Abstract
Detecting and forecasting riverine floods is of paramount importance for adequate disaster risk management and humanitarian response. However, this is challenging in data-scarce and ungauged river basins in developing countries. Satellite remote sensing data offers a cost-effective, low-maintenance alternative to the limited in-situ data when training, parametrizing and operating flood models. Utilizing the signal difference between a measurement (M) and a dry calibration (C) location in Passive Microwave Remote Sensing (PMRS), the resulting rcm index simulates river discharge in the measurement pixel. Whilst this has been demonstrated for several river basins, it is as of yet unknown at what ratio of the spatial scales of the river width vs. the PMRS pixel resolution it remains effective in East-Africa. This study investigates whether PMRS imagery at 37 GHz can be effectively used for flood preparedness in two small-scale basins in Malawi, the Shire and North Rukuru river basins. Two indices were studied: The m index (rcm expressed as a magnitude relative to the average flow) and a new index that uses an additional wet calibration cell: rcmc. Furthermore, the results of both indices were benchmarked against discharge estimates from the Global Flood Awareness System (GloFAS). The results show that the indices have a similar seasonality as the observed discharge. For the Shire River, rcmc had a stronger correlation with discharge (ρ = 0.548) than m (ρ = 0.476), and the former predicts discharge more accurately (R2 = 0.369) than the latter (R2 = 0.245). In Karonga, the indices performed similarly. The indices do not perform well in detecting individual flood events when comparing the signal to a flood impact database. However, these results are sensitive to the threshold used and the impact database quality. The method presented simulated Shire River discharge and detected floods more accurately than GloFAS. It therefore shows potential for river monitoring in data-scarce areas, especially for rivers of a similar or larger spatial scale than the Shire River. Upstream pixels could not directly be used to forecast floods occurring downstream in these specific basins, as the time lag between discharge peaks did not provide sufficient warning time.
Highlights
Natural hazards are considered disasters when they disrupt the functioning of a society and cause human, material, economic or environmental losses that the community or society cannot cope with using its own resources (IFRC, 2020)
The aforementioned phenomenon is especially apparent in cell K0, which is bordered by Lake Malawi (Figure 3B)
In K0, the initial downstream Virtual Gauging Stations (VGSs) along the North Rukuru, the signal of M showed to be more similar in its oscillations to covered by surface water (Cw) (Lake Malawi) than to Cd (Figure 3B)
Summary
Natural hazards are considered disasters when they disrupt the functioning of a society and cause human, material, economic or environmental losses that the community or society cannot cope with using its own resources (IFRC, 2020). Floods have been increasingly causing economic, material, and human losses due to rapid population growth and economic development in developing countries, including in flood-prone areas (Hussain et al, 2005). Better monitoring and forecasting systems can improve respectively humanitarian response and anticipatory action (van den Homberg et al, 2020), whereby vulnerable people are supported in taking early action prior to the hazard happening (an approach called forecast-based financing). In this way, their vulnerability is reduced and resilience increased. There are many data-sparse areas in Africa, and Malawi (Ngongondo et al, 2011; Mwale et al, 2012), where hydrological and meteorological data, important components of these systems, are unavailable or not of satisfactory quality
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