Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Atmosphere atmosphere	2.9	4.1	2010	17.7 Days	CHF 2400
GeoHazards geohazards	-	-	2020	20.7 Days	CHF 1000
Geosciences geosciences	2.7	5.2	2011	23.6 Days	CHF 1800
Remote Sensing remotesensing	5.0	7.9	2009	23 Days	CHF 2700
Water water	3.4	5.5	2009	16.5 Days	CHF 2600

21 pages, 4347 KiB

Open AccessArticle

Hydrological Drought and Flood Projection in the Upper Heihe River Basin Based on a Multi-GCM Ensemble and the Optimal GCM

by Zhanling Li, Yingtao Ye, Xiaoyu Lv, Miao Bai and Zhanjie Li

Atmosphere 2024, 15(4), 439; https://doi.org/10.3390/atmos15040439 - 01 Apr 2024

Viewed by 573

Abstract

To ensure water use and water resource security along “the Belt and Road”, the runoff and hydrological droughts and floods under future climate change conditions in the upper Heihe River Basin were projected in this study, based on the observed meteorological and runoff [...] Read more.

To ensure water use and water resource security along “the Belt and Road”, the runoff and hydrological droughts and floods under future climate change conditions in the upper Heihe River Basin were projected in this study, based on the observed meteorological and runoff data from 1987 to 2014, and data from 10 GCMs from 1987 to 2014 and from 2026 to 2100, using the SWAT model, the Standardized Runoff Index, run length theory, and the entropy-weighted TOPSIS method. Both the multi-GCM ensemble (MME) and the optimal model were used to assess future hydrological drought and flood responses to climate change. The results showed that (1) the future multi-year average runoff from the MME was projected to be close to the historical period under the SSP245 scenario and to increase by 2.3% under the SSP585 scenario, and those from the optimal model CMCC-ESM2 were projected to decrease under both scenarios; (2) both the MME and the optimal model showed that drought duration and flood intensity in the future were projected to decrease, while drought intensity, drought peak, flood duration, and flood peak were projected to increase under both scenarios in their multi-year average levels; (3) drought duration was projected to decrease most after 2080, and drought intensity, flood duration, and flood peak were projected to increase most after 2080, according to the MME. The MME and the optimal model reached a consensus on the sign of hydrological extreme characteristic responses to climate change, but showed differences in the magnitude of trends. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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24 pages, 27091 KiB

Open AccessArticle

Projected Increase in Compound Drought and Hot Days over Global Maize Areas under Global Warming

by Yan He, Yanxia Zhao, Yihong Duan, Xiaokang Hu and Jiayi Fang

Water 2024, 16(4), 621; https://doi.org/10.3390/w16040621 - 19 Feb 2024

Viewed by 708

Abstract

Compound drought and hot events can lead to detrimental impacts on crop yield with grave implications for global and regional food security. Hence, an understanding of how such events will change under unabated global warming is helpful to avoid associated negative impacts and [...] Read more.

Compound drought and hot events can lead to detrimental impacts on crop yield with grave implications for global and regional food security. Hence, an understanding of how such events will change under unabated global warming is helpful to avoid associated negative impacts and better prepare for them. In this article, we comprehensively analyze the projected changes in compound drought and hot days (CDHDs) occurring within the maize-growing season of 2015–2100 over dynamic global maize areas using 10 downscaled Coupled Model Intercomparison Project Phase 6 (CMIP6) models and four socio-economic scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). The results demonstrate a notable increase in the frequency and severity of CDHDs over global maize areas under all four SSPs, of which SSP5-8.5 has the fastest rise, followed by SSP3-7.0, SSP2-4.5 and SSP1-2.6. By the end of 21st century, the global average frequency and severity of CDHDs will reach 18~68 days and 1.0~2.6. Hotspot regions for CDHDs are mainly found in southern Africa, eastern South America, southern Europe and the eastern USA, where drought and heat show the most widespread increases. The increase in CDHDs will be faster than general hot days so that almost all increments of hot days will be accompanied by droughts in the future; therefore, compound dry and hot stresses will gradually become the predominant form of dry and heat stress on maize growth. The results can be applied to optimize adaptation strategies for mitigating risks from CDHDs on maize production worldwide. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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10 pages, 853 KiB

Open AccessArticle

Estimation of Return Levels with Long Return Periods for Extreme Sea Levels by the Average Conditional Exceedance Rate Method

by Jesper Rydén

GeoHazards 2024, 5(1), 166-175; https://doi.org/10.3390/geohazards5010008 - 18 Feb 2024

Viewed by 847

Abstract

Estimation of so-called return levels for environmental extremes is of importance for risk assessment. A particular challenge is to find estimates corresponding to long return periods, as uncertainties in the form of confidence intervals became too wide for practical use when applying conventional [...] Read more.

Estimation of so-called return levels for environmental extremes is of importance for risk assessment. A particular challenge is to find estimates corresponding to long return periods, as uncertainties in the form of confidence intervals became too wide for practical use when applying conventional methodology where large portions of data are not used. A recently proposed technique, the Average Conditional Exceedance Rate (ACER), makes effective use of all available data. For risk analysis related to nuclear infrastructure, usually located along a coastline, extreme sea levels are of concern. We demonstrate, for measurements of the sea level along the Swedish coast at locations close to nuclear power plants, that the methodology results in considerably shorter confidence intervals compared to conventional approaches. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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18 pages, 10783 KiB

Open AccessArticle

The Spatiotemporal Variation Characteristics and Impacts of Summer Heatwaves, Droughts, and Compound Drought and Heatwave Events in Jiangsu Province, China

by Wenyue Wang, Jingcai Wang, Junbo Shao, Bin Wu and Hui Lin

Water 2024, 16(1), 89; https://doi.org/10.3390/w16010089 - 26 Dec 2023

Cited by 1 | Viewed by 843

Abstract

This study used the “Daily meteorological dataset of basic meteorological elements of China National Surface Weather Station (V3.0)” and applied the absolute threshold method and standardized precipitation evapotranspiration index to identify heatwave events and drought events. This study analyzed the spatiotemporal evolution patterns [...] Read more.

