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Hydrometeorological Hazards in the USA and Europe

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 11960

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Special Issue Editor

Prof. Dr. Hatim Sharif

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Guest Editor

Department of Civil and Environmental Engineering, The University of Texas at San Antonio, San Antonio, TX 78249, USA
Interests: hydrometeorology; hydrologic modeling and forecasting; environmental applications of remote sensing; natural hazards; public health; water quality modeling; transportation safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hydrometeorological hazards such as tropical cyclones (hurricanes), severe storms, storm surges, tornados, hailstorms, floods and flash floods, drought, blizzards, heatwaves, and cold spells are responsible for the loss of human lives, infrastructure damage, and massive economic losses in the United States of America and Europe. Extreme, hydrometeorological conditions can also lead to hazards such as landslides, wildland fires, epidemics, and the transport and release of toxic substances. New advances in ground-based and airborne remote sensing platforms and techniques together with the introduction of new products is spurring a revolution in the development of advanced methods and models for the simulation, analysis, forecasting, and prevention of hydrometeorological hazards prevention. Remotely sensed products are also being used in the planning, assessment, management of disaster mitigation efforts. The aim of this Special Issue is to gather contributions on remote sensing (RS) applications in the simulation, analysis, forecasting, and prevention of hydrometeorological hazards. The contributions to this Special Issue will encompass a broad spectrum of topics, including but not limited to:

RS-based hydrometeorological observation and modeling tools;
Innovative RS methods to identify hydrometeorological hazards;
Improvement of hydrometeorological forecasting across various temporal and spatial scales;
RS-based methods to assess the impacts on extreme hydrometeorological events;
RS applications in planning, assessment, management of disaster mitigation measures;
RS applications in disaster risk management, responses, and education.

Prof. Dr. Hatim Sharif
Guest Editor

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Keywords

hydrometeorology
remote sensing
natural hazards
extreme events
hydrologic modeling and forecasting
public health
risk management
disaster mitigation
satellite
radar

Published Papers (7 papers)

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Research

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15 pages, 14134 KiB

Open AccessArticle

Identifying the Drivers of Caribbean Severe Weather Impacts

by Mark R. Jury

Remote Sens. 2023, 15(22), 5282; https://doi.org/10.3390/rs15225282 - 8 Nov 2023

Viewed by 730

Abstract

Severe weather impacts in the central Caribbean are quantified by an objective index of daily maximum wind and rainfall (W•R) in the area 16–19°N, 63–69°W over the period 1970–2021. The index, based on ERA5 hindcast assimilation of satellite and in situ data, peaks from the July to October season as high sea temperatures and weak wind shear promote tropical cyclogenesis. Climate forcing is studied by reducing the W•R index to seasonal values and regressing the time series onto reanalysis fields 10°S–25°N, 180°W–20°E. The outcome reflects Jul–Oct warming in the tropical Atlantic, cooling in the tropical east Pacific (cold tongue), decreased/increased convection over the Pacific/Atlantic, and tropical upper easterly winds. New findings emerge in the Mar–Jun season preceding higher W•R: reduced SW-cloud bands in the northeast Pacific, a convective trough over the equatorial Atlantic, and Caribbean cold-air outbreaks. The multivariate El Niño Southern Oscillation index correlates with Jul–Oct Caribbean W•R at 2-month lead time and shows growing influence. Composite analysis of the top-10 years identifies an anomalous Pacific–Atlantic Walker Circulation favoring higher Caribbean W•R. Salinity is below normal and heat flux is downward across the Atlantic. Anomalous low-level airflow inhibits upwelling in the SW Caribbean, deepening atmospheric moisture. A leading case (TC Fiona 2022) demonstrates the environmental conditions underpinning storm intensification. The key drivers of severe weather impacts yield guidance in strategic planning, risk management and disaster preparedness. New insights are gained from a localized index of severe weather. Full article

(This article belongs to the Special Issue Hydrometeorological Hazards in the USA and Europe)

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16 pages, 6978 KiB

Open AccessArticle

Evaluation of Radar Precipitation Products and Assessment of the Gauge-Radar Merging Methods in Southeast Texas for Extreme Precipitation Events

by Wenzhao Li, Han Jiang, Dongfeng Li, Philip B. Bedient and Zheng N. Fang

Remote Sens. 2023, 15(8), 2033; https://doi.org/10.3390/rs15082033 - 12 Apr 2023

Viewed by 1407

Abstract

Many radar-gauge merging methods have been developed to produce improved rainfall data by leveraging the advantages of gauge and radar observations. Two popular merging methods, Regression Kriging and Bayesian Regression Kriging were utilized and compared in this study to produce hourly rainfall data from gauge networks and multi-source radar datasets. The authors collected, processed, and modeled the gauge and radar rainfall data (Stage IV, MRMS and RTMA radar data) of the two extreme storm events (i.e., Hurricane Harvey in 2017 and Tropical Storm Imelda in 2019) occurring in the coastal area in Southeast Texas with devastating flooding. The analysis of the modeled data on consideration of statistical metrics, physical rationality, and computational expenses, implies that while both methods can effectively improve the radar rainfall data, the Regression Kriging model demonstrates its superior performance over that of the Bayesian Regression Kriging model since the latter is found to be prone to overfitting issues due to the clustered gauge distributions. Moreover, the spatial resolution of rainfall data is found to affect the merging results significantly, where the Bayesian Regression Kriging model works unskillfully when radar rainfall data with a coarser resolution is used. The study recommends the use of high-quality radar data with properly spatial-interpolated gauge data to improve the radar-gauge merging methods. The authors believe that the findings of the study are critical for assisting hazard mitigation and future design improvement. Full article

