ABSTRACT:

This is for the manuscript "Assessing the Effectiveness of Reservoir Operation and Identifying Reallocation Opportunities under Climate Change". Climate change will alter hydroclimatic variability, bringing a set of challenges to existing water management. It remains unclear if current water infrastructure and operational strategies will still be effective in the future. In this study, using 21 federal reservoirs in Texas as examples, we develop data-driven models to represent current reservoir operations and assess their effectiveness under future scenarios. We further explore adaptive strategies for improving water supply reliability without increasing flood risk.

ABSTRACT:

Vegetation plays a crucial role in atmosphere-land water and energy exchanges, global carbon cycle and basin water conservation. Land Surface Models (LSMs) typically represent vegetation characteristics by monthly climatologic index (e.g., green vegetation fraction GVF, leaf area index). However, static vegetation parameterization does not capture dynamic-varying vegetation characteristics, such as responses to climatic fluctuation, long-term trend and interannual variability. This study developed a machine learning accelerated approach to quantify the impacts of dynamic-varying vegetation on the magnitude, seasonality, and biotic and abiotic components of hydrologic fluxes. A deep learning-based surrogate of Noah provided a rapid diagnostic tool to fuse GVF from seven remotely sensed products into LSM. Using the Upper Colorado River Basin (UCRB) as a test case, we found that dynamic-varying vegetation provides more buffering effect to climate fluctuation than the static vegetation configuration, leading to higher total evapotranspiration (thus lower water yield) and smaller evapotranspiration interannual variability. In addition, dynamic-varying vegetation from multi-source remote sensing products consistently predicts larger evaporation abiotic components (e.g., soil evaporation), which are partially compensated by smaller evaporation biotic components (e.g., transpiration). Based on the hydrologic sensitivity analysis to vegetation, we found that vegetation removal in the sparsely vegetated sandy soil regions of the UCRB would lead to the most effective water yield increase. This study highlights the importance of explicit representation of vegetation dynamics in climate change and land management assessment.

ABSTRACT:

Reservoirs are the key hydraulic infrastructure that regulates natural streamflow variability to fulfill various operation targets, including flood control, water supply, hydroelectricity generation and sustaining environmental flow. As an important anthropogenic interference in the hydrologic cycle, reservoir operation behavior remains challenging to be properly represented in hydrologic models, thus limiting the capability of predicting streamflow under the interactions between hydrologic variability and operational preferences. Data-driven models provide a promising approach to capture relationships embedded in historical records. This dataset contains historical daily operations of over 300 major reservoirs across the Contiguous United States with a wide range of streamflow conditions, including inflow, release, storage, elevation, etc. The eastern reservoir data is collected by Duke University (https://nicholasinstitute.duke.edu/reservoir-data/, Patterson et al., 2018. The western reservoir data is accessed via the United States Bureau of Reclamation (https://water.usbr.gov/api/web/app.php/api/).

ABSTRACT:

Irrigation has enhanced food security and biofuel production throughout the world. However, the sustainability of irrigation faces challenges from climate variability and extremes, increasing consumption from irrigated cropland expansion, and competing demands from other water use sectors. In this study, we investigated the agricultural water withdrawal landscape of the contiguous US (CONUS) over 1981-2015, assessed its spatial and temporal changes and analyzed the factors driving the changes. We introduced the concept of “center of mass” to calculate the spatiotemporal trajectory of water withdrawal, along with climatic and agricultural factors at state, regional and CONUS scales. At the CONUS level, the total agricultural water withdrawal has been decreased during 1981-2015, and the centroid of water withdrawal consistently shifts toward the east, caused by reduced water withdrawal in western states and increased withdrawal in the eastern states. While the CONUS irrigation trajectory is resilient to climate variability, prolonged regional drought may interrupt the trend. In the Western US, irrigation withdrawal reduction is mainly achieved by adoption of high-efficiency irrigation technology. Under drought conditions, irrigation withdrawal often switched from surface water to groundwater sources, posing challenges on groundwater sustainability under prolonged droughts. The Eastern US has experienced accelerating agricultural withdrawal from both surface water and groundwater sources. This is mainly driven by increased irrigated acreage in the Midwest and lower Mississippi River, with irrigated croplands supplied by mixed flood irrigation and high-efficiency irrigation methods. At the state level, some states exhibit discrepancy in agricultural withdrawal centroids from surface water and groundwater sources, as results of climate heterogeneity, water availability and infrastructure development. This study provides understanding of the driving forces in the spatiotemporal trends of CONUS agricultural water withdrawal in different regions and implications for predicting future agricultural withdrawal under changing climatic and socioeconomic uncertainties.