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Characterizing, Monitoring and Prediction of Hydrometeorological Extremes under Climate Change

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water and Climate Change".

Deadline for manuscript submissions: closed (15 August 2022) | Viewed by 39690

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Dr. Xushu Wu

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Guest Editor

School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China
Interests: climate change; hydroclimatic extremes; hydrometeorological prediction; big data; AI
Special Issues, Collections and Topics in MDPI journals

Dr. Jiabo Yin

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Guest Editor

State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
Interests: land–atmosphere interaction; climate change; hydrometeorology; machine learning techniques; flood monitoring and forecasting
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Prof. Dr. Shengzhi Huang

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School of Water Resources and Hydropower, Xi’an University of Technology, Xi’an 710048, China
Interests: drought propagation; drought risk assessment; hydrological prediction
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Special Issue Information

Dear Colleagues,

Climate change has altered the hydrological cycle that induces hydrometeorological extremes such as floods and droughts, leading to tremendous impacts on human society and the environment. How to charcterze, monitor and prediction/forecast hydrometeorological extremes are hotspots and crucial for decision making. Compared to the hydrometeorological mean states, the extremes show much more spatiotemporal heterogeneity and are less predictable with larger uncertainties, in particular, under climate change. In this special issue, we welcome the papers focusing on hydrometeorological extremes including, but not limited to, floods and droughts characterization, monitoring and prediction/forecasting. Both general methodological contributions and case studies of hydrometeorological extremes across different regions covering a wide range of spatial scales are welcome.

Dr. Xushu Wu
Dr. Jiabo Yin
Prof. Dr. Shengzhi Huang
Guest Editors

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Keywords

drought
flooding
hydrometeorological extremes
climate change
monitoring
prediction

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Published Papers (11 papers)

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Research

18 pages, 4706 KiB

Open AccessArticle

Trend Analysis of Selected Hydroclimatic Variables for the Hornad Catchment (Slovakia)

by Katarzyna Kubiak-Wójcicka, Patrik Nagy, Agnieszka Pilarska and Martina Zeleňáková

Water 2023, 15(3), 471; https://doi.org/10.3390/w15030471 - 25 Jan 2023

Cited by 5 | Viewed by 2440

Abstract

This study examines the trends in air temperature, precipitation and flow rates over a 50-year observation period (1961–2010) and compares two periods, 1961–1985 and 1986–2010. The research was carried out in terms of annual and monthly values. The research area is the Hornad River in Slovakia. The main aim of the study was to examine the evolution of precipitation, air temperature and flows in the Hornad River catchment area, as well as to identify the regions (sub-catchments) most vulnerable to climate change. Increasing trends in air temperature in the years 1961–2010 were found to be statistically significant (the Sen’s slope was between 0.0197 and 0.0239). On the other hand, a statistically significant downward trend in flows was recorded only at the Stratená station (a small mountain catchment, where the Sen’s slope was −0.0063). The remaining upward and downward trends were not statistically significant. Greater differences in the course of the trends were recorded on a monthly basis in individual multi-years. Increasing trends in air temperature were statistically significant from May to August in the period 1961–2010. No trends in precipitation were recorded in the period 1961–2010, and only an upward trend in precipitation was recorded in June from 1986–2010. Full article

(This article belongs to the Special Issue Characterizing, Monitoring and Prediction of Hydrometeorological Extremes under Climate Change)

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22 pages, 6869 KiB

Open AccessArticle

Determination of River Hydromorphological Features in Low-Land Rivers from Aerial Imagery and Direct Measurements Using Machine Learning Algorithms

by Vytautas Akstinas, Andrius Kriščiūnas, Arminas Šidlauskas, Dalia Čalnerytė, Diana Meilutytė-Lukauskienė, Darius Jakimavičius, Tautvydas Fyleris, Serhii Nazarenko and Rimantas Barauskas

