River Basin Flood Research Articles

Drivers of Widespread Floods in Indian River Basins

Abstract Widespread floods affecting multiple subbasins in a river basin have implications for infrastructure, agriculture, environment, and groundwater recharge. However, the crucial linkage between widespread floods and their drivers remains unexplored for Indian subcontinental river basins. Here, we examine the occurrence and drivers of widespread flooding in seven Indian subcontinental river basins during the observed climate (1959–2020). The peninsular river basins have a high probability of widespread flooding, compared to the transboundary basins of the Ganga and Brahmaputra. Favorable antecedent baseflow and soil moisture conditions, uniform precipitation distribution, and precipitation seasonality determine the probability of widespread floods in Indian river basins. The widespread floods are associated with large atmospheric circulations that cause precipitation in a large part of a river basin. Our findings highlight the prominent drivers and mechanisms of widespread floods with implications for flood mitigation in India.

Assessment of vulnerability to flood risk in the Padma River Basin using hydro-morphometric modeling and flood susceptibility mapping.

An evaluation of flood vulnerability is needed to identify flood risk locations and determine mitigation methods. This research introduces an integrated method combining hydro-morphometric modeling and flood susceptibility mapping to assess Padma River Basin's flood risk. Flood zoning, flooding classes, and resource flood risk were explicitly analyzed in this river basin study. Flood risk was calculated using GIS-based hydro-morphometric modeling. Using Horton's and Strahler's methods, drainage density, stream density, and stream order of the Padma River Basin were determined. The Padma River Basin has five sub-basins: A, B, C, D, and E, with stream densities of 0.53km-2, 0.13km-2, 0.25km-2, 0.30km-2, and 0.28km-2 and drainage densities of 0.63km-1, 0.16km-1, 0.29km-1, 0.35km-1, and 0.33km-1, respectively. Sub-basin A is the most prone to floods due to its high stream and drainage density, whereas B and C are the least susceptible. This study used elevation, TWI, slope, precipitation, NDVI, distance from road, drainage density, distance from river, LU/LC, and soil type to create a flood vulnerability map incorporating GIS and AHP with pair-wise comparison matrix (PCM). The study's flood zoning shows that the northeastern part of this basin is more likely to flood than the southwestern part due to its elevation and high-order streams. Moderate River Flooding, the region's most hazardous flood class, covers 48.19% of the flooding area, including 1078.30 km2 of agricultural land, 94.86 km2 of bare soil, 486.39 km2 of settlements, 586.42 km2 of vegetation cover, and 39.34 km2 of water bodies. The developed hydro-morphometric model, the flood susceptibility map, and the analysis of this data may be utilized to offer long-term advance alarm insight into areas potentially to be invaded by a flood catastrophe, boosting hazard mitigation and planning.

Integrated assessment of flood susceptibility and exposure rate in the lower Niger Basin, Onitsha, Southeastern Nigeria

BackgroundVarious methods have been utilized to investigate and mitigate flood occurrences, yet there is a paucity of literature on factors, such as soil compositions, that contribute to persistent flooding in river basins like the Lower Niger catchment, specifically at Onitsha. Furthermore, the study seeks to furnish essential geospatial data concerning flood vulnerability, flood risks, and exposure rates in the Lower Niger Catchment area, situated in Onitsha, southeastern Nigeria.Materials and methodsSoil samples were collected from 10 specific locations identified through GPS and ground-truthing techniques. Additionally, satellite imagery from the Landsat Enhanced Thematic Mapper (ETM +) was utilized, with supervised classification employed to extract feature classes. Analysis operations were conducted using IDRISI software, resulting in the creation of digital elevation models (DEMs), susceptibility maps, and flood-risk zones.ResultsAnalysis revealed that the predominant soil composition in the study area comprises sandy (84.8%), silt (8.1%), and clayey (7.1%) soils. Utilizing these soil characteristics alongside relevant aerial data, exposure rates were determined at various scales to delineate the most flood-vulnerable zones in the basin. It was found that certain areas, accommodating a population exceeding 79,426 across 2,926.2 ha, were particularly susceptible to flooding. Notably, major markets such as Bridgehead, Textile, and Biafra were identified as highly susceptible, with varying degrees of risk. The prevalence of sandy soil, which facilitates increased rainwater infiltration but is also prone to rapid saturation and runoff, likely contributes to the heightened susceptibility to flooding in these areas.ConclusionGeospatial analysis employing remote sensing data indicates the high susceptibility and exposure to flooding in the lower Niger River Basin around Onitsha. Urgent mitigation efforts are imperative, necessitating the establishment of zoned areas equipped with effective drainage systems to safeguard vulnerable populations.

