Flood Peak Research Articles

Analisis Kapasitas Efektif Tampungan Kali Pancir

Kali Pancir is one of the rivers in Jombang Regency which often experiences flooding. The cause was heavy rain and the critical condition of the embankments. In this research, flood discharge analysis was carried out using synthetic hydrographs and hydraulic modeling for river flow. The data needed is rain data from rain stations around the Pancir River and river profiles. The method used to calculate flood discharge is the Nakayasu Synthetic Unit Hydrograph for 6 different return periods. Simulation using HEC-RAS software is used to simulate flow profiles to determine river cross-sections that are critical to flooding. Based on the results of the calculation analysis, it was found that the peak flood discharge occurred at 3.5 hours. The greater the return period, the greater the river cross section that experiences overflow. At a return period of 1.01 years, the critical cross section starts at STA 45, while at a return period of 50 years, the critical cross section occurs starting at STA 6.

A new scenario free procedure to determine flood peak changes in the Harz Mountains in response to climate change projections

Study areaSix catchments of different hydrological characteristics in the Harz Mountains, Germany. Study focusA new scenario free method for determining changes of flood peaks considering climate change is developed. Compared to existing methods, it accounts for heavy rainfall changes by a newly developed factor with two seasons and establishes a numerical relationship to a greater number of relevant climate predictors. For easier application, it is based on frequently available daily measurements. A functional test of the factor for heavy rainfall changes and its adjustment algorithm for the precipitation time series is performed. Using a regional AR5 ensemble, for the first time the error and the uncertainty of a new scenario free method are estimated and compared with an existing method. The new method is applied in the Harz Mountains, where the sensitivity of the region to different climate predictors is investigated. New hydrological insights for the regionThe adjusting algorithm for precipitation is able to adjust the time series while maintaining mass balance. The new method has a lower error than the reference method, with better matches of changes in the median of the climate ensemble as well as the most ensemble members. In general, the uncertainty of the seasonal results is below the climate uncertainty of the AR5 ensemble. Regarding future flood peaks, the regional catchments are most sensitive to mean precipitation changes, followed by heavy rainfall changes especially in the winter season. Mean temperature changes are of minor significance, but the catchments characteristics are important. The new method can be recommended for assessments of climate change impacts on floods in low to average mountain regions in Germany and Europe.

Predicting peak inundation depths with a physics informed machine learning model

Timely, accurate, and reliable information is essential for decision-makers, emergency managers, and infrastructure operators during flood events. This study demonstrates that a proposed machine learning model, MaxFloodCast, trained on physics-based hydrodynamic simulations in Harris County, offers efficient and interpretable flood inundation depth predictions. Achieving an average R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} of 0.949 and a Root Mean Square Error of 0.61 ft (0.19 m) on unseen data, it proves reliable in forecasting peak flood inundation depths. Validated against Hurricane Harvey and Tropical Storm Imelda, MaxFloodCast shows the potential in supporting near-time floodplain management and emergency operations. The model’s interpretability aids decision-makers in offering critical information to inform flood mitigation strategies, to prioritize areas with critical facilities and to examine how rainfall in other watersheds influences flood exposure in one area. The MaxFloodCast model enables accurate and interpretable inundation depth predictions while significantly reducing computational time, thereby supporting emergency response efforts and flood risk management more effectively.

Evaluation of GPM IMERG Satellite Precipitation Products in Event-Based Flood Modeling over the Sunshui River Basin in Southwestern China

