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-use changes on surface runoff using the Soil and Water Assessment Tool (SWAT) through the lens of the Upper Indus Basin, Pakistan. Future scenarios of the land-use and climate changes are predicted using cellular automata artificial neural network and four bias-corrected general circulation models, respectively. The historical record (2000–2013) was divided into the calibration period (2000–2008) and the validation period (2009–2013). The simulated results demonstrated that the SWAT model performed well. The results obtained from 2000 to 2013 show that climate change (61.61%) has a higher influence on river runoff than land-use change (38.39%). Both climate and land-use changes are predicted to increase future runoff depth in this basin. The influence of climate change (12.76–25.92%) is greater than land-use change (0.37–1.1%). Global weather data has good applicability for simulating hydrological responses in the region where conventional gauges are unavailable. The study discusses that both climate and land-use changes impact runoff depth and concluded some suggestions for water resources managers to bring water environment sustainability.
Understanding the influence of various variables on surface water quality is extremely important for protecting ecosystem health. The principal aim of this study is to assess the direct (DE), indirect (IE) and total effects (TE) of socio-economic, terrestrial and hydrological factors on surface water quality via path analysis through the lens of 15 sub-basins located on Indus basin, Pakistan. Four path models were selected based on Comparative Fit Index (CFI) = 0.999 value. First path model showed that rangelands having low population density decline river runoff which decreases instream Electrical Conductivity (EC) because of lower anthropogenic activities. Second path model depicted that croplands having higher population density enhance river runoff due to irrigation tail water discharge which decline instream EC because of dilution. Third path model showed that croplands with higher population density enhance river runoff which increases instream NO3 concentration because of unscientific application of irrigation water. Fourth path model unveiled that croplands enhance Gross Domestic Product (GDP) which enhance river runoff and instream NO3 concentration. To protect ecosystem health, Best Management Practices (BMPs), precision farming and modern irrigation techniques should be adopted to reduce irrigation tail water discharges containing pollutants entry in Indus River. Doi: 10.28991/cej-2021-03091683 Full Text: PDF
This research study investigates the use of artificial neural network (ANN), recurrent neural network (RNN), and ANFIS for monthly streamflow forecasting in the Hunza river basin of Pakistan. Different models were developed using precipitation, temperature, and discharge data. Two statistical performance indicators, i.e. RMSE and R2, were used to assess the performance of machine learning techniques. Based on these performance indicators, the ANN model predicts monthly streamflow more accurately than the RNN and ANFIS models. To assess the performance of the ANN model, three architectures were used, namely 2-1-1, 2-2-1, and 2-3-1. The ANN architecture with a 2-3-1 configuration had higher R2 values of 0.9522 and 0.96998 for the training and testing phases, respectively. For each RNN architecture, three transfer functions were used, namely Tan-sig, Log-sig, and Purelin. The architecture with a 2-1-1 configuration based on tan-sig transfer function performed well in terms of R2 values, which were 0.7838 and 0.8439 for the training and testing phases, respectively. For the ANFIS model, the R2 values were 0.7023 and 0.7538 for both the training and testing phases, respectively. Overall, the findings suggest that the ANN model with a 2-3-1 architecture is the most effective for predicting monthly streamflow in the Hunza river basin.
Pakistan is primarily dependent on water supplies from the Upper Indus basin for irrigated agriculture. Any changes in available resources due to climate, consequently has the potential to have a significant impact on the environment. Coupled model inter-comparison project phase 6 (CMIP6) based global climate models (GCMs) under shared socioeconomic pathways (SSP245) scenario were assessed to evaluate the study area for climate change effect on river runoff using Soil and water assessment tool (SWAT). Temperature fluctuations have a significant effect on stream flow, since the primary sources of river runoff in the Upper Regions of Indus Basin (URIB) are snow and glacier melting. The temperature (min & max) will likely increase by almost 18% in the future, the projected precipitation pattern will increase by 13-17 %, and the stream flow will increase by 19-30 % in the future due to the warmer temperature. Temperature (min & max), precipitation and stream flow have had different effects in each season, while their variability in the projected annual changes are increasing for mid and late 21 century. Hydroelectricity generation, irrigation, flood prevention, and storage reservoir will be required in the strategies and action plans for the effective water resources management.
Pakistan is primarily dependent on water supplies from the Upper Indus basin for irrigated agriculture. Any changes in available resources due to climate, consequently has the potential to have a significant impact on the environment. Coupled model inter-comparison project phase 6 (CMIP6) based global climate models (GCMs) under shared socioeconomic pathways (SSP245) scenario were assessed to evaluate the study area for climate change effect on river runoff using Soil and water assessment tool (SWAT). Temperature fluctuations have a significant effect on stream flow, since the primary sources of river runoff in the Upper Regions of Indus Basin (URIB) are snow and glacier melting. The temperature (min & max) will likely increase by almost 18% in the future, the projected precipitation pattern will increase by 13-17 %, and the stream flow will increase by 19-30 % in the future due to the warmer temperature. Temperature (min & max), precipitation and stream flow have had different effects in each season, while their variability in the projected annual changes are increasing for mid and late 21st century. Hydroelectricity generation, irrigation, flood prevention, and storage reservoir will be required in the strategies and action plans for the effective water resources management.
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