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The ever increasing pace of computational power, along with continued advances in measurement technologies and improvements in process understanding has stimulated the development of increasingly complex hydrologic models that simulate soil moisture flow, groundwater recharge, surface runoff, root water uptake, and river discharge at different spatial and temporal scales. Reconciling these high-order system models with perpetually larger volumes of field data is becoming more and more difficult, particularly because classical likelihood-based fitting methods lack the power to detect and pinpoint deficiencies in the model structure. Gupta et al. (2008) has recently proposed steps (amongst others) toward the development of a more robust and powerful method of model evaluation. Their diagnostic approach uses signature behaviors and patterns observed in the input-output data to illuminate to what degree a representation of the real world has been adequately achieved and how the model should be improved for the purpose of learning and scientific discovery. In this paper, we introduce approximate Bayesian computation (ABC) as a vehicle for diagnostic model evaluation. This statistical methodology relaxes the need for an explicit likelihood function in favor of one or multiple different summary statistics rooted in hydrologic theory that together have a clearer and more compelling diagnostic power than some average measure of the size of the error residuals. Two illustrative case studies are used to demonstrate that ABC is relatively easy to implement, and readily employs signature based indices to analyze and pinpoint which part of the model is malfunctioning and in need of further improvement. [ABSTRACT FROM AUTHOR]

General filtering approaches in hydrologic data assimilation, such as the ensemble (Caiman filter (EnKF), are based on the assumption that uncertainty of the current background prediction can be reduced by correcting errors in the state variables at the same time step. However, this assumption may not be valid when assimilating stream discharge into hydrological models to correct soil moisture storage due to the time lag between the soil moisture and the discharge. In this paper, we explore the utility of an ensemble Kalman smoother (EnKS) for addressing this time-lag issue. The EnKF and the EnKS are compared for two different updating schemes with the probability distributed model (PDM) via synthetic experiments: (i) updating soil moisture only and (ii) updating soil moisture and routing states simultaneously. The results show that the EnKS is superior to the EnKF when only soil moisture is updated, while the EnKS and the EnKF exhibit similar results when both soil moisture and routing storages are updated. This suggests that the EnKS can better improve the streamflow forecasting for models that do not adopt storage-based routing schemes (e.g., unit-hydrograph-based routing). For models with dynamic routing stores, errors in soil moisture are transferred to the routing stores, which can be corrected effectively by real-time filters. The EnKS-based soil moisture updating scheme is also tested with the GR4H model, for which unit-hydrograph-based routing is used. The result confirms that the EnKS is superior to the EnKF in improving both soil moisture and streamflow forecasting. [ABSTRACT FROM AUTHOR]

The covariability of soil moisture with soil, vegetation, topography, and precipitation is linked by physical relationships. The influence of each of these interdependent physical controls on soil moisture spatial distribution depends on the nature of heterogeneity present in the domain and evolves with time and scale. This paper investigates the effect of three physical controls, i.e., topography (slope), vegetation (type), and soil (texture), on soil moisture spatial distribution in the Little Washita and Walnut Creek watersheds in Oklahoma and Iowa, respectively, at two support scales. Point-support-scale data collected from four soil moisture campaigns (SMEX02, SMEX03, SMEX05, and CLASIC07) and airborne-scale data from three soil moisture campaigns (SGP97, SGP99, and SMEX02) were used in this analysis. The effect of different physical controls on the spatial mean and variability of soil moisture was assessed using Kruskal-Wallis and Shannon entropy respectively. It was found that at both (point and airborne) support scales, nonuniform precipitation (forcing) across the domain can mask the effect of the dominant physical controls on the soil moisture distribution. In order to isolate land-surface controls from the impact of forcing, the effect of precipitation variability was removed. After removing the effect of precipitation variability, it was found that for most soil moisture conditions, soil texture as opposed to vegetation and topography is the dominant physical control at both the point and airborne scales in Iowa and Oklahoma. During a very wet year (2007), however, the effect of topography on the soil moisture spatial variability overrides the effect of soil texture at the point support scale. These findings are valuable for developing any physically based scaling algorithms to upscale or downscale soil moisture between the point and watershed scales in the studied watersheds in humid and subhumid regions of the Great Plains of USA. These results may also be used in designing effective soil moisture field campaigns. [ABSTRACT FROM AUTHOR]

[i] The surface energy fluxes and related évapotranspiration processes across the Indus Basin were estimated for the hydrological year 2007 using satellite measurements. The new ETLook remote sensing model (version 1) infers information on actual Evaporation (E) and actual Transpiration (T) from combined optical and passive microwave sensors, which can observe the land-surface even under persistent overcast conditions. A two-layer Penman-Monteith equation was applied for quantifying soil and canopy evaporation. The novelty of the paper is the computation of ? and ? across a vast area (116.2 million ha) by using public domain microwave data that can be applied under all weather conditions, and for which no advanced input data are required. The average net radiation for the basin was estimated as being 112 Wm~2. The basin average sensible, latent and soil heat fluxes were estimated to be 80, 32, and 0 Wm"2, respectively. The average évapotranspiration (ET) and evaporative fraction were 1.2 mm d~1 and 0.28, respectively. The basin wide ET was 496 ± 16.8 km3 yr-1. Monte Carlo analysis have indicated 3.4% error at 95% confidence interval for a dominant land use class. Results compared well with previously conducted soil moisture, lysimeter and Bowen ratio measurements at field scale (R2 = 0.70 ; RMSE = 0.45 mm d~1 ; RE = -11.5% for annual ET). ET results were also compared against earlier remote sensing and modeling studies for various regions and provinces in Pakistan (R2 = 0.76; RMSE = 0.29 mmd~1 ; RE = 6.5% for annual ET). The water balance for all irrigated areas together as one total system in Pakistan and India (26.02 million ha) show a total ET value that is congruent with the ET value from the ETLook surface energy balance computations. An unpublished validation of the same ETLook model for 23 jurisdictional areas covering the entire Australian continent showed satisfactory results given the quality of the watershed data and the diverging physiographic and climatic conditions (R2 = 0.70 ; RMSE = 0.31 mmd~1 ; RE = -2.8% for annual ET). Eight day values of latent heat fluxes in Heibei (China) showed a good resemblance (R2 = 0.92 ; RMSE = 0.04 mm d~1 ; RE = 9.5% for annual ET). It is concluded that ETLook is a novel model that can be operationalized further--especially after improving the preprocessing of spacebome soil moisture data. This preprocessing includes (1) downscaling of topsoil moisture from 25 to 1 km pixels, and (2) translation of topsoil moisture into subsoil moisture values. [ABSTRACT FROM AUTHOR]

