Understanding the potential impact of anthropogenic climate change on the hydrological regime of the Koshi River Basin (KRB) is very important for sustainable water resources and ecosystem management. The hydrological studies are mainly focused on the annual, seasonal, and peak flows of the hydrological regime; however, the ecologically relevant flows of the hydrological regime are less explored. In this study, we analyzed the different flow characteristics based on the magnitude, intensity, and duration using the Indicator of Hydrologic Alterations (IHA) under the influence of shared socio-economic pathway (SSP) scenarios. We found that the KRB will experience a shift in hydro-climatic events, an increase in rise and fall rates of flow, increase in monthly low flows of the hydrological regime, eventually affecting the livelihoods and ecosystem of the basin. This study highlights the importance of environmental flow components (EFCs) in a hydrological regime to better understand the flow characteristics during the future hydro-climatic variability.

  • IHA and EFCs were grouped into five based on magnitude, intensity, and duration.

  • Increased uncertainty has increased the chances of untimely extreme events in the KRB.

  • Future post-monsoon and winter streamflow increases, sustaining the water table and aquatic life.

Flow is the ‘master variable’ in river ecosystems; if any kind of alteration occurs due to a river regulation project, the whole flow regime is disturbed, affecting the ecosystem and human civilization (Smakhtin et al. 2006). The flow regime is a characteristic which defines the hydrology of any river and shows the consequences toward the ecosystem and human lives. The human lives and society are dependent upon the rivers for their civilization. Based on different seasons, the flow regime changes affecting and uplifting the ecosystem and society. To quantify the flow regime for proper flow management considering ecological and social consequences, the hydrological indicators are introduced (Olden & Poff 2003). Earlier practices were limited to single-flow approaches such as assigning a single minimum flow standard to maintain river ecology. Then these practices were replaced by the concept of flow variability (Yuqin et al. 2019). The concept of flow variability represents the variation of flow in terms of magnitude, frequency, timing, duration, and rate which defines the temporal variation in the river.

The most effective approach for assessing the flow variability and hydrologic alteration is the Index of Hydrologic Alteration/Range of Variability Approach (IHA/RVA) which includes 33 hydrologic indices in five groups that provides a comprehensive analysis of variability of flow and analysis of hydrologic alteration (Richter et al. 1996). Gunawardana et al. (2021) observed the hydrologic alteration in daily runoff time series in the Sre Pok River Basin of the Lower Mekong region using IHA/RVA. Yuqin et al. (2019) used IHA-based RVA and Histogram Comparison Approach (HCA) methods in Kaligandaki River to calculate the flow regime alteration after the construction of a hydropower facility. The changes in flow characteristics of the Koshi River Basin were analyzed using IHA to know the impact of changes in flow on the quality and quantity of riverine ecosystems (Bharati et al. 2019). The changes in flow and hydrologic processes are mainly due to anthropogenic global climate change and human activities (Yang et al. 2012). Furthermore, climate change and land use shift are two main factors affecting the hydrological cycle and eventually flow regime (Chen et al. 2020).

The impact of climate change on rainfall and temperature patterns is expected to affect the hydrological regime and water availability of the Himalayan region (Hock et al. 2019). According to climate change studies, the average annual river flows of Himalayan rivers such as Bagmati, Kaligandaki, Mahakali, and Karnali are expected to increase in future periods (Pandey et al. 2020; Dahal et al. 2021; Mann &Gupta 2022). The rising impact of the climate change in Himalayan regions is triggering climate change-induced hazards which are making people migrate from upstream to downstream which directly affects the water availability downstream, increasing the demand for food and shelter in downstream areas, and pressure on natural resources and much more. The impact of the climate change in the Koshi River Basin is severely affecting water resources, agriculture, local livelihoods, and food security (Hussain et al. 2018). The previous studies have projected the climate and suggest that changes in the extremes will increase the difference between high and low flow regimes and as a result, livelihoods will suffer from frequent floods and droughts (Dixit et al. 2009; Faye 2022). Hussain et al. (2016) found that farmers living in the Koshi Basin are already adopting different practices to cope with the severe impact of climate change such as changing crops according to water requirements, changing cropping techniques, and small changes in crop calendar.

