Transboundary water resources are essential for agricultural sustainability and regional development, and they are intrinsically linked to achieving the United Nations' SDGs and the water-food-energy nexus (WFE-NEXUS) concept. Despite challenges such as conflicting allocation and climate change impacts, effective transboundary water management for irrigation is crucial to meeting the SDGs of eradicating hunger, providing clean water and sanitation, offering affordable and sustainable energy, and taking climate action. This work synthesizes approaches to transboundary water for irrigation optimization, highlighting the significance of a holistic plan that considers both technical and social factors. Remote-sensing technologies, data forecasting, hydrology and hydraulic modelling, and water resource modelling all contribute to maximize water allocation and policy creation, particularly when paired with collaborative government features. This integrated approach to transboundary water for irrigation optimization fosters long-term development by improving livelihoods, resilience, and inclusive growth through efficient resource management.

  • Remote sensing can fill out the absence of field data.

  • Deep and machine learning data predictions provide water dynamics and transboundary water decision-making.

  • Stochastic models, linear programming, and water resource modelling can optimize cross-border water resource management.

  • Technical optimization is not enough; societal challenges like collaborative government must be considered.

ANN

Artificial Neural Network

BRT

Boosted Regression Tree

CFA

Cooperative Framework Agreement

CHIRPS

Climate Hazards Group InfraRed Precipitation with Station data

CH_K2

Channel's Hydraulic Conductivity

CNN

Convolution Neural Networks

CPP

The Chance-Constrained Programming

DL

Deep Learning

DQN

Deep Q-Network

DSFP

Double-Sided Stochastic Fractional Programming

EC

Electrical Conductivity

EVI

Enhanced Vegetation Index

FNN

Feed-Forward Neural Network

FPP

Fuzzy Possibility Planning

GEE

Google Earth Engineering

GPM

Global Precipitation Measurement

GSOD

Global Surface Summary of the Day

GQI

Groundwater Quality Index

GWL

Groundwater Levels

GWO

Gray Wolf Optimization

HEC-HMS

Hydrologic Engineering Center's Hydrologic Modelling System

HEC-RAS

Hydrologic Engineering Center's River Analysis System

ICPR

International Commission for the Protection of the Rhine

IGWO

The Improved Gray Wolf Optimization

IPP

Interval Parameter Programming

IRS-P6

Indian Remote-Sensing Satellite

IWQI

Irrigation Water Quality Index

IWR

Irrigation Water Requirement

KNN

k-nearest Neighbors

LCBC

Lake Chad Basin Commission

LINDO

Linear Interactive and Discrete Optimizer

LISS

Linear Imaging and Self Scanning

LST

Land Surface Temperature

LSTM

Long Short-Term Memory

LU/LC

Land Use/Land Cover

LVBC

Lake Victoria Basin Commission

MARS

Multivariate Adaptive Regression Spline

MCW

Monthly Climatic Data

MIKE SHE

Mike Surface Water – Groundwater Hydrologic Model

MLP

Multilayer Perceptron

MODFLOW

Modular Groundwater Flow

MODSIM

Modular Modelling System

MRC

Mekong River Commission

NDVI

Normalized Difference Vegetation Index

NDWI

Normalized Difference Water Index

NGOs

Non-Governmental Organizations

NOAA

National Oceanic and Atmospheric

PCA

Principal Component Analysis

PI

Permeability Index

PSO

Particle Swarm Optimization

RF

Random Forest

RNN

Recurrent Neural Networks

RSC

Residual Sodium Carbonate

SAVI

Soil Adjusted Vegetation Index

SDGs

Sustainable Development Goals

SOL_AWC

Soil Layer's Available Water Capacity

SOL_K

Saturated Hydraulic Conductivity

SVM

Support Vector Machine

SVR

Support Vector Regression

SWAT

Soil and Water Assessment Tool

TSCCP

Two-Stage Chance-Constrained Programming

TWS

Terrestrial Water Storage

UAVs

Unmanned Aerial Vehicles

USGS

United States Geological Survey

VIC

Variable Infiltration Capacity

WAEN

Water-Agriculture-Energy Nexus Systems

WEAP

Water Evaluation and Planning System

WFE-NEXUS

Water-Food-Energy Nexus

ZAMCOM

Zambezi Watercourse Commission

The escalating demand for water resources is expected to exacerbate the ongoing growth of both the population and the economy. Urbanization intensifies water scarcity problems, particularly during periods of drought, and burdens already strained water resources, resulting in increased conflicts among various water consumers. Furthermore, climate change poses a threat to the availability of water resources by disturbing the normal patterns of the water flow and causing more frequent and intense extreme weather events (Bhaga et al. 2020; Basuki et al. 2022). Therefore, it is essential to apply adaptation approaches to efficiently manage unpredictable water resources and mitigate potential risks (Mishra et al. 2021). However, the management of transboundary water resources presents difficulties because of the differing needs and interests of the countries concerned.

Transboundary water resources are essential for maintaining the long-term viability of agriculture and fostering regional growth. These resources are in accordance with the sustainable development goals (SDGs) of the United Nations and the water-food-energy nexus (WFE-NEXUS) methodology (Tsani et al. 2020; Zarei 2020; Elsayed et al. 2022). Effective management of transboundary water for irrigation is essential in achieving the SDGs, particularly Goals 2, 6, 7, and 13 which eliminate hunger (Mogomotsi et al. 2020), ensure clean water and sanitation (Ladel et al. 2020), promote affordable and clean energy (Saklani et al. 2020), and take climate action (Rajosoa et al. 2021). The WFE-NEXUS plan emphasizes the interconnectedness of water, energy, and food systems, as well as the intricate relationships between these sectors, which are influenced by the success of transboundary irrigation (Botai et al. 2021). Effective governance of transboundary water resources for irrigation is essential for not only attaining specific SDGs but also for promoting integrated and sustainable development across multiple sectors worldwide.

Transboundary water bodies, such as the Nile, Mekong, Danube, and Amazon rivers, exert a substantial influence on irrigation systems spanning entire continents (Dogaru et al. 2019; Merem et al. 2020; Paredes-Trejo et al. 2021; Sridhar et al. 2021). However, the utilization of water for irrigation across different countries faces several challenges. These include conflicting allocation (Gorgoglione et al. 2019), upstream–downstream dynamics and climate change impacts (Munia et al. 2020), population growth (Merem et al. 2020), economic expansion (Tian et al. 2020), and management complexity along with governance issues (Mogomotsi et al. 2020). Disagreements frequently cause conflicting water allocation, resulting in unequal distribution and endangering biodiversity (Merem et al. 2020; Rasul & Neupane 2021). Dam development and other upstream interventions have an impact on downstream water availability, aggravating water stress (Ahmed et al. 2019; Kazemi et al. 2022). Population growth and economic expansion drive up demand, increasing scarcity, particularly during droughts (Liu et al. 2020; Munia et al. 2020). Climate change changes hydrological patterns, resulting in more intense occurrences with many uncertainties (Basheer et al. 2024). Furthermore, managing transboundary water requires balancing several interests, which necessitates strong governance and international cooperation. Thus, the lack of widely recognized frameworks impedes dispute resolution and sustainable resource management.

The progress in knowledge has led to the development of transboundary water for irrigation optimization strategies, which can offer resolutions to the challenges associated with the management of shared water resources. By taking into account technical and socioeconomic factors, these optimization techniques can be adopted to attain both efficiency and equity in transboundary irrigation water management (Hatamkhani & Moridi 2021; Santos et al. 2023). However, earlier research often restricted optimization efforts to certain areas that need attention. The objective of this study is to analyse and consolidate the current approaches and provide a comprehensive overview of the integrated optimization concept, encompassing both technical and socioeconomic elements. This study conducted a comprehensive review of 370 articles obtained from reputable academic sources such as Google Scholars, Publish and Perish, and other relevant sources. The articles were published between 2014 and 2024 and were selected based on their relevance to the topic. The study focused on keywords such as ‘Transboundary River,’ ‘Transboundary watershed,’ ‘Transboundary basin,’ ‘Transboundary water,’ ‘Transboundary irrigation,’ ‘Water optimization,’ ‘Irrigation optimization,’ and ‘Irrigation.’ After eliminating duplicate and irrelevant articles, this study analysed a total of 189 articles. The results of this study are advantageous for developing plans to enhance the administration and utilization of shared water resources for irrigation, ultimately promoting the sustainable growth of agriculture and regional collaboration, especially in developing countries like Indonesia and other similar regions.

Irrigation is essential in agriculture because it ensures food security, increases crop yields, and promotes economic growth. Irrigation may greatly enhance crop yields compared to rainfed agriculture, adding to food production and security (Kumar & Sen 2023). Transboundary water is a vital supply of irrigation in many countries, considerably increasing agricultural output and water security (Merem et al. 2020).

The Nile River Basin is one example of transboundary water for irrigation in Africa, which includes Egypt, Sudan, Ethiopia, and Uganda, which is one of the world's largest transboundary river basins (Abdullah et al. 2020). The Blue Nile, a significant tributary of the Nile River, has enormous transboundary irrigation potential (Tedla et al. 2022). With rising limits on the Blue Nile's water resources due to population and economic growth, as well as climate change, integrated water resource planning and management is required to assure the basin's water, energy, and food security (Basheer et al. 2024). The Grand Ethiopian Renaissance Dam is an example of transboundary water used for irrigation in the Nile River Basin that is expected to considerably boost irrigation opportunities in the region, not only by supplying water for irrigation to millions of hectares of agricultural land but also by providing hydropower generating in Sudan and Egypt (Kahsay et al. 2019).

The Mekong River Basin in Southeast Asia provides another example of transboundary water for irrigation. The Mekong River flows through China, in particular Yunnan Province, Myanmar, Laos, Thailand, Cambodia, and Vietnam providing critical water resources for cultivation in the region (Zhao et al. 2021). With its large network of tributaries and significance in providing livelihoods for millions of people, the Mekong River Basin has tremendous potential for transboundary irrigation projects (Sridhar et al. 2021). Countries in the Mekong area have collaborated to create joint irrigation systems and manage water resources to ensure sustainable agricultural practices and food security for their citizens (Mallick 2022). The Nam Theun 2 Hydropower Project in Laos is a major project in the Mekong River Basin that has helped irrigation and agricultural growth in downstream areas of Thailand and Cambodia, illustrating the favourable effects of transboundary water cooperation for regional irrigation (Khan et al. 2017).

In Europe, the Danube River Basin is another example of transboundary water for irrigation. The Danube River spans 19 countries and provides water for agriculture along its banks (Habersack et al. 2016). The water resources of the Danube are critical for irrigation in the rich plains of Hungary and Serbia, enabling the development of crops such as wheat, maize, and sunflowers (Arsic et al. 2015). Countries in the Danube River Basin work together to improve irrigation infrastructure, increase water efficiency, and promote sustainable agriculture practices (Izakovičová et al. 2020).

In North America, the Colorado River Basin is a well-known example of transboundary water utilized for irrigation. The Colorado River, which runs through several states in the United States and Mexico, provides water for irrigation of farmlands in the region (Fleck & Castle 2022). The river's water sources benefit agriculture in California, Arizona, and Colorado, as well as the Mexicali Valley in Mexico. Several agreements and treaties between the United States and Mexico govern the allocation of water from the Colorado River for irrigation, emphasizing the importance of collaboration in managing transboundary water resources for sustainable agriculture (Wilder et al. 2020; Blumstein & Petersen-Perlman 2021). Aside from that, the Amazon River Basin acts as a model for South America. The Amazon River and its large network of tributaries flow through several countries, including Brazil, Peru, Colombia, Bolivia, and Ecuador. The river and its basin have an abundance of water resources, allowing for transboundary irrigation projects and making the region's agricultural practices more sustainable (Mujica et al. 2021).