This paper describes a soil moisture data set from the 82,000 km2 Murrumbidgee River Catchment in southern New South Wales, Australia. Data have been archived from the Murrumbidgee Soil Moisture Monitoring Network (MSMMN) since its inception in September 2001. The Murrumbidgee Catchment represents a range of conditions typical of much of temperate Australia, with climate ranging from semiarid to humid and land use including dry land and irrigated agriculture, remnant native vegetation, and urban areas. There are a total of 38 soil moisture-monitoring sites across the Murrumbidgee Catchment, with a concentration of sites in three subareas. The data set is composed of 0-5 (or 0-8), 0-30, 30-60, and 60-90 cm average soil moisture, soil temperature, precipitation, and other land surface model forcing at all sites, together with other ancillary data. These data are available on the World Wide Web at http ://www.oznet.org.au. [ABSTRACT FROM AUTHOR]

Estimates of effective parameters for unsaturated flow models are typically based on observations taken on length scales smaller than the modeling scale. This complicates parameter estimation for heterogeneous soil structures. In this paper we attempt to account for soil structure not present in the flow model by using so-called external error models, which correct for bias in the likelihood function of a parameter estimation algorithm. The performance of external error models are investigated using data from three virtual reality experiments and one real world experiment. All experiments are multistep outflow and inflow experiments in columns packed with two sand types with different structures. First, effective parameters for equivalent homogeneous models for the different columns were estimated using soil moisture measurements taken at a few locations. This resulted in parameters that had a low predictive power for the averaged states of the soil moisture if the measurements did not adequately capture a representative elementary volume of the heterogeneous soil column. Second, parameter estimation was performed using error models that attempted to correct for bias introduced by soil structure not taken into account in the first estimation. Three different error models that required different amounts of prior knowledge about the heterogeneous structure were considered. The results showed that the introduction of an error model can help to obtain effective parameters with more predictive power with respect to the average soil water content in the system. This was especially true when the dynamic behavior of the flow process was analyzed. [ABSTRACT FROM AUTHOR]

This work focuses on the description and the use of the probability density functions (pdfs) of travel, residence and évapotranspiration times, which are comprehensive descriptors of the fate of rainfall water particles traveling through catchments, and provide key information on hydrologie flowpaths, partitioning of precipitation, circulation and turnover of pollutants. Exploiting some analytical solutions to the transport problem derived by Botter et al. (2011), this paper analyzes the features of travel, residence and évapotranspiration time pdfs resulting from different assumptions on the mixing processes occurring during streamflow formation and plant uptake (namely, complete mixing and translatory flow). The ensuing analytical solutions are analyzed through numerical Monte Carlo simulations of a stochastic model of soil moisture and streamflow dynamics. Travel and residence time pdfs are shown to be time-variant as they mirror the variability of the relevant hydrological fluxes. In particular, the temporal fluctuations of the mean residence time are shown to reflect rainfall dynamics, whereas the variability of the mean travel time is chiefly driven by streamflow dynamics, with lower frequency and higher amplitude fluctuations. Dry climates enhance the effect of the type of mixing on catchment transport features (e.g., mean travel times and seasonal dynamics of stream concentrations). The implications for the interpretation of tracer experiments are also discussed, showing through specific examples that models disregarding nonstationarity may significantly misestimate travel time pdfs. [ABSTRACT FROM AUTHOR]

Soil hydraulic parameters were upscaled from a 30 m resolution to a 1 km resolution using a new aggregation scheme (described in the companion paper) where the scale parameter was based on the topography. When soil hydraulic parameter aggregation or upscaling schemes ignore the effect of topography, their application becomes limited at hillslope scales and beyond. where topography plays a dominant role in soil deposition and formation. Hence the new upscaling algorithm was tested at the hilislope scale (1 km) across two locations: (I) the Little Washita watershed in Oklahoma, and (2) the Walnut Creek watershed in Iowa. The watersheds were divided into pixels of 1 km resolution and the effective soil hydraulic parameters obtained for each pixel. Each pixel/domain was then simulated using the physically based HYDRUS-3-D modeling platform. In order to account for the surface (runoff/on) and subsurface fluxes between pixels, an algorithm to route infiltration-excess runoff onto downstream pixels at daily time steps and to update the soil moisture states of the downstream pixels was applied. Simulated soil moisture states were compared across scales, and the coarse scale values compared against the airborne soil moisture data products obtained during the hydrology experiment field campaign periods (SGP97 and SMEXO2) for selected pixels with different topographic complexities, soil distnbuttons, and land cover. Results from these comparisons show good correlations between simulated and observed soil moisture states across time, topographic variations, location, elevation, and land cover. Stream discharge comparisons made at two gauging stations in the Little Washita watershed also provide reasonably good results as to the suitability of the upscaling algorithm used. Based only on the topography of the domain, the new upscaling algorithm was able to provide coarse resolution values for soil hydraulic parameters which effectively captured the variations in soil moisture across the watershed domains. [ABSTRACT FROM AUTHOR]

