Flood Simulation Research Articles

A globally applicable framework for compound flood hazard modeling

Abstract. Coastal river deltas are susceptible to flooding from pluvial, fluvial, and coastal flood drivers. Compound floods, which result from the co-occurrence of two or more of these drivers, typically exacerbate impacts compared to floods from a single driver. While several global flood models have been developed, these do not account for compound flooding. Local-scale compound flood models provide state-of-the-art analyses but are hard to scale to other regions as these typically are based on local datasets. Hence, there is a need for globally applicable compound flood hazard modeling. We develop, validate, and apply a framework for compound flood hazard modeling that accounts for interactions between all drivers. It consists of the high-resolution 2D hydrodynamic Super-Fast INundation of CoastS (SFINCS) model, which is automatically set up from global datasets and coupled with a global hydrodynamic river routing model and a global surge and tide model. To test the framework, we simulate two historical compound flood events, Tropical Cyclone Idai and Tropical Cyclone Eloise in the Sofala province of Mozambique, and compare the simulated flood extents to satellite-derived extents on multiple days for both events. Compared to the global CaMa-Flood model, the globally applicable model generally performs better in terms of the critical success index (−0.01–0.09) and hit rate (0.11–0.22) but worse in terms of the false-alarm ratio (0.04–0.14). Furthermore, the simulated flood depth maps are more realistic due to better floodplain connectivity and provide a more comprehensive picture as direct coastal flooding and pluvial flooding are simulated. Using the new framework, we determine the dominant flood drivers and transition zones between flood drivers. These vary significantly between both events because of differences in the magnitude of and time lag between the flood drivers. We argue that a wide range of plausible events should be investigated to obtain a robust understanding of compound flood interactions, which is important to understand for flood adaptation, preparedness, and response. As the model setup and coupling is automated, reproducible, and globally applicable, the presented framework is a promising step forward towards large-scale compound flood hazard modeling.

Quantitative Flood Risk Assessment in Drammenselva River, Norway

Floods are frequent natural hazards, triggering significant negative consequences for the economy every year. Their impact is expected to increase in the near future due to socio-economic development and climate change. In order to minimize the probability and magnitude of expected economic losses and compensation costs, it is essential that flood risk managers are properly informed about potential damage related to hazard features and exposure. In this paper, a flood damage estimation method was proposed for the assessment of flood risk in the Drammen River basin by using a hydraulic model, GIS, and a flood loss estimation model. Hazard variables such as flood depth, flood extent, and flood velocity were computed for the current and future climatic scenarios using the hydraulic model for flood damage assessment. To visualize the flood extent, velocity, depth, and their impact, the results of modelling are illustrated in the form of flood inundation maps produced in GIS. A flood loss estimate included buildings and other infrastructure that are major exposures in flood-prone areas. The flood damage model is formulated based on stage–damage relationships between different flood depths and land-use categories. It calculates the economic loss related to different land-use features based on the simulated flood parameter obtained from the hydraulic model from 100- to 1000-year return periods. For the case study, the results show that the highest proportion of the total damage in each repetition interval (approximately 90–92%) is expected to occur in buildings. In addition, results showed that the effects of climate change will raise the total damage from floods by 20.26%.

Downscaling the Z-R relationship and bias correction solution for flash flood assessment in a data-scarce basin, Thailand.

Weather radar is a form of alternative indirect rainfall measurement for use in mitigating flash flood hazards. It is a challenging task to obtain accurate radar rainfall data without integration with automatic rain gauge networks. This paper investigated transformation equations to convert the calibrated daily Z-R relationship to the sub-hourly scale and proposed optional schemes for downscaling the daily bias adjustment factor into 15 min resolution scale to produce a high-resolution radar rainfall product for flash flood modelling. Radar reflectivity data from three radar stations in Thailand and their corresponding daily gauge rainfall data were used in the analysis. Two bias adjustment schemes (DMFB and DS_DMFB), accounting for the temporal variation, and one spatiotemporal scheme (SPTB_IDS) were used to generate three corresponding rainfall datasets for the unified river basin simulator (URBS) model to simulate flood hydrographs in the Tubma basin, Thailand. The results showed that combining the proposed 15-min Z-R scaling equation and the SPTB_IDS produced the most reliable radar rainfall amount leading to an increase in the accuracy of flood modelling with the lowest uncertainty. This indicated that the temporal downscaling solution together with spatial interpolation technique for sub-hourly radar rainfall assessment could benefit flash flood simulation in a data-scarce basin.

