Hydro-Meteorological Hazards: Forecasting, Assessment and Risk Management

Topic Information

Dear Colleagues,

Hydrometeorological hazards—including floods, droughts, landslides, and storm surges—threaten lives and impact livelihoods. The incidence and severity of extreme weather is projected to increase due to climate change, population growth, land-use change, and urbanization, which consequently increases the number of people at risk from these hazards. A better understanding of the likely impacts and potential responses is needed to enable appropriate adaptation and mitigation measures and ultimately increase resilience. The objective of the Special Issue is to create a valuable opportunity for the interdisciplinary exchange of ideas and experiences among atmospheric–hydrological modelers and members of both the hydrology and earth system modeling communities. Contributions are invited that deal with the complex interactions between surface water, groundwater, and regional climates, with a specific focus on those presenting work on the development or application of coupled hydrometeorological prediction (both deterministic and ensemble) systems for flash floods, droughts, and water resources. The Special Issue welcomes new experiments and practical applications showing successful experiences, as well as problems and failures encountered in the use of uncertain forecasts and ensemble hydro-meteorological forecasting systems. Case studies dealing with different users, temporal and spatial scales, forecast ranges, and data assimilation in coupled model systems are also welcome. Likewise, comments are invited on field experiments and testbeds equipped with complex sensors and measurement systems allowing for multi-variable validation of such complex modeling systems. Hydro-meteorological events drive many hydrologic and geomorphic hazards, such as floods, landslides, and debris flows, which pose a significant threat to modern societies on a global scale. The continuous increase of population and urban settlements in hazard-prone areas in combination with evidence of changes in extreme weather events have led to a continuous increase in the risk associated with weather-induced hazards. To improve resilience and to design more effective mitigation strategies, we need to better understand the aspects of vulnerability, risk, and triggers that are associated with these hazards. This Special Issue aims to gather contributions dealing with various hydro-meteorological hazards that address the aspects of vulnerability analysis, risk estimation, impact assessment, mitigation policies, and communication strategies.

Prof. Dr. Dehua Zhu
Prof. Dr. Dingzhi Peng
Dr. Yunqing Xuan
Dr. Samiran Das
Prof. Dr. Victor Hugo Rabelo Coelho
Dr. Cristiano das Neves Almeida
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Atmosphere atmosphere	3.110	3.7	2010	14.7 Days	2000 CHF
GeoHazards geohazards	-	-	2020	42.9 Days	1000 CHF
Geosciences geosciences	-	4.8	2011	22.5 Days	1500 CHF
Remote Sensing remotesensing	5.349	7.4	2009	19.7 Days	2500 CHF
Water water	3.530	4.8	2009	17.6 Days	2200 CHF

Published Papers (12 papers)

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All Atmosphere GeoHazards Geosciences Remote Sensing Water

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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 673

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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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 575

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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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 1 | Viewed by 570

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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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 680

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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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

Viewed by 866

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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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 787

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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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 3 | Viewed by 949

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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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

Viewed by 1333

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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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 1 | Viewed by 1374

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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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

Viewed by 1191

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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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

Viewed by 984

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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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

Viewed by 1181

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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