Remote sensing of hydrologic variables. Water and energy balance of the soil-vegetation-atmosphere system. Distributed hydrologic modeling. Hydrologic prediction.
S. Lawrence Dingman, Physical Hudrology (3rd Edition), Waveland PressInc, 2015
Learning Objectives
Advanced knowledge of hydrologic processes at watershed and hillslope scales, with in depth reference to soil moisture dynamics, the flood runoff formation and their measurement with new technologies. Knowledge of mathematical techniques for the calibration and uncertainty assessment of a prediction model. Capability of characterizing the hydrologic parameters in a distributed approach, with the aid of remote sensing. Capability of using and calibrating a distributed hydrologic model for the purpose of: assessing the impacts of climate and land use changes; flood prediction.
Prerequisites
Base knowledge (bachelor level) of hydrologic, hydraulics, geographic information systems.
Teaching Methods
Class lectures with multimedia material.
Class and informatic-lab exercises.
Ongoing revisions of group-projects assigned to specific application themes.
Type of Assessment
Presentation and discussion of the pre-assigned group-project. Oral colloquium on course main topics.
Course program
General concepts of environmental remote sensing from satellite and ground platforms. Remote sensing of vegetation, precipitation and soil moisture. Main available remote sensing missions and products for hydrologic use. Hydrologic cycle from watershed to hillslope scale. Mass and energy exchanges in the soil-vegetation-atmosphere system. Soil moisture dynamics. Controls in runoff and flood formation processes. Distributed hydrologic modeling: model characteristics and reference examples. Techniques for the characterization of hydrologic parameters in a distributed modeling approach. Calibration and uncertainty quantification in a hydrologic model. Design flood estimations. Assessment of hydrologic sensitivity to variations in climate and land use characteristics. Assessment of uncertainty and reliability in real time hydrologic prediction.