Flood Estimation Research Articles

Enhancing Flood Management and Early Warning Systems in Mountain Rivers: An ANN-Based Approach

The study focuses on developing an HEC-RAS 1D model to simulate flood hydrographs at desired locations, considering the inflows from two different rivers Rishiganga and Dhauliganga. The objective is to analyze the impact of inflow hydrographs from the aforementioned rivers, utilizing three sets of flood data: high flow (2400 m3/sec), low flow (15 m3/sec), and 1.5 times the high flow (3600 m3/sec) peaks. An Artificial Neural Network (ANN) model, employing the Feed-forward Backpropagation method, is utilized to train the neurons using the maximum flow hydrograph. The trained ANN model is subsequently tested with two unseen inflow hydrographs of low and 1.5 x high flow to evaluate its robustness. The findings reveal that the ANN model performs very well in flood estimation, providing efficient predictions based on the testing with the maximum flow and validating with the minimum and 1.5 times higher inflow hydrographs. Its primary advantage lies in saving time, enabling timely actions when flood warnings are issued. This research significantly contributes to flood management and shall enhance the lead time in disseminating early warning to local authorities and communities. Work shall help in providing timely and accurate flood predictions for proactive measures during any flood event. The study’s significance is rooted in its potential to enhance flood preparedness and response in areas affected by the Rishiganga and Dhauliganga rivers, ultimately ensuring the safety and well-being of local communities and infrastructures.

Modeling surge dynamics improves coastal flood estimates in a global set of tropical cyclones

Tropical cyclone-induced storm surge is a major coastal risk, which will be further amplified by rising sea levels under global warming. Here, we present a computational efficient, globally applicable modeling approach in which ocean surge and coastal inundation dynamics are modeled in a single step by the open-source solver GeoClaw. We compare our approach to two state-of-the-art, globally applicable approaches: (i) using a static inundation model to translate coastal water level time series from a full-scale physical ocean dynamics into inundated areas, and (ii) a fully static approach directly mapping wind fields to inundation areas. For a global set of 71 storms, we compare the modeled flooded areas to satellite-based floodplain observations. We find that, overall, the models have only moderate skill in reproducing the observed floodplains. GeoClaw performs better than the two other modeling approaches that lack a process-based representation of inundation dynamics. The computational efficiency of the presented approach opens up new perspectives for global assessments of coastal risks from tropical cyclones.

Enhancing flood risk mitigation by advanced data-driven approach

Flood events in the Sefidrud River basin have historically caused significant damage to infrastructure, agriculture, and human settlements, highlighting the urgent need for improved flood prediction capabilities. Traditional hydrological models have shown limitations in capturing the complex, non-linear relationships inherent in flood dynamics. This study addresses these challenges by leveraging advanced machine learning techniques to develop more accurate and reliable flood estimation models for the region. The study applied Random Forest (RF), Bagging, SMOreg, Multilayer Perceptron (MLP), and Adaptive Neuro-Fuzzy Inference System (ANFIS) models using historical hydrological data spanning 50 years. The methods involved splitting the data into training (50–70 %) and validation sets, processed using WEKA 3.9 software. The evaluation revealed that the nonlinear ensemble RF model achieved the highest accuracy with a correlation of 0.868 and an root mean squared error (RMSE) of 0.104. Both RF and MLP significantly outperformed the linear SMOreg approach, demonstrating the suitability of modern machine learning techniques. Additionally, the ANFIS model achieved an exceptional R-squared accuracy of 0.99. The findings underscore the potential of data-driven models for accurate flood estimating, providing a valuable benchmark for algorithm selection in flood risk management.

Hydrodynamic modeling of dam breach floods for predicting downstream inundation scenarios using integrated approach of satellite data, unmanned aerial vehicles (UAVs), and Google Earth Engine (GEE)