Recently, Crow et al. (2009) developed an algorithm for enhancing satellite-based land rainfall products via the assimilation of remotely sensed surface soil moisture retrievals into a water balance model. As a follow-up, this paper describes the benefits of modifying their approach to incorporate more complex data assimilation and land surface modeling methodologies. Specific modifications improving rainfall estimates are assembled into the Soil Moisture Analysis Rainfall Tool (SMART), and the resulting algorithm is applied outside the contiguous United States for the first time, with an emphasis on West African sites instrumented as part of the African Monsoon Multidisciplinary Analysis experiment. Results demonstrate that the SMART algorithm is superior to the Crow et al. baseline approach and is capable of broadly improving coarse-scale rainfall accumulations measurements with low risk of degradation. Comparisons with existing multisensor, satellite-based precipitation data products suggest that the introduction of soil moisture information from the Advanced Microwave Scanning Radiometer via SMART provides as much coarse-scale (3 day, 1°) rainfall accumulation information as thermal infrared satellite observations and more information than monthly rain gauge observations in poorly instrumented regions. [ABSTRACT FROM AUTHOR]

Relative permeability of the nonwetting phase in a multiphase flow in porous media is a function of phase saturation. Specific expressions of this function are commonly determined by combining soil water retention curves with relative nonwetting phase permeability models. Experimental evidence suggests that the relative permeability of the nonwetting phase can be significantly overestimated by the existing relative permeability models. A new model for the prediction of relative nonwetting phase permeability from soil water retention curves is proposed in this paper. A closed form expression can be obtained in combination with soil water retention curves. The model is mathematically simple and can easily and efficiently be implemented in numerical models of multiphase flow processes in porous media. The predicting capability of the proposed model is contrasted with well-supported models by comparing the measured and predicted relative air permeability data for 11 soils, representing a wide range of soil textures, from sand to silty clay loam. In most of the cases the proposed model improves the agreement between the predicted relative air permeability and the measured data. [ABSTRACT FROM AUTHOR]

In the shallow subsurface immediately below the land-atmosphere interface, it is widely recognized that the movement of water vapor is closely coupled to thermal processes. However, their mutual interactions are rarely considered in most soil water modeling efforts or in practical applications where it becomes necessary to understand the spatial and temporal distribution of soil moisture. The validation of numerical models that are designed to capture these processes is difficult due to the scarcity of field or laboratory data with accurately known hydraulic and thermal parameters of soils, limiting the testing and refinement of heat and water transfer theories. The goal of this paper is to perform controlled experiments under transient conditions of soil moisture and temperature and use this data to test existing theories and develop appropriate numerical models. Water vapor flow under varying temperature gradients was implemented on the basis of a concept that allows nonequilibrium liquid/gas phase change with gas phase vapor diffusion. To validate this new approach, we developed a long column apparatus equipped with a network of sensors and generated data under well-controlled thermal boundary conditions at the soil surface. The nonequilibrium approach yielded good agreement with the experimental results, validating the hypothesis that transport in the gas phase is better suited to be modeled with nonequilibrium liquid/gas phase change for highly transient field conditions where the thermal conditions at the land-atmosphere interface are constantly changing. [ABSTRACT FROM AUTHOR]

This paper presents time stability analyses of soil moisture at different spatial measurement support scales (point scale and airborne remote sensing (RS) footprint scale 800 m × 800 m) in two different hydroclimatic regions. The data used in the analyses consist of in situ and passive microwave remotely sensed soil moisture data from the Southern Great Plains Hydrology Experiments 1997 and 1999 (SGP97 and SGP99) conducted in the Little Washita (LW) watershed, Oklahoma, and the Soil Moisture Experiments 2002 and 2005 (SMEX02 and SMEX05) in the Walnut Creek (WC) watershed, Iowa. Results show that in both the regions soil properties (i.e., percent silt, percent sand, and soil texture) and topography (elevation and slope) are significant physical controls jointly affecting the spatiotemporal evolution and time stability of soil moisture at both point and footprint scales. In Iowa, using point-scale soil moisture measurements, the WC11 field was found to be more time stable (TS) than the WC12 field. The common TS points using data across the 3 year period (2002-2005) were mostly located at moderate to high elevations in both the fields. Furthermore, the soil texture at these locations consists of either loam or clay loam soil. Drainage features and cropping practices also affected the field-scale soil moisture variability in the WC fields. In Oklahoma, the field having a flat topography (LW21) showed the worst TS features compared to the fields having gently rolling topography (LW03 and LW13). The LW13 field (silt loam) exhibited better time stability than the LW03 field (sandy loam) and the LW21 field (silt loam). At the RS footprint scale, in Iowa, the analysis of variance (ANOVA) tests show that the percent clay and percent sand are better able to discern the TS features of the footprints compared to the soil texture. The best soil indicator of soil moisture time stability is the loam soil texture. Furthermore, the hilltops (slope ∼0%-0.45%) exhibited the best TS characteristics in Iowa. On the other hand, in Oklahoma, ANOVA results show that the footprints with sandy loam and loam soil texture are better indicators of the time stability phenomena. In terms of the hillslope position, footprints with mild slope (0.93%-1.85%) are the best indicators of TS footprints. Also, at both point and footprint scales in both the regions, land use-land cover type does not influence soil moisture time stability. [ABSTRACT FROM AUTHOR]

