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Investigating the effects of climate and land-use changes on surface runoff is critical for water resources management. The majority of studies focused on projected climate change effects on surface runoff, while neglecting future land-use change. Therefore, the main aim of this article is to discriminate the impacts of projected climate and land-u...
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... The physically based distributed model is generally flexible and can be used for most of the types of hydrological responses. For example, one of the versatile SWAT models is used for long-term continuous simulations of flow (Haleem et al., 2022); soil erosion (Mosbahi & Benabdallah, 2020); sediment simulation (Ricci et al., 2018); movement of nutrients in watersheds (Zeiger & Hubbart, 2016); event-based flood simulation (Jodar-Abellan et al., 2019); streamflow drought forecasts (Sohoulande Djebou, 2019); streamflow regionalization to ungauged sites (Kanishka & Eldho, 2020); urban water stormwater simulation (Glick et al., 2023); and water quality evaluation (Olaoye et al., 2021) with different sizes and hydrologic, geological, and climatic variables; and evaluating the effects of climate change, land use and cover, and watershed management (Nilawar & Waikar, 2018). However, specifically, some models have been developed for particular purposes including the APEX model for small watersheds, fields, and plots at a higher spatial resolution, VIC for macroscale watershed, and SWMM for urban watershed flow simulation. ...
Studies reviewed in this paper show anomaly for temperature pertaining to streamflow and rainfall showing different trends, especially in Ethiopia to support the research findings and interpretation. There are many hydrological models, including 54 physically distributed, lumped, and conceptual hydrological models, of which 28 have been used in Ethiopian river basins. The models include the most adaptable and commonly used SWAT model applicable from small areas up to large basins. It is indeed a challenge to use a single hydrological model as the data rely on consistency, limitation-free, and exactly fitted output. The overall performance of individual physically-based, conceptual, and machine learning (ML) models varied at different watersheds. Reasonably, ML performs very well, up to 0.99 for R² and NSE and up to 0.001 for PBIAS. Inopportunely, using a single hydrological model has its limitations; ensemble multi-individual models, coupling or hybridization of physical or conceptual models with machine learning, combining evolutionary optimization algorithms with ML, and also comparisons of multi-single hydrological models, and selecting the best one are recommended options. No single hydrological model is indispensable and can be termed as better than the other for any watershed. Somewhat, ML outperforms SWAT but cannot be considered an absolute substitute. The size of the watershed, the number of data used, and the ratio between calibrations year to validation year do not have a clear correlation with the performance, particularly for the SWAT model accounted for in this review. Optimization algorithms explore multiple options and choosing the right one is a tedious task before a final decision is taken.
... The model accuracy was assessed through several statistical indicators, i.e., Nash-Sutcliffe efficiency (NSE), coefficient of determination (R 2 ), and ratio of root mean square error and standard deviation (RSR). In order to minimize uncertainty, climate models are often evaluated against actual data using a variety of methods if they have little or no relationship with the data (Haleem et al. 2022). ...
... The study analyzed four distinct scenarios, with scenarios 1 and 3 examining the effects of alterations in land-use on river runoff, and scenarios 1 and 2 examining the effects of climate change on river runoff as depicted in Table 3. Determination of Uncorrected Proof the contribution rate was carried out through the utilization of Equations (9) and (10). The variables Q 1 , Q 2 , Q 3 , and Q 4 were employed to represent the mean river runoff obtained from the corresponding scenarios (Haleem et al. 2022). ...
... LS (additive and multiplicative) bias correction technique was used for the present study using CMhyd tool (Worku et al. 2020). The model accuracy was assessed through several statistical indicators, i.e., NSE, coefficient of determination (R 2 ), and ratio of root mean square error and standard deviation (RSR) (Haleem et al. 2022). The results are listed in Table 4. Uncorrected Proof that such corrections are crucial for improving the reliability of climate projections. ...
