Flood Risk Assessment and Regionalization from Past and Future Perspectives at Basin Scale

A framework for the construction of effective landscape ecological network with integrating hydrological connectivity: A case study in Dongjiang River Basin, China

The rise in frequency of extreme climate events has led to notable variation in water storage capacity within many basins around the world, resulting in the simultaneous occurrence of seasonal water shortages and flooding issues. The development of a basin landscape ecological network that is grounded in hydrological connectivity has the potential to markedly improve ecosystem resilience in the basin as well as to facilitate the integrated advancement of ecological conservation and water resource management. This study assessed the hydrological connectivity of the Dongjiang River Basin, China, in terms of Euclidean distance, over the period from 2000 to 2023. Additionally, a boosted regression tree (BRT) model was utilized to ascertain the weights of various ecological resistance factors. The minimum cumulative resistance (MCR) model was subsequently applied to construct a landscape ecological network and to facilitate the identification of ecological pinch points and barriers. Results showed that the mean hydrological connectivity within the Dongjiang River Basin varied between 160 m and 220 m. The overall probability density distribution of hydrological connectivity exhibited characteristics consistent with a semi-normal distribution. The respective contribution rates of elevation, annual average temperature, annual precipitation, and land use type to hydrological connectivity were quantified as 0.57, 0.22, 0.20, and 0.01. In this study, 31 ecological corridors, spanning a cumulative length of 1043.85 km, were identified. Among these corridors, certain ones exhibited a high degree of alignment with the actual distribution of surface water, covering 11.95% of the area, whereas others predominantly traversed forested regions, accounting for 68.58%. The areas designated as ecological pinch points and ecological barriers encompassed 21.78 km2 and 183.37 km2, respectively. These findings offer valuable scientific insights for the ecological protection of basins, the planning and management of water resources, and the prevention and control of flooding in both urban and rural contexts.

Decision-making under flood predictions: A risk perception study of coastal real estate

Flood models, while representing our best knowledge of a natural phenomenon, are continually evolving. Their predictions, albeit undeniably important for flood risk management, contain considerable uncertainties related to model structure, parameterization, and input data. With multiple sources of flood predictions becoming increasingly available through online flood maps, the uncertainties in these predictions present considerable risks related to property devaluation. Such risks stem from real estate decisions, measured by location preferences and willingness-to-pay to buy and rent properties, based on access to various sources of flood predictions. Here, we evaluate the influence of coastal flood predictions on real estate decision-making in the United Kingdom by adopting an interdisciplinary approach, involving flood modeling, novel experimental willingness-to-pay real estate surveys of UK residents in response to flood predictions, statistical modeling, and geospatial analysis. Our main findings show that access to multiple sources of flood predictions dominates real estate decisions relative to preferences for location aesthetics, reflecting a shift in demand toward risk averse locations. We also find that people do not consider flood prediction uncertainty in their real estate decisions, possibly due to an inability to perceive such uncertainty. These results are robust under a repeated experimental survey using an open access long-term flood risk map. We, therefore, recommend getting flood models "right" but recognize that this is a contentious issue because it implies having an error-free model, which is practically impossible. Hence, to reduce real estate risks, we advocate for a greater emphasis on effectively communicating flood model predictions and their uncertainties to non-experts.

Enhancing dynamic flood risk assessment and zoning using a coupled hydrological-hydrodynamic model and spatiotemporal information weighting method

Climate change and intensified human activities are exacerbating the frequency and severity of extreme precipitation events, necessitating more precise and timely flood risk assessments. Traditional models often fail to dynamically and accurately assess flood risks due to their static nature and limited handling of spatiotemporal variations. This study confronts these challenges head-on by developing a novel coupled hydrological-hydrodynamic model integrated with a Block-wise use of the TOPMODEL (BTOP) and the Rainfall-Runoff-Inundation (RRI) model. This integrated approach enables the rapid acquisition of high-precision flood inundation simulation results across large-scale basins, addressing a significant gap in dynamic flood risk assessment and zoning. A critical original achievement of this research lies in developing and implementing a comprehensive vertical-horizontal combined weighting method that incorporates spatiotemporal information for dynamic evaluation indicators, significantly enhancing the accuracy and rationality of flood risk assessments. This innovative method successfully addresses the challenges posed by objective and subjective weighting methods, presenting a balanced and robust framework for flood risk evaluation. The findings from the Min River Basin in China, as a case study, demonstrate the effectiveness of the BTOP-RRI model in capturing the complex variations in runoff and the detailed simulations of flood processes. The model accurately identifies the timing of these peaks, offering insights into the dynamic evolution of flood risks and providing a more precise and timely assessment tool for policymakers and disaster management authorities. The flood risk assessment results demonstrate good consistency with the actual regional conditions. In particular, high-risk areas exhibit distinct characteristics along the river channel, with the distribution area significantly increasing with a sudden surge in runoff. Intense precipitation events expand areas classified as moderate and high risk, gradually shrinking as precipitation levels decrease. This study significantly advances flood risk assessment methodologies by integrating cutting-edge modeling techniques with comprehensive weighting strategies. This is essential for improving the scientific foundation and decision-making processes in regional flood control efforts.