Application of a Fractional Instantaneous Unit Hydrograph in the TOPMODEL: A Case Study in Chengcun Basin, China

The movement of water flow usually has history and path dependence. Fractional calculus is very suitable for describing the process with memory and hereditary properties. In this study, the order of the differential equation in the Nash confluence system was extended from integer order to fractional order. On the basis of the Laplace transform, the fractional instantaneous unit hydrograph was obtained, which was used to describe the long-term memory of the basin confluence system. Furthermore, the enhanced TOPMODEL (FTOP) model was obtained by applying the fractional instantaneous unit hydrograph as the surface runoff calculation. Taking Chengcun Basin in China as an example, the FTOP model was used to simulate the daily runoff and 22 floods from 1989 to 1996. The simulation results were compared with two original TOPMODEL models (the NTOP and ITOP models). The results show that in the daily runoff simulation, the Nash–Sutcliffe efficiency (NSE), relative error (RE), and root mean square error (RMSE) of the FTOP model were 0.82, −11.14%, and 15.25 m3/s, respectively, being slightly better than the other two TOPMODEL models. According to the hydrologic frequency curve, the measured daily runoff was divided into different flow levels. It was found that the FTOP model can improve the simulation effect of the medium-flow (frequency between 10% and 50%) and low-flow (frequency more than 50%) sections to a certain extent. In the flood simulation, the average runoff depth relative error (RDRE), peak discharge relative error (PDRE), peak occurrence time error (POTE), and NSE of 22 floods were 1.99%, 14.06%, −1.27, and 0.88, respectively, indicating that the simulation effect had been improved. Especially in NSE, the improvement was more prominent, meaning that the FTOP model can better simulate the flooding process. However, the flood peak and runoff depth simulation effect were not significantly improved. These conclusions indicate that the confluence method using the fractional instantaneous unit hydrograph as the TOPMODEL model can improve the simulation effect.

ANALYZING THE EFFECTS OF LAND USE / COVER CHANGE (LULCC) SIMULATION ON FLOODING: A CASE STUDY IN LAS NIEVES, AGUSAN DEL NORTE, MINDANAO, PHILIPPINES

Abstract. The municipality of Las Nieves, located in Agusan del Norte, Mindanao, Philippines, has experienced floods and flood-related damages in the floodplain region in recent years. Despite the best intentions of planning and implementing projects anchored in its comprehensive land use plans, recurring flooding significantly impacts its residents. Due to its growing population, changes in land cover are inevitable. The changes can result in increased overland flow and decreased infiltration rates. This study was conducted to determine and quantify the effect of changing the land use/land cover (LULC) on the flooding condition in the municipality. Base LULC map was generated using Sentinel-2 image captured in 2021. Support Vector Machine classifier was used in the classification resulting in an accuracy of 94.07%. The 2021 LULC map was the reference for predicting future LULCs for 2025 and 2029 implemented in MOLUSCE and was utilized for flood simulation. HEC-HMS and HEC-RAS were used to generate flood depth maps of different extreme rainfall scenarios. The results showed that as the rainfall event increased, the extent of affected LULC areas also increased. Based on the rainfall impact assessment results, the annual cropland was the most impacted LULC class across the various LULC classes. The class open forest is the least affected class for 2021, 2025, and 2029. However, the barren was the least affected in the scenario-based build-up increase. These assessments are especially pertinent because storm-related rains have been increasingly severe recently and will continue due to climate change. The simulations and flood mapping knowledge can inform and empower the Local Government Unit of Las Nieves. It will guide them in developing informed decision frameworks for mitigating significant land surface variabilities and adapting effective future land use plans to reduce the adverse effects of flooding.

