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Climate change and human activities increasingly threaten lakes that store 87% of Earth’s liquid surface fresh water. Yet, recent trends and drivers of lake volume change remain largely unknown globally. Here, we analyze the 1972 largest global lakes using three decades of satellite observations, climate data, and hydrologic models, finding statistically significant storage declines for 53% of these water bodies over the period 1992–2020. The net volume loss in natural lakes is largely attributable to climate warming, increasing evaporative demand, and human water consumption, whereas sedimentation dominates storage losses in reservoirs. We estimate that roughly one-quarter of the world’s population resides in a basin of a drying lake, underscoring the necessity of incorporating climate change and sedimentation impacts into sustainable water resources management. [ABSTRACT FROM AUTHOR]

Streamflow often increases after fire, but the persistence of this effect and its importance to present and future regional water resources are unclear. This paper addresses these knowledge gaps for the western United States (WUS), where annual forest fire area increased by more than 1,100% during 1984 to 2020. Among 72 forested basins across the WUS that burned between 1984 and 2019, the multibasin mean streamflow was significantly elevated by 0.19 SDs (P < 0.01) for an average of 6 water years postfire, compared to the range of results expected from climate alone. Significance is assessed by comparing prefire and postfire streamflow responses to climate and also to streamflow among 107 control basins that experienced little to no wildfire during the study period. The streamflow response scales with fire extent: among the 29 basins where >20% of forest area burned in a year, streamflow over the first 6 water years postfire increased by a multibasin average of 0.38 SDs, or 30%. Postfire streamflow increases were significant in all four seasons. Historical fire–climate relationships combined with climate model projections suggest that 2021 to 2050 will see repeated years when climate is more fire-conducive than in 2020, the year currently holding the modern record for WUS forest area burned. These findings center on relatively small, minimally managed basins, but our results suggest that burned areas will grow enough over the next 3 decades to enhance streamflow at regional scales. Wildfire is an emerging driver of runoff change that will increasingly alter climate impacts on water suppliesand runoff-related risks. [ABSTRACT FROM AUTHOR]

This study advances model diagnostics for snowmelt-based hydrological systems using Sobol’ sensitivity analysis, illuminating parameter sensitivities and contrasting model structural differences. We consider several distinct snow-dominated locations in the western United States, running both SNOW-17, a conceptual degree-day model, and the Variable Infiltration Capacity (VIC) snow model, a physically-based model. Model performance is rigorously evaluated through global sensitivity analysis and a temperature warming analysis is conducted to explore how model parameterizations affect portrayals of climate change. Both VIC and SNOW-17 produce comparable results with SNOW-17 performing slightly better for shallower snowpacks and VIC performing better for deeper snowpacks. However, the lack of sensitivity of SNOW-17 to climate warming suggests that it may not be as reliable as a more sensitive model like VIC. Inter-model differences presented here offer insights into physical features with greatest uncertainty and may inform future model development and planning activities. [ABSTRACT FROM AUTHOR]

The semiarid U.S. Great Plains is prone to severe droughts having major consequences for agricultural production, livestock health, and river navigation. The recent 2012 event was accompanied by record deficits in precipitation and high temperatures during the May-August growing season. Here the physics of Great Plains drought are explored by addressing how meteorological drivers induce soil moisture deficits during the growing season. Land surface model (LSM) simulations driven by daily observed meteorological forcing from 1950 to 2013 compare favorably with satellite-derived terrestrial water anomalies and reproduce key features found in the U.S. Drought Monitor. Results from simulations by two LSMs reveal that precipitation was directly responsible for between 72% and 80% of the soil moisture depletion during 2012, and likewise has accounted for the majority of Great Plains soil moisture variability since 1950. Energy balance considerations indicate that a large fraction of the growing season temperature variability is itself driven by precipitation, pointing toward an even larger net contribution of precipitation to soil moisture variability. To assess robustness across a larger sample of drought events, daily meteorological output from 1050 years of climate simulations, representative of conditions in 1979-2013, are used to drive two LSMs. Growing season droughts, and low soil moisture conditions especially, are confirmed to result principally from rainfall deficits. Antecedent meteorological and soil moisture conditions are shown to affect growing season soil moisture, but their effects are secondary to forcing by contemporaneous rainfall deficits. This understanding of the physics of growing season droughts is used to comment on plausible Great Plains soil moisture changes in a warmer world. [ABSTRACT FROM AUTHOR]

Physically based models facilitate understanding of seasonal snow processes but require meteorological forcing data beyond air temperature and precipitation (e.g., wind, humidity, shortwave radiation, and longwave radiation) that are typically unavailable at automatic weather stations (AWSs) and instead are often represented with empirical estimates. Research is needed to understand which forcings (after temperature and precipitation) would most benefit snow modeling through expanded observation or improved estimation techniques. Here, the impact of forcing data availability on snow model output is assessed with data-withholding experiments using 3-yr datasets at well-instrumented sites in four climates. The interplay between forcing availability and model complexity is examined among the Utah Energy Balance (UEB), the Distributed Hydrology Soil Vegetation Model (DHSVM) snow submodel, and the snow thermal model (SNTHERM). Sixty-four unique forcing scenarios were evaluated, with different assumptions regarding availability of hourly meteorological observations at each site. Modeled snow water equivalent (SWE) and snow surface temperature Tsurf diverged most often because of availability of longwave radiation, which is the least frequently measured forcing in cold regions in the western United States. Availability of longwave radiation (i.e., observed vs empirically estimated) caused maximum SWE differences up to 234 mm (57% of peak SWE), mean differences up to 6.2°C in Tsurf, and up to 32 days difference in snow disappearance timing. From a model data perspective, more common observations of longwave radiation at AWSs could benefit snow model development and applications, but other aspects (e.g., costs, site access, and maintenance) need consideration. [ABSTRACT FROM AUTHOR]