Previous studies which focused on climate change's impact on the hydrology of the Koshi River Basin were based on Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models, A2 and B1 climate scenarios from the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios (IPCC-SRES) and Representative Concentration Pathways (RCPs). All the studies projected the future climate and future water availability of the basin. They reported that seasonal variation in runoff was significant in coming near-future (NF) periods (Bharati et al. 2014, 2019; Devkota & Gyawali 2015). Most analyses of climate impacts on river hydrology focus on the effects of temperature and rainfall changes. Furthermore, the analyses are limited to whether the basin will face prolonged drought or frequent floods in future periods. There are very few studies in the Himalayan region which only focus on river ecosystems, flow variability of Himalayan rivers using ecological flow parameters, and hydrological indicators (Bharati et al., 2019; Yuqin et al. 2019). The studies of characteristics of flow regime are very important for understanding river variability and freshwater ecosystems, exploring the influence of streamflow on local communities, hydrologic regionalization analyses and providing an inventory of hydrologic types of water source management (Berhanu et al. 2015). Additionally, the studies of climate change's impact on flow regime are also important because the alteration in both temperature and rainfall are the most important physical effects of change in the climate of river ecosystems.

This study uses IHA-based hydrological indicators and environmental flow components (EFCs) to calculate the observed and future flow variability of the basin. Additionally, we used the latest released CMIP6 General Circulation Models (GCMs) with shared socio-economic pathway (SSP) scenarios to project the future flow regime of the Koshi River Basin, Nepal. As new sets of data were released recently, only a few studies have used the CMIP6 model outputs for the projection of the future climate of Nepal and very few have in the Himalayan River Basin of Nepal (Mishra et al. 2020; Chhetri et al. 2021). The evaluation of uncertainty in hydrology with socio-economic factors is well defined by SSPs. Socio-economic scenarios under different SSPs are related to changes in land use, urbanization, population growth, water-efficient technology, cropping patterns, water demand and industrial growth (O'Neill et al. 2016). There are no studies yet which have projected the future flow and characteristics of the Koshi River Basin under SSP scenarios. Similarly, previous studies are only focused on evaluating changes in mean monthly and annual flows but a more detailed analysis of changes in hydrological characteristics induced by climate change and their effects on riverine ecosystems is missing and highly needed to understand the different flow characteristics based on magnitude, duration, and intensity. There are three important drivers of the river hydrological regime such as climate change, socio-economic growth, and water management practices (Gupta et al. 2022).

Therefore, the present study highlights the observed and future impacts of changes in the climate and hydrological regime of the Koshi River Basin. An understanding of the different hydrological indicators will help to identify the altered flow regime in the basin, which will be beneficial for the decision-makers to design more effective water management policies for preserving the biodiversity and allocation of water for socio-economic development in the basin.

Study area

The Koshi River Basin is located in the eastern part of Nepal with a total catchment area of 74,030 km2 (FMIS 2012). It is divided into different regions depending upon the elevation. The high elevation region is known as the Trans Himalaya region, whereas lower elevation regions are known as Middle Mountains and low-lying plain areas (Rajbhandari et al. 2016). The Tamor, Arun, and Sunkoshi are the major three tributaries in the eastern, middle, and western parts of the basin, but the outlet of the basin is located in the eastern part of Nepal (Figure 1). The Koshi River Basin has experienced a variety of climatic seasons such as pre-monsoon (March–May), monsoon (June–September), post-monsoon (October–November), and winter (December–February) (Bharati et al. 2014). The annual rainfall and temperature of the basin range from 32.5 to 925.25 mm and −3 to 28 °C. The monsoon rainfall contributes 80% of the annual rainfall and the intensity of the rainfall is also high, which causes various hydrological extreme events like landslides, flash floods, floods, etc. (Agarwal et al. 2016). The basin has a wide elevation range from 113 m.a.s.l. in the south to 8,848 m a.s.l. in the north (Khadka et al. 2020). The Koshi River Basin is also dominated by snow and glaciers, which significantly contribute to the runoff of the major rivers (Immerzeel et al. 2009).
Figure 1

Location map of the Koshi River Basin, Nepal.

Figure 1

Location map of the Koshi River Basin, Nepal.

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Methodology

The methodology consists of four main components: (i) projection of the future climate of the basin, (ii) development of the hydrological model, (iii) simulation of future hydrology using future climate data, and (iv) analysis of changes in hydrological indicators (historical and future). The methodology adopted in this study is depicted in Figure 2.
Figure 2

Research methodology used in this study.

Figure 2

Research methodology used in this study.