Multi-Criteria Analysis for Assessing Flood Susceptibility in the Municipality of Conselheiro Lafaiete (MG)

Objective: To evaluate the susceptibility to flooding in the municipality of Conselheiro Lafaiete – Minas Gerais, using the Hierarchical Analytical Process method. Theoretical Framework: Floods are events that affect several Brazilian cities, where the main related factors are denoted by the morphometric characteristics of the river basin(s) that comprise them, associated with slope, hypsometry, soil typology and use and land occupation. In this sense, multi-criteria analyzes are fundamental for mapping areas of susceptibility to flooding in river basins. Method: The study proceeded by characterizing the study area, integrating the diagnosis of the drainage system, morphometric analysis of the river basin in which Conselheiro Lafaiete is integrated, and the application of the Hierarchical Analysis Method (AHP) to map areas susceptibility to flooding of the river basin. Results and Discussion: The results obtained revealed [synthesize the main results of the research]. In the discussion section, these results are contextualized in light of the theoretical framework, highlighting the implications and relationships identified. Possible discrepancies and limitations of the study are also considered in this section. Research Implications: The study indicates gaps in Conselheiro Lafaiete PMSB with regard to urban drainage and points out, through mapping, the areas of the municipality most susceptible to the occurrence of flood events. Originality/Value: The mapping of areas susceptible to flooding carried out for Conselheiro Lafaiete considered a multi-criteria approach, integrating anthropogenic factors. The product can be applied as a basic tool for public management regarding urban drainage and flood risk management in the municipality.

Ciujung river basin flood risk assessment due to climate change and its countermeasure

Ciujung River is experiencing recurrent flooding, which was caused by climate change factor and extensive land use change. Therefore, this study carried out risk assessment in this basin area. First, hydrology analysis was carried out using HEC-HMS software to obtain flood discharge hydrograph, with obtained peak discharge of 3534.3 m3/s for 25-year return period. This hydrograph was then modeled into an inundation map using HEC-RAS two-dimensional model to calculate hazard index. Meanwhile, the vulnerability and capacity index were calculate using the demographic data. Finally, the risk index was calculated by combining hazard, vulnerability, and capacity index using raster calculator on Arc-GIS. Overall, the flood risk analysis shows a medium level. The results shown that hazard index is the most contributing factor in causing a high risk index. Thus, in this study we proposed a structural approach to reduce the hazard index. An embankment with a length of 50.37 km was applied and remodeled to analyze its effectiveness. The modeling results show high effectiveness with a significant reduction in inundation area. The reduction of inundated area reached 39.6%, while the reduction of affected buildings reached 49.9%. Hopefully, the result of this study can provide theoretical support and recommendations for future development.

A Comprehensive Flood Risk Inundation Mapping and Hybrid Model for Flood Forecasting in the Panam River Basin

The forecasting of flooding plays a pivotal role in the field of hydrology and serves as an essential measure for preventing any possible flood damage. This study presents an analysis of the utilization of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to prevent floods in river basins. ANFIS represents a hybrid approach that combines elements of neural networks and fuzzy logic methodologies for the purpose of analyzing and comparing input and output data. Performance evaluation of ANFIS is based on the ratio of Discrepancy Ratio (D) along with the determination coefficient (R2), Coefficient of correlation (R), and Root Mean Square Error (RMSE). Results: The best ANFIS model was the one that gave the best results during training. The RMSE was 622.43, with a correlation coefficient of 0.93 and an R2 of 0.86. Nevertheless, its validation performance demonstrated slightly elevated RMSE figures and lower correlation and R2 values, with RMSE at 2847.99, R at 0.91, and R2 at 0.83. An Artificial Neural Network (ANN) model with three transfer functions (SIGMOID, HYPERBOLIC TANGENT, and LINEAR) is developed and evaluated based on R = correlation and MSE = mean squared error. Furthermore, the study also entails the development of a flood model for the research area, consisting of 38 cross profiles for a detailed analysis of the river’s profile and flood inundation. This analysis aims to provide precise recommendations for flood protection measures.

