1. We tested how strongly aquatic macroinvertebrate taxa richness and composition were associated with natural variation in both flow regime and stream temperatures across streams of the western United States. 2. We used long-term flow records from 543 minimally impacted gauged streams to quantify 12 streamflow variables thought to be ecologically important. A principal component analysis reduced the dimensionality of the data from 12 variables to seven principal component (PC) factors that characterised statistically independent aspects of streamflow: (1) zero flow days, (2) flow magnitude, (3) predictability, (4) flood duration, (5) seasonality, (6) flashiness and (7) base flow. K-means clustering was used to group streams into 4-8 hydrologically different classes based on these seven factors. 3. We also used empirical models to estimate mean annual, mean summer and mean winter stream temperatures at each stream site. We then used invertebrate data from 63 sites to develop Random Forest models to predict taxa richness and taxon-specific probabilities of capture at a site from flow and temperature. We used the predicted taxonspecific probabilities of capture to estimate how well predicted assemblages matched observed assemblages as measured by RIVPACS-type observed ⁄ expected (O ⁄ E) indices and Bray-Curtis dissimilarities. 4. Macroinvertebrate taxon richness was only weakly associated with streamflow and temperature variables, implying that other factors more strongly influenced taxa richness. 5. In contrast to taxa richness, taxa composition was strongly associated with streamflow and temperature. Predictions of taxa composition (O ⁄ E and Bray-Curtis) were most precise when both temperature and streamflow PC factors were used, although predictions based on either streamflow PC factors or temperature alone were also better than null model predictions. Of the seven aspects of the streamflow regime we examined, variation in baseflow conditions appeared to be most directly associated with invertebrate biotic composition. We were also able to predict assemblage composition from the conditional probabilities of hydrological class membership nearly as well as Random Forests models that were based directly on continuous PC factors.6. Our results have direct implication for understanding the relative importance of streamflow and temperature in regulating the structure and composition of stream assemblages and for improving the accuracy and precision of biological assessments.

Adaptation to a changing climate is critical to address future global food and water security challenges. While these challenges are global, successful adaptation strategies are often generated at regional scales; therefore, regional‐scale studies are critical to inform adaptation decision making. While climate change affects both water supply and demand, water demand is relatively understudied, especially at regional scales. The goal of this work is to address this gap, and characterize the direct impacts of near‐term (for the 2030s) climate change and elevated CO2 levels on regional‐scale crop yields and irrigation demands for the Columbia River basin (CRB). This question is addressed through a coupled crop‐hydrology model that accounts for site‐specific and crop‐specific characteristics that control regional‐scale response to climate change. The overall near‐term outlook for agricultural production in the CRB is largely positive, with yield increases for most crops and small overall increases in irrigation demand. However, there are crop‐specific and location‐specific negative impacts as well, and the aggregate regional response of irrigation demands to climate change is highly sensitive to the spatial crop mix. Low‐value pasture/hay varieties of crops—typically not considered in climate change assessments—play a significant role in determining the regional response of irrigation demands to climate change, and thus cannot be overlooked. While, the overall near‐term outlook for agriculture in the region is largely positive, there may be potential for a negative outlook further into the future, and it is important to consider this in long‐term planning.

As managers of agricultural and natural resources are confronted with uncertainties in global change impacts, the complexities associated with the interconnected cycling of nitrogen, carbon, and water present daunting management challenges. Existing models provide detailed information on specific sub-systems (e.g., land, air, water, and economics). An increasing awareness of the unintended consequences of management decisions resulting from interconnectedness of these sub-systems, however, necessitates coupled regional earth system models (EaSMs). Decision makers' needs and priorities can be integrated into the model design and development processes to enhance decision-making relevance and "usability" of EaSMs. BioEarth is a research initiative currently under development with a focus on the U.S. Pacific Northwest region that explores the coupling of multiple stand-alone EaSMs to generate usable information for resource decision-making. Direct engagement between model developers and non-academic stakeholders involved in resource and environmental management decisions throughout the model development process is a critical component of this effort. BioEarth utilizes a bottom-up approach for its land surface model that preserves fine spatialscale sensitivities and lateral hydrologic connectivity, which makes it unique among many regional EaSMs. This paper describes the BioEarth initiative and highlights opportunities and challenges associated with coupling multiple stand-alone models to generate usable information for agricultural and natural resource decision-making.

Abstract. Food supply is affected by a complex nexus of land, atmosphere, and human processes, including short- and long-term stressors (e.g., drought and climate change, respectively). A simulation platform that captures these complex elements can be used to inform policy and best management practices to promote sustainable agriculture. We have developed a tightly coupled framework using the macroscale variable infiltration capacity (VIC) hydrologic model and the CropSyst agricultural model. A mechanistic irrigation module was also developed for inclusion in this framework. Because VIC–CropSyst combines two widely used and mechanistic models (for crop phenology, growth, management, and macroscale hydrology), it can provide realistic and hydrologically consistent simulations of water availability, crop water requirements for irrigation, and agricultural productivity for both irrigated and dryland systems. This allows VIC–CropSyst to provide managers and decision makers with reliable information on regional water stresses and their impacts on food production. Additionally, VIC–CropSyst is being used in conjunction with socioeconomic models, river system models, and atmospheric models to simulate feedback processes between regional water availability, agricultural water management decisions, and land–atmosphere interactions. The performance of VIC–CropSyst was evaluated on both regional (over the US Pacific Northwest) and point scales. Point-scale evaluation involved using two flux tower sites located in agricultural fields in the US (Nebraska and Illinois). The agreement between recorded and simulated evapotranspiration (ET), applied irrigation water, soil moisture, leaf area index (LAI), and yield indicated that, although the model is intended to work on regional scales, it also captures field-scale processes in agricultural areas.

Abstract. Food supply is affected by a complex nexus of land, atmosphere, and human processes, including short- and long-term stressors (e.g., drought and climate change, respectively). A simulation platform that captures these complex elements can be used to inform policy and best management practices to promote sustainable agriculture. We have developed a tightly-coupled framework using the macroscale Variable Infiltration Capacity (VIC) hydrologic model and the CropSyst agricultural model. A mechanistic irrigation module has been developed for inclusion in this framework. The performance of VIC-CropSyst was evaluated using two flux tower sites located in agricultural fields in the U.S. (Nebraska and Illinois). The agreement between recorded and simulated evapotranspiration (ET), applied irrigation water, soil moisture, leaf area index (LAI), and yield indicated that, although the model is intended to work at regional scales, it also captures field scale processes in agricultural areas. We also evaluated the regional simulations of VIC-CropSyst's ET over the Washington, Idaho and Oregon in the U.S. VIC-CropSyst is being used in conjunction with socio-economic models, river system models and atmospheric models to simulate the feedback processes between regional water availability, agricultural water management decisions and land-atmospheric interactions.