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The biophysical processes occurring in the unsaturated zone have a direct impact on the water table dynamics. Representing these processes through the application of unsaturated zone models of different complexity has an impact on the estimates of the volumes of water flowing between the unsaturated zone and the aquifer. These fluxes, known as net recharge, are often used as the shared variable that couples unsaturated to groundwater models. However, as recharge estimates are always affected by a degree of uncertainty, model–data fusion methods, such as data assimilation, can be used to inform these coupled models and reduce uncertainty. This study assesses the effect of unsaturated zone models complexity (conceptual versus physically based) to update groundwater model outputs, through the assimilation of actual evapotranspiration rates, for a water-limited site in South Australia. Actual evapotranspiration rates are assimilated because they have been shown to be related to the water table dynamics and thus form the link between remote sensing data and the deeper parts of the soil profile. Results have been quantified using standard metrics, such as the root mean square error and Pearson correlation coefficient, and reinforced by calculating the continuous ranked probability score, which is specifically designed to determine a more representative error in stochastic models. It has been found that, once properly calibrated to reproduce the actual evapotranspiration–water table dynamics, a simple conceptual model may be sufficient for this purpose; thus using one configuration over the other should be motivated by the specific purpose of the simulation and the information available.

This study presents an assessment of a pre-operational soil moisture product at 1 km resolution derived from satellite data acquired by the European Radar Observatory Sentinel-1 (S-1), representing the first space component of the Copernicus program. The product consists of an estimate of surface soil volumetric water content (SSM) [m/m] and its uncertainty [m/m], both at 1 km. The retrieval algorithm relies on a time series based Short Term Change Detection (STCD) approach, taking advantage of the frequent revisit of the S-1 constellation that performs C-band Synthetic Aperture Radar (SAR) imaging. The performance of the S-1 SSM product is estimated through a direct comparison between 1068 S-1 SSM images against in situ SSM measurements acquired by 167 ground stations located in Europe, America and Australia, over 4 years between January 2015 and December 2020, depending on the site. The paper develops a method to estimate the spatial representativeness error (SRE) that arises from the mismatch between the S-1 SSM retrieved at 1 km resolution and the in situ point-scale SSM observations. The impact of SRE on standard validation metrics, i.e., root mean square error (RMSE), Pearson correlation (R) and linear regression, is quantified and experimentally assessed using S-1 and ground SSM data collected over a dense hydrologic network (4 - 5 stations/km) located in the Apulian Tavoliere (Southern Italy). Results show that for the dense hydrological network the RMSE and correlation are ~0.06 m/m and 0.71, respectively, whereas for the sparse hydrological networks, i.e., 1 station/km, the SRE increases the RMSE by ~0.02 m/m (70% Confidence Level). Globally, the S-1 SSM product is characterized by an intrinsic (i.e., with SRE removed) RMSE of ~0.07 m/m over the SSM range [0.03, 0.60] m/m and R of 0.54. A breakdown of the RMSE per dry, medium and wet SSM ranges is also derived and its implications for setting realistic requirements for SAR-based SSM retrieval are discussed together with recommendations for the density of in situ SSM observations.

Synthetic Aperture Radar (SAR) data are currently the most reliable resource for flood monitoring, though still subject to various uncertainties, which can be objectively represented with probabilistic flood maps. Moreover, the growing number of SAR satellites has increased the likelihood of observing a flood event from space through at least a single SAR image, but generalized methods for flood classification independent of sensor characteristics need to be developed, to fully utilize these images for disaster management. Consequently, a neuro-fuzzy flood mapping technique is proposed for texture-enhanced single SAR images. Accordingly, any SAR image is first processed to generate second-order statistical textures, which are subsequently optimized using a dimensionality reduction technique. The flood and non-flood classes are then modelled within a fuzzy inference system using Gaussian curves. Parameterization is achieved by training a neural network on the image through user-defined polygons. The results of the optimized texture-based neuro-fuzzy classification were compared against the performance of the SAR image alone and that of SAR enhanced with randomly selected texture features. This approach was tested for a COSMO-SkyMed SAR image at two validation sites, for which high resolution aerial photographs were available. An overall accuracy assessment using reliability diagrams demonstrated a reduction of 54.2% in the Weighted Root Mean Squared Error (WRMSE) values compared to the stand-alone use of SAR. WRMSE values estimated for the proposed method varied from 0.027 to 0.196. A fuzzy validation exercise was also proposed to account for the uncertainty in manual flood identification from aerial photography, resulting in fuzzy spatial similarity values ranging from 0.67 to 0.92, with higher values representing better performance. Results suggest that the proposed approach has demonstrated potential to improve operational SAR-based flood mapping.