Projected increase in widespread riverine floods in India under a warming climate

Widespread floods simultaneously affect several subbasins in a large river basin and pose severe challenges to disaster management and flood mitigation efforts. India is highly susceptible to widespread flooding as the country receives more than 70% of annual rainfall in the four-month period from June to September. However, the potential impacts of climate change on the drivers and frequency of widespread floods in India remain unexplored. We use observations, climate model simulations, and a hydrological model to examine the drivers and changes in widespread floods under a projected future climate. The observed increase in extreme precipitation does not translate to flooding in most river basins in India due to its fragmented nature. Translation of extreme precipitation to flood depends on the catchment area. We examined the changes in the widespread floods in Indian river basins using a hydrological model and climate projections. Robust increases in intense precipitation area and frequency are projected under the warming climate. Furthermore, hydrological model simulations indicate a profound increase in widespread floods in Indian river basins under a warming climate. The increase in widespread floods in a warming climate can pose challenges for flood mitigation and management in the future. Consideration of spatial extent of extreme precipitation and catchment area can enhance flood monitoring and prediction efforts. In addition, increases in extreme precipitation may not always lead to rise in flooding.

Water Quality and Toxic Cyanobacteria in Oligohaline Estuary Beaches During the Longest Mississippi River Basin Flood Event in 2019

Water Quality and Toxic Cyanobacteria in Oligohaline Estuary Beaches During the Longest Mississippi River Basin Flood Event in 2019

Observed and projected changes in wet and dry spells for the major river basins in East Asia

AbstractSpatiotemporal changes in wet and dry spells had significantly contributed to climate‐related natural hazards in East Asia, especially in river basins undergoing warming. However, few studies have systematically analysed these changes for river basins at different latitudes. Herein, we investigated the observed and projected variations in wet and dry spells for East Asian river basins at different latitudes. We found that river basins at low latitudes (Yangtze and Pearl rivers) had obvious wet characteristics, whereas those at high latitudes (Amur and Yellow rivers) had obvious dry characteristics. In general, the river basins presented a ‘wet south but dry north’ spatial pattern. Long‐duration wet spells have been shown to be the primary contributor to total wet spells in low‐latitude river basins, while long‐duration dry spells have been found to be the primary contributor to total dry spells in high‐latitude river basins. Importantly, climate is changing from prolonged wet spells to short, intense wet spells in low‐latitude river basins, whereas the climate is changing from single, short dry spells to moderate, prolonged dry spells at high latitudes. These changes indicate a high probability of flash floods in river basins at low latitudes and a high probability of droughts at high latitudes. In the future, more temporally clustered heavy precipitation events and prolonged dry spells may lead to increasingly uneven precipitation in high‐latitude river basins and the Pearl River basin, especially under the higher shared socioeconomic pathway scenarios. In contrast, more frequent consecutive heavy precipitation events may aggravate the risk of flooding in the Yangtze River basin. These findings are expected to serve as a guide for flood and drought risk mitigation in East Asia under a warming climate.

Rhine flood stories: Spatio‐temporal analysis of historic and projected flood genesis in the Rhine River basin

AbstractThe genesis of floods in large river basins often is complex. Streamflow originating from precipitation and snowmelt and different tributaries can superimpose and cause high water levels, threatening cities and communities along the riverbanks. For better understanding the mechanisms (origin and composition) of flood events in large and complex basins, we capture and share the story behind major historic and projected streamflow peaks in the Rhine River basin. Our analysis is based on hydrological simulations with the mesoscale Hydrological Model forced with both meteorological observations and an ensemble of climate projections. The spatio‐temporal analysis of the flood events includes the assessment and mapping of antecedent liquid precipitation, snow cover changes, generated and routed runoff, areal extents of events, and the above‐average runoff from major sub‐basins up to 10 days before a streamflow peak. We introduce and assess the analytical setup by presenting the flood genesis of the two well‐known Rhine floods that occurred in January 1995 and May 1999. We share our extensive collection of event‐based Rhine River flood genesis, which can be used in‐ and outside the scientific community to explore the complexity and diversity of historic and projected flood genesis in the Rhine basin. An interactive web‐based viewer provides easy access to all major historic and projected streamflow peaks at four locations along the Rhine. The comparison of peak flow genesis depending on different warming levels elucidates the role of changes in snow cover and precipitation characteristics in the (pre‐)Alps for flood hazards along the entire channel of the Rhine. Furthermore, our results suggest a positive correlation between flood magnitudes and areal extents of an event. Further hydro‐climatological research is required to improve the understanding of the climatic impact on the Rhine and beyond.