Summary Since 2002, the headwaters of the Colorado River and nearby basins have experienced extensive changes in land cover at sub-annual timescales. Widespread tree mortality from bark beetle infestation has taken place across a range of forest types, elevation, and latitude. Extent and severity of forest structure alteration have been observed through a combination of aerial survey, satellite remote-sensing, and in situ measurements. Additional perturbations have resulted from deposition of dust from regional dry-land sources on mountain snowpacks that strongly alter the snow surface albedo, driving earlier and faster snowmelt runoff. One challenge facing past studies of these forms of disturbance is the relatively small magnitude of the disturbance signals within the larger climatic signal. The combined impacts of forest disturbance and dust-on-snow are explored within a hydrologic modeling framework. We drive the Distributed Hydrology Soil and Vegetation Model (DHSVM) with observed meteorological data, time-varying maps of leaf area index and forest properties to emulate bark beetle impacts, and parameterizations of snow albedo based on observations of dust forcing. Results from beetle-killed canopy alteration suggest slightly greater snow accumulation as a result of less interception and reduced canopy sublimation and evapotranspiration, contributing to overall increases in annual water yield between 8% and 13%. However, understory regeneration roughly halves the changes in water yield. A purely observation-based estimate of runoff coefficient change with cumulative forest mortality shows comparable sensitivities to simulated results; however, positive water yield changes are not statistically significant ( p ⩽ 0.05). The primary hydrologic impact of dust-on-snow forcing is an increased rate of snowmelt associated with more extreme dust deposition, producing earlier peak streamflow rates on the order of 1–3 weeks. Simulations of combined bark beetle and dust-on-snow produced little compounding effects, due to the relatively exclusive nature of their impacts. Potential changes in water yield and peak streamflow timing have important implications for regional water management decisions. [ABSTRACT FROM AUTHOR]

In the Upper Colorado River Basin (UCRB), there is a deep reliance on seasonal snowpack for maintenance of water resources. The term "snow drought" has recently emerged to describe periods of anomalously low snowpack. Unique seasonal patterns in precipitation and temperature that drive snow drought can have distinct hydrologic signatures, and these relationships have not been carefully studied in the UCRB. Here we examine snow drought with a new classification scheme using peak snow water equivalent (SWE) and the ratio of basin‐wide modeled peak SWE to accumulated (onset to peak) precipitation (SWE/P) that clusters snow drought years into three distinct groups—"warm," "dry," and "warm & dry"—that minimize within‐group variance. Over the period 1916–2018, we identify 14 warm years (P¯ = 160 mm; SWE/P¯ = 0.24), 24 dry years (P¯ = 117 mm; SWE/P¯ = 0.35), and 21 warm & dry years (P¯ = 94 mm; SWE/P¯ = 0.23). An elevation‐based analysis reveals two distinct patterns: warm snow droughts see severe SWE reductions primarily at lower (<2600 m) elevations (65% at lower elevations, 37% overall), whereas "dry" scenarios exhibit a consistent reduction across all elevations (39% overall). Using naturalized streamflow data, we also differentiate snow droughts by their earlier streamflow timing and decreased peakedness (warm: 7 days, 2%; dry: 7 days, 2%; warm & dry: 13 days, 5%). This research provides new insights into snow drought patterns relevant for regional water management. [ABSTRACT FROM AUTHOR]

This paper describes a publicly available, long-term (1915-2011), hydrologically consistent dataset for the conterminous United States, intended to aid in studies of water and energy exchanges at the land surface. These data are gridded at a spatial resolution of latitude/longitude and are derived from daily temperature and precipitation observations from approximately 20 000 NOAA Cooperative Observer (COOP) stations. The available meteorological data include temperature, precipitation, and wind, as well as derived humidity and downwelling solar and infrared radiation estimated via algorithms that index these quantities to the daily mean temperature, temperature range, and precipitation, and disaggregate them to 3-hourly time steps. Furthermore, the authors employ the variable infiltration capacity (VIC) model to produce 3-hourly estimates of soil moisture, snow water equivalent, discharge, and surface heat fluxes. Relative to an earlier similar dataset by Maurer and others, the improved dataset has 1) extended the period of analysis (1915-2011 versus 1950-2000), 2) increased the spatial resolution from ⅛° to , and 3) used an updated version of VIC. The previous dataset has been widely used in water and energy budget studies, climate change assessments, drought reconstructions, and for many other purposes. It is anticipated that the spatial refinement and temporal extension will be of interest to a wide cross section of the scientific community. [ABSTRACT FROM AUTHOR]

Highlights: [•] Algorithms for daily shortwave, longwave, and humidity performed well, globally. [•] Shortwave biases at coasts for low daily temperature range. [•] Humidity and longwave biases concentrated in world's deserts. [•] Good overall performance of air temperature diurnal cycle scheme. [•] Morning/afternoon biases in air temperature caused biases in humidity and longwave. [Copyright &y& Elsevier]

