A Unifying Platform for Water Resources Management Using Physically-Based Model and Remote Sensing Data

In recent years, physically-based hydrological models provided a robust approach to better understand the cause-effect relationships of effective hydraulic properties in soil hydrology. These have increased the flexibility of studying the behavior of a soil system under various environmental conditions. One disadvantage of physical models is their inability to model the vertical and horizontal heterogeneity of hydraulic properties in a soil system at the regional scale. In order to overcome this limitation, inverse modeling may be used. Near surface soil moisture, which has been collected routinely by remote sensing (RS) platforms, and evapotranspiration, that is also a pivotal key for water balance near the land surface can be used as alternatives for quantifying the effective soil hydraulic parameters through inverse modeling. However, the new approach suffers from not only the scale discrepancy between RS pixel resolution and model grid resolution, but also its application in complex terrains. Furthermore, hydrological models require a number of required input parameters. Hence, this dissertation focuses on developing a methodology for addressing these problems. The field-scale Soil-Water-Atmosphere-Plant model (SWAP) was extended to regional application, and then coupled with a Genetic Algorithm (GA), to operate as the core of the developed decision support system at the regional level. Also, various stochastic processes were developed and applied to the GA for improving the searching ability of optimization algorithms. The computational simulation-optimization approach was tested and evaluated under various synthetic and field validation experiments demonstrating that the methodology provided satisfactory results. In this dissertation, the proposed methodologies analyzed the spatio-temporal root zone soil moisture with RS and in-situ soil moisture data at the multiple scales. Also, these approaches could provide better input parameters for hydro-climatic models, resulting in better understanding of the hydrologic cycle. Thus, a better understanding of water cycle would help us to be better prepared for efficient water resources management, agriculture, and devastating natural disasters in the real world

Soil hydraulic properties in one-dimensional layered soil profile using layer-specific soil moisture assimilation scheme

We developed a layer-specific soil-moisture assimilation scheme using a simulation-optimization framework, Soil-Water-Atmosphere-Plant model with genetic algorithm (SWAP-GA). Here, we explored the quantification of the soil hydraulic properties in a layered soil column under various combinations of soil types, vegetation covers, bottom boundary conditions and soil layering using idealized (synthetic) numerical studies and actual field experiments. We demonstrated that soil layers and vertical heterogeneity (layering arrangements) could impact to the uncertainty of quantifying soil hydraulic parameters. We also found that, under layered soil system, when the subsurface flows are dominated by upward fluxes, e.g., from a shallow water table, the solution to the inverse problem appears to be more elusive. However, when the soil profile is predominantly draining, the soil hydraulic parameters could be fairly estimated well across soil layers, corroborating the results of past studies on homogenous soil columns. In the field experiments, the layer-specific assimilation scheme successfully matched soil moisture estimates with observations at the individual soil layers suggesting that this approach could be applied in real world conditions

Estimating Effective Soil Hydraulic Properties Using Spatially Distributed Soil Moisture and Evapotranspiration

With the development of many earth-observing remote sensing (RS) platforms, spatially distributed remote sensing products are becoming critical inputs to many hydrologic and meteorological models. Remotely sensed soil moisture (SM) and evapotranspiration (ET) including ground-based data have the potential to be used for estimating pixel-scale soil hydraulic parameters. However, only a few studies have been conducted to better understand the impact of assimilating both SM and ET in estimating soil hydraulic properties of the root zone. In this study, we used inverse modeling based on the Noisy Monte Carlo Genetic Algorithm by linking RS SM and ET derived from the Surface Energy Balance Algorithm for Land for estimating pixel-scale effective soil hydraulic properties. Walnut Creek (Iowa), Brown (Illinois), and Lubbock (Texas) test sites were selected to assess the performance of this approach from point to satellite scales using synthetic and validation experiments. For comparison purposes, inverse modeling results were analyzed under three scenarios (ET only, SM only, and SM + ET in the optimization criteria). These results showed that considering both SM and ET components improved the estimations of effective soil hydraulic properties and reduced their uncertainties better than SM or ET only. Overall, although uncertainty exists, our proposed SM + ET based scheme performed well in estimating effective soil hydraulic properties at multiple spatial scales (point, airborne, and satellite footprints) under various hydroclimatic conditions

Development of a Web-Based L-THIA 2012 Direct Runoff and Pollutant Auto-Calibration Module Using a Genetic Algorithm

