Although many studies have assessed the singular impacts of future land use and climate change on river hydrology, few studies have investigated the distinct and combined impacts of land use and climate change on river flows particularly in developing countries faced with a challenge of limited data. This study addressed the aforementioned gap and applied the Soil and Water Assessment Tool and an ensemble of six CORDEX Regional Climate Models under the moderate (RCP4.5) and high (RCP8.5) emission scenarios in the river Rwizi catchment area in western Uganda for the period 2021–2050. The isolated impacts of land use change and the combined impacts showed an increase in future total annual river flows. However, the isolated impacts of climate change showed a reduction in future total annual flow. The influence of land use changes on total annual runoff was more dominant than that of climate change. The results show that climate change is the dominant factor impacting future high-flow quantiles while future annual flow and extreme low-flow variations were attributed mainly to land use changes. These findings point to the need to plan and implement prudent land use and water resource management practices to mitigate associated risks.

  • Distinct and combined future climate and land use change impacts were assessed.

  • Trend analyses highlight the major contributors to the changes in future river flow.

  • This study sheds light on the performance of reanalysis datasets in data-scarce areas like the river Rwizi catchment.

  • This study informs the need to implement pragmatic catchment management strategies to accelerate progress towards Sustainable Development Goal 6.4–6.6.

Globally, the threats and pressures associated with the emerging impacts of climate and land use change are acute in many low- and middle-income countries in Africa and Asia (Byers et al. 2018; Clarke et al. 2022). These nations face a complex set of circumstances that include economic constraints, competing development priorities, and inability to meet to ambitious international greenhouse gas reduction targets such as those specified in the 2015 Paris Agreement (Vairavamoorthy et al. 2008). In financial-resource-constrained and data-scarce countries particularly in the Global South, understanding quantification and assessment of the isolated impacts as well as the combined impacts of land use change and climate change on water resources is critical for resilient and sustainable management of water resources based on the best available evidence (Shrestha et al 2020; Clarke et al. 2022; Onyutha et al. 2022; Aruho Tusingwiire et al. 2023; Sempewo et al. 2023).

The current trend of increasing water demand caused due to rapid population growth, anthropogenic changes and urbanisation is expected to exacerbate the future water crisis (World Economic Forum 2017; Makarigakis & Jimenez-Cisneros 2019). The water crisis is expected to be further aggravated by emerging climate and land use change threats (Vairavamoorthy et al. 2008; Sharannya et al. 2021).

Several researchers have examined the isolated impacts of climate change on river flows for various regions in the world (e.g. Kumar 2014; Gebre et al. 2015; Zhang et al. 2019). The consensus from most studies is that the temperature will continue to increase during the next century over the world, while precipitation projections show inconsistency among regions. Previous studies that have investigated isolated impacts of both climate and land use change on river flows suggest that the resulting hydrological responses and water availability are affected either positively or negatively by these changes in various regions (Chang et al. 2015; Woldesenbet et al. 2017; Yang et al. 2017; Gebrechorkos et al. 2023). More recent studies (Dong et al. 2014; Zhang et al. 2016; Yin et al. 2017) have investigated the combined impacts of land use and climate change on river flows. Some studies have reported that river flows were more sensitive to land use change than to climate change (e.g. Dong et al. 2014; Yin et al. 2017), while others reported otherwise (e.g. Guo et al. 2008; Shrestha & Htut 2016; Ross et al. 2021). These contradicting results could be attributed to the intensity and types of the land use changes, river catchment characteristics or climate variations of the catchments. The discrepancies to a degree could also be attributed to uncertainties arising from available data and the structure and parameters of the models (Global Climate Models (GCMs) and hydrology model) used in these studies (López-Moreno et al. 2011).

It is, therefore, evident that there is still limited knowledge on the relative influence of climate and land use change on river flows (Howells et al. 2013). This is of great concern for water resource management with respect to mitigating and adapting to future changes in land use and climate changes using the best available evidence. Therefore, catchment-specific studies are required to provide comprehensive and accurate information for water resource management. This study investigated the water-stressed river Rwizi catchment in western Uganda, which is currently the sole source of water supply to Mbarara city, the second largest in Uganda. The river has experienced a reduction in flows over the years which existing studies have attributed to climate change (e.g. Atim 2010; Nyeko-Ogiramoi et al. 2010; Wanyama 2012). Other studies in the catchment attributed the changes to land use and land cover (LULC) changes (e.g. Nseka et al. 2022). A recent study by Onyutha et al. (2021) attempted to provide the distinctive impacts of land use and climate change on the river flows with the study attributing 73% of the flow variations to climate change and 17.1% being attributed to other factors. However, this study did not consider the effect of future climate and land use changes on river flows.

There is, therefore, a lack of knowledge on the driving factors that could cause future changes in the river Rwizi's flow and the region. In this study, the distinct and combined impacts of climate and land use change on river Rwizi flows was investigated using an ensemble of six CORDEX Regional Climate Model (RCM) results and Cellular Automata–based Artificial Neural Network (CA-ANN). Simulations of current and future river flows were undertaken using the Soil and Water Assessment Tool (SWAT) model.

The main objective of this paper is to assess the distinct and combined impacts of future climate and land use change on river Rwizi flows using a combination of statistical downscaling techniques, machine learning and physically based hydrological modelling techniques. Details of the methodology are presented in the following sections.

Description of the study area

This study was conducted in the upper Rwizi catchment (Figure 1). The upper Rwizi catchment, which is part of the Lake Victoria basin, covers an area of 2,087 km2 and is located in the southwest of Uganda. This catchment has undergone extensive land degradation and vast land use changes experienced over time (Kuloba 2017). The upper Rwizi catchment is distinguished by rocky topography to the north and south of the catchment, with its elevations ranging from 1,248 to 2,159 m a.s.l. The average annual precipitation in the upper Rwizi is 1,150 mm with the temperature in the catchment varying between 14 and 33 °C with an average of 23 °C. The upper Rwizi's land use follows a normal topographic sequence, with grazing pastures on the high plateaus, banana-farming on the footslopes and Cyperus papyrus L. wetlands in the river valley bottoms (Ryken et al. 2015).
Figure 1

Location of the Upper Rwizi catchment showing the existing rainfall stations p90300210, p90300270, p90300470 and hydrological station D81224.

Figure 1

Location of the Upper Rwizi catchment showing the existing rainfall stations p90300210, p90300270, p90300470 and hydrological station D81224.

Close modal

Data

The description of the datasets used this study is presented in Tables 1 and 2.

Table 1

Overview of hydrological modelling data: spatial and temporal characteristics, sources and formats for precipitation, temperature, flow, DEM, land use, soil and climatic data used for the study

SNData typeData sourceResolution
FormatRelevance
SpatialTemporal
Precipitation/observed AGMERRA (1980–2010) Point Daily (mm/day) NETCDF Runoff modelling 
Mbarara Meteorological Station (90300030) Point Daily (°C) Text file Bias correction of reanalysis data 
Temperature AGMERRA (1980–2010) Point Daily (°C) NETCDF Runoff modelling 
Flow Directorate of Water Resources Management of Uganda (1980–2015) Point Daily and monthly (m3/s) Excel Model calibration and validation 
DEM USGS-STRM-DEM (2009) 30 × 30 m N/A Geo-tiff Generation of slopes and HRUs 
Land use FAO 30 m N/A Shapefile Helps in creation of HRUs in runoff modelling 
Soil data FAO 1:5,000,000 N/A Shapefile Creation of HRUs in runoff modelling and generation of a curve number map 
Climatic data https://esgf-node.llnl.gov/projects/esgf-llnl/ 25 × 25 km Daily time series NETCDF Analysis of climate change on flow 
SNData typeData sourceResolution
FormatRelevance
SpatialTemporal
Precipitation/observed AGMERRA (1980–2010) Point Daily (mm/day) NETCDF Runoff modelling 
Mbarara Meteorological Station (90300030) Point Daily (°C) Text file Bias correction of reanalysis data 
Temperature AGMERRA (1980–2010) Point Daily (°C) NETCDF Runoff modelling 
Flow Directorate of Water Resources Management of Uganda (1980–2015) Point Daily and monthly (m3/s) Excel Model calibration and validation 
DEM USGS-STRM-DEM (2009) 30 × 30 m N/A Geo-tiff Generation of slopes and HRUs 
Land use FAO 30 m N/A Shapefile Helps in creation of HRUs in runoff modelling 
Soil data FAO 1:5,000,000 N/A Shapefile Creation of HRUs in runoff modelling and generation of a curve number map 
Climatic data https://esgf-node.llnl.gov/projects/esgf-llnl/ 25 × 25 km Daily time series NETCDF Analysis of climate change on flow 

Note: HRU, hydrologic response unit.

Table 2

Hydrological, land use and climate change models used for the study to simulate impacts of land use and climate change

SNModelDescriptionRelevance to the study
SWAT SWAT is a robust hydrological model designed for simulating the impacts of land use and climate change on flow. It provides valuable insights into the response of watersheds to changes in land use and climatic conditions. Simulating impacts of land use and climate change on flow 
MOLUSCE MOLUSCE is a specialised land use change model that facilitates the projection and analysis of land use changes. It plays a crucial role in understanding how land use modifications impact hydrological processes. Land use change projection and analysis 
  • (1)

    RCA4-GFDL-ESM2M

  • (2)

    REMO2009-MPI-ESM-LR

  • (3)

    CCLM4-8-17-CNRM-CM5

  • (4)

    REMO2009-EC-EARTH

  • (5)

    RCA4-CNRM-CM5

  • (6)

    RCA4-CM5A-MR

 
RCM under the CORDEX project. Analysis of climate change on flow 
SNModelDescriptionRelevance to the study
SWAT SWAT is a robust hydrological model designed for simulating the impacts of land use and climate change on flow. It provides valuable insights into the response of watersheds to changes in land use and climatic conditions. Simulating impacts of land use and climate change on flow 
MOLUSCE MOLUSCE is a specialised land use change model that facilitates the projection and analysis of land use changes. It plays a crucial role in understanding how land use modifications impact hydrological processes. Land use change projection and analysis 
  • (1)

    RCA4-GFDL-ESM2M

  • (2)

    REMO2009-MPI-ESM-LR

  • (3)

    CCLM4-8-17-CNRM-CM5

  • (4)

    REMO2009-EC-EARTH

  • (5)

    RCA4-CNRM-CM5

  • (6)

    RCA4-CM5A-MR

 
RCM under the CORDEX project. Analysis of climate change on flow 

To investigate the impacts of climate change and LULC on hydrological processes in the upper Rwizi catchment, land use, topographic and soil and hydro-meteorological data were obtained. A digital elevation model (DEM) of 30 m resolution for the year 2009 was acquired from the United States Geological Survey – Shuttle Radar Topography Mission (USGS-SRTM) in geo-tiff format. The soil map (1: 5,000,000) developed by the Food and Agriculture Organization of the United Nations (FAO-UNESCO) was downloaded from http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/faounesco-soil-map-of-the-world/en/.

Landsat satellite images for the years 1984, 2000 and 2016 were acquired from the United States Geological Survey (USGS) website (https://earthexplorer.usgs.gov/) at a 30 m spatial resolution and classified into LULC types of farmland, bushland, open water, woodland, built-up area, wetland and forested area using the maximum likelihood supervised classification tool in ArcGIS following the generalised classification system (Atay Kaya & Kut Görgün 2020; Palmate et al. 2022). The land use data were in raster format. Land use properties were sourced directly from the SWAT model database. The observed discharge data for the only existing gauged station for River Rwizi Mbarara Water Works (No. D81224) hydrological station for the river Rwizi was acquired from the Directorate of Water Resources Management (DWRM), Ministry of Water and Environment, Uganda, and used for model calibration and validation.

