The simulation, regulation capacity assessment and coping strategy of rainstorm runoff waterlogging in Zhu pai-chong Basin of Nanning, China.

Currently, China is experiencing a phase of rapid urbanization. With the frequent occurrence of extreme rainfall events within the context of climate change, the problem of heavy rainfall and waterlogging in many cities is very prominent. In November 2020, China issued a proposal for the construction of sponge cities across the entire region to significantly enhance the rainfall flood prevention and drainage capacity of cities and effectively improve the resilience of sponge city systems for flooding management. Therefore, this paper selected the Zhu pai-chong watershed in Nanning with frequent waterlogging disasters as an example. Based on underlying surface information, We used a coupled SWMM-LISFOOD model to simulate runoff and waterlogging processes and analyze the spatial and temporal evolution characteristics of the basin under 10 designed rainstorm return periods (0.25a-50a). The results confirm the substantial spatial and temporal variabilities of the runoff coefficient in the study area; impermeability was the main factor contributing to high runoff coefficient values. The spatial distribution characteristics of inundation area was general dispersion and local linear aggregation. Furthermore, this study assessed the effect of the control rate of blue‒green‒gray facilities on the actual storms, and the value ranged from only 48.6% (0.25a)-24.05% (50a). This study quantified the two-dimensional distribution of rainfall storage volume thresholds with or without considering the discharged from the pipe network. Quantitative mapping between the elements of "rainfall-storage volume of blue‒green‒gray facilities-runoff-drainage capacity of the pipe network-waterlogging level" was conducted within the study area as an example. Finally, an overall technical process scheme for rainfall and waterlogging management was proposed. The scheme covered the hydrological‒hydraulic mechanism, storage function of sponge facilities, engineering control response, nonengineering measures and intelligent management of rainfall and waterlogging during sponge city construction, which could provide critical scientific support for effective promotion of the construction of sponge cities in China.

Can atmospheric reanalysis datasets reproduce flood inundation at regional scales? A systematic analysis with ERA5 over Mahanadi River Basin, India.

The prime challenges limiting efficient flood management, especially over large regions, are concurrently related to limited hydro-meteorological observations and exorbitant economics with computational modeling. Reanalysis datasets are a valuable alternative, as they furnish relevant variables at high spatiotemporal resolutions. In recent times, ERA5 has gained significant recognition for its applications in hydrological modeling; however, its efficacy at the inundation scale needs to be understood. The advent of "global flood models" has ensured flood inundation and hazard modeling over large regions, otherwise obscure with regional models. For the first time, the present study explores the fidelity of ERA5 reanalysis at the inundation scale over the Mahanadi River basin, a severely flood-prone region in India. The biases in the discharges within ERA5 are ascertained by comparing them with station-level data at the nascent and extreme levels (i.e., 95th and 99th percentiles). Later, ERA5 is fed to LISFLOOD-FP, an acclaimed global flood model, to reenact the 2006, 2008, 2011, and 2014 flood events. Hit rates exceeding 0.8 compared to MODIS satellite imageries affirm the suitability of ERA5 in accurately capturing flood inundation. Distributed design discharges for 50 yr and 100 yr are derived using a set of extreme value distributions and fed to LISFLOOD-FP to derive design flood inundation and hazards in terms of both "depth" and "product of depth and velocity" of flood waters. Results derived from the study provide vital lessons for efficient land-use planning and adaptation strategies linked to flood protection and resilience.

Predicting flood insurance claims with hydrologic and socioeconomic demographics via machine learning: Exploring the roles of topography, minority populations, and political dissimilarity.