Applications of Advanced Technologies in the Development of Urban Flood Models

Over the past 10 years, urban floods have increased in frequency because of extreme rainfall events and urbanization development. To reduce the losses caused by floods, various urban flood models have been developed to realize urban flood early warning. Using CiteSpace software’s co-citation analysis, this paper reviews the characteristics of different types of urban flood models and summarizes state-of-the-art technologies for flood model development. Artificial intelligence (AI) technology provides an innovative approach to the construction of data-driven models; nevertheless, developing an AI model coupled with flooding processes represents a worthwhile challenge. Big data (such as remote sensing, crowdsourcing geographic, and Internet of Things data), as well as spatial data management and analysis methods, provide critical data and data processing support for model construction, evaluation, and application. The further development of these models and technologies is expected to improve the accuracy and efficiency of urban flood simulations and provide support for the construction of a multi-scale distributed smart flood simulation system.

TOWN PLANNING PRACTICE OF REVIVAL OF COASTAL TERRITORIES

The world experience of revitalization of coastal areas is considered. Vivid examples of revitalization for the citizens of coastal areas are "Hafen City" in Hamburg, "VКstra Namnen" in MalmЪ, "Fjord City" in Oslo, "Canary Wharf" in London. Until the beginning of the 21st century, people were not often seen on the banks of the Yonning River in the Chinese city of Taizhou because the embankment was a concrete road without trees. A 2002 project to create a park along the river was a successful solution to flood control and stormwater management. The effectiveness of such a system was proven by the simulation of regular floods. A wooden structure was added to the natural structure, which performs a technical function, where pedestrian routes were laid. Today, the park is a popular place for residents throughout the year, because the wetland system with pontoon paths is not at risk of flooding due to rising water levels. 3.8 hectares of public space were returned to the residents of Lisbon by building the Museum of Art, Architecture and Technology, and the area on the site of the former road and car park opened access to the Tagus River. It is possible to make the embankment active and accessible by leaving concrete anti-flood structures, as was done on the boulevard along the Vistula in Warsaw. The embankment, which before the reconstruction was not a public space, has become one of the most popular promenade spots in Warsaw after 2017. Domino Park on the waterfront of New York is an example of how a comfortable public space can be built in the context of an industrial area. In many American cities, the revitalization of coastal areas is accompanied by the organization of festival market places in the style of European cities with a predominance of local small shops, unpretentious architecture and the creation of a festive atmosphere.
 It was concluded that complex projects of embankment regeneration involve the construction of new offices and housing, certain tourist attractions - concert halls, museums, walking paths, restaurants and cafes. It is emphasized that the location of the headquarters of large corporations and educational institutions on the coastal territories is an important factor in the further development of such areas. The given examples of revitalization of coastal areas in different parts of the world prove that the creation of public spaces that are optimal in terms of comfort, safety, and aesthetics is the basis of urban planning organization of coastal areas. Attracting investments in the revitalization of coastal areas contributes to solving a number of environmental issues, the aesthetic quality of the urban environment, and the humanization of society.

Ensemble rainfall–runoff and inundation simulations using 100 and 1000 member rainfalls by 4D LETKF on the Kumagawa River flooding 2020

This paper presents the 1000 ensemble flood simulations using ensemble rainfalls simulated by 4D LETKF. The number of ensemble rainfall members is large as 1000 compared to the operational rainfall products of two-digit numbers to avoid sampling errors in the three-dimensional meteorological simulation based on chaotic theory. Using the large data set, 1000 ensemble rainfall–runoff for dam catchments and high-resolution inundation simulations of large area are carried out focusing on the Kumagawa river catchment. Herewith, the comparisons were carried out with 21-member ensemble rainfalls of an operational forecast by Japan Meteorological Agency and 100-member 4D-LETKF ensemble rainfalls simulated independent of 1000-member 4D-LETKF. At the same time, the accuracy of selective 100-member ensembles out of 1000 members is investigated. As a result, although many previous research works show a large number of ensemble simulations are necessary for three-dimensional meteorological field, the number could be reduced in the catchment-average rainfall–runoff and 2.5-dimensional inundation simulations given that the rainfall prediction has a certain level of accuracy since improving the discharge prediction accuracy with lower dimension is sometimes possible by adjusting the horizontally/vertically integrated model parameters determined by topography and soil characteristics in advance against the observed rainfall. Also, the 1000 ensembles could be classified into several patterns in horizontally accumulated 2D rainfall field. Likewise, the flood flow moves toward the low elevation area and river; thus, the resultant 2.5-dimensional flood field does not show much variety as three-dimensional meteorological simulation. The paper summarizes these studies.