This study evaluates the applicability of hourly Global Precipitation Measurement Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) data for event-based flood modeling in the Sunshui River Basin, southwestern China, using the hydrologic modeling system (HEC-HMS) model. The accuracies of IMERG V6, IMERG V7, and the corrected IMERG V7 satellite precipitation products (SPPs) were assessed against ground rainfall observations. The performance of flood modeling based on the original and the corrected SPPs was then evaluated and compared. In addition, the ability of different numbers (one–eight) of ground stations to correct IMERG V7 data for flood modeling was investigated. The results indicate that IMERG V6 data generally underestimate the actual rainfall of the study area, while IMERG V7 and the corrected IMERG V7 data using the geographical discrepancy analysis (GDA) method overestimate rainfall. The corrected IMERG V7 data performed best in capturing the actual rainfall events, followed by IMERG V7 and IMERG V6 data, respectively. The IMERG V7-generated flood hydrographs exhibited the same trend as those of the measured data, yet the former generally overestimated the flood peak due to its overestimation of rainfall. The corrected IMERG V7 data led to superior event-based flood modeling performance compared to the other datasets. Furthermore, when the number of ground stations used to correct the IMERG V7 data in the study area was greater than or equal to four, the flood modeling performance was satisfactory. The results confirm the applicability of IMERG V7 data for fine time scales in event-based flood modeling and reveal that using the GDA method to correct SPPs can greatly enhance the accuracy of flood modeling. This study can act as a basis for flood research in data-scarce areas.

Evaluating the comprehensive flood impact assessment on the head reach of the Chiniot dam project

The major sources of surface water available to Pakistan are perennial flows of the River Indus and its tributaries i.e. Jhelum and Chenab Rivers. The creation of a reservoir of 0.9 MAF Capacity will cause the water level to rise and reduction in flow velocity on the Chenab River upstream of Chiniot Bridge. Consequently, it will cause sediment deposition which would further increase the water level due to the raised river bed level by deposited sediments. To study this storage, the reservoir has been modelled in HEC-RAS software to assess pre-dam and post-dam scenarios to simulate different conditions of river flow for water surface profile and sediment delta modelling of the reservoir reach, using historical daily water and sediment flows (1985–2022). The results of flood routing showed that the reservoir has attenuated the peak flood volume with safe releases at downstream side with proper operation of spillway gates. The average sediment inflow in the reservoir is worked out to be 38.4 metric tons. The results of sediment modelling and reservoir flood management indicated that after every 05 years of normal reservoir operation, reservoir flushing is recommended. Without sediment flushing, the river bed would be further raised beyond 189.0 m due to incoming sediment, resulting in further rise of water level up to 192.8 m which is the existing crest level of upstream training works of Talibwala Motorway Bridge.

Wpływ zmian klimatu na środowisko zabudowane

We are currently in the period of an intensive climate change, resulting from changes in the heat balance of the earth’s surface and causing an increase in the temperature of the lower troposphere levels. According to the latest IPCC report of 2021, it is human activity that has indisputably caused the increase in atmospheric concentrations of greenhouse gases, responsible for this process. The consequences of climate change under Polish conditions, apart from a rise in temperature especially in the spring and winter months, are also changes in the amount and distribution of precipitation totals. A slight increase in precipitation totals is observed, however heavy rainfall is significantly more frequent, interspersed with periods of droughts and heatwaves. Ground frost-free periods are prolonged, and a negative trend in the duration and thickness of snow cover is recorded in most parts of the country. Although extreme phenomena occurring in Poland are permanently inscribed in its climatic conditions, the threat of strong winds has been increasing in recent years, and the intense precipitation that often accompanies them is the cause of peak discharges and flooding. Forecasts for progressive climate change are not optimistic, either on a global scale or for the country in question. The article examines the impact of climate change on the design, construction and maintenance of engineering structures globally and for Poland. Changes in design standards, selected examples of disasters and solutions to adapt and build resilience to climate change have been analysed. For most building disasters, climatic factors were the direct cause of the disaster, although in the course of the analysis it has usually turned out that the disasters exposed human errors in the design, construction and, to a lesser extent, the improper maintenance of engineering structures. However, there is an increasing number of new approaches to creating a climate change resilient built environment, including the latest one, which proposes to use the grey infrastructure of cities to build resilience to climate change.

