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Data sets provided by the Soil Moisture Active Passive (SMAP) and the Global Precipitation Measurement (GPM) satellite missions contain rich information about land surface hydrologic processes. In this study, a unified regression method is proposed and applied to these global data sets to investigate surface soil moisture (SSM) dynamics. Two forms of regressors are implemented: 1) the linear regressors of SSM and precipitation flux and 2) the linear regressors of SSM and precipitation flux with an additional interaction term. Regression results based on 3 years of global SMAP and GPM data show that the unified regression method can identify the SSM characteristics found by several recent studies, including the SSM exponential decay rate, the fraction of precipitation retained in the surface soil layer, and the effective depth of hydrologic storage. Additionally, including the interaction regressor provides a novel way to derive the sensitivity of infiltration/runoff partitioning to antecedent SSM without the need for streamflow observations. These SMAP/GPM regression results are compared with those derived from a global SSM data set simulated by the variable infiltration capacity model. Relative to the satellite data, variable infiltration capacity retains moisture longer in the top layer, retains too much precipitation input in that layer, and exhibits levels of sensitivity of runoff/infiltration partitioning to top‐layer soil moisture that generally match SMAP especially in humid regions. This study demonstrates that the regression‐based method can recover useful process‐level insight from SMAP SSM retrievals and is a viable tool for evaluating the representation of surface processes in hydrologic models. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) measurements contain information about both pre-storm hydrologic states and within-storm rainfall estimates, both of which are required inputs for event-based streamflow simulations. In this study, an existing dual state/rainfall correction system is extended and implemented in the 605 000 km 2 Arkansas–Red River basin with a semi-distributed land surface model. The Soil Moisture Active Passive (SMAP) satellite surface SM retrievals are assimilated to simultaneously correct antecedent SM states in the model and rainfall estimates from the Global Precipitation Measurement (GPM) mission. While the GPM rainfall is corrected slightly to moderately, especially for larger events, the correction is smaller than that reported in past studies due primarily to the improved baseline quality of the new GPM satellite product. In addition, rainfall correction is poorer in regions with dense biomass due to lower SMAP quality. Nevertheless, SMAP-based dual state/rainfall correction is shown to generally improve streamflow estimates, as shown by comparisons with streamflow observations across eight Arkansas–Red River sub-basins. However, more substantial streamflow correction is limited by significant systematic errors present in model-based streamflow estimates that are uncorrectable via standard data assimilation techniques aimed solely at zero-mean random errors. These findings suggest that more substantial streamflow correction will likely require better quality SM observations as well as future research efforts aimed at reducing systematic errors in hydrologic systems. [ABSTRACT FROM AUTHOR]

Data assimilation (DA) techniques have been widely applied to assimilate satellite-based soil moisture (SM) measurements into hydrologic models to improve streamflow simulations. However, past studies have reached mixed conclusions regarding the degree of runoff improvement achieved via SM state updating. In this study, a synthetic diagnostic framework is designed to 1) decompose the random error components in a hydrologic simulation, 2) quantify the error terms that originate from SM states, and 3) assess the effectiveness of SM DA to correct these random errors. The general framework is illustrated through a case study in which surface Soil Moisture Active Passive (SMAP) data are assimilated into a large-scale land surface model in the Arkansas–Red River basin. The case study includes systematic error in the simulated streamflow that imposes a first-order limit on DA performance. In addition, about 60% of the random runoff error originates directly from rainfall and cannot be corrected by SM DA. In particular, fast-response runoff dominates in much of the basin but is relatively unresponsive to state updating. Slow-response runoff is strongly controlled by the bottom-layer SM and therefore only modestly improved via the assimilation of surface measurements. Combined, the total runoff improvement in the synthetic analysis is small (<10% over the basin). Improvements in the real SMAP-assimilated case are further limited due to systematic error and other factors such as inaccurate error assumptions and SMAP rescaling. Findings from the diagnostic framework suggest that SM DA alone is insufficient to substantially improve streamflow estimates in large basins. [ABSTRACT FROM AUTHOR]

Modeling hydrological processes in the Alaskan sub-arctic is challenging because of the extreme spatial heterogeneity in soil properties and vegetation communities. Nevertheless, modeling and predicting hydrological processes is critical in this region due to its vulnerability to the effects of climate change. Coarse-spatial-resolution datasets used in land surface modeling pose a new challenge in simulating the spatially distributed and basin-integrated processes since these datasets do not adequately represent the small-scale hydrological, thermal, and ecological heterogeneity. The goal of this study is to improve the prediction capacity of mesoscale to large-scale hydrological models by introducing a small-scale parameterization scheme, which better represents the spatial heterogeneity of soil properties and vegetation cover in the Alaskan sub-arctic. The small-scale parameterization schemes are derived from observations and a sub-grid parameterization method in the two contrasting sub-basins of the Caribou Poker Creek Research Watershed (CPCRW) in Interior Alaska: one nearly permafrost-free (LowP) sub-basin and one permafrost-dominated (HighP) sub-basin. The sub-grid parameterization method used in the small-scale parameterization scheme is derived from the watershed topography. We found that observed soil thermal and hydraulic properties - including the distribution of permafrost and vegetation cover heterogeneity - are better represented in the sub-grid parameterization method than the coarse-resolution datasets. Parameters derived from the coarse-resolution datasets and from the sub-grid parameterization method are implemented into the variable infiltration capacity (VIC) mesoscale hydrological model to simulate runoff, evapotranspiration (ET), and soil moisture in the two sub-basins of the CPCRW. Simulated hydrographs based on the small-scale parameterization capture most of the peak and low flows, with similar accuracy in both sub-basins, compared to simulated hydrographs based on the coarse-resolution datasets. On average, the small-scale parameterization scheme improves the total runoff simulation by up to 50% in the LowP sub-basin and by up to 10% in the HighP sub-basin from the large-scale parameterization. This study shows that the proposed sub-grid parameterization method can be used to improve the performance of mesoscale hydrological models in the Alaskan sub-arctic watersheds. [ABSTRACT FROM AUTHOR]

