This study examined the Chabagou River watershed in the gully region of the Loess Plateau in China’s Shaanxi Province, and was based on measured precipitation and runoff data in the basin over a 52-year period (1959–2010), land-use types, normalized difference vegetation index (NDVI), and other data. Statistical models and distributed hydrological models were used to explore the influences of climate change and human activity on the hydrological response and on the temporal and spatial evolution of the basin. It was found that precipitation and runoff in the gully region presented a downward trend during the 52-year period. Since the 1970s, the hydrological response to human activities has become the main source of regional hydrological evolution. Evapotranspiration from the large silt dam in the study area has increased. The depth of soil water decreased at first, then it increased by amount that exceeded the evaporation increase observed in the second and third change periods. The water and soil conservation measures had a beneficial effect on the ecology of the watershed. These results provide a reference for water resource management and soil and water conservation in the study area.
AbstractSri Lanka is an important hub connecting Asia-Africa-Europe maritime routes. It receives abundant but uneven spatiotemporal distribution of rainfall and has evident seasonal water shortages. Monitoring water area changes in inland lakes and reservoirs plays an important role in guiding the development and utilisation of water resources. In this study, a rapid surface water extraction model based on the Google Earth Engine remote sensing cloud computing platform was constructed. By evaluating the optimal spectral water index method, the spatiotemporal variations of reservoirs and inland lakes in Sri Lanka were analysed. The results showed that Automated Water Extraction Index (AWEIsh) could accurately identify the water boundary with an overall accuracy of 99.14%, which was suitable for surface water extraction in Sri Lanka. The area of the Maduru Oya Reservoir showed an overall increasing trend based on small fluctuations from 1988 to 2018, and the monthly area of the reservoir fluctuated significantly in 2017. Thus, water resource management in the dry zone should focus more on seasonal regulation and control. From 1995 to 2015, the number and area of lakes and reservoirs in Sri Lanka increased to different degrees, mainly concentrated in arid provinces including Northern, North Central, and Western Provinces. Overall, the amount of surface water resources have increased.
Long-term changes in precipitation and temperature indirectly impact aquifers through groundwater recharge (GWR). Although estimates of future GWR are needed for water resource management, they are uncertain in cold and humid climates due to the wide range in possible future climatic conditions. This work aims to (1) simulate the impacts of climate change on regional GWR for a cold and humid climate and (2) identify precipitation and temperature changes leading to significant long-term changes in GWR. Spatially distributed GWR is simulated in a case study for the southern Province of Quebec (Canada, 36,000 km2) using a water budget model. Climate scenarios from global climate models indicate warming temperatures and wetter conditions (RCP4.5 and RCP8.5; 1951–2100). The results show that annual precipitation increases of >+150 mm/yr or winter precipitation increases of >+25 mm will lead to significantly higher GWR. GWR is expected to decrease if the precipitation changes are lower than these thresholds. Significant GWR changes are produced only when the temperature change exceeds +2 °C. Temperature changes of >+4.5 °C limit the GWR increase to +30 mm/yr. This work provides useful insights into the regional assessment of future GWR in cold and humid climates, thus helping in planning decisions as climate change unfolds. The results are expected to be comparable to those in other regions with similar climates in post-glacial geological environments and future climate change conditions.
Water resource management is a complex engineering problem, due to the stochastic nature of inflow, various demands and environmental flow downstream. With the increase in water consumption for domestic use and irrigation, it becomes more challenging. Many more difficulties, such as non-convex, nonlinear, multi-objective, and discontinuous functions, exist in real-life. From the past two decades, heuristic and metaheuristic optimization techniques have played a significant role in managing and providing better performance solutions. The popularity of heuristic and metaheuristic optimization techniques has increased among researchers due to their numerous benefits and possibilities. Researchers are attempting to develop more accurate and efficient models by incorporating novel methods and hybridizing existing ones. This paper's main contribution is to show the state-of-the-art of heuristic and metaheuristic optimization techniques in water resource management. The research provides a comprehensive overview of the various techniques within the context of a thorough evaluation and discussion. As a result, for water resource management problems, this study introduces the most promising evolutionary and swarm intelligence techniques. Hybridization, modifications, and algorithm variants are reported to be the most successful for improving optimization techniques. This survey can be used to aid hydrologists and scientists in deciding the proper optimization techniques.
Water is essential for food security, industrial output, ecological sustainability, and a country’s socioeconomic progress. Water scarcity and environmental concerns have increased globally in recent years as a result of the ever-increasing population, rapid industrialization and urbanization, and poor water resource management. Even though there are sufficient water resources, their uneven circulation leads to shortages and the requirement for portable fresh water. More than two billion people live in water-stressed areas. Hence, the present study covers all of the research based on water extraction from atmospheric air, including theoretical and practical (different experimental methods) research. A comparison between different results is made. The calculated efficiency of the systems used to extract water from atmospheric air by simulating the governing equations is discussed. The effects of different limitations, which affect and enhance the collectors’ efficiency, are studied. This research article will be very useful to society and will support further research on the extraction of water in arid zones.
