A Multilayer Reservoir Thermal Stratification Module for Earth System Models

Abstract

Thermal stratification in reservoirs is a critical process that regulates downstream riverine energy and biogeochemical cycling. Current stratification models either simplify vertical energy process, reservoir geometry or neglecting the effects of reservoir operation. Here we present a new multilayer reservoir stratification model that can be applied for reservoir and stream temperature simulation at regional or global scale. With a multilayer vertical discretization, we introduce a newly developed storage‐area‐depth dataset to improve parameterization of advection processes in and out of the reservoir. The new model better represents vertical temperature gradient and subsequently temperature of water released to downstream. The stratification model is applied to 1,400 reservoirs over the contiguous United States and validated against observed surface, profile, and outflow temperature data over 130 reservoirs subjected to various levels of regulation. The Nash‐Sutcliffe values are higher than 0.5 for about 77% of the validated reservoirs using surface temperature while the average values of root mean square error and bias are 3.6 °C and −1.1 °C, respectively. Using the new reservoir storage‐area‐depth dataset improves the simulation of surface temperature at over 69% of the validated reservoirs compared to using simplified reservoir geometry. The reservoir stratification model contributes to improving predictive understanding of anthropogenic impact on terrestrial hydrological, ecological, and biogeochemical cycles.

A Near‐Term Iterative Forecasting System Successfully Predicts Reservoir Hydrodynamics and Partitions Uncertainty in Real Time

“…First, FLARE is using a physics-based, 1-D hydrodynamic model, so improvements to the physical parameterization and numerical solution could improve forecast skill, though we note that a 3-D model may be more time-and compute-intensive. Alternatively, thermal stratification models with a more simple structure (e.g., the 2-layer model in Niemeyer et al, 2018) than GLM that leverage global datasets on storage-area-depth (Yigzaw et al, 2018(Yigzaw et al, , 2019) could be used to evaluate the level of complexity required for water temperature forecasting across many waterbodies of varying size. Furthermore, forecasting approaches that combine physics-based models with machine learning (e.g., Jia et al, 2019;Read et al, 2019) could potentially reduce forecast uncertainty by leveraging the power of mechanistic models and machine learning methods.…”

Predicting the Likely Thermal Impact of Current and Future Dams Around the World

“…Summer season correspond to months of JJA in Northern Hemisphere (NH) and DJF in Southern Hemisphere (SH) while the definitions reverse for winter seasons. FUTURIST models per season are necessary because the reservoirs exhibit unique thermal stratification in each season (Yigzaw et al., 2019), with turnover of the regimes during fall/winter in most cases. To capture such an ongoing thermodynamic change to the reservoir water that takes much longer to evolve than the seasonal air temperature, and hence, air temperature alone cannot be a sufficient proxy for seasonal behavior of thermal modification.…”

“…Finally, there should be minimal input data requirement, which is a fundamental prerequisite for working on the data-scarce conditions characterizing the Global South, where the majority of dams are planned. Existing tools, such as WBM-T2PM by Miara et al (2018) or those available in earth system models (see Li et al, 2015;Yigzaw et al, 2019), are complex and based on physically based thermo-hydrodynamic modeling that require extensive data on boundary conditions for calibration. Most importantly, current physically based river temperature modeling tools cannot be applied in a globally consistent manner due to lack of extensive in-situ temperature and hydrodynamic data.…”

Estimating Drivers and Pathways for Hydroelectric Reservoir Methane Emissions Using a New Mechanistic Model

“…To accurately estimate total annual degassing through turbines, we would need a monthly assessment of reservoir stratification. The occurrence and duration of stratification is a function of parameters such as water transparency, wind speed, and reservoir morphology, and can be modeled with significant input data (Lee et al., 2013; Noori et al., 2019; Yigzaw et al., 2019). Since individual reservoir modeling for stratification is beyond the scope of ResME, we instead provide degassing estimates for two potential scenarios: (a) maximum possible degassing if reservoirs were stratified year‐round and thus T b = (0.5 + Bub diss ) x P in each month, and (b) reservoir is stratified for the 2 months before and after peak reservoir temperature (5 months total), with monthly degassing zero otherwise.…”