Application of a Hillslope-Scale Soil Moisture Data Assimilation System to Military Trafficability Assessment

Soil moisture is an important environmental variable that impacts military operations and weapons systems. Accurate and timely forecasts of soil moisture at appropriate spatial scales, therefore, are important for mission planning. We present an application of a soil moisture data assimilation system to military trafficability assessment. The data assimilation system combines hillslope-scale (e.g., 10s to 100s of m) estimates of soil moisture from a hydrologic model with synthetic L-band microwave radar observations broadly consistent with the planned NASA Soil Moisture Active–Passive (SMAP) mission. Soil moisture outputs from the data assimilation system are input to a simple index-based model for vehicle trafficability. Since the data assimilation system uses the ensemble Kalman Filter, the risks of impaired trafficability due to uncertainties in the observations and model inputs can be quantified. Assimilating the remote sensing observations leads to significantly different predictions of trafficability conditions and associated risk of impaired trafficability, compared to an approach that propagates forward uncertainties in model inputs without assimilation. Specifically, assimilating the observations is associated with an increase in the risk of “slow go” conditions in approximately two-thirds of the watershed, and an increase in the risk of “no go” conditions in approximately 40% of the watershed. Despite the simplicity of the trafficability assessment tool, results suggest that ensemble-based data assimilation can potentially improve trafficability assessment by constraining predictions to observations and facilitating quantitative assessment of the risk of impaired trafficability

Hydrologic Remote Sensing and Land Surface Data Assimilation

Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface?atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear updating rule and assumption of jointly normal distribution of errors in state variables and observation

Hydrologic data assimilation of multi-resolution microwave radiometer and radar measurements using ensemble smoothing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2006.Includes bibliographical references (leaves 197-208).Previously, the ensemble Kalman filter (EnKF) has been used to estimate soil moisture and related fluxes by merging noisy low frequency microwave observations with forecasts from a conventional though uncertain land surface model (LSM). Here it is argued that soil moisture estimation is a reanalysis-type problem and thus smoothingis more appropriate than filtering. An ensemble moving batch smoother, an extension of the EnKF in which the state vector is distributed in time, is used to merge synthetic ESTAR observations with modeled soil moisture. Results demonstrate that smoothing can improve over filtering. However, augmentation of the state vector increases the computational cost significantly, rendering this approach unsuitable for spatially distributed problems. The ensemble Kalman smoother (EnKS) is an inexpensive alternative as the costly computations are already performed in the EnKF which provides the initial guess. It is used to assimilate observed L-band radiobrightness temperatures during the Southern Great Plains Experiment 1997. Estimated surface and root zone soil moisture is evaluated using gravimetric measurements and flux tower observations. It is shown that the EnKS can be implemented as a fixed-lag smoother with the required lag determined by the memory in subsurface soil moisture. In a synthetic experiment over the Arkansas-Red river basin, "true" soil moisture from the TOPLATS model is used to generate synthetic Hydros observations which are subsequently merged with modeled soil moisture from the Noah LSM using the EnKS.(cont.) It is shown that the EnKS can be used in a large problem, with a spatially distributed state vector, and spatially-distributed multi-resolution observations. This EnKS-based framework is used to study the synergy between passive and active observations, which have different resolutions and error distributions.by Susan Catherin Dunne.Ph.D

Parameter Sensitivity in LSMs: An Analysis Using Stochastic Soil Moisture Models and ELDAS Soil Parameters

Integration of simulated and observed states through data assimilation as well as model evaluation requires a realistic representation of soil moisture in land surface models (LSMs). However, soil moisture in LSMs is sensitive to a range of uncertain input parameters, and intermodel differences in parameter values are often large. Here, the effect of soil parameters on soil moisture and evapotranspiration are investigated by using parameters from three different LSMs participating in the European Land Data Assimilation System (ELDAS) project. To prevent compensating effects from other than soil parameters, the effects are evaluated within a common framework of parsimonious stochastic soil moisture models. First, soil parameters are shown to affect soil moisture more strongly than the average evapotranspiration. In arid climates, the effect of soil parameters is on the variance rather than the mean, and the intermodel flux differences are smallest. Soil parameters from the ELDAS LSMs differ strongly, most notably in the available moisture content between the wilting point and the critical moisture content, which differ by a factor of 3. The ELDAS parameters can lead to differences in mean volumetric soil moisture as high as 0.10 and an average evapotranspiration of 10%–20% for the investigated parameter range. The parsimonious framework presented here can be used to investigate first-order parameter sensitivities under a range of climate conditions without using full LSM simulations. The results are consistent with many other studies using different LSMs under a more limited range of possible forcing condition