The North American Regional Reanalysis (NARR) is a state-of-the-art land-atmosphere reanalysis product that provides improved representation of the terrestrial hydrologic cycle compared to previous global reanalyses, having the potential to provide an enhanced picture of hydrologic extremes such as floods and droughts and their driving mechanisms. This is partly because of the novel assimilation of observed precipitation, state-of-the-art land surface scheme, and higher spatial resolution. NARR is evaluated in terms of the terrestrial water budget and its depiction of drought at monthly to annual time scales against two offline land surface model [Noah v2.7.1 and Variable Infiltration Capacity (VIC)] simulations and observation-based runoff estimates over the continental United States for 1979-2003. An earlier version of the Noah model forms the land component of NARR and so the offline simulation provides an opportunity to diagnose NARR land surface variables independently of atmospheric feedbacks. The VIC model has been calibrated against measured streamflow and so provides a reasonable estimate of large-scale evapotranspiration. Despite similar precipitation, there are large differences in the partitioning of precipitation into evapotranspiration and runoff. Relative to VIC, NARR and Noah annual evapotranspiration is biased high by 28% and 24%, respectively, and the runoff ratios are 50% and 40% lower. This is confirmed by comparison with observation-based runoff estimates from 1130 small, relatively unmanaged basins across the continental United States. The overestimation of evapotranspiration by NARR is largely attributed to the evapotranspiration component of the Noah model, whereas other factors such as atmospheric forcings or biases induced by precipitation assimilation into NARR play only a minor role. A combination of differences in the parameterization of evapotranspiration and in particular low stomatal resistance values in NARR, the seasonality of vegetation characteristics, the near-surface radiation and meteorology, and the representation of soil moisture dynamics, including high infiltration rates and the relative coupling of soil moisture with baseflow in NARR, are responsible for the differences in the water budgets. Large-scale drought as quantified by soil moisture percentiles covaries closely over the continental United States between the three datasets, despite large differences in the seasonal water budgets. However, there are large regional differences, especially in the eastern United States where the VIC model shows higher variability in drought dynamics. This is mostly due to increased frequency of completely dry conditions in NARR that result from differences in soil depth, higher evapotranspiration, early snowmelt, and early peak runoff. In the western United States, differences in the precipitation forcing contribute to large discrepancies between NARR and Noah/VIC simulations in the representation of the early 2000s drought. [ABSTRACT FROM AUTHOR]

Land surface model experiments are used to quantify, for a number of U.S. river basins, the contributions (isolated and combined) of soil moisture and snowpack initialization to the skill of seasonal streamflow forecasts at multiple leads and for different start dates. Snow initialization has a major impact on skill during the spring melting season. Soil moisture initialization has a smaller but still statistically significant impact during this season, and in other seasons, its contribution to skill dominates. Realistic soil moisture initialization can contribute to skill at long leads (over 6 months) for certain basins and seasons. Skill levels in all seasons are found to be related to the ratio of initial total water storage (soil water plus snow) variance to the forecast period precipitation variance, allowing estimates of the potential for skill in areas outside the verification basins. [ABSTRACT FROM AUTHOR]

A unified land model (ULM) is described that combines the surface flux parameterizations in the Noah land surface model (used in most of NOAA's coupled weather and climate models) with the Sacramento Soil Moisture Accounting model (Sac; used for hydrologic prediction within the National Weather Service). The motivation was to develop a model that has a history of strong hydrologic performance while having the ability to be run in the coupled land-atmosphere environment. ULM takes the vegetation, snow model, frozen soil, and evapotranspiration schemes from Noah and merges them with the soil moisture accounting scheme from Sac. ULM surface fluxes, soil moisture, and streamflow simulations were evaluated through comparisons with observations from the Ameriflux (surface flux), Illinois Climate Network (soil moisture), and Model Parameter Estimation Experiment (MOPEX; streamflow) datasets. Initially, a priori parameters from Sac and Noah were used, which resulted in ULM surface flux simulations that were comparable to those produced by Noah (Sac does not predict surface energy fluxes). ULM with the a priori parameters had streamflow simulation skill that was generally similar to Sac's, although it was slightly better (worse) for wetter (more arid) basins. ULM model performance using a set of parameters identified via a Monte Carlo search procedure lead to substantial improvements relative to the a priori parameters. A scheme for transfer of parameters from streamflow simulations to nearby flux and soil moisture measurement points was also evaluated; this approach did not yield conclusive improvements relative to the a priori parameters. [ABSTRACT FROM AUTHOR]

A negative snow water equivalent (SWE) bias in the snow model of the Noah land surface scheme used in the NCEP suite of numerical weather and climate prediction models has been noted by several investigators. This bias motivated a series of offline tests of model extensions and improvements intended to reduce or eliminate the bias. These improvements consist of changes to the model’s albedo formulation that include a parameterization for snowpack aging, changes to how pack temperature is computed, and inclusion of a provision for refreeze of liquid water in the pack. Less extensive testing was done on the performance of model extensions with alternate areal depletion parameterizations. Model improvements were evaluated through comparisons of point simulations with National Resources Conservation Service (NRCS) Snowpack Telemetry (SNOTEL) SWE data for deep-mountain snowpacks at selected stations in the western United States, as well as simulations of snow areal extent over the conterminous United States (CONUS) domain, compared with observational data from the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS). The combination of snow-albedo decay and liquid-water refreeze results in substantial improvements in the magnitude and timing of peak SWE, as well as increased snow-covered extent at large scales. Modifications to areal snow depletion thresholds yielded more realistic snow-covered albedos at large scales. [ABSTRACT FROM AUTHOR]

Helical piles are increasingly used to support and rehabilitate structures subjected to both tensile and compressive axial loads. This paper presents a detailed investigation into the axial performance of helical piles. The study encompasses 19 full-scale load tests in different soils and numerical modeling using finite element analysis. The ultimate load criteria and load transfer mechanisms for helical piles were examined. In addition, the relationship between the installation effort (torque) and pile capacity was explored to determine its suitability for predicting pile capacity. The piles tested were made of three circular pitched bearing plates welded at a spacing of three helical diameters to a solid-square, slender steel shaft. It is proposed to determine the ultimate pile capacity as the load corresponding to pile head movement equal to 8% of the largest helix diameter plus the pile elastic deflection. A torque correlation factor, KT = 33 m–1 for compression and KT = 24 m–1 for uplift, was established to relate the ultimate pile capacity to the installation torque. It was found that load transfer to the soil is predominantly through a cylindrical shear failure surface that follows the tapered profile of the interhelices soils and the bearing capacity of the lead helix in the direction of loading. [ABSTRACT FROM AUTHOR]