The Long-Term Hydrology Impact Assessment (L-THIA) model has been used as a screening evaluation tool in assessing not only urbanization, but also land-use changes on hydrology in many countries. However, L-THIA has limitations due to the number of available land-use data that can represent a watershed and the land surface complexity causing uncertainties in manually calibrating various input parameters of L-THIA. Thus, we modified the L-THIA model so that could use various (twenty three) land-use categories by considering various hydrologic responses and nonpoint source (NPS) pollutant loads. Then, we developed a web-based auto-calibration module by integrating a Genetic-Algorithm (GA) into the L-THIA 2012 that can automatically calibrate Curve Numbers (CNs) for direct runoff estimations. Based on the optimized CNs and Even Mean Concentrations (EMCs), our approach calibrated surface runoff and nonpoint source (NPS) pollution loads by minimizing the differences between the observed and simulated data. Here, we used default EMCs of biochemical oxygen demand (BOD), total nitrogen (TN), and total phosphorus-TP (as the default values to L-THIA) collected at various local regions in South Korea corresponding to the classifications of different rainfall intensities and land use for improving predicted NPS pollutions. For assessing the model performance, the Yeoju-Gun and Icheon-Si sites in South Korea were selected. The calibrated runoff and NPS (BOD, TN, and TP) pollutions matched the observations with the correlation (R2: 0.908 for runoff and R2: 0.882–0.981 for NPS) and Nash-Sutcliffe Efficiency (NSE: 0.794 for runoff and NSE: 0.882–0.981 for NPS) for the sites. We also compared the NPS pollution differences between the calibrated and averaged (default) EMCs. The calibrated TN and TP (only for Yeoju-Gun) EMCs-based pollution loads identified well with the measured data at the study sites, but the BOD loads with the averaged EMCs were slightly better than those of the calibrated EMCs. The TP loads for the Yeoju-Gun site were usually comparable to the measured data, but the TP loads of the Icheon-Si site had uncertainties. These findings indicate that the web-based auto-calibration module integrated with L-THIA 2012 could calibrate not only the surface runoff and NPS pollutions well, but also provide easy access to users across the world. Thus, our approach could be useful in providing a tool for Best Management Practices (BMPs) for policy/decision-makers

Development of Web GIS-Based VFSMOD System with Three Modules for Effective Vegetative Filter Strip Design

In recent years, Non-Point Source Pollution has been rising as a significant environmental issue. The sediment-laden water problem is causing serious impacts on river ecosystems not only in South Korea but also in most countries. The vegetative filter strip (VFS) has been thought to be one of the most effective methods to reduce the transport of sediment to down-gradient area. However, the effective width of the VFS first needs to be determined before VFS installation in the field. To provide an easy-to-use interface with a scientific VFS modeling engine, the Web GIS-based VFSMOD system was developed in this study. The Web GIS-based VFSMOD uses the UH and VFSM executable programs from the VFSMOD-w model as core engines to simulate rainfall-runoff and sediment trapping. To provide soil information for a point of interest, the Google Map interface to the MapServer soil database system was developed using the Google Map API, Javascript, Perl/CGI, and Oracle DB programming. Three modules of the Web GIS-based VFSMOD system were developed for various VFS designs under single storm, multiple storm, and long-term period scenarios. These modules in the Web GIS-based VFSMOD system were applied to the study watershed in South Korea and these were proven as efficient tools for the VFS design for various purposes

Development of Web GIS-Based VFSMOD System with Three Modules for Effective Vegetative Filter Strip Design

In recent years, Non-Point Source Pollution has been rising as a significant environmental issue. The sediment-laden water problem is causing serious impacts on river ecosystems not only in South Korea but also in most countries. The vegetative filter strip (VFS) has been thought to be one of the most effective methods to reduce the transport of sediment to down-gradient area. However, the effective width of the VFS first needs to be determined before VFS installation in the field. To provide an easy-to-use interface with a scientific VFS modeling engine, the Web GIS-based VFSMOD system was developed in this study. The Web GIS-based VFSMOD uses the UH and VFSM executable programs from the VFSMOD-w model as core engines to simulate rainfall-runoff and sediment trapping. To provide soil information for a point of interest, the Google Map interface to the MapServer soil database system was developed using the Google Map API, Javascript, Perl/CGI, and Oracle DB programming. Three modules of the Web GIS-based VFSMOD system were developed for various VFS designs under single storm, multiple storm, and long-term period scenarios. These modules in the Web GIS-based VFSMOD system were applied to the study watershed in South Korea and these were proven as efficient tools for the VFS design for various purposes

