Sensitivity of flood dynamics to different soil information sources in urbanized areas

Umer, Yakob; Jetten, Victor; Ettema, J.

doi:10.1016/j.jhydrol.2019.123945

Cited by 20 publications

(32 citation statements)

References 20 publications

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“…Similarly, the HIRE time series extracted from the representative grid cell location could be applied as homogeneous input for a flood model to get information on the flood-prone areas in Kampala. In the absence of observed hydrological data (e.g., discharge or water level) and accurate information on sewer system, it is challenging to calibrate and evaluate the output of such hydrological model [23]. The limitation for both applications seems that simulated temporal and spatial variation in rain intensity and volume for this single event is too large to support flood decision making.…”

Section: Discussionmentioning

confidence: 99%

“…The HIRE on 25 June 2012 occurred at the cessation of the long rainy season and caused a substantial flash flood in areas of the city. This rainfall event is an example of a HIRE with a short duration of two hours with a peak intensity of over 100 mm h −1 , which caused a flood depth exceeding 1 m in the flood-prone areas [23]. However, the lack of sufficient rain gauge data is hampering a proper flood hazard assessment, which is vital for city planning.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the WRF Model to Simulate a High-Intensity Rainfall Event over Kampala, Uganda

Umer

Ettema

Jetten

et al. 2021

Water

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Simulating high-intensity rainfall events that trigger local floods using a Numerical Weather Prediction model is challenging as rain-bearing systems are highly complex and localized. In this study, we analyze the performance of the Weather Research and Forecasting (WRF) model’s capability in simulating a high-intensity rainfall event using a variety of parameterization combinations over the Kampala catchment, Uganda. The study uses the high-intensity rainfall event that caused the local flood hazard on 25 June 2012 as a case study. The model capability to simulate the high-intensity rainfall event is performed for 24 simulations with a different combination of eight microphysics (MP), four cumulus (CP), and three planetary boundary layer (PBL) schemes. The model results are evaluated in terms of the total 24-h rainfall amount and its temporal and spatial distributions over the Kampala catchment using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis. Rainfall observations from two gauging stations and the CHIRPS satellite product served as benchmark. Based on the TOPSIS analysis, we find that the most successful combination consists of complex microphysics such as the Morrison 2-moment scheme combined with Grell-Freitas (GF) and ACM2 PBL with a good TOPSIS score. However, the WRF performance to simulate a high-intensity rainfall event that has triggered the local flood in parts of the catchment seems weak (i.e., 0.5, where the ideal score is 1). Although there is high spatial variability of the event with the high-intensity rainfall event triggering the localized floods simulated only in a few pockets of the catchment, it is remarkable to see that WRF is capable of producing this kind of event in the neighborhood of Kampala. This study confirms that the capability of the WRF model in producing high-intensity tropical rain events depends on the proper choice of parametrization combinations.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of the WRF Model to Simulate a High-Intensity Rainfall Event over Kampala, Uganda

Umer

Ettema

Jetten

et al. 2021

Water

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“…While numerous PTF are available in literature (Van Looy et al., 2017), SAR‐PTF is used in this study based on its ability to provide both wilting point and field capacity, ability to account the soil organic matter, and no dependence on the soil bulk density. SAR‐PTF is used in the development of NASA's Goddard Earth Observing System version 5 model for SMAP level 4 products (De Lannoy et al., 2014) along with numerous applications for large‐scale estimation of SWRPs for drought monitoring (Martínez‐Fernández et al., 2016; Mishra et al., 2017), crop yield (Archontoulis et al., 2020) and understanding flood dynamics (Umer et al., 2019), and so on.…”

