Hydrological Prediction and Flooding Risk Assessment

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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 8785

Special Issue Editors

Department of Civil and Environmental Engineering, Brunel University, London, UK
Interests: water–energy–food nexus; water and environmental systems analysis; decision making; uncertainty; hydrological risk
Special Issues, Collections and Topics in MDPI journals
School of Environment, Beijing Normal University, Beijing, China
Interests: water resources management; energy systems planning; climate modeling and impact analysis; environmental pollution control; risk assessment; decision making under uncertainty
Special Issues, Collections and Topics in MDPI journals
School of Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada
Interests: regional climate modeling; climate downscaling; hydrological modeling and flooding risk analysis; energy systems modeling under climate change; climate change impact assessment and adaptation studies; GIS; spatial modeling and analysis; big data analysis and visualization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

River floods are among the costliest natural disasters, which can not only result in severe direct damages and fatalities, but also have considerably wider and longer-term adverse economic consequences. In an age defined by climate change and intensive human activities, ensuring the resilience against flood risk—critical to sustaining economic and population growth—is becoming increasingly important around the world. This challenge is particularly difficult for some coastal regions, where rising emissions from human activities, coupled with unprecedented changes in weather extremes, have resulted in growing floods and posed great threats to the local communities. Consequently, reliable hydrological prediction and flooding risk assessment are of great importance to develop corresponding resilience strategies, especially under a changing environment. However, a great number of challenges need to be carefully considered in hydrological prediction and risk assessment, such as the extensive uncertainties embedded in various hydroclimatic processes, changing climate, and intensified socio-economic activities. Such complexities force researchers to develop more robust mathematical methods and tools to analyze the relevant information, simulate the related processes, assess the potential impacts/risks, and generate sound decision alternatives for flood resilience.

This Special Issue on Hydrological Prediction and Flooding Risk Assessment aims to explore new mathematical techniques to aid decision makers in generating reliable flood predictions and risk inferences. What are new techniques in revealing complexities in hydroclimatic processes? How do we generate sound flood resilience strategies under the consideration of climate change and socio-economic development? Are there appropriate approaches to reflect extensive uncertainties in the process of hydrologic modelling and flood risk assessment? Additionally, case studies from a variety of hydrologic prediction and flood risk assessment issues are welcome.

Dr. Yurui Fan
Prof. Dr. Yongping Li
Dr. Xander Wang
Guest Editors

Manuscript Submission Information

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Keywords

  • hydrologic prediction
  • flood risk assessment
  • climate change and adaptation
  • flood resilience
  • uncertainty quantification

Published Papers (3 papers)

