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Inflow anomalies at varying temporal scales, seasonally varying storage mandates, and multipurpose allocation requirements contribute to reservoir operational decisions. The difficulty of capturing these constraints across many basins in a generalized framework has limited the accuracy of streamflow estimates in land‐surface models for locations downstream of reservoirs. We develop a Piecewise Linear Regression Tree to learn generalized daily operating policies from 76 reservoirs from four major basins across the coterminous US. Reservoir characteristics, such as residence time and maximum storage, and daily state variables, such as storage and inflow, are used to group similar observations across all reservoirs. Linear regression equations are then fit between daily state variables and release for each group. We recommend two models—Model 1 (M1) that performs the best when simulating untrained records but is complex and Model 2 (M2) that is nearly as performant as M1 but more parsimonious. The simulated release median root mean squared error is 49.7% (53.2%) of mean daily release with a median Nash‐Sutcliffe efficiency of 0.62 (0.52) for M1 (M2). Long‐term residence time is shown to be useful in grouping similar operating reservoirs. Release from low residence time reservoirs can be mostly described using inflow‐based variables. Operations at higher residence time reservoirs are more related to previous release variables or storage variables, depending on the current inflow. The ability of the models presented to capture operational dynamics of many types of reservoirs indicates their potential to be used for untrained and limited data reservoirs.

Understanding the nexus between food, energy, and water systems (FEW) is critical for basins with intensive agricultural water use as they face significant challenges under changing climate and regional development. We investigate the food, energy, and water nexus through a regional hydroeconomic optimization (RHEO) modeling framework. The crop production in RHEO is estimated through a hierarchical regression model developed using a biophysical model, AquaCropOS, forced with daily climatic inputs. Incorporating the hierarchical model within the RHEO also reduces the computation time by enabling parallel programming within the AquaCropOS and facilitates mixed irrigation—rainfed, fully irrigated and deficit irrigation—strategies. To demonstrate the RHEO framework, we considered a groundwater‐dominated basin, South Flint River Basin, Georgia, for developing mixed irrigation strategies over 31 years. Our analyses show that optimal deficit irrigation is economically better than full irrigation, which increases the groundwater pumping cost. Thus, considering deficit irrigation in a groundwater‐dominated basin reduces the water, carbon, and energy footprints, thereby reducing FEW vulnerability. The RHEO also could be employed for analyzing FEW nexus under potential climate change and future regional development scenarios.

Changes in annual maximum flood (AMF), which are usually detected using simple trend tests (e.g., Mann‐Kendall test (MKT)), are expected to change design‐flood estimates. We propose an alternate framework to detect significant changes in design‐flood between two periods and evaluate it for synthetically generated AMF from the Log‐Pearson Type‐3 (LP3) distribution due to changes in moments associated with flood distribution. Synthetic experiments show MKT does not consider changes in all three moments of the LP3 distribution and incorrectly detects changes in design‐flood. We applied the framework on 31 river basins spread across the United States. Statistically significant changes in design‐flood quantiles were observed even without a significant trend in AMF and basins with statistically significant trend did not necessarily exhibit statistically significant changes in design‐flood. We recommend application of the framework for evaluating changes in design‐flood estimates considering changes in all the moments as opposed to simple trend tests.

River scientists strive to understand how streamflow regimes vary across space and time because it is fundamental to predicting the impacts of climate change and human activities on riverine ecosystems. Here we tested whether flow periodicity differs between rivers that are regulated or unregulated by large dams, and whether dominant periodicities change over time in response to dam regulation. These questions were addressed by calculating wavelet power at different timescales, ranging from 6 hr to 10 years, across 175 pairs of dam‐regulated and unregulated USGS gages with long‐term discharge data, spanning the conterminous United States. We then focused on eight focal reservoirs with high‐quality and high‐frequency data to examine the spectral signatures of dam‐induced flow alteration and their time‐varying nature. We found that regulation by dams induces changes not only in flow magnitude and variability, but also in the dominant periodicities of a river's flow regime. Our analysis also revealed that dams generally alter multi‐annual and annual periodicity to a higher extent than seasonal or daily periodicity. Based on the focal reservoirs, we illustrate that alteration of flow periodicity is time varying, with dam operations (e.g., daily peaking vs. baseload operation), changes in dam capacity, and environmental policies shifting the relative importance of periodicities over time. Our analysis demonstrates the pervasiveness of human signatures now characterizing the U.S. rivers' flow regimes, and may inform the restoration of environmental periodicity downstream of reservoirs via controlled flow releases—a critical need in light of new damming and dam retrofitting for hydropower globally.

