Research

10 pages, 1802 KiB

Open AccessArticle

Evaluation of Regional Climate Models (RCMs) Performance in Simulating Seasonal Precipitation over Mountainous Central Pindus (Greece)

by Stefanos Stefanidis, Stavros Dafis and Dimitrios Stathis

Water 2020, 12(10), 2750; https://doi.org/10.3390/w12102750 - 02 Oct 2020

Cited by 22 | Viewed by 2789

Abstract

During the last few years, there is a growing concern about climate change and its negative effects on water availability. This study aims to evaluate the performance of regional climate models (RCMs) in simulating seasonal precipitation over the mountainous range of Central Pindus [...] Read more.

During the last few years, there is a growing concern about climate change and its negative effects on water availability. This study aims to evaluate the performance of regional climate models (RCMs) in simulating seasonal precipitation over the mountainous range of Central Pindus (Greece). To this end, observed precipitation data from ground-based rain gauge stations were compared with RCMs grid point’s simulations for the baseline period 1974–2000. Statistical indexes such as root mean square error (RMSE), mean absolute error (MAE), Pearson correlation coefficient, and standard deviation (SD) were used in order to evaluate the model’s performance. The results demonstrated that RCMs fail to represent the temporal variability of precipitation time series with exception of REMO. Although, concerning the model’s prediction accuracy, it was found that better performance was achieved by the RegCM3 model in the study area. In addition, regarding a future projection (2074–2100), it was highlighted that precipitation will significantly decrease by the end of the 21st century, especially in spring (−30%). Therefore, adaption of mountainous catchment management to climate change is crucial to avoid water scarcity. Full article

(This article belongs to the Special Issue Flash Floods in Urban Areas)

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13 pages, 3829 KiB

Open AccessFeature PaperArticle

Urban Flood Hazard Modeling Using Self-Organizing Map Neural Network

by Omid Rahmati, Hamid Darabi, Ali Torabi Haghighi, Stefanos Stefanidis, Aiding Kornejady, Omid Asadi Nalivan and Dieu Tien Bui

Water 2019, 11(11), 2370; https://doi.org/10.3390/w11112370 - 12 Nov 2019

Cited by 40 | Viewed by 4770

Abstract

Floods are the most common natural disaster globally and lead to severe damage, especially in urban environments. This study evaluated the efficiency of a self-organizing map neural network (SOMN) algorithm for urban flood hazard mapping in the case of Amol city, Iran. First, [...] Read more.

Floods are the most common natural disaster globally and lead to severe damage, especially in urban environments. This study evaluated the efficiency of a self-organizing map neural network (SOMN) algorithm for urban flood hazard mapping in the case of Amol city, Iran. First, a flood inventory database was prepared using field survey data covering 118 flooded points. A 70:30 data ratio was applied for training and validation purposes. Six factors (elevation, slope percent, distance from river, distance from channel, curve number, and precipitation) were selected as predictor variables. After building the model, the odds ratio skill score (ORSS), efficiency (E), true skill statistic (TSS), and the area under the receiver operating characteristic curve (AUC-ROC) were used as evaluation metrics to scrutinize the goodness-of-fit and predictive performance of the model. The results indicated that the SOMN model performed excellently in modeling flood hazard in both the training (AUC = 0.946, E = 0.849, TSS = 0.716, ORSS = 0.954) and validation (AUC = 0.924, E = 0.857, TSS = 0.714, ORSS = 0.945) steps. The model identified around 23% of the Amol city area as being in high or very high flood risk classes that need to be carefully managed. Overall, the results demonstrate that the SOMN model can be used for flood hazard mapping in urban environments and can provide valuable insights about flood risk management. Full article

(This article belongs to the Special Issue Flash Floods in Urban Areas)

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20 pages, 16205 KiB

Open AccessArticle

Optimize Short-Term Rainfall Forecast with Combination of Ensemble Precipitation Nowcasts by Lagrangian Extrapolation

by Wooyoung Na and Chulsang Yoo

Water 2019, 11(9), 1752; https://doi.org/10.3390/w11091752 - 22 Aug 2019

Cited by 3 | Viewed by 3061

Abstract

The rainfall forecasts currently available in Korea are not sufficiently accurate to be directly applied to the flash flood warning system or urban flood warning system. As the lead time increases, the quality becomes even lower. In order to overcome this problem, this [...] Read more.