Due to large data gaps (up to 10–15 years) in the observed rainfall data acquired, reanalysis data were used (Morales-Moraga et al. 2019; Nakkazi et al. 2022). This dataset is readily available to the public domain and therefore widely applied in data-scarce regions. Reanalysis data were bias-corrected and evaluated for suitability using the method proposed by Berhanu et al. (2016). A comparison was made between observed rainfall data obtained from the Uganda National Meteorological Authority for the period (2000–2009) with five reanalysis datasets, i.e. AgMERRA, CRSR, CHIRPS, ERA5 and PRINCETON, to determine which data represented the observed catchment rainfall best. The AgMERRA reanalysis dataset that provides daily, relatively high-resolution (0.250° × 0.250°), continuous, meteorological series over the period 1980–2010 achieved a Pearson correlation of 0.93 thereby representing the catchment rainfall best. Therefore, this dataset was adopted for further analysis in this study.

The data series (daily precipitation and maximum and minimum temperatures) in netCDF file format were obtained from https://data.giss.nasa.gov/impacts/agmipcf/agmerra/. The AgMERRA precipitation series were adjusted using the distribution mapping bias-correction method (Teutschbein & Seibert 2012). The bias correction followed the methodology by Ruane et al. (2015) as shown in Equations (1) and (2). A monthly bias factor was obtained between the daily observations from observed rainfall stations p90300210, p90300270 and p90300470 (Figure 1) and that of the AgMERRA reanalysis data at the coordinates of these stations. While calculating the bias, a threshold of 15% of missing data was set as the maximum appropriate gap rate for any given month. If the missing data in the observations dataset at a given station surpassed the threshold for a given month, the data for that month were discarded for all datasets and not included in the computation of the bias factor. This was done through calculation of average monthly biases between the datasets using all days (N) within a given month (m) where given station data existed. The bias correction factor was then multiplied with the reanalysis data for the corresponding month to obtain the bias-corrected precipitation values.
formula
(1)
formula
(2)

Selection of the climate model and its combinations

There are a vast number of climate models available in the public domain for providing climate change projections (Dosdogru et al. 2020; Atay Kaya & Kut Görgün 2020). However, for this study, outputs from CORDEX RCMs were adopted. CORDEX RCMs were chosen for this study because of their higher spatial resolutions (0.44° × 0.44°) as compared with those of the CMIP3 and CMIP5 GCMs. In addition, according to the studies by Onyutha et al. (2019) and Kisembe et al. (2019) on how well the climate models reproduce observed rainfall variability, CORDEX RCMs reproduced variability in daily rainfall over the study area better than the CMIP3 and CMIP5 GCMs. A total of six CORDEX RCMs were adopted based on previous research by Onyutha et al. (2019) as well as the availability of variables from the data portal.

Simulated climate change output variables of both temperature and precipitation for the CORDEX RCMs for both mid-range RCP4.5 and extreme RCP8.5 scenarios were obtained from the website https://esgf-index1.ceda.ac.uk/search/esgf-ceda/. To prepare the simulated climate data for catchment-based hydrologic impact analysis, the data were bias-corrected using the distribution mapping technique (Teutschbein & Seibert 2012; Alramlawi & Fıstıkoğlu 2022). The CMhyd (climate model data for hydrologic modelling) tool developed by Rathjens et al. (2016) was used.

Projection of future land use change

The procedures of projecting future land use change involved three steps: (1) establishing the transition matrix, (2) generating suitability maps and (3) projection of the LULC map. The procedures for projecting land use and land cover change (LULCC) were undertaken using the open-source Module of Land Use Change Evaluation (MOLUSCE) tool for QGIS 2.8.10 version with the CA model, following the procedures of Jogun et al. (2019). A combination of CA-ANN was found to be one of the most efficient methods for simulating complex and non-linear LULC which are also specific to the research area (Jogun et al. 2019). The plugin takes input data from categories of land use and explanatory variables, trains the LULC model using input data and well-known algorithms, forecasts potential LULC based on training input and validates findings based on past reference data (GIS LAB 2014).

For modelling LULC transition probability, ANN multi-layer perception was adopted because it offers the highest accuracy compared with the other algorithms such as logistic regression, multi-criteria analysis (MCA) and weight-of-evidence methods (Jogun et al. 2019). The method was also adopted because it is suitable for data-scarce situations and for capturing complex non-linear features in modelling processes (Li & Yeh 2002).

Then, the generation of suitability maps of the potential driving factors of the LULC transitions was developed for each LULC class using MCA. A simulated land use map of 2048 was produced based on a Monte Carlo CA model approach (Pijanowski et al. 2002). Previous research has outlined the key elements that influence potential land use changes (Park et al. 2011; Jokar Arsanjani et al. 2013; Musa et al. 2016; Shafizadeh-Moghadam et al. 2017). Slope, distance to roadways and urban centres, and population density were the primary variables used in land use change simulation modelling to generate suitability maps. Finally, the projection of the LULC maps was done based on the transition probability matrix and the generation of suitability maps.

Pearson's correlation function of the MOLUSCE plugin in QGIS was used to check the correlation among the spatial–temporal changes and compute the LULC changes and category of each area between 1984 and 2016 to generate two LULC change maps (1984–2000 and 2000–2016). The purpose of this stage was to compute the changes in area in km2 between the LULCs of 1984 and 2016.

Validation of modelling LULC transitions enables the evaluation of the precision of the simulation. The approach followed during validation involved comparing the 2016 reference map and the 2016 simulated LULC map, and this was undertaken using kappa statistics (Equations (1)–(6)): kappa total (Equation (3)), kappa location (Equation (4)) and kappa histogram (Equation (5)). Classification of the performance of the model prediction was done following Landis & Koch (1977).
formula
(3)
formula
(4)
formula
(5)
formula
(6)
where pij is the i,jth cell of the contingency table, piT is the sum of all cells in the ith row, pTj is the sum of all cells in the jth column and c is the count of raster categories.

After achieving satisfactory validation results, the simulated LULC multi-resolution analysis for the 2040s (i.e. 2048) was undertaken to determine the difference between the reference map and the simulated map in position and quantity (Pontius & Suedmeyer 2004). The analysis was undertaken at different spatial resolutions (Jogun et al. 2019). Details of the methods are described in the studies by Costanza (1989) and Castella & Verburg (2007). The approach was adopted because it has been widely applied in the literature (Lin et al. 2011).

Rainfall–runoff modelling

SWAT model

Rainfall–runoff modelling was performed using the SWAT extension in ArcGIS 10.2.1 version. The SWAT model is a physically based, semi-distributed model that can predict the movement of water in complex catchments with varying soils, land use and management conditions over long-term periods (Arnold et al. 1998; Neitsch et al. 2009). The SWAT model was used because it is one of the most widely used hydrological models in the world (Gassman et al. 2010) and is widely applied in data-scarce regions, particularly in middle- and low-income countries (Nakkazi et al. 2022; Sempewo et al. 2023). Furthermore, the model was applied because of its modularity capabilities, computational efficiency, the capacity to predict long-term effects as a continuous model and the ability to use easily accessible global datasets

Sensitivity analysis

Usually, not all parameters of the model have the same effect on stream flow (Abbaspour et al. 2017). Therefore, sensitivity analysis was done with the semi-automated Sequential Uncertainty Fitting (SUFI-2) algorithm using the global sensitivity method. Based on the significance of the ranked values, the t-statistics and p-values of the parameters were used to rank the various parameters considered to affect flow (Arnold et al. 2012).

Calibration and validation

Calibration and validation were done through the semi-automated SUFI-2 algorithm within SWAT Calibration and Uncertainty Procedures (SWAT-CUP) version 5.2.1. For catchments in the sub-tropical and tropical zones like the study area, the SUFI-2 method is the technique that shows the most promise for calibration and uncertainty (Khoi & Thom 2015). In addition, SUFI-2 has a higher computational efficiency than other methods (e.g. ParaSol, Particle Swarm Optimization (PSO) and Generalized Likelihood Uncertainty Estimation (GLUE)) requiring a sample size for one iteration ranging from 500 to 1,000 runs (Khoi & Thom 2015).

The SWAT model was calibrated by comparing the simulated flow and the historical flows of the only gauging station within the catchment from the Mbarara Water Works gauging station D81224 (see Figure 1) for a period from 1 January 1982 to 31 December 1989. The validation of the model was based on the period starting from 1 January 1990 to 31 December 1995 to test if the calibrated parameters adequately represent the catchment processes.

The Nash–Sutcliffe efficiency (NSE) (Equation (7)) was used as the target objective function in the simulations, while the percentage bias (PBIAS) (Equation (8)) and coefficient of determination (R2) (Equation (9)) were also used to evaluate the model as recommended by Moriasi et al. (2007).
formula
(7)
formula
(8)
formula
(9)
where is the simulated flow (m³/s), is the observed flow (m³/s), is the average observed flow (m³/s), is the average simulated flow (m³/s), and n is the number of data points.

Quantification of future land use change and climate change impacts on flow

The calibrated SWAT rainfall–runoff model was used to evaluate the temporal variation in runoff to assess the impacts of future land use and climate change on the upper Rwizi catchment behaviour. Different scenarios were used for analysing the impact of climate and land use changes in the future period. For each scenario, the changes in flow were calculated by comparing stream flow between the reference period and simulated flow (average of all the RCMs) future period.

Determination of extreme flows (high and low)

The change comparison was based on the ten-year extreme quantiles. The high- and low-flow events were extracted as peak over thresholds (POTs) using the frequency analysis considering the non-stationarity tool (Onyutha 2017a, 2017b). The independence criteria for the extraction of the POT were followed using studies by Lang et al. (1999) and Onyutha (2017b). The POTs of observed and future flows were compared on a return period basis in the context of frequency analysis to estimate the impact of climate change, land use and their combined effects on flow.

Attribution analysis of changes in future flows to future land use and climate change
  • (a)

    Annual flows

The contribution of each driver for each scenario was determined based on the work of Tomer & Schilling (2009), who used changes in the proportion of extra water compared with changes in the proportion of excess energy to distinguish the effects of land use and climate change on hydrology. They noted that excess water in the catchment may be described as precipitation (P) minus actual evapotranspiration (ET), and surplus energy as potential evapotranspiration (ETo) minus actual evapotranspiration (ET). Excess water and energy divided by available water and energy yield dimensionless values Pex and Eex on a scale of 0 to 1, which may be stated as follows:
formula
(10)
formula
(11)
where is the proportion of excess energy, is the proportion of excess water, P is the precipitation/rainfall, is the potential evapotranspiration and is the actual evapotranspiration.

Details of the approach can be found in the study by Tomer & Schilling (2009). However, the approach is only suitable for regions where precipitation matches evaporative demand and has been criticised for not being applicable to all hydroclimatic conditions (Renner et al. 2014). To address this limitation, the expanded framework of Marhaento et al. (2017) was adopted.

  • (b)

    High and low flows

In order to distinguish the impacts of future climate change and land use change on hydrological extremes, the study followed the methodology of Chen et al. (2019). In this regard, four scenarios (Table S3, Supplementary Material) were considered, with a reference scenario and altered scenarios as per the tables. Using the SWAT model, the runoff series for each scenario were obtained and the corresponding extreme hydrological indicators were derived.

