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

Soil moisture dynamics from satellite observations, land surface modeling, and field data

Knowledge of soil moisture variability is essential to understand hydrologic processes at a range of scales. In this study, spatio-temporal variability of soil moisture and inter-comparison among different soil moisture products were analyzed. The variability patterns were well characterized by negative exponential fitting as function of observed sampling extent scale. The simple physical soil moisture dynamics model was identified as an alternative approach to characterize statistical soil moisture variability. The soil moisture variability was strongly related to physical properties including rainfall and topography. Normal and log-normal distributions were recognized as the most efficient probability density functions to capture soil moisture variability patterns for all conditions. Further, these variability patterns were well maintained for root zone profile and surface soil moisture time stable characteristics can be used to upper boundary for sub-surface time stability. Through inter-comparison analysis, average soil moisture from remotely sensed measurements, ground-based measurements, and land surface model results showed excellent agreement. However, remotely sensed soil moisture had little variation, especially during the growing season. There were complementary benefits with low random errors for the land surface model and low system errors for the remotely sensed data. The error characteristics of remotely sensed measurements can enhance the utility of satellite observations. The remote sensing measurements can provide relative soil moisture conditions to improve runoff predictions and analyze land surface-atmosphere interactions for regional climate predictions in data limited areas. However, their extremely limited variations must be refined prior to direct application in hydrological processes. Overall, the identified soil moisture variability patterns provide a new understanding of soil moisture dynamics and spatio-temporal variability patterns as related to physical variables. These organized characteristics are essential to predict land-atmosphere interactions, rainfall-runoff processes, and groundwater recharge processes. Practically, these findings can be used to calibrate land surface models and to estimate heterogeneity effects of land surface processes. Additionally, statistical information as a function of scale is critical to develop upscaling and down-scaling methodologies without significant loss of information. This dissertation\u27s findings provide critical insight to hydrologic processes related to soil moisture at a range of scales

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

Understanding and Improving the Soil Moisture Retrieval Algorithm under Space, Time and Heterogeneity

The spatial and temporal monitoring of soil moisture from remote sensing platforms plays a pivotal role in predicting the future food and water security. That is, improving soil moisture estimation at remote sensing platforms has remarkable impacts in the fields of meteorology, hydrology, agriculture, and global climate change. However, remote sensing of soil moisture for long is hindered by spatial heterogeneity in land surface variables (soil, biomass, topography, and temperature) which cause systematic and random errors in soil moisture retrievals. Most soil moisture improvement methods to date focused on the downscaling of either coarse resolution soil moisture or brightness temperature based on fine scale ancillary information of land surface variables. Comparatively little work has been done on improving the parameterization of most sensitive variables to radiative transfer model that impact soil moisture retrieval accuracy. In addition, the classic radiative transfer model assumes the vegetation and surface roughness parameters, as constant with space and time which undermines the retrieval accuracy. Also, it is largely elusive so far the discussion on the non-linearity of microwave radiative transfer model and its relationship with energy and water fluxes. In order to address the above mentioned limitations, this dissertation aims to develop and validate a soil moisture modeling framework with associated improved parameterizations for surface roughness and vegetation optical depth (VOD) in the homogeneous and heterogeneous environments. To this end, the following research work is specifically conducted: (a) conduct comprehensive sensitivity analysis on radiative transfer model with space, time and hydroclimates; (b) develop multi-scale surface roughness model which incorporates small (soil) and large (topography) surface undulations to improve soil moisture retrievals; (c) improve the parameterization of vegetation topical depth (VOD) using within-pixel biomass heterogeneity to improved soil moisture accuracy; (d) investigate the non-linearity in microwave radiative transfer model, and its association with thermal energy fluxes. The results of this study showed that: (a) the total (linear + non-linear) sensitivity of soil, temperature and biomass variables varied with spatial scale (support), time, and hydro climates, with higher non-linearity observed for dense biomass regions. This non-linearity is also governed by soil moisture availability and temperature. Among these variables, surface roughness and vegetation optical depth are most sensitive variables to radiative transfer model (RTM); (b) considering the spatial and temporal variability in parameterization of surface roughness and VOD has improved soil moisture retrieval accuracy, importantly in cropland and forest environments; and (c) the soil moisture estimated through evaporative fraction (EF) correlates higher with VOD corrected soil moisture

Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SM measurement and monitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security

Improvement of Subsurface Flow Predictability Using Land Surface Model in the Unsaturated Zone at Various Spatial Scales