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The future climate projection of the Koshi River Basin was carried out using three climatic variables, i.e., rainfall, maximum, and minimum temperature. The four CMIP6 GCMs data sets were used to project the future climate for three future periods, i.e., the NF (2015–2045), the mid-future (2046–2076), and the far-future (2077–2100) under the SSP2-4.5 and SSP5-8.5 scenarios which were bias-corrected using the Quantile Mapping method. The Soil and Water Assessment Tool (SWAT) model was used to simulate the discharge in the basin under future climatic conditions. The calibration and validation of the model were carried out on the outlet of the basin, i.e., the Chatara station. The calibration period is 1985–2006 and the validation period is 2009–2012 for the Chatara station. The future discharge of the basin is estimated using model outputs under future climate scenarios for all three future periods. The IHA were used for the identification of hydrological extremes of the Koshi River Basin. The indicators are divided into five groups which identify the magnitude, intensity, and duration of river discharge such as (1) the magnitude of the monthly discharge; (2) the magnitude of the annual extreme discharge under different durations; (3) the timing of the annual extreme discharge; (4) rate and frequency of thedischarge change; and (5) the monthly low flows of EFCs (Table 1).

Table 1

Hydrological parameters of the IHA used in this study

IHA parameter groupHydrological parameters
1. Magnitude of monthly water conditions (12 parameters) Mean monthly flow from January to December 
2. Magnitude and duration of annual extreme water conditions (10 parameters) 1-day, 3-day,7-day,30-day, and 90-day minimum flow and maximum flow 
3. Timing of annual extreme water conditions (2 parameters) Julian date of the annual 1-day maximum and minimum water conditions 
4. Rate and frequency of water conditions (2 parameters) Rise rates: Mean or median of all positive differences between consecutive daily values 
Fall rates: Mean or median of all negative differences between consecutive daily values 
5. Environment flow components (EFCs): monthly low flows (12 parameters) EFCs: monthly low flow (January–December) 
IHA parameter groupHydrological parameters
1. Magnitude of monthly water conditions (12 parameters) Mean monthly flow from January to December 
2. Magnitude and duration of annual extreme water conditions (10 parameters) 1-day, 3-day,7-day,30-day, and 90-day minimum flow and maximum flow 
3. Timing of annual extreme water conditions (2 parameters) Julian date of the annual 1-day maximum and minimum water conditions 
4. Rate and frequency of water conditions (2 parameters) Rise rates: Mean or median of all positive differences between consecutive daily values 
Fall rates: Mean or median of all negative differences between consecutive daily values 
5. Environment flow components (EFCs): monthly low flows (12 parameters) EFCs: monthly low flow (January–December) 

Hydrological modeling

The SWAT is a process-based, time series hydrological model used in simulating water quality and quantity of surface water, groundwater and which is also used to predict the impact of climate change and land use change on hydrology (Arnold et al. 2012). The model simulates the water balance components (surface runoff, infiltration, percolation, evapotranspiration, deep and shallow aquifer, and channel flow) of the watershed using climate inputs like daily rainfall, maximum and minimum temperatures, land use/land cover map, and soil map (Arnold et al. 1998).

Model setup and input data

The input data like hydrological data, meteorological data, DEM data, soil properties, and land use/land cover data are used in parameterizing the SWAT model and are summarized in Table 2. The 30 m × 30 m resolution of DEM data was used to determine the watershed and its boundaries of the Koshi River Basin. The 2015 land use/land cover data from the European Space Agency (ESA) and the soil map of 2011 from the Food and Agriculture Organization (FAO) were used for the study. With data from all the input to the model, the watersheds were divided into 2,652 Hydrologic Response Units (HRUs) and 22 sub-basins, with five categories of slope which define the variety of surfaces.