Table 3

Future monthly changes in rainfall (2021–2050)

MonthsObserved (mm)Mean RCP4.5Mean RCP8.5% change RCP4.5% change RCP8.5
JF 57.1 31.3 29.9 −45.2% −47.6% 
MAM 101.4 83.0 84.5 −18.2% −16.7% 
JJAS 63.8 67.1 64.6 5.2% 1.2% 
OND 118.9 110.0 111.1 −7.5% −6.6% 
Annual 85.9 75.8 75.4 −11.7% −12.2% 
MonthsObserved (mm)Mean RCP4.5Mean RCP8.5% change RCP4.5% change RCP8.5
JF 57.1 31.3 29.9 −45.2% −47.6% 
MAM 101.4 83.0 84.5 −18.2% −16.7% 
JJAS 63.8 67.1 64.6 5.2% 1.2% 
OND 118.9 110.0 111.1 −7.5% −6.6% 
Annual 85.9 75.8 75.4 −11.7% −12.2% 
Table 4

Future seasonal changes in Tmax and Tmin (2021–2050)

SeasonObservedMean RCP4.5Mean RCP8.5Change RCP4.5Change RCP8.5
Future seasonal changes in Tmax (°C) 
 JF 28.1 29.4 29.4 1.3 1.3 
 MAM 27.1 27.7 27.9 0.6 0.8 
 JJAS 28.0 29.0 29.2 1.0 1.2 
 OND 26.9 27.6 27.7 0.7 0.8 
 Annual 27.5 28.4 28.5 0.9 1.0 
Future seasonal changes in Tmin (°C) 
 JF 14.9 15.4 15.5 0.5 0.6 
 MAM 15.5 16 16.2 0.5 0.7 
 JJAS 14.7 15.5 15.6 0.8 0.9 
 OND 14.8 15.4 15.6 0.6 0.8 
 Annual 14.9 15.6 15.7 0.7 0.8 
SeasonObservedMean RCP4.5Mean RCP8.5Change RCP4.5Change RCP8.5
Future seasonal changes in Tmax (°C) 
 JF 28.1 29.4 29.4 1.3 1.3 
 MAM 27.1 27.7 27.9 0.6 0.8 
 JJAS 28.0 29.0 29.2 1.0 1.2 
 OND 26.9 27.6 27.7 0.7 0.8 
 Annual 27.5 28.4 28.5 0.9 1.0 
Future seasonal changes in Tmin (°C) 
 JF 14.9 15.4 15.5 0.5 0.6 
 MAM 15.5 16 16.2 0.5 0.7 
 JJAS 14.7 15.5 15.6 0.8 0.9 
 OND 14.8 15.4 15.6 0.6 0.8 
 Annual 14.9 15.6 15.7 0.7 0.8 
Table 5

LULC used for calibration (1984, 2000 and 2016) and the projected LULC of Rwizi River catchment for the year of 2048

ClassesYear
1984
2000
2016
2048
Area km2Area %Area km2Area %Area km2Area %Area km2Area %
Farmland 122 5.84 212.61 10.19 515.44 24.69 516.98 24.77 
Bushland 1,225 58.74 1,194.98 7.25 892.3 42.74 782.37 37.48 
Open water 12 0.57 4.71 0.23 3.05 0.15 1.22 0.06 
Woodland 130 6.23 69.6 3.33 4.21 0.20 2.57 0.12 
Built-up area 50 2.40 77.27 3.70 329.07 15.76 450.69 21.59 
Wetland 338 16.19 335.15 16.06 322.81 15.46 320.82 15.37 
Forests 212 10.16 192.95 9.24 20.64 0.99 12.61 0.60 
Total 2,089 100 2,087.27 100 2,087.52 100 2,087.26 100 
ClassesYear
1984
2000
2016
2048
Area km2Area %Area km2Area %Area km2Area %Area km2Area %
Farmland 122 5.84 212.61 10.19 515.44 24.69 516.98 24.77 
Bushland 1,225 58.74 1,194.98 7.25 892.3 42.74 782.37 37.48 
Open water 12 0.57 4.71 0.23 3.05 0.15 1.22 0.06 
Woodland 130 6.23 69.6 3.33 4.21 0.20 2.57 0.12 
Built-up area 50 2.40 77.27 3.70 329.07 15.76 450.69 21.59 
Wetland 338 16.19 335.15 16.06 322.81 15.46 320.82 15.37 
Forests 212 10.16 192.95 9.24 20.64 0.99 12.61 0.60 
Total 2,089 100 2,087.27 100 2,087.52 100 2,087.26 100 
Table 6

Kappa statistics

Kappa statisticValue
Correctness 66.860 
Kappa (overall) 0.524 
Kappa (histogram) 0.644 
Kappa (location) 0.814 
Kappa statisticValue
Correctness 66.860 
Kappa (overall) 0.524 
Kappa (histogram) 0.644 
Kappa (location) 0.814 

This study recommends two relative action variables to quantitatively distinguish the effects of future land use change and climate change on hydrological extremes. They are and , which are calculated as follows:
formula
(12)
formula
(13)
where denotes the rise in hydrological extremes as a result of the combined effects of the two causes and and denote the increases as a result of land use change and climate change, respectively. To make the sum of the two elements 100%, a redistribution of and is performed as follows:
formula
(14)
formula
(15)
formula
(16)
where and are the percentages of land use change and climate change that cause differences in extreme flow events (high and low), respectively. Equations (12)–(16) are used for calculating probabilities for both high flows and low flows.

Performance of reanalysis precipitation

The results of the performance assessment of the five different reanalysis datasets with respect to simulating mean monthly, mean daily and mean annual precipitation in comparison with the Mbarara Meteorological Station (90300030) are presented in Figure 2.
Figure 2

Comparison of monthly performance of precipitation reanalysis model data from AgMERRA, Princeton, ERA5, CFSR and CHIRPS with actual observation data from a weather station from the Rwizi catchment. The results show that overall, AgMERRA data outperformed other datasets in representing the catchment rainfall best with a good temporal variation as compared with the observed monthly rainfall and close absolute values. Thus, this dataset was adopted for this study.

Figure 2

Comparison of monthly performance of precipitation reanalysis model data from AgMERRA, Princeton, ERA5, CFSR and CHIRPS with actual observation data from a weather station from the Rwizi catchment. The results show that overall, AgMERRA data outperformed other datasets in representing the catchment rainfall best with a good temporal variation as compared with the observed monthly rainfall and close absolute values. Thus, this dataset was adopted for this study.

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Change analysis

Precipitation

Table 3 presents the results on the future monthly changes in rainfall. The results show that the average annual precipitation in the future is expected to decrease by 11.68% (10.02 mm) for RCP4.5 and 12.2% (10.47 mm) for RCP8.5. This indicates that the RCP8.5 will be drier than the RCP4.5. Nevertheless, the mean precipitation for the future period in the June, July, August and September (JJAS) season will increase by around 5% (13.24 mm) and 1% (3.27 mm) for the RCP4.5 and RCP8.5 scenarios, respectively. On the other hand, the mean precipitation for the future period in the January–February (JF) season will decrease by around 45% (51.62 mm) and 48% (54.48 mm) for the RCP4.5 and RCP8.5 scenarios, respectively. This indicates that on average under both RCP4.5 and RCP8.5 scenarios, long dry spells are to be expected in the future.

During the March, April and May (MAM) wet season, a decrease in rainfall of about 18.15% (55.21 mm) and 16.76% (50.98 mm) for RCP4.5 and RCP8.5, respectively, was projected for the future period. Similarly, for the October, November and December (OND) wet season, a decrease in rainfall of about 7.5% (26.75 mm) and 6.57% (23.43 mm) for the RCP4.5 and RCP8.5 scenarios, respectively, was projected for the future period. This indicates that there will be less rainfall in the wet season in the future, as compared with the baseline period for both the RCP4.5 and RCP8.5 scenarios.

However, the months from August to October experienced an increase in rainfall compared with the baseline period for both RCP4.5 and RCP8.5 scenarios.

Maximum and minimum temperature

The results show that there was an increase in Tmax within the Rwizi catchment under both the RCP4.5 and RCP8.5 scenarios (Tables 4). The projected increase in Tmax was about 0.9 and 1.0 °C for the RCP4.5 and RCP8.5 scenarios, respectively. The season of JJAS and JF had the highest increase in Tmax, while the lowest increase in Tmax was projected in the MAM and OND seasons for both scenarios.

According to the findings, there was a general increase in mean annual Tmin within the Rwizi catchment under both the RCP4.5 and RCP8.5 scenarios. The projected increase in mean annual Tmin was about 0.7 and 0.8 °C for RCP4.5 and RCP8.5 scenarios, respectively. On a monthly basis, all months were observed to have had an increase in the average monthly Tmin for both scenarios, i.e. RCP4.5 and RCP8.5. In addition, the season of JJAS had the highest increase in Tmin of about 0.8 and 0.9 °C under the RCP4.5 and RCP8.5 scenarios, respectively.

This means that the dry season will be hotter than in the baseline period. This can be explained from the fact that JF and JJAS are dry seasons while MAM and OND are wet seasons, and the dry season usually experiences higher temperatures than the wet seasons. The results also show that JF season will be drier than the JJAS season under both RCP4.5 and RCP8.5 scenarios.

Results of LULC change projection

Historical land use classification

Figure 3 shows the LULC classifications for the Rwizi catchment in 1984, 2000 and 2016 (the calibration period) and 2048 (the projected period). The results show that Bushland was the most prevalent land use for all three land use maps.
Figure 3

(a) Graphical representation of the land use/cover coverage for the years 1984, 2000, 2016 (used during the calibration of MOLUSCE model) and 2048 (projection) with (b), (c), (d) and (e) showing images of the land cover of the same years for the Rwizi catchment.

Figure 3

(a) Graphical representation of the land use/cover coverage for the years 1984, 2000, 2016 (used during the calibration of MOLUSCE model) and 2048 (projection) with (b), (c), (d) and (e) showing images of the land cover of the same years for the Rwizi catchment.

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Land use change analysis

The results for land use change analysis are presented in Table 5.

The results show that compared with the other classes, the farmland land use class had the highest increase in area with 90.7 km2 (74.3%) from 1984 to 2000, 302.1 km2 (142%) from 2000 to 2016, and 1.54 km2 (0%) from 2016 to 2048 (Figure 3(a), Table 5). There was also a high rise in the built-up area class with a total of 27.5 km2 (55%) from 1984 to 2000, 252.3 km2 (326.8%) from 2000 to 2016, and 121 km2 (36.7%) from 2016 to 2048. On the other hand, bushland had the highest decreases in LULC of −29.9 km2 (−2.4%) from 1984 to 2000, −301.7 km2 (−25.2%) from 2000 to 2016, and −110 km2 (−12%) from 2016 to 2048.

Open water and wetland marginally decreased over the period 1984–2016 by −8.5 km2 (−0.41%) and −15 km2 (−0.7%), respectively. For the period 2016–2048, wetlands are also expected to marginally decrease by 2 km2 (−0.6%). Many people in the region have encroached on the banks of the river Rwizi and other wetlands, which could explain the reduction in the area covered by open water and wetlands.

