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 concludes with some suggestions for water resources managers to bring water environment sustainability.

Identification of nonpoint source (NPS) pollution is essential for effective water management. In this study we used a combined approach of hierarchal cluster analysis (HCA) and positive matrix factorization (PMF) to identify NPS pollution for Huaihe River basin in China. NH₃-N, COD, DO and pH were regularly monitored weekly over 2 years (2011–2012) from 27 monitoring stations subjected to high anthropogenic and natural changes. As identified by multiple correspondence analyses, the monitoring stations #3, #9 and #21 are located away from the rest of sites. HCA classified all the stations into 4 groups. PMF identified four factors on each group and each season. They were associated with the major causes of Huaihe River water quality deterioration resulted by discharges inputs from urban, agricultural and industrial land uses. Seasonal NPS pollution variation was found, and it is possibly linked with natural processes, for instance hydrological regime. This research work demonstrates the usefulness of PMF model for the identification of NPS pollution in surface waters. Furthermore, our study also shows that urban, agricultural and industrial land uses were the main factors impairing surface water quality, and limiting NPS pollution would be critical for enhancing surface water quality in the study area.

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

Streamflow forecasting is essential for planning, designing, and managing watershed systems. This research study investigates the use of artificial neural networks (ANN), recurrent neural networks (RNN), and adaptive neuro-fuzzy inference systems (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., root mean square error (RMSE) and coefficient of determination (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. This research can be helpful for planning, designing, and managing watershed systems, particularly in regions where streamflow forecasting is crucial for effective water resource management.

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.

The global agricultural landscape faces unprecedented challenges, including climate change, soil degradation, and water scarcity, necessitating innovative approaches to enhance crop resilience. Nanotechnology has emerged as a promising avenue for addressing these challenges by offering precise tools to manipulate and engineer materials at the nanoscale. This chapter explores the application of nanotechnology to enhance crop resilience in challenging environments. Nanomaterials, such as nanoparticles and nanocomposites, exhibit unique physicochemical properties that can positively impact plant growth, stress response, and overall crop performance. Engineered nanomaterials have demonstrated the ability to enhance nutrient uptake, mitigate abiotic stressors, and bolster plant defense mechanisms. Additionally, nanoscale delivery systems enable targeted and controlled release of agrochemicals, optimizing their efficacy while minimizing environmental impact. This chapter highlights recent advancements in nanotechnology-driven strategies to improve crop resilience, encompassing nanoscale nutrient delivery, stress-responsive nanomaterials, and precision agriculture technologies. Furthermore, the potential risks and ethical considerations associated with nanotechnology in agriculture are discussed. Overall, the integration of nanotechnology holds great promise for sustainable agriculture, offering novel solutions to ensure food security and mitigate the impact of challenging environmental conditions on global crop production.