Dam breach floods pose significant threats to downstream areas, necessitating accurate prediction of inundation scenarios to mitigate potential damage. This paper presents a novel methodology for hydrodynamic modeling of dam breach floods, leveraging a comprehensive approach that integrates satellite imagery, unmanned aerial vehicles (UAVs), and Google Earth Engine (GEE) to forecast downstream inundation scenarios. Specifically, UAVs were utilized to generate high-resolution Digital Elevation Models (DEMs) of the flood-affected areas, ensuring precise representation of topography in the model. The approach incorporates Cartosat DEM data for catchment modeling, while NASA's Global Precipitation Measurement mission data, integrated with GEE, facilitated accurate estimation of rainfall in ungagged catchment areas. Furthermore, the Hydrological Engineering Center-Hydrological Modeling System was employed for rainfall-runoff simulation and flood hydrograph derivation, followed by application of the HEC River Analysis System (RAS) for hydrodynamic modeling under dam breach conditions. This integrated modeling approach was applied as a case study of Banaskantha district, Gujarat, India. The outcome was the generation of scenario maps based on HEC-RAS results, which include flood extent, water depth, and flow velocity, highlighting downstream areas affected by flooding. Validation of the hydrodynamic dam breach model performance was conducted using actual field measurements and simulated results, employing statistical analysis methods including Support Vector Regression (SVR) and linear regression to determine coefficient of determination (R2), Root-Mean-Square Error, and Mean Absolute Error of observed and simulated data. The coefficient of determination (R2) values for measured and simulated flow (0.91) and water level (0.86) calculated using SVR demonstrate strong correlation between observed and simulated values. This integrated study of hydrodynamic modeling in data-scarce areas aids in accurate estimation of future probable flooding in downstream areas in the event of a dam break, underscoring the potential of advanced surveying and modeling techniques in flood assessment and management. Ultimately, this integration of technologies aims to enhance community resilience and mitigate socioeconomic costs associated with dam breach floods.

Event-based flood estimation in un-gauged sub-basins: a comparative assessment of SCS-UH, CWC-UH and Nash-GIUH based rainfall-runoff models in Shilabati River, Eastern India

Event-based flood estimation in un-gauged sub-basins: a comparative assessment of SCS-UH, CWC-UH and Nash-GIUH based rainfall-runoff models in Shilabati River, Eastern India

Nonstationary multi-site design flood estimation and application to design flood regional composition analysis

Impacts of climate change and human activities may lead to changes in the spatiotemporal composition of the design flood as well as its size. Previous studies mainly focused on changes in design flood size, while there has been relatively little research on changes in its regional composition. In this study, a nonstationary multi-site design flood estimation method is developed, which is useful for the design flood regional composition analysis under nonstationary conditions. Dynamic copula models are first constructed to analyze the change in the joint distribution of the nonstationary multi-site flood variables with the consideration of the nonstationarity of the marginal distribution and copula structure parameters. Then the design flood combinations in multi-site for a specified design standard are calculated by comprehensively applying the equivalent reliability method, the expectation conditional and the most-likely conditional combination strategies, which considers the future precipitation change and design lifespan length impacts on the design flood. Finally, the uncertainty of the multi-site design flood estimation caused by the model parameters uncertainty is evaluated. A case study, based on the annual maximum 7-day (AM7) flood volume in the Yichang (YC) and Cuntan (CT) sites, is conducted to illustrate this method. Results show that flood quantiles in the YC and CT sites exhibit an increasing trend as the precipitation projections will increase in the future, but the flood quantiles in the YC site are less compared to the historical period because of the huge regulation and storage effect of the Three Gorges Reservoir. In addition, the design flood combination in the CT and YC sites are calculated and the CT design floods from the expectation combination strategy are bigger than those provided by the most-likely combination strategy.

Improving Low-Frequency Flood Estimation Using the Partial Duration Series Instead of the Annual Maximum Across the United States

Improving Low-Frequency Flood Estimation Using the Partial Duration Series Instead of the Annual Maximum Across the United States

A Review of Event-Based Conceptual Rainfall-Runoff Models: A Case for Australia

Event-based models focus on modelling of peak runoff from rainfall data. Conceptual models indicate simplified models that provide reasonably accurate answers despite their crude nature. Rainfall-runoff models are used to transform a rainfall event into a runoff event. This paper focuses on reviewing computational simulation of rainfall-runoff processes over a catchment. Lumped conceptual, event-based rainfall-runoff models have remained the dominant practice for design flood estimation in Australia for many years due to their simplicity, flexibility, and accuracy under certain conditions. Attempts to establish regionalization methods for prediction of design flood hydrographs in ungauged catchments have seen little success. Therefore, as well as reviewing key rainfall-runoff model components for design flood estimation with a special focus on event-based conceptual models, this paper covers the aspects of regionalization to promote their applications to ungauged catchments.