Another significant decrease was in the forest cover with a loss of −191.6 km2 (−90.2%) in the period 1984–2016. From 2016 to 2048, the predicted loss is about −8 km2 (−39%). Woodland class also decreased by 126.4 km2 (97.5%) during the period 1984–2016. The decrease in both woodland and forested area could be explained by the fact that on average the population in the Rwizi catchment increased by 2.9% annually in the period 1984 to 2016. With the increase in built-up area and farmland, more land had been cleared for agriculture and settlement (Kuloba 2017). In addition, in the upper Rwizi catchment, over 98% of households use firewood and charcoal for cooking (NEMA 2004), which could also explain the decrease in both woodland and forested areas within the catchment over the study period.

Land use validation and future LULC projection

Table 6 presents the final kappa statistics, while the percentage correctness of simulated land use of 2016 in reproducing reference/classified 2016 land cover during model validation is presented in Table S4 in the Supplementary Material. An overall kappa value of 0.524 and 66.86% correctness estimated by MOLUSCE indicate good ability of the model in projecting future LUCL of the catchment according to Landis & Koch (1977).

Multi-resolution results were also used to check the accuracy of the prediction, and the results are presented in Figure 4. The results indicate a perfect location and medium quantity information of over 78% between the reference and simulated map of 2016, which is also a good predictive ability according to Pontius & Suedmeyer (2004). The LULC map for 2048 was thereafter projected.
Figure 4

Proportion agreement at multi-resolution of simulated LULC map of 2016.

Figure 4

Proportion agreement at multi-resolution of simulated LULC map of 2016.

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Rainfall–runoff modelling results

Sensitivity analysis

The results of the sensitivity analysis for the Rwizi River are presented in Table S1 in the Supplementary Material. The results show that out of the 17 flow parameters, only nine were the most sensitive parameters, and these were further used in the calibration process of the model output. The most sensitive parameter was channel effective hydraulic conductivity (CH_K2) in the Rwizi River, which is in agreement with the previous study by Anaba et al. (2017). The rankings of the flow parameters are presented in Table S1 in the Supplementary Material.

Calibration and validation

The SWAT hydrologic model was calibrated and validated for the Rwizi stream-flow gauges at the Mbarara gauging station No. 81224; the model was able to simulate daily stream-flow with the goodness-of-fit values shown in Table 7, while Figure 5(a) and 5(b) show hydrographs of the predicted stream-flow of the Rwizi River during the calibration period and the validation period.
Table 7

Statistical performance of SWAT simulations for calibration and validation periods

PhaseNSEPBIASR2p-factorr-factor
Calibration (1982–1989) 0.56 0.2 0.57 0.69 0.8 
Validation (1990–1995) 0.53 −3.6 0.55 0.48 0.68 
PhaseNSEPBIASR2p-factorr-factor
Calibration (1982–1989) 0.56 0.2 0.57 0.69 0.8 
Validation (1990–1995) 0.53 −3.6 0.55 0.48 0.68 
Figure 5

Stream flow hydrograph of Rwizi River during (a) calibration period and (b) validation period showing the 95PPU (95% prediction uncertainty), the observation and best simulation of the flow.

Figure 5

Stream flow hydrograph of Rwizi River during (a) calibration period and (b) validation period showing the 95PPU (95% prediction uncertainty), the observation and best simulation of the flow.

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Quantification of future land use change and climate change impacts on river flow

Annual river flow analysis

Table 8 shows the results of annual runoff for different scenarios under RCP4.5 and RCP8.5 during the period 2021–2050.

The results show that the combined impact of land use change and climate change resulted in a 26.92% and 20.95% increase in annual runoff for the RCP4.5 and RCP8.5 scenarios, respectively, for the average values of all the six RCM models, during the period 2021–2050. The land use change scenario alone showed an increase in the total annual runoff by 47.3% during this period. The influence of land use changes on total annual runoff was more dominant than that of climate change. This could be attributed mainly to the increase in built-up area between the baseline period (2000) and the future period (2048), which would lead to an increase in impervious surfaces, reducing infiltration and increasing surface runoff. During this period, there was drastic land use change that occurred in the catchment over the baseline and future periods. Forest land declined drastically from 9.2% coverage in the baseline period to 0.6% coverage, while built-up area rose from 3.7% to 21.6% of the catchment area, which in turn increases the stream flow during the rainy periods due to increased impermeable surfaces in urban areas/pavements (Anaba et al. 2017). These results are also in agreement with assertions by both Tang et al. (2005) and Nie et al. (2011) that increase in built-up areas increases impermeable surfaces, and diminishing forest land speeds up surface runoff. These results are also in agreement with a study by Baker & Miller (2013), who assessed the impacts of land use change on water resources using the SWAT model for the Njoro watershed in Kenya's Rift Valley and reported that land use changes resulted in increased surface runoff. However, the climate change alone scenario led to a reduction in annual runoff by 4.34% and 9.8% under RCP4.5 and RCP8.5, respectively. This decrease in annual runoff can be attributed to a decrease in annual rainfall, which decreased by 11.68% (10.02 mm) and 12.2% (10.47 mm) under RCP4.5 and RCP8.5, respectively.

High river flow analysis

Figure 6(a) and 6(b) give the projected high-flow events under the climate change scenario for RCP4.5 and RCP8.5, respectively. Figure 6(c) and 6(d) give the projected flow events under combined (land use and climate) change scenarios for RCP4.5 and RCP8.5 runs, respectively, while Figure 6(e) presents the projected high-flow events under the land use change scenario.
Figure 6

Extracted high-flow quantiles for (a) and (b) climate change (for six RCM runs), (c) and (d) combined impacts (for six RCM runs) and (e) land use change impacts.

Figure 6

Extracted high-flow quantiles for (a) and (b) climate change (for six RCM runs), (c) and (d) combined impacts (for six RCM runs) and (e) land use change impacts.

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The results also show that LULC and climate change both affect future high flows based on ten-year quantiles indicating a risk of impacting frequent hydrological flood events in the catchment. The average projected increase in high-flow events was 64.7% (22.9 m3/s) and 73.5% (25.9 m3/s) in RCP4.5 and RCP8.5, respectively, for the climate change scenarios in contrast to a projected increase of about 6% (2.03 m3/s) in high-flow quantiles for the land use change scenario. On the other hand, the combined (land use and climate) change scenarios resulted in an average estimated rise of about 77.7% (27.45 m3/s) and 75.05% (26.5 m3/s) for the ten-year high event in RCP4.5 and RCP8.5, respectively. For the high-flow quartiles, LULC had the least impact in contrast to climate change, which had the highest impact.

Low-flow analysis

Figure 7(a) and 7(b) show the projected low-flow quantiles under the climate change scenario for RCP4.5 and RCP8.5, respectively. Figure 7(c) and 7(d) show the projected low-flow quantiles under combined (land use and climate) change scenarios for RCP4.5 and RCP8.5, respectively, while Figure 7(e) gives the projected low-flow quantiles under the land use change scenarios.
Figure 7

Extracted low-flow quantiles for (a) and (b) climate change (for six RCM runs), (c) and (d) combined impacts (for six RCM runs) and (e) land use change impacts.

Figure 7

Extracted low-flow quantiles for (a) and (b) climate change (for six RCM runs), (c) and (d) combined impacts (for six RCM runs) and (e) land use change impacts.

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The average projected increase in low-flow events for the ten-year low event was approximately 46.18% (0.63 m3/s) and 45.92% (0.626 m3/s) for RCP4.5 and RCP8.5, respectively, under the climate change scenarios. For the land use change scenario, a projected decrease of about 32.69% (0.45 m3/s) in low-flow quantiles for the ten-year low event was observed. The combined (land use and climate) change scenarios resulted in an average estimated rise of 44.37% (0.605 m3/s) and 45.79% (0.624 m3/s) for the ten-year low event in RCP4.5 and RCP8.5, respectively.

Drivers of variabilities in future flows

Annual flows

The isolated impact analysis showed that land use change contributed more significantly to the increase in annual runoff compared with climate change with over 72% and 69% for RCP4.5 and RCP8.5, respectively. On the other hand, climate change contributed about 28% and 31% for RCP4.5 and RCP8.5, respectively, considering the average values of the six RCM models as shown in Table 9 and Figure 8(a). Overall, our study shows that land use change contributed more significantly to the increase in annual runoff compared with climate change. These results are in agreement with studies by Dong et al. (2013), who reported that 86% of changes in flow were attributed mainly to changes in land use with about 14.3% being attributed to climate change. In addition, Yin et al. (2017) also reported that runoff fluctuations are more sensitive to land use change than climate change.
Table 8

Simulated annual runoff scenarios for investigating the combined and isolated impact of land use and climate change for RCP4.5 and RCP8.5 simulations

Scenario descriptionLand use mapClimateRCP4.5
RCP8.5
Simulated flow (m3/s)Change% changeSimulated flow (m3/s)Change% change
Baseline 2000 1981–2010 7.90 – – 7.90 – – 
Land use change 2048 1981–2010 11.64 3.74 47.30% 11.64 3.74 47.30% 
Climate change 2000 2021–2050 7.56 −0.34 −4.34% 7.13 −0.77 −9.80% 
Combined 2048 2021–2050 10.03 2.13 26.92% 9.56 1.66 20.95% 
Scenario descriptionLand use mapClimateRCP4.5
RCP8.5
Simulated flow (m3/s)Change% changeSimulated flow (m3/s)Change% change
Baseline 2000 1981–2010 7.90 – – 7.90 – – 
Land use change 2048 1981–2010 11.64 3.74 47.30% 11.64 3.74 47.30% 
Climate change 2000 2021–2050 7.56 −0.34 −4.34% 7.13 −0.77 −9.80% 
Combined 2048 2021–2050 10.03 2.13 26.92% 9.56 1.66 20.95% 
Table 9

Measures of the attribution of changes in annual stream flow to land use and climate-change period