Despite its great promise, using transboundary water for irrigation comes with a host of issues, as shown in Table 1. Included in this list are factors such as water distribution, climate change, hydropower, agriculture, and pollution. The water scarcity problem is exacerbated when countries' competing interests and goals lead to disputes over the distribution of water resources (Katz & Nagabhatla 2023). Because water shortage disturbs ecosystems and lowers habitats in the downstream areas, the continuing conflict increases tensions and threatens the river's biodiversity. The management of transboundary rivers is inherently intricate due to the intricate interplay between nations located upstream and downstream (Hossen et al. 2023). Dams and other extensive irrigation projects disrupt the regular water flow patterns in communities located downstream (Hoque et al. 2022). This alteration will lead to water scarcity and diminished resources for agriculture, industry, and ecosystem well-being and will have a significant effect on the individuals residing downstream (Ingrao et al. 2023), especially when that area is contaminated by severe pollution issues like that of the Rhine due to industrial activities (Chen et al. 2021). Key pollutants such as toxic chemicals from industrial discharge can significantly degrade the river's water quality, which also contributed to a form of water scarcity. This is because polluted water becomes unusable for various critical needs, including drinking, agriculture, and industry.

Table 1

Transboundary water for irrigation system challenges

SourceBasin/riverArea coverageCategoryChallenges
Al-Faraj & Scholz (2014)  Diyala watershed Iraq and Iran Water distribution The upstream water uses, such as dams and large-scale irrigation projects, which have a significant impact on the downstream water availability and stress. The cumulative effects of these, combined with drought conditions, have led to alterations in the natural flow regimes of the river, affecting the water resources for the downstream country 
Roozbahani et al. (2014)  Sefidrud Basin Iran Water distribution The allocation of water in a way that manages conflicting objectives. When a river crosses political borders, increasing the allocated water for one stakeholder can lead to a reduction for others, resulting in competition and potential conflict. The lack of collaboration between stakeholders in transboundary watersheds can lead to unfair water distribution and harmful consequences for river biodiversity 
Porse et al. (2015)  Rio Grande/Bravo river basin United States and Mexico Climate change and agriculture The delicate task of balancing the needs and interests of diverse stakeholders. This is especially arduous given the region's arid, monsoonal climate, which adds a level of complexity and variability to the water management efforts 
Rougé et al. (2018)  The Euphrates and Tigris river basin Turkey, Syria, Iraq, Iran, Saudi Arabia, and Jordan Hydropower and agriculture Water resource vulnerabilities due to changes in infrastructure, such as storage modifications in reservoirs, affecting downstream water availability and water quality issues, such as salinity, which are often exacerbated when water resources are overexploited 
Degefu et al. (2018)  Global – Climate change Transboundary basins in arid and densely populated areas, including regions of Mauritania, Lake Chad, the Niger River in Algeria, and the Tarim river basin in China, face significant water scarcity. This issue is exacerbated by both the dry conditions and high-water demand from large populations. Uncertainties in water footprint and availability data persist, indicating ongoing difficulties in managing transboundary water resources effectively 
Gorgoglione et al. (2019)  Cuareim/Quaraí watershed Uruguay and Brazil Water distribution The increasing water demand due to urbanization, industry, and agriculture, coupled with water scarcity during drought periods leads to difficulties in allocating water resources and can result in conflicts between different administrative regions or countries sharing the watershed. The lack of internationally accepted allocation mechanisms further complicates these conflicts 
Yu et al. (2019)  Lancang–Mekong river basin China, Myanmar, Laos, Thailand, Cambodia, and Vietnam Hydropower and agriculture The water management due to conflicting demands of the riparian countries. Upstream, China utilizes the river for hydropower, while downstream, countries depend on it for agriculture, fisheries, and sustaining wetlands. This variation in usage, especially during dry seasons, leads to water-related conflicts 
Zhu et al. (2019)  Global – Water distribution The increased demand for water resources leading to potential conflicts between users, the need for coordinated planning and international cooperation, and the potential for severe water-related problems that can impair human health and well-being, particularly in the poorest regions of the world 
Bassi et al. (2020)  – India Water distribution The absence of river boards leads states to prefer to control their water resources independently. This is motivated by concerns over losing water share through central government arbitration. As a result, states often carry out water planning and development individually rather than collaboratively considering the entire basin. This siloed approach creates conflict, particularly in times of drought. Complicating the issue are ongoing water-sharing disputes, political challenges, and a lack of a clear and updated water accounting system to guide equitable water allocation among states sharing the river basin 
Rivera-Torres & Gerlak (2021)  Colorado river basin United States and Mexico Climate change and agriculture Climate change has intensified variability in water supply, complicating the management of water resources for irrigation. Population growth exacerbates this challenge by increasing demand, straining already stressed water supplies. Overallocation of water rights further complicates irrigation, particularly during drought periods. Socioeconomic, cultural, and political disparities between countries like the United States and Mexico can hinder effective negotiation and management of water use, including irrigation 
Kaini et al. (2021)  Koshi river Nepal Agriculture Climate change is projected to raise the average flow of the Koshi River, affecting the hydrological regime and potentially increasing the cultivable areas for winter wheat and monsoon paddy rice. The broader impact of these changes on water management and agriculture necessitates the development of adaptation strategies to address altered water availability 
Tedla et al. (2022)  Blue Nile Basin Ethiopia, Sudan, and Egypt Climate change Climate change may lead to an increase in total precipitation, intensity, and frequency of extreme rainfall events, which can consequently lead to greater risks of floods and droughts in the future 
Mehmood et al. (2022)  Indus Basin Pakistan, India, China and Afghanistan Agriculture Overreliance on groundwater for irrigation arises from the inadequacy of surface water supplies, leading to a potential water crisis. The region's aquifers are overdrawn due partly to unreliable water supply infrastructure, specifically inconsistent canal systems that intensify the dependence on subterranean sources. Compounded by the pressures of climate variability, crop irrigation demands are in flux, exacerbating the strain on water resources 
Dembélé et al. (2023)  Volta river basin Benin, Burkina Faso, Côte d'Ivoire, Ghana, Mali, and Togo Climate change and agriculture The increase in water demand projected by 2050 exacerbated variability in rainfall due to climate change, conflicting national water usage priorities between countries in the basin (agriculture in Burkina Faso versus hydropower production in Ghana), potential tensions due to divergent priorities, and difficulties in governance that impact the implementation of effective transboundary water management strategies, despite the establishment of institutions like the Volta Basin Authority and a convention among riparian states 
Genjebo et al. (2023)  Upper Bilate River Ethiopia Water distribution The surface water shortages that cause conflicts between different water users, the necessity to evaluate and optimally distribute water resources, and uneven water allocation based on availability without considering demand. Furthermore, there is a risk of increased conflicts due to unmet water needs, particularly during droughts, and a rising population that could intensify water scarcity 
Mejdar et al. (2023)  Harirud River Iran, Afghanistan, and Turkmenistan Climate change and agriculture The effects of climate change and the impact of upstream development in Afghanistan raised concerns about the future sustainability of agricultural water supplies in Iran and Turkmenistan, with projections suggesting a decline in reliability to less than 3% under the RCP 8.5 climate change scenario. Consequently, the Doosti Dam's capability to meet the domestic water demands of Mashhad, Iran's second-largest city, was called into question, marking it as an unreliable source in the context of the anticipated climatic changes 
Probst et al. (2024)  Danube Basin Germany, Austria, Hungary, Serbia, Romania Water distribution The Danube River, which spans multiple countries, faces competition for shared water resources. This necessitates integrated basin-wide irrigation management 
Basheer et al. (2024)  Blue Nile River Basin Ethiopia, Sudan, and Egypt Hydropower and agriculture The scarce water and limited infrastructure funding, requiring strategic trade-off management. These difficulties are intensified by growing populations, economic expansion, and climate change. Conflicts can arise among different users like those needing water for agriculture and those for urban or industrial purposes and upstream water use can affect downstream hydropower generation 
Chen et al. (2021)  Rhine River Basin Netherlands, Germany, France, Luxembourg, and Switzerland Pollution The pollution from industrial activities degrades water quality and strain transboundary cooperation between riparian states like Germany, France, and the Netherlands. Key issues include the impact of historical industrial spills, such as the Sandoz Spill, which spurred improved environmental regulations 
Shakhman & Bystriantseva (2021)  Dniester River Poland, Moldova, and Ukraine Pollution The river faces severe ecological challenges due to industrial and agricultural pollution, inefficient water use, and poor self-purification capacity, further complicated by inconsistent cross-border management between Ukraine and Moldova. Pollution from heavy industry and agriculture degrades water quality, while high consumption and wastage strain resources, causing effective water scarcity 
Garrick (2017)  Javari (Yavarí) River Basin Brazil and Peru Water distribution The changing river morphology, border management difficulties, and issues related to illicit activities and data inconsistencies 
 Quaraí (Cuareím) River Basin Brazil and Uruguay Pollution and agriculture There is a water scarcity issue due to agricultural demand, pollution from urban and agricultural sources, and challenges arising from differing basin delineation methodologies used by Brazil and Uruguay 
SourceBasin/riverArea coverageCategoryChallenges
Al-Faraj & Scholz (2014)  Diyala watershed Iraq and Iran Water distribution The upstream water uses, such as dams and large-scale irrigation projects, which have a significant impact on the downstream water availability and stress. The cumulative effects of these, combined with drought conditions, have led to alterations in the natural flow regimes of the river, affecting the water resources for the downstream country 
Roozbahani et al. (2014)  Sefidrud Basin Iran Water distribution The allocation of water in a way that manages conflicting objectives. When a river crosses political borders, increasing the allocated water for one stakeholder can lead to a reduction for others, resulting in competition and potential conflict. The lack of collaboration between stakeholders in transboundary watersheds can lead to unfair water distribution and harmful consequences for river biodiversity 
Porse et al. (2015)  Rio Grande/Bravo river basin United States and Mexico Climate change and agriculture The delicate task of balancing the needs and interests of diverse stakeholders. This is especially arduous given the region's arid, monsoonal climate, which adds a level of complexity and variability to the water management efforts 
Rougé et al. (2018)  The Euphrates and Tigris river basin Turkey, Syria, Iraq, Iran, Saudi Arabia, and Jordan Hydropower and agriculture Water resource vulnerabilities due to changes in infrastructure, such as storage modifications in reservoirs, affecting downstream water availability and water quality issues, such as salinity, which are often exacerbated when water resources are overexploited 
Degefu et al. (2018)  Global – Climate change Transboundary basins in arid and densely populated areas, including regions of Mauritania, Lake Chad, the Niger River in Algeria, and the Tarim river basin in China, face significant water scarcity. This issue is exacerbated by both the dry conditions and high-water demand from large populations. Uncertainties in water footprint and availability data persist, indicating ongoing difficulties in managing transboundary water resources effectively 
Gorgoglione et al. (2019)  Cuareim/Quaraí watershed Uruguay and Brazil Water distribution The increasing water demand due to urbanization, industry, and agriculture, coupled with water scarcity during drought periods leads to difficulties in allocating water resources and can result in conflicts between different administrative regions or countries sharing the watershed. The lack of internationally accepted allocation mechanisms further complicates these conflicts 
Yu et al. (2019)  Lancang–Mekong river basin China, Myanmar, Laos, Thailand, Cambodia, and Vietnam Hydropower and agriculture The water management due to conflicting demands of the riparian countries. Upstream, China utilizes the river for hydropower, while downstream, countries depend on it for agriculture, fisheries, and sustaining wetlands. This variation in usage, especially during dry seasons, leads to water-related conflicts 
Zhu et al. (2019)  Global – Water distribution The increased demand for water resources leading to potential conflicts between users, the need for coordinated planning and international cooperation, and the potential for severe water-related problems that can impair human health and well-being, particularly in the poorest regions of the world 
Bassi et al. (2020)  – India Water distribution The absence of river boards leads states to prefer to control their water resources independently. This is motivated by concerns over losing water share through central government arbitration. As a result, states often carry out water planning and development individually rather than collaboratively considering the entire basin. This siloed approach creates conflict, particularly in times of drought. Complicating the issue are ongoing water-sharing disputes, political challenges, and a lack of a clear and updated water accounting system to guide equitable water allocation among states sharing the river basin 
Rivera-Torres & Gerlak (2021)  Colorado river basin United States and Mexico Climate change and agriculture Climate change has intensified variability in water supply, complicating the management of water resources for irrigation. Population growth exacerbates this challenge by increasing demand, straining already stressed water supplies. Overallocation of water rights further complicates irrigation, particularly during drought periods. Socioeconomic, cultural, and political disparities between countries like the United States and Mexico can hinder effective negotiation and management of water use, including irrigation 
Kaini et al. (2021)  Koshi river Nepal Agriculture Climate change is projected to raise the average flow of the Koshi River, affecting the hydrological regime and potentially increasing the cultivable areas for winter wheat and monsoon paddy rice. The broader impact of these changes on water management and agriculture necessitates the development of adaptation strategies to address altered water availability 
Tedla et al. (2022)  Blue Nile Basin Ethiopia, Sudan, and Egypt Climate change Climate change may lead to an increase in total precipitation, intensity, and frequency of extreme rainfall events, which can consequently lead to greater risks of floods and droughts in the future 
Mehmood et al. (2022)  Indus Basin Pakistan, India, China and Afghanistan Agriculture Overreliance on groundwater for irrigation arises from the inadequacy of surface water supplies, leading to a potential water crisis. The region's aquifers are overdrawn due partly to unreliable water supply infrastructure, specifically inconsistent canal systems that intensify the dependence on subterranean sources. Compounded by the pressures of climate variability, crop irrigation demands are in flux, exacerbating the strain on water resources 
Dembélé et al. (2023)  Volta river basin Benin, Burkina Faso, Côte d'Ivoire, Ghana, Mali, and Togo Climate change and agriculture The increase in water demand projected by 2050 exacerbated variability in rainfall due to climate change, conflicting national water usage priorities between countries in the basin (agriculture in Burkina Faso versus hydropower production in Ghana), potential tensions due to divergent priorities, and difficulties in governance that impact the implementation of effective transboundary water management strategies, despite the establishment of institutions like the Volta Basin Authority and a convention among riparian states 
Genjebo et al. (2023)  Upper Bilate River Ethiopia Water distribution The surface water shortages that cause conflicts between different water users, the necessity to evaluate and optimally distribute water resources, and uneven water allocation based on availability without considering demand. Furthermore, there is a risk of increased conflicts due to unmet water needs, particularly during droughts, and a rising population that could intensify water scarcity 
Mejdar et al. (2023)  Harirud River Iran, Afghanistan, and Turkmenistan Climate change and agriculture The effects of climate change and the impact of upstream development in Afghanistan raised concerns about the future sustainability of agricultural water supplies in Iran and Turkmenistan, with projections suggesting a decline in reliability to less than 3% under the RCP 8.5 climate change scenario. Consequently, the Doosti Dam's capability to meet the domestic water demands of Mashhad, Iran's second-largest city, was called into question, marking it as an unreliable source in the context of the anticipated climatic changes 
Probst et al. (2024)  Danube Basin Germany, Austria, Hungary, Serbia, Romania Water distribution The Danube River, which spans multiple countries, faces competition for shared water resources. This necessitates integrated basin-wide irrigation management 
Basheer et al. (2024)  Blue Nile River Basin Ethiopia, Sudan, and Egypt Hydropower and agriculture The scarce water and limited infrastructure funding, requiring strategic trade-off management. These difficulties are intensified by growing populations, economic expansion, and climate change. Conflicts can arise among different users like those needing water for agriculture and those for urban or industrial purposes and upstream water use can affect downstream hydropower generation 
Chen et al. (2021)  Rhine River Basin Netherlands, Germany, France, Luxembourg, and Switzerland Pollution The pollution from industrial activities degrades water quality and strain transboundary cooperation between riparian states like Germany, France, and the Netherlands. Key issues include the impact of historical industrial spills, such as the Sandoz Spill, which spurred improved environmental regulations 
Shakhman & Bystriantseva (2021)  Dniester River Poland, Moldova, and Ukraine Pollution The river faces severe ecological challenges due to industrial and agricultural pollution, inefficient water use, and poor self-purification capacity, further complicated by inconsistent cross-border management between Ukraine and Moldova. Pollution from heavy industry and agriculture degrades water quality, while high consumption and wastage strain resources, causing effective water scarcity 
Garrick (2017)  Javari (Yavarí) River Basin Brazil and Peru Water distribution The changing river morphology, border management difficulties, and issues related to illicit activities and data inconsistencies 
 Quaraí (Cuareím) River Basin Brazil and Uruguay Pollution and agriculture There is a water scarcity issue due to agricultural demand, pollution from urban and agricultural sources, and challenges arising from differing basin delineation methodologies used by Brazil and Uruguay 