Global water systems are facing unprecedented pressures, including climate change‐driven drought and escalating flood risk, environmental contamination, and over allocation. Water management and governance typically lack integration across spatial scales, including relationships between surface and ground water systems. They also routinely ignore connectivity across temporal scales, including the need for intergenerational water planning. As a global and interdisciplinary group of scientists, we seek to highlight how power and scale dynamics influence and determine water outcomes. We argue that attending to complex water systems challenges requires understanding the function and influence of power at different temporal and spatial scales. Building this understanding is key to designing multi‐scalar, reflexive, and pluralistic policy solutions that avoid ineffective or unintended outcomes. We use a co‐learning process to reveal important lessons for the challenge of interdisciplinary research and set a pluralist agenda for understanding power and scale in future water governance.This article is categorized under: Human Water > Water Governance Human Water > Water as Imagined and Represented Human Water > Methods [ABSTRACT FROM AUTHOR]

In nearly all reservoirs, storage capacity is steadily lost due to trapping and accumulation of sediment. Despite critical importance to freshwater supplies, reservoir sedimentation rates are poorly understood due to sparse bathymetry survey data and challenges in modeling sedimentation sequestration. Here, we proposed a novel approach to estimate reservoir sedimentation rates and storage capacity losses using high‐resolution Sentinel‐2 satellites and daily in situ water levels. Validated on eight reservoirs across the central and western United States, the estimated reservoir bathymetry and sedimentation rates have a mean error of 4.08% and 0.05% yr−1, respectively. Estimated storage capacity losses to sediment vary among reservoirs, which overall agrees with the pattern from survey data. We also demonstrated the potential applications of the proposed approach to ungauged reservoirs by combining Sentinel‐2 with sub‐monthly water levels from recent satellite altimeters. Plain Language Summary: Reservoir storage capacity is steadily lost due to sediment filling, which threatens freshwater supplies both now and in the future. Yet, lost reservoir storage capacities to sediment are largely unknown. Here, we develop a generic method to estimate capacity losses and reservoir sedimentation rates by leveraging remote sensing techniques. We tested on eight reservoirs across the central and western United States and found capacity losses and sedimentation rates vary across reservoirs. The proposed method offers a promising alternative to evaluate and predict capacity losses in reservoirs nationwide and globally, and thus supports effective water managements and planning for sustainable freshwater supplies in the future. Key Points: High‐resolution Sentinel‐2 images and daily in situ water levels were used to estimate reservoir sedimentation rates and capacity lossesEstimated reservoir sedimentation rates and storage capacity losses have a mean error of 0.05% yr−1 of full storage capacityPotential applications of this method to ungauged reservoirs are feasible with sub‐monthly level data from recent satellite altimeters [ABSTRACT FROM AUTHOR]

Recently-developed Holocene lake-level reconstructions from the Rocky Mountains offer a quantitative target for testing the skill of state-of-the-art climate system models in simulating hydroclimate change. Here, we use a combination of hydrologic models of catchment streamflow, lake energy balance, and lake water balance to simulate lake level at Little Windy Hill Pond (LWH) in the Medicine Bow Range of Wyoming for a period of severe drought during the mid-Holocene (MH; approximately 6000 years ago). Using Coupled Model Intercomparison Project (CMIP5) output to drive our hydrologic models, we find that none of our simulations reproduce the significantly lowered lake levels at LWH during the MH. Rather, simulated hydroclimate changes for the MH are modest (< 10% reductions in precipitation and streamflow and generally 10–30% increases in lake evaporation), and LWH lake-level changes are buffered by the large volume of snowmelt runoff that the lake receives. Only when winter precipitation is approximately halved in sensitivity experiments do water inputs to the lake become small enough that lake level can be significantly drawn down by year-over-year negative water balances. Possible explanations for the model-data mismatch could lie in the realism of our hydrological modeling framework or in the accuracy of the CMIP5 output, the latter having important implications for projections of future drying in western North America. [ABSTRACT FROM AUTHOR]

Hydrologic modeling and other geophysical applications are sensitive to precipitation forcing data quality, and there are known challenges in spatially distributing gauge-based precipitation over complex terrain. We conduct a comparison of six high-resolution, daily and monthly gridded precipitation datasets over the Western United States. We compare the long-term average spatial patterns, and interannual variability of water-year total precipitation, as well as multi-year trends in precipitation across the datasets. We find that the greatest absolute differences among datasets occur in high-elevation areas and in the maritime mountain ranges of the Western United States, while the greatest percent differences among datasets relative to annual total precipitation occur in arid and rain-shadowed areas. Differences between datasets in some high-elevation areas exceed 200 mm yr −1 on average, and relative differences range from 5 to 60% across the Western United States. In areas of high topographic relief, true uncertainties and biases are likely higher than the differences among the datasets; we present evidence of this based on streamflow observations. Precipitation trends in the datasets differ in magnitude and sign at smaller scales, and are sensitive to how temporal inhomogeneities in the underlying precipitation gauge data are handled. [ABSTRACT FROM AUTHOR]

Corrections to the article "A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States: Update and Extensions" that was published in the June 14, 2013 issue are presented.