Table 2

Summary of the data used in the study

Input dataData durationSource
Topography data (DEM data) (30 m × 30 m)  http://srtm.csi.cgiar.org 
Land use map 2015 European Space Agency (ESA) 
Soil map 2011 www.fao.org 
Meteorological data (rainfall and temperature) 1980–2014 Department of Hydrology and Meteorology (DHM), Nepal 
Hydrological data 1980–2012 Department of Hydrology and Meteorology (DHM), Nepal 
Climate modelsResolutionsSource
BCC-CSM2-MR 1.1° × 1.1° Beijing Climate Center (BCC) 
CNRM-CM6 1.4° ×1.4° Centre National de Recherches Meteorologiques (France) 
CanESM5 2.8° × 2.8° Canadian Centre for Climate Modeling and Analysis, Canada 
IPSL-CM6A-LR 1.27° × 2.5° Institute Pierre-Simon Laplace (France) 
Input dataData durationSource
Topography data (DEM data) (30 m × 30 m)  http://srtm.csi.cgiar.org 
Land use map 2015 European Space Agency (ESA) 
Soil map 2011 www.fao.org 
Meteorological data (rainfall and temperature) 1980–2014 Department of Hydrology and Meteorology (DHM), Nepal 
Hydrological data 1980–2012 Department of Hydrology and Meteorology (DHM), Nepal 
Climate modelsResolutionsSource
BCC-CSM2-MR 1.1° × 1.1° Beijing Climate Center (BCC) 
CNRM-CM6 1.4° ×1.4° Centre National de Recherches Meteorologiques (France) 
CanESM5 2.8° × 2.8° Canadian Centre for Climate Modeling and Analysis, Canada 
IPSL-CM6A-LR 1.27° × 2.5° Institute Pierre-Simon Laplace (France) 

Climate data

The observed meteorological data (35 rain gauges and 18 temperature stations) were obtained from the DHM, Nepal (Table 1). The historical weather data from 1980–2014 in the Koshi River Basin show an average annual maximum temperature of 28 °C, an average annual minimum temperature of 14 °C, and an average annual rainfall of 925.5 mm.

For future climate projections, the latest four different Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs are used from four different institutes (Table 1). The GCMs were bias-corrected using the Quantile Mapping, and future climate was projected for the future period of 2015–2100 based on the observed data for the period 1980–2014 under SSP2-4.5 and SSP5-8.5 scenarios. These two SSP-based scenarios represent the medium and high emissions of GHGs. The SSP2-4.5 scenario represents the medium part of the range of future forcing pathways with a radiative forcing of 4.5 w/m2 in 2100 and an upgraded form of the RCP 4.5 pathway. Similarly, the SSP5-8.5 scenario represents the high end of the range of future pathways with emissions high enough to produce a radiative forcing of 8.5 w/m2 in 2100 and an upgraded form of the RCP 8.5 pathway (O'Neill et al. 2016).

Discharge data

The daily observed discharge data for the outlet station were obtained from the DHM, Nepal (Table 2). The outlet station, i.e., Chatara station was used for the calibration and validation of the hydrological model.

Sensitivity analysis, calibration, validation, and evaluation of model performance

The sensitivity analysis, calibration, and validation processes were carried out automatically in the SWAT-CUP model using the SUFI-2 algorithm. In a shorter period of time, the SWAT-CUP model enables the sensitivity analysis, calibration, validation, and uncertainty analysis of the SWAT model. Sensitivity analysis was carried out using SWAT input parameters to test the p values. If the p-value is less than 0.05 (p-value <0.05) then the parameters are sensitive and should be considered for further calibrations. The parameters like CN2.mgt, ESCO.hru, OV_N.hru, SLSUBBSN.hru, GWQMN.gw, GW_REVAP.gw, REVAPMN.gw, SOL_AWC (). sol is the most significant parameter for baseflow, peak flow, and evapotranspiration (Abbaspour et al. 2015). The sensitive parameters were obtained when the model was run for 200 simulations (Supplementary material, Table S1).

Following sensitivity analysis, the parameters were calibrated using observed daily discharge from 1980 to 2006. After the calibration, the validation from 2009 to 2012 was carried out which verified the calibrated parameters of the model for the Chatara station. Statistical indicators such as the coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE), percent bias (PBIAS), and the ratio of the root mean square error to standard deviation of measured data (RSR) are commonly used to evaluate model performance (Pradhan et al. 2021; Shrestha et al. 2020; Chomba et al. 2022). The indicators are described in Equations (1)–(4) and the classification of the statistical indicator is presented in Supplementary material, Table S2.
formula
(1)
formula
(2)
formula
(3)
formula
(4)
where is the observed data, is the ith simulated data, is the simulated data, is the mean observed data, and n is the total number of observations.

Ecologically relevant flow parameters

Rivers are a fundamental part of human civilization and ecosystems, as they are a primary source of water for agriculture, household activities, hydropower and many more. In addition, river flow regimes are important parts of the ecological integrity of river systems. The alterations in river flow regimes are well described by different indicators of hydrological and ecological flow parameters. This study recognizes the effect of climate change on ecologically relevant hydrologic parameters for the management of the basin.