An integrated model chain for future flood risk prediction under land-use changes

Flood is a very destructive natural disaster in the world that is strongly influenced by land-use change. Therefore, a comprehensive flood risk modeling considering the change in land-use is essential for understanding, predicting, and mitigating flood risk. However, most existing single modeling ignored the derivative effect of land-use change, which may reduce the reality of results. To further address the issue, this study presented an integrated model chain by coupling the Markov-FLUS model, the multiple linear regression and the improved TOPSIS model. By applying it in Guangdong Province, the future land-use simulation, spatialization of hazard-bearing bodies, and determination of flood risk were realized. The results show that the coupled model chain allows for good prediction of flood risk under different scenarios, which could be expressed by flood risk composite index (FRSI). In the natural growth scenario, the flood risk will show markedly increasing trend from 2020 to 2030 (FRSI = 2.06), with the high and highest risk zones will expand significantly. Spatially, these increased high flood risk zones mainly distributed on the periphery of existing built-up lands. On the contrary, the flood risk in ecological protection scenario tends to stabilize (FRSI = 1.98), which may be a reference for alternative development paths. These dynamic information identified by this model chain provides a deeper insight into the spatiotemporal characteristics of future high flood risk areas, which can facilitate reasonable flood mitigation measures to be developed at the most critical locations in the region. In further applications, more efficient spatialization models and climate factor are suggested to be introduced.

System comprehensive risk assessment of urban rainstorm-induced flood-water pollution disasters

The urban rainstorm-induced flood-water pollution disaster is a kind of systematic risk, which may induce secondary disasters that can lead to more serious damage, so this paper first adopts the fuzzy comprehensive evaluation method to determine the flood risk by combining with the submergence depth derived from the risk field and other factors data, and then the grid environmental risk evaluation method, which is improved by increasing the induced possibility based on Bayesian theory, is used to evaluate the flood-induced water pollution risk, and the system comprehensive risk of rainstorm-induced flood-water pollution disasters is finally obtained by constructing risk level matrix, which can well depict the coupling superposition effect. Shenzhen City is selected as the study area, and the results showed that the area with high-risk of both flood and water pollution only accounts for about 0.14% of the total area, mainly distributed in the eastern junction of Longgang district and Pingshan district, where the rainstorms occur frequently and the enterprise risk sources are dense. The system comprehensive risk is mostly very low-low and very high-low, accounting for more than 76% of the total area. It is always necessary to pay attention not only to the areas with high risk level of both disasters, but also to the areas with high risk level of one disaster. The method proposed in this study can not only quantitatively reveal the formation of the induced risk, but also provide reference for early warning.

Climate and landuse change enhance spatio-temporal variability of Dongjiang river flow and ammonia nitrogen

The adverse impacts of climate and landuse change are threatening the availability of water quantity and its quality, yet there are limited understandings in the response of water availability to changing environment at different spatio-temporal scales. Aimed at quantifying the individual and superimposed effects of climate and landuse change on streamflow and ammonia nitrogen (NH₃-N) load in the Dongjiang River Basin (DRB), we dynamically simulated the historical (1981-2010) and future (2030-2070) variation of runoff depth and NH₃-N load coupling multiple regional climate model and landuse data. The increase in runoff depth (avg. +233.9 mm) due to climate change was about 33 times greater than that caused by landuse change (avg. -7.2 mm). Especially in the downstream of DRB (Hong Kong, Shenzhen and Dongguan cities, etc.), the maximum rise of runoff depth under climate change was near twice compared with baseline period, indicating the dominant control of climate change on runoff. Also there existed higher coefficient of variation (C_v) value of runoff in the dry season of downstream DRB, contributing potential great fluctuation in runoff. Besides, the variation of NH₃-N load was jointly influenced by climate and landuse change, revealing an offset or amplification effect. Moreover, the variability of NH₃-N load (C_v value as the metric) increased from 2030, reached a maximum in 2050, following decreased to 2070. The spatial distribution of NH₃-N load, in general, presented a downward trend and concentrated near the water body, while the monthly average NH₃-N load showed distinct peaks in spring and late summer temporally. Overall, the results highlight the significance of investigating the water availability under changing environment and more adaptive strategies should be proposed for better basin water management.

Urban flood risk differentiation under land use scenario simulation

The frequent urban floods have seriously affected the regional sustainable development in recent years. It is significant to understand the characteristics of urban flood risk and reasonably predict urban flood risk under different land use scenarios. This study used the random forest and multi-criteria decision analysis models to assess the spatiotemporal characteristics of flood risk in Zhengzhou City, China, from 2005 to 2020, and proposed a robust method coupling Bayesian network and patch-generating land use simulation models to predict future flood risk probability. We found that the flood risk in Zhengzhou City presented an upward trend from 2005 to 2020, and its spatial pattern was "high in the middle and low in the surrounding areas". In addition, land use patterns under the sustainable development scenario would be more conducive to reducing flood risk. Our results can provide theoretical support for scientifically optimizing land use to improve urban flood risk management.