Snowpack provides the majority of predictive information for water supply forecasts (WSFs) in snow-dominated basins across the western United States. Drought conditions typically accompany decreased snowpack and lowered runoff efficiency, negatively impacting WSFs. Here, we investigate the relationship between snow water equivalent (SWE) and April–July streamflow volume (AMJJ-V) during drought in small headwater catchments, using observations from 31 USGS streamflow gauges and 54 SNOTEL stations. A linear regression approach is used to evaluate forecast skill under different historical climatologies used for model fitting, as well as with different forecast dates. Experiments are constructed in which extreme hydrological drought years are withheld from model training, that is, years with AMJJ-V below the 15th percentile. Subsets of the remaining years are used for model fitting to understand how the climatology of different training subsets impacts forecasts of extreme drought years. We generally report overprediction in drought years. However, training the forecast model on drier years, that is, below-median years (P15, P57.5], minimizes residuals by an average of 10% in drought year forecasts, relative to a baseline case, with the highest median skill obtained in mid- to late April for colder regions. We report similar findings using a modified National Resources Conservation Service (NRCS) procedure in nine large Upper Colorado River basin (UCRB) basins, highlighting the importance of the snowpack–streamflow relationship in streamflow predictability. We propose an "adaptive sampling" approach of dynamically selecting training years based on antecedent SWE conditions, showing error reductions of up to 20% in historical drought years relative to the period of record. These alternate training protocols provide opportunities for addressing the challenges of future drought risk to water supply planning. Significance Statement: Seasonal water supply forecasts based on the relationship between peak snowpack and water supply exhibit unique errors in drought years due to low snow and streamflow variability, presenting a major challenge for water supply prediction. Here, we assess the reliability of snow-based streamflow predictability in drought years using a fixed forecast date or fixed model training period. We critically evaluate different training protocols that evaluate predictive performance and identify sources of error during historical drought years. We also propose and test an "adaptive sampling" application that dynamically selects training years based on antecedent SWE conditions providing to overcome persistent errors and provide new insights and strategies for snow-guided forecasts. [ABSTRACT FROM AUTHOR]

The decrease in mountain snowpack associated with global warming is difficult to estimate in the presence of the large year-to-year natural variability in observations of snow-water equivalent (SWE). A more robust approach for inferring the impacts of global warming is to estimate the temperature sensitivity (λ) of spring snowpack and multiply it by putative past and future temperature rises observed across the Northern Hemisphere. Estimates of λ can be obtained from (i) simple geometric considerations based on the notion that as the seasonal-mean temperature rises by the amount δT, the freezing level and the entire snowpack should rise by the increment δT/Γ, where Γ is the mean lapse rate; (ii) the regression of 1 April SWE measurements upon mean winter temperatures; (iii) a hydrological model forced by daily temperature and precipitation observations; and (iv) the use of inferred accumulated snowfall derived from daily temperature and precipitation data as a proxy for SWE. All four methods yield an estimated sensitivity of 20% of spring snowpack lost per degree Celsius temperature rise. The increase of precipitation accompanying a 1°C warming can be expected to decrease the sensitivity to 16%. Considering observations of temperature rise over the Northern Hemisphere, it is estimated that spring snow-water equivalent in the Cascades portion of the Puget Sound drainage basin should have declined by 8%–16% over the past 30 yr resulting from global warming, and it can be expected to decline by another 11%–21% by 2050. These losses would be statistically undetectable from a trend analysis of the region’s snowpack over the past 30 yr. [ABSTRACT FROM AUTHOR]

Extreme daily precipitation contributes to flooding that can cause significant economic damages, and so is important to properly capture in gridded meteorological datasets. This work examines precipitation extremes, the mean precipitation on wet days, and fraction of wet days in two widely used gridded datasets over the conterminous United States. Compared to the underlying station observations, the gridded data show a 27% reduction in annual 1-day maximum precipitation, 25% increase in wet day fraction, 1.5–2.5 day increase in mean wet spell length, 30% low bias in 20-yr return values of daily precipitation, and 25% decrease in mean precipitation on wet days. It is shown these changes arise primarily from the time adjustment applied to put the precipitation gauge observations into a uniform time frame, with the gridding process playing a lesser role. A new daily precipitation dataset is developed that omits the time adjustment (as well as extending the gridded data by 7 years) and is shown to perform significantly better in reproducing extreme precipitation metrics. When the new dataset is used to force a land surface model, annually averaged 1-day maximum runoff increases 38% compared to the original data, annual mean runoff increases 17%, evapotranspiration drops 2.3%, and fewer wet days leads to a 3.3% increase in estimated solar insolation. These changes are large enough to affect portrayals of flood risk and water balance components important for ecological and climate change applications across the CONUS. [ABSTRACT FROM AUTHOR]

This study compares projected changes of precipitation characteristics in the U.S. Northeast in two analog-based climate downscaling products, Multivariate Adaptive Constructed Analogs (MACA) and Localized Constructed Analogs (LOCA). The level of similarity or differences between the two products varies with the type of precipitation metrics. For the total precipitation amount, the two products project significant annual increases that are similar in magnitude, spatial pattern, and seasonal distribution, with the largest increases in winter and spring. For the overall precipitation intensity or temporal aggregation of heavy precipitation (e.g., number of days with more than one inch of precipitation, the simple intensity index, and the fraction of annual precipitation accounted for by heavy events), both products project significant increases across the region with strong model consensus; the magnitude of absolute increases are similar between the two products, but the relative increases are larger in LOCA due to an underestimation of heavy precipitation in LOCA's training data. For precipitation extremes such as the annual maximum 1-day precipitation, both products project significant increases in the long-term mean, but the magnitude of both the absolute and relative changes are much smaller in LOCA than in MACA, indicating that the extreme precipitation differences in the training data are amplified in future projections as a result of the analog-based downscaling algorithms. The two products differ the most in the intensity and frequency of rare extremes (e.g., 1-in-20-years events) for which MACA projects significant increases while the LOCA-projected changes are inconclusive over much of the study area. [ABSTRACT FROM AUTHOR]

