Direct Integration of Numerous Dams and Reservoirs Outflow in Continental Scale Hydrologic Modeling

Abstract

The world's river system has more than 50,000 dams and reservoirs that offer valuable services to maximize the benefits of surface water resources (ICOLD, 2020). These benefits include flood control, hydropower generation, navigation, and recreational uses (Gao et al., 2012;A. Getirana et al., 2020;Passaia et al., 2020). Largely mandated by the Flood Control Act of 1936, the United States Army Corps of Engineers (USACE) has been building and managing numerous dams and reservoirs. Many of these reservoirs were built to attenuate flood events, thereby lowering peak flows and limiting flood inundation extents in the downstream watersheds (Turner et al., 2020). In the Mississippi River Basin (MRB) (covering 40% of the contiguous Abstract Despite the recent developments in continental-scale streamflow and flood inundation modeling frameworks, effects of time-specific and spatially explicit storage-release dynamics of numerous dams and reservoirs remain underexplored. This paper fills this knowledge gap by directly inserting operational daily flow release data at 175 dam locations into a streamflow simulation of ∼1.2 million river reaches in the Mississippi River Basin (MRB), and therefore quantifying the effect of these regulations on streamflow and flood inundation extents. Using a streamflow routing model called the Routing Application for Parallel computatIon of Discharge (RAPID) and flood inundation mapping model called AutoRoute, two simulation scenarios were constructed respectively including and excluding the daily flow releases from those dams and reservoirs for a 10-year period (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014). Flood inundation maps were simulated for peak flow conditions at a ∼10-m hyper spatial resolution. Kling-Gupta efficiency (KGE) values show that streamflow model performance considerably improved when reservoirs were included in the modeled system, varying from 2% in the eastern region to 380% in the drier western region. Despite small variation of streamflow model improvement with reservoir release inclusion in the eastern region of the basin, the flow model was able to better capture observed peak flows. For a 1% change in streamflow, we observe a 0.8% change in estimated flood inundation. Comparisons to three observed flood events in the MRB demonstrate that the flood inundation estimates improve when percent change in streamflow is relatively high. Overall, inclusion of reservoir release resulted in substantial improvement in continentalscale streamflow and flood inundation mapping.Plain Language Summary Dams and reservoirs are important structures that alter the flow of rivers to provide important services such as flood reduction, hydropower generation, and water storage for irrigation and recreation. We can use computer modeling to simulate the flow of rivers and the operation of dams. Most of the time, researchers approximate the operation of dams using various assumptions. However, we do not fully understand how much the real-life operation of reservoirs impacts rive...

A New Physically Based Index to Quantify the Impact of Multiple Reservoirs on Flood Frequency at the Catchment Scale Based on the Concept of Equivalent Reservoir

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Higher Frozen Soil Permeability Represented in a Hydrological Model Improves Spring Streamflow Prediction From River Basin to Continental Scales

“…Evaluation of model performance is summarized with the Kling‐Gupta Efficiency (KGE; Gupta et al., 2009). Given the mathematical limitations of the Nash‐Sutcliffe Efficiency (NSE) (Gupta et al., 2009), the alternative metric KGE is widely used in modeling studies as an efficiency criterion for model performance calibration and validation (Hirpa et al., 2018; Imhoff et al., 2020; Nashwan et al., 2019; Tavakoly et al., 2017, 2021). KGE is based on a decomposition of NSE into correlation, variability bias, and mean bias components and reflects the Euclidean distance from an ideal state in scaled space with respect to these components.…”

Effect of User Decision and Environmental Factors on Computationally Derived River Networks

“…Use of these models relies on digital river networks, typically in the form of grid‐based flowpath segments connected by boundary conditions at nodal points (Arora, 2001; Fan et al., 2021; Lohmann et al., 2004; Wing et al., 2019; Xia et al., 2012). Application of vector‐based river networks has recently increased in hydrological modeling, particularly at continental and global scales (Gong et al., 2011; Lin et al., 2019; Salas et al., 2018; Tavakoly et al., 2017, 2021). Development of a high‐quality river channel hydrography is critical to conduct large‐scale hydrological modeling and improve monitoring and forecasting of hydrological conditions.…”

“…Optical‐DEM methodologies also suffer from interference in capturing terrain data (Zhang et al., 2019). Errors in vector‐based river networks can also lead to misidentification of monitoring stations and infrastructure such as reservoirs (Mukhopadhyay et al., 2020; Tavakoly et al., 2021).…”