The forest floor in many ecosystems consists of a partially decomposed organic layer (duff), which together with the litter layer comprises the boundary between the atmosphere and the mineral soil. Processes controlling the duff water budget during dry periods (which occur during most of the summer) were investigated using field monitoring, field flow exclusion manipulations, and coupled, multiphasic water and heat budget modeling. The objective of this paper is to model the significant processes that govern the dynamics of the duff water budget during drying. During dry periods the moisture content of the duff's F layer cycles diurnally with minimal moisture movement between the duff and mineral soil. Field exclusion of dew, lateral flow, and mineral soil flow suggests that diurnal drying cycles during the dry period are driven by diurnal atmospheric energy fluxes leading to coupled heat and mass fluxes within the duff. The fine root system and lateral flow do not typically influence drying. TOUGH2 was used to develop a one-dimensional, multiphasic (both liquid and vapor) coupled water and heat budget model which confirmed that the vertical moisture fluxes lead to diurnal cycles. The model reproduced duff drying patterns with Nash-Sutcliffe efficiencies and R2 values greater than 0.910 and 0.970, respectively. Wavelet analysis indicates that the model and observed diurnal cycles in the upper layer's moisture contents are correlated at the 24 h scale. A model flux analysis reveals that lateral fluxes smaller than approximately 360 mm3 h−1 would have little influence on the pattern of drying in the duff layer. Fluxes larger than approximately 5% of the total evaporative flux would slow duff drying and lead to behavior not observed in the field. [ABSTRACT FROM AUTHOR]

The Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency was successfully launched in November 2009 to provide global surface soil moisture and sea surface salinity maps. The SMOS single payload is the Microwave Imaging Radiometer by Aperture Synthesis (MIRAS), an L band two-dimensional aperture synthesis interferometric radiometer with multiangular and polarimetric imaging capabilities. SMOS-derived soil moisture products are expected to have an accuracy of 0.04 m3/m3 over 50 × 50 km2 and a revisit time of 3 days. Previous studies have remarked the necessity of combining SMOS brightness temperatures with auxiliary data to achieve the required accuracy. However, the required auxiliary data and optimal soil moisture retrieval setup need yet to be optimized. Also, the satellite operation mode (dual polarization or full polarimetric) is an open issue to be addressed during the commissioning phase activities. In this paper, an in-depth study of the different retrieval configurations and ancillary data needed for the retrieval of soil moisture from future SMOS observations is presented. A dedicated L2 Processor Simulator software has been developed to obtain soil moisture estimates from SMOS-like brightness temperatures generated using the SMOS End-to-End Performance Simulator (SEPS). Full-polarimetric brightness temperatures are generated in SEPS, and soil moisture retrievals are performed using vertical ( Tvv) and horizontal ( Thh) brightness temperatures and using the first Stokes parameter ( T I). Results show the accuracy obtained with the different retrieval setups for four main surface conditions combining wet and dry soils with bare and vegetation-covered surfaces. Soil moisture retrievals using T I exhibit a significantly better performance than using Thh and Tvv in all scenarios, which indicates that the dual-polarization mode should not be disregarded. The uncertainty of the ancillary data used in the minimization process and its effect on the retrievals is thoroughly evaluated, and an optimum soil moisture retrieval configuration is devised. [ABSTRACT FROM AUTHOR]

The present paper proposes an empirical approach for the modeling of vegetation development, using moisture measurements only. The study is based simply on the use of two databases: one containing soil moisture products derived from ERS scatterometer data over the period 1991-2006 and the other containing normalized difference vegetation indices (NDVI) derived from advanced very high resolution radiometer over the period 1991-2000. The study is applied over the Kairouan plain, the central semiarid region of Tunisia (North Africa). Soil moisture products were first validated on the basis of comparisons with Global Soil Wetness Project, Phase 2 Data, outputs and rainfall events. The soil moisture distribution during the rainy period between October and May is described and is found to be correlated with the vegetation dynamics estimated using the NDVI products. Finally, a semiempirical model is proposed, based on satellite moisture and NDVI products, which allows the NDVI value to be estimated for a period of 1 month during the rainy season as a function of the moisture profile estimations obtained during the previous months. This approach could prove very useful and provide a simple tool for the modeling of vegetation dynamics during rainy seasons in semiarid regions. [ABSTRACT FROM AUTHOR]

In groundwater-dependent ecosystems, interactions between rainfall, water table fluctuations, and vegetation are exerted through the soil water content. The dynamics of soil moisture, in fact, are strongly coupled to fluctuations of the water table and, together, they control the overall ecosystem dynamics. We propose here a simple process-based stochastic model for the study of soil moisture dynamics at a generic depth, to complement the stochastic model of water table depth presented in the companion paper. The model presented here is based on a local and depth-dependent water balance driven by stochastic rainfall (marked Poisson noise) and accounting for processes such as rainfall infiltration, root water uptake, and capillary rise. We obtain a semianalytical formulation of the stationary probability distribution of soil water content at different depths, which is studied for different values of soil, climate, and vegetation parameters. The probability distributions are used to investigate the ecohydrology of groundwater-dependent ecosystems, including the quantitative description of the vegetation-water table-soil moisture interplay and the probabilistic analysis of root water uptake. [ABSTRACT FROM AUTHOR]

In this paper we analyze the probabilistic dynamics resulting from a simplified model of soil moisture and nitrate mass in relatively arid environments, which accounts for relevant hydrologic and biogeochemical processes and for the random characters of rainfall. The soil nitrate balance includes uptake through transpiration, mineralization, nitrification, and denitrification. To allow an exact mathematical treatment, all nitrate fluxes are assumed to be linear functions of the state variables, namely, soil moisture and nitrate mass stored in soils. The range of applicability of the above simplification is investigated by means of numerical Monte Carlo simulations, showing that the linear approach is meaningful in relatively arid environments, where soil moisture contents are generally low. The moment-generating function of the joint probability distribution (pdf) of soil nitrate and water content is derived, thereby allowing a linkage between the main features of soil nitrate statistics and the underlying soil, vegetation, rainfall, and biogeochemical parameters. Exact expressions for the moments of the nitrate pdf and for the covariance of soil moisture and nitrate mass are derived. The pdf of the nitrate mass in storage within the soil is shown to be reasonably approximated by a gamma distribution in many cases of practical interest. Interestingly, nitrogen limitation for the ecosystem is shown to be directly related to low values of the product between the rate of rainfall arrivals and the characteristic time of nitrate removal. [ABSTRACT FROM AUTHOR]

