<p>In recent years, urban flood disasters in China have become increasingly serious. In mid-June 2022, the northern part of Guangdong Province was affected by continuous rainfall, and floods occurred in many rivers in the upper and middle reaches of the Beijiang River in the Pearl River Basin, causing serious floods in many cities, villages, and towns in the basin. Based on the rain and flood processes of these flood disasters and analyses of the specific disaster situations of three typical cities, this paper deeply analyzes the urban water system, vertical topography, etc. The main and secondary causes of flood disasters in the three cities are studied, and the deficiencies in the expansion of cities under different natural geographical conditions are explored through comparisons to address flood disasters. This work provides a basis for the cities to establish flood control systems that are integrated, systematic, and adapted to local conditions.</p>
Paddy field dams are basin-level flood control measures that promote rainwater storage; however, a general runoff model cannot adequately describe the water balance in paddy fields. This study develops a subgrid model for evaluating paddy water balance considering land use on a computational grid. Subgrid models can account for the storage effect of paddy field dams without disregarding the general grid-based distributed rainfall–runoff model framework. To investigate the effect of current paddy field storage and the introduction of paddy field dams on reducing peak flood discharge, rainfall–runoff analysis was conducted using the proposed model in the Kashima River basin, which flows into Lake Inba-numa in Chiba Prefecture, Japan. The computational results indicated that the rainwater storage effect of current paddy fields reduces the peak river discharge, suggesting that the drainage process of the paddy field should be incorporated into runoff models. Furthermore, the storage effect of paddy fields became more pronounced as the height of the drainage pipe in the paddy field dam increased. The calculated results quantitatively show the flood control effect of paddy field storage over the entire basin; thus, the proposed subgrid model may be a useful tool for promoting basin-level flood control measures.
AbstractRiver diversions are a commonly used water management tool throughout the world. Low‐gradient coastal rivers exhibit complex floodplain interactions and are subject to multiple uncertain drivers of future flood risk. The interacting factors make quantification of downstream diversion effects on ambient flood hazards in coastal riverine ecosystems challenging. This article analyzes the effects of a managed ecological diversion (MED) on downstream flood hazards in the flood‐prone tidal freshwater Vermilion River system situated within the Atchafalaya Basin region of Louisiana, USA. A flexible and efficient hydraulic and hydrologic (H&H) modeling setup and counterfactual approach is utilized to develop and analyze 60 simulation scenarios aimed at quantifying the effects of an existing baseflow augmentation program on downstream flood hazards under uncertain future conditions. The analysis demonstrated the significance of preemptive diversion closure on the downstream flood levels in downstream rivers. The role of wetland topography and residual floodplain storage on MED‐induced flood response was also highlighted. A variety of uncertain parameter/forcing scenarios were also evaluated to establish robustness of the findings and to extend the results to more general settings. The study provides insights on downstream flood response induced by MEDs, which are expected to become more prevalent in the future.
China’s economic development has shifted from a high‐speed growth stage to a high‐quality development stage, which promotes the sustainable economic development of the Yangtze River (YR) and also improves the response capacity of the YR basin’s resilience system to floods. Flood resilience (FR) is not only just the achievement of high‐quality economic development (HQED) but also the key to mitigating flood damage (FD). Therefore, based on panel data from 2000 to 2019 from provincial administrative regions (PARs) in the Yangtze River basin (YRb), the entropy weight method and the quantitative regression analysis method were used to empirically investigate the impact of HQED and FR on FD in the YRb. The empirical analysis results show the following. (i) HQED has an inverted U‐shaped effect on FD in the YRb, meaning that HQED will reach a certain point and reduce flood losses. (ii) The HQED model, which has been pushed forward, will significantly improve the level of FR. Various mediating effect test methods revealed that FR has a significant negative mediating effect on the process of HQED to mitigate FD. Taken together, this reveals the importance of insisting on implementing an HQED model to mitigate FD in the YRb, gradually deriving an FR that is adapted to floods and has a high self‐organizing and self‐restoring capacity through high‐quality development.
