Location: Hydrology and Remote Sensing Laboratory
Project Number: 8042-13610-029-000-D
Project Type: In-House Appropriated
Start Date: Dec 22, 2016
End Date: Dec 21, 2021
Objective:
Objective 1: Develop and evaluate new methodologies and tools for characterizing spatiotemporal variability in land-surface water balance components from plot to global scales, integrating multi-sensor remote and in-situ measurement sources.
Sub-objective 1.1: Improve representations of water and energy exchanges in structured agricultural environments, developed using in-situ measurements.
Sub-objective 1.2: Improve multi-sensor tools for mapping water use over irrigated and rainfed crops, forests and rangelands.
Sub-objective 1.3 Improve remote sensing tools for mapping regional and global soil moisture.
Sub-objective 1.4: Develop new techniques for measuring soil moisture variability in situ and upscaling for validation of satellite retrievals.
Sub-objective 1.5: Evaluate the terrestrial water budget at basin scale via the integration of remote sensing with ground observations.
Objective 2: Develop remote sensing and modeling approaches for determining the timing and magnitude of agricultural drought and its impact on agroecosystems and onhe regional hydrology.
Sub-objective 2.1: Improve early warning tools for identifying agricultural drought onset, severity and recovery at local to regional scales.
Sub-objective 2.2: Improve techniques for assessing crop and rangeland phenology and condition and for forecasting yields.
Sub-objective 2.3: Enhance understanding and monitoring of drought impacts on regional hydrologic components.
Objective 3 (short): Assess the hydrologic status and trends within the Lower Chesapeake Bay Long-Term Agroecosystem Research site through measurements, remote sensing, and modeling.
Sub-objective 3.1: Establish long-term data streams for the LCB LTAR project to examine agroecosystem status and trends.
Sub-objective 3.2: Examine the effects of irrigation intensification within the LCB LTAR on trends in regional hydrology and nitrogen dynamics.
Sub-objective 3.3: Improve prediction capability of SWAT in evaluating the effects of both natural riparian and restored wetlands on water quality.
Sub-objective 3.4: Investigate sources and fate of nitrate in the LCB LTAR.
Approach:
This project seeks to develop new tools for agricultural monitoring and management that integrate ground observations, remote sensing data and modeling frameworks. In specific, these multiscale tools will be used to address characterization of water supply (soil moisture), water demand (evapotranspiration), water quality drivers and drought impacts over agricultural landscapes.