(This article belongs to the Special Issue Hydrometeorological Hazards in the USA and Europe)

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16 pages, 4814 KiB

Open AccessArticle

A Summary of Hail Events during the Summer of 2022 in Catalonia: A Comparison with the Period of 2013–2021

by Tomeu Rigo and Carme Farnell

Remote Sens. 2023, 15(4), 1012; https://doi.org/10.3390/rs15041012 - 12 Feb 2023

Cited by 3 | Viewed by 987

Abstract

Hail events are common in Catalonia during the warm season (May to September), but especially between June and August. These cases produce important damages to agriculture and infrastructure. The campaign of 2022 will be remembered by three different phases: the first and last phases, which were very stable and with few events, and the middle phase, which had a large number of episodes. Some of the cases had an important social impact because of the large areas affected or the economical damages. The present analysis used the vertically integrated liquid radar product for estimating the hail swaths. Hail swaths are classified according to different parameters, allowing for the characterization of the campaign and a comparison with the period of 2013–2021. The results show how the month of June had a deficit of cases with respect to the reference period (half of the cases), July presented similar values, and August had a positive anomaly, with five times more cases. In addition, the first ever case of giant hail in Catalonia occurred in August 2022, a month with more than five times the number of cases of severe and very large hail with respect to the average of the period of 2013–2021. Full article

(This article belongs to the Special Issue Hydrometeorological Hazards in the USA and Europe)

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22 pages, 116599 KiB

Open AccessArticle

A Complete Meteo/Hydro/Hydraulic Chain Application to Support Early Warning and Monitoring Systems: The Apollo Medicane Use Case

by Martina Lagasio, Giacomo Fagugli, Luca Ferraris, Elisabetta Fiori, Simone Gabellani, Rocco Masi, Vincenzo Mazzarella, Massimo Milelli, Andrea Parodi, Flavio Pignone, Silvia Puca, Luca Pulvirenti, Francesco Silvestro, Giuseppe Squicciarino and Antonio Parodi

Remote Sens. 2022, 14(24), 6348; https://doi.org/10.3390/rs14246348 - 15 Dec 2022

Cited by 6 | Viewed by 1572

Abstract

Because of the ongoing changing climate, extreme rainfall events’ frequency at the global scale is expected to increase, thus resulting in high social and economic impacts. A Meteo/Hydro/Hydraulic forecasting chain combining heterogeneous observational data sources is a crucial component for an Early Warning System and is a fundamental asset for Civil Protection Authorities to correctly predict these events, their effects, and put in place anticipatory actions. During the last week of October 2021 an intense Mediterranean hurricane (Apollo) affected many Mediterranean countries (Tunisia, Algeria, Malta, and Italy) with a death toll of seven people. The CIMA Meteo/Hydro/Hydraulic forecasting chain, including the WRF model, the hydrological model Continuum, the automatic system for water detection (AUTOWADE), and the hydraulic model TELEMAC-2D, was operated in real-time to predict the Apollo weather evolution as well as its hydrological and hydraulic impacts, in support of the early warning activities of the Italian Civil Protection Department. The WRF model assimilating radar data and in situ weather stations showed very good predictive capability for rainfall timing and location over eastern Sicily, thus supporting accurate river flow peak forecasting with the hydrological model Continuum. Based on WRF predictions, the daily automatic system for water detection (AUTOWADE) run using Sentinel 1 data was anticipated with respect to the scheduled timing to quickly produce a flood monitoring map. Ad hoc tasking of the COSMO-SkyMed satellite constellation was also performed to overcome the S1 data latency in eastern Sicily. The resulting automated operational mapping of floods and inland waters was integrated with the subsequent execution of the hydraulic model TELEMAC-2D to have a complete representation of the flooded area with water depth and water velocity. Full article

(This article belongs to the Special Issue Hydrometeorological Hazards in the USA and Europe)