This study used the IHA software developed by The Nature Conservancy (TNC 2009) which calculates the 67 statistical parameters from daily flow time series to compare and evaluate natural discharge characteristics, flow regime, and EFCs. There are 33 IHA and 34 EFC parameters. In this study, a subgroup of 38 (26 IHA and 12 EFC) parameters were selected based on the magnitude, intensity, and duration to characterize the ecologically relevant flow regime changes in the Koshi River Basin. Moreover, the selection of these parameters was driven by the Koshi River Basin's notable susceptibility to extreme occurrences, with a particular emphasis on flood events. The 38 parameters were divided into the following five groups depending upon the magnitude, intensity, and duration, and they are (1) magnitude of monthly discharge; (2) magnitude of annual extreme discharge under different durations; (3) timing of annual extreme discharge; (4) rate and frequency of discharge change; and (5) monthly low flows of the EFCs (TNC 2009). The simulated and future discharge was analyzed using IHA parameters. The parameters were calculated using IHA software.

Changes in precipitation and temperature under climate change scenarios

The rainfall is expected to rise under both scenarios during all three future periods. The rainfall is expected to increase by 0.5–35%, i.e., 50–160 mm under the SSP2-4.5 scenario (Figure 3(a)). Whereas under the SSP5-8.5 scenario, the rainfall is expected to increase by 20–80%, i.e., 100–300 mm. Most GCMs predicted that rainfall will increase but the BCC-CSM2-MR model predicted rainfall can decrease by 5% in the NF period only under the SSP5-8.5 scenario.
Figure 3

Projected changes in (a) rainfall; (b) maximum temperature; and (c) minimum temperature in the Koshi River Basin under SSP2-4.5 and SSP5-8.5 scenarios for the period 2015–2100.

Figure 3

Projected changes in (a) rainfall; (b) maximum temperature; and (c) minimum temperature in the Koshi River Basin under SSP2-4.5 and SSP5-8.5 scenarios for the period 2015–2100.

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The maximum and minimum temperatures are expected to increase under both SSP scenarios. The maximum temperature is expected to increase between 0.5 and 3.5 °C and between 1 and 6 °C under SSP2-4.5 and SSP5-8.5 scenarios, respectively (Figure 3(b)). The minimum temperature is also expected to rise between 0.5 and 5 °C and between 1 and 7 °C under SSP2-4.5 and SSP5-8.5 scenarios, respectively (Figure 3(c)).

Sensitivity analysis, calibration and validation of the SWAT model

The most sensitive parameters of the Koshi River Basin are CN2.mgt, ALPHA_BF.gw, GW_DELAY.gw, GWQMN.gw, LAT_TIME.hru, TLAPS.sub, and PLAPS.sub. The details of the results of the sensitivity analysis are presented in Supplementary material, Table S1.

The results of the daily observed and simulated flow during the calibration and validation periods are represented using statistical performance indicators such as R2, NSE, PBIAS, and RSR, and are shown in Table 3. If the R2 and NSE value is greater than 0.5, then the performance of the model is acceptable (Moriasi et al. 2007). For the Koshi River Basin, the R2 and NSE value ranges from 0.77 to 0.78 for both calibration and validation which is a very good performance of the model. Similarly, PBIAS and RSR are also within a very good range during calibration and validation periods. The hydrographs (Figure 4) show the observed and simulated discharges by the SWAT in the Koshi River Basin for both calibration and validation periods. The simulated discharge generated by SWAT underestimates the peak of the observed flow, whereas the baseflow is perfectly simulated by the model. The results of the calibrated SWAT model were used to explore the various impacts of climate scenarios in different hydrological phases.
Table 3

Performance of the SWAT model during calibration and validation at the Chatara station

Calibration (1985–2006)
Validation (2009–2012)
Evaluation statisticsR2NSEPBIAS (%)RSRR2NSEPBIAS (%)RSR
 0.77 0.77 0.10 0.21 0.78 0.78 0.50 0.22 
Calibration (1985–2006)
Validation (2009–2012)
Evaluation statisticsR2NSEPBIAS (%)RSRR2NSEPBIAS (%)RSR
 0.77 0.77 0.10 0.21 0.78 0.78 0.50 0.22 
Figure 4

Comparison of observed and simulated daily discharge at the Chatara station in (a) calibration period (1985–2006) and (b) validation period (2009–2012).