Section: Data Setmentioning

confidence: 99%

Global Surface Soil Moisture Drydown Patterns

Sehgal

Mohanty

2021

Water Resources Research

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Soil moisture (SM) accounts for a small fraction of the total global freshwater yet exerts a large influence on the global water cycle (McColl, Alemohammad, et al., 2017). At fine spatial scale, soil governs plant growth, geochemical processes, and groundwater recharge. However at large spatial scales (several hundred meters to several kilometers), SM plays a crucial role in precipitation recycling (Baudena et al., 2008), cloud formation (Fast et al., 2019), land-atmospheric coupling (Schwingshackl et al., 2018), and numerous other critical processes for global energy, water, and carbon cycle. Global SM status and the characteristic soil water retention functions are needed as inputs to the large-scale Earth system models (Bonan & Doney, 2018; Dunne et al., 2012; Flato, 2011; Hurrell et al., 2013). However, the mechanistic understanding (and modeling) of SM dynamics for large-spatial scales is mainly driven by a Darcy-scale perspective, largely due to the unavailability of long-term observed data at large spatial scales. Hence, there is a need to critically reevaluate the process understanding and interpretations about the governing mechanisms of SM, dynamics specific to large spatial scales. The general trajectory of SM drydowns (period of sustained loss of SM) finds its origin in fine-scale studies (Guswa et al., 2002; Rodriguez-Iturbe, 2000; Rodriguez-Iturbe et al., 1999). Laio et al. (2001) proposed that mapping the trajectory of the rate of loss of the observed SM with decreasing SM can provide information about the dominant soil hydrologic regimes and the soil water retention properties like field capacity and wilting point. The pathway of SM drydown was assumed to be a piecewise-linear function where each limb represented a unique soil hydrologic regime. Moreover, the rate of drainage loss and the strength of the land-atmospheric interaction may also be inferred from the observed SM drydown curves. Operating in the L-band microwave frequency, the Soil Moisture Active Passive (SMAP) radiometer provides SM estimates for the top 5 cm of soil with a high retrieval accuracy at a spatial support scale of 36 km. The spatial support (36 km) and global extent make SMAP observations ideal for a global evaluation of SM drydowns at large spatial scales.

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“…A comparison between various existing soil datasets in the context of model applicability concluded that SoilGrids250 is the current state-of-the-art soil dataset [38]. Some hydrologists have already applied it in their studies [39][40][41][42][43].…”

Section: Input Open-source Datasetsmentioning

confidence: 99%

Global BROOK90 R Package: An Automatic Framework to Simulate the Water Balance at Any Location

Vorobevskii

Kronenberg

Bernhofer

2020

Water

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The number of global open-source hydrometeorological datasets and models is large and growing. However, with a constantly growing demand for services and tools from stakeholders, not only in the water sector, we still lack simple solutions, which are easy to use for nonexperts. The new R package incorporates the BROOK90 hydrologic model and global open-source datasets used for parameterization and forcing. The aim is to estimate the vertical water fluxes within the soil–water–plant system of a single site or of a small catchment (<100 km2). This includes data scarce regions where no hydrometeorological measurements or reliable site characteristics can be obtained. The end-user only needs to provide a location and the desired period. The package automatically downloads the necessary datasets for elevation (Amazon Web Service Terrain Tiles), land cover (Copernicus: Land Cover 100 m), soil characteristics (ISRIC: SoilGrids250), and meteorological forcing (Copernicus: ERA5 reanalysis). Subsequently these datasets are processed, specific hydrotopes are created, and BROOK90 is applied. In a last step, the output data of all desired variables on a daily scale as well as time-series plots are stored. A first daily and monthly validation based on five catchments within various climate zones shows a decent representation of soil moisture, evapotranspiration, and runoff components. A considerably better performance is achieved for a monthly scale.

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Sensitivity of flood dynamics to different soil information sources in urbanized areas

Cited by 20 publications

References 20 publications

Evaluation of the WRF Model to Simulate a High-Intensity Rainfall Event over Kampala, Uganda

Evaluation of the WRF Model to Simulate a High-Intensity Rainfall Event over Kampala, Uganda

Global Surface Soil Moisture Drydown Patterns

Global BROOK90 R Package: An Automatic Framework to Simulate the Water Balance at Any Location

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