Subsurface flow in the unsaturated zone is an important component of the hydrologic cycle and plays a significant role in the water and energy balance through affecting various hydrological processes. Land surface models (LSMs) have been developed and extended during the past decades with various enhanced processes to understand and quantify the complex interaction between atmosphere and land surface systems. However, there are still critical deficiencies (e.g., simplified processes and parameterization) remaining in simulating land surface hydrology for land surface modeling. Thus, this dissertation focuses on understanding land surface processes from various land surface models and improving land surface processes and parameterization in land surface modeling in the unsaturated zone at various spatial scales. Two main approaches (Bayesian Model Averaging (BMA) based multi-model simulation and physically based hydrologic connectivity approach) to improve the land surface modeling predictability are presented in this dissertation. The BMA-based multi-model simulation approach was developed to reflect the strengths of the models under various land surface wetness conditions and to quantify the model parameter and structural uncertainties. The physically-based hydrologic connectivity concept was proposed to characterize the subsurface flow variability based on spatially distributed patterns of wetness condition or physical controls (e.g., soil type, vegetation, topography). Hydrologic connectivity is an important concept for understanding local processes in the context of catchment hydrology and defining flow path continuity in surface and subsurface flows. These approaches were applied in land surface modeling and tested in various hydro-climate regions and spatial scales showing significant improvement of modeling predictability. Based on the knowledge and experience gained from this dissertation, the proposed concepts will be useful to improve the hydrological model performance and better understand the subsurface flow variability in the unsaturated zone at various scales

Soil moisture modeling and scaling using passive microwave remote sensing

Soil moisture in the shallow subsurface is a primary hydrologic state governing land-atmosphere interaction at various scales. The primary objectives of this study are to model soil moisture in the root zone in a distributed manner and determine scaling properties of surface soil moisture using passive microwave remote sensing. The study was divided into two parts. For the first study, a root zone soil moisture assessment tool (SMAT) was developed in the ArcGIS platform by fully integrating a one-dimensional vadose zone hydrology model (HYDRUS-ET) with an ensemble Kalman filter (EnKF) data assimilation capability. The tool was tested with dataset from the Southern Great Plain 1997 (SGP97) hydrology remote sensing experiment. Results demonstrated that SMAT displayed a reasonable capability to generate soil moisture distribution at the desired resolution at various depths of the root zone in Little Washita watershed during the SGP97 hydrology remote sensing experiment. To improve the model performance, several outstanding issues need to be addressed in the future by: including "effective" hydraulic parameters across spatial scales; implementing subsurface soil properties data bases using direct and indirect methods; incorporating appropriate hydrologic processes across spatial scales; accounting uncertainties in forcing data; and preserving interactions for spatially correlated pixels. The second study focused on spatial scaling properties of the Polarimetric Scanning Radiometer (PSR)-based remotely sensed surface soil moisture fields in a region with high row crop agriculture. A wavelet based multi-resolution technique was used to decompose the soil moisture fields into larger-scale average soil moisture fields and fluctuations in horizontal, diagonal and vertical directions at various resolutions. The specific objective was to relate soil moisture variability at the scale of the PSR footprint (800 m X 800 m) to larger scale average soil moisture field variability. We also investigated the scaling characteristics of fluctuation fields among various resolutions. The spatial structure of soil moisture exhibited linearity in the log-log dependency of the variance versus scale-factor, up to a scale factor of -2.6 (6100 m X 6100 m) irrespective of wet and dry conditions, whereas dry fields reflect nonlinear (multi-scaling) behavior at larger scale-factors

Understanding Moisture Dynamics in the Vadose Zone: Transcending the Darcy Scale

Soil moisture forms the interface at which the partitioning of the energy, carbon and water budget for the land-surface occurs. Its variability impacts different fields of application at varying extent scales like agriculture at the field scale, meteorology at the regional scale and climate change assessment at the global scale. However, past literature has focused on understanding soil moisture dynamics at this diverse range of extent scales using soil moisture data at the Darcy support scale which cannot effectively cater to soil moisture dynamics for the current eco-hydrologic models that describe complex heterogeneous domains at remote sensing footprint scales. This dissertation serves to push the envelope of our understanding of soil moisture dynamics and its dependence on land-surface heterogeneity at the coarse remote sensing scales. The research questions answered in this dissertation include 1) determining the dominant land-surface controls of near-surface soil moisture dynamics at scales varying between the Darcy (of the order of a few centimeters) support and satellite footprint scale (25.6 km); 2) generating a framework for quantifying the relationships between antecedent wetness, land-surface heterogeneity and near-surface soil moisture at remote sensing scales and 3) evaluating variability in the root zone moisture dynamics as evaluated through evapo-transpiration estimates at different remote sensing footprint scales. The dominant land-surface factors controlling soil moisture distribution at different scales were determined by developing a new Shannon entropy based technique and non-decimated wavelet transforms. It was found that the land-surface controls on soil moisture vary with hydro-climate and antecedent wetness conditions. In general, the effect of soil was found to reduce with coarsening support scale while the effect of topography and vegetation increased. A novel Scale-Wetness-Heterogeneity (SWHET) cuboid was developed to coalesce the relationship between soil moisture redistribution and dominant physical controls at different land-surface heterogeneity and antecedent wetness conditions across remote sensing scales. The SWHET cuboid can potentially enable spatial transferability of the scaling relationships for near-surface soil moisture. It was found that results from the SWHET cuboid enabled spatial transferability of the scaling relationships between two similar hydro-climates (Iowa, U.S.A and Manitoba, Canada) under some wetness and land-surface heterogeneity conditions. Evapotranspiration estimates were computed at varying scales using airborne and satellite borne remotely sensed data. The results indicated that in a semi-arid row cropped orchard environment, a remote sensing support scale comparable to the row spacing and smaller or comparable to the canopy size of trees overestimates the land surface temperature and consequently, underestimates evapotranspiration