Effective coordination and cooperation are necessary to achieve a harmonious balance between the competing needs of agriculture, urbanization, industry, and hydropower generation. The task of managing transboundary water resources becomes extremely difficult when there is a lack of effective leadership and collaboration. International cooperation is essential for the establishment of equitable allocation processes and governance structures, including data sharing. For example, the absence of formal agreements and structured data exchange mechanisms within the Tigris-Euphrates Basin lead to the hesitancy of upstream countries, particularly Turkey, to share hydrological data with downstream nations, which complicates cooperative management (Rougé et al. 2018). Furthermore, the lack of generally accepted frameworks makes it difficult to resolve conflicts and manage shared water resources in the long run (Hossen et al. 2023). Despite the efforts of river organizations such as the Mekong River Commission (MRC), there is no overarching international agreement that fully binds all six riparian countries included to specific data-sharing and water management protocols (Yu et al. 2019). Likewise, the differences in methodologies between the countries complicate data standardization. In Quaraí (Cuareím) River Basin, Uruguay does not use the Otto–Pfafstetter method for basin delineation, which Brazil applies, and this disparity complicates attempts to integrate data for cohesive transboundary management, as varying data scales and resolutions can result in mismatched borders (Garrick 2017). Thus, policies that prioritize cooperation, investments in infrastructure, and equitable distribution processes are crucial for the effective long-term management of transboundary water resources (Santos et al. 2023).

Remote sensing for data acquiring

As previously noted, a lack of data such as hydrological, geographical, topographical, and socioeconomic elements is one of the very first problems with transboundary water for irrigation optimization (Degefu et al. 2018; Al-Addous et al. 2023). The analysis of water availability and distribution across borders relies heavily on field data, including rainfall and river flow rates (de Oliveira et al. 2023). Similarly, topography data aid in identifying natural water flow channels and prospective infrastructure locations, and land use data reveal how water resources are used across varied landscapes. Furthermore, socioeconomic statistics, such as water usage and population trends, provide information about the demand for irrigation resources and the economic activities that rely on them (Santos et al. 2023; Li et al. 2024). The lack of comprehensive data makes it impossible to develop effective management plans and allocate resources efficiently, which impedes collaborative decision-making and long-term transboundary water management (Saha et al. 2021).

Addressing this data gap is critical for increasing collaboration and ensuring equitable and long-term usage of transboundary water resources. Direct field measurement is one step towards achieving this aim, but it requires significant time and financial investment (ter Horst et al. 2023). As a result, in the current situation, remote sensing appears to be a viable option for overcoming data constraints, as shown in Table 2. Remote sensing provides several advantages, including the ability to efficiently cover large geographic areas, provide regular and consistent data updates, and monitor dynamic environmental changes over time (Popescu et al. 2021). Water resource management employs several remote sensing technologies, including satellite imagery and aerial drones (Abdulraheem et al. 2023). These groups can obtain information from satellites like the United States Geological Survey (USGS), Google Earth Engineering (GEE), the Sentinel program of the European Space Agency, the Landsat program of the National Aeronautics and Space Administration, and other private companies (Hemati et al. 2021). In addition, unmanned aerial vehicles are being made to gather data that is more specific to a certain area, which will provide more leeway and accuracy when tracking irrigation systems and water resources (Peladarinos et al. 2023).

Table 2

Remote sensing for data acquiring

SourceData typePurposes
Kucukmehmetoglu & Geymen (2014)  DEM Identifying agricultural and urban demand nodes with their relative elevations and distances to water resources supplies for allocating scarce water resources effectively to different uses like energy generation, urban, and agricultural uses 
Palmate et al. (2017)  Landsat 1, 2, 5, 7, 8, and IRS-P6 LISS III Produce land use/land cover (LU/LC) maps to simulate the future LU/LC using Markov chain and cellular automata model 
Samarkhanov et al. (2019)  Landsat 8 and MODIS MODIS NDVI data were used to monitor land cover changes due to lack of the products that could give detailed classification or temporal changes of land cover types. The correctness of obtained classes was checked with Landsat 8, which shows detailed land cover maps, including rice cropland 
Fujihara et al. (2020)  Landsat 8 Using multiple spectral bands with high spatial resolution to analyse the cropping patterns within the Gash Spate Irrigation System, identify different types of land cover (crops, non-crops, and shrubs), and provide derived data like NDVI, enhanced vegetation index, and soil adjusted vegetation index 
Kempf & Glaser (2020)  Sentinel 2, Landsat 7 and 8 Sentinel 2 is used for optical analysis to monitor vegetation health and detect responses to rainfall anomalies and artificial irrigation activities that helps in comparing and analysing NDVI values across different years to observe spatial differentiation in crop physical condition, based on the Landsat 7 and 8 generated 
Khoshnoodmotlagh et al. (2020)  DEM, Landsat 5, 7, and 8 Producing land cover maps, using normalized difference water index (NDWI) values derived from Landsat 8 for surface water body detection and mapping 
Nhamo et al. (2020)  Landsat 8 and MODIS Landsat 8 NDVI is used to delineate irrigated crop areas, while MODIS NDVI data is used to provide more frequent data collection with a broader spatial coverage compared with Landsat 
Pakdel-Khasmakhi et al. (2022)  CHIRPS, GPM, ERA5, MODIS, SRTM, Landsat 7 and 8 CHIRPS, GPM, and ERA5 are used to assess the effectiveness of satellite-based rainfall data, with CHIRPS chosen for trend analysis due to its better performance for all gauge stations. MODIS Terra and Aqua are used to describe land surface temperature, while topographic information, including the catchment's elevation, is collected using SRTM and DEM. To produce the water mask of the Doosti reservoir, NDWI maps derived from Landsat 7 and 8 are used 
Park et al. (2020)  Sentinel 1 and DEM Proposes a gauging method applicable to a wide range of reservoirs. The SAR data, which provide high-resolution backscatter images that are not significantly affected by cloud cover or time of day, are ideal for monitoring water bodies. When laid over a DEM, the SAR data allow for an estimation of mean slope-corrected elevation points along a reservoir's shoreline, which helps in calculating water levels 
Ruan et al. (2020)  NOAA Monthly precipitation, temperature, sunshine duration, and vapour pressure were derived from the NOAA monthly climatic data, while monthly maximum temperature, minimum temperature, and wind speed data were obtained from the NOAA Global Surface Summary of the Day (GSOD). All the aforementioned data were used to analyse the variations in climate and water resources 
SourceData typePurposes
Kucukmehmetoglu & Geymen (2014)  DEM Identifying agricultural and urban demand nodes with their relative elevations and distances to water resources supplies for allocating scarce water resources effectively to different uses like energy generation, urban, and agricultural uses 
Palmate et al. (2017)  Landsat 1, 2, 5, 7, 8, and IRS-P6 LISS III Produce land use/land cover (LU/LC) maps to simulate the future LU/LC using Markov chain and cellular automata model 
Samarkhanov et al. (2019)  Landsat 8 and MODIS MODIS NDVI data were used to monitor land cover changes due to lack of the products that could give detailed classification or temporal changes of land cover types. The correctness of obtained classes was checked with Landsat 8, which shows detailed land cover maps, including rice cropland 
Fujihara et al. (2020)  Landsat 8 Using multiple spectral bands with high spatial resolution to analyse the cropping patterns within the Gash Spate Irrigation System, identify different types of land cover (crops, non-crops, and shrubs), and provide derived data like NDVI, enhanced vegetation index, and soil adjusted vegetation index 
Kempf & Glaser (2020)  Sentinel 2, Landsat 7 and 8 Sentinel 2 is used for optical analysis to monitor vegetation health and detect responses to rainfall anomalies and artificial irrigation activities that helps in comparing and analysing NDVI values across different years to observe spatial differentiation in crop physical condition, based on the Landsat 7 and 8 generated 
Khoshnoodmotlagh et al. (2020)  DEM, Landsat 5, 7, and 8 Producing land cover maps, using normalized difference water index (NDWI) values derived from Landsat 8 for surface water body detection and mapping 
Nhamo et al. (2020)  Landsat 8 and MODIS Landsat 8 NDVI is used to delineate irrigated crop areas, while MODIS NDVI data is used to provide more frequent data collection with a broader spatial coverage compared with Landsat 
Pakdel-Khasmakhi et al. (2022)  CHIRPS, GPM, ERA5, MODIS, SRTM, Landsat 7 and 8 CHIRPS, GPM, and ERA5 are used to assess the effectiveness of satellite-based rainfall data, with CHIRPS chosen for trend analysis due to its better performance for all gauge stations. MODIS Terra and Aqua are used to describe land surface temperature, while topographic information, including the catchment's elevation, is collected using SRTM and DEM. To produce the water mask of the Doosti reservoir, NDWI maps derived from Landsat 7 and 8 are used 
Park et al. (2020)  Sentinel 1 and DEM Proposes a gauging method applicable to a wide range of reservoirs. The SAR data, which provide high-resolution backscatter images that are not significantly affected by cloud cover or time of day, are ideal for monitoring water bodies. When laid over a DEM, the SAR data allow for an estimation of mean slope-corrected elevation points along a reservoir's shoreline, which helps in calculating water levels 
Ruan et al. (2020)  NOAA Monthly precipitation, temperature, sunshine duration, and vapour pressure were derived from the NOAA monthly climatic data, while monthly maximum temperature, minimum temperature, and wind speed data were obtained from the NOAA Global Surface Summary of the Day (GSOD). All the aforementioned data were used to analyse the variations in climate and water resources 