The rainfall forecasts currently available in Korea are not sufficiently accurate to be directly applied to the flash flood warning system or urban flood warning system. As the lead time increases, the quality becomes even lower. In order to overcome this problem, this study proposes an ensemble forecasting method. The proposed method considers all available rainfall forecasts as ensemble members at the target time. The ensemble members are combined based on the weighted average method, where the weights are determined by applying the two conditions of the unbiasedness and minimum error variance. The proposed method is tested with McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) rainfall forecasts for four storm events that occurred during the summers of 2016 and 2017 in Korea. In Korea, rainfall forecasts are generated every 10 min up to six hours, i.e., there are always a total of 36 sets of rainfall forecasts. As a result, it is found that just six ensemble members is sufficient to make the ensemble forecast. Considering additional ensemble members beyond six does not significantly improve the quality of the ensemble forecast. The quality of the ensemble forecast is also found to be better than that of the single forecast, and the weighted average method is found to be better than the simple arithmetic average method. Full article

(This article belongs to the Special Issue Flash Floods in Urban Areas)

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22 pages, 4829 KiB

Open AccessArticle

Identifying Key Hydrological Processes in Highly Urbanized Watersheds for Flood Forecasting with a Distributed Hydrological Model

by Huanyu Wang and Yangbo Chen

Water 2019, 11(8), 1641; https://doi.org/10.3390/w11081641 - 08 Aug 2019

Cited by 21 | Viewed by 3500

Abstract

The world has experienced large-scale urbanization in the past century, and this trend is ongoing. Urbanization not only causes land use/cover (LUC) changes but also changes the flood responses of watersheds. Lumped conceptual hydrological models cannot be effectively used for flood forecasting in [...] Read more.

The world has experienced large-scale urbanization in the past century, and this trend is ongoing. Urbanization not only causes land use/cover (LUC) changes but also changes the flood responses of watersheds. Lumped conceptual hydrological models cannot be effectively used for flood forecasting in watersheds that lack long time series of hydrological data to calibrate model parameters. Thus, physically based distributed hydrological models are used instead in these areas, but considerable uncertainty is associated with model parameter derivation. To reduce model parameter uncertainty in physically based distributed hydrological models for flood forecasting in highly urbanized watersheds, a procedure is proposed to control parameter uncertainty. The core concept of this procedure is to identify the key hydrological and flood processes in the highly urbanized watersheds and the sensitive model parameters related to these processes. Then, the sensitive model parameters are adjusted based on local runoff coefficients to reduce the parameter uncertainty. This procedure includes these steps: collecting the latest LUC information or estimating this information using satellite remote sensing images, analyzing LUC spatial patterns and identifying dominant LUC types and their spatial structures, choosing and establishing a distributed hydrological model as the forecasting tool, and determining the initial model parameters and identifying the key hydrological processes and sensitive model parameters based on a parameter sensitivity analysis. A highly urbanized watershed called Shahe Creek in the Pearl River Delta area was selected as a case study. This study finds that the runoff production processes associated with both the ferric luvisol and acric ferralsol soil types and the runoff routing process on urban land are key hydrological processes. Additionally, the soil water content under saturated conditions, the soil water content under field conditions and the roughness of urban land are sensitive parameters. Full article

(This article belongs to the Special Issue Flash Floods in Urban Areas)

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17 pages, 5652 KiB

Open AccessArticle

Retrospective Dynamic Inundation Mapping of Hurricane Harvey Flooding in the Houston Metropolitan Area Using High-Resolution Modeling and High-Performance Computing

by Seong Jin Noh, Jun-Hak Lee, Seungsoo Lee and Dong-Jun Seo

Water 2019, 11(3), 597; https://doi.org/10.3390/w11030597 - 22 Mar 2019

Cited by 18 | Viewed by 10308

Abstract

Hurricane Harvey was one of the most extreme weather events to occur in Texas, USA; there was a huge amount of urban flooding in the city of Houston and the adjoining coastal areas. In this study, we reanalyze the spatiotemporal evolution of inundation [...] Read more.

Hurricane Harvey was one of the most extreme weather events to occur in Texas, USA; there was a huge amount of urban flooding in the city of Houston and the adjoining coastal areas. In this study, we reanalyze the spatiotemporal evolution of inundation during Hurricane Harvey using high-resolution two-dimensional urban flood modeling. This study’s domain includes the bayou basins in and around the Houston metropolitan area. The flood model uses the dynamic wave method and terrain data of 10-m resolution. It is forced by radar-based quantitative precipitation estimates. To evaluate the simulated inundation, on-site photos and water level observations were used. The inundation extent and severity are estimated by combining the retrieved water depths, images collected from the impacted area, and high-resolution terrain data. The simulated maximum inundation extent, which is frequently found outside of the designated flood zones, points out the importance of capturing multi-scale hydrodynamics in the built environment under extreme rainfall for effective flood risk and emergency management. Full article

(This article belongs to the Special Issue Flash Floods in Urban Areas)

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20 pages, 8551 KiB

Open AccessArticle

Application of Fuzzy TOPSIS to Flood Hazard Mapping for Levee Failure

by Tae Hyung Kim, Byunghyun Kim and Kun-Yeun Han

Water 2019, 11(3), 592; https://doi.org/10.3390/w11030592 - 21 Mar 2019

Cited by 22 | Viewed by 3798

Abstract

This paper proposes a new approach to consider the uncertainties for constructing flood hazard maps for levee failure. The flood depth, velocity, and arrival time were estimated by the 2-Dimensional model and were considered as flood indices for flood hazard mapping. Each flood [...] Read more.