Water 2022, 14(24), 4114; https://doi.org/10.3390/w14244114 - 16 Dec 2022

Cited by 1 | Viewed by 2892

Abstract

Hydromorphology of rivers assessed through direct measurements is a time-consuming and relatively expensive procedure. The rapid development of unmanned aerial vehicles and machine learning (ML) technologies enables the usage of aerial images to determine hydromorphological units (HMUs) automatically. The application of various direct and indirect data sources and their combinations for the determination of river HMUs from aerial images was the main aim of this research. Aerial images with and without the Sobel filter, a layer of boulders identified using Yolov5x6, and a layer of direct measurements of depth and streamflow velocity were used as data sources. Three ML models were constructed for the cases if one, two, or three data sources were used. The ML models for HMU segmentation were constructed of MobileNetV2 pre-trained on ImageNet data for the feature extraction part and U-net for the segmentation part. The stratified K-fold cross-validation with five folds was carried out to evaluate the performance of the model due to the limited dataset. The analysis of the ML results showed that the measured metrics of segmentation using direct measurements were close to the ones of the model trained only on the combination of boulder layer and aerial images with the Sobel filter. The obtained results demonstrated the potential of the applied approach for the determination of HMUs only from the aerial images, and provided a basis for further development to increase its accuracy. Full article

(This article belongs to the Special Issue Characterizing, Monitoring and Prediction of Hydrometeorological Extremes under Climate Change)

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19 pages, 3034 KiB

Open AccessFeature PaperArticle

A Modified Two-Parameter Monthly Water Balance Model for Runoff Simulation to Assess Hydrological Drought

by Xingjun Hong, Shenglian Guo, Guiya Chen, Na Guo and Cong Jiang

Water 2022, 14(22), 3715; https://doi.org/10.3390/w14223715 - 16 Nov 2022

Cited by 3 | Viewed by 2261

Abstract

Quantitative assessment of the frequency and magnitude of drought events plays an important role in preventing drought disasters and ensuring water security in river basins. In this paper, we modified a parsimonious two-parameter monthly water balance (TPMWB) model by incorporating the generalized proportionality hypothesis with precipitation and potential evapotranspiration as input variables. The modified TPMWB was then used to simulate the monthly hydrological processes of 30 sub-basins in the Han River basin. It is shown that the water balance model can satisfactorily simulate the hydrological regimes in the selected sub-basins. We derived the probability distribution functions of monthly runoff using the principle of maximum entropy to calculate the Standardized Runoff Index (SRI), and assessed the historical hydrological drought conditions. By investigating the correlation between four major drought characteristics (i.e., drought duration, drought severity, drought intensity, and drought inter-arrival time) and four dimensionless parameters representing the climatic and underlying properties of the basin, a conclusion can be drawn that the formation and development of hydrological drought in the Han River basin is mainly controlled by watershed storage factors, and the influence of climatic factors is also significant. The proposed approach provides a potential alternative for regional drought early warning and under changing environmental conditions. Full article

(This article belongs to the Special Issue Characterizing, Monitoring and Prediction of Hydrometeorological Extremes under Climate Change)

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23 pages, 10412 KiB

Open AccessFeature PaperArticle

Potential Effects of Urbanization on Precipitation Extremes in the Pearl River Delta Region, China

by Xiaomeng Song, Jiachen Qi, Xianju Zou, Jianyun Zhang and Cuishan Liu

Water 2022, 14(16), 2466; https://doi.org/10.3390/w14162466 - 9 Aug 2022

Cited by 4 | Viewed by 2815

Abstract

Rapid urbanization plays an indelible role in modifying local climate, with more extreme precipitation in urban areas. Understanding the mechanism of urban-induced precipitation changes and quantifying the potential effects of urbanization on the changes in precipitation extremes have become hotspot issues in hydrometeorology. We examine the spatiotemporal changes of precipitation extremes over the Pearl River Delta region in China using the homogenized daily precipitation dataset from the period 1961–2017, and quantify the urbanization effects on these changes. Most of the extreme precipitation indices show increasing trends, but only the mean precipitation intensity has a significant increase. Urbanization could induce the intensification of extreme precipitation, with a higher amount, intensity, and frequency of precipitation extremes and a larger magnitude of their trends in urban areas by comparison with those rural areas. Moreover, high-level urbanization tends to make a greater contribution to the temporal changes in precipitation extremes, indicating that urbanization effects on precipitation extremes may be related to urbanization levels. However, urbanization level shows little effect on the changes in the spatial patterns of precipitation extremes, with similar spatial distribution in different urbanization stages. Our findings highlight the important role of urbanization in precipitation extremes and offer insights into the feedback of anthropogenic changes into variations in precipitation extremes. Full article