Remote sensing technologies such as Landsat, MODIS, Sentinel, and SAR data, as well as digital elevation model (DEM) information, are critical in data collection for transboundary water for irrigation optimization. Landsat and MODIS data are commonly utilized for land cover monitoring, cropping pattern analysis, and detecting irrigated crop areas because they offer both detailed categorization and extensive spatial coverage (Tariq et al. 2023). Sentinel data track vegetation health and detect responses to environmental changes for assessing agricultural conditions over time (Snevajs et al. 2022). Park et al. (2020) propose employing SAR data to generate high-resolution backscatter images that are unaffected by atmospheric conditions for accurate monitoring of water bodies and determining water levels along reservoir shorelines. DEM data aid in terrain analysis by detecting demand nodes, measuring heights, and precisely estimating water levels, all of which are necessary for effective water resource distribution across diverse landscapes (Wei et al. 2023).

Landsat and Sentinel data have been used to track land cover change and assess agricultural practices in transboundary areas. For example, Landsat 8 data were used to estimate land cover changes over 33 years, focusing on anthropogenic activities in Armenia and the Nakhchivan Autonomous Republic (Khoshnoodmotlagh et al. 2020). Landsat 8, being the primary component, provides critical information for understanding land use changes and finding patterns of anthropogenic impact. Fujihara et al. (2020) also employed Landsat 8 data to analyse agricultural practices within the watershed, finding a preference for 2-year rotation patterns in the top block and 3-year rotation patterns in the bottom block. Furthermore, Sentinel data have been used in conjunction with Landsat to track and assess normalized difference vegetation index (NDVI) values over time, providing information about vegetation health and stress levels (Kempf & Glaser 2020). The accurate delineation of irrigated areas using NDVI from Landsat 8 is critical for monitoring groundwater usage in agriculture, especially during dry seasons when groundwater sources are heavily relied on (Nhamo et al. 2020).

Data forecast and decision-making

Data forecasting and decision-making for irrigation optimization in transboundary water systems have come a long way with the advent of extremely potent technologies like deep learning and machine learning (Saha et al. 2021). Understanding subtle patterns and representations from vast and complex datasets is one of the several advantages of deep learning. Thus, it does not require explicit feature engineering to discover minute connections (Taye 2023). Deep learning does particularly well with high-dimensional, nonlinear data, which are common in transboundary irrigation networks, as shown in Table 3. As new data are collected, deep learning models can be modified and improved upon. This improves accuracy in predicting irrigation demand and optimizing resource allocation (Aldoseri et al. 2023).

Table 3

Data forecast and decision-making using deep learning

SourceStudy areaDL typePurposesNote
Malakar et al. (2021)  India LSTM, RNN, and FNN Groundwater levels forecasting Using multi depth in situ observations from a dense network of monitoring wells to simulate and forecast groundwater level (GWL). The acquired GWL data consist of a broad range of discontinuity, temporal frequency, and time interval. Among these methods, LSTM was found to perform better, outperforming both FNN and RNN in a testing period of five years 
Alibabaei et al. (2022)  Fadagosa, Portugal ANN, LSTM, and CNN Water management decision-making Deep Q-network (DQN) used to optimize irrigation scheduling with the goals of improving water use efficiency, increasing net return, and reducing water consumption. ANN, LSTM, and CNN were used to estimate the Q-table, which is crucial for the DQN decision-making process. However, the LSTM proved to be more effective than the ANN and CNN in estimating the Q-table, leading to better performance in optimizing irrigation. This optimization considered factors such as water wastage minimization, plant stress avoidance towards the end of the growing season, and adjustments for climatic changes and rainfall 
Jia et al. (2022)  North China A hybrid LSTM-CPP-FPP-IPP Water allocation forecasting A hybrid model was used to optimize irrigation water allocation for winter wheat and summer corn in five cities. The LSTM model predicted crop yield per unit area, while CPP-FPP-IPP programming planned the crop area and effective precipitation, considering uncertain factors. By evaluating three rainfall scenarios and four planting intentions, the model aimed to enhance decision-making in agricultural water management, maximize the production profit from crops, and address both economic and water scarcity challenges in the region 
Kilinc (2022)  Orontes Basin, Turkey PSO-LSTM River flow forecasting LSTM models are applied to forecast streamflow data, and the PSO component within the hybrid PSO-LSTM model is utilized to find the optimal parameters for the LSTM model. This combination is aimed at enhancing the precision and dependability of the streamflow forecasts, leveraging the strengths of both methods to achieve greater accuracy in predicting river flow dynamics 
Mumbi et al. (2022)  Nile River, Egypt and Kenya RNN and FFNN Water demand forecasting Forecasting is based on historical data and several input factors like GDP, agriculture, population, and precipitation patterns. However, the performance of the RNN in this study, as indicated in the source, had significant deviation from the actual data, which suggests that the RNN did not perform optimally in forecasting water consumption to optimize water resource management 
Sun et al. (2024)  Mekong River, East and Southeast Asia CNN Barrier detection A specialized collection of satellite images showing different types of structures was used to teach computer models to spot river barriers. CNNs were trained to recognize the features of various barriers. The best CNN was then used to identify many previously unnoticed barriers across the river area 
SourceStudy areaDL typePurposesNote
Malakar et al. (2021)  India LSTM, RNN, and FNN Groundwater levels forecasting Using multi depth in situ observations from a dense network of monitoring wells to simulate and forecast groundwater level (GWL). The acquired GWL data consist of a broad range of discontinuity, temporal frequency, and time interval. Among these methods, LSTM was found to perform better, outperforming both FNN and RNN in a testing period of five years 
Alibabaei et al. (2022)  Fadagosa, Portugal ANN, LSTM, and CNN Water management decision-making Deep Q-network (DQN) used to optimize irrigation scheduling with the goals of improving water use efficiency, increasing net return, and reducing water consumption. ANN, LSTM, and CNN were used to estimate the Q-table, which is crucial for the DQN decision-making process. However, the LSTM proved to be more effective than the ANN and CNN in estimating the Q-table, leading to better performance in optimizing irrigation. This optimization considered factors such as water wastage minimization, plant stress avoidance towards the end of the growing season, and adjustments for climatic changes and rainfall 
Jia et al. (2022)  North China A hybrid LSTM-CPP-FPP-IPP Water allocation forecasting A hybrid model was used to optimize irrigation water allocation for winter wheat and summer corn in five cities. The LSTM model predicted crop yield per unit area, while CPP-FPP-IPP programming planned the crop area and effective precipitation, considering uncertain factors. By evaluating three rainfall scenarios and four planting intentions, the model aimed to enhance decision-making in agricultural water management, maximize the production profit from crops, and address both economic and water scarcity challenges in the region 
Kilinc (2022)  Orontes Basin, Turkey PSO-LSTM River flow forecasting LSTM models are applied to forecast streamflow data, and the PSO component within the hybrid PSO-LSTM model is utilized to find the optimal parameters for the LSTM model. This combination is aimed at enhancing the precision and dependability of the streamflow forecasts, leveraging the strengths of both methods to achieve greater accuracy in predicting river flow dynamics 
Mumbi et al. (2022)  Nile River, Egypt and Kenya RNN and FFNN Water demand forecasting Forecasting is based on historical data and several input factors like GDP, agriculture, population, and precipitation patterns. However, the performance of the RNN in this study, as indicated in the source, had significant deviation from the actual data, which suggests that the RNN did not perform optimally in forecasting water consumption to optimize water resource management 
Sun et al. (2024)  Mekong River, East and Southeast Asia CNN Barrier detection A specialized collection of satellite images showing different types of structures was used to teach computer models to spot river barriers. CNNs were trained to recognize the features of various barriers. The best CNN was then used to identify many previously unnoticed barriers across the river area 

LSTM was found to be a highly effective deep learning technique and is utilized for predictive modelling in many water resource management applications (Omar & Kumar 2021). LSTM outperforms RNN, FNN, ANN, and CNN (Malakar et al. 2021; Alibabaei et al. 2022). LSTM predictive modelling and CPP-FPP-IPP programming techniques are used in the hybrid LSTM-CPP-FPP-IPP model to improve irrigation water allocation. An investigation has shown that this strategy outperforms traditional deterministic planning techniques (Jia et al. 2022). The incorporation of ambiguous components including rainfall patterns, crop output projections, and climatic fluctuations is made possible by this integration. Because these uncertainties are included into the decision-making process, the hybrid model enables decision-makers to look into a wider range of possible scenarios and look for more suitable solutions in ambiguous situations. However, accuracy can be further boosted by combining LSTM with other approaches, such as particle swarm optimization (PSO)-LSTM, which optimizes parameters for LSTM, resulting in more exact and reliable forecasts (Kilinc 2022).

Machine learning approaches offer significant benefits for optimizing transboundary water usage in irrigation (Drogkoula et al. 2023). Machine learning algorithms possess the capacity to effectively process a wide range of data kinds and objectives, enabling its application in numerous scenarios and decision-making contexts within transboundary irrigation management (Peladarinos et al. 2023). Furthermore, machine learning models are recognized for their computational efficacy, allowing for rapid decision-making in relation to changing environmental circumstances and irrigation requirements (Shams et al. 2024).

The management of transboundary water resources extensively uses machine learning in predictive modelling. The applications include, as shown in Table 4, the estimation of runoff coefficient, analysis of land cover change, river flow prediction, evaluation of groundwater quality, computation of irrigation water quality index (IWQI), and categorization of cultivated land and water resources resilience. Machine learning techniques can be used to increase the precision of mapping and model accuracy, including water potential (Sarkar et al. 2024). A study looked at and predicted spring growth using RF, SVM, MARS, and BRT among other data mining methods (Al-Shabeeb et al. 2023). The study concluded that the RF model demonstrated the highest level of accuracy. The utilization of elevation, precipitation, and soil data in the RF model enhances the accuracy of predicting flow coefficients for water resource management and land use planning in water-scarce locations (Yan et al. 2019). Furthermore, the application of machine learning methods can augment the precision of data downscaling procedures, resulting in enhanced accuracy of models and maps. In the previous study, the XG Boost model outperformed the ANN model in successfully fitting anticipated TWS anomalies to GRACE data (Ali et al. 2023). These improved calculations allow for more thorough evaluations utilizing the water storage deficiency index and water storage deficiency analysis, which are essential for assessing the severity and the frequency of droughts.