This study critically assesses the combined effects of climate and land-use change on flood recurrence in the Kokcha River, Afghanistan, spanning the period from 2010 to 2021 and projecting forward to 2088–2099. Through the application of a bias-corrected model, we achieved high accuracy in temperature and precipitation simulations, with notable NSE values of 0.9 and 0.69, and R2 values of 0.92 and 0.78, respectively. Future streamflow simulations under different scenarios highlight climate change as the major driver influencing flood recurrence in the Kokcha River, contributing to 101.1% of the total variation, while land-use change has a minimal contribution of −1.1%. Our analysis of precipitation, average temperature, and streamflow data reveals significant patterns and changes, with future projections indicating a gradual decline in precipitation levels, mean temperature, and streamflow. Flood frequency analysis for return periods of 10, 50, 100, 200, and 500 years, considering different scenarios, underscores the likelihood of floods of varying magnitudes. Notably, the highest streamflow spikes under both scenarios highlight the impact of futuristic air temperature and precipitation on flood recurrence. The study advocates prioritizing climate change adaptation and resilient land-use strategies to ensure sustainable water resource management, emphasizing the mitigation of potential flood risks.
... Likewise, in South Africa, the SWAT model continues to be applied as a tool for assessing different hydrological processes and river basin management options. Gyamfi et al. (2016) employed SWAT to assess the impact of land use changes on the hydrology of the Olifants Basin, where it was revealed that, between 2000 and 2013, the generation of surface runoff increased largely due to urbanization. Nkhonjera et al. (2021) used the SWAT model in the same Olifants River Basin to assess seasonal precipitation variability in the near-and long-term time series. ...
... For example, Idrissou et al. (2022) projected that surface runoff in the Inland Valley Catchment (Burkina Faso) would increase due to the combined impact of climate change and land use changes, whereby in comparison to the reference period, surface runoff was 158% higher when the combined impacts of climate and land use changes were assessed, while the runoff increase was 52% when only climate change was considered. Haleem et al. (2022) also observed that river runoff in the upper Indus basin will increase, whereby the largest increase will be due to climate change than land use change. Gyamfi et al. (2016) also noted that the increase in urbanization in the Olifants Basin in South Africa contributed to the increase in surface runoff up to 47%. ...
... Haleem et al. (2022) also observed that river runoff in the upper Indus basin will increase, whereby the largest increase will be due to climate change than land use change. Gyamfi et al. (2016) also noted that the increase in urbanization in the Olifants Basin in South Africa contributed to the increase in surface runoff up to 47%. Land use plays a substantial role in the overall streamflow as it determines the amount of surface runoff, infiltration, groundwater recharge and the evapotranspiration rate. ...
This study integrated climate projections from five global climate models (GCMs) into the soil and water assessment tool to evaluate the potential impact of climate alterations on the Rietspruit River sub-basin under two representative concentration pathways (RCP4.5 and 8.5). The model's performance was evaluated based on the coefficient of determination (R2), percent bias (PBIAS), Nash–Sutcliffe efficiency (NSE), probability (P)-factor and correlation coefficient (R)-factor. Calibration results showed an R2 of 0.62, NSE of 0.60, PBIAS of 20, P-factor of 0.86 and R-factor of 0.91, while validation produced an R2 of 0.64, NSE of 0.61, PBIAS of 40 and P-factor of 0.85 and R-factor of 1.22. Precipitation is predicted to increase under both RCPs. Maximum temperature is projected to increase under both RCPs, with a major increase in the winter months. Minimum temperatures are projected to decrease under RCP4.5 in the near (−0.99 °C) and mid (−0.23 °C) futures, while the far future is projected to experience an increase of 0.14 °C. Precipitation and temperature changes correspond to increases in streamflow by an average of 53% (RCP4.5) and 47% (RCP8.5). These results indicate a need for an integrated approach in catchment water resource management amid potential climate and land use variations.
