On the Statistical and Scaling Properties of Observed and Simulated Soil Moisture

abstract: Soil moisture (θ) is a fundamental variable controlling the exchange of water and energy at the land surface. As a result, the characterization of the statistical properties of θ across multiple scales is essential for many applications including flood prediction, drought monitoring, and weather forecasting. Empirical evidences have demonstrated the existence of emergent relationships and scale invariance properties in θ fields collected from the ground and airborne sensors during intensive field campaigns, mostly in natural landscapes. This dissertation advances the characterization of these relations and statistical properties of θ by (1) analyzing the role of irrigation, and (2) investigating how these properties change in time and across different landscape conditions through θ outputs of a distributed hydrologic model. First, θ observations from two field campaigns in Australia are used to explore how the presence of irrigated fields modifies the spatial distribution of θ and the associated scale invariance properties. Results reveal that the impact of irrigation is larger in drier regions or conditions, where irrigation creates a drastic contrast with the surrounding areas. Second, a physically-based distributed hydrologic model is applied in a regional basin in northern Mexico to generate hyperresolution θ fields, which are useful to conduct analyses in regions and times where θ has not been monitored. For this aim, strategies are proposed to address data, model validation, and computational challenges associated with hyperresolution hydrologic simulations. Third, analyses are carried out to investigate whether the hyperresolution simulated θ fields reproduce the statistical and scaling properties observed from the ground or remote sensors. Results confirm that (i) the relations between spatial mean and standard deviation of θ derived from the model outputs are very similar to those observed in other areas, and (ii) simulated θ fields exhibit the scale invariance properties that are consistent with those analyzed from aircraft-derived estimates. The simulated θ fields are then used to explore the influence of physical controls on the statistical properties, finding that soil properties significantly affect spatial variability and multifractality. The knowledge acquired through this dissertation provides insights on θ statistical properties in regions and landscape conditions that were never investigated before; supports the refinement of the calibration of multifractal downscaling models; and contributes to the improvement of hyperresolution hydrologic modeling.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201

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

Modeling and application of soil moisture at varying spatial scales with parameter scaling

The dissertation focuses on characterization of subpixel variability within a satellite-based remotely sensed coarse-scale soil moisture footprint. The underlying heterogeneity of coarse-scale soil moisture footprint is masked by the area-integrated properties within the sensor footprint. Therefore, the soil moisture values derived from these measurements are an area average. The variability in soil moisture within the footprint is introduced by inherent spatial variability present in rainfall, and geophysical parameters (vegetation, topography, and soil). The geophysical parameters/variables typically interact in a complex fashion to make soil moisture evolution and dependent processes highly variable, and also, introduce nonlinearity across spatio-temporal scales. To study the variability and scaling characteristics of soil moisture, a quasi-distributed Soil-Vegetation-Atmosphere-Transfer (SVAT) modeling framework is developed to simulate the hydrological dynamics, i.e., the fluxes and the state variables within the satellite-based soil moisture footprint. The modeling framework is successfully tested and implemented in different hydroclimatic regions during the research. New multiscale data assimilation and Markov Chain Monte Carlo (MCMC) techniques in conjunction with the SVAT modeling framework are developed to quantify subpixel variability and assess multiscale soil moisture fields within the coarse-scale satellite footprint. Reasonable results demonstrate the potential to use these techniques to validate multiscale soil moisture data from future satellite mission e.g., Soil Moisture Active Passive (SMAP) mission of NASA. The results also highlight the physical controls of geophysical parameters on the soil moisture fields for various hydroclimatic regions. New algorithm that uses SVAT modeling framework is also proposed and its application demonstrated, to derive the stochastic soil hydraulic properties (i.e., saturated hydraulic conductivity) and surface features (i.e., surface roughness and volume scattering) related to radar remote sensing of soil moisture

Spatiotemporal analyses of soil moisture from point to footprint scale in two different hydroclimatic regions

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

Spatiotemporal analyses of soil moisture from point to footprint scale in two different hydroclimatic regions

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 x 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

Soil moisture and water stage estimation using precipitation radar

In south-western United States, soil moisture data is important for drought studies in the region which is experiencing a drought for many years, whereas in South Florida, water stage data is required by hydrologists to monitor the hydrological flow in wetlands. Soil moisture data and water stage data are not sufficiently available due to sparse monitoring stations. Installation of dense measuring stations over an extended area is costly and labor intensive. Therefore, there is a need to develop an alternative method of measuring soil moisture and water stage. Microwave remote sensing has proven to be a useful tool in the measurement of various surface variables from space. This research explores the capability of microwave remote sensing to measure soil moisture and water stage on the earth from space. Tropical Rainfall Measuring Mission Precipitation Radar (TRMMPR) provides the Ku -band backscatter measurements that are used to measure soil moisture and water stage. Models that relate soil moisture and water stage to TRMMPR backscatter (σ°) are developed. The dependence of σ° on the dielectrical and physical characteristics of the land surface is used as the basis of this research. The soil moisture content affects σ° by changing the dielectric constant of the surface whereas the vegetation density affects σ° by changing the physical characteristics of the surface. Vegetation density in the model is represented by Normalized Difference Vegetation Index (NDVI). Dependence of σ° on partial submergence of vegetation in inundated areas is used to measure water stage in wetlands of South Florida. The effects of the exposed vegetation above the water surface on the model are assessed by comparing two cases of model run3 (a) that includes NDVI in the model, and (b) that does not include NDVI in the model. Eleven years of data is used in this research where 75% of the data is used for calibration of the model and 25% of the data is used for validation. The estimated values of soil moisture and water stage are compared to the observed values and the performance of the models is assessed by calculating correlation coefficients, calculating root mean square errors, and plotting non-exceedance probability plots for the absolute error between observed and modeled values. The soil moisture and water stage models work reasonably well and are able to estimate soil moisture and water stage with low errors. The soil moisture model works better in low vegetated areas because low vegetation allows the incident radiation to penetrate through the canopy cover and provide measurements from underlying surfaces. The water stage model works better in shrublands where there are no tree trunks and the model has an immediate impact from the vegetation canopy. This research provides an alternate way of measurement of soil moisture and water stage using remote sensing

