[1] Soil depth is an important input parameter in hydrological and ecological modeling. Presently, the soil depth data available in national soil databases (STATSGO and SSURGO) from the Natural Resources Conservation Service are provided as averages within generalized land units (map units). Spatial uncertainty within these units limits their applicability for distributed modeling in complex terrain. This work reports statistical models for prediction of soil depth in a semiarid mountainous watershed that are based upon the relationship between soil depth and topographic and land cover attributes. Soil depth was surveyed by driving a rod into the ground until refusal at locations selected to represent the topographic and land cover variation in the Dry Creek Experimental Watershed near Boise, Idaho. The soil depth survey consisted of a model calibration set, measured at 819 locations over 8 subwatersheds representing topographic and land cover variability and a model testing set, measured at 130 more broadly distributed locations in the watershed. Many model input variables were developed for regression to the field data. Topographic attributes were derived from a digital elevation model. Land cover attributes were derived from Landsat remote sensing images and high-resolution aerial photographs. Generalized additive and random forests models were developed to predict soil depth over the watershed. They were able to explain about 50% of the soil depth spatial variation, which is an important improvement over the soil depth extracted from the SSURGO national soil database.

There is evidence that warming leads to greater evapotranspiration and surface drying, thus contributing to increasing intensity and duration of drought and implying that mitigation would reduce water stresses. However, understanding the overall impact of climate change mitigation on water resources requires accounting for the second part of the equation, i.e., the impact of mitigationinduced changes in water demands from human activities. By using integrated, high-resolution models of human and natural system processes to understand potential synergies and/or constraints within the climate-energy-water nexus, we show that in the United States, over the course of the 21st century and under one set of consistent socioeconomics, the reductions in water stress from slower rates of climate change resulting from emission mitigation are overwhelmed by the increased water stress from the emissions mitigation itself. The finding that the human dimension outpaces the benefits from mitigating climate change is contradictory to the general perception that climate change mitigation improves water conditions. This research shows the potential for unintended and negative consequences of climate change mitigation.climate change | mitigation | water deficit | Earth system model | integrated assessment E arlier work addressing the impact of emissions mitigation on water supply and demand has produced conflicting results (1-5). The reasons are complex. Earth system models (ESMs) and climate models are generally in agreement that a lack of climate change mitigation would lead to greater warming and intensification of the global water cycle (6), increasing precipitation intensity (7), changes in runoff that amplify the existing wet/dry patterns (8), and increasing flood risk (9) as well as aridity (10). However, changes in seasonal patterns and the increasing probability of extreme events may complicate the general patterns of wet/dry trends (11). Additionally, changes in water demands caused by socioeconomic drivers alone may surpass the effects of climate change on water availability (12). Several studies (1-5) have assessed the consequences of mitigation on some measure of water deficit. Each study used its own integrated assessment and global hydrologic models, generally with varying underlying socioeconomic and technological assumptions, climate inputs, measures of water deficit, and a wide range of spatial and temporal resolutions. A key distinction of the study presented here is its coupling of regional ESMs and human systems models using finer spatial and/or temporal resolutions than previous efforts.Extending the work of Hejazi et al. (4) and Voisin et al. (13), integrated regional models of human and natural systems with enhanced capabilities are used at high temporal and spatial resolution while maintaining consistency with regional and global climate and economic modeling. In this modeling framework, a regional integrated assessment model (IAM) simulates water demand for both irrigation and nonirrigation sectors (a resu...

Abstract. An integrated model is being developed to advance our understanding of the interactions between human activities, terrestrial system and water cycle, and to evaluate how system interactions will be affected by a changing climate at the regional scale. As a first step towards that goal, a global integrated assessment model, which includes a water-demand model driven by socioeconomics at regional and global scales, is coupled in a one-way fashion with a land surface hydrology-routing-water resources management model. To reconcile the scale differences between the models, a spatial and temporal disaggregation approach is developed to downscale the annual regional water demand simulations into a daily time step and subbasin representation. The model demonstrates reasonable ability to represent the historical flow regulation and water supply over the US Midwest (Missouri, Upper Mississippi, and Ohio river basins). Implications for future flow regulation, water supply, and supply deficit are investigated using climate change projections with the B1 and A2 emission scenarios, which affect both natural flow and water demand. Although natural flow is projected to increase under climate change in both the B1 and A2 scenarios, there is larger uncertainty in the changes of the regulated flow. Over the Ohio and Upper Mississippi river basins, changes in flow regulation are driven by the change in natural flow due to the limited storage capacity. However, both changes in flow and demand have effects on the Missouri River Basin summer regulated flow. Changes in demand are driven by socioeconomic factors, energy and food demands, global markets and prices with rainfed crop demand handled directly by the land surface modeling component. Even though most of the changes in supply deficit (unmet demand) and the actual supply (met demand) are driven primarily by the change in natural flow over the entire region, the integrated framework shows that supply deficit over the Missouri River Basin sees an increasing sensitivity to changes in demand in future periods. It further shows that the supply deficit is six times as sensitive as the actual supply to changes in flow and demand. A spatial analysis of the supply deficit demonstrates vulnerabilities of urban areas located along mainstream with limited storage.

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This work documents version two of the Department of Energy's Energy Exascale Earth SystemModel (E3SM). E3SMv2 is a significant evolution from its predecessor E3SMv1, resulting in a model that is nearly twice as fast and with a simulated climate that is improved in many metrics. We describe the physical climate model in its lower horizontal resolution configuration consisting of 110 km atmosphere, 165 km land, 0.5° river routing model, and an ocean and sea ice with mesh spacing varying between 60 km in the mid-latitudes and 30 km at the equator and poles. The model performance is evaluated with Coupled Model Intercomparison Project Phase 6 Diagnosis, Evaluation, and Characterization of Klima simulations augmented with historical simulations as well as simulations to evaluate impacts of different forcing agents. The simulated climate has many realistic features of the climate system, with notable improvements in clouds and precipitation compared to E3SMv1. E3SMv1 suffered from an excessively high equilibrium climate sensitivity (ECS) of 5.3 K. In E3SMv2, ECS is reduced to 4.0 K which is now within the plausible range based on a recent World Climate Research Program assessment. However, a number of important biases remain including a weak Atlantic Meridional Overturning Circulation, deficiencies in the characteristics and spectral distribution of tropical atmospheric variability, and a significant underestimation of the observed warming in the second half of the historical period. An analysis of single-forcing simulations indicates that correcting the historical temperature bias would require a substantial reduction in the magnitude of the aerosol-related forcing.