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Water-resources planners use regional water management models (WMMs) to identify vulnerabilities to climate change. Frequently, dynamically downscaled climate inputs are used in conjunction with land-surface models (LSMs) to provide hydrologic streamflow projections, which serve as critical inputs for WMMs. Here, we show how even modest projection errors can strongly affect assessments of water availability and financial stability for irrigation districts in California. Specifically, our results highlight that LSM errors in projections of flood and drought extremes are highly interactive across timescales, path-dependent, and can be amplified when modeling infrastructure systems (e.g., misrepresenting banked groundwater). Common strategies for reducing errors in deterministic LSM hydrologic projections (e.g., bias correction) can themselves strongly distort projected climate vulnerabilities and misrepresent their inferred financial consequences. Overall, our results indicate a need to move beyond standard deterministic climate projection and error management frameworks that are dependent on single simulated climate change scenario outcomes. [ABSTRACT FROM AUTHOR]

Irrigation efficiency plays an important role in agricultural productivity; it affects farm-scale water demand, and the partitioning of irrigation losses into evaporative and non-evaporative components. This partitioning determines return flow generation and thus affects water availability. Over the last two decades, hydrologic and agricultural research communities have significantly improved our understanding of the impacts of climate change on water availability and food productivity. However, the impacts of climate change on the efficiency of irrigation systems, particularly on the partitioning between evaporative and non-evaporative losses, have received little attention. In this study, we incorporated a process-based irrigation module into a coupled hydrologic/agricultural modeling framework (VIC-CropSyst). To understand how climate change may impact irrigation losses, we applied VIC-CropSyst over the Yakima River basin, an important agricultural region in Washington State, U.S. We compared the historical period of 1980–2010 to an ensemble of ten projections of climate for two future periods: 2030–2060 and 2060–2090. Results averaged over the watershed showed that a 9% increase in evaporative losses will be compensated by a reduction of non-evaporative losses. Therefore, overall changes in future efficiency are negligible (−0.4%) while the Evaporative Loss Ratio (ELR) (defined as the ratio of evaporative to non-evaporative irrigation losses) is enhanced by 10%. This higher ELR is associated with a reduction in return flows, thus negatively impacting downstream water availability. Results also indicate that the impact of climate change on irrigation losses depend on irrigation type and climate scenarios. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Farmers' investment in more efficient irrigation systems represents a primary adaptation strategy when confronting climate change. However, the regional benefits of these investments and their influence on the conflicting demands among different water dependent stakeholders for intensely irrigated regions remains an open question. Using the Pacific Northwest of the United States as an illustrative region of focus, we show that higher irrigation efficiency has diverse effects across stakeholders that are contingent on many local climatic, institutional and infrastructural factors such as the availability of water storage, the location of hydropower generators, and water rights. These complexities limit simple abstractions of irrigation efficiency as broader policy challenge and are central to its inclusion within the class of "wicked problems". Additionally, we argue that the widely used rebound effect concept, which implicitly discourages irrigation efficiency supporting policies, should not be assumed to fully capture the nuances of the complex suite of regional impacts that emerge from irrigation efficiency investments. Consequently, the evaluation of irrigation efficiency investments requires a broader framing across a diversity of perspectives. policies and actions that are pluralistic, context-specific, and closely engage various groups of stakeholders in the policymaking process. • Climate change impacts on supply and demand will interact with IE effects. • Higher IE improves the overall performance of agricultural systems during droughts. • IE can generate complex and path-dependent implications for various stakeholders. • Efficiency policymakers should consider region-specific institutional contexts. • The Rebound Effect concept can oversimplify IE impacts and lead to biased policies. [ABSTRACT FROM AUTHOR]

Soil thermal conductivity is an important property of soil that has various applications in simulation of land-surface water and energy processes, and geothermal energy calculations. In this study we conduct laboratory experiments to evaluate the impacts of various soil properties on soil thermal conductivity. The experiments included three soil types (i.e., sand, sandy loam, silt loam), four soil salinity levels, and a wide range of soil water content. To find the most effective factors we utilized different statistical methods (i.e., linear regression, information theoretic sensitivity index, Pearson's correlation coefficient, and random forest). Moreover, we developed and tested three soil thermal conductivity models (i.e., linear regression, random forest and quadratic equations). The results show that soil water content has the highest level of influence on thermal conductivity. Also, the quadratic relationship between thermal conductivity and independent variables provide the most accurate predictions among the models evaluated in this study. [ABSTRACT FROM AUTHOR]

