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Garry L. Schaefer
,
Michael H. Cosh
, and
Thomas J. Jackson

Abstract

Surface soil moisture plays an important role in the dynamics of land–atmosphere interactions and many current and upcoming models and satellite sensors. In situ data will be required to provide calibration and validation datasets. Therefore, there is a need for sensor networks at a variety of scales that provide near-real-time soil moisture and temperature data combined with other climate information for use in natural resource planning, drought assessment, water resource management, and resource inventory. The U.S. Department of Agriculture (USDA)–Natural Resources Conservation Service (NRCS)–National Water and Climate Center has established a continental-scale network to address this need, called the Soil Climate Analysis Network (SCAN). This ever-growing network has more than 116 stations located in 39 states, most of which have been installed since 1999. The stations are remotely located and collect hourly atmospheric, soil moisture, and soil temperature data that are available to the public online in near–real time. New stations are located on benchmark soils when possible. Future plans for the network include increasing the number of stations, improving on user-friendly data summaries, increasing data quality, and scaling the stations to the surrounding region.

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Evan J. Coopersmith
,
Michael H. Cosh
, and
Jennifer M. Jacobs

Abstract

The continuity of soil moisture time series data is crucial for climatic research. Yet, a common problem for continuous data series is the changing of sensors, not only as replacements are necessary, but as technologies evolve. The Illinois Climate Network has one of the longest data records of soil moisture; yet, it has a discontinuity when the primary sensor (neutron probes) was replaced with a dielectric sensor. Applying a simple model coupled with machine learning, the two time series can be merged into one continuous record by training the model on the latter dielectric model and minimizing errors against the former neutron probe dataset. The model is able to be calibrated to an accuracy of 0.050 m3 m−3 and applying this to the earlier series and applying a gain and offset, an RMSE of 0.055 m3 m−3 is possible. As a result of this work, there is now a singular network data record extending back to the 1980s for the state of Illinois.

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Diego G. Miralles
,
Wade T. Crow
, and
Michael H. Cosh

Abstract

The validation of satellite surface soil moisture products requires comparisons between point-scale ground observations and footprint-scale (>100 km2) retrievals. In regions containing a limited number of measurement sites per footprint, some of the observed difference between the retrievals and ground observations is attributable to spatial sampling error and not the intrinsic error of the satellite retrievals themselves. Here, a triple collocation (TC) approach is applied to footprint-scale soil moisture products acquired from passive microwave remote sensing, land surface modeling, and a single ground-based station with the goal of the estimating (and correcting for) spatial sampling error in footprint-scale soil moisture estimates derived from the ground station. Using these three soil moisture products, the TC approach is shown to estimate point-to-footprint soil moisture sampling errors to within 0.0059 m3 m−3 and enhance the ability to validate satellite footprint-scale soil moisture products using existing low-density ground networks.

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Peter J. Shellito
,
Eric E. Small
, and
Michael H. Cosh

Abstract

Soil hydraulic properties (SHPs) control infiltration and redistribution of moisture in a soil column. The Noah land surface model (LSM) default simulation selects SHPs according to a location’s mapped soil texture class. SHPs are instead estimated at seven sites in North America through calibration. A single-objective algorithm minimizes the root-mean-square difference (RMSD) between simulated surface soil moisture and observations from 1) a dense network of in situ probes, 2) Soil Moisture Ocean Salinity (SMOS) satellite retrievals, and 3) SMOS retrievals adjusted such that their mean equals that of the in situ network. Parameters are optimized in 2012 and validated in 2013 against the in situ network. RMSD and unbiased RMSD (ubRMSD) assess resulting surface soil moisture behavior. At all sites, assigning SHP parameters from a different soil texture than the one that is mapped decreases the RMSD by an average of 0.029 cm3 cm−3. Similar improvements result from calibrating parameters using in situ network data (0.031 cm3 cm−3). Calibrations using remotely sensed data show comparable success (0.029 cm3 cm−3) if the SMOS product has no bias. Calibrated simulations are superior to texture-based simulations in their ability to decrease ubRMSD at times of year when the default simulation is worst. Changes to both RMSD and ubRMSD are small when the default simulation is already good. Most calibrated simulations have higher runoff ratios than do texture-based simulations, a change that warrants further evaluation. Overall, parameter selection using SMOS data shows good potential where biases are low.