In a companion paper, a conceptual model was presented to predict two important vegetation parameters from climatic constraints in water limited conditions, notably photosynthetic capacity and internal carbon dioxide concentration. In this study, the model is evaluated using data of four experimental forest plots in sub-Mediterranean Slovenia which were selected for their topography induced differences in climate and contrasting vegetation characteristics. Data were collected during a regular (2004) and an exceptionally dry year (2003). Measurements showed that photosynthetic capacity decreases with vapor pressure deficit, and internal carbon dioxide concentration correlates positively with available water. Variations in soil water storage at the start of the dry season and vapor pressure deficit during the dry season are responsible for a large part of these differences. Winter precipitation has a large effect on the shape of the seasonal course of transpiration during the following season. The model explained observed differences among sites and years in photosynthetic capacity and the seasonal cycle of transpiration. Although the magnitude of calculated optimum internal carbon dioxide concentrations agreed with observations, the model could not explain observed differences in internal carbon dioxide concentration or the correlation between internal carbon dioxide concentration and water availability. The optimality hypothesis, despite its limitations, can be used to predict the seasonal cycle of transpiration. [ABSTRACT FROM AUTHOR]

Two field data sets are used to model near-surface soil temperature profiles in a bare soil. It is shown that the commonly used solutions to the heat flow equations by Van Wijk perform well when applied at deeper soil layers, but result in large errors when applied to near surface layers, where more extreme variations in temperature occur. The reason for this is that these approaches do not consider heat sources or sinks below the surface. This paper proposes a new approach for modeling the surface soil temperature profiles from a single observation depth. This approach consists of two parts: 1) modeling an instantaneous ground flux profile based on net radiation and the ground heat flux at 5 cm depth; and 2) use of this ground heat flux profile to extrapolate a single temperature observation to a complete surface temperature profile. The new model is validated under different field and weather conditions showing low RMS errors of 1-3 K for wet to dry conditions. Finally, the proposed model is tested under limitations in input data that are associated with remote sensing applications. It is shown that these limitations result in only small increases in the overall error. This approach may be useful for satellite-based global energy balance applications. [ABSTRACT FROM AUTHOR]

This paper describes the data set from the 6540-km2 Goulburn River experimental catchment in New South Wales, Australia. Data have been archived from this experimental catchment since its inception in September 2002. Land use in the northern half of the catchment is predominantly cropping and grazing on basalt-derived soils, with the south being cattle and sheep grazing on sandstone-derived soils; only the floodplains are cleared of trees in the south. Monitoring sites are mainly concentrated in the nested Merriwa (651 km2) and Krui (562 km2) subcatchments in the northern half of this experimental catchment with a few monitoring sites located in the south. The data set comprises soil temperature and moisture profile measurements from 26 locations; meteorological data from two automated weather stations (data from a further three stations are available from other sources) including precipitation, atmospheric pressure, air temperature and relative humidity, wind speed and direction, soil heat flux, and up- and down-welling short- and long-wave radiation; streamflow observations at five nested locations (data from a further three locations are available from other sources); a total of three surface soil moisture maps across a 40 km × 50 km region in the north from ∼200 measurement locations during intensive field campaigns; and a high-resolution digital elevation model (DEM) of a 175-ha microcatchment in the Krui catchment. These data are available on the World Wide Web at http://www.sasmas.unimelb.edu.au. [ABSTRACT FROM AUTHOR]

Observations of hydrological response often exhibit considerable scatter that is difficult to interpret. In this paper, we examine runoff production of 53 sprinkling experiments on the water-repellent soils in the southern Alps of Switzerland; simulated plot scale tracer transport in the macroporous soils at the Weiherbach site, Germany; and runoff generation data from the 2.3-km2 Tannhausen catchment, Germany, that has cracking soils. The response at the three sites is highly dependent on the initial soil moisture state as a result of the threshold dynamics of the systems. A simple statistical model of threshold behavior is proposed to help interpret the scatter in the observations. Specifically, the model portrays how the inherent macrostate uncertainty of initial soil moisture translates into the scatter of the observed system response. The statistical model is then used to explore the asymptotic pattern of predictability when increasing the number of observations, which is normally not possible in a field study. Although the physical and chemical mechanisms of the processes at the three sites are different, the predictability patterns are remarkably similar. Predictability is smallest when the system state is close to the threshold and increases as the system state moves away from it. There is inherent uncertainty in the response data that is not measurement error but is related to the observability of the initial conditions. [ABSTRACT FROM AUTHOR]

The paper aims at analyzing subsurface flow and streamflow generation in steep hillslopes after rainfall. The principal scope of the work is to assess the role played by the spatial variability of the hydraulic properties on the subsurface flow patterns and hillslope discharge. We try to address this task through a three-dimensional numerical model which solves the Richards equation under prescribed initial and boundary conditions. Along an established practice, the relevant hydraulic parameters are modeled as random space functions with given statistical properties. Realistic features of the spatially variable formation parameters and the precipitation data are incorporated into the simulation model. It is found that spatial heterogeneity has a significant impact on the movement of water in the hillslope and the streamflow generation. In particular, the heterogeneous systems are more sensitive to rain input and display an increase of both the water table and saturation which is faster than the homogeneous case. The same is for water velocity, which is everywhere larger when heterogeneity is considered. As a consequence, the dynamics of subsurface stormflow (recognized as the leading mechanism for streamflow generation in the simulations) and the outflow discharge produced by the hillslope after a rain event are enhanced by heterogeneity. Despite of the possible lack of generality of the numerical experiments, they provide useful insight into the complex dynamics occurring in hillslope systems. The simulations show the importance of including realistic spatial heterogeneity in hillslope hydrology studies. [ABSTRACT FROM AUTHOR]