Current research on flooding risk often focuses on understanding hazards, de-emphasizing the complex pathways of exposure and vulnerability. We investigated the use of both hydrologic and social demographic data for flood exposure mapping with Random Forest (RF) regression and classification algorithms trained to predict both parcel- and tract-level flood insurance claims within New York State, US. Topographic characteristics best described flood claim frequency, but RF prediction skill was improved at both spatial scales when socioeconomic data was incorporated. Substantial improvements occurred at the tract-level when the percentage of minority residents, housing stock value and age, and the political dissimilarity index of voting precincts were used to predict insurance claims. Census tracts with higher numbers of claims and greater densities of low-lying tax parcels tended to have low proportions of minority residents, newer houses, and less political similarity to state level government. We compared this data-driven approach and a physically-based pluvial flood routing model for prediction of the spatial extents of flooding claims in two nearby catchments of differing land use. The floodplain we defined with physically based modeling agreed well with existing federal flood insurance rate maps, but underestimated the spatial extents of historical claim generating areas. In contrast, RF classification incorporating hydrologic and socioeconomic demographic data likely overestimated the flood-exposed areas. Our research indicates that quantitative incorporation of social data can improve flooding exposure estimates.

The impact of lake and reservoir parameterization on global streamflow simulation.

Lakes and reservoirs affect the timing and magnitude of streamflow, and are therefore essential hydrological model components, especially in the context of global flood forecasting. However, the parameterization of lake and reservoir routines on a global scale is subject to considerable uncertainty due to lack of information on lake hydrographic characteristics and reservoir operating rules. In this study we estimated the effect of lakes and reservoirs on global daily streamflow simulations of a spatially-distributed LISFLOOD hydrological model. We applied state-of-the-art global sensitivity and uncertainty analyses for selected catchments to examine the effect of uncertain lake and reservoir parameterization on model performance. Streamflow observations from 390 catchments around the globe and multiple performance measures were used to assess model performance. Results indicate a considerable geographical variability in the lake and reservoir effects on the streamflow simulation. Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) metrics improved for 65% and 38% of catchments respectively, with median skill score values of 0.16 and 0.2 while scores deteriorated for 28% and 52% of the catchments, with median values -0.09 and -0.16, respectively. The effect of reservoirs on extreme high flows was substantial and widespread in the global domain, while the effect of lakes was spatially limited to a few catchments. As indicated by global sensitivity analysis, parameter uncertainty substantially affected uncertainty of model performance. Reservoir parameters often contributed to this uncertainty, although the effect varied widely among catchments. The effect of reservoir parameters on model performance diminished with distance downstream of reservoirs in favor of other parameters, notably groundwater-related parameters and channel Manning's roughness coefficient. This study underscores the importance of accounting for lakes and, especially, reservoirs and using appropriate parameterization in large-scale hydrological simulations.

Integrating remotely sensed surface water extent into continental scale hydrology.

In hydrological forecasting, data assimilation techniques are employed to improve estimates of initial conditions to update incorrect model states with observational data. However, the limited availability of continuous and up-to-date ground streamflow data is one of the main constraints for large-scale flood forecasting models. This is the first study that assess the impact of assimilating daily remotely sensed surface water extent at a 0.1° × 0.1° spatial resolution derived from the Global Flood Detection System (GFDS) into a global rainfall-runoff including large ungauged areas at the continental spatial scale in Africa and South America. Surface water extent is observed using a range of passive microwave remote sensors. The methodology uses the brightness temperature as water bodies have a lower emissivity. In a time series, the satellite signal is expected to vary with changes in water surface, and anomalies can be correlated with flood events. The Ensemble Kalman Filter (EnKF) is a Monte-Carlo implementation of data assimilation and used here by applying random sampling perturbations to the precipitation inputs to account for uncertainty obtaining ensemble streamflow simulations from the LISFLOOD model. Results of the updated streamflow simulation are compared to baseline simulations, without assimilation of the satellite-derived surface water extent. Validation is done in over 100 in situ river gauges using daily streamflow observations in the African and South American continent over a one year period. Some of the more commonly used metrics in hydrology were calculated: KGE', NSE, PBIAS%, R2, RMSE, and VE. Results show that, for example, NSE score improved on 61 out of 101 stations obtaining significant improvements in both the timing and volume of the flow peaks. Whereas the validation at gauges located in lowland jungle obtained poorest performance mainly due to the closed forest influence on the satellite signal retrieval. The conclusion is that remotely sensed surface water extent holds potential for improving rainfall-runoff streamflow simulations, potentially leading to a better forecast of the peak flow.

