Agriculture Related Collaboration Study:
Dr. Fang, along with Dr. Jianzhong Su, UTA math professor and chairman of the Department of Mathematics, is currently working on a four-year grant for an Alliance for Smart Agriculture in the Internet of Things Era project. This project features both research and undergraduate education. It will encourage graduate and undergraduate UTA STEM students to expand their opportunities in the agriculture sector. A team of graduate and undergraduate students under Dr. Fang and Dr. Su’s advice formed a Smart Agriculture learning community and are being mentored in a research oriented learning process through experiential learning, internship and research opportunities. There are serval research directions under this grand and one of them is a machine-learning enhanced smart irrigation system. The goal of this research project io optimize irrigation system including scheduling, equipment, and other factors. In order to optimize the irrigation scheduling, to have a comprehensive understanding of the soil moisture content is the first step. However, most of the municipal landscaping sites rely on a sparse network of point measurement to estimate irrigation requirement. To evaluate the feasibility of spatially-variable irrigation management, an innovative system known as Precision Irrigation Soil Moisture Mapper (PrISMM) was developed in which an Unmanned Aerial Vehicle (UAV) equipped with multispectral sensors is used to estimate volumetric water content (VWC) using a thermal inertia approach. PrISMM consists of four central components, including (1) high-resolution thermal and optical remotely-sensed data, (2) site-specific soil analysis, (3) surface energy balance modeling and (4) machine learning module.
Flood Alert System (FAS) Related Projects:
The state of Texas is vulnerable to various extreme weather events, including record amounts of rain, such as the multiple events in 2015 and 2016, and the catastrophic flooding caused by Hurricane Harvey. Recognizing the state’s long and well-documented history of flooding and hurricanes, as well as its ongoing efforts to mitigate future disaster effects in its most vulnerable areas, the Texas Water Development Board (TWDB) stepped up to lead the efforts of rebuilding a disaster-resilient Texas. How the impacts of these events need to be mitigated will depend on many solutions, and one of such is Flood Early Warning Systems (FEWS). The FEWS, as non-structural flood mitigation tools, have become more popular among the flood-prone communities than ever due to their life-saving functions like rainfall and river level monitoring, real-time flood forecasting, and estimating potential damages to different communities while remaining low cost compared to other infrastructure-related mitigation solutions. Many communities in Texas have years of experience in developing and enhancing the application of FEWS with individual practical needs and flood level threats.
To better share the past experience and provide a useful guidance with the communities who are considering investing in FEWS in the near future, the TWDB has tasked a strong research team led by Dr. Nick Fang (PI) (the University of Texas at Arlington), Dr. Philip B. Bedient (Co-PI) (Rice University), Professor Michael Zaretsky (Co-PI) (the University of Texas at Arlington), and Dr. Samuel Brody (Co-PI) (the Texas A&M University at Galveston) to (1) gather and organize lessons learned from the communities using FEWS to cope with repetitive flooding events, (2) make recommendations to the TWDB and state officials for regional oversight and coordination of flood mitigations, and (3) create an effective FEWS guidance manual that will be particularly tailored for the communities in Texas. Not only will the FEWS technical guidance serve for future needs in the flood mitigation grant applications but also become a reference guidance for community leaders, county judges, and floodplain managers to aid in the mitigation from future flooding events. The pertinent information will be gathered in an organized database in collaboration with TWDB, and provided for use by the TWDB, other governmental entities, and the public.
Hydrologic Statistics:
This project shall research the generalized (regional) skew coefficients (RegSkew) and other measures of distribution shape in and near Texas. RegSkews, which are derived by procedures integrating sample at-site skew values obtained at many stream gages, are important for peak-streamflow frequency (flood frequency) analyses because of the high sampling variability. The measures of distribution geometry are normally derived from research of United States Geological Survey (USGS) instantaneous annual peak streamflow data and ancillary watershed properties. However, identification of applicable time periods of the USGS observational record is complex and critical for execution of this research, and the USGS peak-values database provides only qualitative information to this effect. Texas is in need of new RegSkews for hydrologic design due to the currently used being out of date relative to Federal guidelines. Future flood frequency analyses shall inherently be more reliable and shall decrease uncertainties when new RegSkews are in use and in particular with the Advisory Committee on Water Information (ACWI) Expected Moments Algorithm of Bulletin 17C. Bulletin 17C currently recommends Bayesian generalized least squares (B-GLS) concepts to estimate RegSkews because B-GLS reflects the precision of available estimates, their cross correlations, and the precision of the regional model. This project shall report on the results of B-GLS for Texas. The complexity of the Texas flood hydrology, due to a broad spectrum of wide ranging climatic, rural to urban development conditions, and potential flood-flow regulation effects, requires further research of spatial and temporal trends in annual peaks and empirical distributions. Further, RegSkew and other measures of distribution shape concepts and methods that incorporate machine learning and generalized additive models shall be explored in this project to fully discern probability distribution shape and prediction for the distal tail estimation of flood frequency. The project shall also produce products and training materials suitable for self-training and inclusion in workforce development facilitated training.
Loss Rate Study:
As a main part of hydrologic losses, infiltration plays an important role in runoff estimation, which can further serve as design criteria and flood forecast information. Among various infiltration models, the initial abstraction and constant loss method (IA&CL) is widely applied based on a concept that any watershed can be assumed to store an absolute depth of rainfall at the beginning of the rainfall as initial abstraction (IA) and then reduce the rainfall rate at a constant loss (CL) rate. Due to the simplicity and the lack of physical equivalent properties, conceptual methods like IA&CL are often subject to issues in parameterization/calibration. Therefore, the UTA team is tasked by the USACE–Fort Worth District to better evaluate these initial and constant loss rates that have been observed to occur during rare or extreme precipitation events in Texas.
The UTA research team was particularly tasked to evaluate and correlate the initial and constant losses with soil types, development of storage in the basins, and rare frequency peak discharges (i.e. 2%, 1%, and 0.2% annual chance exceedance) for a list of watersheds that the USACE-Fort Worth District has already developed models for. This loss rate study would help USACE gain a better knowledge in understanding how hydrologic responses react to initial and constant losses during extreme events by completing the following tasks:
A storm catalog with at least 50 observed storms in Texas that have precipitation depths greater than 10 inches in 24 hours.
- A sensitivity analysis of hydrologic loss rate parameters used in the current hydrologic models for the selected watersheds.
- Calibration of loss parameters using extreme storms from the storm catalog.
- Evaluation of antecedent soil moisture and watershed storage for the selected events.
- Incorporation of soil types, basin storage and rare frequency peak flow into initial and constant losses parameters based on available information.