Figure 4

Comparison of observed and simulated daily discharge at the Chatara station in (a) calibration period (1985–2006) and (b) validation period (2009–2012).

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Climate change impact on water conditions based upon IHA indicators

The simulated discharge obtained by simulating the SWAT model with each of the climate predictions, i.e., SSP2-4.5 and SSP5-8.5 were compared with observed discharge from the baseline period. The projected annual average discharge for the future period was 1,708 and 1,998 m3/s under SSP2-4.5 and SSP5-8.5 scenarios, respectively. It is projected that the annual average discharge will increase by ∼12% for SSP2-4.5 and ∼30% for SSP5-8.5 in the future as compared to the average daily discharge in the baseline period (1,534 m3/s). The differences in the discharge mean values for each IHA parameter, which are grouped in five groups between two SSP scenarios are discussed in the following sections.

Group 1 (magnitude of mean monthly flows)

Group 1 describes the magnitude of mean monthly flows. The magnitude of the mean monthly flow change for the monsoon season (June–September) is less than the post-monsoon and winter seasons under future climate scenarios. Figure 5 shows the absolute changes in monthly flow for each future period and scenario relative to the baseline period (1980–2014).
Figure 5

Changes in mean monthly flow (Group 1) in the Koshi River in the near future (NF), mid-future (MF), and far-future (FF) under SSP scenarios.

Figure 5

Changes in mean monthly flow (Group 1) in the Koshi River in the near future (NF), mid-future (MF), and far-future (FF) under SSP scenarios.

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The future discharge for the NF period is predicted to decrease especially in June and July by 100–200 m3/s under both SSP scenarios. In contrast, a significant increase in future discharge is estimated for the post-monsoon and winter seasons. The future discharge for October–November and December–February will increase by 200 m3/s for the NF and by more than 300–400 m3/s for both mid-future and far-future periods under the SSP2-4.5 scenario. Similarly, under the SSP5-8.5 scenario, the discharge is expected to increase by more than 500 m3/s for both mid-future and far-future periods. These changes in streamflow are mainly driven by changes in precipitation and it is predicted that precipitation will increase with a higher percentage in the winter season rather than the monsoon season (Pradhan & Shrestha 2022).

Group 2 (magnitude and duration of annual extreme water conditions)

Group 2 describes the 10 different parameters of the magnitude of extreme (min–max) annual water conditions of various durations, ranging from daily (1, 3, 7 days) to seasonal (30 and 90 days). The 1-, 3-, and 7-day annual maxima are 5,210; 4,598; 4,374 m3/s and for 30- and 90-day annual maxima they are 408 and 3,684 m3/s for the baseline period. Similarly, for 1-, 3- and 7-day the annual minima are 251, 254, 260 m3/s and for 30- and 90-day the annual minima are 279 and 319 m3/s respectively.

The change in the mean value of the annual minimum does not have much variation in all durations and seasons. Whereas the change in the mean value of the annual maximum has significant variation in all durations ranging from daily to seasonal (Figure 6).
Figure 6

The variability of the magnitude of extreme (min and max) annual water conditions (Group 2) in the Koshi River in all three future periods under SSP scenarios.

Figure 6

The variability of the magnitude of extreme (min and max) annual water conditions (Group 2) in the Koshi River in all three future periods under SSP scenarios.

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Figure 7

Changes in the timing (in days) of annual extreme streamflow occurrence (Group 3) in the Koshi River in the near future (NF), mid-future (MF), and far-future (FF) under SSP scenarios.

Figure 7

Changes in the timing (in days) of annual extreme streamflow occurrence (Group 3) in the Koshi River in the near future (NF), mid-future (MF), and far-future (FF) under SSP scenarios.

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Figure 8

Percentage change in rate and frequency of streamflow (Group 4) in the Koshi River in the near future (NF), mid-future (MF), and far-future (FF) under SSP scenarios.

Figure 8

Percentage change in rate and frequency of streamflow (Group 4) in the Koshi River in the near future (NF), mid-future (MF), and far-future (FF) under SSP scenarios.