Soil moisture affects and is affected by root water uptake and at the same time drives soil CO2 dynamics. Selecting root water uptake formulations in models is important since this affects the estimation of actual transpiration and soil CO2 efflux. This study aims to compare different models combining the Richards equation for soil water flow to equations describing heat transfer and air-phase CO2 production and flow. A root water uptake model (RWC), accounting only for root water compensation by rescaling water uptake rates across the vertical profile, was compared to a model (XWP) estimating water uptake as a function of the difference between soil and root xylem water potential; the latter model can account for both compensation (XWPRWC) and hydraulic redistribution (XWPHR). Models were compared in a scenario with a shallow water table, where the formulation of root water uptake plays an important role in modeling daily patterns and magnitudes of transpiration rates and CO2 efflux. Model simulations for this scenario indicated up to 20% difference in the estimated water that transpired over 50 days and up to 14% difference in carbon emitted from the soil. The models showed reduction of transpiration rates associated with water stress affecting soil CO2 efflux, with magnitudes of soil CO2 efflux being larger for the XWPHR model in wet conditions and for the RWC model as the soil dried down. The study shows the importance of choosing root water uptake models not only for estimating transpiration but also for other processes controlled by soil water content.

Synthetic Aperture Radar (SAR) enables 24-hour, all-weather flood monitoring. However, accurate detection of inundated areas can be hindered by the extremely complicated electromagnetic interaction phenomena between microwave pulses, and horizontal and vertical targets. This manuscript focuses on the problem of inundation mapping in areas with emerging vegetation, where spatial and seasonal heterogeneity makes the systematic distinction between dry and flooded backscatter response even more difficult. In this context, image interpretation algorithms have mostly used detailed field data and reference image(s) to implement electromagnetic models or change detection techniques. However, field data are rare, and despite the increasing availability of SAR acquisitions, adequate reference image(s) might not be readily available, especially for fine resolution acquisitions. To by-pass this problem, this study presents an algorithm for automatic flood mapping in areas with emerging vegetation when only single SAR acquisitions and common ancillary data are available. First, probability binning is used for statistical analysis of the backscatter response of wet and dry vegetation for different land cover types. This analysis is then complemented with information on land use, morphology and context within a fuzzy logic approach. The algorithm was applied to three fine resolution images (one ALOS-PALSAR and two COSMO-SkyMed) acquired during the January 2011 flood in the Condamine-Balonne catchment (Australia). Flood extent layers derived from optical images were used as validation data, demonstrating that the proposed algorithm had an overall accuracy higher than 80% for all case studies. Notwithstanding the difficulty to fully discriminate between dry and flooded vegetation backscatter heterogeneity using a single SAR image, this paper provides an automatic, data parsimonious algorithm for the detection of floods under vegetation.

The objective of the study is to investigate the potential of retrieving superficial soil moisture content (mv) from multi-temporal L-band synthetic aperture radar (SAR) data and hydrologic modelling. The study focuses on assessing the performances of an L-band SAR retrieval algorithm intended for agricultural areas and for watershed spatial scales (e.g. from 100 to 10 000 km2). The algorithm transforms temporal series of L-band SAR data into soil moisture contents by using a constrained minimization technique integrating a priori information on soil parameters. The rationale of the approach consists of exploiting soil moisture predictions, obtained at coarse spatial resolution (e.g. 15–30 km2) by point scale hydrologic models (or by simplified estimators), as a priori information for the SAR retrieval algorithm that provides soil moisture maps at high spatial resolution (e.g. 0.01 km2). In the present form, the retrieval algorithm applies to cereal fields and has been assessed on simulated and experimental data. The latter were acquired by the airborne E-SAR system during the AgriSAR campaign carried out over the Demmin site (Northern Germany) in 2006. Results indicate that the retrieval algorithm always improves the a priori information on soil moisture content though the improvement may be marginal when the accuracy of prior mv estimates is better than 5%.