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

Landslide susceptibility mapping through enhanced dynamic slope stability analysis using earth observing satellite measurements

Landslides are common throughout the world and can be triggered by earthquakes, volcanoes, floods, and heavy continuous rainfall in mountainous regions. For most types of slope failure, soil moisture plays a critical role because increased pore water pressure reduces the soil strength and increases stress. The combined effect of soil moisture in unsaturated soil layers and pore water pressure in saturated soil layers is critical to accurately predict landslides. However, dynamic in-situ soil moisture profiles are rarely measured on regional or global scales. The dynamic soil moisture can be estimated by a soil vegetation atmosphere transfer (SVAT) model or satellite. While a SVAT model can estimate soil moisture profile, satellite estimates are limited to the upper thin surface (0-5 cm). However, considering the complex database needed for a SVAT model, satellite data can be obtained quickly and may produce promising results in less data-rich regions at the global scale. While no previous landslide studies have used remotely-sensed soil moisture data, Advanced Microwave Scanning Radiometer (AMSR-E) has the potential to be useful in characterizing soil moisture profiles. First this study investigated relationships among landslides, AMSR-E soil moisture and Tropical Rainfall Measuring Mission (TRMM) in landslide prone regions of California, U.S., Leyte, Philippines and Dhading, Nepal. Then, a modified infinite slope stability model was developed and applied at Cleveland Corral, California, US and Dhading Nepal, using variable infiltration capacity (VIC-3L) soil moisture and AMSR-E soil moisture to develop dynamic landslide susceptibility maps at regional scale. Results show a strong relationship among remotely sensed soil moisture, rainfall and landslide events. Results also show a modified infinite slope stability model that directly includes vadose zone soil moisture can produce promising landslide susceptibility maps at regional scale using either VIC-3L or AMSR-E soil moisture. Vadose zone soil moisture has a significant role in shallow slope failure. Results show promising agreement between the susceptible area predicted by the model and the actual slope movements and slope failures observed in the study region. This model is quite reasonable to use in shallow slope stability analysis at a regional or global scale

Hydrological simulations at basin scale using distributed model and remote sensing with a focus of soil moisture

Remotely-sensed precipitation and soil moisture products are becoming increasingly important sources of information in earth science system. However, there are still high degree of uncertainties inherited in remotely-sensed precipitation and soil moisture products, and limited studies have focused on evaluation of these products. In this study, GEOtop model (Rigon et al. 2006), which is physically-based distributed hydrological model, is used to assess the use of remotely-sensed precipitation and soil moisture products for hydrological applications. The study area is Little Washita watershed (583 km2), Oklahoma, USA. To assess these products, the model has to be first calibrated and validated at different locations in the watershed using extensive ground-based measurements. The Southern Great Plains 1997 (SGP97) and SGP99 Hydrology Experiment are used for model calibration and validation, respectively. The model is reasonably calibrated and validated at watershed scale at different locations in the watershed for: heat fluxes, soil temperature profiles, soil moisture profiles, and streamflows. Regarding soil moisture evolution, we studied the spatial variability of the near-surface soil moisture from GEOtop simulations and estimates from Electronically Scanned Thinned Array Radiometer (ESTAR). Results show that GEOtop simulations and ESTAR estimates show very different magnitude and spatial patterns of near-surface soil moisture. Spatial patterns derived from GEOtop simulations are in agreement with the previous findings obtained from the same study area using ground-based measurements of soil moisture and theoretical model simulations. We conclude that GEOtop simulation results are more accurate and that ESTAR estimates are not a reliable source of data for characterizing the spatial variability of near-surface soil moisture. GEOtop simulations show that the spatial distribution of near-surface soil moisture is highly controlled by soil texture and river network. Furthermore, we investigated the effect of vegetation, surface roughness, and topography on ESTAR. Results show that there are insignificant effects of vegetation except for interception, surface roughness, and topography on ESTAR. In addition, we investigated the scaling properties of near-surface soil moisture. Results show that near-surface soil moisture has multiscaling behaviour. On the other hand, spatial soil moisture patterns are studied using geostatistical techniques: Ordinary kriging, external drift kriging and conditional Gaussian simulations (CGSs). Krigings show that soil moisture patterns in the watershed are highly controlled by gradient and cosine aspect. All CGSs clearly show soil moisture patterns. Spatial soil moisture patterns produced by CGSs are much better than the patterns reproduced by kriging algorithms. Regarding remotely-sensed precipitation products, we have investigated the utility of these products for hydrological simulations during non-winter seasons. Results show that all remotely-sensed precipitation products (Climate Prediction Center’s morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Cloud Classification System (PERSIANN-CCS)- and Next Generation Weather Radar (NEXRAD Stage III)) are fairly reproducing the streamflows, but CMORPH often overestimates streamflows. Thus it concluded that all the above mentioned remotely-sensed precipitation products have value for streamflow simulations