... Sylhet, a major administrative division of northeastern Bangladesh, is situated on the bank of the Surma river, and Sunamganj is a District within the Sylhet Division. The Surma flows for 215 km through Sylhet before re-joining the Kushiyara to form the Meghna River in Bhairab and Kishoreganj Districts (Haleem et al., 2022;Rahman et al., 2018). ...
... In recent years, the variation in climate as well as land use changes had drastic impacts in many parts of the globe and has resulted in changes in hydrological patterns, catchment, water yield as well as stream flow characteristics (Haleem et al., 2022;Yao et al., 2022). Past researchers reported that the Surma river's channel pattern is altering continually due to the development of new channels and the abandonment of old, larger channels. ...
Globally, Bangladesh is one of the most climate-vulnerable countries due to its low-lying topography, high population density, and unique geographic location. It is well-established that the influences of changing climate are inducing variability in the temperature and rainfall patterns of the country, which in turn impacts the hydrological conditions of the major river systems. Surma river, a transboundary river that flows through Sylhet Division in Bangladesh, drains one of the world’s heaviest rainfall areas—Cherrapunji of Meghalaya in India. The present study aims to investigate the impact of climate change on the Surma river’s morphological features such as depth, width, bank-line shifting, etc. by employing a bibliometric assessment of relevant literature and data analysis from USGS and NASA. The findings indicate that between 2016 and 2021, the percentage of water bodies in the upstream and downstream portions of Surma river decreased from 18.3% and 31.7% to 8.2% and 24.1%, respectively, impacting the rate of groundwater recharge. In addition, increased rainfall, erosion, and sediment deposition near the riverbank caused the river’s retaining capacity to decrease and consequently, resulting in frequent flooding events. Moreover, the effects of climate change on the Surma river are impacting the fisheries and agricultural activities of the surrounding area. It is anticipated that the findings of the present study will assist in developing an effective water resource management strategy to mitigate impacts of climatic change-induced natural disasters, such as floods and other hydrologic events in this region.
... The Atmosphere and Ocean Research Institute (The University of Tokyo) built the first three climate models, while the Met Office Hadley Centre in Exeter, UK, developed MOHC HaDGEM2 ES. These models were chosen based on their widespread usage in hydrological and changing climate research in Asia (Jena et al. 2016;Shrestha et al. 2016a;Xuan et al. 2017;Xu et al. 2017;Aghakhani Afshar et al. 2018;Wang et al. 2018;Wang et al. 2019;Rahimi et al. 2019;Jain et al. 2019;Shiny et al. 2019;Haleem et al. 2022), their excellent meteorological performance, and the availability of bias-corrected downscaled data (Deng et al. 2013;Seth et al. 2013;Sperber et al. 2013;Su et al. 2013;Wang and Chen 2014;Zhou et al. 2014;Ogata et al. 2014). The downscaled future datasets for these GCMs (2020-2030) have been obtained from the Climate Change, Agriculture, and Food Security website (http:// www. ...
Groundwater is an important resource for a multitude of purposes, and change in groundwater levels is an important factor in determining the increase and decrease of groundwater quantity in aquifers. This study has been carried out for Gomti River basin, where groundwater is the major drinking water source for the entire region. This paper evaluates the behavior of the groundwater levels using Visual MODFLOW under RCP 4.5 and RCP 8.5 scenarios. For modeling of groundwater flow, the entire study area was discretized into 108480 (X-direction) and 207090 (Y-direction) gridal network with each cell size of 1000 m × 1000 m. The model was calibrated for the period 2002 to 2008 and validated for the period 2009 to 2013 using data of 24 observation wells. During the calibration period of Visual MODFLOW, residual mean ranged between 0.658 and 0.268 m, and absolute residual mean ranged between 2.035 and 2.174 m. The calibrated Visual MODFLOW model was employed to evaluate the influence of climate change on future groundwater levels. Future groundwater recharge (2020–2030), obtained after the application of four (MIROC-ESM, MIROC-ESM-CHEM, MIROC MIROC5, and MOHC-HADGEM2-ES) global climate model (GCM) data into prior SWAT model, was applied to Visual MODFLOW. After applying, the projected future (2020–2030) trend in the groundwater levels was found continuously decreasing due to low groundwater recharge and excessive groundwater withdrawal in the basin in future.