Table 4

Data forecast and decision-making using machine learning

SourceStudy areaMethodPurposesNote
Khoshnoodmotlagh et al. (2020)  Aras River Basin, South Caucasus Countries ANN-MLP Land cover change forecasting The study employs an ANN-MLP, within a larger land cover change modelling framework to optimize the understanding and prediction of land cover, such as converting forest to agriculture or bare land to rangeland. By simulating these conversions, the model aims to enhance the accuracy of forecasting future land cover scenarios based on historical data and influential environmental variables 
Mirzaei et al. (2020)  Aras River Basin, Turkey ANN-MLP Land cover change forecasting The study employs an ANN-MLP approach to establish the relationship between environmental variables and land cover changes. By testing various scenarios with different configurations of the MLP, the goal is to optimize the model's structure for the most accurate prediction of transition potentials between land cover classes, considering a range of environmental drivers which are essential for creating reliable predictions that can inform water resource management and land use planning 
Liu et al. (2021)  Heilongjiang Province, Northeastern China GWO-SVM and GSA-SVM Water resource resiliency classifier The improved GWO-SVM is an advanced optimization method that refines the original GWO for tuning an SVM. This approach focuses on enhancing the SVM's parameters, using innovative mathematical strategies to ensure diversity, avoid early convergence, and accelerate the achievement of optimal solutions aimed at increasing the accuracy of the SVM in assessing the resilience of water resource systems in irrigation areas. The optimized IGWO-SVM model outperforms its precursors, GWO-SVM and GSA-SVM, by showing superior metrics, making it a more reliable and efficient tool for advancing sustainable agriculture 
Magidi et al. (2021)  Mpumalanga Province, East of South Africa RF Cultivated lands classifier The study sought to enhance the precision of mapping irrigated lands by automating classification on the Google Earth Engine and processing the results with R-programming by utilizing both static variables, like terrain and geographic data, and dynamic inputs from remote sensing like NDVI values and climate data to distinguish irrigated areas, particularly during the dry season, to develop an accurate spatial model to aid decision-making related to water management, land planning, and agricultural practices at a provincial scale, helping to address critical concerns such as crop water needs and the influence of irrigation on hydrological cycles 
Rahman et al. (2022)  Upper Indus Basin, North of Pakistan MLP Streamflow forecasting The model incorporates tasks such as nonlinear precipitation prediction, streamflow and sediment modelling, rainfall–runoff modelling, and river stage-discharge modelling, alongside sensitivity analyses, calibration, and validation processes. These procedures were implemented to pinpoint and optimize the most influential parameters impacting streamflow simulation. Sensitive parameters including curve number, the SOL_AWC, SOL_K, snowmelt factors, and the main CH_K2 were identified as critical for the accuracy of the SWAT model and thus were concentrated on for optimization 
Awasthi et al. (2023)  India PCA Quality of groundwater forecasting To assess groundwater quality for drinking and irrigation purposes, the study conducted a detailed hydrochemical analysis of groundwater samples. This included the application of PCA to distil a large set of variables into a new, smaller, uncorrelated subset. The data underwent z-scale transformation for standardization and to ensure accurate classification. The results, combined with GIS techniques, provided a spatial representation of water quality across the transboundary aquifers 
Derdour et al. (2023)  Southwestern of Algeria SVM and KNN Quality of groundwater forecasting The study aims to optimize the prediction of the groundwater quality index in arid environments through classification techniques, such as a cubic SVM, which has the highest accuracy when working with normalized hydrochemical input parameters like sodium adsorption ratio, electrical conductivity, bicarbonate level, chloride concentration, and sodium concentration. This method enhances the efficiency and reduces the cost of evaluating water quality, serving as a valuable tool in regions with limited resources for extensive groundwater testing and analysis 
Hussein et al. (2024)  Wilaya of Naama, Southwestern of Algeria XGBoost, SVR, and KNN Irrigation water quality index forecasting The study aimed to optimize the prediction of the IWQI by evaluating groundwater sustainability with hydrochemical parameters (cations, anions, pH, and EC), qualitative indices (SAR, RSC, Na%, MH, and PI), and geospatial data. Among the machine learning algorithms tested, XGBoost stood out for its high predictive accuracy in assessing groundwater quality, which is essential for determining its suitability for agricultural irrigation 
SourceStudy areaMethodPurposesNote
Khoshnoodmotlagh et al. (2020)  Aras River Basin, South Caucasus Countries ANN-MLP Land cover change forecasting The study employs an ANN-MLP, within a larger land cover change modelling framework to optimize the understanding and prediction of land cover, such as converting forest to agriculture or bare land to rangeland. By simulating these conversions, the model aims to enhance the accuracy of forecasting future land cover scenarios based on historical data and influential environmental variables 
Mirzaei et al. (2020)  Aras River Basin, Turkey ANN-MLP Land cover change forecasting The study employs an ANN-MLP approach to establish the relationship between environmental variables and land cover changes. By testing various scenarios with different configurations of the MLP, the goal is to optimize the model's structure for the most accurate prediction of transition potentials between land cover classes, considering a range of environmental drivers which are essential for creating reliable predictions that can inform water resource management and land use planning 
Liu et al. (2021)  Heilongjiang Province, Northeastern China GWO-SVM and GSA-SVM Water resource resiliency classifier The improved GWO-SVM is an advanced optimization method that refines the original GWO for tuning an SVM. This approach focuses on enhancing the SVM's parameters, using innovative mathematical strategies to ensure diversity, avoid early convergence, and accelerate the achievement of optimal solutions aimed at increasing the accuracy of the SVM in assessing the resilience of water resource systems in irrigation areas. The optimized IGWO-SVM model outperforms its precursors, GWO-SVM and GSA-SVM, by showing superior metrics, making it a more reliable and efficient tool for advancing sustainable agriculture 
Magidi et al. (2021)  Mpumalanga Province, East of South Africa RF Cultivated lands classifier The study sought to enhance the precision of mapping irrigated lands by automating classification on the Google Earth Engine and processing the results with R-programming by utilizing both static variables, like terrain and geographic data, and dynamic inputs from remote sensing like NDVI values and climate data to distinguish irrigated areas, particularly during the dry season, to develop an accurate spatial model to aid decision-making related to water management, land planning, and agricultural practices at a provincial scale, helping to address critical concerns such as crop water needs and the influence of irrigation on hydrological cycles 
Rahman et al. (2022)  Upper Indus Basin, North of Pakistan MLP Streamflow forecasting The model incorporates tasks such as nonlinear precipitation prediction, streamflow and sediment modelling, rainfall–runoff modelling, and river stage-discharge modelling, alongside sensitivity analyses, calibration, and validation processes. These procedures were implemented to pinpoint and optimize the most influential parameters impacting streamflow simulation. Sensitive parameters including curve number, the SOL_AWC, SOL_K, snowmelt factors, and the main CH_K2 were identified as critical for the accuracy of the SWAT model and thus were concentrated on for optimization 
Awasthi et al. (2023)  India PCA Quality of groundwater forecasting To assess groundwater quality for drinking and irrigation purposes, the study conducted a detailed hydrochemical analysis of groundwater samples. This included the application of PCA to distil a large set of variables into a new, smaller, uncorrelated subset. The data underwent z-scale transformation for standardization and to ensure accurate classification. The results, combined with GIS techniques, provided a spatial representation of water quality across the transboundary aquifers 
Derdour et al. (2023)  Southwestern of Algeria SVM and KNN Quality of groundwater forecasting The study aims to optimize the prediction of the groundwater quality index in arid environments through classification techniques, such as a cubic SVM, which has the highest accuracy when working with normalized hydrochemical input parameters like sodium adsorption ratio, electrical conductivity, bicarbonate level, chloride concentration, and sodium concentration. This method enhances the efficiency and reduces the cost of evaluating water quality, serving as a valuable tool in regions with limited resources for extensive groundwater testing and analysis 
Hussein et al. (2024)  Wilaya of Naama, Southwestern of Algeria XGBoost, SVR, and KNN Irrigation water quality index forecasting The study aimed to optimize the prediction of the IWQI by evaluating groundwater sustainability with hydrochemical parameters (cations, anions, pH, and EC), qualitative indices (SAR, RSC, Na%, MH, and PI), and geospatial data. Among the machine learning algorithms tested, XGBoost stood out for its high predictive accuracy in assessing groundwater quality, which is essential for determining its suitability for agricultural irrigation 

Hydrological and hydraulic modelling

Hydrological and hydraulic models can be used with enough data to gain a thorough understanding of water dynamics and improve transboundary water for irrigation system management (Wang et al. 2023). These models provide precise estimates of water flow, distribution, and behaviour throughout the basin and irrigation infrastructure (Garcia-Espinal et al. 2024). These models, which incorporate high-quality data on precipitation, soil quality, land use, and terrain, can provide information on water availability, predict probable flood or drought scenarios, and optimize water distribution systems (Xiao et al. 2024). Hydrological and hydraulic modelling can also aid in the identification of irrigation network vulnerabilities, estimate the effects of climate change, and develop adaptive management techniques (Kypreos et al. 2024).

Depending on the water management aims and study fields, a variety of advanced modelling approaches can be utilized to conduct hydrological and hydraulic modelling as shown in Table 5. Tools such as the Hydrologic Engineering Center's Hydrologic Modelling System (HEC-HMS), Hydrologic Engineering Center's River Analysis System (HEC-RAS), MIKE SHE, MIKE 11, soil and water assessment tool (SWAT), variable infiltration capacity (VIC), and modular groundwater flow (MODFLOW) are utilized.