Close modal

The results of the SSP2-4.5 scenario show that the change in the mean value of 1-, 3-, 7-, 30-, and 90-day annual minimum flow is expected to increase by 21, 20, and 20% and for the SSP5-8.5 scenario the annual minima flow is expected to increase by 24, 23, and 23% for a NF period. Similarly, under the SSP2-4.5 scenario, the 1-, 3-, and 7-day mean annual maxima flow is expected to increase by 47, 37, and 28%, and the 30- and 90-day mean annual maximum flow is expected to increase by 11 and 1%, respectively. In addition, the 1-day annual maximum flow under the SSP5-8.5 scenario is expected to increase by 45% in the NF period. Overall, the plot shows that the change in annual maximum and minimum flow is high in short-term duration periods for all future periods.

Group 3 (timing of annual extreme flow conditions)

There are two parameters in Group 3 which are critical for the seasonal features of the hydrological conditions. These two parameters measure the Julian date of the annual 1-day maximum and minimum water conditions. The timings of annual extreme flows for SSP2-4.5 and SSP5-8.5 were similar to the baseline period. The change in timings of annual extremes shows that the Julian date of the annual 1-day minimum flow was predicted to be 22 days earlier for the SSP2-4.5 scenario and 4 days later for the SSP5-8.5 scenario compared to the baseline period. Whereas the Julian date of 1-day maximum flow is predicted to be 36 days earlier for the SSP2-4.5 scenario and 1 day later for the SSP5-8.5 scenario, respectively (Figure 7). These indicators show that the basin will experience a shift in annual streamflow extreme events which directly affects the life cycle of the crop, life cycles of aquatic species, increases the stress for the aquatic organism, and many more.

Group 4 (rate and frequency of water conditions)

The two parameters in Group 4 measure the mean rate and number for both negative and positive changes in water conditions. The streamflow is expected to rise and fall at a faster rate with a high percentage change in future climate scenarios. The rise rate of streamflow is gradually increasing with future time periods under both SSP scenarios. The change in the fall rate for the NF period is 30% under both scenarios. Under both scenarios, the fall rate and rise rate were expected to increase in the far-future period by 80% compared to the baseline period (Figure 8). As the rise and fall rates are increasing with higher percentages it is likely that the Koshi River Basin will experience flash floods often.

Group 5 (monthly low flows)

Group 5 describes the monthly low flow of EFCs. These 12 parameters describe the mean values of low flow during each calendar month. For the month of July, the SSP2-4.5 scenario predicts a more than 600 m3/s reduction in the monthly low flows as compared to the baseline period (Figure 9). Similarly, the SSP5-8.5 scenario predicts the decline in low flow throughout the monsoon season. Contrarily, it is projected that the monthly low flows will rise from October to April under both future climatic scenarios. The monthly low flows are predicted to rise by more than 25%, i.e., 100–200 m3/s for the SSP2-4.5 scenario and more than 30%, i.e., 200–400 m3/s for the SSP5-8.5 scenario from November to February. This group is important for maintaining the water table levels in floodplains, and providing adequate habitat for aquatic organisms. The monthly low flows are expected to increase in future with a high percentage in the pre-monsoon and winter seasons which means that even during the low flow season the Koshi River Basin will have a good environment for aquatic organisms, plants, fisheries, terrestrial animals, and many more.
Figure 9

Percentage change in monthly low EFCs (Group 5) at the Chatara station in the Koshi River for all three future periods under SSP scenarios.

Figure 9

Percentage change in monthly low EFCs (Group 5) at the Chatara station in the Koshi River for all three future periods under SSP scenarios.

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Variability in projected river flows

This study analyzed different hydrological indicators and projected the flow of the Koshi River Basin. The projection indicates high uncertainty and variability in future flows. The percentage change in flow for each SSP scenario and time period is based on hydrological indicators derived from four CMIP6 GCMs and their ensemble representing the hydrological extremes and climatic extremes. From the above analysis, most of the hydrological indicator's variability increases with time as compared to the baseline period. The projected mean annual flow under the SSP2-4.5 scenario varies from −16 to 20%, −6 to 45%, and 3–45% for NF, mid-future, and far-future periods, respectively. Under the SSP5-8.5 scenario, the flow varies from −18 to 26%, 3 to 55%, and 18 to 80%. The variation in the mean annual flow is higher under the SSP5-8.5 scenario than under the SSP2-4.5 scenario in the mid-future and far-future periods. All the GCM ensembles project higher river flow in the post-monsoon season for both scenarios and time periods.

