Investigating flash floods potential areas using ASCAT and TRMM satellites in the Western Cape Province, South Africa

Bangira, Tsitsi; Maathuis, B.H.P.; Dube, Timothy; Gara, Tawanda W.

doi:10.1080/10106049.2014.997302

Cited by 18 publications

(11 citation statements)

References 36 publications

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“…Therefore, operational utilization is very advanced, especially in numerical weather prediction (de Rosnay et al, 2013). Further examples of ASCAT applications are, e.g., the improvement of rainfall (Ciabatta et al, 2016; Wanders et al, 2015) and discharge estimates (Laiolo et al, 2016), the identification of potential flash‐flood areas (Bangira et al, 2015) and the prediction of floods (Alvarez‐Garreton et al, 2015), and identification of vegetation drought (Schroeder et al, 2016).…”

Section: Near‐real‐time Remote Sensing Soil Moisture Products For Opementioning

confidence: 99%

Soil Moisture Remote Sensing: State‐of‐the‐Science

Mohanty

Cosh

Lakshmi

et al. 2017

Vadose Zone Journal

272

143

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Core Ideas Satellites, particularly at L‐band frequency, can globally map near‐surface soil moisture. Near‐surface moisture is extended to the root zone using models and data assimilation. Validation uses core monitoring sites, monitoring networks, field campaigns, and multi‐satellite comparisons. Efforts are underway to associate soil moisture variability dynamics with land surface attributes. This is an update to the special section “Remote Sensing for Vadose Zone Hydrology—A Synthesis from the Vantage Point” [Vadose Zone Journal 12(3)]. Satellites (e.g., Soil Moisture Active Passive [SMAP] and Soil Moisture and Ocean Salinity [SMOS]) using passive microwave techniques, in particular at L‐band frequency, have shown good promise for global mapping of near‐surface (0–5‐cm) soil moisture at a spatial resolution of 25 to 40 km and temporal resolution of 2 to 3 d. C‐ and X‐band soil moisture records date back to 1978, making available an invaluable data set for long‐term climate research. Near‐surface soil moisture is further extended to the root zone (top 1 m) using process‐based models and data assimilation schemes. Validation of remotely sensed soil moisture products has been ongoing using core monitoring sites, sparse monitoring networks, intensive field campaigns, as well as multi‐satellite comparison studies. To transfer empirical observations across space and time scales and to develop improved retrieval algorithms at various resolutions, several efforts are underway to associate soil moisture variability dynamics with land surface attributes in various energy‐ and water‐rich environments. We describe the most recent scientific and technological advances in soil moisture remote sensing. We anticipate that remotely sensed soil moisture will find many applications in vadose zone hydrology in the coming decades.

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Section: Near‐real‐time Remote Sensing Soil Moisture Products For Opementioning

confidence: 99%

Soil Moisture Remote Sensing: State‐of‐the‐Science

Mohanty

Cosh

Lakshmi

et al. 2017

Vadose Zone Journal

272

143

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“…These reservoirs are of various sizes, ranging from 0.5-5 km 2 . Most of these dams are ungauged and setting up, maintaining, and managing conventional in situ surveys, gauge stations, and telemetry networks would be prohibitively expensive and time-consuming [8].…”