(This article belongs to the Special Issue Characterizing, Monitoring and Prediction of Hydrometeorological Extremes under Climate Change)

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18 pages, 3110 KiB

Open AccessArticle

Evaluation of TIGGE Precipitation Forecast and Its Applicability in Streamflow Predictions over a Mountain River Basin, China

by Yiheng Xiang, Tao Peng, Qi Gao, Tieyuan Shen and Haixia Qi

Water 2022, 14(15), 2432; https://doi.org/10.3390/w14152432 - 5 Aug 2022

Cited by 6 | Viewed by 2086

Abstract

The number of numerical weather prediction (NWP) models is on the rise, and they are commonly used for ensemble precipitation forecast (EPF) and ensemble streamflow prediction (ESP). This study evaluated the reliabilities of two well-behaved NWP centers in the Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE), the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP), in EPF and ESP over a mountain river basin in China. This evaluation was carried out based on both deterministic and probabilistic metrics at a daily temporal scale. The effectiveness of two postprocessing methods, the Generator-based Postprocessing (GPP) method, and the Bayesian Model Averaging (BMA) method were also investigated for EPF and ESP. Results showed that: (1) The ECMWF shows better performances than NCEP in both EPF and ESP in terms of evaluation indexes and representation of the hydrograph. (2) The GPP method performs better than BMA in improving both EPF and ESP performances, and the improvements are more significant for the NCEP with worse raw performances. (3) Both ECMWF and NCEP have good potential for both EPF and ESP. By using the GPP method, there are desirable EPF performances for both ECMWF and NCEP at all 7 lead days, as well as highly skillful ECMWF ESP for 1~5 lead days and average moderate skillful NCEP ESP for all 7 lead days. The results of this study can provide a reference for the applications of TIGGE over mountain river basins. Full article

(This article belongs to the Special Issue Characterizing, Monitoring and Prediction of Hydrometeorological Extremes under Climate Change)

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25 pages, 6705 KiB

Open AccessArticle

Orographic Precipitation Extremes: An Application of LUME (Linear Upslope Model Extension) over the Alps and Apennines in Italy

by Andrea Abbate, Monica Papini and Laura Longoni

Water 2022, 14(14), 2218; https://doi.org/10.3390/w14142218 - 14 Jul 2022

Cited by 6 | Viewed by 2923

Abstract

Critical hydrometeorological events are generally triggered by heavy precipitation. In complex terrain, precipitation may be perturbed by the upslope raising of the incoming humid airflow, causing in some cases extreme rainfall. In this work, the application of LUME—Linear Upslope Model Extension—to a group of extreme events that occurred across mountainous areas of the Central Alps and Apennines in Italy is presented. Based on the previous version, the model has been “extended” in some aspects, proposing a methodology for physically estimating the time-delay coefficients as a function of precipitation efficiency. The outcomes of LUME are encouraging for the cases studied, revealing the intensification of precipitation due to the orographic effect. A comparison between the reference rain gauge data and the results of the simulations showed good agreement. Since extreme precipitation is expected to increase due to climate change, especially across the Mediterranean region, LUME represents an effective tool to investigate more closely how these extreme phenomena originate and evolve in mountainous areas that are subject to potential hydrometeorological risks. Full article

(This article belongs to the Special Issue Characterizing, Monitoring and Prediction of Hydrometeorological Extremes under Climate Change)

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27 pages, 8341 KiB

Open AccessArticle

Recent Hydrological Droughts in Brazil and Their Impact on Hydropower Generation

by Luz Adriana Cuartas, Ana Paula Martins do Amaral Cunha, Jessica Anastácia Alves, Larissa Milena Pinto Parra, Karinne Deusdará-Leal, Lidiane Cristina Oliveira Costa, Ruben Dario Molina, Diogo Amore, Elisangela Broedel, Marcelo Enrique Seluchi, Christopher Cunningham, Regina Célia dos Santos Alvalá and José Antonio Marengo