Прогнозирование характеристик половодья 2024 года на реках Ишим, Тобол и Урал

The Hydrometeorological Center of Russia has developed a system of methods for forecasting spring flood characteristics, which was implemented for eight stations on the Ishim, Tobol, and Ural rivers in 2024. Specific features of the methods are the simplicity of their obtaining and implementation for a particular river reach and a possibility of the fast correction of forecasts as the current information is available. The peak water level at the station was predicted based on its dependence on the peak water level at the upstream station. The date of the flood peak was predicted with account of the data of the peak at the upstream station and probable values of the travel time from the upstream gauge to the predicted downstream one. The date of the water level drop to the level of a severe event was forecasted taking into account the height and date of the flood peak using the calculated recession curve obtained by the statistical analysis of flood recessions in the previous years. The date of the water level drop to the level of an adverse event was predicted by extrapolating the flood recession observed during 10–15 days after the flood peak. The mean forecast lead time is 9 days. The forecasts were issued in a deterministic and probabilistic form and gave quite satisfactory results, which were used in the organization and implementation of measures to protect the population and economic facilities from the floods observed in 2024. The efficiency of the developed methods allows recommending them for use in predicting flood characteristics in various regions of Russia.

A combined qualitative–quantitative method for adaptive configuration of urban flood mitigation measure

Inappropriately selecting flood mitigation measures can lead to ineffective governance and wastage of social resources. To address this issue, this research proposes a framework for adaptive configuration of urban flood mitigation measure. This framework aims to achieve a two-sided matching between the disaster reduction capacity of measures and the specific demands of the construction site. The framework was employed to the urban flood mitigation strategy in Longkungou drainage system, an optimal allocation scheme for addressing urban flood disasters under the design rainfall with 50-year return period was determined. The results revealed that green mitigation measures are primarily concentrated in the drainage system within the upstream catchment. Stormwater pumping stations are mainly situated at the drainage outlets and adjacent river channels in the study area. By reducing the water level in the river channel, these stations effectively prevent and manage flood volume resulting from river overflow. As a result of the flood mitigation strategy, the flood volume in the Longkungou drainage system was reduced by 591,186 m3, representing a peak total flood volume reduction rate of 42.44%. The severity of flood disasters in each sub-catchment has significantly diminished, indicating that the flood mitigation strategy has successfully achieved the desired improvement.

Quantification of the flood mitigation ecosystem service by coupling hydrological and hydrodynamic models

Flood mitigation service provides crucial information for reducing flood disasters and assessing ecosystem capacities by quantifying how much damage is reduced and how many benefiting areas are protected during flood events. However, there remains a gap in the full-process quantification, which results in less precise simulation outcomes. In this study, we introduce a novel methodology to accurately quantify the flood mitigation service of ecosystems by coupling hydrological and hydrodynamic models. We utilized the Hydrological Simulation Program-Fortran (HSPF) model to simulate peak flow and flood volume and then used these data as inputs for the Environmental Fluid Dynamics Code (EFDC) hydrodynamic model to simulate the spatial extent and depth of flood inundation. The contribution and capacity of the ecosystem are reflected through the reduction in peak flow, flood volume, and inundation areas. We used the Nandu Basin flood event in October 2010 as a case study to illustrate our approach, comparing our assessment results with those simulated by the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and the Height Above Nearest Drainage (HAND) model. The results demonstrate that coupling the HSPF model (R2 = 0.93) with the EFDC model (overlap ratio = 83.71 %) allows for precise quantification of flood mitigation service. The process-based hydrological and hydrodynamic models show a high correlation with the simpler and faster InVEST and HAND model simulations, with the full-process models reducing relative errors by 7.66 % and 5.25 % respectively. This study offers a promising approach for accurately and comprehensively assessing flood mitigation ecosystem service and provides a basis for model selection.