Bare-earth DEM generation from ArcticDEM and its use in flood simulation

Abstract. In urban areas, topography data without above-ground objects are typically preferred in wide-area flood simulation but are not yet available for many locations globally. High-resolution satellite photogrammetric DEMs, like ArcticDEM, are now emerging and could prove extremely useful for global urban flood modelling; however, approaches to generate bare-earth DEMs from them have not yet been fully investigated. In this paper, we test the use of two morphological filters (simple morphological filter – SMRF – and progressive morphological filter – PMF) to remove surface artefacts from ArcticDEM using the city of Helsinki (192 km2) as a case study. The optimal filter is selected and used to generate a bare-earth version of ArcticDEM. Using a lidar digital terrain model (DTM) as a benchmark, the elevation error and flooding simulation performance for a pluvial scenario were then evaluated at 2 and 10 m spatial resolution, respectively. The SMRF was found to be more effective at removing artefacts than PMF over a broad parameter range. For the optimal ArcticDEM-SMRF the elevation RMSE was reduced by up to 70 % over the uncorrected DEM, achieving a final value of 1.02 m. The simulated water depth error was reduced to 0.3 m, which is comparable to typical model errors using lidar DTM data. This paper indicates that the SMRF can be directly applied to generate a bare-earth version of ArcticDEM in urban environments, although caution should be exercised for areas with densely packed buildings or vegetation. The results imply that where lidar DTMs do not exist, widely available high-resolution satellite photogrammetric DEMs could be used instead.

Examine of Hybrid-discharge-prediction Model to Manage Dam by Machine Learning

Recently, as it occurs flood disasters due to climate change, it is important to operate dams. Especially, for hydroelectrical power plants of the dams, it is also necessary to predict the discharge to the dams appropriately at the phase of during usual and flood water for managing power generation and preparing for flood. However, conventional methods to predict the discharge have been developed at each phase of usual and flood water separately. In this report, we developed a hybrid-discharge-prediction model, which is composed by a state discriminator and discharge prediction models with machine learning and a flood simulator. This hybrid-discharge-prediction model can detect the state of discharge and adopt an appropriate discharge prediction model each state of discharge and prediction time. As a result, it was shown that the hybrid-discharge-prediction model can detect 7 states and predict the discharge to the dam in 5 hours at the phase from usual to flood water.

ОПЫТ ПРИМЕНЕНИЯ КОНЦЕПТУАЛЬНОЙ МОДЕЛИ GR4J ДЛЯ РАСЧЕТА СТОКА РЕК БАССЕЙНА УССУРИ

The open-source low-parametric conceptual hydrological model GR4J was used for runoff simulations of 17 nested catchments of the Upper Ussuri River basin with areas ranging from 133 to 24400 km2, including the Kirovskii outlet. The data of standard hydrometeorological observations of Primorye Administration for Hydrometeorology and Environmental Monitoring were used for the model calibration and verification. Runoff simulations were performed with a daily step. The criteria commonly used in hydrological community, namely, the Nash-Sutcliffe model efficiency coefficient (NSE), coefficient of determination ( R 2), and bias (BIAS) were used to assess the modeling efficiency. According to the specified criteria, modeling results are mostly in “satisfactory” or better categories. The base of the unit hydrograph expectedly increases with the catchment area, but there were no other relationships found between the values of calibrated model parameters, the annual water content, and the main morphometric parameters of the catchments. It was shown for the studied catchments that the stabilization and the maxima of the modeling efficiency scores are reached at the catchment areas of 1200-1700 km2. The influence of representativeness of the weather station network on the modeling efficiency was demonstrated using complementary meteorological observations from experimental catchments in the Upper Ussuri basin for the simulation of the catastrophic flood in 2016 that was caused by typhoon Lionrock.

