Aaron A. Berg

Field observations of soil moisture variability across scales

In this study, over 36,000 ground-based soil moisture measurements collected during the SGP97, SGP99, SMEX02, and SMEX03 field campaigns were analyzed to characterize the behavior of soil moisture variability across scales. The field campaigns were conducted in Oklahoma and Iowa in the central USA. The Oklahoma study region is sub-humid with moderately rolling topography, while the Iowa study region is humid with low-relief topography. The relationship of soil moisture standard deviation, skewness and the coefficient of variation versus mean moisture content was explored at six distinct extent scales, ranging from 2.5 m to 50 km. Results showed that variability generally increases with extent scale. The standard deviation increased from 0.036 cm3/cm3 at the 2.5-m scale to 0.071 cm3/cm3 at the 50-km scale. The log standard deviation of soil moisture increased linearly with the log extent scale, from 16 m to 1.6 km, indicative of fractal scaling. The soil moisture standard deviation versus mean moisture content exhibited a convex upward relationship at the 800-m and 50-km scales, with maximum values at mean moisture contents of roughly 0.17 cm3/cm3 and 0.19 cm3/cm3, respectively. An empirical model derived from the observed behavior of soil moisture variability was used to estimate uncertainty in the mean moisture content for a fixed number of samples at the 800-m and 50-km scales, as well as the number of ground-truth samples needed to achieve 0.05 cm3/cm3 and 0.03 cm3/cm3 accuracies. The empirical relationships can also be used to parameterize surface soil moisture variations in land surface and hydrological models across a range of scales. To our knowledge, this is the first study to document the behavior of soil moisture variability over this range of extent scales using ground-based measurements. Our results will contribute not only to efficient and reliable satellite validation, but also to better utilization of remotely sensed soil moisture products for enhanced modeling and prediction.

The Second Phase of the Global Land–Atmosphere Coupling Experiment: Soil Moisture Contributions to Subseasonal Forecast Skill

AbstractThe second phase of the Global Land–Atmosphere Coupling Experiment (GLACE-2) is a multi-institutional numerical modeling experiment focused on quantifying, for boreal summer, the subseasonal (out to two months) forecast skill for precipitation and air temperature that can be derived from the realistic initialization of land surface states, notably soil moisture. An overview of the experiment and model behavior at the global scale is described here, along with a determination and characterization of multimodel “consensus” skill. The models show modest but significant skill in predicting air temperatures, especially where the rain gauge network is dense. Given that precipitation is the chief driver of soil moisture, and thereby assuming that rain gauge density is a reasonable proxy for the adequacy of the observational network contributing to soil moisture initialization, this result indeed highlights the potential contribution of enhanced observations to prediction. Land-derived precipitation forec...

Global Soil Moisture from Satellite Observations, Land Surface Models, and Ground Data: Implications for Data Assimilation

Abstract Three independent surface soil moisture datasets for the period 1979–87 are compared: 1) global retrievals from the Scanning Multichannel Microwave Radiometer (SMMR), 2) global soil moisture derived from observed meteorological forcing using the NASA Catchment Land Surface Model, and 3) ground-based measurements in Eurasia and North America from the Global Soil Moisture Data Bank. Time-average soil moisture fields from the satellite and the model largely agree in the global patterns of wet and dry regions. Moreover, the time series and anomaly time series of monthly mean satellite and model soil moisture are well correlated in the transition regions between wet and dry climates where land initialization may be important for seasonal climate prediction. However, the magnitudes of time-average soil moisture and soil moisture variability are markedly different between the datasets in many locations. Absolute soil moisture values from the satellite and the model are very different, and neither agrees...

Realistic Initialization of Land Surface States: Impacts on Subseasonal Forecast Skill

Forcing a land surface model (LSM) offline with realistic global fields of precipitation, radiation, and nearsurface meteorology produces realistic fields (within the context of the LSM) of soil moisture, temperature, and other land surface states. These fields can be used as initial conditions for precipitation and temperature forecasts with an atmospheric general circulation model (AGCM). Their usefulness is tested in this regard by performing retrospective 1-month forecasts (for May through September, 1979‐93) with the NASA Global Modeling and Assimilation Office (GMAO) seasonal prediction system. The 75 separate forecasts provide an adequate statistical basis for quantifying improvements in forecast skill associated with land initialization. Evaluation of skill is focused on the Great Plains of North America, a region with both a reliable land initialization and an ability of soil moisture conditions to overwhelm atmospheric chaos in the evolution of the meteorological fields. The land initialization does cause a small but statistically significant improvement in precipitation and air temperature forecasts in this region. For precipitation, the increases in forecast skill appear strongest in May through July, whereas for air temperature, they are largest in August and September. The joint initialization of land and atmospheric variables is considered in a supplemental series of ensemble monthly forecasts. Potential predictability from atmospheric initialization dominates over that from land initialization during the first 2 weeks of the forecast, whereas during the final 2 weeks, the relative contributions from the two sources are of the same order. Both land and atmospheric initialization contribute independently to the actual skill of the monthly temperature forecast, with the greatest skill derived from the initialization of both. Land initialization appears to contribute the most to monthly precipitation forecast skill.