An unmixing algorithm for remotely sensed soil moisture

We present an unmixing method, based on genetic algorithm-soil-vegetation-atmosphere-transfer modeling to extract subgrid information of soil and vegetation from remotely sensed soil moisture (downscaled; e.g., soil hydraulic properties, area fractions of soil-vegetation combinations, and unmixed soil moisture time series) that most land surface models use. The unmixing method was evaluated using numerical experiments comprising mixed pixels with simple and complex soil-vegetation combinations, in idealized case studies (with or without uncertainty) and under actual field conditions (Walnut Creek (WC11) field, Soil Moisture Experiment 2005, Iowa). Additional validation experiments were conducted at an airborne-remote sensing footprint (Little Washita (LW21) site, Southern Great Plains 1997 hydrology campaign, Oklahoma) using Electronically Scanning Thin Array Radiometer (ESTAR). Results of the idealized experiments suggest that the unmixing method can extract optimal or near-optimal solutions to the inverse problem under different hydrologic and climatic conditions. Errors in soil moisture data and initial and boundary conditions can compound uncertainty in the solution. The solutions generated under actual field conditions (WC11 field) were able to match soil moisture observations. Analysis showed that typical soil moisture retention curves of cataloged dominant soils in WC11 field did not match well with the measurements, but those derived from actual field-scale soil moisture inversion matched better. The unmixing method performed well in replicating soil hydraulic behavior at the ESTAR footprint. Unlike in WC11 field, the typical soil moisture retention curves of cataloged soils in LW21 field matched better with the measurements. We envisaged that the unmixing method can provide quick and easy way of extracting subgrid soil moisture variability and soil-vegetation information in a pixel

A Numerical Investigation of Delamination Response of CNT/Epoxy Film Interleaved Composite

In this study, numerical modeling through the cohesive zone theory was performed to simulate the end notch flexure (ENF) test with same conditions of the experimental results of previous study that investigated the effect of a carbon nanotube (CNT)/epoxy film in carbon fiber reinforced polymer (CFRP) composite through the mode II interlaminar fracture toughness of a non-interleaved, epoxy film interleaved, CNT/epoxy film interleaved CFRP laminate specimen. The effect of the presence of CNT/epoxy film interleave on a composite laminate was modeled. The influence of the interleave cohesive parameters was studied to link the parameters to the material strength and energy release rate. Cohesive parameter identification was performed by matching the initial loading and the damage evolution phase by dividing the cohesive zones into cohesive front and remaining cohesive zones. This is because, when modeling with a single cohesive zone, the critical load point that causes delamination or the curve after load drop do not match the experimental values. Results showed that the divided cohesive zone model is in good agreement with the experimental results and that there is a clear relationship between the cohesive energy of the interface and CNT/epoxy film parameters

Development of Web-Based RECESS Model for Estimating Baseflow Using SWAT

Groundwater has received increasing attention as an important strategic water resource for adaptation to climate change. In this regard, the separation of baseflow from streamflow and the analysis of recession curves make a significant contribution to integrated river basin management. The United States Geological Survey (USGS) RECESS model adopting the master-recession curve (MRC) method can enhance the accuracy with which baseflow may be separated from streamflow, compared to other baseflow-separation schemes that are more limited in their ability to reflect various watershed/aquifer characteristics. The RECESS model has been widely used for the analysis of hydrographs, but the applications using RECESS were only available through Microsoft-Disk Operating System (MS-DOS). Thus, this study aims to develop a web-based RECESS model for easy separation of baseflow from streamflow, with easy applications for ungauged regions. RECESS on the web derived the alpha factor, which is a baseflow recession constant in the Soil Water Assessment Tool (SWAT), and this variable was provided to SWAT as the input. The results showed that the alpha factor estimated from the web-based RECESS model improved the predictions of streamflow and recession. Furthermore, these findings showed that the baseflow characteristics of the ungauged watersheds were influenced by the land use and slope angle of watersheds, as well as by precipitation and streamflow

Development of Dynamic Ground Water Data Assimilation for Quantifying Soil Hydraulic Properties from Remotely Sensed Soil Moisture

Several inversion modeling-based approaches have been developed/used to extract soil hydraulic properties (α, n, θres, θsat, Ksat) from remotely sensed (RS) soil moisture footprints. Hydrological models with shallow ground water (SGW) table depths in soils simulate daily root zone soil moisture dynamics based on the extracted soil parameters. The presence of SGW table depths in soils significantly influences model performances; however, SGW table depths are usually unknown in the field, thus, unknown SGW table depths might cause uncertainties in the model outputs. In order to overcome these drawbacks, we developed a dynamic ground water (DGW) data assimilation approach that can consider SGW table depths across time for quantifying effective soil hydraulic properties in the unsaturated zone. In order to verify the DGW data assimilation scheme, numerical experiments comprising synthetic and field validation experiments were conducted. For the numerical studies, the Little Washita (LW) watershed in Oklahoma and Olney (OLN)/Bondville (BOND) sites in Illinois were selected as different hydroclimatic regions. For the synthetic conditions, we tested the DGW scheme using various soil textures and vegetation covers with fixed and dynamically changing SGW table depths across time in homogeneous and heterogeneous (layered) soil columns. The DGW-based soil parameters matched the observations under various synthetic conditions better than those that only consider fixed ground water (FGW) table depths in time. For the field validations, our proposed data assimilation scheme performed well in predicting the soil hydraulic properties and SGW table depths at the point, airborne sensing, and satellite scales, even though uncertainties exist. These findings support the robustness of our proposed DGW approach in application to regional fields. Thus, the DGW scheme could improve the availability and applicability of pixel-scale soil moisture footprints based on satellite platforms