Many studies have projected that the river flow in the Himalayan River Basin will increase due to an increase in rainfall and high temperatures (Lutz et al. 2014; Devkota & Gyawali 2015; Wu et al. 2019). Siderius et al. (2013) states that snowmelts have an equal contribution to runoff generation in the basin. Similarly, there are studies addressing the increases in temperature that will affect the glacier and snowmelt of the basin contributing to the greater river flow (Shrestha & Aryal 2011; Khadka et al. 2016). According to our research and other studies, the Koshi River Basin's water availability will probably increase in the coming future periods (Bharati et al. 2019, Kaini et al. 2021). However, most of these studies were based on CMIP5 climate model data. This study used the latest released CMIP6 GCM model with SSP scenarios that guides the climate change research with new adaptation and mitigation strategies in the basin. The uncertainty in the future river flow of the basin is high and needs to be considered in making decisions on planning, development, and management of water resources.

Impacts of climate change on the flow of Koshi River Basin on agriculture and water availability

About 13.7 million people and their livelihoods in the basin are mostly dependent on agriculture (Hussain et al. 2018). For example, Chatara is the outlet of the Koshi River Basin and the livelihoods of the people living near the outlet solely depend upon agriculture. The major crops in the basin are rice, maize, and wheat but they grow different crops depending upon the season. During the monsoon season, spring paddy rice, sugarcane, maize, jute, and vegetables are cultivated. During the winter season, sugarcane, potato, wheat, pulses, oilseed, and maize are cultivated (Neupane et al. 2015). The rainfall, temperature, and flow patterns are the major drivers for agricultural production and climate change has a greater impact on these drivers.

The results of our study indicate that the future average monthly flow will increase in the pre-monsoon and post-monsoon seasons while decreasing during the monsoon season. Kaini et al. (2021) also projects that the flow will increase by more than 20% during the winter season in the Koshi basin under RCP scenarios. The future flow projections of the basin suggest that monsoon-dominated crop production will decrease and affect the socio-economic lives of farmers living in the basin. The farmers are already adjusting their agricultural activities according to the changing climate of the basin such as changing crop varieties, small changes in the cropping calendar, and exploring improved seed varieties (Hussain et al. 2016). Despite making changes in their agricultural activities, farmers are not able to improve their crop production and want to improve their socio-economic lives with off-farm activities (Sarkar et al. 2012).

This study examines the impact of the climate change on hydrological indicators and EFCs in the Koshi River Basin. The hydrological model (SWAT) was applied to simulate the hydrological regime of the basin and investigate the future hydrological trends under SSP scenarios. Additionally, hydrological indicators were analyzed using IHA software to examine the flow parameters under the influence of SSP scenarios. The main findings of the study are summarized as follows:

  • The changes in rainfall patterns have influenced the high streamflow to shift from monsoon to pre-monsoon season affecting the ecosystem of the hydrological regime.

  • In future periods, annual maximum flows are expected to increase by more than 50%, potentially leading to more frequent flood events and soil erosion, which could harm aquatic organisms and livelihoods.

  • It is expected that maximum flow events could shift earlier by 36 days and minimum flow shifts later by 1 day, which can have a very high impact on aquatic ecosystems and agriculture practices of the basin.

  • The study found that monthly low flows will increase by 25% during the dry season (November–February) which indicates there will be enough water for agricultural purposes and maintain a suitable habitat for aquatic and terrestrial species.

The main aim of the study was to evaluate the hydrological regime based on the magnitude, intensity, and duration of the flow using SSP scenarios. Despite having limited datasets related to hydrological flow characteristics, this research offered a comprehensive understanding and analysis of future flow characteristics based on magnitude, duration, and intensity. However, most studies in the Koshi River Basin have primarily focused on evaluating seasonal, annual, and peak flows (Bharati et al. 2014; Nepal 2016; Kaini et al. 2019). This study and its methodology have highlighted several promising directions for managing water resources and ecosystems in the Koshi River Basin. Future research can focus on understanding how climate change affects aquatic ecosystems. The study also predicts shifts in hydro-climatic events within the basins, which could impact agricultural activities. Further research could focus on enhancing agricultural practices, including crop rotation, adapting crop varieties to mitigate the impact of climate change, and using high-quality datasets for more precise crop yield predictions. In summary, the findings of this study hold the potential to assist policymakers in revising policies related to hydrological extremes such as floods and droughts in the Koshi River Basin. This research can contribute to more effective planning and management of water resources in the region by taking into consideration the influence of climate change on water availability, hydrological extremes, agriculture, and ecosystems.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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Supplementary data