This paper proposes a new approach to consider the uncertainties for constructing flood hazard maps for levee failure. The flood depth, velocity, and arrival time were estimated by the 2-Dimensional model and were considered as flood indices for flood hazard mapping. Each flood index predicted from the 2-D flood analysis based on several scenarios was fuzzified to reflect the uncertainties of the indices. The fuzzified flood indices were integrated using the Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), resulting in a single graded flood hazard map. This methodology was applied to the Gam river in South Korea and confirmed that the Fuzzy MCDM (Multiple Criteria Decision Making) technique can be used to produce flood hazard maps. The flood hazard map produced in this study compared with the current flood hazard map of MOLIT (Ministry of Land, Infrastructure and Transports). This study found that the proposed methodology was more advantageous than the current methods with regard to the accuracy and grading of the flood areas, as well as in regard to an integrated single map. This report is expected to be expand upon other floods, including dam failure and urban flooding. Full article

(This article belongs to the Special Issue Flash Floods in Urban Areas)

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Review

Jump to: Research, Other

19 pages, 1793 KiB

Open AccessReview

Recommendations for Improving Integration in National End-to-End Flood Forecasting Systems: An Overview of the FFIR (Flooding From Intense Rainfall) Programme

by David L. A. Flack, Christopher J. Skinner, Lee Hawkness-Smith, Greg O’Donnell, Robert J. Thompson, Joanne A. Waller, Albert S. Chen, Jessica Moloney, Chloé Largeron, Xilin Xia, Stephen Blenkinsop, Adrian J. Champion, Matthew T. Perks, Niall Quinn and Linda J. Speight

Water 2019, 11(4), 725; https://doi.org/10.3390/w11040725 - 08 Apr 2019

Cited by 23 | Viewed by 9248

Abstract

Recent surface-water and flash floods have caused millions of pounds worth of damage in the UK. These events form rapidly and are difficult to predict due to their short-lived and localised nature. The interdisciplinary Flooding From Intense Rainfall (FFIR) programme investigated the feasibility [...] Read more.

Recent surface-water and flash floods have caused millions of pounds worth of damage in the UK. These events form rapidly and are difficult to predict due to their short-lived and localised nature. The interdisciplinary Flooding From Intense Rainfall (FFIR) programme investigated the feasibility of enhancing the integration of an end-to-end forecasting system for flash and surface-water floods to help increase the lead time for warnings for these events. Here we propose developments to the integration of an operational end-to-end forecasting system based on the findings of the FFIR programme. The suggested developments include methods to improve radar-derived rainfall rates and understanding of the uncertainty in the position of intense rainfall in weather forecasts; the addition of hydraulic modelling components; and novel education techniques to help lead to effective dissemination of flood warnings. We make recommendations for future advances such as research into the propagation of uncertainty throughout the forecast chain. We further propose the creation of closer bonds to the end users to allow for an improved, integrated, end-to-end forecasting system that is easily accessible for users and end users alike, and will ultimately help mitigate the impacts of flooding from intense rainfall by informed and timely action. Full article

(This article belongs to the Special Issue Flash Floods in Urban Areas)

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Other

Jump to: Research, Review

11 pages, 2277 KiB

Open AccessTechnical Note

Extraction of Urban Waterlogging Depth from Video Images Using Transfer Learning

by Jingchao Jiang, Junzhi Liu, Cheng-Zhi Qin and Dongliang Wang

Water 2018, 10(10), 1485; https://doi.org/10.3390/w10101485 - 21 Oct 2018

Cited by 20 | Viewed by 4399

Abstract

Urban flood control requires real-time and spatially detailed information regarding the waterlogging depth over large areas, but such information cannot be effectively obtained by the existing methods. Video supervision equipment, which is readily available in most cities, can record urban waterlogging processes in [...] Read more.

Urban flood control requires real-time and spatially detailed information regarding the waterlogging depth over large areas, but such information cannot be effectively obtained by the existing methods. Video supervision equipment, which is readily available in most cities, can record urban waterlogging processes in video form. These video data could be a valuable data source for waterlogging depth extraction. The present paper is aimed at demonstrating a new approach to extract urban waterlogging depths from video images based on transfer learning and lasso regression. First, a transfer learning model is used to extract feature vectors from a video image set of urban waterlogging. Second, a lasso regression model is trained with these feature vectors and employed to calculate the waterlogging depth. Two case studies in China were used to evaluate the proposed method, and the experimental results illustrate the effectiveness of the method. This method can be applied to video images from widespread cameras in cities, so that a powerful urban waterlogging monitoring network can be formed. Full article

(This article belongs to the Special Issue Flash Floods in Urban Areas)

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