The paper deals with numerical studies of basin-scale dynamics of soil moisture in arbitrarily heterogeneous conditions (i.e., in presence of heterogeneity of climate, soil, vegetation and land use). Its relevance stems from comparative analysis of the probabilistic structure of spatially averaged soil moisture fields with the corresponding exact solutions of the underlying simplified stochastic point processes. The probabilistic structure of coarse-grained soil moisture fields is largely controlled by temporal fluctuations of intermittent rainfall fields. Averaged properties are also affected by heterogeneous soil and vegetation features and by the spatial scale of aggregation. Here, we employ results from extended Montecarlo simulations of a continuous model of the hydrologic response that proved suitable to describe observed events. The comparison of numerically derived soil moisture probability density functions with exact simplified solutions suggests, somewhat unexpectedly, that the analytical model can reasonably describe the large-scale behavior of spatially-averaged hydrologic fluxes through physically meaningful, basin-scale soil and vegetation parameters. The application of a seasonally variable, stochastic climate model shows pronounced daily fluctuations in the relationship between water losses and soil moisture, related to the underlying climatic fluctuations. The resulting spatially averaged soil moisture probability density functions in heterogeneous catchments, however, do not show appreciable differences with respect to the ones obtained assuming constant mean climate conditions. We thus conclude that effective basin-scale states, which average highly heterogeneous (spatial/temporal) properties allowing exact probabilistic descriptions, indeed exist, with implications for large scale estimates of soil-atmosphere interactions. [ABSTRACT FROM AUTHOR]

It is well known that the presence and development stage of vegetation largely influences the soil moisture content. In its turn, soil moisture availability is of major importance for the development of vegetation. The objective of this paper is to assess to what extent the results of a fully coupled hydrology-crop growth model can be optimized through the assimilation of observed leaf area index (LAI) or soil moisture values. For this purpose the crop growth module of the World Food Studies (WOFOST) model has been coupled to a fully process based water and energy balance model (TOPMODEL-Based Land-Atmosphere Transfer Scheme (TOPLATS)). LAI and soil moisture observations from 18 fields in the loamy region in the central part of Belgium have been used to thoroughly validate the coupled model. An observing system simulation experiment (OSSE) has been performed in order to assess whether soil moisture and LAI observations with realistic uncertainties are useful for data assimilation purposes. Under realistic conditions (biweekly observations with a noise level of 5 volumetric percent for soil moisture and 0.5 for LAI) an improvement in the model results can be expected. The results show that the modeled LAI values are not sensitive to the assimilation of soil moisture values before the initiation of crop growth. Also, the modeled soil moisture profile does not necessarily improve through the assimilation of LAI values during the growing season. In order to improve both the vegetation and soil moisture state of the model, observations of both variables need to be assimilated. [ABSTRACT FROM AUTHOR]

This paper aims at a first theoretical step toward the probabilistic modeling of nutrient contents in catchment-scale soil states. To this end, we wish to characterize probabilistically the slow, leaching-prone component of the hydrologic response, which chiefly determines the export of dissolved nutrients from soil, as a result of interactions between mobile/immobile phases along the pathways of runoff formation. The influence of temporal fluctuations of soil moisture on slow components of the catchment-scale runoff is thus investigated by means of a stochastic framework, where the intermittency of rainfall is modeled by a marked Poisson process with exponentially distributed intensities. The probability distribution of the relevant runoff components and its moment-generating function are derived by coupling a stochastic description of soil moisture dynamics with a suitably simplified flow model. New exact solutions are achieved in two different cases, namely, when (1) infiltration rates are assumed proportional to the incoming rainfall depths, i.e., when surface runoff is negligible, and (2) infiltration rates are upwardly bounded by episodical soil saturations (e.g., for shallow soils). In both cases, the derived probability density functions of slow components of runoff are well described by a Gamma distribution, whose shape is controlled by the ratio between the runoff frequency and the inverse of the mean residence time of subsurface flow. The framework developed allows one to link the probabilistic structure of slow components of runoff with simple (pluviometric, soil, vegetation, and geomorphologic) macroscopic parameters, with implications for the ecohydrology of fluvial systems and for drought prediction in ungauged basins. Comparisons with Monte Carlo simulations of a more detailed rainfall-runoff model to a real catchment located in northeastern Italy suggest the ability of the approach proposed to capture the main features of runoff probability distributions in heterogeneous catchments. [ABSTRACT FROM AUTHOR]

The performance of any model depends on how well its associated parameters are estimated. In the current application, a localized Bayesian recursive estimation (LOBARE) approach is devised for parameter estimation. The LOBARE methodology is an extension of the Bayesian recursive estimation (BARE) method. It is applied in this paper on two different types of models: an artificial intelligence (AI) model in the form of a support vector machine (SVM) application for forecasting soil moisture and a conceptual rainfall-runoff (CRR) model represented by the Sacramento soil moisture accounting (SAC-SMA) model. Support vector machines, based on statistical learning theory (SLT), represent the modeling task as a quadratic optimization problem and have already been used in various applications in hydrology. They require estimation of three parameters. SAC-SMA is a very well known model that estimates runoff. It has a 13-dimensional parameter space. In the LOBARE approach presented here, Bayesian inference is used in an iterative fashion to estimate the parameter space that will most likely enclose a best parameter set. This is done by narrowing the sampling space through updating the 'parent' bounds based on their fitness. These bounds are actually the parameter sets that were selected by BARE runs on subspaces of the initial parameter space. The new approach results in faster convergence toward the optimal parameter set using minimum training/calibration data and fewer sets of parameter values. The efficacy of the localized methodology is also compared with the previously used BARE algorithm. [ABSTRACT FROM AUTHOR]

