The September 2019 floods in Spain: An example of the utility of satellite data for the analysis of extreme hydrometeorological events

Major floods in Spain in September 9–13, 2019 resulted in seven casualties and massive losses to agriculture, property and infrastructure. This paper investigates the utility of satellite data to: (1) characterize the event when input into a hydrological model, and to provide an accurate picture of the evolution of the floods; and (2) inform meteorologists in real time in order to complement model forecasts. It is shown that the precipitation estimates from the Global Precipitation Measurement (GPM) Core Observatory (GPM-CO, available since 2014) and the merged satellite estimates provide an extraordinary improvement over previous technologies to monitor severe hydrometeorological episodes in near real time. In spite of known biases and errors, these new satellite precipitation estimates can be of broad practical interest to deal with emergencies and long-term readiness, especially for semi-arid areas potentially affected by ongoing global warming. Comparisons of satellite data of the September event with model outputs and more direct observations such as rain gauges and ground radars reinforce the idea that satellites are fundamental for an appropriate management of hydrometeorological events.Funding from projects PID2019-108470RB-C21, PID2019-108470RB-C22 (AEI/FEDER, UE), CGL2016-80609-R, and 1365002970/KMA2018-00721 (Korean Meteorological Agency, Korea) is gratefully acknowledged

A multiscale accuracy assessment of moisture content predictions using time-lapse electrical resistivity tomography in mine tailings

Accurate and large-scale assessment of volumetric water content (VWC) plays a critical role in mining waste monitoring to mitigate potential geotechnical and environmental risks. In recent years, time-lapse electrical resistivity tomography (TL-ERT) has emerged as a promising monitoring approach that can be used in combination with traditional invasive and point-measurements techniques to estimate VWC in mine tailings. Moreover, the bulk electrical conductivity (EC) imaged using TL-ERT can be converted into VWC in the field using petrophysical relationships calibrated in the laboratory. This study is the first to assess the scale effect on the accuracy of ERT-predicted VWC in tailings. Simultaneous and co-located monitoring of bulk EC and VWC are carried out in tailings at five different scales, in the laboratory and in the field. The hydrogeophysical datasets are used to calibrate a petrophysical model used to predict VWC from TL-ERT data. Overall, the accuracy of ERT-predicted VWC is , and the petrophysical models determined at sample-scale in the laboratory remain valid at larger scales. Notably, the impact of temperature and pore water EC evolution plays a major role in VWC predictions at the field scale (tenfold reduction of accuracy) and, therefore, must be properly taken into account during the TL-ERT data processing using complementary hydrogeological sensors. Based on these results, we suggest that future studies using TL-ERT to predict VWC in mine tailings could use sample-scale laboratory apparatus similar to the electrical resistivity Tempe cell presented here to calibrate petrophysical models and carefully upscale them to field applications

Towards the improvement of machine learning peak runoff forecasting by exploiting ground- and satellite-based precipitation data: A feature engineering approach