2021–2050 (RCP4.5)
 1981–2010 (baseline)123456Avg
ETo 1,095.4 1,383.7 1,370.6 1,357.7 1,360.0 1,356.7 1,359.2 1,364.7 
ET 839 634.8 674.5 668.7 659.2 81.5 85.4 467.4 
P 1,043.7 755.6 861.3 773.9 853.9 782.2 736.3 793.9 
ET/P 0.8 0.8 0.8 0.9 0.8 0.1 0.1 0.6 
ET/ETo 0.8 0.5 0.5 0.5 0.5 0.1 0.1 0.3 
Pex 0.2 0.2 0.2 0.1 0.2 0.9 0.9 0.4 
Eex 0.2 0.5 0.5 0.5 0.5 0.9 0.9 0.7 
R  0.3 0.3 0.3 0.3 1.0 1.0 0.5 
  23.1 24.0 23.9 23.8 18.0 18.4 21.5 
L  70% 69% 69% 69% 75% 75% 72% 
C  30% 31% 31% 31% 25% 25% 28% 
2021–2050 (RCP8.5)
Period1981–2010 (baseline)123456Avg
ETo 1,095.4 1,376.7 1,376.9 1,361.3 1,371.6 1,361.2 1,367.7 1,369.2 
ET 839 673.8 674.1 689.3 644.5 682.9 674.9 673.3 
P 1,043.7 799.1 830.8 828.0 791.5 789.5 700.8 790.0 
ET/P 0.8 0.8 0.8 0.8 0.8 0.9 1.0 0.9 
ET/ETo 0.8 0.5 0.5 0.5 0.5 0.5 0.5 0.5 
Pex 0.2 0.2 0.2 0.2 0.2 0.1 0.0 0.1 
Eex 0.2 0.5 0.5 0.5 0.5 0.5 0.5 0.5 
R  0.3 0.7 0.7 0.7 0.8 0.7 0.7 
  23.9 24.0 24.3 23.5 24.1 23.1 23.9 
L  69% 69% 69% 70% 69% 70% 69% 
C  31% 31% 31% 30% 31% 30% 31% 
2021–2050 (RCP4.5)
 1981–2010 (baseline)123456Avg
ETo 1,095.4 1,383.7 1,370.6 1,357.7 1,360.0 1,356.7 1,359.2 1,364.7 
ET 839 634.8 674.5 668.7 659.2 81.5 85.4 467.4 
P 1,043.7 755.6 861.3 773.9 853.9 782.2 736.3 793.9 
ET/P 0.8 0.8 0.8 0.9 0.8 0.1 0.1 0.6 
ET/ETo 0.8 0.5 0.5 0.5 0.5 0.1 0.1 0.3 
Pex 0.2 0.2 0.2 0.1 0.2 0.9 0.9 0.4 
Eex 0.2 0.5 0.5 0.5 0.5 0.9 0.9 0.7 
R  0.3 0.3 0.3 0.3 1.0 1.0 0.5 
  23.1 24.0 23.9 23.8 18.0 18.4 21.5 
L  70% 69% 69% 69% 75% 75% 72% 
C  30% 31% 31% 31% 25% 25% 28% 
2021–2050 (RCP8.5)
Period1981–2010 (baseline)123456Avg
ETo 1,095.4 1,376.7 1,376.9 1,361.3 1,371.6 1,361.2 1,367.7 1,369.2 
ET 839 673.8 674.1 689.3 644.5 682.9 674.9 673.3 
P 1,043.7 799.1 830.8 828.0 791.5 789.5 700.8 790.0 
ET/P 0.8 0.8 0.8 0.8 0.8 0.9 1.0 0.9 
ET/ETo 0.8 0.5 0.5 0.5 0.5 0.5 0.5 0.5 
Pex 0.2 0.2 0.2 0.2 0.2 0.1 0.0 0.1 
Eex 0.2 0.5 0.5 0.5 0.5 0.5 0.5 0.5 
R  0.3 0.7 0.7 0.7 0.8 0.7 0.7 
  23.9 24.0 24.3 23.5 24.1 23.1 23.9 
L  69% 69% 69% 70% 69% 70% 69% 
C  31% 31% 31% 30% 31% 30% 31% 

Notes: P, mean annual rainfall (mm); ETo, mean annual potential evapotranspiration (mm); ET, mean annual evapotranspiration (mm); Pex, excess water divided by available water; Eex, excess energy divided by available energy; R, resultant length (dimensionless); , angle of changes (degrees); L, attribution to land use change (%); C, attribution to climate change (%); 1, CCLM4-8-17-CNRM-CERFACS-CNRM-CM5; 2, RCA4-IPSL-IPSL-CM5A; 3, RCA4-CNRM-CERFACS-CNRM-CM5; 4, RCA4-NOAA-GFDL-GFDL-ESM2M; 5, REMO2009-ICHEC-EC-EARTH; 6, REMO2009-MPI-M-MPI-ESM-LR.

Figure 8

(a) Isolated impact of land use and climate change on runoff variability, (b) isolated impact of land use and climate change on magnitudes of high flow, (c) isolated impacts of land use and climate change on magnitude of low flows.

Figure 8

(a) Isolated impact of land use and climate change on runoff variability, (b) isolated impact of land use and climate change on magnitudes of high flow, (c) isolated impacts of land use and climate change on magnitude of low flows.

Close modal

Contrary to the findings of this study, Onyutha et al. (2021) attributed the changes in historical flow to climate variability in the upper Rwizi catchment. However, Zhang et al. (2016) reported that climate change has a greater impact than land use change in both historical and future periods, although they cautioned that the effects of land use change might be significant if the climate gets drier in the future. It is indeed true, as this study had reported, that there is a decrease in precipitation in the future for both RCP4.5 and RCP8.5, which means the future could be drier as per these results. This could explain the reason for land use having a more pronounced impact on annual flows that climate changes, as reported by Zhang et al. (2016).

Furthermore, the attribution analysis was carried out across the six RCM models to cater for uncertainty across the models. This was done for the combined scenario to try and understand the contribution of future land use and climate change of future flow variations. The highest contribution by climate change under RCP4.5 of about 31% was recorded by 2 = RCA4-IPSL-IPSL-CM5A, 3 = RCA4-CNRM-CERFACS-CNRM-CM5 and 4 = RCA4-NOAA-GFDL-GFDL-ESM2M. Under the RCP8.5 scenario, 1 = CCLM4-8-17-CNRM-CERFACS-CNRM-CM5, 2 = RCA4-IPSL-IPSL-CM5A, 3 = RCA4-CNRM-CERFACS-CNRM-CM5 and 5 = REMO2009-ICHEC-EC-EARTH also contributed over 31% to the changes in future flow changes. The climate change contribution to the future flow variations for all the models under both RCP4.5 and RCP8.5 ranged between 25% and 31%. It should also be noted that the contributions of the climate changes to flow variations under RCP8.5 were on average either equal to or higher than those under RCP4.5 as shown in Table 9.

High-flow quantiles

The isolated impact of land use and climate change on magnitudes of high flow is presented in Figure 8(b). The results show that climate change contributed to 8% and 7%, while land use change contributed to 92% and 93% of the increase in the total magnitude of flow under RCP4.5 and RCP8.5, respectively. The results indicate that climate change is the dominant factor contributing to changes in future extreme flow quantiles within the Rwizi catchment. The increase in the magnitude of high-flow quantiles can be attributed to the increase in rainfall and the decrease in the bushland area, favouring arable land and built-up areas in the catchment.

Low-flow quantiles

The isolated impact of land use and climate change on magnitudes of high flow is presented in Figure 8(c). The results show that climate change contributed to 0.5% and 0.4% (almost zero), while land use change contributed to 99.5% and 99.6% (almost 100%) under RCP4.5 and RCP8.5, respectively, of the increase in the magnitude of future low-flow quantiles. It can be observed that all regional climate model runs project an increase in low-flow quantiles with increasing return periods.

The results indicated that the future changes in low flows were majorly influenced by land use changes with the changes in low flow attribution being about 99.5% and 99.6% for RCP4.5 and RCP8.5, respectively, which is consistent with the study conducted for the Gilgay Abay River basin by Dile et al. (2013), who revealed that climate change appears to have an insignificant impact on low-flow conditions of the river basin. The land cover impacts are like those of high flow. A reduction in grassland and forest area helps alleviate the duration and severity of low flows in both basins. Furthermore, land cover changes are proportionate to changes in low flows.

Performance of reanalysis precipitation

The study results suggest that PRINCETON, ERA5 and CFSR reanalysis data tended to overestimate the mean monthly precipitation, while the CHIRPS reanalysis data underestimated the mean monthly precipitation for the Mbarara rain gauge station. Based on the study results, it is suggested that in the absence of in situ observed rainfall data, AGMERRA reanalysis data provide the best dataset for use in studies in catchments within the same climatic zone and with precipitation patterns similar to the Rwizi catchment.

Change analysis

Precipitation

The results of this study are in line with the CORDEX RCM study findings by Onyutha (2020), which suggested that the future annual amount of rainfall in the study region would decrease. The results are also in line with the study by Onyutha et al. (2019), which that utilised the CMIP3, CMIP5 and CORDEX RCMs to project a decrease in future rainfall for the study area using both RCP4.5 and RCP8.5 scenarios.

However, some studies have reported an increase in rainfall contrary to the findings of this study within the study area, e.g. Nyeko-Ogiramoi et al. (2012) and Nyeko-Ogiramoi (2011), which projected an increase in precipitation in the Rwizi catchment over the future period. In another study, Nyenje & Batelaan (2009) projected an increase in precipitation in the nearby Sezibwa catchment using the HadCM3 GCM. The difference could be attributed to the fact that in both these studies, GCM outputs were used for climate change projection, which are on a coarser resolution than the CORDEX RCMs. This is indeed true as Onyutha et al. (2019) observed that climate change conditions varied from one set of climate models to another, which they attributed to the spatial resolution in the models. This is because the coarser resolution of the GCM models causes the GCMs to smoothly reflect the orography, thus reducing the ability to depict the effect of topographical characteristics on rainfall variability across regions (Onyutha et al. 2019).

Maximum and minimum temperature

The findings of this study are in agreement with earlier findings from Jassogne et al. (2013), who articulated an increasing trend in minimum and maximum temperature over the next 50 years beginning from 2015. Diffenbaugh & Scherer (2011) also asserted that if there were a continued rise in carbon dioxide concentrations, a permanent heat regime over the next four decades would be experienced in different locations over the globe. Consequently, the increase and decrease in Tmax and Tmin would mean an increase in the difference between Tmax and Tmin, and thus an increase in evaporation (Nyeko-Ogiramoi 2011). An increase in evapotranspiration would mean a reduction in the stream flow for the case study catchment.

LULC change projection

The study results are in agreement with Barasa et al. (2010), who established that the highest increases in land use systems were in agriculture and grasslands, while the highest losses were in degraded grasslands and woodland/forest in Uganda. Also, Wanyama (2012) reported a decrease in grassland and forest area of 17,529 ha (−17.7%) and −1,406 ha (−39.1%), respectively, between the period 1954–2010, which is in close agreement with the findings of this study. The study results suggest the need for focused efforts on afforestation and wetland protection as suitable adaptation measures for the Rwizi catchment. Furthermore, the study results suggest the need for alternative cooking-fuel options to reduce the demand for firewood and charcoal in the Rwizi catchment.

Quantification of future land use and climate change impacts on river flow

Annual river flow analysis

The study results suggest that for the case study catchment, the influence of land use change was more predominant when compared with climate change. These results are in agreement with studies by Piras et al. (2014) and Islam et al. (2014), who both reported that climate change led to a reduction in mean annual rainfall which resulted in a decline in mean annual runoff. In addition, according to the findings, the combined effects of land use and climate change on changes in flow are significantly different from the effects of each factor alone. This highlights the need to consider the effects of both land use change and climate change when planning, designing and managing water resource systems.

High river flow analysis

The results agree with the findings of Dosdogru et al. (2020), who found that LULC change did not have a substantial impact on peak stream flows in the Upper Cahaba River catchment. More impact on the high flows will, however, further compound the soil erosion situation and increase sediment transport, making stream-flow muddy and impacting aquatic life and hence downgrading ecosystem services (Dosdogru et al. 2020).

Low-flow analysis

The results show that LULC and climate change both affect future low flows, indicating a risk of impacting frequent hydrological droughts in the catchment. The projections suggest that these impacts are compounded with the increase in return periods.

Drivers of variabilities in future flows

The results suggest that the projected decrease in future precipitation for both RCP4.5 and RCP8.5 scenarios will amplify the effects of land use change on annual average river flows. However, the study results also suggest that climate change will have a greater impact on peak flows when compared with land use change. The study findings are affirmed in a previous study by Hung et al. (2020), which concluded that climate change has a greater impact on peak flows compared with land use change. Another study by Dibaba et al. (2020) in highland Ethiopia also found that climate change had a predominant influence on stream flow extremes compared with LULC effects. The percentage increases in high-flow quantiles caused by the combined land use and climate change scenarios were higher than the increases observed in individual scenarios of land use change and climate change.