Flood‐frequency curves, critical for water infrastructure design, are typically developed based on a stationary climate assumption. However, climate changes are expected to violate this assumption. Here, we propose a new, climate‐informed methodology for estimating flood‐frequency curves under non‐stationary future climate conditions. The methodology develops an asynchronous, semiparametric local‐likelihood regression (ASLLR) model that relates moments of annual maximum flood to climate variables using the generalized linear model. We estimate the first two marginal moments (MM) – the mean and variance – of the underlying log‐Pearson Type‐3 distribution from the ASLLR with the monthly rainfall and temperature as predictors. The proposed methodology, ASLLR‐MM, is applied to 40 U.S. Geological Survey streamgages covering 18 water resources regions across the conterminous United States. A correction based on the aridity index was applied on the estimated variance, after which the ASLLR‐MM approach was evaluated with both historical (1951–2005) and projected (2006–2035, under RCP4.5 and RCP8.5) monthly precipitation and temperature from eight Global Circulation Models (GCMs) consisting of 39 ensemble members. The estimated flood‐frequency quantiles resulting from the ASLLR‐MM and GCM members compare well with the flood‐frequency quantiles estimated using the historical period of observed climate and flood information for humid basins, whereas the uncertainty in model estimates is higher in arid basins. Considering additional atmospheric and land‐surface conditions and a multi‐level model structure that includes other basins in a region could further improve the model performance in arid basins.

Studies have quantified the contribution of tropical cyclones (TCs) toward seasonal precipitation, but limited analysis is available on TC contribution toward seasonal streamflow over the southeastern and southcentral (SESC) United States (U.S.). Using an extensive network of hydroclimatic data that consists of 233 TC tracks and daily precipitation and streamflow, we estimate TC contribution toward precipitation and streamflow during the hurricane season over the SESC U.S. We found that TCs account for 12% of seasonal streamflow and 6% of seasonal precipitation over the region. Florida, North Carolina, and Louisiana have the highest fractional occurrence of TC‐generated annual maximum precipitation (∼20%–32%) and streamflow (∼15%–27%). We also found the fractional occurrence of TCs associated with peak‐over threshold precipitation (streamflow) events ranges from 5% to 8% in coastal regions (10%–20% over FL and 5%–10% over coastal NC). Increased antecedent moisture results in increased TCs contribution to streamflow leading to different land‐surface responses for similar hurricane events.

Large dams degrade the river’s health by heavily regulating the natural flows. Despite a long history of research on flow regulation due to dams, most studies focused only on the impact of a single dam and ignored the combined impact of flow regulation on a river network. We propose a new Dynamic Flow Alteration Index (DFAI) to quantify the local and cumulative degree of regulation by comparing the observed controlled flows with the naturalized flows based on a moving time horizon for the highly regulated Colorado River Basin. The proposed DFAI matches closely to dam’s localized regulation for headwater gages and starts to diverge as we move downstream due to increase in cumulative impact of the dams. DFAI considers the impact of dam operations on flow characteristics such as shifting of peak flow occurrence and dampening of peak flows. DFAI estimates the degree of regulation to be small for upstream dams and finds the maximum network regulation to be 2.52 years at Glen Canyon reservoir. DFAI also successfully captures the reduction in cumulative regulation when dam operations (e.g., Hoover Dam) bring the altered flow in synchronization with natural regime due to downstream flow requirements. The impact of San Juan River Basin Recovery Implementation Program is also captured by DFAI as the reduction in network regulation drops by 1.5 years for Navajo Dam. Our findings using DFAI suggest the need to develop naturalized flows for major river basins to quantify the flow alteration under continually changing climate and human influences.