Modelling daily streamflow in a temporary karst river system: comparing three approaches using the SWAT model

ABSTRACT This work tests different options for simulating hydrology in basins with karst areas. The Soil and Water Assessment Tool (SWAT) model was applied to the Canale d’Aiedda (Puglia, Italy), a Mediterranean temporary karst river basin. Different basin delineations and model parameterizations were adopted that include: (i) cutoff of the karst areas (configuration A); (ii) basin set-up including the karst areas (configuration B) and (iii) model parameterization considering a bypass flow in karst sub-basins (configuration C). The model performance was satisfactory for daily streamflow for configurations B and C and good for A. A better simulated large floods. C presented the best fit for monthly streamflow from May to July. Regarding the water balance, C showed higher values of surface runoff and lower values of total water yield than A and B. Bypass flow proved to be a valid option to improve the simulation of the hydrological processes in karst areas.

Simulation of Urban Areas Exposed to Hazardous Flash Flooding Scenarios in Hail City

According to the United Nations (UN), an additional 1.35 billion people will live in cities by 2030. Well-planned measures are essential for reducing the risk of flash floods. Flash floods typically inflict more damage in densely populated areas. The province of Hail encompasses 120,000 square kilometers, or approximately 6% of the total land area of the Kingdom of Saudi Arabia. Due to its innate physiographic and geologic character, Hail city is susceptible to a wide variety of geo-environmental risks such as sand drifts, flash floods, and rock falls. The aim of this work is to evaluate the rate of urban sprawl in the Hail region using remote sensing data and to identify urban areas that would be affected by simulated worst-case flash floods. From 1984 to 2022, the global urbanization rate increased from 467 to 713% in the Hail region. This is a very high rate of expansion, which means that the number of urban areas exposed to the highest level of flood risk is rising every year. With Gridded Surface Subsurface Hydrologic Analysis (GSSHA), a wide range of hydrologic scenarios can be simulated. The data sources for the soil type, infiltration, and initial moisture were utilized to create the coverage and index maps. To generate virtual floods, we ran the GSSHA model within the Watershed Modeling System (WMS) program to create the hazard map for flash flooding. This model provides a suitable method based on open access data and remote data that can help planners in developing countries to create the risk analysis for flash flooding.

Research on Parameter Regionalization of Distributed Hydrological Model Based on Machine Learning

In the past decade, more than 300 people have died per year on average due to mountain torrents in China. Mountain torrents mostly occur in ungauged small and medium-sized catchments, so it is difficult to maintain high accuracy of flood prediction. In order to solve the problem of the low accuracy of flood simulation in the ungauged areas, this paper studies the influence of different methods on the parameter regionalization of distributed hydrological model parameters in hilly areas of Hunan Province. According to the terrain, landform, soil and land use characteristics of each catchment, we use Shortest Distance, Attribute Similarity, Support Vector Regression, Generative Adversarial Networks, Classification and Regression Tree and Random Forest methods to create parameter regionalization schemes. In total, 426 floods of 25 catchments are selected to calibrate the model parameters, and 136 floods of 8 catchments are used for verification. The results showed that the average values of the Nash–Sutcliffe coefficients of each scheme were 0.58, 0.64, 0.60, 0.66, 0.61 and 0.68, and the worst values were 0.27, 0.31, 0.25, 0.43, 0.35 and 0.59. The random forest model is the most stable solution and significantly outperforms other methods. Using the random forest model to regionalize parameters can improve the accuracy of flood simulation in ungauged areas, which is of great significance for flash flood forecasting and early warning.