Although LULC has also impacted the duration and severity of high flow, their significance is affected by the amount of land cover change. Many recent scholars have postulated that land use change increases peak flow and runoff because of decreased infiltration capabilities of the soils (Anaba et al. 2017). On the other hand, reduction in grassland and forest area produces shorter duration and more severe high flows. These results highlight the significance of considering both land use change and climate change together when analysing the impacts on high-flow events.

On the other hand, the low-flow study findings emphasise the importance of considering both climate change and land use change when assessing the impacts on low-flow events. The contribution of land use change to the increase in low flows can be attributed to the large, combined area of grassland and forest in the catchment during the projection period. In addition, it could also be attributed to the increase in population and hence settlement, which further leads to an increase in impervious surfaces through urbanisation and infrastructure development like roads, buildings and pavements. These surfaces would prevent water from infiltrating into the ground and as a result lead to reduced groundwater recharge during the rainy periods. This would result in reduced baseflow contribution to the river in the dry spells.

This aim of the study was to undertake a comprehensive assessment of the distinct and combined impacts of future climate and land use change on river Rwizi flows using a combination of statistical downscaling, machine learning and physically based hydrological modelling techniques.

The assessment was undertaken using the physically based semi-distributed SWAT model using three scenarios, i.e. LULC change, climate change and combined (climate and LULC change). The study also applied an ensemble of six CORDEX RCM climate models under the moderate (RCP4.5) and high (RCP8.5) emission scenarios to assess changes in future climate for 2050. The 2050 future land use was projected using a CA-ANN.

This study was driven by the desire to provide evidence-based insights into the key factors responsible for the changing hydrology of water-stressed catchments with limited observed data. The findings from such assessments can act as a benchmark for government ministries responsible for water resources management, river basin organisations and other non-governmental organisations to make decisions on water regulation, water conservation and regulation of plans for hydraulic infrastructure (Bai et al. 2019; Onyutha et al. 2021; Bahati et al. 2021).

The results demonstrated that land use change and climate change as well as their interactions place substantial pressures on river flow. More specifically, the results showed that changes in future annual flow and low flow were attributable mainly to land use change, while future high flows were attributed to climate change. The isolated impacts of land use change and the combined impacts showed an increase in future total annual river flows of 47.3% and 20.9%, respectively. However, the isolated impacts of climate change showed a reduction in future total annual flow of 4.34% and 9.8% under RCP4.5 and RCP8.5, respectively. The influence of land use changes on total annual runoff was more dominant than that of climate change. The findings show that LULC change and climate change affect the duration and severity for both high and low flows. Specifically, the more the land cover changes, the more the duration and severity of high- and low-flow events will be affected.

Land use change alters the composition and configuration of river catchments hence impacting the river catchment ecosystems (Bai et al. 2019; Onyutha et al. 2021; Bahati et al. 2021). Studies have shown that land use significantly impacts river flow through changes in pervious surfaces/runoff coefficients (e.g. Anaba et al. 2017). Spatial changes in land use over time have significant impacts on hydrological risks and the capacity of catchments to provide ecosystem services (Jogun et al. 2019). Scholars have recently used LULC change as a proxy for assessing impacts on river catchment ecosystems (Paudyal et al. 2019). Climate change is equally a key driver impacting hydrological flood and drought risks (Onyutha et al. 2021).

Similar to studies elsewhere, climate change has been found to be responsible for escalating the frequency and severity of extreme events, such as temperature and precipitation patterns. Climate change affects river catchment ecosystems and flows by modifying the biophysical processes, which in turn pushes the flows to the extremes. This is expected to increasingly threaten river flow and ecosystems and is expected to become a severe threat in the future (Nakkazi et al. 2022).

Although previous studies agree that LULC and climate change impact hydrological flow and extreme events, it should be noted that the impacts can produce varied hydrologic responses for the same catchment under different geographic and climatic conditions due to data variability and limitations. While bias correction has been adopted to overcome data limitations, further investigations will be required particularly when more data become available. Despite the limitations in this study, the results have methodological, practical and policy applications, which support the use of hydrological modelling to inform allocation, prioritisation and decision-making in a bid to accelerate progress towards the United Nations Sustainable Development Goal (SDG) target number 6.4–6.6. The study also supports the hypothesis that a combined scenario analysis is key to evaluating effects of driving factors on future hydrological changes in river basins. These tools facilitate a better understanding of the change impacts and projections on river basins, enabling the generation of tailored catchment-specific policy suggestions. However, it should be noted that impacts will vary from location to location (Bai et al. 2019; Qu et al. 2020). While this analysis has been applied to the Rwizi catchment, the same approach can be applied to other water-stressed catchments to come up with location-specific results and mitigation strategies like planting trees and restoring forests to enhance water retention, regulate stream flow and mitigate the impacts of climate change since trees play a crucial role in maintaining hydrological balance by absorbing and releasing water through transpiration (e.g. Azarnivand et al. 2020; Meaza et al. 2022; Kingsbury-Smith et al. 2023). In addition, implementing sustainable agricultural and land use practices, such as contour ploughing, cover cropping and reduced tillage, can minimise soil erosion and improve water retention (Kajembe et al. 2005; Nyssen et al. 2010; Sarvade et al. 2019; Telles et al. 2019; Rajbanshi et al. 2023). This helps maintain a more stable stream flow pattern (Näschen et al. 2019).

Finally, planning for water resources management in the Rwizi catchment should consider both land use change and climate impact as they are important in assessing the adequacy of water management strategies. While designing management strategies to prevent future extreme events like flood and drought, climate change deserves more attention. Pragmatic strategies for sustainable water and environmental resources in the catchment should be developed.

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

The authors declare there is no conflict.

