Massive Spatiotemporal Watershed Hydrological Storm Event Response Model (MHSERM) with Time-Lapsed NEXRAD Radar Feed

Correctly and efficiently estimating hydrological responses corresponding to a specific storm event at the streams in a watershed is the main goal of any sound water resource management strategy. Methods for calculating a stream flow hydrograph at the selected streams typically require a great deal of spatial and temporal watershed data such as geomorphological data, soil survey, landcover, precipitation data, and stream network information to name a few. However, extracting and preprocessing such data for estimation and analysis is a hugely time-consuming task, especially for a watershed with hundreds of streams and lakes and complicated landcover and soil characteristics. To deal with the complexity, traditional models have to simplify the watershed and the streams network, use average values for each subcatchment, and then indirectly validate the model by adjusting the parameters through calibration and verification. To obviate such difficulties, and to better utilize the new, high precision spatial/temporal data, a new massive spatiotemporal watershed hydrological storm event response model (MHSERM) was developed and implemented on ESRI ArcMap platform. Different from other hydrological modeling systems, the MHSERM calculated the rainfall run off at a resolution of finer grids that reflects high precision spatial/temporal data characteristics of the watershed, not at conventional catchment or subcatchment scales, and that can simulate the variations of terrain, vegetation and soil far more accurately. The MHSERM provides a framework to utilize the USGS DEM and Landcover data, NRCS SSURGO and STATSGO soil data and National Hydrology Dataset (NHD) by handling millions of elements (grids) and thousands of streams in a real watershed and utilizing the Spatiotemporal NEXRAD precipitation data for each grid in pseudo real-time. Specifically, the MHSERM model has the following new functionalities: (1) Grid the watershed on the basis of high precision data like USGS DEM and Landcover data, NRCS SSURGO and STATSGO soil data, e.g., at a 30 meter by 30 meter resolution; (2) Delineate catchments based on the USGS National Digital Elevation Model (DEM) and the stream network data of the National Hydrography Dataset (NHD); (3) Establish the stream network and routing sequence for a watershed with hundreds of streams and lakes extracted from the National Hydrography Dataset (NHD) either in a supervised or unsupervised manner; (4) Utilize the NCDC NEXRAD precipitation data as spatial and temporal input, and extract the precipitation data for each grid; (5) Calculate the overland runoff volume, flow path and slope to the stream for each grid; (6) Dynamically estimates time of concentration to the stream for each interval, and only for the grids with rainfall excess, not for the whole catchment; (7) Deal with different hydrologic conditions (Good, Fair, Poor) for landcover data and different Antecedent Moisture Condition (AMC); (8) Process single or a series of storm events automatically; thus, the MHSERM model is capable of simulating both discrete and continuous storm events; (9) Calculate the temporal flow rate (i.e., hydrograph) for all the streams in the stream network within the watershed, save them to a database for further analysis and evaluation of various what-if scenarios and BMP designs. In MHSERM model, the SCS Curve number method is used for calculating overland flow runoff volume, and the Muskingum-Cunge method is used for flow routing of the stream network

Riverine flooding using GIS and remote sensing

Floods are caused by extreme meteorological and hydrological changes that are influenced directly or indirectly by human activities within the environment. The flood trends show that floods will reoccur and shall continue to affect the livelihoods, property, agriculture and the surrounding environment. This research has analyzed the riverine flood by integrating remote sensing, Geographical Information Systems (GIS), and hydraulic and/or hydrological modeling, to develop informed flood mapping for flood risk management. The application of Hydrological Engineering Center River Analysis System (HEC RAS) and HEC HMS models, developed by the USA Hydrologic Engineering Center of the Army Corps of Engineers in a data-poor environment of a developing country were successful, as a flood modeling tools in early warning systems and land use planning. The methodology involved data collection, preparation, and model simulation using 30m Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) as a critical data input of HEC RAS model. The findings showed that modeling using HEC-RAS and HEC HMS models in a data-poor environment requires intensive data enhancements and adjustments; multiple utilization of open sources data; carrying out multiple model computation iterations and calibration; multiple field observation, which may be constrained with time and resources to get reasonable output