This study used the “Daily meteorological dataset of basic meteorological elements of China National Surface Weather Station (V3.0)” and applied the absolute threshold method and standardized precipitation evapotranspiration index to identify heatwave events and drought events. This study analyzed the spatiotemporal evolution patterns of three types of summer disaster events, namely, heatwave events, drought events, and compound drought and heatwave events, in Jiangsu Province from 1960 to 2018. Additionally, it investigated and verified the concurrent historical data of the identified years with the most severe occurrence of compound drought and heatwave events and calculated the monthly drought centers and summer accumulations of the standardized precipitation evapotranspiration index (SPEI-3). The results indicate that over the 59 years analyzed, the number of days with a threshold of 35 °C, which were considered hot days, was 503.2, accounting for 9.27% of the total summer days in Jiangsu Province. Both the number of hot days and the frequency of heatwave events showed a clear increasing trend from the northeastern coastal areas to the southwestern regions of Jiangsu Province. The total frequency of drought events at different stations in Jiangsu Province from 1960 to 2018 fell within the range of 50–64. The fitted slope of the frequency of compound drought and heatwave events in Jiangsu Province was −0.021 for the period 1960 to 1989, and 0.079 for the period 1990 to 2018, indicating a higher frequency compared with the preceding 30 years. This trend aligned with the rise in heatwave events experienced in Jiangsu Province in recent years. The frequency and duration of compound drought and heatwave events in Jiangsu Province exhibited an increasing spatial pattern from the southwestern parts to the northeastern parts. This study’s verification established that the identification of compound drought and heatwave events was relatively accurate. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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18 pages, 2006 KiB

Open AccessArticle

Development of the Chrono-Systemic Timeline as a Tool for Cross-Sectional Analysis of Droughts—Application in Wallonia

by Kevin Thibaut, Pierre-Alain Ayral and Pierre Ozer

Water 2023, 15(23), 4150; https://doi.org/10.3390/w15234150 - 29 Nov 2023

Viewed by 1126

Abstract

Drought is a complex hazard with multiple and often dramatic impacts, depending on the environmental and societal context of the affected area. In recent years, due to global warming, this phenomenon has been occurring more intensely and frequently, affecting regions worldwide, including Wallonia, [...] Read more.

Drought is a complex hazard with multiple and often dramatic impacts, depending on the environmental and societal context of the affected area. In recent years, due to global warming, this phenomenon has been occurring more intensely and frequently, affecting regions worldwide, including Wallonia, the southern part of Belgium. This study aims to enhance our understanding of the interdisciplinary dynamics of drought in order to improve its anticipation and crisis management by stakeholders. To achieve these objectives, a cross-disciplinary analysis tool has been developed: the chrono-systemic timeline. Applied here to the severe drought of 2018 in Wallonia, this tool provides a comprehensive visual representation of the crisis, simultaneously offering temporal and multi-sectoral perspectives. The data incorporated into the model encompass environmental conditions, economic and social contexts, as well as political and administrative decisions made during the case study. The analysis of the chrono-systemic timeline reveals numerous interdisciplinary connections, a prolonged period of significant impacts, a gradual return to a ‘normal’ situation, and a reactive form of crisis management. In conclusion, the study emphasizes the importance of giving due consideration to the risks associated with water deficits and advocates for the implementation of anticipatory and adaptive management strategies to enhance our ability to effectively address droughts. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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21 pages, 7385 KiB

Open AccessArticle

Determination of Hazard Due to Debris Flows

by Ricardo A. Bocanegra, Carlos A. Ramírez, Elkin de J. Salcedo and María Paula Lorza Villegas

Water 2023, 15(23), 4057; https://doi.org/10.3390/w15234057 - 23 Nov 2023

Viewed by 881

Abstract

Debris flows have generated major disasters worldwide due to their great destructive capacity, which is associated with their high energy levels and short response times. To achieve adequate risk management of these events, it is necessary to define as accurately as possible the [...] Read more.

Debris flows have generated major disasters worldwide due to their great destructive capacity, which is associated with their high energy levels and short response times. To achieve adequate risk management of these events, it is necessary to define as accurately as possible the different hazard levels to which the territory is exposed. This article develops a new methodology to estimate this hazard based on the hydrodynamic characteristics of the flow and the granulometry of the sediments that can be mobilized by the flow. The hydrodynamic characteristics of the flow are determined via mathematical modeling that considers the rheology of non-Newtonian flows and the different volumes of sediments that could be transported during events corresponding to different return periods. The proposed methodology was implemented in the Jamundí River basin (Colombia). The results obtained indicate that in the upper part of this basin, there is a low hazard level, while in the lower part of the basin, approximately 15% of the affected territory has a medium hazard level, and the remaining 85% has a low hazard level. The methodology developed is simple to implement but technically rigorous since it considers all relevant aspects in the generation of debris flows. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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21 pages, 13502 KiB

Open AccessArticle

Application of Gated Recurrent Unit Neural Network for Flood Extraction from Synthetic Aperture Radar Time Series

by Ming Zhang, Chou Xie, Bangsen Tian, Yanchen Yang, Yihong Guo, Yu Zhu and Shuaichen Bian

Water 2023, 15(21), 3779; https://doi.org/10.3390/w15213779 - 29 Oct 2023

Viewed by 1177

Abstract

Floods are a sudden and influential natural disaster, and synthetic aperture radar (SAR) can image the Earth’s surface almost independently of time and weather conditions, making it particularly suitable for extracting flood ranges in time. Platforms such as Google Earth Engine (GEE) can [...] Read more.