The impact of land–atmosphere anomaly coupling on land variability is investigated using a new two-stage climate model experimental design called the ‘‘GLACE-Hydrology’’ experiment. First, as in the GLACECMIP5 experiment, twin sets of coupled land–atmosphere climate model (CAM5-CLM4.5) ensembles are performed, with each simulation using the same prescribed observed sea surface temperatures and radiative forcing for the years 1971–2014. In one set, land–atmosphere anomaly coupling is removed by prescribing soil moisture to follow the control model’s seasonally evolving soil moisture climatology (‘‘land–atmosphere uncoupled’’), enabling a contrast with the original control set (‘‘land–atmosphere coupled’’). Then, the atmospheric outputs from both sets of simulations are used to force land-only ensemble simulations, allowing investigation of the resulting soil moisture variability and memory under both the coupled and uncoupled scenarios. This study finds that in midlatitudes during boreal summer, land–atmosphere anomaly coupling significantly strengthens the relationship between soil moisture and evapotranspiration anomalies, both in amplitude and phase. This allows for decreased moisture exchange between the land surface and atmosphere, increasing soilmoisturememory and often its variability as well. Additionally, land–atmosphere anomaly coupling impacts runoff variability, especially in wet and transition regions, and precipitation variability, although the latter has surprisingly localized impacts on soilmoisture variability. As a result of these changes, there is an increase in the signal-to-noise ratio, and thereby the potential seasonal predictability, of SST-forced hydroclimate anomalies in many areas of the globe, especially in the midlatitudes. This predictability increase is greater for soil moisture than precipitation and has important implications for the prediction of drought. [ABSTRACT FROM AUTHOR]

Severe and persistent 21st-century drought in southwestern North America (SWNA) motivates comparisons to medieval megadroughts and questions about the role of anthropogenic climate change. We use hydrological modeling and new 1200-year tree-ring reconstructions of summer soil moisture to demonstrate that the 2000-2018 SWNA drought was the second driest 19-year period since 800 CE, exceeded only by a late-1500s megadrought. The megadrought-like trajectory of 2000-2018 soil moisture was driven by natural variability superimposed on drying due to anthropogenic warming. Anthropogenic trends in temperature, relative humidity, and precipitation estimated from 31 climate models account for 47% (model interquartiles of 35 to 105%) of the 2000-2018 drought severity, pushing an otherwise moderate drought onto a trajectory comparable to the worst SWNA megadroughts since 800 CE. [ABSTRACT FROM AUTHOR]

Climate change may affect the US electricity system through changes in electricity demand, mediated by increases in average surface temperature, and through changes in electricity supply, mediated by changes in both surface temperature and regional water availability. By coupling projections from four general circulation models (GCMs) with a state-of-the-art US electricity system model—the Regional Energy Deployment System (ReEDS)—this study evaluates both the isolated and combined effects of different climate-mediated drivers of US electricity system change through 2050. Comparing results across climate models allows us to evaluate which effects are robust to uncertainty in projected climate outcomes. Comparing effects of different drivers in isolation and in combination allows us to determine the relative contributions of the climate-mediated effects on system evolution. Our results indicate that national-level energy and economic impacts are largely driven by increases in electricity demand that follow from a consistent increase in surface air temperature that is largely robust to the choice of climate model. Other electricity system changes can be equally or more significant in some regions, but these effects are more regionally variable, less significant when aggregated to the national scale, and less robust to the choice of climate model. The findings show that the impacts of climate change on the electricity system can be understood in terms of fewer drivers and with greater certainty at the national level than at the regional level. [ABSTRACT FROM AUTHOR]

Soil moisture anomalies within the root zone (roughly, soil depths down to ~0.4 m) typically persist only a few months. Consequently, land surface–related climate predictability research has often focused on subseasonal to seasonal time scales. However, in this study of multidecadal in situ datasets and land data assimilation products, we find that root zone soil moisture anomalies can recur several or more seasons after they were initiated, indicating potential interannual predictability. Lead–lag correlations show that this recurrence often happens during one fixed season and also seems related to the greater memory of soil moisture anomalies within the layer beneath the root zone, with memory on the order of several months to over a year. That is, in some seasons, notably spring and summer when the vertical soil water potential gradient reverses sign throughout much of North America, deeper soil moisture anomalies appear to return to the surface, thereby restoring an earlier root zone anomaly that had decayed. We call this process "reemergence," in analogy with a similar seasonally varying process (with different underlying physics) providing winter-to-winter memory to the extratropical ocean surface layer. Pronounced spatial and seasonal dependence of soil moisture reemergence is found that is frequently, but not always, robust across datasets. Also, some of its aspects appear sensitive to spatial and temporal sampling, especially within the shorter available in situ datasets, and to precipitation variability. Like its namesake, soil moisture reemergence may enhance interannual-to-decadal variability, notably of droughts. Its detailed physics and role within the climate system, however, remain to be understood. [ABSTRACT FROM AUTHOR]

One way to adapt to and mitigate current and future water scarcity is to manage and store water more efficiently. Reservoirs act as critical buffers to ensure agricultural and municipal water deliveries, mitigate flooding, and generate hydroelectric power, yet they often lose significant amounts of water through evaporation, especially in arid and semiarid regions. Despite this fact, reservoir evaporation has been an inconsistently and inaccurately estimated component of the water cycle within the water resource infrastructure of the arid and semiarid western United States. This paper highlights the increasing importance and challenges of correctly estimating and forecasting reservoir evaporation in the current and future climate, as well as the need to bring new ideas and state-of-the-art practices for the estimation of reservoir evaporation into operational use for modern water resource managers. New ideas and practices include i) improving the estimation of reservoir evaporation using up-to-date knowledge, state-of-the-art instrumentation and numerical models, and innovative experimental designs to diagnose processes and accurately forecast evaporation; ii) improving our understanding of spatial and temporal variations in evaporative water loss from existing reservoirs and transferring this knowledge when expanding reservoirs or siting new ones; and iii) implementing an adaptive management plan that incorporates new knowledge, observations, and forecasts of reservoir evaporation to improve water resource management. [ABSTRACT FROM AUTHOR]