Prediction of Flood Zone Areas for Frankfort, Kentucky, using Lidar Data

Flooding is a common, serious, and costly natural disaster. Kentucky is ranked second among flood-related disaster areas in the United States. Preparation of flood zone and flood hazard maps are essential to identify flood risk zones, and then to potentially reduce the impact of flooding. Light Detection and Ranging (Lidar) provides high-resolution and high-accuracy elevation data as input for flood modeling and in studying flood impacts. The objectives of this study are: (1) to use topographic elevation data for delineating flood inundation areas of the Kentucky River at Frankfort; and (2) to demonstrate a flood analysis technique using Lidar as a source of highly accurate elevation data. Lidar point cloud tiles from the Kentucky Geography Network were used to create Triangulated Irregular Networks (TIN), then used to generate water surface elevation. Results showed Lidar-based topography data accurately delineated flood inundation areas and water depth. A simulated flood at a level of 14.77 m, approximating the flood of 1978, inundated 7.85 ha within downtown Frankfort and vicinity, causing considerable physical and monetary damage. Resulting maps of flood inundation areas can be used as supplementary information for flood modeling and analysis. This information can be used in land-use management, town planning, and estimating flood insurance to minimize damage from flooding.

Floods Simulation on the Vedea River (Romania) Using Hydraulic Modeling and GIS Software: A Case Study

Extreme hydro-meteorological phenomena have become more frequent in recent years compared to the year 2000 in Europe, including Romania. Flooding occurs from heavy rainfalls favored by natural and anthropogenic factors such as the valley’s flat slope or settlements situated near the river. Ţigăneşti and Brânceni villages (from southern Romania) are no exception and have been affected by floods many times. One of these events is that from 2005, when the flow reached 676 m3/s (a value 80 times higher than the normal flow of the Vedea River) in Brȃnceni. This paper aims to present a simulation of the flood that occurred during 3–6 July 2005 and its impact on the settlements, roads, and land, using field observation (including some from 2005), GIS software (ArcGIS), software for flood simulations (HEC-RAS—Hydrologic Engineering Center River Analysis System), and flow data from the Romanian National Institute of Hydrology. Simulations were run in HEC-RAS. The obtained flooded areas imported back into GIS (Geographic Information System) were used to determine the area covered by water and the length of affected roads. The surface and number of flooded buildings were calculated using different tools from ArcMap. Results were interpreted, commented on, and compared with data and maps provided by the Romanian Water National Administration. The simulation shows that the villages would be protected from the flood by building a levee along the Vedea River. Significant losses can be prevented, and money can be saved.

Himalayan hazard cascades – modern and medieval outburst floods in Pokhara, Nepal

AbstractIn May 2012, a sediment‐laden flood along the Seti Khola (= river) caused 72 fatalities and widespread devastation for > 40 km in Pokhara, Nepal's second largest city. The flood was the terminal phase of a hazard cascade that likely began with a major rock‐slope collapse in the Annapurna Massif upstream, followed by intermittent ponding of meltwater and subsequent outburst flooding. Similar hazard cascades have been reported in other mountain belts, but peak discharges for these events have rarely been quantified. We use two hydrodynamic models to simulate the extent and geomorphic impacts of the 2012 flood and attempt to reconstruct the likely water discharge linked to even larger medieval sediment pulses. The latter are reported to have deposited several cubic kilometres of sediment in the Pokhara Valley. The process behind these sediment pulses is debated. We traced evidence of aggradation along the Seti Khola during field surveys and from RapidEye satellite images. We use two steady‐state flood models, HEC‐RAS and ANUGA, and high‐resolution topographic data, to constrain the initial flood discharge with the lowest mismatch between observed and predicted flood extents. We explore the physically plausible range of simplified flood scenarios, from meteorological (1000 m3 s−1) to cataclysmic outburst floods (600,000 m3 s−1). We find that the 2012 flood most likely had a peak discharge of 3700 m3 s−1 in the upper Seti Khola and attenuated to 500 m3 s−1 when arriving in Pokhara city. Simulations of larger outburst floods produce extensive backwater effects in tributary valleys that match with the locations of upstream‐dipping medieval‐age slackwater sediments in several tributaries of the Seti Khola. Our findings are consistent with the notion that the medieval sediment pulses were linked to outburst floods with peak discharges of >50,000 m3 s−1, though discharge may have been an order of magnitude higher.