... In a Central Himalayan watershed, predicted streamflow rose throughout the monsoon and post-monsoon seasons but dropped during the dry season, according to [55,56]. Snowmelt accounts for over 34% of total streamflow in this region, while glacier melt accounts for 26%. ...
The potential impacts of climate change on water resources in the Upper Indus Basin of Pakistan, a region heavily reliant on these resources for irrigated agriculture. We employ state-of-the-art global climate models from the CMIP6 project under the SSP245 scenario to evaluate changes in river runoff using the Soil and Water Assessment Tool (SWAT). Our findings indicate that temperature fluctuations play a crucial role in streamflow dynamics, given that the primary sources of river runoff in the Upper Indus Basin are snow and glacier melting. We project a substantial increase of approximately 18% in both minimum and maximum temperatures, precipitation pattern increases of 13–17%, and a significant rise in streamflow by 19–30% in the future, driven by warmer temperatures. Importantly, our analysis reveals season-specific impacts of temperature, precipitation, and streamflow, with increasing variability in projected annual changes as we progress into the mid and late 21st century. To address these changes, our findings suggest the need for integrated strategies and action plans encompassing hydroelectricity generation, irrigation, flood prevention, and reservoir storage to ensure effective water resource management in the region.
... Hussain & Khan (2020) conducted a study on the Hunza River Basin using ML techniques, they also concluded that ML algorithms/models can be used for forecasting river flow with high accuracy which will further improve water and hazard management. Haleem et al. (2022) studied the Upper Indus Basin in Pakistan using a semi-distributed model called the Soil and Water Assessment Tool (SWAT). They conclude that climate change will result in an increase in overall streamflow. ...
This study investigates changes in river flow patterns, in the Hunza Basin, Pakistan, attributed to climate change. Given the anticipated rise in extreme weather events, accurate streamflow predictions are increasingly vital. We assess three machine learning (ML) models-artificial neural network (ANN), recurrent neural network (RNN), and adaptive fuzzy neural inference system (ANFIS)-for streamflow prediction under the Coupled Model Intercomparison Project 6 (CMIP6) Shared Socioeconomic Pathways (SSPs), specifically SSP245 and SSP585. Four key performance indicators, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2), guide the evaluation. These models employ monthly precipitation, maximum and minimum temperatures as inputs, and discharge as the output, spanning 1985-2014. The ANN model with a 3-10-1 architecture outperforms RNN and ANFIS, displaying lower MSE, RMSE, MAE, and higher R 2 values for both training (MSE ¼ 20417, RMSE ¼ 142, MAE ¼ 71, R 2 ¼ 0.94) and testing (MSE ¼ 9348, RMSE ¼ 96, MAE ¼ 108, R 2 ¼ 0.92) datasets. Subsequently, the superior ANN model predicts streamflow up to 2100 using SSP245 and SSP585 scenarios. These results underscore the potential of ANN models for robust futuristic streamflow estimation, offering valuable insights for water resource management and planning.
... Hydrological models are based on fundamental hydrological processes which transform rainfall into runoff. These models use complex mathematical equations and theoretical principles which are based on physical processes within the watershed (Haleem et al. 2022). It simulates the catchment response to meteorological and terrestrial determinants and generates historical and futuristic streamflow which can be used by water managers for various purposes namely water budgeting, water portioning and inundation mapping (Zhang et al. 2023). ...
