Maki Postdoctoral Program
As part of the Maki Endowment, postdoctoral researchers were hired in the DRI Division of Hydrologic Sciences. The postdoctoral researchers will be focusing their research efforts on southern Nevada water issues. The current postdocs are:
Dr. Abhinav Gupta
Dr. Abhinav Gupta’s work focuses on quantifying uncertainties in hydrologic modeling using both hydrologic and statistical reasoning. Specifically, he has developed methods to quantify structural uncertainty and separate structural and measurement uncertainties using a combination of machine learning and hydrologic principles. As a Maki postdoctoral research fellow, he will focus on the problem of non-stationarity: how to make projections of streamflow in response to climate change if future and observed hydrologic regimes in a watershed are expected to be different.
“A Machine Learning Model to Assess the Impact of Climate Change on Colorado River Flows”
In the western United States, approximately 37 percent of precipitation falls as snow, which contributes to 53 percent of the total runoff in the region. Due to climate change, more precipitation is falling as rain rather than snow and this trend is expected to continue throughout the twenty-first century. Consequently, a change in the hydrological regime of the western United States is also expected, with more water available in winter months and less water available in summer months. Quantifying the impact of this change along with the associated uncertainty is needed to manage water availability and all related ecosystems. In this study, a machine learning (ML) model will be developed to simulate streamflow in the western United States and project streamflows at a daily timescale in response to climate change. Machine learning models have proven to be useful for predicting streamflows, especially in regions where runoff is dominated by rainfall. However, studies in regions where runoff is dominated by snow are lacking. There are two challenges associated with developing an ML model for climate impact assessment. First, there is a large time lag between precipitation and runoff in snow-dominated regions. Second, the data currently available for calibration may not be enough to generate reliable future predictions because of non-stationarities. In this study, these problems will be addressed using state-of-the-art ML methods and the idea of space-time symmetry. Finally, the streamflows predicted by the ML models will be compared against those predicted by the variable infiltration capacity model.
Dr. Rubab Saher
Dr. Rubab Saher’s research focuses on incorporating the effects of microclimates into existing irrigation models for urban climates in arid regions. Dr. Saher uses remote sensing algorithms to improve evapotranspiration and irrigation models to specifically address the needs of urban landscapes and improve irrigation scheduling. Her other research interests include understanding the potential for emerging approaches/technologies such as Internet of Things (IoT) and unmanned autonomous vehicles to improve the science of hydrologic datasets and better understand urban ecosystems.
“Understanding the Gap between Irrigation Water Requirements and Irrigation Water Application in Arid Climates”
In light of ongoing drought conditions, communities in arid regions have devised turf grass removal or replacement policies. Recent policies in the Southwest include the Southern Nevada Water Authority’s Water Smart Landscapes program, for which turf grass lawns were removed for a certain amount of money (2 USD/ft2). Although the program reduced per capita water consumption by 50 percent, its long-term effect regarding changes in irrigation water requirements and microclimates has remained understudied. Additionally, considerable discrepancies in irrigation water requirements and applications have been reported, but the key drivers such as microclimate effects and homeowner income and awareness have not been studied. Therefore, this project aims to understand the responses of low- and high-water-use landscapes to irrigation water requirements and explain irrigation patterns. It also aims to investigate the relationship between homeowner income and irrigation water application. The project will simulate microclimate effects in three-dimensional landscapes to estimate the irrigation water requirements at a one-meter spatial resolution. Microclimate impacts of irrigation will be analyzed for five years (2015-2020) for the hottest days of summer and coldest days of winter to understand microclimate extremes. The gap between irrigation water requirement, irrigation application, and homeowner income will be investigated using regression analysis. This study will help water managers and landscape designers understand the response of diverse landscapes to irrigation water in arid regions, as well as the role of homeowners in saving water.
Dr. Guo Yu
Dr. Guo Yu’s research focuses on hydroclimate extremes, involving extreme rainfall and the floods it causes, and how they will change in a warming future. Dr. Yu uses the physics-based hydrologic model WRF-Hydro (i.e., the core of the National Water Model), high-resolution precipitation data, and a stochastic storm transposition tool to derive process-based rainfall and flood frequency distributions (e.g., 1,000-year flood). In a warming future, the “recipes” of flooding will change, such as rainfall intensity, snowpack, and watershed antecedent soil moisture. He is also interested in understanding how changes in individual driving factors along with their interactions affect future flood hazards.
“Process-based Understanding of Rainfall and Flood Frequencies in an Arid Region Under Current and Future Climate Conditions”
Despite Nevada’s arid climate, the region is not immune to extreme precipitation and the flash floods they cause. Understanding the severity and corresponding probability of hydroclimate extremes is central to flood management and is referred to as a frequency analysis. Conventional rainfall and flood frequency analysis approaches focus on solving the math problem of the estimation of frequency severity but neglect the hydrometeorological information associated with these extreme events. This project presents a process-based framework that combines gridded precipitation, stochastic storm transposition, a storm typing and characterization algorithm, and physics-based distributed hydrologic modeling. The proposed approach will be used to examine the upper tail of rainfall and flood frequencies over Nevada. More importantly, the roles of storm types and characteristics in driving extreme rainfalls and their interactions with watershed morphology and initial conditions to shape the probabilities of rare floods will be investigated. For example, similar rainfall depths can cause large variability in flood magnitudes at the outlet of a watershed, depending on the watershed antecedent conditions and the storm location relative to the watershed outlet. In addition, incorporating regional climate model simulation into the proposed process-based framework will shed light on how rainfall and flood hazards will change in future due to climate change. Finally, this proposed research will be useful in private-sector engineering and for state and local authorities involved in flood risk management, such as the Clark County Regional Flood Control District.