Water 2022, 14(4), 601; https://doi.org/10.3390/w14040601 - 16 Feb 2022

Cited by 35 | Viewed by 10463

Abstract

Brazil has endured the worst droughts in recorded history over the last decade, resulting in severe socioeconomic and environmental impacts. The country is heavily reliant on water resources, with 77.7% of water consumed for agriculture (irrigation and livestock), 9.7% for the industry, and 11.4% for human supply. Hydropower plants generate about 64% of all electricity consumed. The aim of this study was to improve the current state of knowledge regarding hydrological drought patterns in Brazil, hydrometeorological factors, and their effects on the country’s hydroelectric power plants. The results show that since the drought occurred in 2014/2015 over the Southeast region of Brazil, several basins were sharply impacted and remain in a critical condition until now. Following that event, other regions have experienced droughts, with critical rainfall deficit and high temperatures, causing a pronounced impact on water availability in many of the studied basins. Most of the hydropower plants end the 2020–2021 rainy season by operating at a fraction of their total capacity, and thus the country’s hydropower generation was under critical regime. Full article

(This article belongs to the Special Issue Characterizing, Monitoring and Prediction of Hydrometeorological Extremes under Climate Change)

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18 pages, 7495 KiB

Open AccessFeature PaperArticle

Mitigating Drought Conditions under Climate and Land Use Changes by Applying Hedging Rules for the Multi-Reservoir System

by Zejun Li, Bensheng Huang, Zhifeng Yang, Jing Qiu, Bikui Zhao and Yanpeng Cai

Water 2021, 13(21), 3095; https://doi.org/10.3390/w13213095 - 3 Nov 2021

Cited by 8 | Viewed by 2446

Abstract

Climate and land use changes have substantially affected hydrologic cycles and increased the risk of drought. Reservoirs are one of the important means to provide resilience against hydrologic variability and achieve sustainable water management. Therefore, adaptive reservoir operating rules are needed to mitigate their adverse effects. In this study, the Hanjiang River Basin in southeast China was selected as the study area. Future climate and land use projections were produced by the Delta method and CA-Markov model, respectively. Future climate forcings and land use patterns were then incorporated into a distributed hydrologic model to evaluate river flow regime shifts. Results revealed that climate and land use changes may lead to severe drought conditions in the future. Lower flows are shown to be more sensitive to environmental changes and a decline of monthly flows could reach up to nearly 30% in the dry season. To address the threat of increasing drought uncertainties in the water supply system, the aggregation-decomposition method incorporated with hedging rules was applied to guide the multi-reservoir operation. Parameters of optimal hedging rules were obtained by a multi-objective optimization algorithm. The performance of hedging rules was evaluated by comparison to standard operating policies and conventional operating rules with respect to reliability, resiliency, vulnerability, and sustainability indices. Results showed that the multi-reservoir system guided by hedging rules can be more adaptive to the environmental changes. Full article

(This article belongs to the Special Issue Characterizing, Monitoring and Prediction of Hydrometeorological Extremes under Climate Change)

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12 pages, 2113 KiB

Open AccessArticle

Passive Microwave Remote Sensing Soil Moisture Data in Agricultural Drought Monitoring: Application in Northeastern China

by Tao Cheng, Siyang Hong, Bensheng Huang, Jing Qiu, Bikui Zhao and Chao Tan

Water 2021, 13(19), 2777; https://doi.org/10.3390/w13192777 - 7 Oct 2021

Cited by 5 | Viewed by 2486

Abstract

Drought is the costliest disaster around the world and in China as well. Northeastern China is one of China’s most important major grain producing areas. Frequent droughts have harmed the agriculture of this region and further threatened national food security. Therefore, the timely and effective monitoring of drought is extremely important. In this study, the passive microwave remote sensing soil moisture data, i.e., the SMOS soil moisture (SMOS-SM) product, was compared to several in situ meteorological indices through Pearson correlation analysis to assess the performance of SMOS-SM in monitoring drought in northeastern China. Then, maps based on SMOS-SM and in situ indices were created for July from 2010 to 2015 to identify the spatial pattern of drought distributions. Our results showed that the SMOS-SM product had relatively high correlation with in situ indices, especially SPI and SPEI values of a nine-month scale for the growing season. The drought patterns shown on maps generated from SPI-9, SPEI-9 and sc-PDSI were also successfully captured using the SMOS-SM product. We found that the SMOS-SM product effectively monitored drought patterns in northeastern China, and this capacity would be enhanced when field capacity information became available. Full article

