Improving the use of ground-based radar rainfall data for monitoring and predicting floods in the Iguaçu river basin

Falck, Aline S.; Maggioni, Viviana; Tomasella, Javier; Diniz, Fábio L. R.; Mei, Yiwen; Beneti, Cesar; Herdies, Dirceu Luís; Neundorf, R.; Caram, R. Oliveira; Rodríguez, Daniel Andrés

doi:10.1016/j.jhydrol.2018.10.046

Cited by 20 publications

(14 citation statements)

References 30 publications

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“…Regarding indirect methods, weather radars provide precipitation estimates with high spatial and temporal resolution but have limited accuracy in mountainous regions and cold climates [5,8]. On the other hand, satellite estimates of precipitation provide vast spatial and temporal coverage and are freely available.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Paraná State, Brazil

et al. 2021

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The lack of measurement of precipitation in large areas using fine-resolution data is a limitation in water management, particularly in developing countries. However, Version 6 of the Integrated Multi-satellitE Retrievals for GPM (IMERG) has provided a new source of precipitation information with high spatial and temporal resolution. In this study, the performance of the GPM products (Final run) in the state of Paraná, located in the southern region of Brazil, from June 2000 to December 2018 was evaluated. The daily and monthly products of IMERG were compared to the gauge data spatially distributed across the study area. Quantitative and qualitative metrics were used to analyze the performance of IMERG products to detect precipitation events and anomalies. In general, the products performed positively in the estimation of monthly rainfall events, both in volume and spatial distribution, and demonstrated limited performance for daily events and anomalies, mainly in mountainous regions (coast and southwest). This may be related to the orographic rainfall in these regions, associating the intensity of the rain, and the topography. IMERG products can be considered as a source of precipitation data, especially on a monthly scale. Product calibrations are suggested for use on a daily scale and for time-series analysis.

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Section: Introductionmentioning

confidence: 99%

Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Paraná State, Brazil

et al. 2021

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“…Some authors [2,4,5] provide reviews of the application of machine learning-based models for this purpose. Several streamflow forecasting studies [23][24][25][26][27] use data-driven models employing radar-derived rainfall as input; this requires a preliminary step for transforming reflectivity (native ground radar variable) into rainfall rate [14]. Either way, derivation of radar rainfall estimates remains an intensive task that needs to be tackled before using ground radar data for discharge forecasting.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Native Radar Reflectivity and Radar Rainfall Estimates for Discharge Forecasting in Mountain Catchments with a Random Forest Model

et al. 2020

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Discharge forecasting is a key component for early warning systems and extremely useful for decision makers. Forecasting models require accurate rainfall estimations of high spatial resolution and other geomorphological characteristics of the catchment, which are rarely available in remote mountain regions such as the Andean highlands. While radar data is available in some mountain areas, the absence of a well distributed rain gauge network makes it hard to obtain accurate rainfall maps. Thus, this study explored a Random Forest model and its ability to leverage native radar data (i.e., reflectivity) by providing a simplified but efficient discharge forecasting model for a representative mountain catchment in the southern Andes of Ecuador. This model was compared with another that used as input derived radar rainfall (i.e., rainfall depth), obtained after the transformation from reflectivity to rainfall rate by using a local Z-R relation and a rain gauge-based bias adjustment. In addition, the influence of a soil moisture proxy was evaluated. Radar and runoff data from April 2015 to June 2017 were used. Results showed that (i) model performance was similar by using either native or derived radar data as inputs (0.66 < NSE < 0.75; 0.72 < KGE < 0.78). Thus, exhaustive pre-processing for obtaining radar rainfall estimates can be avoided for discharge forecasting. (ii) Soil moisture representation as input of the model did not significantly improve model performance (i.e., NSE increased from 0.66 to 0.68). Finally, this native radar data-based model constitutes a promising alternative for discharge forecasting in remote mountain regions where ground monitoring is scarce and hardly available.

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“…Precipitation is a major component of the hydrologic cycle and is the critical input for hydrological models [62][63][64][65][66][67][68][69][70][71][72][73][74]. Accurate and continuous precipitation estimates are essential for reliable hydrological simulations of fluxes and states [17,73,75]. However, poor precipitation observations (e.g., poor continuity in time and space) may lead to non-linear propagated errors in streamflow simulations [66,[76][77][78][79].…”

Section: Remotely Sensed Precipitationmentioning

confidence: 99%

“…Therefore, prior to their implementation to the hydrological model, SREs require thorough validation and commonly need bias correction based on rain gauge data [17,71,74,78,89,91,102,103]. Falck et al (2018) [75] concluded that the corrected radar rainfall estimates reduced the systematic error of the streamflow ensemble for most sub-basins compared with the rain gauge, and significantly improved the simulated streamflow during nine flood events. Zhang et al (2019) [17] discovered that the adjusted TMPA 3b42V7 improved the performance of the simulated streamflow better than the original TMPA 3b42V7 data, and performed even better than rain-gauge observation in the validation period.…”

Section: Remotely Sensed Precipitationmentioning

confidence: 99%

The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review

Jiang

Wang

2019

Water

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A hydrological model is a useful tool to study the effects of human activities and climate change on hydrology. Accordingly, the performance of hydrological modeling is vitally significant for hydrologic predictions. In watersheds with intense human activities, there are difficulties and uncertainties in model calibration and simulation. Alternative approaches, such as machine learning techniques and coupled models, can be used for streamflow predictions. However, these models also suffer from their respective limitations, especially when data are unavailable. Satellite-based remote sensing may provide a valuable contribution for hydrological predictions due to its wide coverage and increasing tempo-spatial resolutions. In this review, we provide an overview of the role of satellite-based remote sensing in streamflow simulation. First, difficulties in hydrological modeling over highly regulated basins are further discussed. Next, the performance of satellite-based remote sensing (e.g., remotely sensed data for precipitation, evapotranspiration, soil moisture, snow properties, terrestrial water storage change, land surface temperature, river width, etc.) in improving simulated streamflow is summarized. Then, the application of data assimilation for merging satellite-based remote sensing with a hydrological model is explored. Finally, a framework, using remotely sensed observations to improve streamflow predictions in highly regulated basins, is proposed for future studies. This review can be helpful to understand the effect of applying satellite-based remote sensing on hydrological modeling.

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Improving the use of ground-based radar rainfall data for monitoring and predicting floods in the Iguaçu river basin

Cited by 20 publications

References 30 publications

Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Paraná State, Brazil

Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Paraná State, Brazil

Assessment of Native Radar Reflectivity and Radar Rainfall Estimates for Discharge Forecasting in Mountain Catchments with a Random Forest Model

The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review

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