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Evan J. Coopersmith
,
Michael H. Cosh
,
Walt A. Petersen
,
John Prueger
, and
James J. Niemeier

Abstract

Soil moisture monitoring with in situ technology is a time-consuming and costly endeavor for which a method of increasing the resolution of spatial estimates across in situ networks is necessary. Using a simple hydrologic model, the estimation capacity of an in situ watershed network can be increased beyond the station distribution by using available precipitation, soil, and topographic information. A study site was selected on the Iowa River, characterized by homogeneous soil and topographic features, reducing the variables to precipitation only. Using 10-km precipitation estimates from the North American Land Data Assimilation System (NLDAS) for 2013, high-resolution estimates of surface soil moisture were generated in coordination with an in situ network, which was deployed as part of the Iowa Flood Studies (IFloodS). A simple, bucket model for soil moisture at each in situ sensor was calibrated using four precipitation products and subsequently validated at both the sensor for which it was calibrated and other proximal sensors, the latter after a bias correction step. Average RMSE values of 0.031 and 0.045 m3 m−3 were obtained for models validated at the sensor for which they were calibrated and at other nearby sensors, respectively.

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Sujay V. Kumar
,
Christa D. Peters-Lidard
,
David Mocko
,
Rolf Reichle
,
Yuqiong Liu
,
Kristi R. Arsenault
,
Youlong Xia
,
Michael Ek
,
George Riggs
,
Ben Livneh
, and
Michael Cosh

Abstract

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.

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Sujay V. Kumar
,
Benjamin F. Zaitchik
,
Christa D. Peters-Lidard
,
Matthew Rodell
,
Rolf Reichle
,
Bailing Li
,
Michael Jasinski
,
David Mocko
,
Augusto Getirana
,
Gabrielle De Lannoy
,
Michael H. Cosh
,
Christopher R. Hain
,
Martha Anderson
,
Kristi R. Arsenault
,
Youlong Xia
, and
Michael Ek

Abstract

The objective of the North American Land Data Assimilation System (NLDAS) is to provide best-available estimates of near-surface meteorological conditions and soil hydrological status for the continental United States. To support the ongoing efforts to develop data assimilation (DA) capabilities for NLDAS, the results of Gravity Recovery and Climate Experiment (GRACE) DA implemented in a manner consistent with NLDAS development are presented. Following previous work, GRACE terrestrial water storage (TWS) anomaly estimates are assimilated into the NASA Catchment land surface model using an ensemble smoother. In contrast to many earlier GRACE DA studies, a gridded GRACE TWS product is assimilated, spatially distributed GRACE error estimates are accounted for, and the impact that GRACE scaling factors have on assimilation is evaluated. Comparisons with quality-controlled in situ observations indicate that GRACE DA has a positive impact on the simulation of unconfined groundwater variability across the majority of the eastern United States and on the simulation of surface and root zone soil moisture across the country. Smaller improvements are seen in the simulation of snow depth, and the impact of GRACE DA on simulated river discharge and evapotranspiration is regionally variable. The use of GRACE scaling factors during assimilation improved DA results in the western United States but led to small degradations in the eastern United States. The study also found comparable performance between the use of gridded and basin-averaged GRACE observations in assimilation. Finally, the evaluations presented in the paper indicate that GRACE DA can be helpful in improving the representation of droughts.

