Your search found 2 records
1 Batra, N; Yang, Y. C. E.; Choi, H. I.; Kumar, P.; Cai, X.; de Fraiture, Charlotte. 2008. Understanding hydrological cycle dynamics due to changing land use and land cover: Congo Basin study. In IEEE International Geoscience and Remote Sensing Symposium, Boston, Massachusetts, USA, 6-11 July 2008. Los Alamitos, CA, USA: IEEE Publications Office. Vol. 5. pp.V491-V494.
Remote sensing ; Simulation models ; Hydrology ; GIS ; Land use ; Land cover ; River basins ; Forests ; Case studies ; Water balance ; Precipitation ; Evapotranspiration ; Runoff / Africa / Congo River Basin / Congo Forest
(Location: IWMI HQ Call no: e-copy only Record No: H042121)
(0.29 MB)
Land use and land cover changes (LULCC) significantly modify the hydrological flow regime of the watersheds, affecting water resources and environment from regional to global scale. In recent years, with an increased number of launched satellites, regular updates of land-cover databases are available. This study seeks to advance and integrate water and energy cycle observation, scientific understanding, and its prediction to enable society to cope with future climate adversities due to LULCC. We use the Common Land Model [1] which is developed with enhanced spatial and temporal resolution, physical complexity, hydrologic theory and processes to quantify the impact of LULCC on hydrological cycle dynamics. A consistent global GIS-based dataset is constructed for the surface boundary conditions of the model from existing observational datasets available in various resolutions, map projections and data formats. Incorporation of the projected LULCC of Intergovernmental Panel on Climate Change (IPCC) A1B scenario [2] into our hydrologic model enhances scientific understanding of LULCC impact on the seasonal hydrological dynamics. An interesting case study is addressed over the Congo basin located in the western central Africa which has the second largest rain forest area in the world. It is surrounded by plateaus merging into savannas in the south, mountainous terraces and grassland in the west and mountainous glaciers in the east. Savanna and Evergreen Broadleaf forest are projected to be cleared off in places to be replaced by dryland, cropland and pasture. By 2100, there would be a 10% decrease in savanna and 2% decrease in evergreen forest under A1B scenario of IPCC. Each land cover class has a particular set of characteristics defined in the model and any change in land cover type changes the vegetation properties, rooting depth, roughness length, etc. which results in a change of energy and water fluxes. Deforestation of evergreen forests and intense land clearing of savanna leads to reduction in evapotranspiration. Model results show that the gain in runoff follows the pattern of loss in evapotranspiration.
2 Batra, N.; Amarnath, Giriraj. 2023. Development of a flood index insurance product for Zambia. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Climate Resilience. 18p.
(Location: IWMI HQ Call no: e-copy only Record No: H052656)
(1.12 MB)
Weather Risk Management Services Pvt Ltd (WRMS) is collaborating with the International Water Management Institute (IWMI) as part of the CGIAR Initiative on Climate Resilience (ClimBeR) to develop financial solutions for post-extreme climatic events. The focus is on creating parametric insurance solutions to aid vulnerable populations in managing and mitigating loss and damage caused by natural disasters, with a primary emphasis on floods. The project's scope involves a comprehensive approach to enhance community resilience through financial solutions for flood. It begins with identifying vulnerable locations using secondary data sources. The subsequent steps include developing parametric insurance products, setting triggers and damage ratios based on past events, and evaluating community vulnerability. Zambia is particularly prone to seasonal floods from November to April. Major rivers like Zambezi, Kafue, and Luangwa can lead to widespread inundation, impacting lives, displacing communities, damaging infrastructure, and disrupting agriculture. The project focuses on flood risk in specific regions, the Kafue flats area in Lusaka and Southern Provinces. Data sources crucial for assessing flood severity include river water level, dam discharge data, and rainfall. The study utilizes historical data from 1980-2023 for water level and discharge, and 2000-2020 for rainfall, collected from multiple locations in the Kafue plains area. The development of the flood index-based insurance product involves analyzing data to determine triggers for flash floods and riverine floods. The shortlisted region experienced severe flooding in the past, and detailed analysis has been done to validate if the collected data sets capture both the intensity and duration of those extreme events. The proposed index insurance product features triggers based on water level and rainfall data, offering fast and transparent settlement with low administrative costs. For riverine floods, payouts depend on the increase in daily water level from a set benchmark, considering the number of days above the threshold. For flash floods, compensation is triggered by excess rainfall over a specified period. Ultimately, the project aims to offer a combined flood coverage product addressing both flash and riverine floods, contributing to the overall goal of strengthening disaster resilience through integrated risk analysis, financial solutions, and actionable protocols.
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