Research Lab
The WRES Lab: A Hub for Hydrological Research
The WRES Lab employs a multifaceted approach to understanding the terrestrial hydrological cycle, combining remote sensing, advanced modeling techniques, machine learning, and extensive data analysis. Computational facilities, including high-performance workstations and a robust data server, support our research endeavors.
Computational Resources:
- High-performance workstations: Equipped with Xeon Precision processors and dual-screen monitors for efficient data analysis and modeling.
- Data server: Offers ample storage (24 TB) and memory (64 GB RAM) for large datasets.
- Software: A comprehensive suite of software tools, including:
- Hydrogeologic and water resources modeling: GMS (v10.4)
- Geographic Information Systems (GIS): ArcGIS Pro, QGIS
- Remote sensing: ENVI (v5.4), Agisoft Metashape Professional, Pix4D, IDL
- Data analysis and programming: MATLAB, SPSS, RStudio
- Hydrological modeling: SWAT, TerrSet
- In-house developed codes and open-source tools
Field and Remote Sensing Capabilities:
- Unmanned Aircraft Systems (UAS): DJI M200 V2 and Phantom 4D drones equipped with FLIR thermal, RGB, and multispectral sensors for data collection.
- Field equipment: Ground truthing instruments and equipment for in-situ data collection.
Data Management:
- Organized data storage system for in-situ, GIS, and remote sensing data.
Research Focus: Our research focuses on quantifying the impacts of climate and human impacts on water resources, developing hydrological models for improved water management, and assessing the effectiveness of water conservation strategies.
Location: The WRES Lab is located in FSH, Rm 205.
Example Research Projects:
Unmanned Aircraft Systems (UAS) for Remote Sensing
With advancement of sensors miniaturization, battery power, and availability, UAS are widely used for civilian applications such as monitoring of vegetation, forest fire, wildlife, crops, and surveying and construction applications. Unlike space-based sensors, UAS can be easily deployed at low altitude at a convenient time and provides very high-resolution data. In our lab, we are determining the utility of UAS for hydrology and environmental monitoring. We are developing acquisition, processing, and analysis of UAS data for water cycle monitoring including discharge, water inundation, water quality, soil moisture, and snow/ice. We are testing and developing methods for agricultural applications such as plant phenology analysis, plant health, and water stress studies. Further, we are developing UAS capability for subsurface characterization such as groundwater, geological, and agricultural (e.g. tile drainage mapping) applications. Evaluation and integration of UAS data with in-situ observation and hydrologic models is our target to quantify the water cycle and improve predictive capacity at various scales, from field to watershed scale.
Past, present, and future climatic impact in man-made and natural reservoirs
Man-made and natural reservoirs are essential water resources. Reservoirs in small-scale watershed having limited areal extent and highly relied on intensity and frequency of precipitation. As a result storage in these reservoirs is prone to climatic variability, this will potentially exacerbated by future climate change. Understanding how these systems are responding to short- and long-term climate variability as well as to future climate condition is essential for a sustainable water future.
Climate sensitivity to shallow subsurface water Dynamics
Land, ocean, and atmospheric processes interact with each other and have the potential to impact the climate in various ways. Ocean-atmosphere interactions such as those produced through sea surface temperature variations, for instance, often impact meteorological conditions that may enhance extreme
precipitation and drought events. This project explores the relationship between hydrological changes related to shallow subsurface waters and how those fluctuations may relate to and impact climate.
Satellite applications in hydrology
Most basins throughout the world, specifically those in non-industrialized countries, are poorly gauged, as a result prediction of the hydrologic cycle is challenging. Fortunately, recent advancements in satellite-based hydrology have demonstrated that some water cycle components can be directly or indirectly estimated from space. In this theme, the group is working on enhancing the utility of satellite products (e.g., GRACE, TRMM, LANDSAT) in monitoring the water cycle, by merging satellite products with machine learning models.
The effect of cover crops in nutrients load reduction
Nutrients loading into streams and rivers from agricultural runoff is a major concern in the Mississippi River Basin: (1) reducing the fertility of soil and (2) deteriorating the quality of streams and rivers, including eutrophying surface water bodies, and creating water quility problems in watersheds downstream. Various best management practices (BMP) are being implemented to reduce nutrient loading in the Mid-west region. Our team is collaborating with research groups across campus on a project aimed at characterizing and simulating the effect of management practices in nutrient loss reduction.