Floods are a sudden and influential natural disaster, and synthetic aperture radar (SAR) can image the Earth’s surface almost independently of time and weather conditions, making it particularly suitable for extracting flood ranges in time. Platforms such as Google Earth Engine (GEE) can provide a large amount of SAR data and preprocess it, providing powerful assistance for real-time flood monitoring and time series analysis. However, the application of long-term series data combined with recurrent neural networks (RNNs) to monitor floods has been lacking in current research, and the accuracy of flood extraction in open water surfaces remains unsatisfactory. In this study, we proposed a new method of near real-time flood monitoring with a higher accuracy. The method utilizes SAR image time series to establish a gated recurrent unit (GRU) neural network model. This model was used to predict normal flood-free surface conditions. Flood extraction was achieved by comparing and analyzing the actual flood surface conditions with the predicted conditions, using a parameter called Scores. Our method demonstrated significant improvements in accuracy compared to existing algorithms like the OTSU algorithm, Sentinel-1 Dual Polarized Water Index (SDWI) algorithm, and Z-score algorithm. The overall accuracy of our method was 99.20%, which outperformed the Copernicus Emergency Management Service (EMS) map. Importantly, our method exhibited high stability as it allowed for fluctuation within the normal range, enabling the extraction of the complete flood range, especially in open water surfaces. The stability of our method makes it suitable for the flood monitoring of future open-access SAR data, including data from future Sentinel-1 missions. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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14 pages, 2532 KiB

Open AccessArticle

Spatial Dependence Analysis of Weekly Moving Cumulative Rainfall for Flood Risk Assessment

by Prapawan Chomphuwiset, Tossapol Phoophiwfa, Wanlop Kannika, Palakorn Seenoi, Sujitta Suraphee, Jeong-Soo Park and Piyapatr Busababodhin

Atmosphere 2023, 14(10), 1525; https://doi.org/10.3390/atmos14101525 - 01 Oct 2023

Viewed by 1151

Abstract

Climate change has intensified the frequency and severity of extreme weather events, necessitating a nuanced understanding of flood patterns for effective risk management. This study examines flood risk in the Chi watershed, Thailand, using Weekly Moving Cumulative Rainfall (WMCR) data from 1990 to [...] Read more.

Climate change has intensified the frequency and severity of extreme weather events, necessitating a nuanced understanding of flood patterns for effective risk management. This study examines flood risk in the Chi watershed, Thailand, using Weekly Moving Cumulative Rainfall (WMCR) data from 1990 to 2021. We employ extreme value copula analysis to assess spatial dependence between meteorological stations in the watershed. Nine bivariate generalized extreme value (BGEV) models were evaluated using the Akaike Information Criterion (AIC) and the Likelihood Ratio test (LRT) to ensure model robustness. The BGEV model revealed higher tail dependence among stations near the bay of the watershed. We also calculated the flood recurrence period to estimate flood events’ frequency and potential severity. Stations ST5 (Khon Kaen), ST6 (Tha Phra Khon Kaen), and ST8 (Maha Sarakham) were identified as potential hotspots, with higher probabilities of experiencing extreme rainfall of approximately 200 (mm.) during the rainy season. These findings provide valuable insights for flood management and mitigation strategies in the Chi watershed and offer a methodological framework adaptable to other regions facing similar challenges. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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24 pages, 13722 KiB

Open AccessArticle

Prediction of Water Quality in Reservoirs: A Comparative Assessment of Machine Learning and Deep Learning Approaches in the Case of Toowoomba, Queensland, Australia

by Syeda Zehan Farzana, Dev Raj Paudyal, Sreeni Chadalavada and Md Jahangir Alam

Geosciences 2023, 13(10), 293; https://doi.org/10.3390/geosciences13100293 - 27 Sep 2023

Cited by 3 | Viewed by 1676

Abstract

The effective management of surface water bodies, such as rivers, lakes, and reservoirs, necessitates a comprehensive understanding of water quality status. Altered precipitation patterns due to climate change may significantly affect the water quality and influence treatment procedures. This study aims to identify [...] Read more.