La predicción de picos de caudal en sistemas montañosos complejos presenta desafíos en hidrología debido a la falta de datos y las limitaciones de los modelos físicos. El aprendizaje automático (ML) ofrece una solución al permitir la integración de técnicas y productos satelitales de precipitación (SPPs). Sin embargo, se ha debatido sobre la efectividad del ML debido a su naturaleza de "caja negra" que dificulta la mejora del rendimiento y la reproducibilidad de los resultados. Para abordar estas preocupaciones, se han propuesto estrategias de ingeniería de características (FE) para incorporar conocimiento físico en los modelos de ML, mejorando la comprensión y precisión de las predicciones. Esta investigación doctoral tiene como objetivo mejorar la predicción de picos de caudal mediante la integración de conceptos hidrológicos a través de técnicas de FE y el uso de datos de precipitación in-situ y SPPs. Se exploran técnicas y estrategias de ML para mejorar la precisión en sistemas hidrológicos macro y mesoescala. Además, se propone una estrategia de FE para aprovechar la información de SPPs y superar la escasez de datos espaciales y temporales. La integración de técnicas avanzadas de ML y FE representa un avance en hidrología, especialmente para sistemas montañosos complejos con limitada o nula red de monitoreo. Los hallazgos de este estudio serán valiosos para tomadores de decisiones e hidrólogos, facilitando la mitigación de los impactos de los picos de caudal. Además, las metodologías desarrolladas se pueden adaptar a otros sistemas de macro y mesoescala, beneficiando a la comunidad científica en general.Peak runoff forecasting in complex mountain systems poses significant challenges in hydrology due to limitations in traditional physically-based models and data scarcity. However, the integration of machine learning (ML) techniques offers a promising solution by balancing computational efficiency and enabling the incorporation of satellite precipitation products (SPPs). However, debates have emerged regarding the effectiveness of ML in hydrology, as its black-box nature lacks explicit representation of hydrological processes, hindering performance improvement and result reproducibility. To address these concerns, recent studies emphasize the inclusion of FE strategies to incorporate physical knowledge into ML models, enabling a better understanding of the system and improved forecasting accuracy. This doctoral research aims to enhance the effectiveness of ML in peak runoff forecasting by integrating hydrological concepts through FE techniques, utilizing both ground-based and satellite-based precipitation data. For this, we explore ML techniques and strategies to enhance accuracy in complex macro- and mesoscale hydrological systems. Additionally, we propose a FE strategy for a proper utilization of SPP information which is crucial for overcoming spatial and temporal data scarcity. The integration of advanced ML techniques and FE represents a significant advancement in hydrology, particularly for complex mountain systems with limited or inexistent monitoring networks. The findings of this study will provide valuable insights for decision-makers and hydrologists, facilitating effective mitigation of the impacts of peak runoffs. Moreover, the developed methodologies can be adapted to other macro- and meso-scale systems, with necessary adjustments based on available data and system-specific characteristics, thus benefiting the broader scientific community.0000-0002-7683-37680000-0002-6206-075XDoctor (PhD) en Recursos HídricosCuenc

Near-Real-Time Flood Forecasting Based on Satellite Precipitation Products

Floods, storms and hurricanes are devastating for human life and agricultural cropland. Near-real-time (NRT) discharge estimation is crucial to avoid the damages from flood disasters. The key input for the discharge estimation is precipitation. Directly using the ground stations to measure precipitation is not efficient, especially during a severe rainstorm, because precipitation varies even in the same region. This uncertainty might result in much less robust flood discharge estimation and forecasting models. The use of satellite precipitation products (SPPs) provides a larger area of coverage of rainstorms and a higher frequency of precipitation data compared to using the ground stations. In this paper, based on SPPs, a new NRT flood forecasting approach is proposed to reduce the time of the emergency response to flood disasters to minimize disaster damage. The proposed method allows us to forecast floods using a discharge hydrograph and to use the results to map flood extent by introducing SPPs into the rainfall–runoff model. In this study, we first evaluated the capacity of SPPs to estimate flood discharge and their accuracy in flood extent mapping. Two high temporal resolution SPPs were compared, integrated multi-satellite retrievals for global precipitation measurement (IMERG) and tropical rainfall measurement mission multi-satellite precipitation analysis (TMPA). The two products are evaluated over the Ottawa watershed in Canada during the period from 10 April 2017 to 10 May 2017. With TMPA, the results showed that the difference between the observed and modeled discharges was significant with a Nash–Sutcliffe efficiency (NSE) of −0.9241 and an adapted NSE (ANSE) of −1.0048 under high flow conditions. The TMPA-based model did not reproduce the shape of the observed hydrographs. However, with IMERG, the difference between the observed and modeled discharges was improved with an NSE equal to 0.80387 and an ANSE of 0.82874. Also, the IMERG-based model could reproduce the shape of the observed hydrographs, mainly under high flow conditions. Since IMERG products provide better accuracy, they were used for flood extent mapping in this study. Flood mapping results showed that the error was mostly within one pixel compared with the observed flood benchmark data of the Ottawa River acquired by RadarSat-2 during the flood event. The newly developed flood forecasting approach based on SPPs offers a solution for flood disaster management for poorly or totally ungauged watersheds regarding precipitation measurement. These findings could be referred to by others for NRT flood forecasting research and applications