Table 5

Hydrological and hydraulic modelling

SourceMethodPurposesNote
Traore et al. (2015)  HEC-RAS Flow characteristics modelling The model is used to evaluate the impact of hydraulic structures on water flow and estimate floodplains, ultimately informing water resource management decisions focused on irrigated agriculture. The objective is to optimize the operation of these structures to ensure they are managed effectively, maximizing water availability for crops such as rice. This involves strategic control over water storage and release from dams, facilitating efficient support for agricultural activities and contributing to food security by reducing the cereal deficit in the area 
Usmanov et al. (2016)  MIKE SHE Water balance modelling The model is used to simulate interactions between surface and groundwater, providing a detailed and accurate water balance. This approach enabled quantification of the water cycle's components, notably revealing that actual evapotranspiration due to large-scale irrigation is the predominant factor in water loss. The goal was to optimize water resource management by enhancing the understanding of hydrological interactions and parameters’ spatial variability, thus informing sustainable strategies for the region's water use 
Paparrizos & Maris (2017)  MIKE SHE Water balance modelling The study aimed to model the accurate simulation of the river discharge at three key points along the main riverbed and the comprehensive modelling of the river's water balance. The process involved using a well-calibrated, yet limited number of parameters to maintain model accuracy. The successful calibration resulted in simulation outputs that closely matched observed hydrological data, demonstrating the model's effectiveness in representing the river basin's hydrological characteristics 
Salazar-Briones et al. (2018)  Integration of HEC-HMS and HEC-RAS Storm design and flood areas modelling HEC-HMS and HEC-RAS were used in tandem to develop accurate hydrological and hydraulic models for flood prediction in a transboundary river basin. The study focused on calibrating sensitive parameters like the Curve Number to ensure the models’ fidelity to real-life flooding events. This modelling aimed to improve flood hazard understanding in arid regions, ultimately aiding in urban infrastructure planning and protection from potential floods 
Rodriguez et al. (2020)  MODFLOW Groundwater flow modelling The use of visual MODFLOW to create a hydrogeological model aimed at evaluating the aquifer flow patterns with an emphasis on the river system and adjacent corridor. By analysing the volumetric fluxes and groundwater movement, their goal was to ascertain if the aquifer is transboundary, meaning whether it allows for groundwater flow across the countries border, thus determining its classification as a transboundary groundwater flow system 
Talchabhadel et al. (2021)  MODFLOW Groundwater flow modelling The model developed for the aquifer features 10 vertical layers for accurate geological representation, enabling simulation of monthly water stress and is calibrated with well data and pumping records. The goal is to optimize holistic management, addressing water quality, quantity, and climate change impacts, by making informed decisions on recharge, pumping, and withdrawals. This approach balances use and conservation, ensuring the aquifer's sustainability, and incorporates graphical models to integrate expert and stakeholder inputs into management strategies 
Azzam et al. (2022)  HEC-HMS River flow modelling The model was employed to predict and simulate peak flow and runoff, crucial due to severe water crises and the need for a bilateral water-sharing agreement. The model was calibrated and validated to ensure accuracy, demonstrating its effectiveness by producing a simulated peak flow closely matching the observed data. This approach proved valuable as a rapid prediction tool utilizing accessible rainfall and snow-cover data, vital for water resource management and international negotiations in this data-scarce region 
Mirlas et al. (2022)  HYDRUS and MODFLOW Groundwater flow modelling The integration of HYDRUS-1D and MODFLOW modelling tools is used to optimize groundwater supply for livestock on remote pastures by evaluating water movement through soil to groundwater and predicting the impact on groundwater levels from well water extraction. This enables informed strategies to meet the water needs of livestock sustainably, ensuring a dependable groundwater supply that supports the permissible livestock population without causing excessive well drawdown and aligning water demand with sustainable resource management 
Waseem et al. (2018)  SWAT, integration of MIKE SHEE and MIKE11 Nitrogen dynamics modelling SWAT can simulate surface hydrological processes like runoff and nitrogen transport but requires coupling with another model for hydraulic processes since it does not handle backwater effects well. While MIKE 11 combined with MIKE SHE provides comprehensive hydraulic modelling capabilities, including river hydraulics, flood risk, and water quality processes, making it ideal for riverine hydraulic and nitrogen transport simulations in transboundary watersheds, due to its integrated framework that handles complex hydraulic interactions and nitrogen dynamics effectively 
Khan et al. (2017)  SWAT Hydrological processes modelling The study integrates SWAT with agent-based modelling (ABM). The framework linked SWAT with an ABM to enable interaction between natural hydrological processes and human decisions. Agents in the ABM made decisions on water usage for agriculture, hydropower, and ecosystem health. SWAT provided the hydrological data needed for these decisions, enabling a two-way feedback loop between the environmental system and human actions 
Taraky et al. (2021)  Integration of SWAT and HEC-RAS Storm design and flood inundation modelling The study simulated flood scenarios under different climate projections and dam conditions, finding that proposed dams significantly reduce flood inundation areas. For instance, under severe climate scenarios, inundation reductions ranged from 34 to 38% with dams. The results show that climate change could exacerbate flood risks due to increased precipitation and glacial melt, particularly impacting regions downstream 
Lilhare et al. (2020)   VIC Hydrological processes modelling The different climate data sources affect seasonal and annual hydrological simulations. By using various datasets, it can be investigated how these uncertainties propagate through the VIC model's outputs, particularly in terms of streamflow, soil moisture, and evapotranspiration. The study seeks to pinpoint which VIC model parameters most significantly influence streamflow and other hydrological components. This helps improve model calibration by focusing on the most impactful parameters 
SourceMethodPurposesNote
Traore et al. (2015)  HEC-RAS Flow characteristics modelling The model is used to evaluate the impact of hydraulic structures on water flow and estimate floodplains, ultimately informing water resource management decisions focused on irrigated agriculture. The objective is to optimize the operation of these structures to ensure they are managed effectively, maximizing water availability for crops such as rice. This involves strategic control over water storage and release from dams, facilitating efficient support for agricultural activities and contributing to food security by reducing the cereal deficit in the area 
Usmanov et al. (2016)  MIKE SHE Water balance modelling The model is used to simulate interactions between surface and groundwater, providing a detailed and accurate water balance. This approach enabled quantification of the water cycle's components, notably revealing that actual evapotranspiration due to large-scale irrigation is the predominant factor in water loss. The goal was to optimize water resource management by enhancing the understanding of hydrological interactions and parameters’ spatial variability, thus informing sustainable strategies for the region's water use 
Paparrizos & Maris (2017)  MIKE SHE Water balance modelling The study aimed to model the accurate simulation of the river discharge at three key points along the main riverbed and the comprehensive modelling of the river's water balance. The process involved using a well-calibrated, yet limited number of parameters to maintain model accuracy. The successful calibration resulted in simulation outputs that closely matched observed hydrological data, demonstrating the model's effectiveness in representing the river basin's hydrological characteristics 
Salazar-Briones et al. (2018)  Integration of HEC-HMS and HEC-RAS Storm design and flood areas modelling HEC-HMS and HEC-RAS were used in tandem to develop accurate hydrological and hydraulic models for flood prediction in a transboundary river basin. The study focused on calibrating sensitive parameters like the Curve Number to ensure the models’ fidelity to real-life flooding events. This modelling aimed to improve flood hazard understanding in arid regions, ultimately aiding in urban infrastructure planning and protection from potential floods 
Rodriguez et al. (2020)  MODFLOW Groundwater flow modelling The use of visual MODFLOW to create a hydrogeological model aimed at evaluating the aquifer flow patterns with an emphasis on the river system and adjacent corridor. By analysing the volumetric fluxes and groundwater movement, their goal was to ascertain if the aquifer is transboundary, meaning whether it allows for groundwater flow across the countries border, thus determining its classification as a transboundary groundwater flow system 
Talchabhadel et al. (2021)  MODFLOW Groundwater flow modelling The model developed for the aquifer features 10 vertical layers for accurate geological representation, enabling simulation of monthly water stress and is calibrated with well data and pumping records. The goal is to optimize holistic management, addressing water quality, quantity, and climate change impacts, by making informed decisions on recharge, pumping, and withdrawals. This approach balances use and conservation, ensuring the aquifer's sustainability, and incorporates graphical models to integrate expert and stakeholder inputs into management strategies 
Azzam et al. (2022)  HEC-HMS River flow modelling The model was employed to predict and simulate peak flow and runoff, crucial due to severe water crises and the need for a bilateral water-sharing agreement. The model was calibrated and validated to ensure accuracy, demonstrating its effectiveness by producing a simulated peak flow closely matching the observed data. This approach proved valuable as a rapid prediction tool utilizing accessible rainfall and snow-cover data, vital for water resource management and international negotiations in this data-scarce region 
Mirlas et al. (2022)  HYDRUS and MODFLOW Groundwater flow modelling The integration of HYDRUS-1D and MODFLOW modelling tools is used to optimize groundwater supply for livestock on remote pastures by evaluating water movement through soil to groundwater and predicting the impact on groundwater levels from well water extraction. This enables informed strategies to meet the water needs of livestock sustainably, ensuring a dependable groundwater supply that supports the permissible livestock population without causing excessive well drawdown and aligning water demand with sustainable resource management 
Waseem et al. (2018)  SWAT, integration of MIKE SHEE and MIKE11 Nitrogen dynamics modelling SWAT can simulate surface hydrological processes like runoff and nitrogen transport but requires coupling with another model for hydraulic processes since it does not handle backwater effects well. While MIKE 11 combined with MIKE SHE provides comprehensive hydraulic modelling capabilities, including river hydraulics, flood risk, and water quality processes, making it ideal for riverine hydraulic and nitrogen transport simulations in transboundary watersheds, due to its integrated framework that handles complex hydraulic interactions and nitrogen dynamics effectively 
Khan et al. (2017)  SWAT Hydrological processes modelling The study integrates SWAT with agent-based modelling (ABM). The framework linked SWAT with an ABM to enable interaction between natural hydrological processes and human decisions. Agents in the ABM made decisions on water usage for agriculture, hydropower, and ecosystem health. SWAT provided the hydrological data needed for these decisions, enabling a two-way feedback loop between the environmental system and human actions 
Taraky et al. (2021)  Integration of SWAT and HEC-RAS Storm design and flood inundation modelling The study simulated flood scenarios under different climate projections and dam conditions, finding that proposed dams significantly reduce flood inundation areas. For instance, under severe climate scenarios, inundation reductions ranged from 34 to 38% with dams. The results show that climate change could exacerbate flood risks due to increased precipitation and glacial melt, particularly impacting regions downstream 
Lilhare et al. (2020)   VIC Hydrological processes modelling The different climate data sources affect seasonal and annual hydrological simulations. By using various datasets, it can be investigated how these uncertainties propagate through the VIC model's outputs, particularly in terms of streamflow, soil moisture, and evapotranspiration. The study seeks to pinpoint which VIC model parameters most significantly influence streamflow and other hydrological components. This helps improve model calibration by focusing on the most impactful parameters 

HEC-HMS is hydrological cycle modelling software that simulates surface runoff, surface runoff, and base flow in a drainage basin (Davamani et al. 2024). It enables the investigation of rainfall, infiltration, evapotranspiration, and runoff formation, all of which are critical for a thorough understanding of the hydrological cycle in a specific area (Bronstert et al. 2023). Meanwhile, HEC-RAS is a hydraulic modelling programme used mostly along rivers and waterways to investigate water flow in open channels by computing surface elevations, flow velocities, flow distributions, and flood potentials (Chomba et al. 2021). HEC-RAS is widely used to assess how hydraulic constructions such as dams, bridges, and irrigation channel networks affect water flow patterns (Kamran et al. 2021). Furthermore, integrated systems that use multiple software packages, such as HEC-HMS and HEC-RAS, improve flood prediction accuracy. This is critical for arid region urban development and flood prevention since it improves flood hazard comprehension and aids in urban infrastructure planning and protection (Salazar-Briones et al. 2018).

MIKE SHE, created by the DHI Group, is a hydrological modelling software required for optimizing transboundary irrigation (Rahim & Yusoff 2023). MIKE SHE fully incorporates surface and groundwater dynamics, allowing for comprehensive modelling of hydrological systems, which makes it easier to simulate important processes as precipitation, evapotranspiration, infiltration, surface runoff, groundwater flow, and surface–groundwater interaction (Prucha et al. 2016). Furthermore, when modelling nitrogen dynamics in hydrological systems, tools like MIKE SHE combined with MIKE 11 are particularly effective (Waseem et al. 2018). These models allow for detailed simulation of nitrogen transport and transformation processes. Other than that, MODFLOW Model can also simulate a wide range of hydrogeological phenomena, including groundwater flow, interactions between groundwater and surface water, and recharge and discharge in porous medium aquifers (Condon et al. 2021; Davamani et al. 2024). MODFLOW's ability to estimate groundwater levels, evaluate pollutant movement, and assess the effects of land use changes makes it a valuable tool for optimizing transboundary irrigation techniques and guaranteeing sustainable water resource management (Davamani et al. 2024). Lesser-used software, such as HYDRUS, can help with transboundary irrigation optimization by simulating water transport and successfully managing groundwater resources (Mirlas et al. 2022). By tying water demand to sustainable practices, HYDRUS assures a consistent water supply for agriculture while reducing the risk of groundwater depletion. Despite its low profile, HYDRUS delivers critical insights into groundwater dynamics that are essential for effective irrigation control.

Moreover, SWAT and VIC models are widely used in hydrological and water quality modelling, of which SWAT is a physical model designed to simulate long-term impacts of agricultural activities on water quality and quantity in large watersheds, covering parameters such as evapotranspiration, erosion, and nitrogen dynamics (Waseem et al. 2018). It is particularly useful for analysing nitrogen pollution in intensively farmed areas and assessing human interventions. VIC, on the other hand, focuses on simulating the hydrological cycle by incorporating key inputs like precipitation and air temperature, allowing for sensitivity and uncertainty analysis in water processes, particularly in cold or snow-covered regions (Lilhare et al. 2020). VIC is suitable for predicting streamflow variations and improving water management in cold climates, making it an essential tool for transboundary irrigation systems (Safeeq et al. 2014).