Continental-domain assessments of climate change impacts on water resources typically rely on statistically downscaled climate model outputs to force hydrologic models at a finer spatial resolution. This study examines the effects of four statistical downscaling methods [bias-corrected constructed analog (BCCA), bias-corrected spatial disaggregation applied at daily (BCSDd) and monthly scales (BCSDm), and asynchronous regression (AR)] on retrospective hydrologic simulations using three hydrologic models with their default parameters (the Community Land Model, version 4.0; the Variable Infiltration Capacity model, version 4.1.2; and the Precipitation-Runoff Modeling System, version 3.0.4) over the contiguous United States (CONUS). Biases of hydrologic simulations forced by statistically downscaled climate data relative to the simulation with observation-based gridded data are presented. Each statistical downscaling method produces different meteorological portrayals including precipitation amount, wet-day frequency, and the energy input (i.e., shortwave radiation), and their interplay affects estimations of precipitation partitioning between evapotranspiration and runoff, extreme runoff, and hydrologic states (i.e., snow and soil moisture). The analyses show that BCCA underestimates annual precipitation by as much as −250 mm, leading to unreasonable hydrologic portrayals over the CONUS for all models. Although the other three statistical downscaling methods produce a comparable precipitation bias ranging from −10 to 8 mm across the CONUS, BCSDd severely overestimates the wet-day fraction by up to 0.25, leading to different precipitation partitioning compared to the simulations with other downscaled data. Overall, the choice of downscaling method contributes to less spread in runoff estimates (by a factor of 1.5-3) than the choice of hydrologic model with use of the default parameters if BCCA is excluded. [ABSTRACT FROM AUTHOR]

The current California drought has cast a heavy burden on statewide agriculture and water resources, further exacerbated by concurrent extreme high temperatures. Furthermore, industrial-era global radiative forcing brings into question the role of long-term climate change with regard to California drought. How has human-induced climate change affected California drought risk? Here, observations and model experimentation are applied to characterize this drought employing metrics that synthesize drought duration, cumulative precipitation deficit, and soil moisture depletion. The model simulations show that increases in radiative forcing since the late nineteenth century induce both increased annual precipitation and increased surface temperature over California, consistent with prior model studies and with observed long-term change. As a result, there is no material difference in the frequency of droughts defined using bivariate indicators of precipitation and near-surface (10 cm) soil moisture, because shallow soil moisture responds most sensitively to increased evaporation driven by warming, which compensates the increase in the precipitation. However, when using soil moisture within a deep root zone layer (1 m) as covariate, droughts become less frequent because deep soil moisture responds most sensitively to increased precipitation. The results illustrate the different land surface responses to anthropogenic forcing that are relevant for near-surface moisture exchange and for root zone moisture availability. The latter is especially relevant for agricultural impacts as the deep layer dictates moisture availability for plants, trees, and many crops. The results thus indicate that the net effect of climate change has made agricultural drought less likely and that the current severe impacts of drought on California's agriculture have not been substantially caused by long-term climate changes. [ABSTRACT FROM AUTHOR]

The article examines the some of the possible causes of drought in East Africa during the period April-June 2014 using the Variable Infiltration Capacity model. Topics covered include the influence of warm Eastern African air temperatures, reduction in rainfall associated with intense West Pacific Index and the effect of anthropogenic warming in reducing soil moisture, evapotranspiration and rainfall.

Gridded spatiotemporal maps of precipitation are essential for hydrometeorological and ecological analyses. In the United States, most of these datasets are developed using the Cooperative Observer (COOP) network of ground-based precipitation measurements, interpolation, and the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) to map these measurements to places where data are not available. Here, we evaluate two daily datasets gridded at ° resolution against independent daily observations from over 100 snow pillows in California's Sierra Nevada from 1990 to 2010. Over the entire period, the gridded datasets performed reasonably well, with median total water-year errors generally falling within ±10%. However, errors in individual storm events sometimes exceeded 50% for the median difference across all stations, and in many cases, the same underpredicted storms appear in both datasets. Synoptic analysis reveals that these underpredicted storms coincide with 700-hPa winds from the west or northwest, which are associated with post-cold-frontal flow and disproportionately small precipitation rates in low-elevation valley locations, where the COOP stations are primarily located. This atmospheric circulation leads to a stronger than normal valley-to-mountain precipitation gradient and underestimation of actual mountain precipitation. Because of the small average number of storms (<10) reaching California each year, these individual storm misses can lead to large biases (~20%) in total water-year precipitation and thereby significantly affect estimates of statewide water resources. [ABSTRACT FROM AUTHOR]