Reliable information about the wetted dimensions of soil under drip irrigation helps designers to determine optimal emitter flow rates and spacings to reduce system equipment costs and provide better soil water conditions for the most efficient and effective use of water. This study presents a new empirical formula that predicts soil wetted dimensions around a drip emitter. The coefficients were obtained by using regression analysis on the results of field experiments done on the Pardis Agricultural Farm of Tehran University in Karaj, Iran. These data were also used to evaluate the semiempirical model of Zur and Schwartzman, the empirical model of Amin and Ekhmaj, and the analytical model WetUp. Statistical comparisons (mean error, root mean square error, and model efficiency) are made of the simulated data with the observed data. To evaluate the models, published experimental data by Risse et al. and Li et al. were also used. The results demonstrate that the suggested equations can be used for a wide range of discharge rates and soil types. The best result was obtained from the new empirical model proposed in this investigation. The lowest mean error for the wetted radius and wetted depth was 8.21 and 8.62 cm, respectively. [ABSTRACT FROM AUTHOR]

Land surface models such as the Community Land Model version 5 (CLM5) seek to enhance understanding of terrestrial hydrology and aid in the evaluation of anthropogenic and climate change impacts. However, the effects of parametric uncertainty on CLM5 hydrologic predictions across regions, timescales, and flow regimes have yet to be explored in detail. The common use of the default hydrologic model parameters in CLM5 risks generating streamflow predictions that may lead to incorrect inferences for important dynamics and/or extremes. In this study, we benchmark CLM5 streamflow predictions relative to the commonly employed default hydrologic parameters for 464 headwater basins over the conterminous United States (CONUS). We evaluate baseline CLM5 default parameter performance relative to a large (1,307) Latin Hypercube Sampling‐based diagnostic comparison of streamflow prediction skill using over 20 error measures. We provide a global sensitivity analysis that clarifies the significant spatial variations in parametric controls for CLM5 streamflow predictions across regions, temporal scales, and error metrics of interest. The baseline CLM5 shows relatively moderate to poor streamflow prediction skill in several CONUS regions, especially the arid Southwest and Central U.S. Hydrologic parameter uncertainty strongly affects CLM5 streamflow predictions, but its impacts vary in complex ways across U.S. regions, timescales, and flow regimes. Overall, CLM5's surface runoff and soil water parameters have the largest effects on simulated high flows, while canopy water and evaporation parameters have the most significant effects on the water balance. Plain Language Summary: Large‐scale land surface computer models, such as the Community Land Model version 5 (CLM5), play a central role in projecting future changes in water resources and hydrological extreme events such as floods and droughts. These models require representations of land surface processes and properties that vary significantly across space. Consequently, these representations require a large suite of input parameters to distinguish different soils, vegetation, land‐cover, and other features of the landscape that shape flood and drought dynamics. Given the large number of these input parameters required by CLM5 and limits in the information needed to specify them, we hypothesize that they strongly increase uncertainty in our ability to predict floods or droughts. In this study, we develop and apply a diagnostic approach to better understand how CLM5 input parameter uncertainty influences water predictions and the relative importance of different input parameters across the continental United States. We argue and illustrate that addressing model input parameter uncertainty is crucial as the results of uncertainty characterization may have important implications for decision making. This study provides region‐specific insights into CLM5 model input parameter uncertainty and dominant processes for a variety of hydrologic application contexts. Key Points: Community Land Model Version 5 (CLM5) streamflow predictions use default parameters are vulnerable to poor performance especially in the arid Southwest and Central U.SThe effects of CLM5 hydrologic parameter uncertainty are significantly higher for drought versus flood predictions in humid regionsThe dominant hydrologic parameters in CLM5 vary significantly across regions, diagnostic error metrics, and temporal scales [ABSTRACT FROM AUTHOR]