AbstractIncorporating 50 years of flood data for the Manas River Kenswat Hydrological Station from 1957 to 2006, the Pettitt test and Mann–Kendall trend test are used to analyse non‐stationarity of the flood characteristic sequences. Moreover, the Pearson type‐III (P‐III) distribution, the mixed distribution (MD) and conditional probability distribution (CPD) models are employed to analyse frequency and to calculate the design flood process line. The results showed that the annual maximum peak discharge and the annual maximum flood volume are most likely to change in 1993. The MD model considering the non‐stationarity of the flood sequence is more accurate than the CPD model and the traditional P‐III distribution model. There are significant differences in the design flood process lines of the 1996 typical flood process obtained by the three methods using the same frequency scaling method. In addition, under different design standards, the design value of the MD model is 20–53% smaller than the design value approved in 2008 (approved by China Renewable Energy Engineering Institute) and 4–48% higher than the traditional P‐III distribution design value. The results can provide a new reference for the management of non‐stationary floods in Manas River.
Abstract. Understanding trends in flood severity and the persistence in peak discharge timing along a vast river network is vital for basin-scale flood risk management and reinsurance purposes. While earlier studies have primarily focused on analysis of either trends in floods or its seasonality independently, here for the first time, we assess coincidence of changes in peak discharge and shifts in its timing in one of the largest peninsular rivers (drainage area of 141 589 km2), Mahanadi River Basin (MRB), in India during 1970–2016. Our research is motivated by the recent six major consecutive floods over MRB during the years 2001, 2003, 2006, 2008, 2011, and 2013. We analyze flood properties using peak fluvial discharge indicators, Monsoonal (from June 1–end of September, during the Indian summer monsoon period), Maxima Flood (MMF) and Peak over Threshold Flood (POTF) events. While we find a blend of (insignificant) up/downward trends in flood magnitude at Upper MRB (Region I), the middle reaches of the basin (Region II) showed an upward trend in flood magnitude with a larger number of sites detect significant trends in floods for the POTF events. Although the average dates of peak discharge in the basin are concentrated in August, notwithstanding the nature of flood samplings, a delayed (or earlier) shift in flood timing is apparent for most of sites. Further, we detect potential hotspots, where up/downward trends in flood magnitude coincide with early (or delayed) dates of flood occurrences. Based on observational evidence, here we show that up to one-third of sites show an up/downward trend in peak discharge with a distinct shift in the flood timing throughout the MRB. The outcomes of the study call for developing efficient adaptation strategies to ensure regional flood resilience since variations in the peak discharge timing should not be confounded with (insignificant) changes in its magnitude.
Floods, responsible for 44% of global natural disasters and impacting over 1.6 billion people between 2000 and 2019, are increasing in frequency and severity due to climate change and human activities. In the Amazon River Basin, this trend is evident with rising flood frequency and intensity since 2000, yet detailed flood susceptibility maps for the region remain scarce. To address this limitation, this study utilized Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) to develop flood susceptibility maps for the Amazon River Basin. The analysis incorporated a flood inventory dataset along with fourteen conditioning factors, encompassing meteorological, hydrological, topographical, and geological variables. The multicollinearity among the variables was addressed through Variance Inflation Factor (VIF) analysis. The models' performance was evaluated using accuracy, precision, recall, F1-score, and Kappa score. To enhance the interpretability of both models, SHAP (SHapley Additive exPlanations) was employed to identify and evaluate the key factors influencing the models' outcomes. Results confirmed the effectiveness of both models, with XGBoost delivering an accuracy of 0.91 and a Kappa score of 0.83, outperforming RF’s accuracy of 0.90 and Kappa score of 0.81. SHAP results revealed that for both models the most important factors were land use/land cover, rainfall, elevation, curve number, slope, drainage density, and soil. We assessed the robustness of the models by removing the least important features. Both models demonstrated stable performance, maintaining consistent accuracy, precision, recall, and F1-scores, with XGBoost surpassing RF. Ultimately, RF and XGBoost proved effective in generating accurate and reliable flood susceptibility maps for large regions like the Amazon River Basin, with SHAP offering significant insights into the interpretability of model outputs. Funding:This research was supported by Disaster-Safety Platform Technology Development Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT. (No. 2022M3D7A1090338).