James S. Famiglietti

Satellite-based estimates of groundwater depletion in India

Groundwater is a primary source of fresh water in many parts of the world. Some regions are becoming overly dependent on it, consuming groundwater faster than it is naturally replenished and causing water tables to decline unremittingly. Indirect evidence suggests that this is the case in northwest India, but there has been no regional assessment of the rate of groundwater depletion. Here we use terrestrial water storage-change observations from the NASA Gravity Recovery and Climate Experiment satellites and simulated soil-water variations from a data-integrating hydrological modelling system to show that groundwater is being depleted at a mean rate of 4.0 ± 1.0 cm yr-1 equivalent height of water (17.7 ± 4.5 km3 yr-1) over the Indian states of Rajasthan, Punjab and Haryana (including Delhi). During our study period of August 2002 to October 2008, groundwater depletion was equivalent to a net loss of 109 km3 of water, which is double the capacity of India’s largest surface-water reservoir. Annual rainfall was close to normal throughout the period and we demonstrate that the other terrestrial water storage components (soil moisture, surface waters, snow, glaciers and biomass) did not contribute significantly to the observed decline in total water levels. Although our observational record is brief, the available evidence suggests that unsustainable consumption of groundwater for irrigation and other anthropogenic uses is likely to be the cause. If measures are not taken soon to ensure sustainable groundwater usage, the consequences for the 114,000,000 residents of the region may include a reduction of agricultural output and shortages of potable water, leading to extensive socioeconomic stresses.

Multiscale modeling of spatially variable water and energy balance processes

This paper presents the model development component of a body of research which addresses aggregation and scaling in multiscale hydrological modeling. Water and energy balance models are developed at the local and catchment scales and at the macroscale by aggregating a simple soil-vegetation-atmosphere transfer scheme (SVATS) across scales in a topographic framework. A spatially distributed approach is followed to aggregate the SVATS to the catchment scale. A statistical-dynamical approach is utilized to simplify the large-scale modeling problem and to aggregate the SVATS to the macroscale. The resulting macroscale hydrological model is proposed for use as a land surface parameterization in atmospheric models. It differs greatly from the current generation of land surface parameterizations owing to its simplified representation of vertical process physics and its statistical representation of horizontally heterogeneous runoff and energy balance processes. The spatially distributed model formulation is explored to understand the role of spatial variability in determining areal-average fluxes and the dynamics of hydrological processes. The simpler macroscale formulation is analyzed to determine how it represents these important dynamics, with implications for the parameterization of runoff and energy balance processes in atmospheric models.

Variability in surface moisture content along a hillslope transect: Rattlesnake Hill, Texas

Abstract Surface soil moisture content exhibits a high degree of spatial and temporal variability. The purpose of this study was (a) to characterize variations in moisture content in the 0–5 cm surface soil layer along a hillslope transect by means of intensive sampling in both space and time; and (b) to make inferences regarding the environmental factors that influence this variability. Over a period of seven months, soil moisture content was measured (gravimetric method) on a near-daily basis at 10 m intervals along a 200 m downslope transect at the Rattlesnake Hill field site in Austin, Texas. Results indicate that significant variability in soil moisture content exists along the length of the transect; that variability decreases with decreasing transectmean moisture content as the hillslope dries down following rain events; and that the dominant influences on moisture content variability are dependent upon the moisture conditions on the hillslope. While topographic and soil attributes operate jointly to redistribute soil water following storm events, under wet conditions, variability in surface moisture content is most strongly influenced by porosity and hydraulic conductivity, and under dry conditions, correlations are strongest to relative elevation, aspect and clay content. Consequently, the dominant influence on soil moisture variability gradually changes from soil heterogeneity to joint control by topographic and soil properties as the transect dries following significant rain events.

Satellites measure recent rates of groundwater depletion in California's Central Valley

In highly-productive agricultural areas such as Californias Central Valley, where groundwater often supplies the bulk of the water required for irrigation, quantifying rates of groundwater depletion remains a challenge owing to a lack of monitoring infrastructure and the absence of water use reporting requirements. Here we use 78 months (October, 2003–March, 2010) of data from the Gravity Recovery and Climate Experiment satellite mission to estimate water storage changes in Californias Sacramento and San Joaquin River Basins. We find that the basins are losing water at a rate of 31.0 ± 2.7 mm yr−1 equivalent water height, equal to a volume of 30.9 km3 for the study period, or nearly the capacity of Lake Mead, the largest reservoir in the United States. We use additional observations and hydrological model information to determine that the majority of these losses are due to groundwater depletion in the Central Valley. Our results show that the Central Valley lost 20.4 ± 3.9 mm yr−1 of groundwater during the 78-month period, or 20.3 km3 in volume. Continued groundwater depletion at this rate may well be unsustainable, with potentially dire consequences for the economic and food security of the United States.

Integration of soil moisture remote sensing and hydrologic modeling using data assimilation

The feasibility of synthesizing distributed fields of soil moisture by the novel application of four-dimensional data assimilation (4DDA) applied in a hydrological model is explored. Six 160-km2 push broom microwave radiometer (PBMR) images gathered over the Walnut Gulch experimental watershed in southeast Arizona were assimilated into the Topmodel-based Land-Atmosphere Transfer Scheme (TOPLATS) using several alternative assimilation procedures. Modification of traditional assimilation methods was required to use these high-density PBMR observations. The images were found to contain horizontal correlations that imply length scales of several tens of kilometers, thus allowing information to be advected beyond the area of the image. Information on surface soil moisture also was assimilated into the subsurface using knowledge of the surface- subsurface correlation. Newtonian nudging assimilation procedures are preferable to other techniques because they nearly preserve the observed patterns within the sampled region but also yield plausible patterns in unmeasured regions and allow information to be advected in time.

Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water

Monitoring Earths terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (∼10–100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earths terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface-subsurface interactions due to fine-scale topography and vegetation; improved representation of land-atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 109 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a “grand challenge” to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

Ground‐based investigation of soil moisture variability within remote sensing footprints During the Southern Great Plains 1997 (SGP97) Hydrology Experiment

Surface soil moisture content is highly variable in both space and time. While remote sensing provides an effective methodology for mapping surface moisture content over large areas, it averages within-pixel variability thereby masking the underlying heterogeneity observed at the land surface. This variability must be better understood in order to rigorously evaluate sensor performance and to enhance the utility of the larger-scale remotely sensed averages by quantifying the underlying variability that remote sensing cannot record explicitly. In support of the Southern Great Plains 1997 (SGP97) Hydrology Experiment (a surface soil moisture mapping mission conducted between June 18 and July 17, 1997, in central Oklahoma) an investigation was conducted to characterize soil moisture variability within remote sensing footprints (approximately 0.64 km2) with more certainty than would be afforded with conventional gravimetric moisture content sampling. Nearly every day during the experiment period, portable impedance probes were used to intensively monitor volumetric moisture content in the 0- to 6-cm surface soil layer at six footprint-sized fields scattered over the SGP97 study area. A minimum of 49 daily moisture content measurements were made on most fields. Higher-resolution grid and transect data were also collected periodically. In total, more than 11,000 impedance probe measurements of volumetric moisture content were made at the six sites by over 35 SGP97 participants. The wide spatial distribution of the sites, combined with the intensive, near-daily monitoring, provided a unique opportunity (relative to previous smaller-scale and shorter-duration soil moisture studies) to characterize variations in surface moisture content over a range of wetness conditions. In this paper the range and temporal dynamics of the variability in moisture content within each of the six fields are described, as are general relationships between the variability and footprint-mean moisture content. Results indicate that distinct differences in mean moisture content between the six sites are consistent with variations in soil type, vegetation cover, and rainfall gradients. Within fields the standard deviation, coefficient of variation, skewness, and kurtosis increased with decreasing moisture content; the distribution of surface moisture content evolved from negatively skewed/nonnormal under very wet conditions, to normal in the midrange of mean moisture content, to positively skewed/nonnormal under dry conditions; and agricultural practices of row tilling and terracing were shown to exert a major control on observed moisture content variations. Results presented here can be utilized to better evaluate sensor performance, to extrapolate estimates of subgrid-scale variations in moisture content across the entire SGP97 region, and in the parameterization of soil moisture dynamics in hydrological and land surface models.

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.

Basin scale estimates of evapotranspiration using GRACE and other observations

Evapotranspiration is integral to studies of the Earth system, yet it is difficult to measure on regional scales. One estimation technique is a terrestrial water budget, i.e., total precipitation minus the sum of evapotranspiration and net runoff equals the change in water storage. Gravity Recovery and Climate Experiment (GRACE) satellite gravity observations are now enabling closure of this equation by providing the terrestrial water storage change. Equations are presented here for estimating evapotranspiration using observation based information, taking into account the unique nature of GRACE observations. GRACE water storage changes are first substantiated by comparing with results from a land surface model and a combined atmospheric-terrestrial water budget approach. Evapotranspiration is then estimated for 14 time periods over the Mississippi River basin and compared with output from three modeling systems. The GRACE estimates generally lay in the middle of the models and may provide skill in evaluating modeled evapotranspiration.

Analysis of terrestrial water storage changes from GRACE and GLDAS

Since March 2002, the Gravity Recovery and Climate Experiment (GRACE) has provided first estimates of land water storage variations by monitoring the time-variable component of Earths gravity field. Here we characterize spatial-temporal variations in terrestrial water storage changes (TWSC) from GRACE and compare them to those simulated with the Global Land Data Assimilation System (GLDAS). Additionally, we use GLDAS simulations to infer how TWSC is partitioned into snow, canopy water and soil water components, and to understand how variations in the hydrologic fluxes act to enhance or dissipate the stores. Results quantify the range of GRACE-derived storage changes during the studied period and place them in the context of seasonal variations in global climate and hydrologic extremes including drought and flood, by impacting land memory processes. The role of the largest continental river basins as major locations for freshwater redistribution is highlighted. GRACE-based storage changes are in good agreement with those obtained from GLDAS simulations. Analysis of GLDAS-simulated TWSC illustrates several key characteristics of spatial and temporal land water storage variations. Global averages of TWSC were partitioned nearly equally between soil moisture and snow water equivalent, while zonal averages of TWSC revealed the importance of soil moisture storage at low latitudes and snow storage at high latitudes. Evapotranspiration plays a key role in dissipating globally averaged terrestrial water storage. Latitudinal averages showed how precipitation dominates TWSC variations in the tropics, evapotranspiration is most effective in the midlatitudes, and snowmelt runoff is a key dissipating flux at high latitudes. Results have implications for monitoring water storage response to climate variability and change, and for constraining land model hydrology simulations.