Research
Advancing Drought Monitoring Using a Small Unmanned Aerial Systems (UAS) for Conjunctive Water Management in a Changing Climate", FY16-Present
Climate variability, weather extremes and climate change continue to threaten the sustainability of water resources in the western United States. Given current climate change projections, increasing temperature is likely to modify the timing, form, and intensity of precipitation events, which consequently affect regional and local hydrologic cycles. As a result, drought, water shortages, and subsequent water conflicts may become an increasing threat in monotone hydrologic systems in arid lands. In this study, we apply a small unmanned aerial systems (sUAS) to advance drought monitoring and outlooks so that planners and end users can more effectively manage and meter out limited water resources. Images of Normalized difference vegetation index (NDVI) on the left represent how crops respond to different irrigation rate and various manure applications in the farm field.
Impact Assessment of Urbanization and Land Use Change Using Low Impact Development (LID) to Characterize Water Quantity and Quality in the Boise River, FY14-FY16
In recent years, urbanization and land use change are very active in the Boise areas due to economic development and population growth. People are moving to Boise to pursue job opportunities in secondary and tertiary industries as a result of increased agricultural productivity and also people are looking for better environmental and social living at the rural-urban interface. Consequently, concerns of water resources management and non-point source (NPS) pollution control due to rapid growth often lead research agenda and policy making to mitigate impacts driven by urbanization. The research aims to reduce potential flood risks and to mitigate water quality impacts using LID technologies, such as bioretention. The bioretention options are incorporated into HSPF hydrological modelling framework to evaluate how LID can mitigate hydrological and environmental impacts induced by urbanization and land use change at the Boise River since 1992.
Streamflow Disaggregation from Monthly to Daily Value
Knowing stream and river flows for each day of the week helps water resources managers, for reservoir operation, water quality studies, and environmental modeling, for example, make more informed decisions. Streamflow disaggregation at shorter time scales, such as daily, is an important research avenue in water resources management and practice. A better understanding of the ecological health of river system biodiversity, and biological responses to fluctuating high and low stream flow events becomes possible with fine-scale information about water quantity and how it changes from day to day over the course of time. The tool developed in an Excel environment is fairly simple, broadly applicable with less computational burden, and provides a solution to the disaggregation problem which is of interest to many state and local agencies at present. This tool can be applied directly at any site to conduct streamflow disaggregation beyond Idaho watersheds.
Development of Decision Support Tools for Sustainable Water Resources Planning and Management
A System Dyanmics (SD) was applied to address dynamically complex problems with management of the Eastern Snake Plain Aquifer (ESPA) system and associated surface-water and groundwater interactions. Recharge and discharge dynamics within the aquifer system are characterized in an environmental modeling setting to identify long-term behavior of aquifer responses to uncertain future climate variability. This research shows that the system dynamics is a promising modeling tool to develop sustainable water resources planning and management in a collaborative decision-making framework, such as Comprehensive Aquifer Management Plan (CAMP).
Climate Extremes in the State of Idaho
Climate extreme and its linkage to regional drought has been investigated to identify consequences of climate variability on agricultural and regional water resource. Various climate and drought indices, including precipitation, mean temperature and maximum temperature, Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI) are used to identify spatial and temporal distribution of climatic extreme and variability as well as drought frequency and magnitude. The result indicates that decreasing trends and increasing trends are identified for precipitation and temperature, respectively. Given current climate conditions, the result also implies that these trends will continue in the future possibly driven by uncertain climate variability. We anticipate that this research will contribute to drought-forecasting capability in this region. The tool developed in an Excel environment is fairly simple, broadly applicable with less computational burden, and provides a solution to the disaggregation problem which is of interest to many state and local agencies at present. This tool can be applied directly at any site to conduct streamflow disaggregation beyond Idaho watersheds.
Toward mapping gridded drought indices to evaluate local drought in a rapidly changing global environment
Uncertain future climate, recent persistent droughts, and subsequent water conflicts increasingly threaten the sustainability of regional water resources around the world. Climate change and ongoing water disputes brought about by changes in water availability and timing emphasize the need for decision makers to develop proactive adaptive management strategies to mitigate losses. Developing a drought monitoring and management system equipped with advanced visualization settings is critical to lay out drought development at local scales. Although much research on drought at national, regional, and local scales has been conducted to mitigate drought impacts, still drought claims economic losses estimated at about $8.5 billion per year. One possible reason for such huge losses may be a lack of clear understanding of the characteristics of drought at local scales that the end user can relate to the particular water management constraints of their basin. This research aims to evaluate drought characteristics of tens to hundreds of thousands of interacting and diffusing water flows at high spatial resolution using big data, such as remotely sensed data and Unidata products by means of widely used drought indices. Computer parallelism in high-performance computing (HPC) environment for drought analysis is being implemented in this research.
Distributed Model Intercomparison Project Phase 2 (DMIP2)
In January 2000, the Hydrology Laboratory (HL) of the National Oceanic and Atmospheric Administration/National Weather Service (NOAA/NWS) initiated the Distributed Model Intercomparison Project (DMIP) to improve river and streamflow forecasts associated with modeling systems based on Next Generation Data (NEXRAD) multi-sensor precipitation products. Results of the first phase (2000-2002) suggest that the spatial rainfall derived from NEXRAD, as opposed to point observations, can improve streamflow forecast in higher order of stream embedded basins.
This phase also provided a significant emphasis on the hydrologic modeling framework and compared the performances between existing distributed models and lumped models in terms of model formulation, parameterization, and levels of calibration. Based on key findings from DMIP, NOAA/NWS extended intercomparison work to a second phase of DMIP (DMIP2) to identify more critical modeling issues associated with computational requirements in an operational environment and forecasting setting.
As a one of participants, I had decided to utilize the Hydrological Simulation Program-Fortran (HSPF) model system in this study not only because it is a rainfall-runoff model linking surface dynamics to groundwater recharges through climate forcing data, but also it provides a wide range of flexibility for model formulation and calibration processes. Although participants were allowed to use any physically based rainfall-runoff models, the selected models should be able to meet a list of specific requirements of the NWS, such as capability of distributed modeling, adoptability of high-resolution data sets (e.g., NEXRAD), ability of ungaged streamflow simulation, and potential of streamflow forecast.