Variational assimilation of remote sensing data for land surface hydrologic applications

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2000.Includes bibliographical references (p. 283-192).Soil moisture plays a major role in the global hydrologic cycle. Most importantly, soil moisture controls the partitioning of available energy at the land surface into latent and sensible heat fluxes. We investigate the feasibility of estimating large-scale soil moisture profiles and related land surface variables from low-frequency (L-band) passive microwave remote sensing observations using weak-constraint variational data assimilation. We extend the iterated indirect representer method, which is based on the adjoint of the hydrologic model, to suit our application. The four-dimensional (space and time) data assimilation algorithm takes into account model and measurement uncertainties and provides optimal estimates by implicitly propagating the full error covariances. Explicit expressions for the posterior error covariances are also derived. We achieve a dynamically consistent interpolation and extrapolation of the remote sensing data in space and time, or equivalently, a continuous update of the model predictions from the data. Our hydrologic model of water and energy exchange at the land surface is expressly designed for data assimilation. It captures the key physical processes while remaining computationally efficient. The assimilation algorithm is tested with a series of experiments using synthetically generated system and measurement noise. In a realistic environment based on the Southern Great Plains 1997 (SGP97) hydrology experiment, we assess the performance of the algorithm under ideal and non ideal assimilation conditions. Specifically, we address five topics which are crucial to the design of an operational soil moisture assimilation system. (1) We show that soil moisture can be satisfactorily estimated at scales finer than the resolution of the brightness images (downscaling), provided sufficiently accurate fine-scale model inputs are available. (2) The satellite repeat cycle should be shorter than the average interstorm period. (3) The loss of optimality by using shorter assimilation intervals is offset by a substantial gain in computational efficiency. (4) Soil moisture can be satisfactorily estimated even if quantitative precipitation data are not available. (5) The assimilation algorithm is only weakly sensitive to inaccurate specification of the soil hydraulic properties. In summary, we demonstrate the feasibility of large-scale land surface data assimilation from passive microwave observations.by Rolf H. Reichle.Ph.D

Near-surface soil moisture assimilation for quantifying effective soil hydraulic properties using genetic algorithm: 1. Conceptual modeling

We used a genetic algorithm (GA) to identify soil water retention (h) and hydraulic conductivity K(h) functions by inverting a soil-water-atmosphere-plant (SWAP) model using observed near-surface soil moisture (0-5 cm) as search criterion. Uncertainties of parameter estimates were estimated using multipopulations in GA and considering data and modeling errors. Three hydrologic cases were considered: (1) homogenous free-draining soil column, (2) homogenous soil column with shallow water table, and (3) heterogeneous soil column under free-drainage condition, considering three different rainfall patterns in northern Texas. Results (cases 1 and 2) showed the identifiability of soil hydraulic parameters improving at coarse and fine scales of the soil textural class. Medium-textured soils posed identifiability problems when the soil is dry. Nonlinearity in (h) and K(h) is greater at drier conditions, and some parameters are less sensitive for estimation. Flow regimes controlled by upward fluxes were found less successful, as the information content of observed near-surface data may no longer influence the hydrologic processes in the subsurface. The identifiability of soil hydraulic parameters was found better when the soil profile is predominantly draining. In case 3, top soil layer hydraulic properties were defined using near-surface data alone as criterion. Adding evapotranspiration (ET) improved identification of the second soil layer, although not all parameters were identifiable. Under uncertainties, (h) was found to be well defined while K(h) is more uncertain. Finally, we applied the method to a validation site in Little Washita watershed, Oklahoma, where derived effective soil hydraulic properties closely matched the measured ones at the field site

Final Report of the DAUFIN project

DAUFIN = Data Assimulation within Unifying Framework for Improved river basiN modeling (EC 5th framework Project