Improving the understanding of coupled natural human systems (CNHS) can better inform environmental policymaking. We investigated the co-evolution (i.e. , bidirectional interactions) issues in CNHS via two-way coupling RiverWare (RW; a river-reservoir routing model) with agent-based models (ABMs, human decision models) in the Yakima River Basin in Washington, US. Results show that coupled models can better capture the historical irrigation diversion (human) and streamflow (nature) dynamics. We further demonstrated the effect of social norms (i.e. , the influence of neighbors) among farmers and tested a "water reallocation" scenario to evaluate the influence of water policies on irrigation diversion behaviors. Detailed model structure and parameter uncertainty analysis are suggested to further quantify the benefit of CNHS models in multi-level water resources governance. • Coupled models can better capture the water diversion and streamflow dynamics • The social norms effect is influenced by policy rules and equifinality • Different water allocations show the feedback effect of water policy on diversion behaviors [ABSTRACT FROM AUTHOR]

This study introduces the California Food-Energy-Water System (CALFEWS) simulation model to describe the integrated, multi-sector dynamics that emerge from the coordinated management of surface and groundwater supplies throughout California's Central Valley. The CALFEWS simulation framework links the operation of state-wide, interbasin transfer projects (i.e., State Water Project, Central Valley Project) with coordinated water management strategies abstracted to the scale of irrigation/water districts. This study contributes a historic baseline (October 1996–September 2016) evaluation of the model's performance against observations, including reservoir storage, inter-basin transfers, environmental endpoints, and groundwater banking accounts. State-aware, rules-based representations of critical component systems enable CALFEWS to simulate adaptive management responses to alternative climate, infrastructure, and regulatory scenarios. Moreover, CALFEWS has been designed to maintain interoperability with electric power dispatch and agricultural production models. As such, CALFEWS provides a platform to evaluate internally consistent scenarios for the integrated management of water supply, energy generation, and food production. • Rules-based simulation model of water storage and conveyance in California's Central Valley. • State-aware adaptation enables representation of key institutional decision-making. • Supports Monte Carlo exploratory modelling results. • Interoperable with power dispatch and agricultural production models. [ABSTRACT FROM AUTHOR]

Integrated water resource management ( IWRM) requires accounting for many interrelated facets of water systems, water uses and stakeholders, and water management activities. The consequence is that project analysis must account for the nonseparability among the component parts of IWRM plans. This article presents a benefit-cost (B-C) analysis of a set of projects included in the Yakima Basin Integrated Plan proposed for the Yakima Basin in south-central Washington State. The analysis accounts for interdependence among proposed water storage projects and between water storage and water market development in the context of historical and more adverse projected future climate scenarios. Focusing on irrigation benefits from storage, we show that the value of a given proposed storage project is lower when other proposed storage projects in the basin are implemented, and when water markets are functioning effectively. We find that none of the water storage projects satisfy a B-C criterion, and that assuring proposed instream flow augmentation is less expensive by purchasing senior diversion rights than relying on new storage to provide it. [ABSTRACT FROM AUTHOR]

Subsurface drip irrigation (SDI) systems are increasingly being used in agriculture in attempts to use the available water more efficiently. The proper design and management of SDI systems requires knowledge of precise distribution of water around emitters. We conducted both field and numerical experiments to evaluate the soil water content distributions between two neighboring emitters when their wetting patterns started to overlap. The experiments involved SDI systems with emitters installed at different depths and with different spacings along the drip lateral. The HYDRUS software package was used to analyze the field data, assuming modeling approaches in which emitters were represented as (i) a point source in an axisymmetrical two-dimensional domain, (ii) a line source in a planar two-dimensional domain, or (iii) a point source in a fully three-dimensional domain. Results indicated that SDI systems can be accurately described using an axisymmetrical two-dimensional model only before wetting patterns start to overlap, and a planar two-dimensional model only after full merging of the wetting fronts from neighboring emitters. A fully three-dimensional model appears to be required for describing subsurface drip irrigation processes in their entirety. [ABSTRACT FROM AUTHOR]