Water resource management and optimization modelling

With enough information and thorough data, transboundary water for irrigation optimization is possible by modelling the water resource and management (De Keyser et al. 2023). Typically, this optimization process involves using a variety of approaches geared to the complexities of cross-border water resource management (Derepasko et al. 2021; Santos et al. 2023). Machine learning, stochastic methods, linear programming, and particular water resource management and optimization models are examples of methodology and modelling tools used in transboundary water for irrigation optimization (Agastya et al. 2024). Aside from data predicting, machine learning can also be used for optimization, which frequently involves stochastic modelling or linear programming.

Stochastic modelling adds randomness and uncertainty into the optimization process, resulting in more accurate representations of complex systems, and this technique is especially effective in dynamic and uncertain conditions, when typical deterministic approaches may fail. For example, two-stage chance-constrained programming (TSCCP) approach blends pre-emptive decision-making based on expected situations with tailored adjustments in reaction to random events, resulting in a dynamic solution for handling uncertainties such as weather changes. In the initial step of the optimization model, decision variables are determined before any random occurrences occur, functioning as preventative actions based on projected scenarios within the agricultural irrigation system, considering the field water cycle. In the second step, decision variables are established after random events have occurred, and they are modified to handle the details of the uncertainties that have been identified, such as weather changes or water supply. Integrating these two steps into the decision-making process can thus optimize agricultural irrigation water allocation, improving the effectiveness and sustainability of water resource management in agriculture while considering the field water cycle (Yan & Li 2018). In addition, the double-sided stochastic fractional programming (DSFP) model handles uncertainty in water-agriculture-energy nexus (WAEN) systems, with a specific emphasis on climate change consequences (Zhang et al. 2024). The strategy is utilized to build an optimal water allocation methodology for a WAEN system, with a focus on mitigating the effects of climate change. To determine the best water allocation plans for a transboundary river basin, a DSFP-WAEN model was developed and applied to 48 different scenarios to analyse the long-term planning implications of various factors such as climate change, improved irrigation efficiency, and varying levels of system risk. The overarching goal is to optimize water allocation schemes by balancing economic advantages against system hazards, all while effectively managing the inherent uncertainties and intricate interrelationships of water supplies for agricultural and energy output.

In addition to forecasting and decision-making, stochastic modelling can optimize transboundary irrigation. Copula-based stochastic multi-objective programming, for example, optimizes irrigation plans by maximizing economic advantages while conserving water in drought-prone locations (Zhang et al. 2023). To effectively analyse and manage the effects of seasonal agricultural drought, the Copula model is used, which combines regional net irrigation water demand and stream runoff. This model addresses the uncertainty and complexity of drought scenarios by optimizing three core objectives: maximizing economic benefits while considering the cost-effectiveness of water uses and conservation, ensuring the highest crop yield during drought conditions through efficient water allocation, and improving irrigation strategies for greater water savings, which is critical in areas experiencing water scarcity. Similarly, Ortiz-Partida et al. (2019) employed a two-stage stochastic optimization model to propose robust procedures for a multifunctional reservoir. The first part of the project comprises developing a robust set of reservoir releases that address a variety of objectives, including water supply, flood control, hydropower generation, and ecological considerations across a wide range of hydroclimatic situations. The goal of this stage is to plan for efficient and cost-effective water management in the face of climate and water availability uncertainties. The second stage evaluates these planned releases by comparing their performance to a wide range of future hydroclimatic conditions, allowing for any necessary adjustments to maximize economic advantages while reducing potential dangers from changeable hydroclimatic events.

Linear programming, on the other hand, is a mathematical optimization technique for determining the optimal outcome in a model according to particular limitations. Linear programming can be used to reduce water use by assessing precipitation efficacy and estimating crop irrigation water requirements, with the goal of maintaining agricultural yields (Difallah et al. 2017). The study's linear programming model combines the ‘knapsack’ problem decisional form to analyse the efficacy or ineffectiveness of precipitation and to forecast the amount of irrigation water needed to optimize its use in agricultural activities. Their proposal addressed worries about water scarcity while also promoting good agricultural water management. Linear programming, like stochastic modelling, seeks to maximize economic advantages by maximizing, reducing, or combining the two to accomplish desired results within given constraints (Kumar & Sen 2020). Other studies sought to maximize net economic benefits by distributing water to urban and agricultural consumers while conforming to supply and regulatory restrictions (Kucukmehmetoglu & Geymen 2014). Their research began with a linear programming model but grew into a mixed integer programming model to better handle linear, binary, and integral decisions, allowing for trade-off evaluation and collaborative efforts among countries sharing water resources. This improvement facilitates the assessment of trade-offs and cooperative efforts among countries that share these water resources. Furthermore, linear programming maximizes agricultural production benefits by balancing planting patterns and water requirements against seasonal water availability (Juwono et al. 2018). The model aimed to maximize yield or profit by efficiently utilizing water resources within the restrictions of land availability, water supply, and crop requirements, hence increasing the benefits of crops grown in irrigated zones.

More than that, transboundary optimization can also be achieved using water resource management modelling tools as shown in Table 6. Modelling tools, including the water evaluation and planning (WEAP) System, SWAT, modular modelling system (MODSIM), MIKE HYDRO, and linear interactive and discrete optimizer (LINDO), have been employed in research to provide comprehensive frameworks for monitoring and managing water resources across borders, incorporating a diverse set of data sources and modelling methodologies to aid decision-making. Water resource management modelling tools assist in the optimization of transboundary irrigation systems by simulating water allocation, usage, and distribution scenarios. This promotes sustainable water use while maximizing economic benefits and lowering environmental costs (Dinar 2024).

Table 6

Water resource management modelling

SourceMethodPurposesNote
Psomas et al. (2016)  Integration of WEAP and SWAT Water efficient modelling The study integrated models to optimize the balance water demand with supply across urban, tourism, industrial, and agricultural sectors. WEAP analysed regional water needs and identified potential efficiency gains, while SWAT examined detailed hydrological processes in agriculture, focusing on optimizing irrigation to save water. Together, these models were used to propose strategies ensuring that water is used judiciously and sustainably in the catchment 
Emami & Koch (2018)  Integration of MODSIM and SWAT Hydroeconomic modelling The study utilizes the SWAT model for detailed simulations of hydrology and crop yields influenced by land use and climate conditions, alongside the MODSIM model for strategic water resources allocation and management. Together, these tools facilitate the optimization of multi-crop planning and water resource management with the aim of maximizing agricultural water productivity and achieving a balance between water use efficiency, crop yields, and economic returns, all within the broader context of adapting to climate change's impact on water availability 
Fadaeizadeh & Shourian (2019)  Integration of MODSIM and PSO Water efficient modelling The study uses MODSIM coupled with the PSO algorithm to optimize agricultural water demand. The focus is on reducing these demands to achieve a satisfactory reliability level in water supply, with the aim of sustainably managing water resources and maintaining an appropriate allocation for agricultural purposes, amidst pressing water scarcity challenges. The combination of MODSIM and PSO helps in the determination of suitable water allocations that meet reliability constraints 
Malamataris et al. (2020)  Integration of UTHBAL, MIKE SHE, MIKE HYDRO River and Basin Water availability modelling The study aims to optimize water management strategies basin by estimating the availability of surface water and groundwater using hydrological models. These models simulate various scenarios to discern the effects of climate change and human activities, including scenarios absent of human intervention. By predicting the future state of water resources and analyzing potential impacts on the water balance and levels, these models provide critical insights that guide the development of sustainable water management practices 
Ramadan et al. (2021)  LINDO Water efficient and allocation modelling A mathematical model utilizing physical mass balance constraints was developed and integrated with ArcGIS to analyse canal networks and expected water shortages in the agriculture sector. This model is designed to optimize water allocation by considering available resources including canal water, groundwater, and reclaimed water. The objective is to redistribute water strategically based on supply deficits and demand across diverse nodes such as agricultural and domestic sectors 
Taraky et al. (2021)  Integration of SWAT and HEC-RAS Hydrological and flood risk modelling The study utilized SWAT for simulating hydrological processes such as precipitation, snowmelt, and surface runoff under diverse climate and dam management scenarios, while HEC-RAS was used to model river flood routing to determine potential flood extents, involving water surface profiles and spatial flood characteristics. The goal was to optimize flood risk management by devising a transboundary water management strategy that enhances cooperation, aiming to mitigate flood risks and bring socioeconomic benefits to both countries sharing river basin 
Leta et al. (2022)  HEC-Res PRM and SWAT Hydropower reservoir operation modelling SWAT is employed to analyse how land use changes impact water inflows, a vital component for optimizing the operation of the Hydropower Reservoir, especially in regions without direct flow measurements. Concurrently, HEC-Res PRM is used to refine reservoir management, balancing water releases, storage, and stakeholder demands, with the specific goal of determining the optimal monthly releases to meet watershed needs under different land cover scenarios, thereby enhancing hydropower production and adapting to increasing inflows and storage requirements 
Yimere & Assefa (2022)  MIKE HYDRO Water allocation modelling The study uses the model to evaluate present and future irrigation potential and requirements, simulating historical irrigation water requirement and projecting future scenarios. The aim is to optimize irrigation patterns and water use efficiency under the scenarios, considering crop needs, water losses, and climate change impacts to achieve sustainable and effective irrigation management in the region 
Babel et al. (2023)  LINDO Water allocation modelling The study aims to maximize the economic return by strategically allocating water resources among key sectors such as electricity generation, flood control, domestic and agricultural demands, recreation, and salinity control. It seeks to establish a sustainable balance within the water-energy-food nexus to optimize productivity and address the competition for water among these interconnected sectors, taking into account various socioeconomic development scenarios in the basin 
Genjebo et al. (2023)  Integration of HEC-HMS and WEAP Water resource and allocation modelling The study employs the HEC-HMS model to model watershed hydrology, enabling the analysis of peak flows and runoff characteristics which is crucial for understanding the watershed's hydrologic responses. This is coupled with the WEAP model to strategically manage and allocate the available surface water to satisfy the different competitive requirements, such as irrigation, livestock, and domestic needs, in a sustainable manner. The integrated approach aims to optimize the allocation of surface water resources by balancing current and projected future water demands, mitigating potential conflicts over water use, encouraging efficient water usage, and fostering economic development within the context of water scarcity and competing water uses 
SourceMethodPurposesNote
Psomas et al. (2016)  Integration of WEAP and SWAT Water efficient modelling The study integrated models to optimize the balance water demand with supply across urban, tourism, industrial, and agricultural sectors. WEAP analysed regional water needs and identified potential efficiency gains, while SWAT examined detailed hydrological processes in agriculture, focusing on optimizing irrigation to save water. Together, these models were used to propose strategies ensuring that water is used judiciously and sustainably in the catchment 
Emami & Koch (2018)  Integration of MODSIM and SWAT Hydroeconomic modelling The study utilizes the SWAT model for detailed simulations of hydrology and crop yields influenced by land use and climate conditions, alongside the MODSIM model for strategic water resources allocation and management. Together, these tools facilitate the optimization of multi-crop planning and water resource management with the aim of maximizing agricultural water productivity and achieving a balance between water use efficiency, crop yields, and economic returns, all within the broader context of adapting to climate change's impact on water availability 
Fadaeizadeh & Shourian (2019)  Integration of MODSIM and PSO Water efficient modelling The study uses MODSIM coupled with the PSO algorithm to optimize agricultural water demand. The focus is on reducing these demands to achieve a satisfactory reliability level in water supply, with the aim of sustainably managing water resources and maintaining an appropriate allocation for agricultural purposes, amidst pressing water scarcity challenges. The combination of MODSIM and PSO helps in the determination of suitable water allocations that meet reliability constraints 
Malamataris et al. (2020)  Integration of UTHBAL, MIKE SHE, MIKE HYDRO River and Basin Water availability modelling The study aims to optimize water management strategies basin by estimating the availability of surface water and groundwater using hydrological models. These models simulate various scenarios to discern the effects of climate change and human activities, including scenarios absent of human intervention. By predicting the future state of water resources and analyzing potential impacts on the water balance and levels, these models provide critical insights that guide the development of sustainable water management practices 
Ramadan et al. (2021)  LINDO Water efficient and allocation modelling A mathematical model utilizing physical mass balance constraints was developed and integrated with ArcGIS to analyse canal networks and expected water shortages in the agriculture sector. This model is designed to optimize water allocation by considering available resources including canal water, groundwater, and reclaimed water. The objective is to redistribute water strategically based on supply deficits and demand across diverse nodes such as agricultural and domestic sectors 
Taraky et al. (2021)  Integration of SWAT and HEC-RAS Hydrological and flood risk modelling The study utilized SWAT for simulating hydrological processes such as precipitation, snowmelt, and surface runoff under diverse climate and dam management scenarios, while HEC-RAS was used to model river flood routing to determine potential flood extents, involving water surface profiles and spatial flood characteristics. The goal was to optimize flood risk management by devising a transboundary water management strategy that enhances cooperation, aiming to mitigate flood risks and bring socioeconomic benefits to both countries sharing river basin 
Leta et al. (2022)  HEC-Res PRM and SWAT Hydropower reservoir operation modelling SWAT is employed to analyse how land use changes impact water inflows, a vital component for optimizing the operation of the Hydropower Reservoir, especially in regions without direct flow measurements. Concurrently, HEC-Res PRM is used to refine reservoir management, balancing water releases, storage, and stakeholder demands, with the specific goal of determining the optimal monthly releases to meet watershed needs under different land cover scenarios, thereby enhancing hydropower production and adapting to increasing inflows and storage requirements 
Yimere & Assefa (2022)  MIKE HYDRO Water allocation modelling The study uses the model to evaluate present and future irrigation potential and requirements, simulating historical irrigation water requirement and projecting future scenarios. The aim is to optimize irrigation patterns and water use efficiency under the scenarios, considering crop needs, water losses, and climate change impacts to achieve sustainable and effective irrigation management in the region 
Babel et al. (2023)  LINDO Water allocation modelling The study aims to maximize the economic return by strategically allocating water resources among key sectors such as electricity generation, flood control, domestic and agricultural demands, recreation, and salinity control. It seeks to establish a sustainable balance within the water-energy-food nexus to optimize productivity and address the competition for water among these interconnected sectors, taking into account various socioeconomic development scenarios in the basin 
Genjebo et al. (2023)  Integration of HEC-HMS and WEAP Water resource and allocation modelling The study employs the HEC-HMS model to model watershed hydrology, enabling the analysis of peak flows and runoff characteristics which is crucial for understanding the watershed's hydrologic responses. This is coupled with the WEAP model to strategically manage and allocate the available surface water to satisfy the different competitive requirements, such as irrigation, livestock, and domestic needs, in a sustainable manner. The integrated approach aims to optimize the allocation of surface water resources by balancing current and projected future water demands, mitigating potential conflicts over water use, encouraging efficient water usage, and fostering economic development within the context of water scarcity and competing water uses 