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Research

14 pages, 3738 KiB  
Article
Predictability of Seasonal Streamflow Forecasting Based on CSM: Case Studies of Top Three Largest Rivers in China
Water 2021, 13(2), 162; https://doi.org/10.3390/w13020162 - 12 Jan 2021
Cited by 4 | Viewed by 1820
Abstract
Accurate seasonal streamflow forecasting is important in reservoir operation, watershed planning, and water resource management, and streamflow forecasting is often based on hydrological models driven by coupled global climate models (CGCMs). To understand streamflow forecasting predictability, this study considered the three largest rivers [...] Read more.
Accurate seasonal streamflow forecasting is important in reservoir operation, watershed planning, and water resource management, and streamflow forecasting is often based on hydrological models driven by coupled global climate models (CGCMs). To understand streamflow forecasting predictability, this study considered the three largest rivers in China and explored deterministic and probabilistic skill metrics on the monthly scale according to ensemble streamflow hindcasts from the hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV) driven by multiple climate forcings from the climate system model by the Beijing Climate Center (BCC_CSM1.1m). The effects of initial conditions (ICs) and meteorological forcings (MFs) on skill were investigated using the conventional ensemble streamflow prediction (ESP) and reverse-ESP (revESP). The results revealed the following: (1) Skill declines as lead time increases, and forecasting is generally the most skillful for lead month 1; (2) skill is higher for dry rivers than wet rivers, and higher for dry target months than wet months for the Yellow and Yangtze Rivers, suggesting greater skill in potential drought forecasting than flood forecasting; (3) the relative operating characteristic (ROC) area is greater for abnormal terciles than the near-normal tercile for all three rivers, greater for the above-normal tercile than the below-normal tercile for the Yellow and Yangtze Rivers, but slightly greater for the below-normal tercile than the above-normal tercile for the Xijiang River; and (4) the influence of ICs outweighs that of MFs in dry months, and the period of influence varies from 1 to 3 months; however, the influence of MFs is dominant in wet target months. These findings will help improve the understanding of both the seasonal streamflow forecasting predictability based on coupled climate system/hydrological models and of streamflow forecasting for variable rivers and seasons. Full article
(This article belongs to the Special Issue Hydrological Prediction and Flooding Risk Assessment)
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20 pages, 4825 KiB  
Article
Characterizing Hydrological Drought and Water Scarcity Changes in the Future: A Case Study in the Jinghe River Basin of China
Water 2020, 12(6), 1605; https://doi.org/10.3390/w12061605 - 04 Jun 2020
Cited by 8 | Viewed by 2531
Abstract
The assessment of future climate changes on drought and water scarcity is extremely important for water resources management. A modeling system is developed to study the potential status of hydrological drought and water scarcity in the future, and this modeling system is applied [...] Read more.
The assessment of future climate changes on drought and water scarcity is extremely important for water resources management. A modeling system is developed to study the potential status of hydrological drought and water scarcity in the future, and this modeling system is applied to the Jinghe River Basin (JRB) of China. Driven by high-resolution climate projections from the Regional Climate Modeling System (RegCM), the Variable Infiltration Capacity model is employed to produce future streamflow projections (2020–2099) under two Representative Concentration Pathway (RCP) scenarios. The copula-based method is applied to identify the correlation between drought variables (i.e., duration and severity), and to further quantify their joint risks. Based on a variety of hypothetical water use scenarios in the future, the water scarcity conditions including extreme cases are estimated through the Water Exploitation Index Plus (WEI+) indicator. The results indicate that the joint risks of drought variables at different return periods would decrease. In detail, the severity of future drought events would become less serious under different RCP scenarios when compared with that in the historical period. However, considering the increase in water consumption in the future, the water scarcity in JRB may not be alleviated in the future, and thus drought assessment alone may underestimate the severity of future water shortage. The results obtained from the modeling system can help policy makers to develop reasonable future water-saving planning schemes, as well as drought mitigation measures. Full article
(This article belongs to the Special Issue Hydrological Prediction and Flooding Risk Assessment)
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17 pages, 13465 KiB  
Article
Flood Routing Process and High Dam Interception of Natural Discharge from the 2018 Baige Landslide-Dammed Lake
Water 2020, 12(2), 605; https://doi.org/10.3390/w12020605 - 23 Feb 2020
Cited by 7 | Viewed by 3531
Abstract
The outburst flood of the Baige landslide dam caused tremendous damage to infrastructure, unfinished hydraulic buildings, roads, and bridges that were built or under construction along the Jinsha River. Can downstream hydraulic buildings, such as high dams with flood control and discharge function, [...] Read more.
The outburst flood of the Baige landslide dam caused tremendous damage to infrastructure, unfinished hydraulic buildings, roads, and bridges that were built or under construction along the Jinsha River. Can downstream hydraulic buildings, such as high dams with flood control and discharge function, accommodate outburst floods or generate more serious losses due to wave overtopping? In this study, the unsteady flow of a one-dimensional hydraulic calculation was used to simulate natural flood discharge. Assuming a high dam (Yebatan arch dam) is constructed downstream, the flood processes were carried out in two forms of high dam interception (complete interception, comprehensive flood control of blocking and draining). Moreover, three-dimensional visualization of the inundation area was performed. Simulation results indicate that the Yebatan Hydropower Station can completely eliminate the outburst flood risk even under the most dangerous situations. This station can reduce the flood peak and delay the peak flood arrival time. Specifically, the flood peak decreased more obviously when it was closer to the upstream area, and the flood peak arrival time was more delayed when the flood spread further downstream. In addition, the downstream water depth was reduced by approximately 10 m, and the inundation area was reduced to half of the natural discharge. This phenomenon shows that hydraulic buildings such as high dams can reduce the inundation area of downstream farmlands and extend the evacuation time for downstream residents during the flood process, thus reducing the loss of life and property. Full article
(This article belongs to the Special Issue Hydrological Prediction and Flooding Risk Assessment)
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