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Abstract Effective flood prediction supports developing proactive risk management strategies, but its application in ungauged basins faces tremendous challenges due to limited/no streamflow record. This study investigates the potential for integrating streamflow derived from synthetic aperture radar (SAR) data and U.S. National Water Model (NWM) reanalysis estimates to develop improved predictions of above-normal flow (ANF) over the coterminous US. Leveraging the SAR data from the Global Flood Detection System to estimate the antecedent conditions using principal component regression, we apply the spatial-temporal hierarchical model (STHM) using NWM outputs for improving ANF prediction. Our evaluation shows promising results with the integrated model, STHM-SAR, significantly improving NWE, especially in 60% of the sites in the coastal region. Spatial and temporal validations underscore the model’s robustness, with SAR data contributing to explained variance by 24% on average. This approach not only improves NWM prediction, but also uniquely combines existing remote sensing data with national-scale predictions, showcasing its potential to improve hydrological modeling, particularly in regions with limited stream gauges. 

Large-scale downscaling plays an important role in assessing global impacts on hydrological sphere due to climate changes. In such downscaling efforts, it is essential to consider the various climate regimes. Although previous studies have indirectly suggested that the accuracy of downscaling might differ among climate regimes, research that systematically understands or quantifies the variability of this accuracy remains scarce. This study addresses this gap by systematically quantifying the performance of five different large-scale downscaling methods across various climate regimes in the context of downscaling hydroclimatic indicators. Our findings indicate that large-scale downscaling yields the highest accuracy on average when applied to temperature, precipitation, and runoff in tropical, arid, and temperate climate regimes, respectively, while showing poor accuracy in polar regimes for all variables. The maximum difference of normalized root mean squared errors for hydroclimate indicators is 69 % across climate zones, and the spatial distribution of downscaling accuracy aligns with spatial distribution of climate zones. The variation of downscaling accuracy is particularly significant in temperature, precipitation, and seasonal runoff indicators. Furthermore, linkages between accuracy of climate and hydrological indicators differ by climate zones. The underlying reasons for the different accuracy of downscaling are spatially different accuracy of global climate models (GCMs) and interaction of downscaling structure and climate regimes. This study articulated the source of spatially different accuracy/uncertainties for large-scale downscaling that have never been addressed before. The findings of this study provide valuable support in selecting appropriate downscaling methods, ultimately enhancing the spatial reliability and accuracy of large-scale downscaling methods. 

Abstract Floods cause hundreds of fatalities and billions of dollars of economic loss each year in the United States. To mitigate these damages, accurate flood prediction is needed for issuing early warnings to the public. This situation is exacerbated in larger model domains flood prediction, particularly in ungauged basins. To improve flood prediction for both gauged and ungauged basins, we propose a spatio‐temporal hierarchical model (STHM) using above‐normal flow estimation with a 10‐day window of modeled National Water Model (NWM) streamflow and a variety of catchment characteristics as input. The STHM is calibrated (1993–2008) and validated (2009–2018) in controlled, natural, and coastal basins over three broad groups, and shows significant improvement for the first two basin types. A seasonal analysis shows the most influential predictors beyond NWM streamflow reanalysis are the previous 3‐day average streamflow and the aridity index for controlled and natural basins, respectively. To evaluate the STHM in improving above‐normal streamflow in ungauged basins, 20‐fold cross‐validation is performed by leaving 5% of sites. Results show that the STHM increases predictive skill in over 50% of sites' by 0.1 Nash‐Sutcliffe efficiency (NSE) and improves over 65% of sites' streamflow prediction to an NSE > 0.67, which demonstrates that the STHM is one of the first of its kind and could be employed for flood prediction in both gauged and ungauged basins. 

Abstract 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. 

Abstract 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. 

Abstract 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. 

Abstract 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.