The Soil Moisture and Ocean Salinity (SMOS) mission has the potential to improve the predictive skill of land surface models through the assimilation of its observations. Several alternate products can be distinguished: the observed brightness temperature (TB) data at coarse scale, indirect estimates of soil moisture (SM) through the inversion of the coarse-scale TB observations, and fine-scale soil moisture through the a priori downscaling of coarse-scale soil moisture. The SMOS TB products include observations over a large range of incidence angles at both H- and V-polarizations, which allows the merit of assimilating the full set of multi-angular/polarization observations, as opposed to specific sub-sets of observations, to be assessed. This study investigates the performance of various observation scenarios with respect to soil moisture and streamflow predictions in the Murray Darling Basin. The observations are assimilated into the Variable Infiltration Capacity (VIC) model, coupled to the Community Microwave Emission Modeling (CMEM) platform, using the Ensemble Kalman filter. The assimilation of these various observation products is assessed under similar realistic assimilation settings, without optimization, and validated by comparison of the modeled soil moisture and streamflow to in situ measurements across the basin. The best results are achieved from assimilation of the coarse-scale SM observations. The reduced improvement using downscaled SM is probably due to a lower number of observations, as a result of cloud cover effects on the downscaling method. The assimilation of TB was found to be a promising alternative, which led to improvements in soil moisture prediction approaching those of the coarse-scale SM assimilation. publisher: Elsevier articletitle: Assimilation of SMOS soil moisture and brightness temperature products into a land surface model journaltitle: Remote Sensing of Environment articlelink: http://dx.doi.org/10.1016/j.rse.2015.10.033 content_type: article copyright: © 2015 Elsevier Inc. All rights reserved. ispartof: Remote Sensing of Environment vol:180 pages:292-304 status: published

Summary An important tool in the management of floods is the use of rainfall–runoff models to predict the arrival of discharge peaks. These models generally use rainfall and potential evapotranspiration rates as input, and relate these to the catchment discharge through a number of conceptual equations. The parameters of these equations are estimated through a comparison of the modeled discharge to the observations. Only one variable, catchment discharge, is thus generally used to calibrate and validate these models. The objective of this paper is to validate the internal model dynamics of two widely used rainfall–runoff models using baseflow estimates. The baseflow time series used in this paper are obtained through the use of a physically-based digital baseflow filter. These models, the Hydrologiska Byrans Vattenbalansavdelning (HBV) model and the Probability Distributed Model (PDM), were calibrated using 1 year of hourly discharge data. The HBV model uses a linear reservoir for the modeling of groundwater flow, while the PDM uses a cubic reservoir for this purpose. In order to assess the impact of the type of reservoir choice, the cubic reservoir in the PDM was also replaced by a linear reservoir. Two different parameter estimation algorithms were used for model calibration. The Shuffled Complex Evolution (SCE-UA) algorithm minimizes the Root Mean Square Error between the model simulations and the observations, while Multistart Weight-Adaptive Recursive Parameter Estimation (MWARPE) uses the Extended Kalman Filter equations in an iterative, Monte-Carlo framework. MWARPE was found to lead to the best discharge simulations, but the differences with the results obtained from the SCE-UA algorithm were relatively small. When only the modeled discharge was analyzed, no clear picture emerged of which model produced the best results. However, it has been found that the replacement of the cubic groundwater reservoir in the PDM by a linear reservoir resulted in a strongly improved model performance with respect to the baseflow. Further, MWARPE consistently led to the best baseflow estimates for the three models, while the HBV model resulted in the best simulations of the baseflow, regardless of the calibration algorithm. The overall conclusion of this paper is that, even though it may be difficult to assess which model and calibration algorithm resulted in the best discharge estimates, the MWARPE calibration algorithm and the HBV model consistently led to the best internal model dynamics.

The objective of this study is to assess the validity of a theoretical expression for the outflow from an initially dry aquifer subject to recharge. This equation states that, for small values of time, this outflow is proportional to the square root of time. Here we report on experiments performed with two laboratory hillslopes in the Wageningen rainfall simulator. Three different aquifer slopes were investigated with four replicates for each slope. The relations between the measured outflows and the square root of time exhibited very high coefficients of determination (all but one >0.9). The aquifer saturated hydraulic conductivity (k) and drainable porosity were determined using a base flow recession analysis, with k being consistent with soil physical laboratory measurements. The recharge rates obtained using the coefficient of the short-time expression and the steady state discharge were consistent as well. Overall, these results confirm the theoretical relationship between the aquifer outflow for the hydrograph rising limb and the square root of time.