The effective management of surface water bodies, such as rivers, lakes, and reservoirs, necessitates a comprehensive understanding of water quality status. Altered precipitation patterns due to climate change may significantly affect the water quality and influence treatment procedures. This study aims to identify the most suitable water quality prediction models for the assessment of the water quality status for three water supply reservoirs in Toowoomba, Australia. It employed four machine learning and two deep learning models for determining the Water Quality Index (WQI) based on five parameters sensitive to rainfall impact. Temporal WQI variations over a period of 22 years (2000–2022) are scrutinised across 4 seasons and 12 months. Through regression analysis, both machine learning and deep learning models anticipate WQI gauged by seven accuracy metrics. Notably, XGBoost and GRU yielded exceptional outcomes, showcasing an R² value of 0.99. Conversely, Bidirectional LSTM (BiLSTM) demonstrated moderate accuracy with results hovering at 88% to 90% for water quality prediction across all reservoirs. The Coefficient of Efficiency (CE) and Willmott Index (d) showed that the models capture patterns well, while MAE, MAPE and RMSE provided good performance metrics for the RFR, XGBoost and GRU models. These models have provided valuable knowledge that can be utilised to assess the adverse consequences of extreme climate events such as shifts in rainfall patterns. These insights can be used to improve strategies for managing water bodies more effectively. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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15 pages, 6566 KiB

Open AccessArticle

Effects of Extreme Precipitation on Runoff and Sediment Yield in the Middle Reaches of the Yellow River

by Zongping Ren, Xiaoni Ma, Kaibo Wang and Zhanbin Li

Atmosphere 2023, 14(9), 1415; https://doi.org/10.3390/atmos14091415 - 08 Sep 2023

Cited by 3 | Viewed by 804

Abstract

Understanding the link between extreme precipitation and changes in runoff and sediment yield is of great significance for regional flood disaster response and soil and water conservation decision-making. This study investigated the spatial and temporal distribution of extreme precipitation (characterized by 10 extreme [...] Read more.

Understanding the link between extreme precipitation and changes in runoff and sediment yield is of great significance for regional flood disaster response and soil and water conservation decision-making. This study investigated the spatial and temporal distribution of extreme precipitation (characterized by 10 extreme precipitation indices recommended by the Expert Team on Climate Change Detection and Indices) in the Toudaoguai–Longmen section of the middle Yellow River from 1960 to 2021 and quantified the effects of extreme precipitation on runoff and sediment yield based on the method of partial least squares regression (PLSR). The extreme precipitation index showed an obvious upward trend in the last 20 years, with the increases in the central and northern regions (upstream) being stronger than the increase in the southern region (downstream). However, the runoff and sediment yield decreased significantly due to the implementation of large-scale soil and water conservation measures on the Loess Plateau, with average rates of 94.7 million m³/a and 13.3 million t/a during 1960–2021, respectively. The change points of runoff and sediment yield change occurred in 1979. Compared with those in the period from 1960 to 1979, the reductions in runoff and sediment yield in the years 1980–2021 were 52.7% and 70.6%, respectively. Moreover, extreme precipitation contributed 35.3% and 6.2% to the reduction in runoff in the 1980–1999 and 2000–2021 periods, respectively, and contributed 84.3% and 40.0% to the reduction in sediment yield, respectively. It indicated that other factors (such as large-scale soil and water conservation construction) played main roles in the decrease in runoff and sediment yield in the study area in recent 20 years. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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12 pages, 1654 KiB

Open AccessArticle

Non-Stationary Flood Discharge Frequency Analysis in West Africa

by Aymar Yaovi Bossa, Jean de Dieu Akpaca, Jean Hounkpè, Yacouba Yira and Djigbo Félicien Badou

GeoHazards 2023, 4(3), 316-327; https://doi.org/10.3390/geohazards4030018 - 11 Aug 2023

Cited by 1 | Viewed by 1046

Abstract

With climate change and intensification of the hydrological cycle, the stationarity of hydrological variables is becoming questionable, requiring appropriate flood assessment models. Frequency analysis is widely used for flood forecasting. This study aims to determine the most suitable models (stationary and non-stationary) for [...] Read more.

With climate change and intensification of the hydrological cycle, the stationarity of hydrological variables is becoming questionable, requiring appropriate flood assessment models. Frequency analysis is widely used for flood forecasting. This study aims to determine the most suitable models (stationary and non-stationary) for estimating the maximum flows observed at some stations spread across West Africa. A statistical analysis of the annual maximum flows in terms of homogeneity, stationarity, and independence was carried out through the Pettitt, modified Mann–Kendall, and Wald–Wolfowitz tests, respectively, to identify the stations whose flows are non-stationary. After that, the best-correlated climate covariates with the annual maximum flows of the non-stationary stations were determined. The covariates explored are the climatic indices of sea surface temperatures (SST). Finally, different non-stationary GEV models were derived by varying the scale and position parameters of the best-correlated index for each station. The results indicate that 56% of the annual maximum flow series are non-stationary. As per the Bayes information criterion (BIC) values, the performance of the non-stationary models (GEV, generalized extreme values) is largely greater than that of the stationary models. These good performances of non-stationary models using climatic indices open perspectives for the prediction of extreme flows in the study area. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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25 pages, 8593 KiB

Open AccessArticle

Member Formation Methods Evaluation for a Storm Surge Ensemble Forecast System in Taiwan

by Chun-Wei Lin, Tso-Ren Wu, Yu-Lin Tsai, Shu-Chun Chuang, Chi-Hao Chu and Chuen-Teyr Terng

Water 2023, 15(10), 1826; https://doi.org/10.3390/w15101826 - 10 May 2023

Viewed by 1466

Abstract

The forecast of typhoon tracks remains uncertain and is positively related to the accuracy of the storm surge forecast. The storm surge prediction error increases dramatically when the forecast track error is larger than 100 km. This study aims to develop an ensemble [...] Read more.

