Near-real-time satellite precipitation data ingestion into peak runoff forecasting models

Muñoz, Paul; Corzo, Gerald; Solomatine, Dimitri; Feyen, Jan; Célleri, Rolando

doi:10.1016/j.envsoft.2022.105582

Cited by 7 publications

(7 citation statements)

References 66 publications

(88 reference statements)

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“…The Random Forest is a machine learning technique, and it has been widely employed in hydrological forecasting [1,15,16,20,36]. The strength of RF lies in its ensemble nature, where each decision tree within the forest is trained on a distinct data subset, promoting diversity and minimizing potential bias.…”

Section: Random Forest (Rf) Algorithm For Regressionmentioning

confidence: 99%

“…The effectiveness of employing FE strategies in hydrological models is supported in several studies [19][20][21][22][23][24][25]. In the specific case of precipitation-runoff models, there are studies; for example, in the one conducted by Muñoz et al [20], they employed FE through a spatiotemporal object-based approach. This object-based approach is derived from the framework proposed in the study by Laverde-Barajas et al [26].…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Peak Runoff Forecasting through Feature Engineering Applied to X-Band Radar Data

Álvarez-Estrella,

Muñoz,

Bendix

et al. 2024

Water

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Floods cause significant damage to human life, infrastructure, agriculture, and the economy. Predicting peak runoffs is crucial for hazard assessment, but it is challenging in remote areas like the Andes due to limited hydrometeorological data. We utilized a 300 km2 catchment over the period 2015–2021 to develop runoff forecasting models exploiting precipitation information retrieved from an X-band weather radar. For the modeling task, we employed the Random Forest (RF) algorithm in combination with a Feature Engineering (FE) strategy applied to the radar data. The FE strategy is based on an object-based approach, which derives precipitation characteristics from radar data. These characteristics served as inputs for the models, distinguishing them as “enhanced models” compared to “referential models” that incorporate precipitation estimates from all available pixels (1210) for each hour. From 29 identified events, enhanced models achieved Nash-Sutcliffe efficiency (NSE) values ranging from 0.94 to 0.50 for lead times between 1 and 6 h. A comparative analysis between the enhanced and referential models revealed a remarkable 23% increase in NSE-values at the 3 h lead time, which marks the peak improvement. The enhanced models integrated new data into the RF models, resulting in a more accurate representation of precipitation and its temporal transformation into runoff.

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Section: Random Forest (Rf) Algorithm For Regressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing Peak Runoff Forecasting through Feature Engineering Applied to X-Band Radar Data

Álvarez-Estrella,

Muñoz,

Bendix

et al. 2024

Water

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“…The Jubones basin has extremely variable climatic conditions with multiple ecosystems and landscapes, influenced by the Andes Mountain range, ocean currents from the Pacific Ocean, and trade winds from the southeast (Hasan and Wyseure, 2018). Consequently, the climate in the basin varies from humid to semi-arid, with annual precipitation regions ranging from 290 to 925 mm (Muñoz et al, 2023).…”

Section: Study Areamentioning

confidence: 99%

“…In recent decades, ML models have become popular among hydrologists due to their advantages, including shorter simulation times, the possibility of computationally inexpensive real-time operation, and less overfitting compared to models based on physical processes (Solomatine and Ostfeld, 2008;Muñoz et al, 2018;Kwon et al, 2020;Adnan et al, 2021;Huang and Lee, 2021;Moreido et al, 2021). The Random Forest (RF) algorithm and the Long Short-Term Memory (LSTM) networks are among the most commonly used ML techniques for hydrological time-series forecasting (Muñoz et al, 2018(Muñoz et al, , 2023de la Fuente et al, 2019;Campozano et al, 2020;Li et al, 2022;Zhou et al, 2023). Recent studies have shown the superiority of LSTM over RF models for sub-daily runoff forecasting (Zhou et al, 2023).…”

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confidence: 99%

Integrating geographic data and the SCS-CN method with LSTM networks for enhanced runoff forecasting in a complex mountain basin