Flash Flood Simulation for Hilly Reservoirs Considering Upstream Reservoirs—A Case Study of Moushan Reservoir

With the advancement of society and the impact of various factors such as climate change, surface conditions, and human activities, there has been a significant increase in the frequency of extreme rainfall events, leading to substantial losses from flood disasters. The presence of numerous small and medium-sized water conservancy projects in the basin plays a crucial role in influencing runoff production and rainwater confluence. However, due to the lack of extensive historical hydrological data for simulation purposes, it is challenging to accurately predict floods in the basin. Therefore, there is a growing emphasis on flood simulation and forecasting that takes into account the influence of upstream water projects. Moushan Reservoir basin is located in a hilly area of an arid and semi-arid region in the north of China. Flooding has the characteristics of sudden strong, short confluence time, steep rise, and steep fall, especially floods caused by extreme weather events, which have a high frequency and a wide range of hazards, and has become one of the most threatening natural disasters to human life and property safety. There are many small and medium-sized reservoirs in this basin, which have a significant influence on the accuracy of flood prediction. Therefore, taking Moushan Reservoir as an example, this paper puts forward a flash flood simulation method for reservoirs in hilly areas, considering upstream reservoirs, which can better solve the problem of flood simulation accuracy. Using the virtual aggregation method, the 3 medium-sized reservoirs and 93 small upstream reservoirs are summarized into 7 aggregated reservoirs. Then, we construct the hydrological model combining two method sets with different runoff generation and confluence mechanisms. Finally, after model calibration and verification, the results of different methods are analyzed in terms of peak discharge error, runoff depth error, difference in peak time, and certainty coefficient. The results indicate that the flooding processes simulated by the proposed model are in line with the observed ones. The errors of flood peak and runoff depth are in the ranges of 2.3% to 15% and 0.1% to 19.6%, respectively, meeting the requirements of Class B accuracy of the “Water Forecast Code”. Method set 1 demonstrates a better simulation of floods with an average flood peak error of 5.63%. All these findings illustrate that the developed model, utilizing aggregate reservoirs and dynamic parameters to reflect regulation and storage functions, can effectively capture the impact of small water conservancy projects on confluence. This approach addresses challenges in simulating floods caused by small and medium-sized reservoirs, facilitating basin-wide flood prediction.

Assessment of the Impact of Land Use and Land Cover Change on the Surface Runoff of Hadejia River System, Kano, Nigeria

Land use and land cover changes, mostly driven by anthropogenic activities, affect the processes of the water cycle. The impacts of land use (LU) and land cover (LC) changes between 1995 and 2015 on the surface runoff of the Hadejia River System (HRS) were investigated. The LULC changes obtained through re-classifications of selected Landsat satellite images and their effects on runoff peak discharges and volumes were assessed using selected hydrologic models for runoff generation and routing available within the HEC-HMS. Physically-based parameters of the models were estimated from the LULC change maps together with a digital elevation model and soil datasets of the basin. The simulated flows from the 90 sub-catchments were routed to the basin outlet afterwards to obtain the accrued effects in the entire river basin. Model results obtained generally revealed significant and varying increases in the runoff peak discharges and volumes within some sub-basins in the whole catchment, though the change was not significant at the basin outlet. In the sub-catchments within Kano and Jigawa states, increase between 15-20% and 10-15% were observed in the peak discharge respectively. These are the areas with the highest increase in agricultural activities and urbanization within the whole catchment. In the entire basin, however, the flood peak discharges and volumes increased by at least 3.57% and 8.18% respectively. From these results, the study concludes that changes were more pronounced in Kano and Jigawa states due to the increase of urbanization and farming activities in those areas, leading to reduction of infiltration and hence, increase in surface runoff. The study successfully outlined the hydrological consequences of land cover changes, emphasizing the importance of sustainable land use and catchment management strategies. Hence, integration of remote sensing, GIS, and the hydrological model (HEC-HMS) can be used to solve hydrological problems in a river basin.