Flood simulation using LISFLOOD and inundation effects: A case study of Typhoon In-Fa in Shanghai.

Urban flooding threatens residents and their property, necessitating timely and accurate flood simulations to enhance prevention measures. However, as a megacity, Shanghai presents a complex underlying surface that proves challenging to assess accurately in existing studies. To simulate the dynamic flooding caused by Typhoon In-Fa in Shanghai from July 23rd to 28th 2021, we employed the LISFLOOD hydrodynamic model with multi-source data and validated the flooded area using the S1FLOOD deep learning model with Sentinel-1 satellite imagery. Based on simulated flood results and a flood depth classification system, we quantified the impacts of flood inundation on population, land use, and buildings. Key findings include: (1) The most severe flooding period in Shanghai occurred on July 25th and 26th 2021. (2) The LISFLOOD model effectively captured the extent of inundation, with the very-high flood depth zone covering 98.07 % of the area identified as flooded by the S1FLOOD and Sentinel-1. (3) Peak-affected individuals were recorded on July 25th 2021. (4) Farmland experienced the most extensive flooding among land use types, while residential buildings were notably affected among building types. Our study reconstructed the spatiotemporal dynamics of Typhoon In-Fa-induced flooding in Shanghai. We mapped the spatial extent and water depths, revealing the dynamic impacts of inundation on population, land use, and buildings across urban areas. This comprehensive framework for flood simulation and inundation impact analysis offers a valuable approach to improve urban flood emergency response.

WITHDRAWN: A preliminary assessment on spatial prioritization for integrated urban flood management based on source tracking method.

This article has been withdrawn at the request of the editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/policies/article-withdrawal.

Gaussian process emulation of spatio-temporal outputs of a 2D inland flood model.

The computational limitations of complex numerical models have led to adoption of statistical emulators across a variety of problems in science and engineering disciplines to circumvent the high computational costs associated with numerical simulations. In flood modelling, many hydraulic and hydrodynamic numerical models, especially when operating at high spatiotemporal resolutions, have prohibitively high computational costs for tasks requiring the instantaneous generation of very large numbers of simulation results. This study examines the appropriateness and robustness of Gaussian Process (GP) models to emulate the results from a hydraulic inundation model. The developed GPs produce real-time predictions based on the simulation output from LISFLOOD-FP numerical model. An efficient dimensionality reduction scheme is developed to tackle the high dimensionality of the output space and is combined with the GPs to investigate the predictive performance of the proposed emulator for estimation of the inundation depth. The developed GP-based framework is capable of robust and straightforward quantification of the uncertainty associated with the predictions, without requiring additional model evaluations and simulations. Further, this study explores the computational advantages of using a GP-based emulator over alternative methodologies such as neural networks, by undertaking a comparative analysis. For the case study data presented in this paper, the GP model was found to accurately reproduce water depths and inundation extent by classification and produce computational speedups of approximately 10,000 times compared with the original simulator, and 80 times for a neural network-based emulator.

Tailings dams: Assessing the long-term erosional stability of valley fill designs.

Tailings is a generic term for waste material from the extraction and processing of minerals and frequently contain mineral and chemical residues. They are usually highly erodible and transportable via fluvial processes. Tailings are commonly stored in 'tailings dams' and such dams are a feature of many mine sites. As they impound water and sediment, tailings dams can be at risk from both catastrophic and gradual failure, especially if unmanaged. A fundamental question for their management is, can tailings dams ever be walk-away structures? Catastrophic failure occurs when there is a large scale rapid structural failure of the dam wall suddenly releasing large volumes of water and sediment. However, over time, there will the increased risk of gradual failure by the slow infilling of the dam and the erosion of the dam wall. Failure can occur where water overtops the dam wall and then incises through the wall due to a loss of freeboard in the dam, a situation which is more likely in legacy tailings dams where they have been filled, vegetated and abandoned. Here, firstly, a computer based landscape evolution model (CAESAR-Lisflood) is employed to assess a hypothetical tailings dam failure by erosion. Secondly, using an idealised example, it is demonstrated that given average climate conditions a dam can be sufficiently robust to last centuries. Thirdly, and longer term it is demonstrated that the tailings can be contained if (a) maintenance is conducted to increase the dam wall height over time or (b) a more robust dam wall is constructed to manage extreme events. However, erosion and infill will continue to reduce the integrity of any structure over time. Therefore, it is highly likely that tailings dams will require continued monitoring and maintenance. The method outlined provides a new tool for assessing any tailings facility for its erosional stability.