Effects of different cropping systems on ammonia nitrogen load in a typical agricultural watershed of South China

The excessive application of agricultural irrigation water and chemical fertilizer has increased crop yields to help meet the demand for food, but it has also led to major water environment problem, i.e. non-point source (NPS) pollution, which needs to be addressed to achieve sustainable development targets. Although numerous studies have focused on the control and reduction of agricultural NPS pollution from the perspective of irrigation and fertilizer, the effects of different cropping systems on NPS pollution (ammonia nitrogen (NH₃-N)) in the Dongjiang River Basin (DRB) were seldom assessed. Specifically, variation in the NH₃-N load was simulated and analyzed at the annual and semi-annual scales under ten different cropping systems using the Soil and Water Assessment Tool (SWAT) model, which was calibrated and validated with satisfactory statistical index values in the DRB. The results indicated that the NH₃-N load decreased, distinctly increased, slightly decreased when sweet potato, peanut, and rice were planted, respectively. Compared with mono-cropping, crop rotation could reduce the NH₃-N load, and the planting sequence of crops could affect the NH₃-N load to a certain extent. Planting peanuts in spring would dramatically increase NH₃-N load. To evaluate NH₃-N pollution, a critical threshold of NH₃-N emission (5.1 kg·ha^-1·year^-1) was proposed. Meeting the NH₃-N emission threshold cannot be achieved by altering the cropping system alone; additional measures are needed to reduce agricultural NPS pollution. This study facilitates the development of cropping systems and provides relevant information to aid the sustainable development of agriculture in the DRB.

Flood Risk Assessment in Urban Areas of Southern Taiwan

A flood risk assessment of urban areas in Kaohsiung city along the Dianbao River was performed based on flood hazards and social vulnerability. In terms of hazard analysis, a rainfall-runoff model (HEC-HMS) was adopted to simulate discharges in the watershed, and the simulated discharges were utilized as inputs for the inundation model (FLO-2D). Comparisons between the observed and simulated discharges at the Wulilin Bridge flow station during Typhoon Kongrey (2013) and Typhoon Megi (2016) were used for the HEC-HMS model calibration and validation, respectively. The observed water levels at the Changrun Bridge station during Typhoon Kongrey and Typhoon Megi were utilized for the FLO-2D model calibration and validation, respectively. The results indicated that the simulated discharges and water levels reasonably reproduced the observations. The validated model was then applied to predict the inundation depths and extents under 50-, 100-, and 200-year rainfall return periods to form hazard maps. For social vulnerability, the fuzzy Delphi method and the analytic hierarchy process were employed to select the main factors affecting social vulnerability and to yield the weight of each social vulnerability factor. Subsequently, a social vulnerability map was built. A risk map was developed that compiled both flood hazards and social vulnerability levels. Based on the risk map, flood mitigation strategies with structural and nonstructural measures were proposed for consideration by decision-makers.

Scenario-based flood risk assessment for urbanizing deltas using future land-use simulation (FLUS): Guangzhou Metropolitan Area as a case study

Preparing cities for sea-level rise is one of the critical challenges of the twenty-first century. Extreme weather events, natural hazards, and the failure of climate mitigation and adaptation are substantial risks. These risks are especially significant in fast-urbanizing deltas, such as the Pearl River Delta in China, because the conflict between urbanization and flooding caused by climate change will be more significant in the future. This paper elaborates on an approach that employs a future land-use simulation (FLUS) model for scenario-based 100-year coastal flood risk assessment. Storylines of future scenarios from the Intergovernmental Panel on Climate Change (IPCC), called the representative concentration pathways (RCPs) 2.6 and 8.5, are utilized in the present study. The Guangzhou Metropolitan Area (GMA) is used as a case study to explore the probable implications of future land-use changes due to the ongoing urbanization process in the region in relation to projected environmental changes (sea-level rise, storm surge, and land subsidence). The results indicate that there will be a significant increase in flooded urban areas in the future. The simulations show that, as compared to 2015, the built-up area in the GMA will increase by 246.57 km² in 2030 and 513.03 km² in 2050. As compared to 2015, the flooding of built-up areas in 2030 and 2050 will respectively increase by about 31.32 km² and 48.49 km² under the RCP 8.5 scenario. It is also found that, as the main driving factor, urbanization will increase the flooding of built-up areas in Guangzhou in 2030 and 2050 by about 1.9 km² and 5.9 km², respectively, under the RCP 2.6 scenario as compared to 2015. Additionally, due to environmental changes, the flooding of built-up areas in Guangzhou will increase by about 24.2 km² and 26.8 km², respectively, under the RCP 8.5 scenario by 2030 and 2050 as compared to 2015. This increasing flood risk information determined by the simulation provides insight into the spatial distribution of future flood-prone urban areas to facilitate the development and prioritization of flood mitigation measures at the most critical locations in the region.