An extreme forecast index-driven runoff prediction approach using stacking ensemble learning

Runoff prediction plays a crucial role in hydropower generation and flood prevention, enhancing prediction accuracy in hydrology. This study proposes an extreme forecast index (EFI)-driven runoff prediction approach using stacking ensemble learning to improve prediction performance. EFI is introduced as an input into four machine learning models (Support Vector Regression, Multi-layer Perceptron, Gradient Boosting Decision Tree, and Ridge Regression) for runoff prediction with lead times of 24 h, 48 h, and 72 h. The stacking ensemble learning framework comprises four base-models and a meta-model, and model hyperparameters are re-optimized using the particle swarm optimization algorithm. The approach focuses on predicting the inflow processes of the Geheyan Reservoir in the Qing River using EFI and runoff time series. Results demonstrate that the EFI-runoff simulation can improve runoff prediction capability due to EFI’s higher sensitivity to observed runoff, and the proposed stacking ensemble learning model outperforms the individual model in predicting runoff with all lead times. The relative flood peak error, mean relative error, root mean square error, and Nash-Sutcliffe efficiency coefficient of the model’s one-day-ahead prediction are 7.987%, 22.421%, 632.871 m3/s, and 0.771, respectively. Therefore, this approach can be effectively utilized to improve accuracy in short-term runoff prediction applications.

Investigating the impacts of climate change on hydroclimatic extremes in the Tar-Pamlico River basin, North Carolina

Evaluating the forthcoming impacts of climate change is important for formulating efficient and flexible approaches to water resource management. General Circulation Models (GCMs) are primary tools that enable scientists to study both past and potential future climate changes, as well as their impacts on policies and actions. In this work, we quantify the future projected impacts of hydroclimatic extremes on the coastal, risk-prone Tar-Pamlico River basin in North Carolina using GCMs from the Sixth International Coupled Model Intercomparison Project (CMIP6). These models incorporate projected future societal development scenarios (Shared Socioeconomic Pathways, SSPs) as defined in the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Specifically, we have utilized historical residential expansion data, the Soil and Water Assessment Tool Plus (SWAT+), the Standardized Precipitation Index (SPI), and the Interquartile Range (IQR) method for analyzing extremes from 2024 to 2100. Our findings include: (1) a trend toward wetter conditions is identified with an increase in flood events toward 2100; (2) projected increases in the severity of flood peaks are found, quantified by a rise of 21% compared to the 2000–2020 period; (3) downstream regions are forecast to experience severe droughts up to 2044; and (4) low-lying and coastal regions are found as particularly susceptible to higher flood peaks and more frequent drought events between 2045 and 2100. This work provides valuable insights into the anticipated shifts in natural disaster patterns and supports decision-makers and authorities in promoting adaptive strategies and sustainable policies to address challenges posed by future climate changes in the Tar-Pamlico region and throughout the state of North Carolina, United States.

Comparative Performance of Non-Stationary Intensity-Duration-Frequency (NS-IDF) Models for Selected Gauge Stations in the Niger Delta

This study focused on a comparative analysis of developed Non-stationary rainfall intensity-duration-frequency (NS-IDF) models with existing IDF models for the Niger Delta with Uyo, Benin, Port Harcourt, and Warri as selected stations. Applied was 24-hourly (daily) annual maximum series (AMS) data with downscaling models also used to downscale the time series data. Uyo and Benin had statistically significant trends with Port Harcourt and Warri showing mild trends. The best linear behavioural parameter extremes model integrating time as co-variate was selected for each station for computation of the General extreme value (GEV) distribution fitted NS-IDF models with the open-access R-studio software. The Non-stationary intensity values were higher than computed stationary ones, with significant differences at a 5% significance level for a given return period. For example, for 2 and 10-year return periods for 1-hour storms the differences of 22.71% & 17.0%, 15.24% & 9.40%, 5.09% & 4.04%, and 6.15% & 4.43% for Uyo, Benin, Port Harcourt and Warri, respectively were recorded. While, the percentage difference in intensities was very high between the Non-stationary and existing, Stationary IDF models. For a return period of 2 years at 15 and 60 min durations, the differences were 97.9 & 3.2%, 240.6 & 67.2%, 78.2 & 0%, and 121.6 & 50.1% for Uyo, Benin, Port Harcourt and Warri, respectively. Such extreme value difference in intensity underestimates the peak flood and exagerate the flood risk. The general NS-IDF calibrated models showed very good match and fit with R<sup>2</sup> = 0.977, 0.999, 0.999 & 0.999, and MSE accuracy = 193.5, 1.011, 4.1552 & 1.011 for Uyo, Benin, Port Harcourt, and Warri, respectively. Erosion and flood control facilities in the Niger Delta require upgrading using the calibrated general NS-IDF models to accommodate extra-value rainfall intensities due to climate change.

