Evaluating the impacts of climate change projections on streamflow in the Panjshir watershed

The assessment of future discharge impacts on the Gandak River Basin is crucial for understanding potential climate change effects and planning effective water resource management. This study employs the Soil and Water Assessment Tool (SWAT) model integrated with machine learning techniques to evaluate and predict the future discharge patterns in the basin. The Gandak River Basin, a significant tributary of the Ganges, plays a vital role in regional agriculture, hydropower, and ecosystem services, making it imperative to understand the potential changes in its hydrological dynamics. The SWAT model, a comprehensive, semi-distributed hydrological model, simulates the effects of land management practices, climate variability, and water management strategies on water, sediment, and agricultural chemical yields in large complex watersheds. SWAT’s capability to incorporate various climatic inputs, land use, soil properties, and topography enables it to simulate hydrological processes with high accuracy. However, the complexity and non-linearity of hydrological processes often necessitate the incorporation of advanced data-driven techniques to enhance prediction accuracy and robustness. In this study, machine learning algorithms, including Random Forest, Support Vector Machines, and Neural Networks, are integrated with SWAT to improve the model’s predictive performance. These algorithms are trained on historical discharge data, climate variables, and SWAT-simulated outputs to capture the non-linear relationships and complex interactions within the hydrological system. The hybrid model leverages the strengths of both physically-based and data-driven approaches, providing a more comprehensive understanding of the future discharge scenarios under various climate change projections. The research involves hbias-correcting climate projections from General Circulation Models (GCMs) to derive high-resolution climate inputs for the SWAT model. Scenarios based on Shared Socio-Economic Pathways (SSPs) are employed to simulate future climatic conditions. The SWAT model is calibrated and validated using observed discharge data from the Gandak River Basin, ensuring the reliability of the simulations. Subsequently, the machine learning models are trained on the SWAT outputs and historical data, creating an ensemble approach to predict future discharge. Results indicate significant variability in future discharge patterns under different climate scenarios. The integrated SWAT and machine learning model captures the seasonal and inter-annual variability in discharge more accurately than the standalone SWAT model. The findings suggest potential increases in peak discharge events during the monsoon season, with implications for flood risk management. Conversely, reduced discharge during the dry season could impact water availability for agriculture and domestic use, necessitating adaptive water management strategies. The study highlights the importance of combining physically-based hydrological models with machine learning techniques to enhance the prediction of hydrological responses to climate change. The integrated approach provides valuable insights for policymakers and stakeholders in the Gandak River Basin, aiding in the development of sustainable water resource management plans to mitigate the adverse impacts of future climate variability. This research underscores the need for continuous monitoring, adaptive management, and the incorporation of advanced modeling techniques to address the complexities of climate change impacts on river basins.

Modeling the joint behavior of flood characteristics under climate change is necessary for understanding the potential changes in associated flood risk and hazards. In this study, we assessed the changes in flood duration, peak, and volume between historical and future periods through copula-based flood frequency analysis, employing the Soil and Water Assessment Tool (SWAT) hydrological model for modeling flood risk in a tropical watershed (Govindpur) lying in eastern India. Observed streamflow at the watershed outlet is obtained for the baseline period (1990-2014) for flood analysis. A suitable copula model is selected for bivariate flood frequency analysis while assuming copula parameters vary between baseline and future periods under climate change. In this study, high-resolution (12-km) climate reanalysis dataset from the Indian Monsoon Data Assimilation and Analysis (IMDAA) and future climate projections from general circulation models (BCC-CSM2-MR, MPI-ESM1-2-HR) after downscaling and bias correction, are used for simulating flood events using SWAT. The use of high-resolution climate data for hydrological modeling and flood frequency analysis is a novel aspect of the presented study. Uncertainty in the estimation of joint return periods of flood events under climate change due to climate model selection and assumption of stationarity is also quantified in this study for the near future (2041-2070) period under the shared socio-economic pathway (SSP585) scenario. Among the GCMs used, BCC-CSM2-MR performed relatively better in simulating baseline period streamflow in the study watershed. In this study, the Clayton copula is obtained as the most suitable based on its lowest Akaike information criterion (AIC) value, and joint return periods are then derived with the help of a conditional copula. It is found that flood events are projected to become more severe in the near future; the flood peak value increased by more than 90%, while the duration is projected to decrease. Flood volume may likely double in the future, as per our analysis, suggesting the need for mitigation and precautionary measures to reduce flood risk in the watershed. Based on the analysis, uncertainty in flood return period estimation under changed future climate is to be accounted for extreme event studies, and that can aid in managing and minimizing the flood-associated risks. Keywords: Climate Change, Flood Frequency Analysis, Soil and Water Assessment Tool, Copula, General Circulation Model, Uncertainty Analysis

Water Supply issues a formal withdrawal in relation to the above article by Cen Li, Xin Guo, Liping Chen, Majid Khayatnezhad and Fatemeh Gholinia. This decision has been made due to concerns that were raised regarding potential citation manipulation due to inappropriate references, and significant authorship changes during peer review. The journal did not receive a satisfactory response to these concerns and as such the Editors-in-Chief no longer have confidence in the integrity of the article.