Inventory of River Basin Flood Events for Drainage System Examination Using Multi-Temporal Composite Remote Sensing Data

Palembang City is divided into two regions by the Musi river, and there are 114 tributaries streamed within Palembang City, with 31.58% located in the Seberang Ilir region and 68.42% in the Seberang Ulu region. In the lowland topography of Palembang City, with most of the region below 5 m above mean sea level, flooding becomes repetitive. This paper examines the flooded area within the Bendung river basin using multi-temporal composite remote sensing radar data from Sentinel 1. Flood events in November 2018 and December 2021 were selected. The flood extent and affected populations will be calculated using Google Earth Engine (GEE) and Geographic Information System (GIS) application. In this study, flood extent was delineated and compared for different digital elevation models. It is found that the threshold value, the DEM, and the availability of radar data in conjunction with the flood event will better delineate the affected area to the flood. The DEM contributed a 37.5% to 83% difference in the delineated area calculation. The script in the GEE is customizable for different areas of interest and can be applied to different areas with adjustments to the threshold value that should be a minimum of 1.0 or higher.

Developing Flood Risk Zones during an Extreme Rain Event from the Perspective of Social Insurance Management

Recently, Japan has been hit by more frequent and severe rainstorms and floods. Typhoon Hagibis caused heavy flooding in many river basins in central and eastern Japan from 12–13 October 2019, resulting in loss of life, substantial damage, and many flood insurance claims. Considering that obtaining accurate assessments of flood situations remains a significant challenge, this study used a geographic information system (GIS)-based analytical hierarchy process (AHP) approach to develop flood susceptibility maps for the Abukuma, Naka, and Natsui River Basins during the Typhoon Hagibis event. The maps were based on population density, building density, land-use profile, distance from the river, slope, and flood inundation. A novel approach was also employed to simulate the flood inundation profiles of the river basins. In addition, a crosscheck evaluated the relationship between flood insurance claims and the developed flood risk zones within the river basins. Over 70% of insurance claims were concentrated in high to very high risk zones identified by the flood susceptibility maps. These findings demonstrate the effectiveness of this type of assessment in identifying areas that are particularly vulnerable to flood damage, which can be a useful reference for flood disaster management and related stakeholder concerns for future extreme flood events.

Statistical Modeling for Flood Frequency in Upper Chaophraya River Basin

The research deals with the probabilistic estimates of annual maximum flood peaks in the upper Chaophraya basin (Thailand) used the extreme value theory, the Block Maxima. The Generalized Extreme Value (GEV) distribution model, the Frèchet distribution (EV2), the extension of the Frèchet distribution such as the Kumaraswamy Frèchet distribution and the new distribution as called the Exponentiated Kumaraswamy Frèchet distribution satisfied the Goodness of fit test (Kolmokorov-smirnov test). The return levels are estimated for 3, 5, 10, 30, 50, 100, 500 and 1000 years which are consistently increasing for designs of flood protection in future. The return period of flood for each stations are estimated.
 The investigation of the new distribution and appropriated estimation technique for the flood frequency in upper Chaophraya river basin as we called the Exponentiated Kumaraswamy Frèchet distribution and the differential evolution maximum likelihood estimation were done. We derived the properties of the Frèchet family; such as the Frèchet distribution (EV2), the Kumaraswamy Frèchet distribution (KF) and the Exponentiated Kumaraswamy Frèchet distribution (EKF). We also compared Bias, Variance, Mean Square Error and Mean Absolute Percentage Error for all parameters in each distribution by generating the Frèchet family random number. For the effectiveness of analytical solutions of the parameters we provided the numerical solutions (differential evolution method) to obtain estimates for all parameters by using Scilab program.
 Accuracy of flood assessment of extreme event is of fundamental importance for many safety, engineering and financial application. In part of application we provided the probabilistic estimates of annual maximum flood peaks or momentary peak data in the upper Chaophraya river basin (Thailand). The Generalized Extreme Value (GEV) distribution model were used to be gain to compare with as the Frèchet distribution (EV2), the Kumaraswamy Frèchet distribution (KF) and the Exponentiated Kumaraswamy Frèchet distribution (EKF). The Goodness of fit test, the return level and return period were done. The return periods of flood were classified by hazard class using GEV found that in upper Chaophraya river basin flood occurred highly. The result from the Frèchet family also occurred highly, but the return period and return level from the Kumaraswamy Frèchet distribution (KF) quite closed to GEV more than another distribution.