Recent work by Isham et al. and Rodrìguez-Iturbe et al. has characterized the space-time variability of soil moisture through its analytically derived covariance function which depends on soil properties, vegetation structure, and rainfall patterns typical of a region. This paper uses such characterization to address the strategies and methodologies for the sampling of soil moisture fields. The focus is on the estimation of the long-term mean soil moisture and the daily soil moisture averaged over a given area as a function of the network geometry, number of stations, number of sampling days and landscape heterogeneity. It is found that the spatial geometry of the network has a significant impact on the sampling of the average soil moisture over an area in any particular day, while it is much less relevant for the sampling of the long-term mean daily soil moisture over the region. In the latter case, the length of the record is a commanding factor in what concerns the variance of estimation, specially for soils with shallow rooted vegetation. Spatial vegetation heterogeneity plays an important role on the variance of estimation of the soil moisture, being particularly critical for the sampling of the average soil moisture over an area for a given day. [ABSTRACT FROM AUTHOR]

Comprehension of the soil moisture dynamics in the rhizosphere is fundamental in the study of the water balance, vegetation productivity in water-limited ecosystems, soil microbial activity, and atmospheric boundary layer dynamics. This paper develops a vertically extended model for soil moisture, which accounts for (1) the stochasticity of precipitation, (2) the vertical distribution of plant roots, and (3) the (linear or nonlinear) dependence of transpiration on soil water content. The stochastic model is mathematically formulated under a few simplifying hypotheses, and the depth-dependent steady state probability density function of soil moisture is obtained analytically. The dependence of these solutions on climate conditions is discussed through some exemplifying cases. [ABSTRACT FROM AUTHOR]

This paper investigates the sensitivity of regional hydrology to climate change using a physically based model. The model partitions precipitation into surface runoff, groundwater runoff, and evapotranspiration by describing these fluxes first at the local instantaneous scale and then integrating over spatial and temporal distributions of soil saturation, precipitation, and wet environment evapotranspiration to calculate basin-wide climatic mean fluxes and soil saturation. The sensitivities of the mean fluxes are calculated by changing the mean precipitation and wet environment evapotranspiration. The model is applied to the Illinois River basin, and the impact of the basin's characteristics on the sensitivities is studied. For a relatively broad range of conditions the runoff processes tend to amplify climate change signals in precipitation and wet environment evapotranspiration, while evapotranspiration processes tend to dampen the same signals. These results indicate that it may be easier to detect climate changes in runoff measurements than in precipitation measurements. [ABSTRACT FROM AUTHOR]

This paper presents a framework for studying regional water balance in which the physical processes are first described at the local instantaneous scale and then integrated to the annual, basin-wide scale. The integration treats the relative soil saturation (i.e., the soil moisture divided by the porosity) and precipitation intensities as stochastic variables in space and time. A statistical equilibrium characterizes the annual water balance, resulting in a specific relation that predicts the space-time average of soil saturation in terms of soil, climate, and vegetation parameters. Specific relationships are proposed to relate the space-time average soil saturation to runoff, groundwater recharge, and evapotranspiration. This framework is applied to the Illinois River Basin. The shape of the spatial and temporal distributions of soil saturation are determined from observations. The other parameters are determined from the physical characteristics of the basin and calibration procedures. The resulting model is able to reproduce an observed relation between the space-time average soil saturation and precipitation. It is also able to reproduce observed relations between space-time average soil saturation and space-time average evapotranspiration, surface runoff, and groundwater runoff. [ABSTRACT FROM AUTHOR]

Soil moisture is an important component of the hydrological cycle. It is a control in the partitioning of energy and water related to evapotranspiration and runoff and thereby influences the hydrological response of an area. Characterizing the temporal and spatial distribution of soil moisture has important hydrologic applications, yet soil moisture varies in response to many processes acting over a variety of scales; the relative importance of different temporal and spatial controls on soil moisture is still poorly understood. In this paper we analyze both temporal and spatial soil moisture data empirically for two catchments in Australia and a further three in New Zealand. Hydrological conditions at these field sites covered a wide range over a 2 year period. The ground-based soil moisture data set is unique in its temporal and, in particular, its spatial coverage. Analyses attempt to isolate and quantify different deterministic sources of variability, measurement error, and a remaining unexplained component of variability. Because of limited data (especially relating to soils) we take a pragmatic approach of removing patterns that we can define in time and space (namely, seasonality and terrain) and then analyzing the unexplained variation. We then look for consistent patterns in this unexplained variability and argue that these are related to meteorological conditions, especially precipitation events, in the temporal case, and a combination of soils and vegetation in the spatial case. We were able to explain most of the observed variance in time and space, and the temporal variance was typically 5 times larger than spatial variance. Seasonality is the dominant source of temporal variability at our sites, although this conclusion obviously depends on climate and does not hold where soil water storage is limited. Most importantly, in controlling the distribution of soil moisture in space, the spatial distribution of soils and vegetation seems to be of similar importance to that of topography, a fact often ignored in hydrological modeling, or else surrogate soils patterns are used, but these are often not well correlated to the actual patterns [ Grayson and Blöschl, 2000]. Better methods for defining the spatial properties of soils and vegetation as they affect soil moisture patterns are a key challenge. [ABSTRACT FROM AUTHOR]

The influences of vegetation and its spatial and temporal heterogeneity on the detection of soil moisture can be significant and may limit the applicability of satellite passive microwave sensors. Sensitivity analysis of an applied soil moisture algorithm using ground-based measurements can show where problems can arise and how they may be circumvented. This paper investigates a method of retrieving a one-dimensional soil moisture profile and the surface and canopy temperature, under the influence of different vegetations and dynamics, by integrating numerical models and passive microwave, visible, and near-infrared measurements via a novel application of data assimilation. The land surface scheme (LSS), which is at the heart of the present land data assimilation scheme (LDAS), is a biophysically based model (simplified biosphere model 2: SiB2) of soil, vegetative and atmospheric interactions. The ground-based microwave radiometer (GBMR) measurements, gathered over the soil moisture experiments in 2002 (SMEX02) in Iowa, were assimilated into the LSS via a radiative transfer model (RTM) using LDAS. Compared to open loop simulations, the results of LDAS are in better agreement with observations. [ABSTRACT FROM AUTHOR]

