Abstract
Extreme rainfall can be a catastrophic trigger for natural disaster events at urban scales. However, there remains large uncertainties as to how satellite precipitation can identify these triggers at a city scale. The objective of this study is to evaluate the potential of satellite-based rainfall estimates to monitor natural disaster triggers in urban areas. Rainfall estimates from the Global Precipitation Measurement (GPM) mission are evaluated over the city of Rio de Janeiro, Brazil, where urban floods and landslides occur periodically as a result of extreme rainfall events. Two rainfall products derived from the Integrated Multi-satellite Retrievals for GPM (IMERG), the IMERG Early and IMERG Final products, are integrated into the Noah Multi-Parameterization (Noah-MP) land surface model in order to simulate the spatial and temporal dynamics of two key hydrometeorological disaster triggers across the city over the wet seasons during 2001–2019. Here, total runoff (TR) and rootzone soil moisture (RZSM) are considered as flood and landslide triggers, respectively. Ground-based observations at 33 pluviometric stations are interpolated, and the resulting rainfall fields are used in an in-situ precipitation-based simulation, considered as the reference for evaluating the IMERG-driven simulations. The evaluation is performed during the wet seasons (November-April), when average rainfall over the city is 4.4 mm/day. Results show that IMERG products show low spatial variability at the city scale, generally overestimate rainfall rates by 12–35%, and impacts on TR and RZSM vary spatially mostly as a function of land cover and soil types. Results based on statistical and categorical metrics show that IMERG skill in detecting extreme events is moderate, with IMERG Final performing slightly better for most metrics. By analyzing two recent storms, we observe that IMERG detects mostly hourly extreme events, but underestimates rainfall rates, resulting in underestimated TR and RZSM. An evaluation of normalized time series using percentiles shows that both satellite products have significantly improved skill in detecting extreme events when compared to the evaluation using absolute values, indicating that IMERG precipitation could be potentially used as a predictor for natural disasters in urban areas.
Highlights
There is a notion that disasters are outcomes of inappropriate adjustments between people and environment [1]
An evaluation of normalized time series using percentiles shows that both satellite products have significantly improved skill in detecting extreme events when compared to the evaluation using absolute values, indicating that Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation could be potentially used as a predictor for natural disasters in urban areas
Brazil’s nationwide rain gauge network [77], normally available for the international scientific community, is not available in Rio de Janeiro and remains scarce in the surrounding region. This network excludes the municipal rain gauge network used in this study. Such a geographic limitation may result in biases over coastal areas where orographic and other meteorological processes dominate precipitation patterns and where the satellite retrieval algorithms may be less sensitive to land-ocean background states
Summary
There is a notion that disasters are outcomes of inappropriate adjustments between people and environment [1]. An example of inappropriate adjustments is the fast-urban growth observed in the past decades, putting humans in increased natural hazard risks related to environmental degradation that are intrinsic to urban development. Many large cities in developing countries lack urban planning and management, resulting in inappropriate land use, often composed of informal and irregular settlements in high hazard risk areas (e.g., along steep hills and riverbanks) [2]. A key hydrological variable in assessing urban flood risks, is primarily related to the rainfall intensity and land use. Intense and/or prolonged rainfall is the predominate triggering factor in landslide-prone areas where increased soil saturation and runoff can serve to mobilize the downstream transport of debris, rock, and soil. Disaster monitoring and forecasting are examples of non-structural measures that can be used to provide stakeholders with information on how to implement or improve structural measures such as river canalization, dykes and dams, or slope retrofitting as well as other non-structural measures such as flood and landslide mapping for improved characterization of past hazards, land use regulation and early warning systems
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