Influence of large-scale teleconnections on annual and seasonal floods in Godavari and Narmada River basins

AbstractIndian summer monsoon rainfall is strongly influenced by large-scale atmosphere-ocean oscillations including Pacific Decadal Oscillation (PDO), El Niño-Southern Oscillation (ENSO), and Indian Ocean Dipole (IOD). Researchers have shown that the negative phase of PDO or La Niña episodes of ENSO produce higher magnitude rainfall and hence relatively wetter years. So, it is imperative to have better knowledge of flood characteristics in the Indian watersheds for optimal planning and design of various infrastructure, and for optimal planning and management of reservoir operations. Traditionally, such information is estimated using flood frequency analysis (FFA), however the adequacy of traditionally accepted assumption that the annual peak flows are independent and identically distributed (i.i.d.) is questioned globally. This study evaluates the adequacy of this assumption in Godavari and Narmada River basins and assesses the influence of PDO, ENSO and IOD on flood characteristics. The results indicate that the flood characteristics at the majority of gauges are significantly influenced by these oscillations, higher magnitude floods are associated with negative episodes. A very few gauges are inversely related to these teleconnections, although statistically not significant. Overall, the signal of all the three teleconnections is found in the annual and seasonal floods in the majority of gauging stations.

Tropical Pacific and North Atlantic Sea Surface Temperature Patterns Modulate Mississippi Basin Hydroclimate Extremes Over the Last Millennium

AbstractMississippi River basin floods impart large socioeconomic impacts over the central United States. Improving flood predictability depends on our understanding of the dynamical controls on Mississippi basin hydroclimate. However, short instrumental records make it difficult to constrain the connections between flooding and climate variability. Here, we use the Paleo Hydrodynamics Data Assimilation product, spanning the Last Millennium, to investigate the impacts of tropical Pacific and North Atlantic sea surface temperature (SST) variability on hydrological extremes across the Mississippi River and its major tributaries. Wet extremes are associated with strong El Niño‐like warming over the tropical Pacific, but specific SST patterns matter: dry (wet) conditions occur during Central Pacific (Eastern Pacific) El Niño events. The influence of North Atlantic SSTs is less clear, but cool SSTs contribute to Ohio basin wet extremes. These results are relevant for seasonal‐to‐interannual flood hazard prediction on the fourth largest river basin in the world.

Разработка нейросетевого алгоритма оперативного детектирования разливов бассейна реки Амур на основе данных космического аппарата Sentinel-2

In this paper, the authors propose an algorithm for automatic operational flood detection of the Amur River basin based on data from the MSI instrument installed on the Sentinel-2 satellite. To solve the problem, a U-net convolutional neural network is used, improved due to the specifics of the data by adding an additional layer that reduces sampling and normalization layers after each neuron activation. As a training set, Sentinel-2 Level-2A data was used, which underwent the atmospheric correction procedure and represents 4 channels of the MSI instrument with a spatial resolution of 10 m, as well as index images built on their basis. As reference information, vector maps of river floods were used, built in an interactive mode by decoder specialists from the Far-Eastern Center of State Research Center for Space Hydrometeorology “Planeta”. The results of the neural network algorithm after the learning process were evaluated according to the metrics that amounted to: Precision – 94.91%, Recall – 90.76%, F1-measure – 92.79%. High accuracy ratings and fast operation speed make it possible to use the developed algorithm for automatic operational flood detection of the Amur River basin floods in the tasks of integrated monitoring of flood conditions. The work is a complete technical solution and has been put into trial operation at the Far-Eastern Center of State Research Center for Space Hydrometeorology “Planeta”. In the future, the results obtained by the authors are planned to be adapted to the data of the Russian satellite of the Meteor-M series with the KMSS-2 multi-zone satellite imagery complex installed on board. The expected results will improve the quality of the thematic products provided and will make it possible to switch over to the use of domestic data when creating our own technologies for processing satellite information.

River flooding on the French Mediterranean coast and its relation to climate and land use change over the past two millennia