Blue and green water accounting for climate change adaptation in a water scarce river basin

Robust climate change adaptation pathways in agricultural water management

Our study aims to understand how the hydrological cycle is affected by climate change in river basins. This study focused on the Karnali River Basin (KRB) to examine the impact of extreme weather events like floods and heat waves on water security and sustainable environmental management. Our research incorporates precipitation and temperature projections from ten Global Circulation Models (GCMs) under the Coupled Model Intercomparison Project Phase 6 (CMIP6). We applied thirteen statistical bias correction methods for precipitation and nine for temperatures to make future precipitation and temperature trend projections. The research study also utilized the Soil and Water Assessment Tool (SWAT) model at multi-sites to estimate future streamflow under the Shared Socioeconomic Pathway (SSP) scenarios of SSP245 and SSP585. Additionally, the Web-based Hydrograph Analysis Tool (WHAT) was used to distinguish between baseflow and streamflow. Our findings, based on the Multi-Model Ensemble (MME), indicate that precipitation will increase by 7.79–16.25% under SSP245 (9.43–27.47% under SSP585) and maximum temperatures will rise at rates of 0.018, 0.048, and 0.064 °C/yr under SSP245 (0.022, 0.066, and 0.119 °C/yr under SSP585). We also anticipate that minimum temperatures will increase at rates of 0.049, 0.08, and 0.97 °C/yr under SSP245 (0.057, 0.115, and 0.187 °C/yr under SSP585) for near, mid, and far future periods, respectively. Our research predicts an increase in river discharge in the KRB by 27.12% to 54.88% under SSP245 and 45.4% to 93.3% under SSP585 in different future periods. Our finding also showed that the expected minimum monthly baseflow in future periods will occur earlier than in the historical period. Our study emphasizes the need for sustainable and adaptive management strategies to address the effects of climate change on water security in the KRB. By providing detailed insights into future hydrological conditions, this research serves as a critical resource for policymakers and stakeholders, facilitating informed decision-making for the sustainable management of water resources in the face of climate change.

Can the best management practices resist the combined effects of climate and land-use changes on non-point source pollution control?

The present study aims at documenting the impact of different climate and land use change scenarios on runoff in the Kangsabati River basin. While the study relies on India Meteorological Department (IMD), National Oceanic and Atmospheric Administration's Physical Sciences Laboratory (NOAA-PSL), and a multi-model ensemble of six driving models from Coordinated Regional Downscaling Experiment-Regional Climate Models (CORDEX RCM) for climate data input, it depends on IDRISI Selva's Land Change Modeller (LCM) and Soil and Water Assessment Tool (SWAT) model to generate projected land use land change maps and simulate its streamflow response, respectively. A total of four land use and land cover (LULC) scenarios, representing four projected land use change, were modelled across three climatic scenarios, called Representative Concentration Pathways (RCPs). With runoff being predominantly impacted more by climate change than LULC, volumetric runoff is expected to be 12-46% higher than the baseline period of 1982-2017. Conversely, while surface runoff is expected to decrease by 4-28% in lower parts of the basin, it will increase by 2-39% in the rest of it, depending on the subtle alterations in land use and climatic variability.

The present study aims to analyse the effect of climate and land cover changes on the discharge of the Upper Beas River basin. The study used climate, soil, land use/land cover, and elevation data in the Soil and Water Assessment Tool (SWAT) model to estimate the discharge that is calibrated and validated using the observed discharge data of the Thalout gauge site. The findings suggest that basin hydrology is more sensitive to climate change than land cover changes. Mean annual discharge shows a rise between 0.31 and 9.65% under climate change scenarios, but it may decline by 9% under land cover change scenarios by the mid-21st century. Under all the climate change and land cover scenarios, seasonal variations in discharge are more prominent than annual changes. Water availability would be more in pre-monsoon season because of warming in the future. These changes would be beneficial in the short run, but may adversely affect the water availability in the long run.