Anaba
L. A.
,
Banadda
N.
,
Kiggundu
N.
,
Wanyama
J.
,
Engel
B.
&
Moriasi
D.
2017
Application of SWAT to assess the effects of land use change in the Murchison Bay Catchment in Uganda
.
Comput. Water Energy Environ. Eng.
6
,
24
40
.
https://doi.org/10.4236/cweee.2017.61003
.
Alramlawi
K.
&
Fıstıkoğlu
O.
2022
Estimation of intensity-duration-frequency (IDF) curves from large scale atmospheric dataset by statistical downscaling
.
Teknik Dergi
33
(
1
),
11591
11615
.
https://doi.org/10.18400/tekderg.874035.
Arnold
J. G.
,
Srinivasan
R.
,
Muttiah
R. S.
&
Williams
J. R.
1998
Large area hydrologic modeling and assessment part I: model development
.
JAWRA Journal of the American Water Resources Association
34
,
73
89
.
Arnold
J. G.
,
Moriasi
D. N.
,
Gassman
P. W.
,
Abbaspour
K. C.
,
White
M. J.
,
Srinivasan
R.
,
Santhi
C.
,
Harmel
R. D.
,
van Griensven
A.
,
Van Liew
M. W.
,
Kannan
N.
&
Jha
M. K.
2012
SWAT: model use, calibration, and validation
.
Transactions of the ASABE
55
,
1491
1508
.
http://dx.doi.org/10.13031/2013.42256
.
Aruho Tusingwiire, M., Tumutungire, M. D., Sempewo, J. I. & Semiyaga, S. 2023 Impacts of climate and land use/cover change on mini-hydropower generation in river Kyambura watershed in southwestern part of Uganda. Water Practice & Technology 18 (6), 1576–1597.
https://doi.org/10.2166/wpt.2023.079.
Atim
J.
2010
Application of Integrated Water Resources Management in Computer Simulation of River Basin's Status – Case Study of River Rwizi. Master's thesis, Vaal University of Technology, Vanderbijlpark, South Africa
.
Azarnivand
A.
,
Camporese
M.
,
Alaghmand
S.
&
Daly
E.
2020
Modeling hydrological impacts of afforestation on intermittent streams
.
Sci. Total Environ.
728
,
138748
.
Baker
T. J.
&
Miller
S. N.
2013
Using the Soil and Water Assessment Tool (SWAT) to assess land use impact on water resources in an East African watershed
.
J. Hydrol.
486
,
100
111
.
https://doi.org/10.1016/j.jhydrol.2013.01.041
.
Barasa
B.
,
Egeru
A.
,
Okello
P.
&
Mutuzo
F.
2010
Dynamics of land use/cover trends in Kanungu District, south-western Uganda
.
J. Appl. Sci. Environ. Manag.
14
(
4
),
67
70
.
https://doi.org/10.4314/jasem.v14i4.63260
.
Bahati
H. K.
,
Ogenrwoth
A.
&
Sempewo
J. I.
2021
Quantifying the potential impacts of land-use and climate change on hydropower reliability of Muzizi hydropower plant, Uganda
.
Journal of Water and Climate Change
12
,
2526
2554
.
https://doi.org/10.2166/wcc.2021.273.
Berhanu
B.
,
Seleshi
Y.
,
Amare
M.
&
Melesse
A. M.
2016
Upstream–downstream linkages of hydrological processes in the Nile River Basin
. In:
Landscape Dynamics, Soils and Hydrological Processes in Varied Climates
(
Melesse
A. M.
, &
Abtew
W.
, eds),
Springer
,
Cham, Switzerland
, pp.
207
223
.
https://doi.org/10.1007/978-3-319-18787-7_11.
Byers
E.
,
Gidden
M.
,
Leclère
D.
,
Balkovic
J.
,
Burek
P.
,
Ebi
K.
,
Greve
P.
,
Grey
D.
,
Havlik
P.
,
Hillers
A.
,
Johnson
N.
,
Kahil
T.
,
Krey
V.
,
Langan
S.
,
Nakicenovic
N.
,
Novak
R.
,
Obersteiner
M.
,
Pachauri
S.
,
Palazzo
A.
,
Parkinson
S.
,
Rao
N. D.
,
Rogelj
J.
,
Satoh
Y.
,
Wada
Y.
,
Willaarts
B.
&
Riahi
K.
2018
Global exposure and vulnerability to multi-sector development and climate change hotspots
.
Environ. Res. Lett.
13
,
055012
.
https://doi.org/10.1088/1748-9326/aabf45
.
Castella
J. C.
&
Verburg
P. H.
2007
Combination of process-oriented and pattern-oriented models of land-use change in a mountain area of Vietnam
.
Ecological Modelling
202
(
3–4
),
410
420
.
https://doi.org/10.1016/j.ecolmodel.2006.11.011.
Chang
J.
,
Wang
Y.
,
Istanbulluoglu
E.
,
Bai
T.
,
Huang
Q.
,
Yang
D.
&
Huang
S.
2015
Impact of climate change and human activities on runoff in the Weihe River Basin, China
.
Quat. Int.
380–381
,
169
179
.
https://doi.org/10.1016/j.quaint.2014.03.048
.
Chen
Q.
,
Chen
H.
,
Wang
J.
,
Zhao
Y.
,
Chen
J.
&
Xu
C.
2019
.
Clarke
B.
,
Otto
F.
,
Stuart-Smith
R.
&
Harrington
L.
2022
Extreme weather impacts of climate change: an attribution perspective
.
Environ. Res. Clim.
1
,
012001
.
https://doi.org/10.1088/2752-5295/ac6e7d
.
Costanza
R.
1989
Model goodness of fit: a multiple resolution procedure
.
Ecological Modelling
47
(
3–4
),
199
215
.
https://doi.org/10.1016/0304-3800(89)90001-X.
Diffenbaugh
N. S.
&
Scherer
M.
2011
Observational and model evidence of global emergence of permanent, unprecedented heat in the 20th and 21st centuries
.
Clim. Change
107
,
615
624
.
https://doi.org/10.1007/s10584-011-0112-y
.
Dile
Y. T.
,
Berndtsson
R.
&
Setegn
S. G.
2013
Hydrological response to climate change for Gilgel Abay River, in the Lake Tana Basin – Upper Blue Nile Basin of Ethiopia
.
PLoS One
8
,
e79296
.
https://doi.org/10.1371/journal.pone.0079296
.
Dong
W.
,
Cui
B.
,
Liu
Z.
&
Zhang
K.
2013
Relative effects of human activities and climate change on the river runoff in an arid basin in northwest China
.
Hydrol. Process.
28
,
4854
4864
.
https://doi.org/10.1002/hyp.9982
.
Dong
H.
,
Geng
Y.
,
Fujita
T.
,
Fujii
M.
,
Hao
D.
&
Yu
X.
2014
Uncovering regional disparity of China's water footprint and inter-provincial virtual water flows
.
Sci. Total Environ.
500–501
,
120
130
.
https://doi.org/10.1016/j.scitotenv.2014.08.094.
Gassman
P. W.
,
Arnold
J. G.
,
Srinivasan
R.
&
Reyes
M.
2010
The worldwide use of the SWAT model: technological drivers, networking impacts, and simulation trends. In: 21st Century Watershed Technology: Improving Water Quality and Environment 2010, American Society of Agricultural and Biological Engineers, St Joseph, MI, USA, pp. 226–233
.
Gebre
S. L.
,
Tadele
K.
&
Mariam
B. G.
2015
Potential impacts of climate change on the hydrology and water resources availability of Didessa catchment, Blue Nile River Basin, Ethiopia
.
J. Geol. Geophys.
4
,
193
.
https://doi.org/10.4172/2329-6755.1000193
.
Gebrechorkos
S. H.
,
Peng
J.
,
Dyer
E.
,
Miralles
D. G.
,
Vicente-Serrano
S. M.
,
Funk
C.
,
Beck
H. E.
,
Asfaw
D. T.
,
Singer
M. B.
&
Dadson
S. J.
2023
Global high-resolution drought indices for 1981–2022
.
Earth System Science Data
15
,
5449
5466
.
https://doi.org/10.5194/essd-15-5449-2023.
GIS LAB 2014 Landscape change analysis with MOLUSCE – methods and algorithms.
http://wiki.gis-lab.info/w/Landscape_change_analysis_with_MOLUSCE_-_methods_and_algorithms (accessed July 11, 2019).
Guo
H.
,
Hu
Q.
&
Jiang
T.
2008
Annual and seasonal streamflow responses to climate and land-cover changes in the Poyang Lake basin, China
.
J. Hydrol.
355
,
106
122
.
https://doi.org/10.1016/j.jhydrol.2008.03.020
.
Howells
M.
,
Hermann
S.
,
Welsch
M.
,
Bazilian
M.
,
Segerström
R.
,
Alfstad
T.
,
Gielen
D.
,
Rogner
H.
,
Fischer
G.
,
van Velthuizen
H.
,
Wiberg
D.
,
Young
C.
,
Roehrl
R. A.
,
Mueller
A.
,
Steduto
P.
&
Ramma
I.
2013
Integrated analysis of climate change, land-use, energy and water strategies
.
Nature Climate Change
3
,
621
626
.
https://doi.org/10.1038/nclimate1789.
Hung
C.-L. J.
,
James
L. A.
,
Carbone
G. J.
&
Williams
J. M.
2020
Impacts of combined land-use and climate change on streamflow in two nested catchments in the Southeastern United States
.
Ecological Engineering
143
,
105665
.
https://doi.org/10.1016/j.ecoleng.2019.105665.
Islam
M. S.
,
Han
S.
,
Ahmed
M. K.
&
Masunaga
S.
2014
Assessment of trace metal contamination in water and sediment of some rivers in Bangladesh
.
Journal of Water and Environment Technology
12
(
2
),
109
121
.
https://doi.org/10.2965/jwet.2014.109.
Jassogne
L.
,
Läderach
P.
&
van Asten
P.
2013
The Impact of Climate Change on Coffee in Uganda: Lessons from a Case Study in the Rwenzori Mountains
. Oxfam, Oxford,
UK
.
Jogun
T.
,
Lukić
A.
&
Gašparović
M.
2019
Simulation model of land cover changes in a post-socialist peripheral rural area: Požega-Slavonia County, Croatia
.
Hrvat. Geogr. Glas.
81
(
1
),
31
59
.
https://doi.org/10.21861/HGG.2019.81.01.02
.
Jokar Arsanjani
J.
,
Helbich
M.
,
Kainz
W.
&
Darvishi Boloorani
A.
2013
Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion
.
Int. J. Appl. Earth Obs. Geoinf.
21
,
265
275
.
https://doi.org/10.1016/j.jag.2011.12.014
.
Khoi
D. N.
&
Thom
V. T.
2015
Parameter uncertainty analysis for simulating streamflow in a river catchment of Vietnam
.
Glob. Ecol. Conserv.
4
,
538
548
.
https://doi.org/10.1016/j.gecco.2015.10.007
.
Kingsbury-Smith
L.
,
Willis
T.
,
Smith
M.
,
Boisgontier
H.
,
Turner
D.
,
Hirst
J.
,
Kirkby
M.
&
Klaar
M.
2023
Evaluating the effectiveness of land use management as a natural flood management intervention in reducing the impact of flooding for an upland catchment
.
Hydrol. Processes
37
(
4
),
e14863
.
Kisembe
J.
,
Favre
A.
,
Dosio
A.
,
Lennard
C.
,
Sabiiti
G.
&
Nimusiima
A.
2019
Evaluation of rainfall simulations over Uganda in CORDEX regional climate models
.
Theor. Appl. Climatol.
137
,
1117
1134
.
https://doi.org/10.1007/s00704-018-2643-x
.
Kuloba
M. P.
2017
Land Use Transitions and Vegetation Dynamics in the Rwizi Catchment, Uganda. Master's thesis
,
Makerere University
,
Kampala, Uganda
.
Kumar
N.
2014
Impacts of Climate Change and Land-Use Change on the Water Resources of the Upper Kharun Catchment, Chhattisgarh, India
.
PhD thesis, Rheinischen Friedrich-Wilhelms-Universität zu Bonn, Bonn, Germany
, pp.
16
17
.
Landis
J. R.
&
Koch
G. G.
1977
The measurement of observer agreement for categorical data
.
Biometrics
33
,
159
174
.
https://doi.org/10.2307/2529310
.
Lang
M.
,
Ouarda
T. B. M. J.
&
Bobée
B.
1999
Towards operational guidelines for over-threshold modeling
.
J. Hydrol.
225
,
103
117
.
https://doi.org/10.1016/S0022-1694(99)00167-5
.
Li
X.
&
Yeh
A. G.-O.
2002
Neural-network-based cellular automata for simulating multiple land use changes using GIS
.
Int. J. Geogr. Inf. Sci.
16
,
323
343
.
https://doi.org/10.1080/13658810210137004
.
Lin
J. Y.
,
Yang
M. D.
,
Lin
B. R.
&
Lin
P. S.
2011
Risk assessment of debris flows in Songhe Stream, Taiwan
.
Engineering Geology
123
(
1–2
),
100
112
.
https://doi.org/10.1016/j.enggeo.2011.07.003.
López-Moreno
J. I.
,
Vicente-Serrano
S. M.
,
Moran-Tejeda
E.
,
Zabalza
J.
,
Lorenzo-Lacruz
J.
&
García-Ruiz
J. M.
2011
Impact of climate evolution and land use changes on water yield in the Ebro basin
.
Hydrol. Earth Syst. Sci.
15
,
311
322
.
https://doi.org/10.5194/hess-15-311-2011.
Makarigakis
A. K.
&
Jimenez-Cisneros
B. E.
2019
UNESCO's contribution to face global water challenges
.
Water
11
,
388
.
https://doi.org/10.3390/w11020388
.
Marhaento
H.
,
Booij
M. J.
&
Hoekstra
A. Y.
2017
Attribution of changes in stream flow to land use change and climate change in a mesoscale tropical catchment in Java, Indonesia
.
Hydrol. Res.
48
,
1143
1155
.
https://doi.org/10.2166/nh.2016.110
.
Morales-Moraga
D.
,
Meza
F. J.
,
Miranda
M.
&
Gironás
J.
2019
Spatio-temporal estimation of climatic variables for gap filling and record extension using Reanalysis data
.
Theor. Appl. Climatol.
137
,
1089
1104
.
Moriasi
D. N.
,
Arnold
J. G.
,
Van Liew
M. W.
,
Bingner
R. L.
,
Harmel
R. D.
&
Veith
T. L.
2007
Model evaluation guidelines for systematic quantification of accuracy in watershed simulations
.