River flooding triggered by extreme rainfall events is a major natural hazard that frequently causes fatalities and damages in southeastern France. A 2000-year record of flooding from the Hérault River basin was reconstructed using magnetic susceptibility data from lagoon deposits, supported by documentary databases of floods in coastal river basins from Languedoc over the last centuries. Flooding was particularly important between 800 and 1350 cal CE, i.e. during the warm climatic conditions of the Medieval Climate Anomaly. Moreover, several periods of increased flooding occurred in the 2nd, 6th-7th, 16th, 18th and 20th centuries. Increased flooding was significantly correlated with higher air temperature in the northwestern Mediterranean and with higher sea surface temperature in the Gulf of Lions. The analysis of relationships between river flooding and atmospheric conditions shows that autumn flood events in Languedoc were generally more frequent during the positive phase of the East Atlantic pattern, which is associated with southerly to southeasterly winds in the Gulf of Lions. Besides, increased flooding could also have been controlled by forest decline and human activities after 500 cal CE. Between 1400 and 1800 cal CE, palaeobotanical data show an intensification of land use characterized by an expansion of grasslands and cultivated areas in the coastal lowlands east of the Hérault River. Forest clearings and the development of agriculture seem to have led to a relative increase in river flooding, especially in the late Middle Ages and early modern period.

Multiday Precipitation Is a Prominent Driver of Floods in Indian River Basins

AbstractIndia witness floods during the summer monsoon (June–September) that disproportionately affect the socioeconomic well‐being of millions of people. Nonstructural measures such as flood early warning systems play a crucial role in mitigating the impacts; however, these require a proper understanding of flood drivers. The drivers of floods in the Indian river basins have not been examined for the observed and projected future climate. Here using a novel framework, we examine antecedent moisture conditions and precipitation characteristics before high flow events. We estimate the probability of occurrence of flood drivers and their association with peak flood magnitude under the observed and projected future climate in Indian river basins. Multiday precipitation, a proxy to heavy precipitation on wet soil conditions, was found as the predominant flood driver in the observed and projected future climate. We show that multiday precipitation is more prominent driver than extreme soil moisture conditions in larger rivers basins while extreme precipitation drives floods in smaller river basins. The frequency of major drivers of floods is projected to rise in the future, which may pose a greater risk to agriculture and infrastructure under the warming climate.

Optimization of Reservoir Flood Control Operation Based on Multialgorithm Deep Learning

With the rapid development of China's social economy, it is the most important task for the water conservancy industry to make use of the existing water conservancy engineering measures to carry out the research on river basin flood control dispatching. Large-scale joint operation of river basins usually needs to consider meteorological and hydrological conditions, historical flood data, multireservoir engineering conditions, and multiple flood control targets, which is a complex decision-making problem. Therefore, electing the optimal operation model of reservoir flood control optimization is very important. In this paper, Luanhe River Basin is taken as the research area, and three kinds of constraints, namely, water balance constraint, reservoir flood control capacity constraint, and water release decision constraint, are set to construct the flood control optimization model. Taking the minimum square of the sum of reservoir discharge and interval flood discharge as the objective function, genetic algorithm (GA), particle swarm optimization (PSO), Spider swarm optimization (SSO), and grey wolf optimization (GWO) are introduced into flood control optimal operation to seek the minimum value of objective function, and the results are compared and analyzed. Through the analysis of optimization results, the optimization ability and convergence effect of grey wolf optimization algorithm are better than those of genetic algorithm and particle algorithm, and the results are more stable than those of spider swarm algorithm. It has a good model structure and can make full use of the results of three wolf groups for optimization. Through the analysis of scheduling results, the results of genetic algorithm and particle swarm optimization algorithm are similar, while those of spider swarm optimization algorithm and grey wolf optimization algorithm are similar and slightly better than those of the first two. Moreover, the search range of grey wolf optimization algorithm for solving long sequence problems is wider and the calculation time is shorter. Therefore, the grey wolf optimization algorithm can be applied to solve the flood control operation optimization model of Panjiakou Reservoir Group.

Catastrophic Floods in Large River Basins: Surface Water and Groundwater Interaction under Dynamic Complex Natural Processes–Forecasting and Presentation of Flood Consequences

A unique approach has been developed for explaining and forecasting the processes of flood and/or mudflow (debris) formation and their spread along riverbeds in mountainous areas, caused by flash increases in the water masses involved (considerably increasing in their expected level because of precipitation intensity) due to groundwater contributions. Three-dimensional crack-nets within the confines of unified rivershed basins in mountain massifs are a natural transportation system (as determined by some dynamic external stress factors) for groundwater, owing to hydrostatic/hydrodynamic pressure distribution, varied due to different reasons (e.g., earthquakes). This process reveals a wave nature characterized by signs of obvious self-organization, and can be described via the soliton model in nonlinear hydrodynamics on the surface propagation after a local exit of groundwater as the trigger type. This approach (and related concepts) might result in a more reliable forecasting and early warning system in case of natural water hazards/disasters, taking into account a groundwater-dominant role in some cases.