AbstractWhile the Sustainable Development Goals (SDG) are broadly framed with 17 goals, the goals and their targets inherently connect with each other forming a complex system. Actions supporting one goal may influence progress in other goals, either positively (synergies) or negatively (trade-offs). Effective managing the synergies and trade-offs is a prerequisite for ensuring policy coherence. This is particular relevant at the river basin scale where the implementation of national policies may generate inequalities at the sub-basin levels, such as the upstream and the downstream. In the existing literature, there is still a lack of methodologies to assess the SDG interlinkages and their differences at the subnational levels. This paper presents a methodology on the development of an SDG interlinkages analysis model at the basin scale and its application to a case study in China’s Luanhe River Basin (LRB). Seven broad areas, namely land use and land cover change, climate change, ecosystem services, flood risks, water sector, urbanisation, and energy, were set as the scope of study. Through a systematic review, key elements of the SDG interlinkages system were identified and their interactions were mapped. The resulting generic SDG interlinkages model were validated with expert survey and stakeholders’ consultation and tailored to the LRB. Quantification of the SDG interlinkages was conducted for 27 counties in the LRB and demonstrated by the results of 3 selected counties located in the upstream, midstream and downstream areas, respectively. The methodology and its applications can be used to support integrated water resource management in river basins.
River flow modeling plays a crucial role in water resource management and ensuring its sustainability. Therefore, in recent years, in addition to the prediction of hydrological processes through modeling, applicable and highly reliable methods have also been used to analyze the sustainability of water resources. Artificial neural networks and deep learning-based hybrid models have been used by scientists in river flow predictions. Therefore, in this study, we propose a hybrid approach, integrating long-short-term memory (LSTM) networks and a genetic algorithm (GA) for streamflow forecasting. The performance of the hybrid model and the benchmark model was taken into account using daily flow data. For this purpose, the daily river flow time series of the Beyderesi-Kılayak flow measurement station (FMS) from September 2000 to June 2019 and the data from Yazıköy from December 2000 to June 2018 were used for flow measurements on the Euphrates River in Turkey. To validate the performance of the model, the first 80% of the data were used for training, and the remaining 20% were used for the testing of the two FMSs. Statistical methods such as linear regression was used during the comparison process to assess the proposed method’s performance and to demonstrate its superior predictive ability. The estimation results of the models were evaluated with RMSE, MAE, MAPE, STD and R2 statistical metrics. The comparison of daily streamflow predictions results revealed that the LSTM-GA model provided promising accuracy results and mainly presented higher performance than the benchmark model and the linear regression model.
AbstractNatural ecosystems are fundamental to local water cycles and the water ecosystem services that humans enjoy, such as water provision, outdoor recreation, and flood protection. However, integrating ecosystem services into water resources management requires that they be acknowledged, quantified, and communicated to decision-makers. We present an indicator framework that incorporates the supply of, and demand for, water ecosystem services. This provides an initial diagnostic for water resource managers and a mechanism for evaluating tradeoffs through future scenarios. Building on a risk assessment framework, we present a three-tiered indicator for measuring where demand exceeds the supply of services, addressing the scope (spatial extent), frequency, and amplitude for which objectives (service delivery) are not met. The Ecosystem Service Indicator is measured on a 0–100 scale, which encompasses none to total service delivery. We demonstrate the framework and its applicability to a variety of services and data sources (e.g., monitoring stations, statistical yearbooks, modeled datasets) from case studies in China and Southeast Asia. We evaluate the sensitivity of the indicator scores to varying levels data and three methods of calculation using a simulated test dataset. Our indicator framework is conceptually simple, robust, and flexible enough to offer a starting point for decision-makers and to accommodate the evolution and expansion of tools, models and data sources used to measure and evaluate the value of water ecosystem services.
Water is an essential component for the survival of mankind and for balancing the ecosystem and livelihood. The world is experiencing a scarcity of water, both in terms of quality and quantity. Although there are several in-situ measurement techniques, they seem insufficient for large areas involving several parameters. Analysis of satellite images for estimating the quality and quantity of natural water has become an accepted tool for better spatial planning. With the increase in variety, volume, and velocity of satellite image, a tool for faster and accurate processing of the data is needed. Google Earth Engine (GEE) is one such cloud-based geo-big data platform. This chapter reviews the work of several researchers worldwide who have used and demonstrated the capability of satellite images with other geo-big data such as elevation, landcover, etc. for water resource management on the GEE platform. It can be concluded from the review work that GEE can help in estimating the water quality parameters with reasonable accuracy, comparable to the in-situ measurement, albeit quickly.