Using GRanD Database and Surface Water Data to Constrain Area–Storage Curve of Reservoirs

Mengfei Mu,Songjun Han,Xiaomang Liu,Qiuhong Tang,Huijuan Cui

doi:10.3390/w12051242

Abstract

Basic information on global reservoirs is well documented in databases such as GRanD (Global Reservoir and Dam) and ICOLD (International Commission on Large Dams). However, though playing a critical role in estimating reservoir storage variations from remote sensing or hydrological models, area–storage curves of reservoirs are not conveniently obtained nor publicly shared. In this paper, we combine the GRanD database and Landsat-based global surface water extent (GSW) data to derive area–storage curves of reservoirs. The reported storage capacity in the GRanD database and water surface area from GSW data were used to constrain the area–storage curve. The proposed method has the potential to derive area–storage curves of reservoirs larger than 1 km2 archived in the GRanD database. The derived curves are validated with in situ reservoir data collected in US and China, and the results show that in situ records are well captured by the derived curves both in large and small reservoirs with various shapes. The derived area–storage curves could be employed to advance global monitoring or modeling of reservoir storage dynamics.

Highlights

As one of the most important human water resource management activities, artificial reservoir operation, greatly affects water and energy balance both at the global and local scale [1,2,3]
We demonstrate an approach to derive area–storage curves of reservoirs using readily available datasets, i.e., the Global Reservoir and Dam (GRanD) database and Landsat-based global surface water extent (GSW) dataset
It should be noted that this approach relies heavily on reservoir storage data archived in the GRanD database and surface water area from the Landsat-based global surface water extent (GSW) data

Summary

Introduction

As one of the most important human water resource management activities, artificial reservoir operation, greatly affects water and energy balance both at the global and local scale [1,2,3]. Macro-scale simulation-based reservoir operation algorithms [2,4,5,6,7,8,9,10] have been developed to simulate the storage and release of global reservoirs, but there are still huge gaps in representing the realistic operating rules and reproducing historic reservoir storage dynamics. Due to the unavailability of observation data or characteristic data of most reservoirs, these algorithms are basically calibrated in several selected reservoirs, and the calibrated uniform modeling parameters are used in all regions, ignoring the heterogeneity of the operating rules. I.e., water level, area or storage, could considerably improve the efficiency of these algorithms through calibrating the optimal modeling parameters [11]. Remote sensing products, which usually have global coverage, have offered an important source to provide reservoirs storage estimates [12]

Methods

Results

Conclusion