The forecast of typhoon tracks remains uncertain and is positively related to the accuracy of the storm surge forecast. The storm surge prediction error increases dramatically when the forecast track error is larger than 100 km. This study aims to develop an ensemble storm surge prediction system using parametric weather models to account for the uncertainty in typhoon track prediction. The storm surge model adopted in this study is COMCOT-SS storm surge forecast system. Two methods are introduced and analyzed to generate the ensemble members in this study. One is from the weather ensemble prediction system (WEPS), and the other is from the error distribution of the deterministic forecasts (EDF). The ensemble prediction results show that the ensemble mean of WEPS performs similarly to the deterministic forecast. However, the maximum surge height of WEPS is often lower than one from EDF. The verification results suggest that, for disaster prevention, EDF provides stronger warnings to the coastal region than WEPS. However, it may provide overestimated forecasts in some cases. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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14 pages, 6448 KiB

Open AccessArticle

Effect of Tide Level Change on Typhoon Waves in the Taiwan Strait and Its Adjacent Waters

by Cheng Chen, Chen Peng, Hong Xiao, Minjian Wei and Tingyu Wang

Water 2023, 15(10), 1807; https://doi.org/10.3390/w15101807 - 09 May 2023

Viewed by 1476

Abstract

In recent years, most research on typhoons in the Taiwan Strait and its adjacent waters has focused on simulating typhoon waves under the influence of wind fields. In order to study the influence of tidal level changes on typhoon waves, a numerical model [...] Read more.

In recent years, most research on typhoons in the Taiwan Strait and its adjacent waters has focused on simulating typhoon waves under the influence of wind fields. In order to study the influence of tidal level changes on typhoon waves, a numerical model was established in the Taiwan Strait based on the third-generation ocean wave model SWAN. The simulation results of the tide level during the corresponding typhoon landing time were incorporated into the model to optimize its performance. Subsequently, the wave height of the typhoon landing at the lowest tide level was compared with that at the highest tide level. This comparison serves as a reference and warning for ocean engineering, highlighting the hazards of the typhoon landing at high tide. The simulation results were verified and analyzed using the measured data of significant wave heights and wind speeds when typhoons Mekkhala (2006) and Maria (0607) approached. The results show that after optimization, the relative error of the significant wave peak is reduced. Furthermore, there has been a decrease in the maximum wind speed, bringing it closer to the measured value. These improvements signify enhanced model accuracy. The tide level has a great influence on the typhoon wave, and the tide level height at the time of the typhoon landing is positively correlated with the significant wave height of the waves generated by the typhoon. When the typhoon’s landing time coincides with the high tide level, the resulting waves are significantly higher, reaching up to 0.71 m. This has a substantial impact on the safety of marine structures, particularly breakwaters. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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17 pages, 13223 KiB

Open AccessArticle

Accurate Retrieval of the Whole Flood Process from Occurrence to Recession Based on GPS Original CNR, Fitted CNR, and Seamless CNR Series

by Zhifeng Tong, Mingkun Su, Fu Zheng, Junna Shang, Juntao Wu, Xiaoliang Shen and Xin Chang

Remote Sens. 2023, 15(9), 2316; https://doi.org/10.3390/rs15092316 - 27 Apr 2023

Cited by 2 | Viewed by 1237

Abstract

The CNR (Carrier-to-Noise Ratio) of GPS (Global Positioning System) satellites is highly relevant to the multipath error. The multipath error is more serious in the flood environment since the reflection and diffraction coefficients of water are much higher compared to dry soil. Thus, [...] Read more.

The CNR (Carrier-to-Noise Ratio) of GPS (Global Positioning System) satellites is highly relevant to the multipath error. The multipath error is more serious in the flood environment since the reflection and diffraction coefficients of water are much higher compared to dry soil. Thus, the amplitude of CNR will decrease in the flood environment. In this study, the relationship between multipath error, flooding, and CNR is introduced in theory. Then, by using the characteristic of the orbital repetition period, the stability of CNR between 2 adjacent days in a static observation environment is demonstrated by 32 MGEX (Multi-GNSS Experiment) stations in different latitude and longitude regions of the world. The results show that the average RMS of different CNRs between two adjacent days is only about 0.62 dB-Hz. In addition, the correlation coefficient of CNRs between two adjacent days is analyzed. The correlation coefficient of the original signal CNR is 0.997. Moreover, after mitigating the influence of random noise and lower CNR, the correlation coefficients of the fitted CNRs larger than 40 dB-Hz can reach 0.999. Thus, based on the fluctuation in original CNR, fitted CNR, and seamless series characteristics of CNR, the whole flood process from occurrence to recession can be retrieved. A flood that occurred in Zhengzhou City, China, from DOY 200 to DOY 202, 2021 is used to demonstrate the process of retrieval. The experimental results indicate that the flood appeared at about 15:30 pm on DOY 200, reached a peak at approximately 8:30 am on DOY 202, and totally subsided at about 10:00 am on DOY 202. In conclusion, the CNR can be effectively used to retrieve the whole process of the flood, which lays a foundation for researching flood detection and warning based on GPS satellites. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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15 pages, 5998 KiB

Open AccessArticle

Research on Water Level Anomaly Data Alarm Based on CNN-BiLSTM-DA Model

by Cancan Hu, Lanting Zhou, Yunzhu Gong, Yufei Li and Siyuan Deng

Water 2023, 15(9), 1659; https://doi.org/10.3390/w15091659 - 24 Apr 2023

Viewed by 2057

Abstract

With frequent extreme rainfall events caused by rapid changes in the global climate, many cities are threatened by urban flooding. Timely issuance of flood warnings can help prepare for disasters and minimize losses caused by floods. In this study, we propose a method [...] Read more.