Section: Introductionmentioning

confidence: 99%

Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water

et al. 2019

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S.M.alfieri-1@tudelft.nl (S.M.A.); M.Menenti@tudelft.nl (M.M.)Abstract: Small reservoirs play an important role in mining, industries, and agriculture, but storage levels or stage changes are very dynamic. Accurate and up-to-date maps of surface water storage and distribution are invaluable for informing decisions relating to water security, flood monitoring, and water resources management. Satellite remote sensing is an effective way of monitoring the dynamics of surface waterbodies over large areas. The European Space Agency (ESA) has recently launched constellations of Sentinel-1 (S1) and Sentinel-2 (S2) satellites carrying C-band synthetic aperture radar (SAR) and a multispectral imaging radiometer, respectively. The constellations improve global coverage of remotely sensed imagery and enable the development of near real-time operational products. This unprecedented data availability leads to an urgent need for the application of fully automatic, feasible, and accurate retrieval methods for mapping and monitoring waterbodies. The mapping of waterbodies can take advantage of the synthesis of SAR and multispectral remote sensing data in order to increase classification accuracy. This study compares automatic thresholding to machine learning, when applied to delineate waterbodies with diverse spectral and spatial characteristics. Automatic thresholding was applied to near-concurrent normalized difference water index (NDWI) (generated from S2 optical imagery) and VH backscatter features (generated from S1 SAR data). Machine learning was applied to a comprehensive set of features derived from S1 and S2 data. During our field surveys, we observed that the waterbodies visited had different sizes and varying levels of turbidity, sedimentation, and eutrophication. Five machine learning algorithms (MLAs), namely decision tree (DT), k-nearest neighbour (k-NN), random forest (RF), and two implementations of the support vector machine (SVM) were considered. Several experiments were carried out to better understand the complexities involved in mapping spectrally and spatially complex waterbodies. It was found that the combination of multispectral indices with SAR data is highly beneficial for classifying complex waterbodies and that the proposed thresholding approach classified waterbodies with an overall classification accuracy of 89.3%. However, the varying concentrations of suspended sediments (turbidity), dissolved particles, and aquatic plants negatively affected the classification accuracies of the proposed method, whereas the MLAs (SVM in particular) were less sensitive to such variations. The main disadvantage of using MLAs for operational waterbody mapping is the requirement for suitable training samples, representing both water and non-water land covers. The dynamic nature of reservoirs (many reservoirs are depleted at least once a year) makes the re-use of training data unfeasible. The study found that aggregating (combining) the thresholding results of two SAR and multispectral features, namely the S1 VH polari...

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“…The CTT inputs are more effective in estimating rainfall from convective systems and less so for frontal or orographic regimes (Thorne et al, 2001). The upper Breede catchment rainfall is highly influenced by the warm Indian Ocean and cold Atlantic Ocean air masses (Bangira et al, 2015), which often meet along the Western Cape coast resulting in convergence and frontal rainfall. Thus, this mechanism of rainfall development seems to influence the accuracy of the satellite rainfall estimates (Thorne et al, 2001;Maidment et al, 2017).…”

Section: Validation Of Satellite-derived Rainfall Productsmentioning

confidence: 99%

Assessment of satellite-derived rainfall and its use in the ACRU agro-hydrological model

Suleman

Chetty

Clark

et al. 2020

WSA

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Unfortunately, for various reasons, in-situ rain gauge networks are diminishing, especially in southern Africa, resulting in sparse networks whose records give a poor representation of rainfall occurrence, patterns and magnitudes. Hydrological models are used to inform decision making; however, model performance is directly linked to the quality of input data, such as rainfall. Therefore, the use of satellite-derived rainfall is being increasingly advocated as a viable alternative or supplement. The aim of this study was to evaluate the representativeness of satellite-derived rainfall and its utility in the ACRU agro-hydrological model to simulate streamflow magnitudes, distributions and patterns. The satellite-derived rainfall products selected for use in this study were TRMM3B42, FEWSARC2.0, FEWSRFE2.0, TAMSAT 3.0 and GPM-IMERG4. The satellite rainfall products were validated against available historical observed records and then were used to drive simulations using the ACRU agro-hydrological model in the upper uMngeni, upper uThukela and upper and central Breede catchments in South Africa. At the daily timescale, satellite-derived and observed rainfall were poorly correlated and variable among locations. However, monthly, seasonal and yearly rainfall totals and simulated streamflow volumes were in closer agreement with historical observations than the daily correlations; more so in the upper uMngeni and uThukela than in the upper and central Breede (e.g. FEWSARC2.0 and FEWSRFE2.0, producing relative volume errors of 3.18%, 4.63%, −5.07% and 2.54%, 9.54%, −1.67%, respectively, at Gauges V2E002, 0268883 and 02396985). Therefore, the satellite-derived rainfall shows promise for use in applications operating at coarser temporal scales than at finer daily ones. Complex topographical rainfall generation and varying weather systems, e.g. frontal rainfall, affected the accuracy of satellite-derived product estimates. This study focused on utilising the wealth of available raw satellite data; however, it is clear that the raw satellite data need to be corrected for bias and/or downscaled to provide more accurate results.

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Investigating flash floods potential areas using ASCAT and TRMM satellites in the Western Cape Province, South Africa

Cited by 18 publications

References 36 publications

Soil Moisture Remote Sensing: State‐of‐the‐Science

Soil Moisture Remote Sensing: State‐of‐the‐Science

Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water

Assessment of satellite-derived rainfall and its use in the ACRU agro-hydrological model

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