Assessment of the SMAP Passive Soil Moisture Product

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 km Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 m3/m3 unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m3/m3.

Impact of bias correction to reanalysis products on simulations of North American soil moisture and hydrological fluxes

Simulating land surface hydrological states and fluxes requires a comprehensive set of atmospheric forcing data at consistent temporal and spatial scales. At the continental-to-global scale, such data are not available except in weather reanalysis products. Unfortunately, reanalysis products are often biased due to errors in the host weather forecast model. This paper explores whether the error in model predictions of the initial soil moisture status and hydrological fluxes can be minimized through a bias reduction scheme to the European Centre for Medium Range Weather Forecast and National Center for Environmental Prediction/National Center for Atmospheric Research reanalysis products. The bias reduction scheme uses both difference and ratio corrections based upon global observational data sets. Both the corrected and original forcing data were used to simulate land surface states and fluxes with a land surface model (LSM) over North America. Soil moisture, snow depth, and runoff output from the LSM are compared to observations to assess the impact of the bias correction on simulation accuracy. Results of this study demonstrate the sensitivity of LSMs to bias in the forcing data, and that implementation of a bias reduction scheme reduces errors to the simulation of soil moisture, runoff, and snow water equivalence. Accordingly, the initial soil moisture fields produced should be more representative of actual conditions, and therefore more useful to the climate modeling community. Results suggest that modelers using reanalysis products for forcing LSMs, in particular for the establishment of initial conditions, should consider a bias reduction strategy when preparing their input forcing fields.

Evaluation of 10 Methods for Initializing a Land Surface Model

Abstract Improper initialization of numerical models can cause spurious trends in the output, inviting erroneous interpretations of the earth system processes that one wishes to study. In particular, soil moisture memory is considerable, so that accurate initialization of this variable in land surface models (LSMs) is critical. The most commonly employed method for initializing an LSM is to spin up by looping through a single year repeatedly until a predefined equilibrium is achieved. The downside to this technique, when applied to continental- to global-scale simulations, is that regional annual anomalies in the meteorological forcing accumulate as artificial anomalies in the land surface states, including soil moisture. Nine alternative approaches were tested and compared using the Mosaic LSM and 15 yr of global meteorological forcing. Results indicate that the most efficient way to initialize an LSM, if possible and given that multiple years of preceding forcing are not available, is to use climatologi...

Spatial distribution of soil moisture over 6 and 30 cm depth, Mahurangi river catchment, New Zealand

Ground-based measurement of the spatial distribution of soil moisture can be difficult because sampling is essentially made at a point and the choice of both sample depth and sample spacing affects the interpretation of the measurements. Hydrological interest has generally been in soil moisture of the root zone. Microwave Remote Sensing methods are now available that allow the interpretation of spatial distributions of soil moisture, however, their signals respond to moisture in the upper few centimetres of soil. These instruments are still being developed, but one of the questions surrounding their application is how to interpret the surface moisture in a hydrological context. In this study we compare measurements of soil moisture in 0-30 cm of soil with those in 0-6 cm to examine how representative this surface measure is with regard to the root zone. Detailed spatial measurements of soil moisture were conducted at three pasture sites in the 50 km2 Mahurangi River catchment of northern New Zealand as part of a comprehensive hydrology project; MARVEX (MAhurangi River Variability EXperiment). In three field sites, on each of three occasions, field measurements were made using both 30 and 6 cm dielectric-based instruments. Spatial grids of several hundred moisture measurements were collected over 0-30 cm and compared with those collected simultaneously over 0-6 cm. Results indicate that temporal and spatial issues interfere with correlation of the two sets of series. Rapid wetting of 0-6 cm compared with 0-30 cm is seen following storm activity. Some evidence of the decoupling of moisture content response is also evident when sites are measured on days following a storm. Rapid, but not unrealistic, response to intense rainfall was also observed. Implications are that detailed and accurate knowledge of local soil conditions and a sound model of soil water redistribution are required before surface soil moisture measurements can be used to infer root zone behaviour. Such knowledge was not available in this study, from either published data or field observation. In this study, without suitable a priori knowledge, soil property information was found via calibration.