With frequent extreme rainfall events caused by rapid changes in the global climate, many cities are threatened by urban flooding. Timely issuance of flood warnings can help prepare for disasters and minimize losses caused by floods. In this study, we propose a method based on a convolutional neural network-bidirectional long short-term memory-difference analysis (CNN-BiLSTM-DA) model for water level prediction analysis and flood warning. The method calculates and analyzes the difference sequence between water level monitoring values and water level prediction values, compares historical flood data to determine the alarm threshold for abnormal water level data, and achieves real-time flood warnings to provide technical references for flood prevention and mitigation. Taking Yancheng city, a low-lying city located in the plain area of Jiangsu Province in China, as an example, this study verifies the accuracy of the CNN-BiLSTM model in water level prediction, which can achieve an accuracy rate above 95%. This provides a reliable data basis for further determination of warning thresholds using the DA model. The CNN-BiLSTM-DA model achieves an accuracy rate of 85.71% in flood warnings without any missed reports, demonstrating that this method has scientific, practical, and accurate features in addressing flood warning issues. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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24 pages, 6039 KiB

Open AccessArticle

Assessment of the Breaching Event, Breach Parameters and Failure Mechanisms of the Spillway Collapse in the Swa Dam, Myanmar

by Pa Pa Shwe Sin Kyaw and Taro Uchida

Water 2023, 15(8), 1513; https://doi.org/10.3390/w15081513 - 12 Apr 2023

Cited by 1 | Viewed by 1871

Abstract

The spillway of the Swa earthen dam, constructed in Yedashe Township, Bago Region, Myanmar, collapsed suddenly on 29 August 2018 and resulted in a huge flood to downstream areas causing fatalities and the displacement of thousands of localities. This study aimed to assess [...] Read more.

The spillway of the Swa earthen dam, constructed in Yedashe Township, Bago Region, Myanmar, collapsed suddenly on 29 August 2018 and resulted in a huge flood to downstream areas causing fatalities and the displacement of thousands of localities. This study aimed to assess the spillway breaching process in terms of the breaching parameters such as the average breach width, failure time and peak outflow, and failure mechanisms. We analyzed the event from the changes in the study site before and after the event and used water discharge conditions from satellite data and water level records during the event. We compared the breaching parameters using empirical equations from past failed events with tested scenarios for failure mechanisms, such as overtopping and piping. According to satellite data, 97% of the storage from the reservoir was discharged, and the peak breach outflow rate was 7643 m³/s calculated from the water level records. The selected empirical formulas were applied, and the estimated average breach widths, failure times and peak discharge from the formulas were larger in overtopping and nearer in piping than that of the observed data for the Swa Dam. Thus, a concrete spillway might impact the erodibility rate of breaching compared with concrete-faced and earthen dam types. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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20 pages, 14943 KiB

Open AccessArticle

Assessment of the Urban Extreme Precipitation by Satellite Estimates over Mainland China

by Yu Li, Bo Pang, Ziqi Zheng, Haoming Chen, Dingzhi Peng, Zhongfan Zhu and Depeng Zuo

Remote Sens. 2023, 15(7), 1805; https://doi.org/10.3390/rs15071805 - 28 Mar 2023

Viewed by 1596

Abstract

The accurate estimation of urban extreme precipitation is essential for urban design and risk management, which is hard for developing countries, due to the fast urbanization and sparse rain gauges. Satellite precipitation products (SPPs) have emerged as a promising solution. Not only near [...] Read more.

The accurate estimation of urban extreme precipitation is essential for urban design and risk management, which is hard for developing countries, due to the fast urbanization and sparse rain gauges. Satellite precipitation products (SPPs) have emerged as a promising solution. Not only near real-time SPPs can provide critical information for decision making, but post-processed SPPs can also offer essential information for climate change adaption, risk management strategy development, and related fields. However, their ability in urban extreme precipitation estimation has not been examined in detail. This study presents a comprehensive evaluation of four recent SPPs that are post-processed, including IMERG, GSMaP_Gauge, MSWEP, and CMFD, for their ability to capture urban extreme precipitation in mainland China at the national, city, and inner-city scales. The performance of the four SPPs was assessed using daily observations from the 821 urban gauges from 2001 to 2018. The assessment includes: (1) the extreme precipitation estimates from the four SPPs in the total urbanized areas of mainland China were evaluated using correlation coefficients (CC), absolute deviation (AD), relative deviation (RB), and five extreme precipitation indices; (2) The extreme precipitation estimates over 21 Chinese major cities were assessed with the two most important extreme indices, namely the 99th percentile of daily precipitation on wet days (R99) and total precipitation when daily precipitation exceeding R99 (R99TOT); and (3) Bivariate Moran’s I (BMI) was adopted to assess the inner-city spatial correlation of R99 and R99TOT between SPPs and gauge observations in four major cities with most gauges. The results indicate that MSWEP has the highest CC of 0.79 and the lowest AD of 1.61 mm at the national scale. However, it tends to underestimate urban precipitation, with an RB of −8.5%. GSMaP_Gauge and IMERG performed better in estimating extreme values, with close extreme indices with gauge observations. According to the 21 major cities, GSMaP_Gauge also shows high accuracy in estimating R99 and R99TOT values, with the best RB and AD in these cities, while CMFD and MSWEP exhibit the highest CC values for R99 and R99TOT, respectively, indicating a strong correlation between their estimates and those obtained from gauge observations. At the inner-city scale, MSWEP shows advantages in monitoring the spatial distribution of urban extreme precipitation in most of cities. The study firstly provided the multiscale assessment of urban extreme precipitation by SPPs over mainland China, which is useful for their applications. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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21 pages, 9686 KiB