Hydrologic evaluation of the global precipitation measurement mission over the U.S.: Flood peak discharge and duration

In recent years, a great amount of research has been done towards evaluating precipitation data generated by satellites, but less has focused on how these estimates and their uncertainties manifest further into the water cycle. In this study, ten years of satellite-based and ground-based radar data are used as forcings for a distributed hydrologic model across the Continental United States. They are compared using a methodology designed to assess the flood signals and characteristics generated by the model. By focusing on how well the model reproduces flood characteristics rather than fits traditional bulk statistics, this research provides robust insights into satellite precipitation deficiencies. It is found that satellite data has greater success at resolving lower magnitude flood events while tending to generate floods of longer durations. Additionally, flood managers should note that satellites tend to generate floods that characteristically both begin earlier and end later than the ground radar reference. Subsequent research is recommended into other satellite data products in order to better understand these discrepancies and mitigate or plan for them in the future. Plain Language SummaryIn recent years, satellites have been increasingly used to provide valuable insights on rainfall across the globe, especially in locations where radars are unable to be installed on the ground. By nature, however, the rainfall data provided by satellites has uncertainties. Large amounts of research has gone into the difference in accuracy between satellite radar and ground references, but less has focused on how these differences impact flood simulations, respectively. In this study it was found that the rainfall data from satellites tends to predict floods that are more severe (have higher magnitudes), last longer (have longer durations), and have different timings (start earlier and end later) than the reference. It is also found that satellites struggle with predictions involving extreme rainfall values. Further research is suggested to better understand these issues, as well as compare against other forms of satellite rainfall data.

Flood Predictability of One-Way and Two-Way WRF Nesting Coupled Hydrometeorological Flow Simulations in a Transboundary Chenab River Basin, Pakistan

Remote sensing precipitation or precipitation from numerical weather prediction (NWP) is considered to be the best substitute for in situ ground observations for flood simulations in transboundary, data-scarce catchments. This research was aimed to evaluate the possibility of using a combination of a satellite precipitation product and NWP precipitation for better flood forecasting in the transboundary Chenab River Basin (CRB) in Pakistan. The gauge-calibrated satellite precipitation product, i.e., Global Satellite Mapping of Precipitation (GSMaP_Gauge), was selected to calibrate the Integrated Flood Analysis System (IFAS) model for the 2016 flood event in the Chenab River at the Marala Barrage gauging site in Pakistan. Precipitation from the Global Forecast System (GFS) NWP, with nine different lead times up to 4 days, was used in the calibrated IFAS model to predict the flood hydrograph in the Chenab River. The hydrologic simulations, with global GFS forecasts, were unable to predict the flood peak for all lead times. Then, the Weather Research and Forecasting (WRF) model was used to downscale the precipitation forecasts with one-way and two-way nesting approaches. In the WRF model, the CRB was centered in two domains of 25 km and 5 km resolutions. The downscaled precipitation forecasts were subsequently supplied to the IFAS model, and the predicted simulations were compared to obtain the optimal flood peak simulation in the Chenab River. It was found in this study that the simulated hydrographs, at different lead times, from the precipitation of two-way WRF nesting exhibited superior performance with the highest R2 and Nash–Sutcliffe efficiency (NSE) and the lowest percent bias (PBIAS) compared with one-way nesting. Moreover, it was concluded that the combination of GFS forecast and two-way WRF nesting can provide high-quality precipitation prediction to simulate flood hydrographs with a remarkable lead time of 96 h when applying coupled hydrometeorological flow simulation.

Model-Based Mechanism Analysis of “7.20” Flash Flood Disaster in Wangzongdian River Basin

With digital information technology based on limited data, disaster simulation review is an important guideline for analyzing disaster mechanisms, planning post-disaster reconstruction, and improving defense capability. Taking the “7.20” flash flood in the Wangzongdian river basin as a research area, a hydrological-hydrodynamic model was established using limited measured data. The results showed that the extreme rainstorm caused flooding in mountainous areas and the collapse of subgrade water damming, and the high-level flood quickly flowed into Wangzongdian Village in a short distance, which was the main cause of this serious disaster. Considering the collapse due to the congestion of the upstream bridge, the simulated flood flow in Wangzongdian Village reached 782 m3/s, which was basically consistent with the post-disaster survey results, with a relative error of only +8%. The modeling strategy proposed in this paper is applicable in the review of sudden heavy rainstorms and flash floods and can provide technical guidance for future flash flood simulation review analysis in other areas.