Coarse soil moisture fields and nonlinear relationships between fluxes and soil moisture combine to yield errors in both diagnostic and predictive estimates of large-scale mass and energy fluxes. Efforts to empirically define the dynamics of subgrid spatial variance of soil moisture have led to contradictory results. Moreover, most reports of soil moisture variability range from qualitative to descriptively quantitative, owing to the lack of a robust theoretical framework for moisture variance dynamics. In this paper we derive a conservation equation for the spatial variance of subgrid root zone soil moisture, based on first principles of statistical fluid mechanics. We arrive at a variance budget in which explicit covariances between moisture fields and land surface flux fields act to produce or destroy variance through time (according to the sign of the correlation between the flux and state fields). A series of examples are used to explore how simple forms of soil, vegetation, precipitation, topography, and initial moisture variability lead to evolving covariances between spatial fields of soil moisture and particular land surface fluxes and how these covariances relate to the temporal trajectory of the spatial variance of soil moisture. We isolate a set of processes and conditions that demonstrate variance production through time and a set that demonstrate variance destruction. Of particular interest is the tendency for transpiration and infiltration-runoff processes to either produce or destroy variance, depending on the background wetness regime. Field data are also employed and shown to demonstrate a temporal behavior of the spatial variance that is readily described by the proposed approach. Ultimately, this work should aid field data interpretation and, when supplemented with a closure model for the variance budget, lead to improved land surface flux predictability over coarse grids. [ABSTRACT FROM AUTHOR]

Remotely sensed microwave measurements provide useful but indirect observations of surface soil moisture. Ground-based measurements are more direct but are very localized and limited in coverage. Model predictions provide a more regional perspective but rely on many simplifications and approximations and depend on inputs that are difficult to obtain over extensive areas. The only effective way to achieve soil moisture estimates with the accuracy and coverage required for hydrologic and meteorological applications is to merge information from satellites, ground-based stations, and models. In this paper we describe a convenient data merging (or data assimilation) procedure based on an ensemble Kalman filter. This procedure is illustrated with an application to the Southern Great Plains 1997 (SGP97) field experiment. It uses land surface and radiative transfer models to derive soil moisture estimates from airborne L band microwave observations and ground-based measurements of micrometeorological variables, soil texture, and vegetation type. The ensemble filter approach is appealing because (1) it can accommodate a wide range of models, (2) it can account for input and measurement uncertainties, (3) it provides information on the accuracy of its estimates, and (4) it is relatively efficient, making large-scale applications feasible. Results from our SGP97 application of the ensemble Kalman filter include large-scale maps (∼10,000 km2) of soil moisture estimates and estimation error standard deviations for the entire month long experiment and comparisons of filter soil moisture and latent heat estimates to ground truth measurements (gravimetric and flux tower observations). The ground truth comparisons show that the filter is able to track soil moisture fluctuations. The filter estimates are significantly better than those from an 'open loop' simulation that includes the same ground-based data but does not incorporate radio brightness measurements. Overall, the results from this field test indicate that the ensemble Kalman filter is an accurate, efficient, and flexible data assimilation procedure that is able to extract useful information from remote sensing measurements. [ABSTRACT FROM AUTHOR]

An integrated system for collecting, recording, and analyzing data describing the behavior of water flowing through a forest soil has been developed. The collecting and recording components are located on a lower terrace of the City of Seattle Cedar River watershed in western Washington. This system includes tension lysimeters installed such that the overlying soil and rooting systems are undisturbed. The lysimeters are constructed from a fused aluminum oxide disk 11 inches (28 cm) in diameter. The suction to each lysimeter is maintained through a single controlled vacuum source. Solutions collected during periods of flow are passed through flow cells measuring conductivity, acidity, and rate of water flow. The sensitivity of the soil moisture flowmeter is in excess of 0.002 cm/hr. The electrical output from the flow cells is coupled to a data-logging facility and printed as perforations in paper tape. Computer analysis converts these data to tabular and graphic form. Examples demonstrating the functioning of the system during periods of flow are presented. [ABSTRACT FROM AUTHOR]

An exact solution is derived for the second term of J. R. Philip's [1957] series expansion originally developed for his numerical method of solution of the diffusion equation with diffusivity concentration dependent. The exact solution is made possible by the adoption of simple algebraic functions to describe the water content-capillary suction and the water content-capillary conductivity relationships of the porous medium and of a solution for horizontal infiltration presented in an earlier paper [ Brutsaert, 1968]. Because of the fast convergence of the series, all terms of order higher than the second are negligible. Thus the present solution for the second term used in combination with the first term, representing the solution for horizontal infiltration, provides an adequate solution for vertical infiltration. Good agreement is obtained with published experimental data. [ABSTRACT FROM AUTHOR]

From analysis of the water profiles obtained by gamma ray absorption during infiltration into soil columns, and of the wetting branch of the soil water-suction water content relation, this paper suggests a method for checking the validity of the generalized Darcy's law and for computing the values of the hydraulic conductivity at various water contents. Tests have been made for horizontal infiltration and for capillary rise. In both tests, for a given water content, the capillary conductivity k is constant, and the generalized Darcy's law is valid and may be used to describe a transient flow in unsaturated soils. [ABSTRACT FROM AUTHOR]

The article announces that the paper "Controls on Topographic Dependence and Temporal Instability in Catchment-Scale Soil Moisture Patterns," has been withdrawn by "Water Resources Research" journal due to an error in the calibration used in the model.