Open AccessArticle

Possible Future Climate Change Impacts on the Meteorological and Hydrological Drought Characteristics in the Jinghe River Basin, China

by Tingting Huang, Zhiyong Wu, Peiqing Xiao, Zhaomin Sun, Yu Liu, Jingshu Wang and Zhihui Wang

Remote Sens. 2023, 15(5), 1297; https://doi.org/10.3390/rs15051297 - 26 Feb 2023

Cited by 11 | Viewed by 2071

Abstract

Revealing the impact of future climate change on the characteristics and evolutionary patterns of meteorological and hydrological droughts and exploring the joint distribution characteristics of their drought characteristics are essential for drought early warning in the basin. In this study, we considered the [...] Read more.

Revealing the impact of future climate change on the characteristics and evolutionary patterns of meteorological and hydrological droughts and exploring the joint distribution characteristics of their drought characteristics are essential for drought early warning in the basin. In this study, we considered the Jinghe River Basin in the Loess Plateau as the research object. The standardized precipitation index (SPI) and standardized runoff index (SRI) series were used to represent meteorological drought and hydrological drought with monthly runoff generated by the SWAT model. In addition, the evolution laws of the JRB in the future based on Copula functions are discussed. The results showed that: (1) the meteorological drought and hydrological drought of the JRB displayed complex periodic change trends of drought and flood succession, and the evolution laws of meteorological drought and hydrological drought under different spatiotemporal scales and different scenario differ significantly. (2) In terms of the spatial range, the JRB meteorological and hydrological drought duration and severity gradually increased along with the increase in the time scale. (3) Based on the joint distribution model of the Copula function, the future meteorological drought situation in the JRB will be alleviated when compared with the historical period on the seasonal scale, but the hydrological drought situation is more serious. The findings can help policy-makers explore the correlation between meteorological drought and hydrological drought in the background of future climate change, as well as the early warning of hydrological drought. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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16 pages, 3189 KiB

Open AccessArticle

Snowmelt Runoff in the Yarlung Zangbo River Basin and Runoff Change in the Future

by Haoyu Ji, Dingzhi Peng, Yu Gu, Xiaoyu Luo, Bo Pang and Zhongfan Zhu

Remote Sens. 2023, 15(1), 55; https://doi.org/10.3390/rs15010055 - 22 Dec 2022

Cited by 7 | Viewed by 2288

Abstract

Comprehending the impacts of climate change on regional hydrology and future projections of water supplies is of great value to manage the water resources in the Yarlung Zangbo River Basin (YZRB). However, large uncertainties from both input data and the model itself exert [...] Read more.

Comprehending the impacts of climate change on regional hydrology and future projections of water supplies is of great value to manage the water resources in the Yarlung Zangbo River Basin (YZRB). However, large uncertainties from both input data and the model itself exert obstacles to accurate projections. In this work, a hydrological modeling framework was established over the YZRB linking the Variable Infiltration Capacity (VIC) with an empirical formulation, called the degree-day glacier-melt scheme (VIC–Glacier). The model performance was evaluated through three aspects, including streamflow, snow cover area, and glacier area. Nine GCM models and three emission scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) in CMIP6 were chosen to drive the calibrated VIC–Glacier model. The results showed that both precipitation and temperature resulted in an increase of around 25% and 13%, respectively, in multi-year average runoff from June to September, under SSP5-8.5 and SSP1-2.6. The precipitation runoff was projected to increase, as compensation for the decrease of glacier runoff and snow runoff by the end of the 21st century. An apparent increasing trend in the runoff was expected over the YZRB before 2050 and after the year 2060 under SSP 5-8.5, with a steeply decreasing trend from 2050 to 2060, and a negligible decreasing trend under SSP1-2.6 from 2020 to 2060, in contrast to an increasing trend from 2060 to 2100. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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18 pages, 7581 KiB

Open AccessArticle

Improvement of the SWAT Model for Snowmelt Runoff Simulation in Seasonal Snowmelt Area Using Remote Sensing Data

by Hongling Zhao, Hongyan Li, Yunqing Xuan, Changhai Li and Heshan Ni

Remote Sens. 2022, 14(22), 5823; https://doi.org/10.3390/rs14225823 - 17 Nov 2022

Cited by 9 | Viewed by 2923

Abstract

The SWAT model has been widely used to simulate snowmelt runoff in cold regions thanks to its ability of representing the effects of snowmelt and permafrost on runoff generation and confluence. However, a core method used in the SWAT model, the temperature index [...] Read more.