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Wesley J. Rondinelli
,
Brian K. Hornbuckle
,
Jason C. Patton
,
Michael H. Cosh
,
Victoria A. Walker
,
Benjamin D. Carr
, and
Sally D. Logsdon

Abstract

Soil moisture affects the spatial variation of land–atmosphere interactions through its influence on the balance of latent and sensible heat fluxes. Wetter soils are more prone to flooding because a smaller fraction of rainfall can infiltrate into the soil. The Soil Moisture Ocean Salinity (SMOS) satellite carries a remote sensing instrument able to make estimates of near-surface soil moisture on a global scale. One way to validate satellite observations is by comparing them with observations made with sparse networks of in situ soil moisture sensors that match the extent of satellite footprints. The rate of soil drying after significant rainfall observed by SMOS is found to be higher than the rate observed by a U.S. Department of Agriculture (USDA) soil moisture network in the watershed of the South Fork Iowa River. This leads to the conclusion that SMOS and the network observe different layers of the soil: SMOS observes a layer of soil at the soil surface that is a few centimeters thick, while the network observes a deeper soil layer centered at the depth at which the in situ soil moisture sensors are buried. It is also found that SMOS near-surface soil moisture is drier than the South Fork network soil moisture, on average. The conclusion that SMOS and the network observe different layers of the soil, and therefore different soil moisture dynamics, cannot explain the dry bias. However, it can account for some of the root-mean-square error in the relationship. In addition, SMOS observations are noisier than the network observations.

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Qing Liu
,
Rolf H. Reichle
,
Rajat Bindlish
,
Michael H. Cosh
,
Wade T. Crow
,
Richard de Jeu
,
Gabrielle J. M. De Lannoy
,
George J. Huffman
, and
Thomas J. Jackson

Abstract

The contributions of precipitation and soil moisture observations to soil moisture skill in a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). Soil moisture skill (defined as the anomaly time series correlation coefficient R) is assessed using in situ observations in the continental United States at 37 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at 4 USDA Agricultural Research Service (“CalVal”) watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill of AMSR-E retrievals is R = 0.42 versus SCAN and R = 0.55 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R = 0.43 and R = 0.47, respectively, versus SCAN measurements. MERRA surface moisture skill is R = 0.56 versus CalVal measurements. Adding information from precipitation observations increases (surface and root zone) soil moisture skills by ΔR ~ 0.06. Assimilating AMSR-E retrievals increases soil moisture skills by ΔR ~ 0.08. Adding information from both sources increases soil moisture skills by ΔR ~ 0.13, which demonstrates that precipitation corrections and assimilation of satellite soil moisture retrievals contribute important and largely independent amounts of information.

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C. Bruce Baker
,
Michael Cosh
,
John Bolten
,
Mark Brusberg
,
Todd Caldwell
,
Stephanie Connolly
,
Iliyana Dobreva
,
Nathan Edwards
,
Peter E. Goble
,
Tyson E. Ochsner
,
Steven M. Quiring
,
Michael Robotham
,
Marina Skumanich
,
Mark Svoboda
,
W. Alex White
, and
Molly Woloszyn

Abstract

Soil moisture is a critical land surface variable, impacting the water, energy, and carbon cycles. While in situ soil moisture monitoring networks are still developing, there is no cohesive strategy or framework to coordinate, integrate, or disseminate these diverse data sources in a synergistic way that can improve our ability to understand climate variability at the national, state, and local levels. Thus, a national strategy is needed to guide network deployment, sustainable network operation, data integration and dissemination, and user-focused product development. The National Coordinated Soil Moisture Monitoring Network (NCSMMN) is a federally led, multi-institution effort that aims to address these needs by capitalizing on existing wide-ranging soil moisture monitoring activities, increasing the utility of observational data, and supporting their strategic application to the full range of decision-making needs. The goals of the NCSMMN are to 1) establish a national “network of networks” that effectively demonstrates data integration and operational coordination of diverse in situ networks; 2) build a community of practice around soil moisture measurement, interpretation, and application—a “network of people” that links data providers, researchers, and the public; and 3) support research and development (R&D) on techniques to merge in situ soil moisture data with remotely sensed and modeled hydrologic data to create user-friendly soil moisture maps and associated tools. The overarching mission of the NCSMMN is to provide coordinated high-quality, nationwide soil moisture information for the public good by supporting applications like drought and flood monitoring, water resource management, agricultural and forestry planning, and fire danger ratings.

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