Accurate measurements of the turbulent exchanges of mass and energy at the land surface are necessary for a good understanding of the various components of the hydrological cycle. The two most commonly used methods to measure evapotranspiration rates are the Bowen ratio energy balance (BREB) and the Eddy correlation (EC) methods. These methods are applicable when a number of requirements, mostly with respect to terrain topography and homogeneous fetch extension, are fulfilled. On the other hand, meteorological variables can be used to calculate evapotranspiration rates. The two most frequently used methods for this purpose are the Penman-Monteith (PM) combination equation and the Priestley-Taylor (PT) approximation. The objective of this paper is to compare these different methods under non-ideal conditions, more specifically for a wet sloping grassland. The BREB-based and EC-based latent heat fluxes are intercompared, and a good agreement between the estimates from both methods is found. A comparison between the results of the PM and PT methods and the measured latent heat fluxes is then done. A strong annual cycle in the calculated values for the PT alfa factor ( α ), with a mean annual average value of 1.21 ± 0.79, has been observed. This annual cycle is related to the annual cycle of the humidity of the soil, which can be evaluated by either the soil moisture or the vapor deficit of the air. A strong annual cycle in the inverted surface resistances has also been observed. A relationship between the inverted surface resistances and α has been found, in which the highest values for α coincide with low surface resistances, and vice versa. The results indicate that the methods to measure and calculate latent heat fluxes are in a good agreement, and that imposing an annual cycle in the surface resistance and α leads to an improvement in the estimated evapotranspiration rates. The results suggest that it is possible to measure or model evapotranspiration rates in situations where the theoretical requirements (more specifically a non-sloping surface) are not met.

This paper presents the solution of the linearized Boussinesq equation for an inclined, ditch-drained aquifer, with a temporally varying recharge rate. Water-table profiles and flow rates into the ditches are calculated. As an initial condition the steady-state profile for a constant recharge rate is used, and the linearized Boussinesq equation is solved for a different recharge rate. Then, at a specified time, the transient water table profile is used as initial condition for the Boussinesq equation with a new recharge rate. The transient solution at a new specified time is then used as the initial condition for the Boussinesq equation with a different recharge rate, and so on. Using the Darcy equation, analytical expressions for the flow rates into the ditches can be obtained. The solution allows the calculation of the transient behavior of the groundwater table and its flow rates due to temporally variable recharge rates.

This paper describes the development of a process-based water and energy balance model for use in high latitudes under both summertime and wintertime conditions. The model is developed as a part of the Boreal Ecosystem-Atmosphere Study (BOREAS). The model differs from its original version in the representation of hydrological processes specific to climatic and ecologic conditions in high latitudes: the impact of an organic layer on the water and energy balance of the boreal forest; the parameterization of frozen ground and snow accumulation and ablation; and the effect of open water bodies on the calculation of the radiative energy and water budget. The model can be run either in a fully distributed or an aggregated statistical mode. The resulting macroscale hydrological model can be used, off line, to study the energy and water balance of the boreal forest and potentially can be used as a land-atmosphere parameterization in atmospheric models.

In support of the overall scientific objective of the Boreal Ecosystem-Atmosphere Study (BOREAS), which encompasses the improved understanding of the interactions between the boreal forest and the atmosphere, a process-based water and energy balance model is applied to observed forcing data, and the results are presented and discussed. Observed tower forcing and validation data are analyzed. A consistent diurnal pattern in the energy balance closure of the validation data is obtained. Simulations are performed for a number of BOREAS flux tower sites. The model successfully simulates the temporally averaged Bowen ratio and the evaporative part of precipitation over the different BOREAS flux tower sites during the 1994 and 1996 intensive field campaigns. At finer temporal scales a small phase shift in sensible heat flux and net radiation exists between the observed and model-derived quantities. The ground heat flux is found to be slightly larger than the observations during the course of the day. It is suggested that the sensitivity of the model to parameters such as the moss thickness, thermal conductivity, and heat capacity is responsible for these differences. The moss moisture content and the different components of the energy balance were very well matched for a continuous simulation during 1996. Overall, the accuracy performance of the model is equivalent to the accuracy of the input forcing data.