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35 pages, 9866 KiB

Open AccessArticle

Understanding Intensity–Duration–Frequency (IDF) Curves Using IMERG Sub-Hourly Precipitation against Dense Gauge Networks

by Alcely Lau and Ali Behrangi

Remote Sens. 2022, 14(19), 5032; https://doi.org/10.3390/rs14195032 - 9 Oct 2022

Cited by 3 | Viewed by 1838

Abstract

The design storm derived from intensity–duration–frequency (IDF) curves is the main input for hydrologic analysis or hydraulic design for flood control. The regions with higher flood risks due to extreme precipitation are often deficient in precipitation gauges. This study presents a detailed evaluation of IDF curves derived using IMERG Final half-hourly precipitation (V06), fitted with the widely used CDFs: Gumbel and MLE, Gumbel and MM, Pearson 3, and GEV. As benchmarks and following the same method, we also derived IDF curves using areal average gridded precipitation constructed from two dense gauges networks over (1) the WegenerNET Feldbach region in the Alpine forelands of Austria and (2) the gauge network of the Walnut Gulch Experimental Watershed, in a semiarid region of the United States. In both regions, the frequency analysis for return periods between 2 and 100 years was based on half-hourly rainfall and compared at a grid-scale with a spatial resolution of IMERG, 0.1° × 0.1° lat/lon. The impact of order in which the gridded gauge-based precipitation average is performed within an IMERG grid was evaluated by computing two different Annual Maximum Series (AMS). In one, the average was computed before obtaining the AMS (AB-AMS), and in the other, the average was computed after obtaining the AMS for each gauge grid (AA-AMS) within the IMERG grid. The evaluation revealed that IMERG AMS agrees better with AB-AMS than AA-AMS for the two study regions. Lastly, it was found that the use of Gumbel distribution in calculating IMERG IDF curves results in better agreement with the ground truth than the use of the other three distributions studied here. The outcomes should provide valuable knowledge for the application of IMERG precipitation over regions with sparse gauges. Full article

(This article belongs to the Special Issue Hydrometeorological Hazards in the USA and Europe)

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10 pages, 2484 KiB

Open AccessArticle

Evaluation of the Analysis of Record for Calibration (AORC) Rainfall across Louisiana

by Hanbeen Kim and Gabriele Villarini

Remote Sens. 2022, 14(14), 3284; https://doi.org/10.3390/rs14143284 - 8 Jul 2022

Cited by 5 | Viewed by 1844

Abstract

The use of a long-term and high-quality precipitation dataset is crucial for hydrologic modeling and flood risk management. This study evaluates the Analysis of Period of Record for Calibration (AORC) dataset, a newly released product with high temporal and spatial resolutions. Our study region is centered on Louisiana because of the major flooding it has been experiencing. We compare the AORC hourly precipitation to other widely used gridded rainfall products and rain-gauge observations. To evaluate the performance of rainfall products according to different weather conditions causing severe flooding, we stratify the analyses depending on whether precipitation is associated with a tropical cyclone (TC) or not. Compared to observations, our results show that the AORC has the highest correlation coefficients (i.e., values above 0.75) with respect to observations among all rainfall products for both TC and non-TC periods. When the skill metric is decomposed into the potential skill and biases, the AORC clearly shows the highest potential skill with relatively small biases for the whole period. In addition, the AORC performs better for the TC period compared to the non-TC ones. Our results suggest that AORC precipitation shows good potential to be viable for hydrologic modeling and simulations of TC and non-TC events. Full article

(This article belongs to the Special Issue Hydrometeorological Hazards in the USA and Europe)

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Other

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12 pages, 5281 KiB

Open AccessTechnical Note

Mapping Flash Flood Hazards in Arid Regions Using CubeSats

by Zhaocheng Wang and Enrique R. Vivoni

Remote Sens. 2022, 14(17), 4218; https://doi.org/10.3390/rs14174218 - 26 Aug 2022

Cited by 4 | Viewed by 2560

Abstract

Flash flooding affects a growing number of people and causes billions of dollars in losses each year with the impact often falling disproportionally on underdeveloped regions. To inform post-flood mitigation efforts, it is crucial to determine flash flooding extents, especially for extreme events. Unfortunately, flood hazard mapping has often been limited by a lack of observations with both high spatial and temporal resolution. The CubeSat constellation operated by Planet Labs can fill this key gap in Earth observations by providing 3 m near-daily multispectral imagery at the global scale. In this study, we demonstrate the imaging capabilities of CubeSats for mapping flash flood hazards in arid regions. We selected a severe storm on 13–14 August 2021 that swept through the town of Gila Bend, Arizona, causing severe flood damages, two deaths, and the Declaration of a State of Emergency. We found the spatial extent of flooding can be mapped from CubeSat imagery through comparisons of the near-infrared surface reflectance prior to and after the flash flood event (ΔNIR). The unprecedented spatiotemporal resolution of CubeSat imagery allowed the detection of ponded (ΔNIR ≤ −0.05) and flood-affected (ΔNIR ≥ +0.02) areas that compared remarkably well with the 100-year flood event extent obtained by an independent hydraulic modeling study. Our findings demonstrate that CubeSat imagery provides valuable spatial details on flood hazards and can support post-flood activities such as damage assessments and emergency relief. Full article

(This article belongs to the Special Issue Hydrometeorological Hazards in the USA and Europe)

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