We evaluate the implications of ten twenty-first century climate scenarios for snow, soil moisture, and fuel moisture across the conterminous western USA using the Variable Infiltration Capacity (VIC) hydrology model. A decline in mountain snowpack, an advance in the timing of spring melt, and a reduction in snow season are projected for five mountain ranges in the region. For the southernmost range (the White Mountains), spring snow at most elevations will disappear by the end of the twenty-first century. We investigate soil and fuel moisture changes for the five mountain ranges and for six lowland regions. The accelerated depletion of mountain snowpack due to warming leads to reduced summer soil moisture across mountain environments. Similarly, warmer and drier summers lead to decreases of up to 25% in dead fuel moisture across all mountain ranges. Collective declines in spring mountain snowpack, summer soil moisture, and fuel moisture across western mountain ranges will increase fire potential in flammability-limited forested systems where fuels are not limiting. Projected changes in fire potential in predominately fuel-limited systems at lower elevations are more uncertain given the confounding signals between projected changes in soil moisture and fuel moisture. [ABSTRACT FROM AUTHOR]

The severity-area-duration (SAD) method is used in conjunction with the Variable Infiltration Capacity model (VIC) to identify the major historical total moisture (TM; soil moisture plus snow water equivalent) droughts over the Pacific Northwest region, defined as the Columbia River basin and the region's coastal drainages, for the period 1920-2013. The motivation is to understand how droughts identified using TM (a measure similar to that used in the U.S. Drought Monitor) relate to sector-specific drought measures that are more relevant to users. It is found that most of the SAD space is dominated by an extended drought period during the 1930s, although the most severe shorter droughts are in the 1970s (1976-78) and early 2000s (2000-04). The impact of the three severe TM droughts that dominate most of the SAD space are explored in terms of sector-specific measures relevant to dryland and irrigated agriculture, hydropower generation, municipal water supply, and recreation. It is found that in many cases the most severe droughts identified using the SAD method also appear among the most severe sector-specific droughts; however, there are important exceptions. Two types of inconsistencies are examined and the nature of the conditions that give rise to them are explored. [ABSTRACT FROM AUTHOR]

Man-made reservoirs play a key role in the terrestrial water system. They alter water fluxes at the land surface and impact surface water storage through water management regulations for diverse purposes such as irrigation, municipal water supply, hydropower generation, and flood control. Although most developed countries have established sophisticated observing systems for many variables in the land surface water cycle, long-term and consistent records of reservoir storage are much more limited and not always shared. Furthermore, most land surface hydrological models do not represent the effects of water management activities. Here, the contribution of reservoirs to seasonal water storage variations is investigated using a large-scale water management model to simulate the effects of reservoir management at basin and continental scales. The model was run from 1948 to 2010 at a spatial resolution of 0.25° latitude-longitude. A total of 166 of the largest reservoirs in the world with a total capacity of about 3900 km3 (nearly 60% of the globally integrated reservoir capacity) were simulated. The global reservoir storage time series reflects the massive expansion of global reservoir capacity; over 30 000 reservoirs have been constructed during the past half century, with a mean absolute interannual storage variation of 89 km3. The results indicate that the average reservoir-induced seasonal storage variation is nearly 700 km3 or about 10% of the global reservoir storage. For some river basins, such as the Yellow River, seasonal reservoir storage variations can be as large as 72% of combined snow water equivalent and soil moisture storage. [ABSTRACT FROM AUTHOR]

The implementation of a multimodel drought monitoring system is described, which provides near-real-time estimates of surface moisture storage for the global land areas between 50°S and 50°N with a time lag of about 1 day. Near-real-time forcings are derived from satellite-based precipitation estimates and modeled air temperatures. The system distinguishes itself from other operational systems in that it uses multiple land surface models (Variable Infiltration Capacity, Noah, and Sacramento) to simulate surface moisture storage, which are then combined to derive a multimodel estimate of drought. A comparison of the results with other historic and current drought estimates demonstrates that near-real-time nowcasting of global drought conditions based on satellite and model forcings is entirely feasible. However, challenges remain because hydrological droughts are inherently defined in the context of a long-term climatology. Changes in observing platforms can be misinterpreted as droughts (or as excessively wet periods). This problem cannot simply be addressed through the addition of more observations or through the development of new observing platforms. Instead, it will require careful (re)construction of long-term records that are updated in near-real time in a consistent manner so that changes in surface meteorological forcings reflect actual conditions rather than changes in methods or sources. [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]

Describes a daily set of surface meteorological forcings, model-derived moisture fluxes and state variables for global land areas between 1979 and 1993. Facilitation of global simulations; Evaluation of land surface parameterizations; Variable infiltration capacity.

Measuring gravimetric soil water content is a relatively elementary laboratory procedure; however, the inaccessibility of a laboratory oven makes this activity impractical for K-12 students. The Soil Moisture Campaign (SMC) Project of the Global Learning and Observation to Benefit the Environment (GLOBE) Program was developed to use gravimetric water content measurement as an introduction to earth science research for K-12 students. We have designed and tested a low-budget, low-technology "light-bulb oven" that is affordable enough for use in a classroom. The light-bulb oven was tested by drying replicate samples of four different soil types in the light-bulb oven and in a traditional laboratory oven. The results show that the performance of the light-bulb oven was comparable to that of a traditional laboratory oven. [ABSTRACT FROM AUTHOR]