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Increasing climate aridity and drought, exacerbated by global warming, are increasing risks for western United States of America (U.S.A.) rainfed farming, and challenging producers’ capacity to maintain production and profitability. With agricultural water demand in the region exceeding limited supplies and fewer opportunities to develop new water sources, rainfed agriculture is under increasing pressure to meet the nation’s growing food demands. This study examines three major western U.S.A. rainfed crops: barley, spring wheat, and winter wheat. We analyzed the relationship between crop repurposing (the ratio of acres harvested for grain to the total planted acres) to seasonal climatic water deficit (CWD). To isolate the climate signal from economic factors, our analysis accounted for the influence of crop prices on grain harvest. We used historical climate and agricultural data between 1958 and 2020 to model crop repurposing (e.g. forage) across the observed CWD record using a fixed effect model. Our methodology is applicable for any region and incorporates regional differences in farming and economic drivers. Our results indicate that farmers are less likely to harvest barley and spring wheat for grain when the spring CWD is above average. Of the major winter wheat growing regions, only the Northern High Plains in Texas showed a trend of decreasing grain harvest during high CWD. For the majority of major crop growing regions, grain prices increased with lower levels of grain harvest. Interestingly, winter wheat repurposing is significantly higher in the southern Great Plains (∼50% harvested for grain) compared to the rest of the West (∼90%). Our results highlight that the major barley and spring wheat regions’ grain harvests are vulnerable to high spring CWD and low summer CWD, while winter wheat grain harvest is unaffected by variable CWD in most of the West.

 

Quantitative estimations of ecohydrological water partitioning into evaporation and transpiration remains mostly based on plot‐scale investigations that use well‐instrumented, small‐scale experimental catchments in temperate regions. Here, we attempted to upscale and adapt the conceptual tracer‐aided ecohydrology model STARRtropics to simulate water partitioning, tracer, and storage dynamics over daily time steps and a 1‐km grid larger‐scale (2565 km²) in a sparsely instrumented tropical catchment in Costa Rica. The model was driven by bias‐corrected regional climate model outputs and was simultaneously calibrated against daily discharge observations from 2 to 30 years at four discharge gauging stations and a 1‐year, monthly streamwater isotope record of 46 streams. The overall model performance for the best discharge simulations ranged in KGE values from 0.4 to 0.6 and correlation coefficients for streamflow isotopes from 0.3 to 0.45. More importantly, independent model‐derived transpiration estimates, point‐scale residence time estimates, and measured groundwater isotopes showed reasonable model performance and simulated spatial and temporal patterns pointing towards an overall model realism at the catchment scale over reduced performance in the headwaters. The simulated catchment system was dominated by low‐seasonality and high precipitation inputs and a marked topographical gradient. Climatic drivers overrode smaller, landcover‐dependent transpiration fluxes giving a seemingly homogeneous rainfall‐runoff dominance likely related to model input bias of rainfall isotopes, oversimplistic Potential Evapotranspiration (PET) estimates and averaged Leaf Area Index (LAI). Topographic influences resulted in more dynamic water and tracer fluxes in the headwaters that averaged further downstream at aggregated catchment scales. Modelled headwaters showed greater storage capacity by nearly an order of magnitude compared to the lowlands, which also favoured slightly longer residence times (>250 days) compared to superficially well‐connected groundwater contributing to shorter streamflow residence times (<150 days) in the lowlands. Our findings confirm that tracer‐aided ecohydrological modelling, even in the data‐scarce Tropics, can help gain a first, but crucial approximation of spatio‐temporal dynamics of how water is partitioned, stored and transported beyond the experimental catchment scale of only a few km².

 

The Western United States (U.S.) relies heavily on scarce water resources for both ecological services and irrigation. However, the response of irrigation water use during drought is not well documented. Irrigation decision‐making is complex and influenced by human and environmental factors such as water deliveries, crop yields, equipment, labor, crop prices, and climate variability. While few irrigation districts have plans to curtail water deliveries during droughts, water rights, fallowing patterns, crop rotations, and profit expectations also influence irrigation management at the farm scale. This study uses high‐resolution satellite data to examine the response of irrigators to drought by using a novel measure of irrigation management, the Standardized Irrigation Management Index. We assess the state of drought at the field and basin scales in terms of climate and streamflow and analyze the importance of variations in crop price and drought status on decision‐making and water use. We show significant variability in field‐scale response to drought and that crop type, irrigation type, and federal management explain regional and field‐scale differences. The relative influence of climate and prices on crop transitions indicate prices more strongly drive crop planting decisions. The study provides insights into irrigation management during drought, which is crucial for sustainable water supply in the face of the ongoing water supply crisis in the U.S. Southwest.