Seasonal and interannual variations of suspended particulate matter in a West-African lagoon (Nokoué lagoon, Benin): Impact of rivers and wind

This study, based on five years of monthly in situ data collected from 2018 to 2022, examines the seasonal and interannual fluctuations of suspended particulate matter (SPM) concentration in Nokoué Lagoon, Benin. Seasonally, SPM exhibits significant variations primarily influenced by changes in river discharge. During low-flow periods (December to May), SPM concentrations are relatively low (<15 mg L-1) throughout the lagoon. During this time, slight temporal variations are correlated with wind energy and likely associated with wind-induced resuspension of sediments. This is confirmed by slightly higher concentrations of SPM in the bottom layers compared to the surface. Resuspension appears to be lower in the west than in the east, likely due to the increased presence of acadjas (brush parks) in the west, reducing fetch and wind intensity, thus decreasing resuspension. At the onset of the river flood period (July–August), associated with the West African monsoon, increased river flow generates a significant turbid plume extending from the northeast of the lagoon to the Cotonou channel, connecting the lagoon to the Atlantic Ocean. SPM levels then increase considerably (>100 mg L−1), with a pronounced SPM gradient from the western to eastern regions of the lagoon. The less dense freshwater laden with sediment from the rivers flows over the denser saline water of the lagoon, leading to slightly higher SPM concentrations in the surface layers. Between September and November, SPM concentration gradually decreases as river flows reach their peak values. Thus, on a seasonal scale, the relationships between SPM and river discharge show a temporal lag, resulting in a clockwise hysteresis cycle. This is explained by the early mobilization of fine sediments during rising river flows, followed by reduced sediment availability and dilution effects as the flood peaks. On an interannual scale, SPM variations are relatively low with slight temporal shifts observed in the formation and expansion of the turbid plume and peak SPM levels. The total SPM mass in the lagoon ranges from 0.2 to 0.3 × 104 tonnes during low-flow periods to 20-30 × 104 tonnes at the onset of flooding. We also discuss uncertainties associated with SPM determination, estimated at approximately 5–15%. This study leverages a unique database in West Africa and provides valuable insights into the hydro-sedimentary dynamics of Nokoué Lagoon.

Comprehensive analysis of the impacts of climate and land use changes on flood damage (Case study: AqQala, Iran)

ABSTRACT Population growth, community development, and waste generation impact climate, land use, water resources, and urban heat island effect. This research assesses the impacts of climate and land use changes on flood damage for different return periods (RPs) in AqQala area, Iran. Climate change anticipation is conducted using the CMCC-ESM2 model under shared socioeconomic pathways (SSPs) 126, 370, and 585 of the Intergovernmental Panel on Climate Change (IPCC) sixth assessment report (Coupled Model Intercomparison Project Phase 6 – CMIP6). The LARS-WG model is used to downscale climatic information, and land use mapping is processed through Landsat satellite images in ENVI 5.3. The Markov chain method is implemented for 2050 and 2080 via TerrSet. Hydrographs and inundation maps are generated by the Autodesk Storm and Sanitary Analysis (SSA) and Hydrologic Engineering Center's River Analysis System (HEC-RAS) models. Results show an increase in average annual precipitation (up to 46%) and temperatures (up to 3.39 °C) under different SSP scenarios until 2080. The result indicates that land use changes are more significant than climate change. Peak flood discharge and damages could escalate by 38 and 29%, respectively, in the worst-case scenario. Eco-friendly design and implementation are crucial for improving the situation.

