Quantifying the potential for reservoirs to secure future surface water yields in the world’s largest river basins

Abstract

Surface water reservoirs provide us with reliable water supply, hydropower generation, flood control and recreation services. Yet reservoirs also cause flow fragmentation in rivers and lead to flooding of upstream areas, thereby displacing existing land-use activities and ecosystems. Anticipated population growth and development coupled with climate change in many regions of the globe suggests a critical need to assess the potential for future reservoir capacity to help balance rising water demands with long-term water availability. Here, we assess the potential of large-scale reservoirs to provide reliable surface water yields while also considering environmental flows within 235 of the world's largest river basins. Maps of existing cropland and habitat conservation zones are integrated with spatially-explicit population and urbanization projections from the Shared Socioeconomic Pathways to identify regions unsuitable for increasing water supply by exploiting new reservoir storage. Results show that even when maximizing the global reservoir storage to its potential limit (∼4.3-4.8 times the current capacity), firm yields would only increase by about 50% over current levels. However, there exist large disparities across different basins. The majority of river basins in North America are found to gain relatively little firm yield by increasing storage capacity, whereas basins in Southeast Asia display greater potential for expansion as well as proportional gains in firm yield under multiple uncertainties. Parts of Europe, the United States and South America show relatively low reliability of maintaining current firm yields under future climate change, whereas most of Asia and higher latitude regions display comparatively high reliability. Findings from this study highlight the importance of incorporating different factors, including human development, land-use activities, and climate change, over a time span of multiple decades and across a range of different scenarios when quantifying available surface water yields and the potential for reservoir expansion.

Surface Depression and Wetland Water Storage Improves Major River Basin Hydrologic Predictions

“…Given our methodological approach, the variation in water yield between the two models stemmed solely from surface depressions (e.g., floodplain and non‐floodplain wetlands, ponds, and other water storage systems). This is a new finding commonly overlooked in basin‐scale/global water availability assessments (e.g., Liu et al, 2018; Veldkamp et al, 2017; Zhou et al, 2016). While Lehner et al (2011) similarly suggested the considerable potential influence of small surface water storage systems on downstream waters, our study is, to our knowledge, the first to quantify this phenomenon in one of the world's major river basins.…”

Recognition of district-wise groundwater stress zones using the GLDAS-2 catchment land surface model during lean season in the Indian state of West Bengal

“…After that, most five sensitive parameters have been performed for inter-connectivity between dominant characteristic using Kendall, Pearson, and Spearman methods (Table 4 ). Kendall, Pearson, and Spearman are rank correlation coefficient methods that indicate an original association between two variables (Dhar et al 2014 ; Liu et al 2018 ). It is a non-parametric hypothesis test for a relationship between two variables.…”

The NExus Solutions Tool (NEST) v1.0: an open platform for optimizing multi-scale energy–water–land system transformations

“…The second and third inequality constraints are the capacity limitations both in terms of system size (Z) and rate of commodity transfer ( Z). Storage losses (i.e., evaporation and seepage) are given by the factor λ and computed as a function of the estimated evaporation from the hydrological model and a linear area-volume relationship (Liu et al, 2018c). The sub-annual time period duration t converts the storage change calculated as a rate into a volume consistent with the storage level.…”