The accurate knowledge of soil moisture and snow conditions is important for the skillful characterization of agricultural and hydrologic droughts, which are defined as deficits of soil moisture and streamflow, respectively. This article examines the influence of remotely sensed soil moisture and snow depth retrievals toward improving estimates of drought through data assimilation. Soil moisture and snow depth retrievals from a variety of sensors (primarily passive microwave based) are assimilated separately into the Noah land surface model for the period of 1979-2011 over the continental United States, in the North American Land Data Assimilation System (NLDAS) configuration. Overall, the assimilation of soil moisture and snow datasets was found to provide marginal improvements over the open-loop configuration. Though the improvements in soil moisture fields through soil moisture data assimilation were barely at the statistically significant levels, these small improvements were found to translate into subsequent small improvements in simulated streamflow. The assimilation of snow depth datasets were found to generally improve the snow fields, but these improvements did not always translate to corresponding improvements in streamflow, including some notable degradations observed in the western United States. A quantitative examination of the percentage drought area from root-zone soil moisture and streamflow percentiles was conducted against the U.S. Drought Monitor data. The results suggest that soil moisture assimilation provides improvements at short time scales, both in the magnitude and representation of the spatial patterns of drought estimates, whereas the impact of snow data assimilation was marginal and often disadvantageous. [ABSTRACT FROM AUTHOR]

The article "Decomposing supply-side and demand-side impacts of climate change on the US electricity system through 2050". [ABSTRACT FROM AUTHOR]

Soil temperature can exhibit considerable memory from weather and climate signals and is among the most important initial conditions in numerical weather and climate models. Consequently, a more accurate long-term land surface soil temperature dataset is needed to improve weather and climate simulation and prediction, and is also important for the simulation of agricultural crop yield and ecological processes. The North American Land Data Assimilation phase 2 (NLDAS-2) has generated 31 years (1979-2009) of simulated hourly soil temperature data with a spatial resolution of ⅛°. This dataset has not been comprehensively evaluated to date. Thus, the purpose of this paper is to assess Noah-simulated soil temperature for different soil depths and time scales. The authors used long-term (1979-2001) observed monthly mean soil temperatures from 137 cooperative stations over the United States to evaluate simulated soil temperature for three soil layers (0-10, 10-40, and 40-100 cm) for annual and monthly time scales. Short-term (1997-99) observed soil temperatures from 72 Oklahoma Mesonet stations were used to validate simulated soil temperatures for three soil layers and for daily and hourly time scales. The results showed that the Noah land surface model generally matches observed soil temperature well for different soil layers and time scales. At greater depths, the simulation skill (anomaly correlation) decreased for all time scales. The monthly mean diurnal cycle difference between simulated and observed soil temperature revealed large midnight biases in the cold season that are due to small downward longwave radiation and issues related to model parameters. [ABSTRACT FROM AUTHOR]

Land managers often need to predict watershed-scale erosion rates after disturbance or other land cover changes. This study compared commonly used hillslope erosion models to simulate post-fire sediment yields (SY) at both hillslope and watershed scales within the High Park Fire, Colorado, U.S.A. At hillslope scale, simulated SY from four models— RUSLE, AGWA/KINEROS2, WEPP, and a site-specific regression model—were compared to observed SY at 29 hillslopes. At the watershed scale, RUSLE, AGWA/KINEROS2, and WEPP were applied to simulate spatial patterns of SY for two 14–16 km2 watersheds using different scales (0.5–25 ha) of hillslope discretization. Simulated spatial patterns were compared between models and to densities of channel heads across the watersheds. Three additional erosion algorithms were implemented within a land surface model to evaluate effects of parameter uncertainty. At the hillslope scale, SY was only significantly correlated to observed SY for the empirical model, but at the watershed scale, sediment loads were significantly correlated to observed channel head densities for all models. Watershed sediment load increased with the size of the hillslope sub-units due to the nonlinear effects of hillslope length on simulated erosion. SY's were closest in magnitude to expected watershed-scale SY when models were divided into the smallest hillslopes. These findings demonstrate that current erosion models are fairly consistent at identifying areas with low and high erosion potential, but the wide range of predicted SY and poor fit to observed SY highlight the need for better field observations and model calibration to obtain more accurate simulations. • Evaluated performance of erosion models at hillslope and watershed scales. • Simulated hillslope sediment yields did not correlate with measured values. • Simulated watershed sediment yields were correlated with observed rill densities. • Longer hillslopes within watershed simulations led to greater sediment loads. • Parameter uncertainty caused >2 orders of magnitude variability in sediment yields. [ABSTRACT FROM AUTHOR]

We describe an approach (ESMAP; Evaporation–Soil Moisture Active Passive) to estimate direct evaporation from soil, Esoil, by combining remotely-sensed soil drying rates with model calculations of the vertical fluxes in and out of the surface soil layer. Improved knowledge of Esoil can serve as a constraint in how total evapotranspiration is partitioned. The soil drying rates used here are based on SMAP data, but the method could be applied to data from other sensors. We present results corresponding to ten SMAP pixels in North America to evaluate the method. The ESMAP method was applied to intervals between successive SMAP overpasses with limited precipitation (<2 mm) to avoid uncertainty associated with precipitation, infiltration, and runoff. We used the Hydrus 1-D model to calculate the flux of water across the bottom boundary of the 0 to 50 mm soil layer sensed by SMAP, qbot. During dry intervals, qbot typically transfers water upwards into the surface soil layer from below, usually <0.5 mm day−1. Based on a standard formulation, transpiration from the surface soil layer, ET_s, is usually < 0.1 mm day−1, and, thus, generally not an important flux. Soil drying rates (converted to equivalent water thickness) are typically between 0 and 1 mm day−1. Evaporation is almost always greater than soil drying rates because qbot is typically a source of water to the surface soil and ET_s is negligible. Evaporation is typically between 0 and 1.5 mm day−1, with the highest values following rainfall. Soil evaporation summed over SMAP overpass intervals with precipitation <2 mm (60% of days) accounts for 15% of total precipitation. If evaporation rates are similar during overpasses with substantial precipitation, then the total evaporation flux would account for ~25% of precipitation. ESMAP could be used over spatially continuous domains to provide constraints on Esoil, but model-based Esoil would be required during intervals with substantial precipitation. [ABSTRACT FROM AUTHOR]