The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12): Prelaunch Calibration and Validation of the SMAP Soil Moisture Algorithms

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite is scheduled for launch in January 2015. In order to develop robust soil moisture retrieval algorithms that fully exploit the unique capabilities of SMAP, algorithm developers had identified a need for long-duration combined active and passive L-band microwave observations. In response to this need, a joint Canada-U.S. field experiment (SMAPVEX12) was conducted in Manitoba (Canada) over a six-week period in 2012. Several times per week, NASA flew two aircraft carrying instruments that could simulate the observations the SMAP satellite would provide. Ground crews collected soil moisture data, crop measurements, and biomass samples in support of this campaign. The objective of SMAPVEX12 was to support the development, enhancement, and testing of SMAP soil moisture retrieval algorithms. This paper details the airborne and field data collection as well as data calibration and analysis. Early results from the SMAP active radar retrieval methods are presented and demonstrate that relative and absolute soil moisture can be delivered by this approach. Passive active L-band sensor (PALS) antenna temperatures and reflectivity, as well as backscatter, closely follow dry down and wetting events observed during SMAPVEX12. The SMAPVEX12 experiment was highly successful in achieving its objectives and provides a unique and valuable data set that will advance algorithm development.

Reply to comment by H. Vereecken et al. on “Field observations of soil moisture variability across scales”

WATER RESOURCES RESEARCH, VOL. 44, W12602, doi:10.1029/2008WR007323, 2008 Reply to comment by H. Vereecken et al. on ‘‘Field observations of soil moisture variability across scales’’ James S. Famiglietti, 1 Dongryeol Ryu, 2 Aaron A. Berg, 3 Matthew Rodell, 4 and Thomas J. Jackson 2 Received 30 July 2008; revised 30 July 2008; accepted 15 September 2008; published 4 December 2008. Citation: Famiglietti, J. S., D. Ryu, A. A. Berg, M. Rodell, and T. J. Jackson (2008), Reply to comment by H. Vereecken et al. on ‘‘Field observations of soil moisture variability across scales,’’ Water Resour. Res., 44, W12602, doi:10.1029/2008WR007323. [ 1 ] We welcome and appreciate the comments of Vereecken et al. [2008] on our recent paper [Famiglietti et al., 2008]. Their comments provide the opportunity to reemphasize the complex nature of the processes driving the observed behavior of soil moisture variability across scales presented by Famiglietti et al. [2008]. [ 2 ] Vereecken et al. [2008] suggest that the spatial vari- ability of soil moisture content versus its field mean, as presented in Famiglietti et al. [2008], can be largely explained by the shape of soil moisture retention curves for a homogeneous soil, and, for a heterogeneous soil, it is related to soil variability of the constitutive model param- eters which control the shape of retention curves. As an example, Vereecken et al. [2008] show soil moisture content (q) versus soil water capacity C(q) and Dq on the basis of the van Genuchten model, which forms concave curves with peaks exiting between the two end-members (i.e., residual water content and porosity) of soil moisture. Indeed, the behavioral similarity between the variability observed by Famiglietti et al. [2008] and the curves calculated using stochastic theory of unsaturated flow [Vereecken et al., 2007, 2008] has encouraged us to explore the fundamental causality that may exist between them. And we agree with Vereecken et al. [2008] in that, with given uniformly distributed suction head in a homogeneous soil, soil mois- ture variability peaks in the medium range of mean soil moisture content, and that progressively decreasing unsatu- rated hydraulic conductivity implied in the moisture reten- tion curve plays an important role in reducing soil moisture variability toward the dry end of field mean soil moisture content. [ 3 ] However, we would like to point out that some of the basic assumptions underlying the stochastic theory are rarely if ever met in the real field conditions, which makes it difficult to directly link our observations to the theory. For Department of Earth System Science, University of California, Irvine, California, USA. Hydrology and Remote Sensing Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, Maryland, USA. Department of Geography, University of Guelph, Guelph, Ontario, Canada. Hydrological Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA. Copyright 2008 by the American Geophysical Union. 0043-1397/08/2008WR007323 instance, a heterogeneous soil in Vereecken et al. [2007] and Zhang et al. [1998] is defined as a uniform texture soil with hydraulic properties that are stationary random variables (e.g., lnK s , a, b). On the contrary, most regional-scale fields (e.g., 50-km-scale fields) in Famiglietti et al. [2008] contain more than one classes of soil texture, and the different drainage rates among the textural groups are an important source of high soil moisture variability in the midrange of mean soil moisture content. Another important source of peak variability in the midrange of soil moisture content is fractional rainfall coverage that typically occurs within regional-scale fields. In fact, Ryu and Famiglietti [2005] show that, at the regional scale, fractional rainfall has larger impact on soil moisture spatial variability than soil hetero- geneity. Even at local (