WEAP is a commonly used tool for managing water resources for the sustainable management and allocation of available surface water to meet competing demands such as irrigation, livestock, and domestic use (Esraa et al. 2023). When combined with HEC-HMS, WEAP optimizes surface water allocation by balancing current and projected future water demands, reducing potential water use conflicts, encouraging efficient water use, and promoting economic development in the face of water scarcity and competing water uses (Genjebo et al. 2023). SWAT is another important hydrological modelling method, notably for determining the effects of land management practices on water, sediment, and agricultural chemical yields (Wang et al. 2019; Nda et al. 2020; Al Khoury et al. 2023). SWAT is frequently coupled with other modelling methodologies, including hydrological and hydraulic modelling, as well as water resource management modelling (Nda et al. 2020). For example, it can be combined with HEC-RAS or WEAP to provide complete water resource and flood management (Psomas et al. 2016; Taraky et al. 2021). MODSIM is a versatile modelling programme that may be used for hydroeconomic analysis as well as strategic water resource management and allocation, which is frequently combined with other models, such as SWAT or PSO, to improve irrigation techniques and water distribution systems (Emami & Koch 2018; Fadaeizadeh & Shourian 2019). MIKE HYDRO consists of the MIKE HYDRO River and MIKE HYDRO Basin modules (Vashist & Singh 2022). MIKE HYDRO River simulates river hydraulics, whereas MIKE HYDRO Basin models basin-level water availability (Yimere & Assefa 2022). These techniques are frequently integrated with MIKE SHE to provide comprehensive insights into water management strategies that account for both surface and groundwater dynamics (Malamataris et al. 2020). LINDO is a mathematical optimization tool for linear, integer, and nonlinear programming (Paramjita et al. 2019). Its strength stems from its capacity to efficiently address complicated optimization problems, particularly those connected to water resource management, such as water efficiency and allocation models (Ramadan et al. 2021). The benefits of using LINDO include the ability to quickly uncover optimal solutions, improve decision-making processes, and develop cost-effective resource allocation approaches (Babel et al. 2023).

Holistic strategy to optimizing transboundary water for irrigation is critical to obtaining optimal results as shown in Figure 1. This is reinforced by studies that show that both technological and socioeconomic elements must be examined concurrently (Gorgoglione et al. 2019; Merem et al. 2020). Technical components of optimization are critical for recognizing the different technical parameters that influence rainfall patterns, land use, soil types, and topographic characteristics. The input data used, such as field observation data and calibrated and validated remote sensing data, form a solid foundation for such technical analysis. One of the most important field data is a crop pattern. Crop patterns are valuable sources of field observation data for optimization (Taghdisian et al. 2022). However, given the micro-level nature of crop patterns, this assessment does not go into detail about them, focusing instead on macro-scale irrigation. Nonetheless, determining crop patterns at the micro-level serves as a crucial initial step before commencing macro-scale or watershed-wide optimization processes (Sadeghi et al. 2020). The prevailing crop patterns within irrigation areas offer essential insights into specific water requirements. Thus, selected crop patterns should align with soil characteristics and the water needs of each crop during different seasons (Neupane & Guo 2019). The integration of field data and remote sensing data proves to be an effective strategy for attaining a more comprehensive understanding of transboundary water for irrigation optimization, with remote sensing data playing a vital role in complementing poorly recorded field data with the advantages discussed earlier (Yalew et al. 2023).
Figure 1

Integration approach of transboundary water for irrigation optimization.

Figure 1

Integration approach of transboundary water for irrigation optimization.

Close modal

Furthermore, socioeconomic aspects must be included as input data to make informed water management decisions. Policies and governance components are critical socioeconomic factors to consider when integrating technology characteristics (Shao et al. 2020). First, a clear legal and legislative framework is essential for sustainable water use, as evidenced by the 1964 Columbia River Treaty between the United States and Canada. This treaty defines water allocation and flood control measures, establishing a systematic framework for collaborative management of the Columbia River Basin (Zhang et al. 2021). Similarly, the MRC, created in 1995, ensures equitable use and management of the Mekong River Basin among member nations by legally binding agreements (Feng et al. 2019). Second, effective stakeholder engagement and empowerment are critical for inclusivity and support for water management initiatives, as demonstrated in the Rhine River Basin by the International Commission for the Protection of the Rhine (ICPR), which engages diverse stakeholders in collaborative decision-making (Booth et al. 2020). Furthermore, the Lake Chad Basin Commission (LCBC), which was created in 1964, actively engages communities and non-governmental organizations in decision-making processes (Mbaigoto et al. 2023). Third, increased information sharing and data openness promote trust and informed decision-making, as seen by the Danube River Basin Commission's extensive data-sharing platform (Lipponen 2020). Fourth, institutional coordination and capacity building are essential, as demonstrated by the Lake Victoria Basin Commission (LVBC) and the Zambezi Watercourse Commission (ZAMCOM) (Paisley & Curlier 2019). Finally, dispute resolution and consensus building are critical, as evidenced by the Indus Water Treaty (IWT) between India and Pakistan and the Nile Basin Cooperative Framework Agreement (Rigi & Warner 2020; Wehling 2020).

The IWT of 1960, mediated by the World Bank, governs water sharing between India and Pakistan, primarily allocating the Indus Basin's rivers while providing dispute resolution mechanisms like the Permanent Indus Commission and international arbitration. Both countries have at times linked water issues to broader geopolitical concerns, such as terrorism, complicating cooperation. Pakistan's alliance with China on water projects further affects negotiations, as these agreements bolster Pakistan's position and reduce India's leverage. The securitization of water resources and issue-linkage strategies have reinforced the strategic and contentious nature of transboundary water diplomacy between the two nations. Other than that, the Nile Basin Cooperative Framework Agreement was developed as a legal framework for managing the Nile waters equitably among the riparian states. Despite its intent to foster cooperation, only a subset of the 11 basin countries initially signed the CFA in 2010. The CFA aims to align with international principles, such as equitable and reasonable utilization and the no-harm rule, adapted from the 1997 UN Watercourses Convention. Egypt and Sudan have refrained from signing due to concerns over water security and existing rights, while other nations like Ethiopia ratified it, reflecting varied regional perspectives on shared water management.

Despite the importance of initiatives, as previously stated, difficulties still exist in these areas. This suggests that studying socioeconomic criteria alone does not guarantee the success of irrigation projects. Likewise, relying solely on technical aspects will not ensure success, as effective irrigation requires a balanced consideration of both technical and socioeconomic aspects. As a result, it is vital to integrate these two dimensions with all stakeholders, including the community and farmers, fulfilling their tasks with utmost dedication (Shao et al. 2020). To ensure the long-term management of irrigation water resources, stakeholders may need to work effectively together, obey regulations, and actively participate in decision-making processes (Barua et al. 2019; Rivera-Torres & Gerlak 2021).

With good input data and the integration of processes involving forecast data and decision-making factors, simulations of water quantity and quality, and optimization itself, yield significant outcomes (Xue et al. 2021). The medium-term outputs of this optimization are adaptive capacity, sustainable practices, and resilient communities. Irrigation systems can dynamically react to changing environmental circumstances and water demand by leveraging predictive data and decision-making criteria (Nikolaou et al. 2020). This enables quick responses to significant weather changes, water quality decreases, or policy alterations, improving the irrigation system's ability to manage new difficulties. Furthermore, optimization of transboundary water for irrigation can result in more sustainable agricultural practices (Guo et al. 2022; Ruan et al. 2023). Decisions regarding water usage and land management can be made by considering environmental, economic, and social aspects comprehensively (Li et al. 2020). More sustainable agriculture can be achieved by effective water usage, environmentally friendly irrigation systems, and enhanced soil conservation practices (Velasco-Muñoz et al. 2019). Furthermore, by increasing adaptive ability and implementing sustainable agriculture practices, irrigation-dependent areas, the countries, and communities will become more robust to environmental and social change, and thus, they will be more prepared to address issues such as droughts, floods, policy changes, water resource sustainability, and food security (Srivastav et al. 2021). These three outcomes are extremely related to the SDGs, particularly eradicating hunger, providing clean water and sanitation, affordable and clean energy, climate action, and the WFE-NEXUS idea. All of these efforts eventually yield long-term economic benefits, demonstrating the connection of water, food, energy, and economic development.

This study underscores the critical need for a comprehensive approach to transboundary water management for irrigation, addressing challenges like competing water demands, climate change impacts, and governance complexities. Key technologies, such as remote sensing and predictive modelling, are essential for enhancing data collection and improving water allocation strategies. Integrating these technical solutions with socioeconomic policies promotes adaptive capacity and fosters sustainable development, aligning with global goals like the SDGs and the Water-Food-Energy Nexus.

Future research should focus on refining predictive models that incorporate both environmental and socioeconomic variables, as well as exploring ways to strengthen cross-border cooperation frameworks. In addition, advancing the use of machine learning in water management could improve accuracy in forecasting and decision-making under uncertainty. For practical applications, continued emphasis on building resilience through sustainable practices will be crucial to mitigating the effects of water scarcity and variability, thereby supporting long-term resource sustainability and regional prosperity.

The authors would like to thank Jember University as a funder and IDB Internal Support Grant provider.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

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