Compositional data, such as soil texture, are hard to deal with in the geosciences as standard statistical methods are often inappropriate to analyse this type of data. Especially in sensitivity analysis, the closed character of the data is often ignored. To that end, we developed a method to assess the local sensitivity of a model output with resect to a compositional model input. We adapted the finite difference technique such that the different parts of the input are perturbed simultaneously while the closed character of the data is preserved. This method was applied to a hydrologic model and the sensitivity of the simulated soil moisture content to local changes in soil texture was assessed. Based on a high number of model runs, in which the soil texture was varied across the entire texture triangle, we identified zones of high sensitivity in the texture triangle. In such zones, the model output uncertainty induced by the discrepancy between the scale of measurement and the scale of model application, is advised to be reduced through additional data collection. Furthermore, the sensitivity analysis provided more insight into the hydrologic model behaviour as it revealed how the model sensitivity is related to the shape of the soil moistureretention curve.

[1] For simulations in basins where soil information is limited to soil type maps, a methodology is presented to quantify the uncertainty of soil hydraulic parameters arising from within-soil-class variability and to assess the impact of this uncertainty on soil moisture modeling. Continuous pedotransfer functions were applied to samples with different texture within each soil class to construct discrete probability distributions of the soil hydraulic parameters. When propagating the parameter distributions through a hydrologic model, a wide range of simulated soil moisture was generated within a single soil class. The pedotransfer function was found to play a crucial role in assessing the uncertainty in the modeled soil moisture, and the geographic origin of the pedotransfer function (region specific versus nonregion specific) highly affected the range and shape of the probability distribution of the soil hydraulic parameters. Furthermore, the modeled soil moisture distribution was found to be non-Gaussian. An accurate uncertainty assessment therefore requires the characterization of its higher-order moments. As an extension of this research, we have shown that applying continuous region-specific pedotransfer functions to the central point of a soil class is a better alternative to standard (often nonregion-specific) class pedotransfer functions for determining an average set of soil hydraulic parameters.

In most hydrologic modeling studies, the hypothesis is made that an improvement in the modeled soil moisture leads to an improvement in the modeled surface energy balance. The objective of this paper is to assess whether this hypothesis is true. The study was performed over the winter wheat fields in the AgriSAR 2006 domain. Remotely sensed soil moisture values and latent heat fluxes were used, in combination with in situ observations. First, the land cover and saturated subsurface flow parameters were estimated using the in situ observations. A spatially distributed model simulation was then performed, for which the Brooks-Corey parameters were derived from a soil texture map, and of which the results were validated using the remote sensing data. The remotely sensed soil moisture values were then used to optimize the Brooks-Corey parameters. As expected, a better performance with respect to the soil moisture estimation was obtained. However, this did not improve the latent heat flux estimates. This can be explained by the consumption of water from the deeper soil layers by the vegetation. The overall conclusion is that, under conditions where evapotranspiration is limited by energy and not by the soil moisture content, surface soil moisture values alone are not sufficient for the optimization of hydrologic model results. More data sets are needed for this purpose.

This paper investigates the use of dense time series of C-band SAR data (i.e. acquired with revisit time within 1–2 weeks) for the retrieval of volumetric soil moisture content (m v ) underneath agricultural crops. Its final aim is to contribute at assessing retrieval strategies for monitoring agricultural areas using near future frequent-revisit SAR missions, such as the forthcoming European Sentinel-1. A recently developed m v retrieval algorithm is firstly presented and then applied to a time series of ASAR HH and HV data collected in 2006 over the agricultural DEMMIN site (Germany). The assessment of the algorithm is provided by comparing the SAR-derived m v maps over the DEMMIN site with hydrologically modelled m v maps. Results indicate that m v can be retrieved with accuracy ranging between 5% and 6%.

[1] The estimation of catchment-averaged aquifer hydraulic conductivity values is usually performed through a base flow recession analysis. Relationships between the first time derivatives of the base flow and the base flow values themselves, derived for small and large values of time, are used for this purpose. However, in the derivation of the short-time equations, an initially fully saturated aquifer without recharge with sudden drawdown is assumed, which occurs very rarely in reality. It is demonstrated that this approach leads to a nonnegligible error in the parameter estimates. A new relationship is derived, valid for the rising limb of a base flow hydrograph, succeeding a long rainless period. Application of this equation leads to accurate estimates of the aquifer lower layer saturated hydraulic conductivity. Further, it has been shown analytically that, if base flow is modeled using the linearized Boussinesq equation, the base flow depends on the effective aquifer depth and the ratio of the saturated hydraulic conductivity to the drainable porosity, not on these three parameters separately. The results of the new short-time expression are consistent with this finding, as opposed to the use of a traditional base flow recession analysis. When base flow is modeled using the nonlinear Boussinesq equation, the new expression can be used, without a second equation for large values of time, to estimate the aquifer lower layer hydraulic conductivity. Overall, the results in this paper suggest that the new methodology outperforms a traditional recession analysis for the estimation of catchment-averaged aquifer hydraulic conductivities.

