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It is well known that floods account for the most pervasive mortality and economic damages among all known weather-related natural disasters. Recent statistics indicate that global flood events during March 2019 alone accounted for a mammoth USD 8 billion of financial losses. They further apprise that although countries at all levels of development face various degrees of flood risk, the vast majority of the exposed population, i.e., ~ 89% reside in the low- and middle-income countries, most widespread impacts of floods noticed over South-Asia. The bitter truth is that flood events have been increasing manifold, majorly driven by climate change impacts and changes in socio-economic dynamics. One way of comprehending this emerging concern is by quantifying flood hazards, which provides a transparent knowledge of the regions and how they will be affected by flooding. Among the well-known approaches, hydrodynamic modeling is identified as the most precise one, as it can model the propagation of flood waves. Moreover, the increasing usability of satellite and remotely sensed information has contributed to the improved performance of hydrodynamic models.
The 2-hour session aims to build a knowledge-based platform on hydrodynamic modeling approaches, covering the latest trends, developments, and new dimensions in computer-based simulation. The participants will learn to access relevant hydro-meteorological and GIS datasets such as rainfall, streamflow, land use land cover, population, and terrain data. In addition, participants will get exposure to building a simple hydrodynamic flood model setup using a sophisticated model to facilitate flood risk.
A Climate Impact Indicator (CII) is an aggregate quantitative measure used to show the impact of climate change on complex environmental phenomena in terms of trends and variability. The CIIs can describe global/regional climate change, trace climate hazards, assess sensitivity of water resources and society, and can be used in raising awareness and informing climate change adaptation policies and actions.
Training will be divided into three sessions.
Sustainable water resources management in remote regions is the most critical challenge water resources planners and hydrologists face due to the scarcity of ground-based monitoring stations. The problem is aggravated in heterogeneous terrains such as Himalayan catchments, where local climate systems play a significant role. Hydrological modeling plays a valuable role in such cases by providing an estimate of water resources availability under the absence of in-situ hydrological data. Hydrological models are the tools that simulate different hydrological processes such as runoff generation, infiltration, groundwater recharge, evapotranspiration, etc. These models also help us understand the watershed behaviors under different scenarios of climate change and anthropogenic alterations.
Hydrological modeling requires various inputs on catchment physiographic characteristics and hydrometeorology, but these inputs (particularly in-situ meteorological data) are generally unavailable for remote catchments. Among different inputs, precipitation is one of the essential forcing inputs of the water cycle and is crucial in calculating various land surface processes in the hydrological models. In addition, the accuracy of precipitation measurements and their spatiotemporal representations greatly influence the predictions of hydrological models. Under limited availability of in-situ precipitation observations, the hydrological models fail to simulate the true rainfall-runoff relationship, leading to large uncertainty in model predictions. Therefore, an accurate spatial representation of precipitation is required to reduce these uncertainties in simulating the hydrological processes of watersheds.
Several Satellite Precipitation Products (SPPs) have recently emerged, which compensate for the lack of in-situ observations. SPPs have now been widely used for hydrological applications such as climate extremes predictions, early drought warning, flood forecasting and control, and streamflow simulation in gauged and ungauged basins. The SPPs provide many advantages, such as better spatial coverage and resolutions and consistent temporal resolution, but there are many challenges associated with SPPs in hydrologic applications. SPPs suffer from uncertainties that arise from measurement errors related to observations, sampling, retrieval algorithms, and bias correction processes. Therefore, it is essential to evaluate the quality and applicability of SPPs using both quantitative statistical and hydrological modeling evaluation strategies.
This 2-hours hands-on session will demonstrate:
The session will use open-source Satellite Precipitation Products (SPP), in-situ meteorological observations, and hydrological modeling. The participants will learn to download, preprocess, and utilize an SPP in a hydrological modeling framework to simulate a catchment's rainfall-runoff relationship.
Training will be divided into three sessions.