(This article belongs to the Special Issue Characterizing, Monitoring and Prediction of Hydrometeorological Extremes under Climate Change)

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18 pages, 3094 KiB

Open AccessArticle

Impacts of Climate Change on Urban Drainage Systems by Future Short-Duration Design Rainstorms

by Han Zhang, Zhifeng Yang, Yanpeng Cai, Jing Qiu and Bensheng Huang

Water 2021, 13(19), 2718; https://doi.org/10.3390/w13192718 - 1 Oct 2021

Cited by 21 | Viewed by 4359

Abstract

The adverse impacts of climate change and urbanization are converging to challenge the waterlogging control measures established in the Dong Hao Chong (DHC) Basin. Based on representative concentration pathway (RCP) scenarios, the future (2030–2050) waterlogging was assessed for the DHC basin and combined with future design rainfall. The delta change factors were projected using the regional climate model, RegCM4.6, and the annual maximum one-day rainstorm was modified to develop the annual maximum value method. By combining the delta change and annual maximum value methods, a future short-duration design rainstorm formula is developed in this study. The Chicago hyetograph shapes indicated that the peak rainfall intensity and amount both increase in the five return periods with two RCP scenarios. The InfoWorks ICM urban flood model is used to simulate the hydrological response. The results show that climate change will exacerbate urban waterlogging in DHC Basin. The maximum inundation volume and number of inundation nodes were expected to increase in the five return periods under the RCP4.5 and RCP8.5 scenarios, respectively. The submerged area is increasing due to climate change. This study highlights the link between climate change and urban drainage systems, and suggests that the effect of climate change in extreme rainfall should be considered in urban waterlogging management and drainage system design. Full article

(This article belongs to the Special Issue Characterizing, Monitoring and Prediction of Hydrometeorological Extremes under Climate Change)

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21 pages, 4618 KiB

Open AccessArticle

River Runoff Modelling and Hydrological Drought Assessment Based on High-Resolution Brightness Temperatures in Mainland China

by Xing Qu, Ziyue Zeng, Zhe Yuan, Junjun Huo, Yongqiang Wang and Jijun Xu

Water 2021, 13(17), 2429; https://doi.org/10.3390/w13172429 - 3 Sep 2021

Cited by 1 | Viewed by 3035

Abstract

Under the background of global climate change, drought is causing devastating impacts on the balance of the regional water resources system. Hydrological drought assessment is critical for drought prevention and water resources management. However, in China to assess hydrological drought at national scale is still challenging basically because of the difficulty of obtaining runoff data. In this study, we used the state-of-the-art passive microwave remote sensing techniques in river runoff modelling and thus assessed hydrological drought in Mainland China in 1996–2016. Specifically, 79 typical hydrological stations in 9 major basins were selected to simulate river runoff using the M/C signal method based on a high-resolution passive microwave bright temperature dataset. The standardized runoff index (SRI) was calculated for the spatial and temporal patterns of hydrological drought. Results show that passive microwave remote sensing can provide an effective way for runoff modelling as 92.4% and 59.5% of the selected 79 stations had the Pearson correlation coefficient (R) and the Nash-Sutcliffe efficiency coefficient (NS) scores greater than 0.5. Especially in areas located on Qinghai-Tibet Plateau in the Inland and the Southwest River Basin, the performance of the M/C signal method is quite outstanding. Further analysis indicates that stations with small rivers in the plateau areas with sparse vegetation tend to have better simulated results, which are usually located in drought-prone regions. Hydrological drought assessment shows that 30 out of the 79 stations present significant increasing trends in SRI-3 and 18 indicate significant decreasing trends. The duration and severity of droughts in the non-permanent dry areas of the Hai River Basin, the middle reaches of the Yangtze River Basin and the Southwest of China were found out to be more frequent and severe than other regions. This work can provide guidance for extending the applications of remote sensing data in drought assessment and other hydrological research. Full article

(This article belongs to the Special Issue Characterizing, Monitoring and Prediction of Hydrometeorological Extremes under Climate Change)

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