It is widely recognized that Synthetic Aperture Radar (SAR) data are a very valuable source of information for the modeling of the interactions between the land surface and the atmosphere. During the last couple of decades, most of the research on the use of SAR data in hydrologic applications has been focused on the retrieval of land and biogeophysical parameters (e.g., soil moisture contents). One relatively unexplored issue consists of the optimization of soil hydraulic model parameters, such its, for example, hydraulic conductivity, values, through remote sensing. This is due to the fact that no direct relationships between the remote-sensing observations, more specifically radar backscatter values, and the parameter values can be derived. However, land surface models can provide these relationships. The objective of this paper is to retrieve a number of soil physical model parameters through a combination of remote sensing anti land surface modeling. Spatially distributed and multitemporal SAR-based soil moisture maps are the basis of the study. The surface soil moisture values are used in a parameter estimation procedure basest on the Extended Kalman Filter equations. In fact, the land surface model is, thus, used to determine the relationship between the soil physical parameters and the remote-sensing data. An analysis is then performed, relating the retrieved soil parameters to the soil texture data available over the study area. The results of the study show that there is a potential to retrieve soil physical model parameters through a combination of land surface modeling and remote sensing.

[1] An ensemble Kalman filter for state estimation and a bias estimation algorithm were applied to estimate individual soil moisture profiles in a small corn field with the CLM2.0 model through the assimilation of measurements from capacitance probes. Both without and with inclusion of bias correction, the effect of the assimilation frequency, the assimilation depth, and the number of observations assimilated per profile were studied. Assimilation of complete profiles had the highest impact on deeper soil layers, and the optimal assimilation frequency was about 1–2 weeks, if bias correction was applied. The optimal assimilation depth depended on the calibration results. Assimilation in the surface layer had typically less impact than assimilation in other layers. Through bias correction the soil moisture estimate greatly improved. In general, the correct propagation of the innovations for both the bias-blind state and bias filtering from any layer to other layers was insufficient. The approximate estimation of the a priori (bias) error covariance and the choice of a zero-initialized persistent bias model made it impossible to estimate the bias in layers for which no observations were available.

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.

Some techniques are proposed to limit the representativeness error of point soil moisture observations as estimates for spatial mean soil moisture in the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) field site. First, representative measurement locations for the spatial mean soil moisture are sought by ranking of the sensors based on the time-mean differences between the point values and the spatial mean. Next, simple statistical methods, as well as models in both the time and frequency domain are explored to scale up point measurements to field averaged soil moisture. Upscaling by a simple linear relationship and cumulative distribution function (cdf) matching generally provide the best estimates of the temporal evolution of the spatial mean soil moisture. © 2007 Elsevier B.V. All rights reserved. ispartof: Journal of Hydrology vol:343 issue:1 pages:1-11 status: published

[1] The Community Land Model (CLM2.0) has been used to simulate land surface processes in a small corn field. The subdivision of grid cells into patches in the CLM2.0 was explored for the generation of Monte Carlo simulations for use in calibration and ensemble generation. A distributed multiobjective calibration was developed for the optimal estimation of parameters and initial state variables for 36 soil moisture profiles. Since the resulting parameter and initial state values did not lead to perfect simulations for soil moisture, and in order to better understand the forecast uncertainty, ensemble runs were generated. The ensembles generated by CLM2.0 have been verified by several methods that are commonly used in meteorology. It was shown that the perfect model approach cannot be applied for bounded hydrological applications and that perturbation of parameters is a necessity to obtain a realistic assessment of the forecast error. Perturbation of forcings only captures more of the model uncertainty than perturbation of initial conditions only, but also causes a too limited spread in the ensembles. The generation of ensemble members through perturbation of the parameter set, found through calibration, does not necessarily result in ensembles that surround the calibrated deterministic control run for soil moisture. This is partially due the nonlinearity of the model in the parameters. It may also indicate that some parameter sets are not robust and not appropriate to perturb for ensemble generation. Consequently, the resulting ensemble mean may not represent the best forecast or a priori state estimation. During periods of extreme drought or precipitation, the ensemble probability density function (pdf) deviates far from normality and the model behaves very nonlinearly. For state estimation, methods like the ensemble Kalman filter are best suited for the propagation of the first moments to account for the nonlinear dynamics during crucial events for hydrological simulations. However, the a posteriori estimate for this technique will only be optimal in the limited class of linear filters, since the underlying pdfs cannot be assumed to be Gaussian.