Geo-Spatial Analysis in Hydrology

Geo-spatial analysis has become an essential component of hydrological studies to process and examine geo-spatial data such as hydrological variables (e.g., precipitation and discharge) and basin characteristics (e.g., DEM and land use land cover). The advancement of the data acquisition technique helps accumulate geo-spatial data with more extensive spatial coverage than traditional in-situ observations. The development of geo-spatial analytic methods is beneficial for the processing and analysis of multi-source data in a more efficient and reliable way for a variety of research and practical issues in hydrology. This book is a collection of the articles of a published Special Issue Geo-Spatial Analysis in Hydrology in the journal ISPRS International Journal of Geo-Information. The topics of the articles range from the improvement of geo-spatial analytic methods to the applications of geo-spatial analysis in emerging hydrological issues. The results of these articles show that traditional hydrological/hydraulic models coupled with geo-spatial techniques are a way to make streamflow simulations more efficient and reliable for flood-related decision making. Geo-spatial analysis based on more advanced methods and data is a reliable resolution to obtain high-resolution information for hydrological studies at fine spatial scale

Nitrate Removal and Placement of Floating Treatment Wetlands in the Midwest

The Midwestern United States is vulnerable to eutrophic conditions from high nutrient concentrations. Recommendations for nonpoint source pollution management include runoff treatment (i.e., filter strips, riparian buffers) and in-situ lake treatment practices (i.e., aluminum sulfate (alum) treatments, aeration, up/downdraft pumping, floating treatment wetlands). Floating treatment wetlands (FTWs) are an innovative wetland design for nutrient removal from nonpoint sources and provide a unique in-situ treatment. Best management practice studies have commonly focused on adjacent to water practices, which have resulted in a gap for guidance for in-situ treatment placement and design. Therefore, the objectives of this project were to (1) Quantify nitrate removal for Midwestern floating treatment wetlands during the establishment year and (2) develop a lake mapping method utilizing chemical and physical water sensors in conjunction with visualization software to characterize the dynamics of a nutrient enriched lake in Lancaster County, NE. This study provides new insight on the impacts of water quality treatment of floating treatment wetlands based on growing season, plant species, and carbon amendments for nitrate-N removal performance during the establishment year and presents a novel monitoring assessment technique for in-situ best management practice implementation within waterbodies. Advisor: Tiffany L. Messe

Development of UAS-Based Construction Stormwater Inspections & Soil Loss Model

Erosion and sediment control practices on construction sites provide vital protection for the environment, minimizing the impact from sediment-laden runoff associated with construction activities. Federal, state, and local regulations require regular inspections of erosion and sediment control practices to ensure their performance is adequate. This study developed an innovative approach to stormwater inspections and design guidance by integrating tools and guidance into aerial stormwater inspection outcomes. Aerial inspections were integrated with photogrammetry, geospatial information systems, and deep learning-based object detection applications to assist in performing inspections and develop site plans, hydrologic analyses, practice detection, and soil loss modeling. Orthomosaic views were used for creating site plans and developing object detection data sources. Digital Surface Models (DSMs) were developed as datasets for evaluating the performance of the E&SC practices on site. These surface models were used for running hydrologic analyses and developing soil loss models. The use of DSMs improves stormwater inspections and design approaches since DSMs serve as datasets for evaluating design efficiency with the incorporation of aerial inspection outcomes. Trial inspections were performed at the U.S. highway 30 construction site in Tama County, Iowa. Preliminary results were prepared to demonstrate a comprehensive framework for aerial inspections in future studies. This research introduces aerial inspections as an effective method to streamline inspection procedures. This could be in the form of using fewer inspectors, providing better record keeping, having faster inspection procedures and developing efficient outcomes to evaluate the performance of practices. The study highlights the potential for this technology and developed approaches to be used in the construction industry

The Application of Microbial Source Tracking to aid in Site Prioritization for Remediation in Lower Michigan

Non-point source fecal pollution is a threat to both the environment and public health. Climate change, aging infrastructure, and intensified agricultural practices are predicted to accentuate this issue. In Michigan, due to the high instance of aging infrastructure and intensified agriculture, non-point source fecal pollution has caused many waterbodies to exceed the state standards posing a risk to recreational activities and source water. Due to this threat, there is an increased effort to identify and remediate these sources. My study focused on improving the identification of non-point source fecal pollution through a combination of culture-based and molecular fecal indicator bacteria (FIB) identification, combined with geospatial and statistical modeling approaches. In Chapter 2, I assessed associations between measured FIB and key watershed characteristics in two watersheds located in Ottawa County, Michigan: Bass River and Deer Creek. Results indicated several associations between watershed characteristics and monitored FIB, which should be considered in future non-point source monitoring efforts. In Chapter 3, I created a new tool to aid stakeholders in interpreting FIB monitoring results. This tool was applied to FIB data from the previous chapter as well as FIB data from five public beaches in Macomb County, Michigan. Results indicated that the framework could improve the interpretation of monitored data. Using this tool, stakeholders can better identify and remediate the most impaired areas first, maximizing their impact and minimizing costs. In Chapter 4, I assessed potential improvements to components of a commonly used geospatial model, the Agricultural Conservation Planning Framework (ACPF), and looked at the model’s ability to assess non-point source fecal pollution from runoff derived events. To determine this, I first updated the sediment delivery ratio (SDR) in runoff risk and compared the updated outputs to measured FIB to identify ACPF’s ability to assess FIB concentrations. Results indicated a significant difference between model outputs, but limitations in experimental design precluded an adequate assessment of my objective for this chapter. Recommendations on future studies to properly assess these objectives were offered