Quantifying the Impacts of Land-Cover Change on the Hydrologic Response to Hurricane Ida in the Lower Mississippi River Basin

Abstract The Lower Mississippi River basin (LMRB) has experienced significant changes in land cover and is one of the most vulnerable regions to hurricanes in the United States. Here, we study the impacts of land-cover change on the hydrologic response to Hurricane Ida in LMRB. By using an integrated surface–subsurface hydrologic model, Energy Exascale Earth System Model (E3SM) Land Model coupled with the three-dimensional ParFlow subsurface flow model (ELM-ParFlow), we simulate the effects of land-cover change on the flood volume and peak timing induced by rainfall from Hurricane Ida. The results show that land-cover changes from 1850 to 2015, which resulted in a smoother surface and less vegetation, exacerbated both flood peak time and volume induced by Hurricane Ida. The effects of land-cover changes can be decomposed into two mechanisms: a smoother surface routes more water faster to a watershed outlet and less vegetation allows more water to contribute to surface runoff. By comparing scenarios in which the two mechanisms were isolated, we found that changes in soil moisture due to vegetation cover change have more dominant effects on floods in the southern part and changes in Manning’s coefficient have the largest effect on floods in the northern part of the LMRB. The study provides important insights into the complex relationship between land-use, land-cover, and hydrologic processes in coastal regions.

Quantifying the Regulation Capacity of the Three Gorges Reservoir on Extreme Hydrological Events and Its Impact on Flow Regime in a Changing Climate

AbstractThe Three Gorges Reservoir (TGR) is one of the world's largest hydropower projects and plays an important role in water resources management in the Yangtze River. For the sake of disaster prevention and catchment management, it is crucial to understand the regulation capacity of the TGR on extreme hydrological events and its impact on flow regime in a changing climate. This study obtains historical inflows of the TGR from 1961 to 2019 and uses a distributed hydrological model to simulate the future inflows from 2021 to 2070. These data are adopted to drive a machine learning‐based TGR operation model to obtain the simulated outflow with TGR operation, which are then compared with the natural flow without TGR operation to assess the impact of TGR. The results indicate that the average flood peaks and total flooding days in the historical period could have been reduced by 29.2% and 53.4% with the operation of TGR. The relative declines in drought indicators including duration and intensity were generally less than 10%. Faced with more severe extreme hydrological events in the future, the TGR is still expected to alleviate floods and droughts, but cannot bring them down to historical levels. The impact of TGR operation on flow regime will also evolve in a changing climate, potentially altering the habitats of river ecosystems. This study proposes feasible methods for simulating the operation of large reservoirs and quantifying the impact on flow regime, and provides insights for integrated watershed management in the upper Yangtze River basin.

Modeling arboreal vegetation-induced friction and its influence on river flooding using a two-dimensional model

Extensive research on vegetation flow resistance at the laboratory scale has yielded numerous formulations for determining vegetation friction coefficients. However, implementing and validating these equations at the river scale is often complex due to the lack of data on vegetation features and flow characteristics during floods, resulting in a limited number of field application studies. In this research, we use the Telemac-2D hydrodynamic model in a new case study to evaluate the performance of five vegetation friction formulations applied to arboreal vegetation. Employing vegetation data acquired through field surveys and LiDAR, along with flow data collected during one of the highest peak floods recorded on the Piave River, we built a two-dimensional (2D) river model. This model considers existing arboreal vegetation and terrain conditions to compare observed water surface elevations (WSE) during the flood with those simulated using vegetation friction equations. Our findings reveal that all tested equations perform similarly in all simulations. While these equations overestimated WSE throughout most of the flood, they accurately predicted levels at the highest peak and its proximity. Additionally, we explore several vegetation scenarios, varying density and percentage of area covered by arboreal vegetation, to assess their effect on water levels and flood risk.

Estimation of peak discharge and flood volume in ungauged basins using HydroCAD software

Estimation of peak discharge and flood volume in ungauged basins using HydroCAD software