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Understanding spatially correlated floods and modeling joint hazard associated with threshold exceedances across multiple locations is crucial for accurate estimation of continental‐scale portfolio risk. This work uses a non‐parametric copula‐based spatial simulator to analyze peak floods across the United States to derive the first‐of‐its‐kind continental portfolio risk estimates at the 10‐ and 100‐year return levels. We find significant interdependence in floods across the nation, revealing the recurring pattern of extreme events affecting the Northeast, Central, West, and Northwest United States in the same year. The stochastic simulator effectively manages high‐dimensional data and offers reliable uncertainty estimates for both spatially dependent floods and the aggregated flood losses at the continental level. El Niño‐Southern Oscillation and Atlantic Multidecadal Oscillation are identified as statistically significant tele‐connectors of aggregate loss. This research aims to advance the understanding of compound continental flood hazard and the potential large‐scale climate teleconnections that lead to such compound floods.

A key strategy for agriculture to adapt to climate change is by switching crops and relocating crop production. We develop an approach to estimate the economic potential of crop reallocation using a Bayesian hierarchical model of yields. We apply the model to six crops in the United States, and show that it outperforms traditional empirical models under cross-validation. The fitted model parameters provide evidence of considerable existing climate adaptation across counties. If crop locations are held constant in the future, total agriculture profits for the six crops will drop by 31% for the temperature patterns of 2070 under RCP 8.5. When crop lands are reallocated to avoid yield decreases and take advantage of yield increases, half of these losses are avoided (16% loss), but 57% of counties are allocated crops different from those currently planted. Our results provide a framework for identifying crop adaptation opportunities, but suggest limits to their potential.

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.

Food demands are rising due to an increasing population with changing food preferences, placing pressure on agricultural production. Additionally, climate extremes have recently highlighted the vulnerability of the agricultural system to climate variability. This study seeks to fill two important gaps in current knowledge: how irrigation impacts the large-scale response of crops to varying climate conditions and how we can explicitly account for uncertainty in yield response to climate. To address these, we developed a statistical model to quantitatively estimate historical and future impacts of climate change and irrigation on US county-level crop yields with uncertainty explicitly treated. Historical climate and crop yield data for 1970–2009 were used over different growing regions to fit the model, and five CMIP5 climate projections were applied to simulate future crop yield response to climate. Maize and spring wheat yields are projected to experience decreasing trends with all models in agreement. Winter wheat yields in the Northwest will see an increasing trend. Results for soybean and winter wheat in the South are more complicated, as irrigation can change the trend in projected yields. The comparison between projected crop yield time series for rainfed and irrigated cases indicates that irrigation can buffer against climate variability that could lead to negative yield anomalies. Through trend analysis of the predictors, the trend in crop yield is mainly driven by projected trends in temperature-related indices, and county-level trend analysis shows regional differences are negligible. This framework provides estimates of the impact of climate and irrigation on US crop yields for the 21st century that account for the full uncertainty of climate variables and the range of crop response. The results of this study can contribute to decision making about crop choice and water use in an uncertain future climate.

Large dams are a leading cause of river ecosystem degradation. Although dams have cumulative effects as water flows downstream in a river network, most flow alteration research has focused on local impacts of single dams. Here we examined the highly regulated Colorado River Basin (CRB) to understand how flow alteration propagates in river networks, as influenced by the location and characteristics of dams as well as the structure of the river network—including the presence of tributaries. We used a spatial Markov network model informed by 117 upstream‐downstream pairs of monthly flow series (2003–2017) to estimate flow alteration from 84 intermediate‐to‐large dams representing >83% of the total storage in the CRB. Using Least Absolute Shrinkage and Selection Operator regression, we then investigated how flow alteration was influenced by local dam properties (e.g., purpose, storage capacity) and network‐level attributes (e.g., position, upstream cumulative storage). Flow alteration was highly variable across the network, but tended to accumulate downstream and remained high in the main stem. Dam impacts were explained by network‐level attributes (63%) more than by local dam properties (37%), underscoring the need to consider network context when assessing dam impacts. High‐impact dams were often located in sub‐watersheds with high levels of native fish biodiversity, fish imperilment, or species requiring seasonal flows that are no longer present. These three biodiversity dimensions, as well as the amount of dam‐free downstream habitat, indicate potential to restore river ecosystems via controlled flow releases. Our methods are transferrable and could guide screening for dam reoperation in other highly regulated basins.