Previous experiments demonstrated the relationships between the radar backscattering coefficient, sigma /sub o/ and crop parameters such as fresh biomass, plant height and Leaf Area Index (LAI). Topsoil water content also influences the backscattered signal and is as such a required input parameter in the physical and semi-empirical models that extract vegetation parameters from sigma /sub o/. In an operational environment, it is not possible to measure soil moisture over an entire agricultural region. As the vegetation cover hampers the radar remote sensing of soil moisture, near surface soil moisture can be simulated using a hydrological model. In this paper, it is investigated whether soil moisture values obtained through the hydrological model TOPLATS can be used in a crop parameter retrieval algorithm. The data set used for this investigation was collected from March to September 2003 in the Loamy Region, Belgium. During this period, 18 agricultural fields were sampled for vegetation parameters and soil moisture. In addition, 11 ERS-2 images of that period were acquired of which 6 coincided with the field measurement dates. Because the necessary catchment data were not available, TOPLATS was calibrated on a point scale for every field with in situ soil moisture. The calibrated TOPLATS model was applied to simulate soil moisture values at the ERS-2 acquisition dates for which no soil moisture field measurements were available. In parallel, the Water Cloud model was calibrated using the biophysical parameters measured on the field in order to retrieve LAI estimates from ERS SAR time series. In a second step, the simulated soil moisture values corresponding to the SAR acquisition dates were used as input in the Cloud model as substitutes of field measurements, and the propagation of the soil moisture estimate error in the LAI retrieval algorithm was studied. Finally the experimental results were discussed in the perspective of a regional crop monitoring system and the operational feasibility is assessed.

Remote sensing offers a very interesting means to estimate the soil moisture state of a hydrological system. However, practical use for small scale agricultural applications is still limited. Ground truth data remain necessary to validate the inversion from the measured quantities to soil moisture content, to understand small scale processes in the horizontal plane, and to assess the distribution of water over a soil profile. Additionally, land surface models offer basic knowledge of the physical and physiological processes affecting the soil moisture state. A combination of both sources of information yields an optimal estimate of the system state and offers the best knowledge available to decision makers. In this study, ground measurements of soil moisture in the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE 3 ) field (near Washington D.C.) of the United States Department of Agriculture (USDA) were assimilated into the Community Land Model (CLM2.0). Some practical problems that prevent optimal state estimation are discussed, such as the presence of bias in the model or observations, and the limited knowledge of the correlation structure of e.g. model error. Some case studies revealed that the influence of assimilation of upper layer soil moisture, as provided by remote sensing, improves the model results, but is not as persistent for profile estimation as assimilation of soil moisture in deeper layers.

During the last two decades, the potential of radar remote sensing in the retrieval of the water content of the near-surface unsaturated soil zone has been explored. This water content is usually referred to as soil moisture. The inversion of radar observations into soil moisture values has been hampered by an insufficient characterization of the soil roughness. However, some studies have focused on relating temporal changes of the radar signal to hydrologic relevant information. One result is presented here, where we show that variable source areas, which are mainly responsible for runoff in a catchment, can be visualized through a principal component analysis. A second part of this paper shows how soil moisture information obtained from radar imagery can be incorporated into hydrologic models. A first example uses an extended Kalman filtering technique, which adjusts the state variables from a hydrologic model. Through this technique, one-dimensional soil moisture profiles are retrieved with high accuracy. In a second example, we show a data-assimilation method which uses both the statistics and the spatial distribution of radar-retrieved soil moisture values, to adjust the modeled soil moisture profile. This methodology enables a better modeling of the rainfall-runoff behavior of the catchment.