Reservoir storage-area-depth relationships are the most important factors controlling thermal stratification in reservoirs and, more broadly, the water, energy, and biogeochemical dynamics in the reservoirs and subsequently their impacts on downstream rivers. However, most land surface or Earth system models do not account for the gradual changes of reservoir surface area and storage with the changing depth, inhibiting a consistent and accurate representation of mass, energy, and biogeochemical balances in reservoirs. Here we present a physically coherent parameterization of reservoir storage-area-depth data set at the global scale. For each reservoir, the storage-area-depth relationships were derived from an optimal geometric shape selected iteratively from five possible regular geometric shapes that minimize the error of total storage and surface area estimation. We applied this algorithm to over 6,800 reservoirs included in the Global Reservoir and Dam database. The relative error between the estimated and observed total storage is no more than 5% and 50% for 66% and 99% of all Global Reservoir and Dam reservoirs, respectively. More importantly, the storage-depth profiles derived from the approximated reservoir geometry compared well with remote sensing based estimation at 40 major reservoirs from previous studies and ground-truth measurements for 34 reservoirs in the United States and China. The new global reservoir storage-area-depth data set is critical for advancing future modeling and understanding of reservoir processes and subsequent effects on the terrestrial hydrological, ecological, and biogeochemical cycles at the regional and global scales.Previous land surface modeling studies used simplified storage-area-depth relationships for reservoir modeling. For example, Neumann (1959) and Lehman (1975) used depth ratio (mean depth to maximum YIGZAW ET AL.10,372

Reservoir operations may alter the characteristics of Flood Frequency Curve (FFC) and challenge the basic assumption of stationarity used in flood frequency analysis. This paper presents a combined data‐modeling analysis of reservoir as a nonlinear filter of runoff routing that alters the FFCs. A dimensionless Reservoir Impact Index (RII), defined as the total upstream reservoir storage capacity normalized by the annual streamflow volume, is used to quantify reservoir regulation effects. Analyses are performed for 388 river stations in the contiguous U.S. using the first two moments of the FFC, mean annual maximum flood (MAF) and coefficient of variations (CV), calculated for the pre and post‐dam periods. It is found that MAF generally decreases with increasing RII but stabilizes when RII exceeds a threshold value, and CV increases with RII until a threshold value beyond which CV decreases with RII. Hence depending on the magnitude of RII, reservoir regulation acts as a filter to increase or reduce the nonlinearity of the natural runoff routing process and alters flood characteristics. The nonlinear relationships of MAF and CV with RII can be captured by three reservoir models with different levels of complexity, suggesting that they emerge from the basic flood control function of reservoirs. However, the threshold RII values in the nonlinear relationships depend on the more detailed reservoir operations and objectives that can only be captured by the more complex reservoir models. Our conceptual model may help improve flood‐risk assessment and mitigation in regulated river systems at the regional scale.

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