Assessing the impact of human interventions on floods and low flows in the Wei River Basin in China using the LISFLOOD model.

Floods are extreme hydroclimatic events that threaten societies and ecosystems. The effects of these events are greatly influenced by the changes that humans have imposed on the environment. The LISFLOOD model is a physically based rainfall-runoff model that simulates the hydrological processes in a catchment. Using globally available land cover, soil, and vegetation as well as meteorological and geographical datasets as input, the LISFLOOD model has the potential to be applied worldwide, even for regions where data are lacking. This study first calibrated and validated the LISFLOOD model in the Wei River Basin in China (432,000 km2) for the years between 2000 and 2010 at 0.05° resolution with a monthly Nash-Sutcliffe model efficiency coefficient of 0.79 at the Huaxian station located at the catchment outlet. The outlets of 17 tributaries draining into the main river were then identified in order to assess the contribution of each tributary to the total runoff occurring as a result of flooding. Four categories of scenarios focusing on human interventions in the basin were created and evaluated: 1) Business as usual, 2) Additional reservoirs constructed in different catchments, 3) Land use as in 1980, and 4) Water diversion plan with a pipeline injection of a fixed daily inflow from an adjacent catchment. The results of the scenarios are presented for three strategically important cities located on the floodplain. In general, the construction of the reservoirs could have an effect on reducing peak flows and decreasing the flood return periods while increasing the low flows. The water diversion plan scenarios increased the low flow by 41 times averaged for the three cities. In conclusion, the LISFLOOD model is a sophisticated model for land and water management planning on the catchment scale for reducing the effects of flood and drought.

Uncertainty of modelled flow regime for flow-ecological assessment in Southern Europe.

Sustainable water basin management requires characterization of flow regime in river networks impacted by anthropogenic pressures. Flow regime in ungauged catchments under current, future, or natural conditions can be assessed with hydrological models. Developing hydrological models is, however, resource demanding such that decision makers might revert to models that have been developed for other purposes and are made available to them ('off-the-shelf' models). In this study, the impact of epistemic uncertainty of flow regime indicators on flow-ecological assessment was assessed at selected stations with drainage areas ranging from about 400 to almost 90,000km2 in four South European basins (Adige, Ebro, Evrotas and Sava). For each basin, at least two models were employed. Models differed in structure, data input, spatio-temporal resolution, and calibration strategy, reflecting the variety of conditions and purposes for which they were initially developed. The uncertainty of modelled flow regime was assessed by comparing the modelled hydrologic indicators of magnitude, timing, duration, frequency and rate of change to those obtained from observed flow. The results showed that modelled flow magnitude indicators at medium and high flows were generally reliable, whereas indicators for flow timing, duration, and rate of change were affected by large uncertainties, with correlation coefficients mostly below 0.50. These findings mirror uncertainty in flow regime indicators assessed with other methods, including from measured streamflow. The large indicator uncertainty may significantly affect assessment of ecological status in freshwater systems, particularly in ungauged catchments. Finally, flow-ecological assessments proved very sensitive to reference flow regime (i.e., without anthropogenic pressures). Model simulations could not adequately capture flow regime in the reference sites comprised in this study. The lack of reliable reference conditions may seriously hamper flow-ecological assessments. This study shows the pressing need for improving assessment of natural flow regime at pan-European scale.