Transactions of the ASABE
50
(
3
),
885
900
.
https://doi.org/10.13031/2013.23153.
Musa
S. I.
,
Hashim
M.
&
Reba
M. N. M.
2016
A review of geospatial-based urban growth models and modelling initiatives
.
Geocarto International
32
,
813
833
.
https://doi.org/10.1080/10106049.2016.1213891
.
Näschen
K.
,
Diekkrüger
B.
,
Evers
M.
,
Höllermann
B.
,
Steinbach
S.
&
Thonfeld
F.
2019
The impact of land use/land cover change (LULCC) on water resources in a tropical catchment in Tanzania under different climate change scenarios
.
Sustainability
11
(
24
),
7083
.
Nakkazi
M. T.
,
Sempewo
J. I.
,
Tumutungire
M. D.
&
Byakatonda
J.
2022
Performance evaluation of CFSR, MERRA-2 and TRMM3B42 data sets in simulating river discharge of data-scarce tropical catchments: a case study of Manafwa, Uganda
.
Journal of Water and Climate Change
13
(
2
),
522
541
.
https://doi.org/10.2166/wcc.2021.174.
Neitsch
S. L.
,
Arnold
J. G.
,
Kiniry
J. R.
&
Williams
J. R.
2009
Soil and Water Assessment Tool: Theoretical Documentation. Version 2009. Texas Water Resources Institute Technical Report No. 406, Texas A&M University, College Station, TX, USA
.
NEMA
2004
State of Environment Report: Mbarara District
.
National Environment Management Authority
,
Kampala, Uganda
.
Nie
W.
,
Yuan
Y.
,
Kepner
W.
,
Nash
M. S.
,
Jackson
M.
&
Erickson
C.
2011
Assessing impacts of Landuse and Landcover changes on hydrology for the upper San Pedro watershed
.
J. Hydrol.
407
,
105
114
.
https://doi.org/10.1016/j.jhydrol.2011.07.012.
Nseka
D.
,
Opedes
H.
,
Mugagga
F.
,
Ayesiga
P.
,
Semakula
H.
,
Wasswa
H.
&
Ologe
D.
2022
Implications of land use and cover changes on upper river Rwizi macro-watershed health in south western Uganda
.
In: Water Conservation – Inevitable Strategy (Eyvaz, M., Albahnasawi, A., Gürbulak, E. & Yüksel, E., eds), IntechOpen, London, UK, pp. 39–60.
Nyeko-Ogiramoi
P.
2011
Climate Change Impacts on Hydrological Extremes and Water Resources in Lake Victoria Catchments, Upper Nile Basin. PhD thesis
.
KU Leuven
,
Heverlee, Belgium
.
Nyeko-Ogiramoi
P.
,
Ngirane-Katashaya
G.
,
Willems
P.
&
Ntegeka
V.
2010
Evaluation and inter-comparison of Global Climate Models’ performance over Katonga and Ruizi catchments in Lake Victoria basin
.
Phys. Chem. Earth
35
,
618
633
.
https://doi.org/10.1016/j.pce.2010.07.037
.
Nyeko-Ogiramoi
P.
,
Willems
P.
,
Ngirane-Katashaya
G.
&
Ntegeka
V.
2012
Nonparametric statistical downscaling of precipitation from global climate models. In: Climate Models (Druyan, L. M., ed.), InTech, Rijeka, Croatia, pp. 109–136
.
Nyenje
P. M.
&
Batelaan
O.
2009
Estimating the effects of climate change on groundwater recharge and baseflow in the upper Ssezibwa catchment, Uganda
.
Hydrol. Sci. J.
54
,
713
726
.
https://doi.org/10.1623/hysj.54.4.713
.
Nyssen
J.
,
Clymans
W.
,
Descheemaeker
K.
,
Poesen
J.
,
Vandecasteele
I.
,
Vanmaercke
M.
, Zenebe, A., Van Camp, M., Haile, M., Haregeweyn, N., Moeyersons, J., Martens, K., Gebreyohannes, T., Deckers, J. &
Walraevens
K.
2010
Impact of soil and water conservation measures on catchment hydrological response – a case in north Ethiopia
.
Hydrol. Processes
24
(
13
),
1880
1895
.
Onyutha
C.
2017a
FAN-Stat – Frequency analyses considering non- stationarity: a tool for flood and drought assessment. Available from: https://sites.google.com/site/conyutha/tools-to-download
.
Onyutha
C.
,
Rutkowska
A.
,
Nyeko-Ogiramoi
P.
&
Willems
P.
2019
How well do climate models reproduce variability in observed rainfall? A case study of the Lake Victoria basin considering CMIP3, CMIP5 and CORDEX simulations
.
Stochastic Environ. Res. Risk Assess.
33
,
687
707
.
https://doi.org/10.1007/s00477-018-1611-4
.
Onyutha
C.
,
Nyesigire
R.
&
Nakagiri
A.
2021
Contributions of human activities and climatic variability to changes in river Rwizi flows in Uganda, East Africa
.
Hydrology
8
,
145
.
https://doi.org/10.3390/hydrology8040145.
Onyutha
C.
,
Kerudong
P. A.
,
Guma
B. E.
&
Mugisha
C.
2022
Impacts of upstream water abstraction and climate variability on river Mpanga hydropower production in Uganda
.
International Journal of Energy and Water Resources
6
(
1
),
49
66
.
https://doi.org/10.1007/s42108-021-00137-1.
Palmate
S. S.
,
Wagner
P. D.
,
Fohrer
N.
&
Pandey
A.
2022
Assessment of uncertainties in modelling land use change with an integrated cellular automata–Markov chain model
.
Environ. Model. Assess.
27
,
275
293
.
Park
S.
,
Jeon
S.
,
Kim
S.
&
Choi
C.
2011
Prediction and comparison of urban growth by land suitability index mapping using GIS and RS in South Korea
.
Landsc. Urban Plan.
99
,
104
114
.
https://doi.org/10.1016/j.landurbplan.2010.09.001
.
Pijanowski
B. C.
,
Brown
D. G.
,
Shellito
B. A.
&
Manik
G. A.
2002
Using neural networks and GIS to forecast land use changes: a Land Transformation Model
.
Computers, Environment and Urban Systems
26
,
553
575
.
Piras
M.
,
Mascaro
G.
,
Deidda
R.
&
Vivoni
E. R.
2014
Quantification of hydrologic impacts of climate change in a Mediterranean basin in Sardinia, Italy, through high-resolution simulations
.
Hydrol. Earth Syst. Sci.
18
,
5201
5217
.
https://doi.org/10.5194/hess-18-5201-2014.
Pontius
R. G.
&
Suedmeyer
B.
2004
Components of agreement between categorical maps at multiple resolutions
.
In: Remote Sensing and GIS Accuracy Assessment (Lunetta, R. S. & Lyon, J. G., eds), CRC Press, Boca Raton, FL, USA
, pp.
233
251
.
Rathjens
H.
,
Bieger
K.
,
Srinivasan
R.
,
Chaubey
I.
&
Arnold
J. G.
2016
CMhyd User Manual: Documentation for Preparing Simulated Climate Change Data for Hydrologic Impact Studies. Texas A&M University, College Station, TX, USA
.
Renner
M.
,
Brust
K.
,
Schwärzel
K.
,
Volk
M.
&
Bernhofer
C.
2014
Separating the effects of changes in land cover and climate: a hydro-meteorological analysis of the past 60yr in Saxony, Germany
.
Hydrol. Earth Syst. Sci.
18
,
389
405
.
https://doi.org/10.5194/hess-18-389-2014
.
Ross
L.
,
Alahmed
S.
,
Smith
S. M. C.
&
Roberts
G.
2021
Tidal and subtidal transport in short, tidally-driven estuaries with low rates of freshwater input
.
Continental Shelf Research
224
,
104453
.
https://doi.org/10.1016/j.csr.2021.104453.
Ruane
A. C.
,
Goldberg
R.
&
Chryssanthacopoulos
J.
2015
Climate forcing datasets for agricultural modeling: merged products for gap-filling and historical climate series estimation
.
Agric. For. Meteorol.
200
,
233
248
.
https://doi.org/10.1016/j.agrformet.2014.09.016
.
Ryken
N.
,
Vanmaercke
M.
,
Wanyama
J.
,
Isabirye
M.
,
Vanonckelen
S.
,
Deckers
J.
&
Poesen
J.
2015
Impact of papyrus wetland encroachment on spatial and temporal variabilities of stream flow and sediment export from wet tropical catchments
.
Sci. Total Environ.
511
,
756
766
.
https://doi.org/10.1016/j.scitotenv.2014.12.048
.
Sarvade
S.
,
Upadhyay
V. B.
,
Kumar
M.
&
Imran Khan
M.
2019
Soil and water conservation techniques for sustainable agriculture
. In:
Sustainable Agriculture, Forest and Environmental Management
, pp.
133
188
.
Sempewo
J. I.
,
Twite
D.
,
Nyenje
P.
&
Mugume
S. N.
2023
Comparison of SWAT and HEC-HMS model performance in simulating catchment runoff
.
Journal of Applied Water Engineering and Research
11
(
4
),
481
495
.
https://doi.org/10.1080/23249676.2022.2156401.
Shafizadeh-Moghadam
H.
,
Tayyebi
A.
,
Ahmadlou
M.
,
Delavar
M. R.
&
Hasanlou
M.
2017
Integration of genetic algorithm and multiple kernel support vector regression for modeling urban growth
.
Comput. Environ. Urban Syst.
65
,
28
40
.
https://doi.org/10.1016/j.compenvurbsys.2017.04.011
.
Sharannya
T. M.
,
Venkatesh
K.
,
Mudbhatkal
A.
,
Dineshkumar
M.
&
Mahesha
A.
2021
Effects of land use and climate change on water scarcity in rivers of the Western Ghats of India
.
Environ. Monit. Assess.
193
,
820
.
https://doi.org/10.1007/s10661-021-09598-7
.
Shrestha
S.
&
Htut
A. Y.
2016
Land use and climate change impacts on the hydrology of the Bago River Basin, Myanmar
.
Environ. Model. Assess.
21
,
819
833
.
https://doi.org/10.1007/s10666-016-9511-9
.
Shrestha
M.
,
Shrestha
S.
&
Shrestha
P. K.
2020
Evaluation of land use change and its impact on water yield in Songkhram River basin, Thailand
.
International Journal of River Basin Management
18
(
1
),
23
31
.
https://doi.org/10.1080/15715124.2019.1566239.
Tang
Z.
,
Engel
B. A.
,
Pijanowski
B. C.
&
Lim
K. J.
2005
Forecasting land use change and its environmental impact at a watershed scale
.
Journal of Environmental Management
76
(
1
),
35
45
.
https://doi.org/10.1016/j.jenvman.2005.01.006.
The worldwide use of the SWAT model: technological drivers, networking impacts, and simulation trends
. In:
21st Century Watershed Technology: Improving Water Quality and Environment 2010
,
American Society of Agricultural and Biological Engineers
, St Joseph, MI,
USA
, pp.
226
233
Telles
T. S.
,
Righetto
A. J.
,
da Costa
G. V.
,
Volsi
B.
&
de Oliveira
J. F.
2019
Conservation agriculture practices adopted in southern Brazil
.
Int. J. Agric. Sustain.
17
(
5
),
338
346
.
Teutschbein
C.
&
Seibert
J.
2012
Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods
.
J. Hydrol.
456–457
,
12
29
.
https://doi.org/10.1016/j.jhydrol.2012.05.052
.
Tomer
M. D.
&
Schilling
K. E.
2009
A simple approach to distinguish land-use and climate-change effects on watershed hydrology
.
J. Hydrol.
376
,
24
33
.
https://doi.org/10.1016/j.jhydrol.2009.07.029
.
Vairavamoorthy
K.
,
Gorantiwar
S. D.
&
Pathirana
A.
2008
Managing urban water supplies in developing countries – climate change and water scarcity scenarios
.
Phys. Chem. Earth
33
,
330
339
.
https://doi.org/10.1016/j.pce.2008.02.008
.
Wanyama
J.
2012
Effect of Land-Use/Cover Change on Land Degradation in the Lake Victoria Basin: Case of Upper Rwizi Catchment, Southwestern Uganda
.
PhD thesis, KU Leuven
,
Heverlee, Belgium
.
Woldesenbet
T. A.
,
Elagib
N. A.
,
Ribbe
L.
&
Heinrich
J.
2017
Hydrological responses to land use/cover changes in the source region of the Upper Blue Nile Basin, Ethiopia
.
Sci. Total Environ.
575
,
724
741
.
https://doi.org/10.1016/j.scitotenv.2016.09.124.
World Economic Forum 2017 The Global Risks Report 2017, 12th edn. World Economic Forum, Geneva,
Switzerland
.
Yang
L.
,
Feng
Q.
,
Yin
Z.
,
Wen
X.
,
Si
J.
,
Li
C.
&
Deo
R. C.
2017
Identifying separate impacts of climate and land use/cover change on hydrological processes in upper stream of Heihe River, Northwest China
.
Hydrol. Process.
31
,
1100
1112
.
https://doi.org/10.1002/hyp.11098.
Yin
J.
,
He
F.
,
Xiong
Y. J.
&
Qiu
G. Y.
2017
Effects of land use/land cover and climate changes on surface runoff in a semi-humid and semi-arid transition zone in northwest China
.
Hydrol. Earth Syst. Sci.
21
,
183
196
.
https://doi.org/10.5194/hess-21-183-2017
.
Zhang
H.
,
Wang
B.
,
Li Liu
D.
,
Zhang
M.
,
Feng
P.
,
Cheng
L.
,
Yu
Q.
&
Eamus
D.
2019
Impacts of future climate change on water resource availability of eastern Australia: a case study of the Manning River basin
.
J. Hydrol.
573
,
49
59
.
https://doi.org/10.1016/j.jhydrol.2019.03.067.
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