 

Forests face accelerating threats due to increases in the severity and frequency of drought and heat stress associated with climate change. In particular, changing patterns of forest regeneration after disturbance will be important in predicting future forest distribution across the western United States, where patterns of recurring fire and regrowth are important in establishing landscape dynamics. To predict shifting landscape patterns, it will be important to identify environmental boundaries for forest regeneration using environmental variables with clear consequences for seedling survival. Here, we explore soil surface temperature as an environmental variable with direct consequences for seedling survival and forest regeneration potential. We conducted a literature search to identify five previous laboratory experiments, spanning a period of 1924 to 1986, that exposed conifer seedlings to elevated soil surface temperatures for varying durations. We then synthesized the data from these studies to explore the survival of western U.S. conifer species in response to differing surface temperature levels. We found mortality thresholds consistent with previously reported measurements in field and lab studies, but found that as surface temperatures reach these lethal thresholds the duration of exposure matters greatly to survival outcomes. This work leverages an intuitive climate metric with clear consequences for seedling survival as an indicator of forest regeneration potential. 

Abstract. Quantifying how vegetation mediates water partitioning at different spatialand temporal scales in complex, managed catchments is fundamental forlong-term sustainable land and water management. Estimations fromecohydrological models conceptualising how vegetation regulates theinterrelationships between evapotranspiration losses, catchment water storage dynamics, and recharge and runoff fluxes are needed to assess water availability for a range of ecosystem services and evaluate how these might change under increasing extreme events, such as droughts. Currently, the feedback mechanisms between water and mosaics of different vegetation and land cover are not well understood across spatial scales, and the effects of different scaleson the skill of ecohydrological models needs to be clarified. We used thetracer-aided ecohydrological model EcH2O-iso in an intensively monitored 66 km2 mixed land use catchment in northeastern Germany to quantify water flux–storage–age interactions at four model grid resolutions (250, 500, 750, and 1000 m). This used a fusion of field (including precipitation, soil water, groundwater, and stream isotopes) and remote sensing data in the calibration. Multicriteria calibration across the catchment at each resolution revealed some differences in the estimation of fluxes, storages, and water ages. In general, model sensitivity decreased and uncertainty increased with coarser model resolutions. Larger grids were unable to replicate observed streamflow and distributed isotope dynamics in the way smaller pixels could. However, using isotope data in the calibration still helped constrain the estimation of fluxes, storage, and water ages at coarserresolutions. Despite using the same data and parameterisation for calibration at different grid resolutions, the modelled proportion of fluxes differed slightly at each resolution, with coarse models simulating higher evapotranspiration, lower relative transpiration, increased overland flow, and slower groundwater movement. Although the coarser resolutions also revealed higher uncertainty and lower overall model performance, the overall results were broadly similar. The study shows that tracers provide effective calibration constraints on larger resolution ecohydrological modelling and help us understand the influence of grid resolution on the simulation of vegetation–soil interactions. This is essential in interpreting associated uncertainty in estimating land use influence on large-scale “blue” (ground and surface water) and “green” (vegetation and evaporated water) fluxes, particularly for future environmental change. 

Accurate monitoring of crop condition is critical to detect anomalies that may threaten the economic viability of agriculture and to understand how crops respond to climatic variability. Retrievals of soil moisture and vegetation information from satellite-based remote-sensing products offer an opportunity for continuous and affordable crop condition monitoring. This study compared weekly anomalies in accumulated gross primary production (GPP) from the SMAP Level-4 Carbon (L4C) product to anomalies calculated from a state-scale weekly crop condition index (CCI) and also to crop yield anomalies calculated from county-level yield data reported at the end of the season. We focused on barley, spring wheat, corn, and soybeans cultivated in the continental United States from 2000 to 2018. We found that consistencies between SMAP L4C GPP anomalies and both crop condition and yield anomalies increased as crops developed from the emergence stage (r: 0.4–0.7) and matured (r: 0.6–0.9) and that the agreement was better in drier regions (r: 0.4–0.9) than in wetter regions (r: −0.8–0.4). The L4C provides weekly GPP estimates at a 1-km scale, permitting the evaluation and tracking of anomalies in crop status at higher spatial detail than metrics based on the state-level CCI or county-level crop yields. We demonstrate that the L4C GPP product can be used operationally to monitor crop condition with the potential to become an important tool to inform decision-making and research.