Accurate streamflow estimation is vital for effective water resources management, including flood mitigation, drought warning, and reservoir operation. This paper aims to evaluate four machine learning (ML) algorithms, namely, Long Short-Term Memory (LSTM), Regression Tree, AdaBoost, and Gradient Boosting algorithms, to predict the futuristic streamflow of the Swat River basin. Ten General Circulation Models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socioeconomic Pathways (SSPs) 245 and 585 were used for futuristic streamflow assessment. The ML models were developed using maximum temperature, minimum temperature, and precipitation as the input variables while streamflow as the target variable. The performance of ML models was assessed via statistical performance indicators, namely the coefficient of determination (R2), mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), Nash Sutcliffe Efficiency (NSE) and Percent BIAS (PBIAS). The AdaBoost exhibits exceptional performance (R2: 0.99 during training, 0.86 during testing). The futuristic streamflow projection shows an increase in mean annual streamflow between 2050 and 2080 s from 3.26 to 7.52% for SSP245 and 3.77–13.55% for SSP585. ML models, notably adaboost, provide a reliable method for projecting streamflow, will assist in hazard and water management in the area.
... Many scholars have conducted research at national, watershed, and regional scales using the Representative Concentration Pathway (RCP) scenarios of CMIP5 to explore the response and changes in water resources under the separate or coupled effects of climate change and land use (Getachew et al., 2021;Loukika et al., 2022;Okwala et al., 2020;Soonthornrangsan and Lowry, 2021). Studies have indicated that the impact of climate change on the water supply is greater than that of land use (Chen et al., 2020a;Haleem et al., 2022;Shang et al., 2019;Yang et al., 2019a). Land use changes can result in greater regional variations in water resources (Ross and Randhir, 2022). ...
Coupling land use and climate change under shared socioeconomic pathways and representative concentration pathway (SSP-RCP) scenarios can provide more accurate predictions of water supply risks, thereby supporting decision-making for spatial planning with a focus on climate adaptation. Climate change exhibits spatial and temporal differences. To meet the requirements of spatial planning, further research is needed to assess water supply risks at different basin or regional scales. In this study, we selected four SSP-RCP scenarios for analysis, considering the temporal scale of spatial planning. The climate modeling capabilities of five global climate models (GCMs) and a multi-model ensemble (MME) were evaluated using a Taylor diagram, which assesses the performance of climate element simulations. The modeling framework that consisted of system dynamics (SD), patch-generating land-use simulations (PLUS), and Soil and Water Assessment Tool (SWAT) was employed to analyze synergistic changes in climate, land use, and water supply. The Ganjiang River Basin (GRB) serves as a case study for climate-adaptive planning at the basin scale, given its characteristics of high agricultural water demand and vulnerability to droughts and floods. The study aims to provide decision-making support for such planning. In our projections, precipitation in the GRB showed a slightly increasing trend from 2021 to 2050. Monthly precipitation increases during the flood season in August and decreases during the dry season from October to December. The maximum and minimum temperatures showed an increasing trend on both the yearly and monthly scales, with slightly higher increases during fall and winter. During the planning phase, the land use quantities of SSP126 and SSP245 showed similar variations. SSP370 experienced the most significant reduction in farmland, while SSP585 displayed a more scattered and punctuated layout of construction land. The annual water supply in the GRB showed a slightly decreasing trend from 2021 to 2035 and 2036-2050, and the largest reduction was found in SSP370. The trend of variation in the monthly water supply was complex. There was a consistent trend of decreasing water supply during the dry season, whereas the changes during the flood season were more difficult. Seasonal variations in water supply are a major water security concern for the basin's future. It is necessary to strengthen the agricultural water security planning in the northern region of the basin and enhance its ability to adapt to droughts and floods.
... It is well-founded knowledge that the SWAT model is excessively used for streamflow assessment (Cibin et al. 2010;Grusson et al. 2017). Haleem et al. (2022) used SWAT to isolate the impacts of climate change and anthropogenic activities on streamflow in the Upper Indus Basin (UIB). Wisal et al. (2020) assessed the suitability of gridded precipitation data for streamflow forecasting using the SWAT model. ...
... and 67.33-81.83°E (Haleem et al. 2022). UIB contributes to over half of Pakistan's surface water resources and is crucial to the country's long-term economic growth. ...