Merizalde,

Muñoz,

Corzo

et al. 2023

Front. Water

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IntroductionIn complex mountain basins, hydrological forecasting poses a formidable challenge due to the intricacies of runoff generation processes and the limitations of available data. This study explores the enhancement of short-term runoff forecasting models through the utilization of long short-term memory (LSTM) networks.MethodsTo achieve this, we employed feature engineering (FE) strategies, focusing on geographic data and the Soil Conservation Service Curve Number (SCS-CN) method. Our investigation was conducted in a 3,390 km2 basin, employing the GSMaP-NRT satellite precipitation product (SPP) to develop forecasting models with lead times of 1, 6, and 11 h. These lead times were selected to address the needs of near-real-time forecasting, flash flood prediction, and basin concentration time assessment, respectively.Results and discussionOur findings demonstrate an improvement in the efficiency of LSTM forecasting models across all lead times, as indicated by Nash-Sutcliffe efficiency values of 0.93 (1 h), 0.77 (6 h), and 0.67 (11 h). Notably, these results are on par with studies relying on ground-based precipitation data. This methodology not only showcases the potential for advanced data-driven runoff models but also underscores the importance of incorporating available geographic information into precipitation-ungauged hydrological systems. The insights derived from this study offer valuable tools for hydrologists and researchers seeking to enhance the accuracy of hydrological forecasting in complex mountain basins.

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“…El monitoreo de los eventos hidrológicos presenta una limitación en la data disponible para la modelación computacional de los eventos y comprender su complejidad (Muñoz et al, 2023). Los datos son proporcionados por las estaciones meteorológicas que son de representación puntual, pero en zonas con una baja densidad de estaciones meteorológicas es necesario buscar alternativa que brinden solución a la problemática de escasa data a nivel espacial, surgen métodos de interpolación, reanálisis y validación de datos satelitales (Essou et al, 2017;C.M et al, 2022;Vaheddoost et al, 2023).…”

Section: Introductionunclassified

Validación de la data de precipitación (1981-2016) del producto PISCO v2.1: Estación meteorológica, elevación, estacionalidad del año y región climática en la cuenca Tumbes, Perú

QUIROZ

2023

Manglar

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The scarce information that is counted and its importance in hydrological studies helps us to understand, simulate, and predict the events associated with the behavior of water. I think it is necessary to check the gridded data PISCOpm V.2.1 monthly rainfall with the observations in our Tumbes basin. For this, a comparative study was carried out between the observed and estimated values, concerning the weather station, elevation, seasonality of the year, and climatic region. Indicators such as the Coefficient of Determination(R2), Nash-Sutcliffe Efficiency Index (NS), and Percentage Statistical Bias (PBIAS%) were considered. At the monthly level, good correlation results were obtained, with values greater than 0,61, in the Nash - Sutcliffe efficiency index values greater than 0,60 and a minimum variation in the Percentage Statistical Bias. Regarding the elevation of the basin, data were obtained with a minimum adjustment in all the indicators. For the seasonal arrangement, summer presents the best average fit, followed by fall and spring. Winter presents a very good adjustment for 4 out of 9. Finally, of the 3 climatic regions, the D(i)A' region presents the best adjustment in the 3 indicators. It was possible to verify the adjustment of the data, having different results according to the evaluated criterion. This supports the use of observed data and its usefulness for studies in the Tumbes basin such as floods, droughts, hydrological and hydraulic modeling, completion of missing data, etc. Based on the results, studies of water availability, analysis of extreme events, etc. can be carried out.

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Near-real-time satellite precipitation data ingestion into peak runoff forecasting models

Cited by 7 publications

References 66 publications

Enhancing Peak Runoff Forecasting through Feature Engineering Applied to X-Band Radar Data

Enhancing Peak Runoff Forecasting through Feature Engineering Applied to X-Band Radar Data

Integrating geographic data and the SCS-CN method with LSTM networks for enhanced runoff forecasting in a complex mountain basin

Validación de la data de precipitación (1981-2016) del producto PISCO v2.1: Estación meteorológica, elevación, estacionalidad del año y región climática en la cuenca Tumbes, Perú

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