Prioritization of watershed management scenarios under climate change in the Jemma sub-basin of the Upper Blue Nile Basin, Ethiopia

First posted June 21, 2023 For additional information, contact: For more information about this publication, contactProgram CoordinatorU.S. Geological SurveyWater Availability and Use Science ProgramNational Water Quality ProgramEmail: wausp-info@usgs.govFor additional information, visithttps://www.usgs.gov/programs/national-water-quality-programContact Pubs Warehouse Water resources in the coastal region of North Carolina and South Carolina (Coastal Carolinas) are currently under stress from competing ecological and societal needs. Projected changes in climate and population are expected to place even more stress on water resources in the region. The Coastal Carolinas Focus Area Study was initiated by the U.S. Geological Survey Water Availability and Use Science Program’s National Water Census to investigate these stressors and their effects on water resources for the Coastal Carolinas. As part of that study, the Soil and Water Assessment Tool (SWAT) model was used to investigate future streamflow and irrigation demand under six scenarios for the Cape Fear and Pee Dee River Basins, which flow through the Coastal Carolinas and into the Atlantic Ocean.For each river basin, historical (2000 through 2014) Soil and Water Assessment Tool models were minimally calibrated, and future (2055 through 2065) scenario models were developed based on three alternative global climate models, two alternative urban growth projections, and water-use projections that correspond to each global climate model and urban growth projection pair. The river basins were delineated into 2,928 and 5,678 subbasins for the Cape Fear and Pee Dee, respectively, each approximately 2.6 square miles (mi2) in size. The best available water-use and wastewater discharge data were used for historical model calibration. The models simulated monthly mean streamflow with median Nash-Sutcliffe efficiency values of 0.53 (n = 36) and 0.61 (n = 33) in the Cape Fear and Pee Dee River Basins, respectively. Average percent bias was −4.8 percent for the Cape Fear River Basin and −1.2 percent for the Pee Dee River Basin. Catchments for streamgages chosen for model calibration that were small (less than 100 mi2) to medium (100–1,000 mi2) in area tended to perform better than larger catchments (greater than 1,000 mi2).Historical models were used to develop future model scenarios by replacing historical weather, land-use, and water-use input datasets with projected datasets. One small, gaged catchment was selected to illustrate how the models can be used to evaluate the relative differences in simulated streamflow resulting from alternative global climate models and urban growth projections. For the selected catchment, future climate projections had a much greater influence on simulated streamflow than urban growth projections. Simulated cumulative monthly mean streamflow results for this catchment differed by 26 percent under alternative global climate models and differed by 2.4 percent under alternative urban growth projections.Irrigation demand was modeled for subbasins with cropland. Simulated differences in irrigation demand were more pronounced and widespread across the model domain under the alternative future climate scenarios compared to alternative urban growth scenarios.The calibrated and future scenario models have the capability to run on a daily time step and simulate streamflow and irrigation demand for thousands of small subbasins in the Cape Fear and Pee Dee River Basins. The models and underlying datasets enable future analyses for large and small areas within the basins.

Distinct and combined impacts of climate and land use scenarios on water availability and sediment loads for a water supply reservoir in northern Morocco

The present study predicts and assesses the individual, combined, and synergistic effect of land-use change and climate change on streamflow, sediment, and total phosphorus (TP) loads under the present and future scenarios by using the Soil and Water Assessment Tool (SWAT). To predict the impacts of climate and land-use change on streamflow, sediment, and TP loads, there are 46 scenarios composed of historical climate, baseline period climate, eight climate models of Coupled Model Intercomparison Project phase 5 (CMIP5) of two representative emission pathways (RCP4.5 and RCP8.5), after downscaled and bias-corrected, two observed land-use maps (LULC 1995, LULC 2015) and the projected two future land-use maps (LU2055 and LU 2075) with the help of CA-Markov model to be fed into SWAT. The central tendency of streamflow, sediment, and TP loads under future scenarios is represented using the annual average. The intra-/inter-annual variation of streamflow, sediment, and TP loads simulated by SWAT is also analyzed using the coefficient of variation. The results show that future land-use change has a negligible impact on annual streamflow, sediment, TP loads, and intra-annual and inter-annual variation. Climate change is likely to amplify the annual streamflow and sediment and reduce the annual TP loads, which is also expected to reduce its inter-/intra-annual variation of TP loads compared with the baseline period (2000–2019). The combined impact of land-use and climate change on streamflow, sediment, and TP loads is greater than the sum of individual impacts for climate change and land-use change, especially for TP loads. Moreover, the synergistic impact caused by the interaction of climate and land use varies with variables and is more significant for TP loads. Thus, it is necessary to consider the combined climate and land-use change scenarios in future climate change studies due to the non-negligible synergistic impact, especially for TP loads. This research rare integrates the individual/combined/synergistic impact of land-use and climate change on streamflow, sediment, and TP loads and will help to understand the interaction between climate and land-use and take effective climate change mitigation policy and land-use management policy to mitigate the non-point source pollution in the future.