The SWAT model has been widely used to simulate snowmelt runoff in cold regions thanks to its ability of representing the effects of snowmelt and permafrost on runoff generation and confluence. However, a core method used in the SWAT model, the temperature index method, assumes both the dates for maximum and minimum snowmelt factors and the snowmelt temperature threshold, which leads to inaccuracies in simulating snowmelt runoff in seasonal snowmelt regions. In this paper, we present the development and application of an improved temperature index method for SWAT (SWAT+) in simulating the daily snowmelt runoff in a seasonal snowmelt area of Northeast China. The improvements include the introduction of total radiation to the temperature index method, modification of the snowmelt factor seasonal variation formula, and changing the snowmelt temperature threshold according to the snow depth derived from passive microwave remote sensing data and temperature in the seasonal snowmelt area. Further, the SWAT+ model is applied to study climate change impact on future snowmelt runoff (2025–2054) under the climate change scenarios including SSP2.6, SSP4.5, and SSP8.5. Much improved snowmelt runoff simulation is obtained as a result, supported by several metrics, such as MAE, RE, RMSE, R², and NSE for both the calibration and validation. Compared with the baseline period (1980–2019), the March–April ensemble average snowmelt runoff is shown to decrease under the SSP2.6, SSP4.5, and SSP8.5 scenario during 2025–2054. This study provides a valuable insight into the efficient development and utilization of spring water resources in seasonal snowmelt areas. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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18 pages, 7223 KiB

Open AccessArticle

Impact of Storm Surge on the Yellow River Delta: Simulation and Analysis

by Liang Huang, Shenliang Chen, Shunqi Pan, Peng Li and Hongyu Ji

Water 2022, 14(21), 3439; https://doi.org/10.3390/w14213439 - 28 Oct 2022

Cited by 1 | Viewed by 2170

Abstract

Storm surges can lead to serious natural hazards and pose great threats to coastal areas, especially developed deltas. Assessing the risk of storm surges on coastal infrastructures is crucial for regional economic development and disaster mitigation. Combining in situ observations, remote sensing retrievals, [...] Read more.

Storm surges can lead to serious natural hazards and pose great threats to coastal areas, especially developed deltas. Assessing the risk of storm surges on coastal infrastructures is crucial for regional economic development and disaster mitigation. Combining in situ observations, remote sensing retrievals, and numerical simulation, storm surge floods in the Yellow River Delta (YRD) were calculated in different scenarios. The results showed that NE wind can cause the largest flooding area of 630 km², although the overall storm surge risk in the delta is at lower levels under various conditions. The coastal oilfields are principally at an increasing storm surge risk level. E and NE winds would result in storm surges of 0.9–1.4 m, increasing the risk of flooding in the coastal oilfields. Nearshore seabed erosion in storm events resulted in a decrease in inundation depths and inundation areas. To prevent and control storm surge disasters, we should adapt to local conditions. Different measures should be taken to prevent the disaster of storm surges on different seashores, such as planting saltmarsh vegetation to protect seawalls, while the key point is to construct and maintain seawalls on high-risk shorelines. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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15 pages, 2435 KiB

Open AccessArticle

A New Approach for Assessing Heat Balance State along a Water Transfer Channel during Winter Periods

by Tiejie Cheng, Jun Wang, Jueyi Sui, Haijing Zhao, Zejia Hao, Minghai Huang and Zhicong Li

Water 2022, 14(20), 3269; https://doi.org/10.3390/w14203269 - 17 Oct 2022

Cited by 2 | Viewed by 1554

Abstract

Ice problems in channels for water transfer in cold regions seriously affect the capacity and efficiency of water conveyance. Sometimes, ice problems such as ice jams in water transfer channels create risk during winter periods. Recently, water temperature and environmental factors at various [...] Read more.

Ice problems in channels for water transfer in cold regions seriously affect the capacity and efficiency of water conveyance. Sometimes, ice problems such as ice jams in water transfer channels create risk during winter periods. Recently, water temperature and environmental factors at various cross-sections along the main channel of the middle route of the South-to-North Water Transfer Project in China have been measured. Based on these temperature data, the heat balance state of this water transfer channel has been investigated. A principal component analysis (PCA) method has been used to analyze the complex factors influencing the observed variations of the water temperature, by reducing eigenvector dimension and then extracting the principal component as the input feature. Based on the support vector machine (SVM) theory, a new approach for judging the heat loss or heat gain of flowing water in a channel during winter periods has been developed. The Gaussian radial basis is used as the kernel function in this new approach. Then, parameters have been optimized by means of various methods. Through the supervised machine learning process toward the observed water temperature data, it is found that the air–water temperature difference and thermal conditions are the key factors affecting the heat loss or heat absorption of water body. Results using the proposed method agree well with those of measurements. The changes of water temperature are well predicted using the proposed method together with the state of water heat balance. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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18 pages, 7867 KiB

Open AccessArticle

A Spatial Pattern Extraction and Recognition Toolbox Supporting Machine Learning Applications on Large Hydroclimatic Datasets

by Han Wang and Yunqing Xuan

Remote Sens. 2022, 14(15), 3823; https://doi.org/10.3390/rs14153823 - 08 Aug 2022

Cited by 2 | Viewed by 2518

Abstract

This paper presents the development and applications of a new, open-source toolbox that aims to provide automatic identification and classification of hydroclimatic patterns by their spatial features, i.e., location, size, orientation, and shape, as well as the physical features, i.e., the areal average, [...] Read more.

This paper presents the development and applications of a new, open-source toolbox that aims to provide automatic identification and classification of hydroclimatic patterns by their spatial features, i.e., location, size, orientation, and shape, as well as the physical features, i.e., the areal average, total volume, and spatial distribution. The highlights of this toolbox are: (1) incorporating an efficient algorithm for automatically identifying and classifying the spatial features that are linked to hydroclimatic extremes; (2) use as a frontend for supporting AI-based training in tracking and forecasting extremes; and (3) direct support for short-term nowcasting of extreme rainfall via tracking rainstorm centres and movement. The key design and implementation of the toolbox are discussed alongside three case studies demonstrating the application of the toolbox and its potential in helping build machine learning applications in hydroclimatic sciences. Finally, the availability of the toolbox and its source code is included. Full article

(This article belongs to the Topic Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management)

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