Methods and insights on enabling geovisualisation for coastal communities of the North Shore of Vancouver

Open Access via SHEDL agreementPeer reviewedPublisher PD

Field Deployment and Integration of Wireless Communication & Operation Support System for the Landscape Irrigation Runoff Mitigation System

The study of water conservation technologies is critically important due to the rapid growth in urban population leading to a shortage in potable water supplies throughout the world. Current water supplies are not expected to meet the water demand in the coming decades; this could seriously affect human lives and socio-economic stability. About 30 percent of the current municipal supplies are being used for outdoor irrigation such as gardening and landscaping. These numbers are increasing due to the increase in urban population. Due to the current inefficient or improper landscape irrigation practices, substantial amounts of water are lost in the form of runoff or due to evaporation. Runoff occurs when the irrigation precipitation rate exceeds the infiltration rate of the soil, which depends on the soil and site characteristics such as soil type and the slope of the site. Runoff being an obvious water wastage, it also poses a great problem to the environment with its potential for transporting fertilizers and pesticides into storm sewers and, eventually, surface waters. Thus, this study focuses on designing a smart operational support system for landscape irrigation that has the potential to reduce runoff and also decrease water losses in the form of evaporation. The system consists of two main units, the landscape irrigation runoff mitigation system (LIRMS) and an operational support system (OSS). The combined system is referred to as the second-generation LIRMS. The LIRMS is installed at the border of a field/lawn. The LIRMS consists of a central controller unit and a runoff sensor. Based on the feedback from the runoff sensor, the controller unit pauses and resumes irrigation as needed in order to reduce runoff. The main purpose of OSS is to automate the scheduling of the irrigation process. A multilayer perceptron based OSS was designed and implemented on a dedicated web-server. The OSS processes historical irrigation data and the environmental/weather data to choose an optimal schedule to irrigate on a given day. The OSS aims to reduce irrigation water losses due to natural environmental factors such as evaporation and rain. A wireless communication link is established between LIRMS and OSS for monitoring and analyzing irrigation events. The second-generation LIRMS was installed in the Texas A&M Turfgrass Research Field Laboratory, College Station, TX for performing irrigation tests. The preliminary results show that the average soil wetting efficiency has increased with the use of the operational support system when compared to previous tests performed without the operational support system. Also the results suggest that the second generation LIRMS has comparable runoff reductions when compared to the first-generation LIRMS. Yet, more tests are required to quantify the overall water savings

A Markov Chain Random Field Cosimulation-Based Approach for Land Cover Post-classification and Urban Growth Detection

The recently proposed Markov chain random field (MCRF) approach has great potential to significantly improve land cover classification accuracy when used as a post-classification method by taking advantage of expert-interpreted data and pre-classified image data. This doctoral dissertation explores the effectiveness of the MCRF cosimulation (coMCRF) model in land cover post-classification and further improves it for land cover post-classification and urban growth detection. The intellectual merits of this research include the following aspects: First, by examining the coMCRF method in different conditions, this study provides land cover classification researchers with a solid reference regarding the performance of the coMCRF method for land cover post-classification. Second, this study provides a creative idea to reduce the smoothing effect in land cover post-classification by incorporating spectral similarity into the coMCRF method, which should be also applicable to other geostatistical models. Third, developing an integrated framework by integrating multisource data, spatial statistical models, and morphological operator reasoning for large area urban vertical and horizontal growth detection from medium resolution remotely sensed images enables us to detect and study the footprint of vertical and horizontal urbanization so that we can understand global urbanization from a new angle. Such a new technology can be transformative to urban growth study. The broader impacts of this research are concentrated on several points: The first point is that the coMCRF method and the integrated approach will be turned into open access user-friendly software with a graphical user interface (GUI) and an ArcGIS tool. Researchers and other users will be able to use them to produce high-quality land cover maps or improve the quality of existing land cover maps. The second point is that these research results will lead to a better insight of urban growth in terms of horizontal and vertical dimensions, as well as the spatial and temporal relationships between urban horizontal and vertical growth and changes in socioeconomic variables. The third point is that all products will be archived and shared on the Internet