Climate change will profoundly affect hydrological processes at various temporal and spatial scales. This study is focused on assessing the alteration of water resources availability and low flows frequencies driven by changing climates in different time periods of the 21st century. This study evaluates the adaptability of prevailing Global Circulation Models (GCMs) on a particular watershed through streamflow regimes. This analysis was conducted in the Great Miami River Watershed, Ohio by analyzing historical and future simulated streamflow using 10 climate model outputs and the Soil and Water Assessment Tool (SWAT). The climate change scenarios, consisting of ten downscaled Coupled Model Intercomparision Project Phase 5 (CMIP5) climate models in combination with two Representative Concentration Pathways (RCP 4.5 and RCP 8.5) were selected based on the correlation between observed records and model outputs. Streamflow for three future periods, 2016-2043, 2044-2071 and 2072-2099, were independently analyzed and compared with the baseline period (1988-2015). Results from the average of ten models projected that 7-day low flows in the watershed would increase by 19% in the 21st century under both RCPs. This trend was also consistent for both hydrological (7Q10, 1Q10) and biological low flow statistics (4B3, 1B3). Similarly, average annual flow and monthly flows would also increase in future periods, especially in the summer. The flows simulated by SWAT in response to the majority of climate model projections showed a consistent increase in low flow patterns. However, the flow estimates using the Max-Planck-Institute Earth System Model (MPI-ESM-LR) climate output resulted in the biological based low flows (4B3, 1B3) decreasing by 22.5% and 33.4% under RCP 4.5 and 56.9% and 63.7% under RCP 8.5, respectively, in the future when compared to the baseline period. Regardless, the low flow ensemble from the 10 climate models for the 21st century seemed to be slightly higher than that of historical low flows. Keywords: climate change, low flows, SWAT, climate models, Great Miami River Watershed DOI: 10.25165/j.ijabe.20191201.4486 Citation: Shrestha S, Sharma S, Gupta R, Bhattarai R. Impact of global climate change on stream low flows: A case study of the great Miami river watershed, Ohio, USA. Int J Agric & Biol Eng, 2019; 12(1): 84–95.

Climate change will profoundly affect hydrological processes at various temporal and spatial scales. This study is focused on assessing the alteration of water resources availability and low flows frequencies driven by changing climates in different time periods of the 21st century. This study evaluates the adaptability of prevailing Global Circulation Models (GCMs) on a particular watershed through streamflow regimes. This analysis was conducted in the Great Miami River Watershed, Ohio by analyzing historical and future simulated streamflow using 10 climate model outputs and the Soil and Water Assessment Tool (SWAT). The climate change scenarios, consisting of ten downscaled Coupled Model Intercomparision Project Phase 5 (CMIP5) climate models in combination with two Representative Concentration Pathways (RCP 4.5 and RCP 8.5) were selected based on the correlation between observed records and model outputs. Streamflow for three future periods, 2016-2043, 2044-2071 and 2072-2099, were independently analyzed and compared with the baseline period (1988-2015). Results from the average of ten models projected that 7-day low flows in the watershed would increase by 19% in the 21st century under both RCPs. This trend was also consistent for both hydrological (7Q10, 1Q10) and biological low flow statistics (4B3, 1B3). Similarly, average annual flow and monthly flows would also increase in future periods, especially in the summer. The flows simulated by SWAT in response to the majority of climate model projections showed a consistent increase in low flow patterns. However, the flow estimates using the Max-Planck-Institute Earth System Model (MPI-ESM-LR) climate output resulted in the biological based low flows (4B3, 1B3) decreasing by 22.5% and 33.4% under RCP 4.5 and 56.9% and 63.7% under RCP 8.5, respectively, in the future when compared to the baseline period. Regardless, the low flow ensemble from the 10 climate models for the 21st century seemed to be slightly higher than that of historical low flows. Keywords: climate change, low flows, SWAT, climate models, Great Miami River Watershed DOI: 10.25165/j.ijabe.20191201.4486 Citation: Shrestha S, Sharma S, Gupta R, Bhattarai R. Impact of global climate change on stream low flows: A case study of the great Miami river watershed, Ohio, USA. Int J Agric & Biol Eng, 2019; 12(1): 84–95.