A GIS-based Approach to Spatially Explicit Mean Monthly Areal Rainfall Estimation of Krishna District, Andhra Pradesh, India

Rainfall analysis is pivotal for understanding hydrological systems, crucial for effective water resource management. In this study spatial rainfall analysis is carried out for Krishna District of Andhra Pradesh using ArcGIS software. Rainfall data for a period of 30 years (1992-2021) was collected from the Directorate of Economics and Statistics, Vijayawada, Andhra Pradesh. Utilizing the spatial interpolation techniques in Geographic Information System (GIS) software, mean monthly areal rainfall was found using the three methods viz., Arithmetic mean, Thiessen’s polygon and isohyet method. This study utilized Geographic Information Systems (GIS) to visualize spatial patterns of rainfall across Krishna district. To account for missing data in the rainfall record, the normal ratio method was implemented. Spatial maps were generated to depict mean monthly areal rainfall variations. The analysis yielded mean monthly areal rainfall for Krishna district as 84.72 mm, 85.22 mm and 83.97 mm using the arithmetic mean, Thiessen polygon and Isohyet methods, respectively. Isohyet method is considered to be more accurate and rainfall is the basic data required for all hydrological analysis as an input layer for runoff generation, water resources planning, evapotranspiration studies, crop planning, flood estimation and water budget estimations etc.

Development of a convolutional neural network based regional flood frequency analysis model for South-east Australia

Flood is one of the worst natural disasters, which causes significant damage to economy and society. Flood risk assessment helps to reduce flood damage by managing flood risk in flood affected areas. For ungauged catchments, regional flood frequency analysis (RFFA) is generally used for design flood estimation. This study develops a Convolutional Neural Network (CNN) based RFFA technique using data from 201 catchments in south-east Australia. The CNN based RFFA technique is compared with multiple linear regression (MLR), support vector machine (SVM), and decision tree (DT) based RFFA models. Based on a split-sample validation using several statistical indices such as relative error, bias and root mean squared error, it is found that the CNN model performs best for annual exceedance probabilities (AEPs) in the range of 1 in 5 to 1 in 100, with median relative error values in the range of 29–44%. The DT model shows the best performance for 1 in 2 AEP, with a median relative error of 24%. The CNN model outperforms the currently recommended RFFA technique in Australian Rainfall and Runoff (ARR) guideline. The findings of this study will assist to upgrade RFFA techniques in ARR guideline in near future.

Modelling non-stationarity in extreme rainfall using large-scale climate drivers

Flood estimates used in engineering design are commonly based on intensity–duration–frequency (IDF) curves derived from historical extreme rainfalls. Under global warming these extreme rainfalls are increasing, threatening the capacity of existing infrastructure to resist failure as IDF curves traditionally assume no change in rainfall magnitude. Hence, there is a need to investigate the implications of non-stationarity in extreme rainfalls used to derive IDFs across storm durations and annual exceedance probabilities (AEPs). One way of doing this is to incorporate covariates into the fitted probability distribution. However, there are few studies which examine non-stationarity in extreme rainfall using large-scale climate drivers as covariates, with little consensus on which covariate is the most appropriate. Here we evaluate non-stationarity in extreme rainfall across different durations, from the 1 in 5 AEP to the 1 in 100 AEP, using potential large-scale climate drivers including global and continental mean temperature, continental diurnal temperature range, continental dewpoint temperature, continental precipitable water, the Indian Ocean Dipole, the El Niño Southern Oscillation, and the Southern Annular Mode. These covariates are linked to the three parameters of the Generalized Extreme Value distribution to identify the most appropriate form of non-stationary probability model. We analyse 16 different storm durations from 6 min to 7 day annual maxima across 46 stations in Australia. Based on the Akaike Information Criteria, precipitable water is the superior covariate at a large proportion of stations irrespective of storm duration. However, when the modelled quantile changes are inspected, only global temperature is able to adequately capture the variability in changes across both storm duration and annual exceedance probability. Further, when regional average values of mean temperature, diurnal temperature range, dewpoint temperature and precipitable water were used as covariates, there was no improvement in the model performance compared to continental-wide covariates, particularly for short durations. The results using a mean global temperature covariate show that stationary historical rainfall quantile estimates are underestimated by 12 % – 9 % for frequent (1 in 5 AEP) and 23 % – 13 % for rare (1 in 100 AEP) short duration (6 min – 30 min) events. Moving forward our results suggests infrastructure design needs to incorporate non-stationarity in IDFs to ensure infrastructure and flood planning levels are not under-designed.

Open-access digital elevation model (DEM) selection for flood inundation modelling using HEC-RAS in Capital City of Nusantara

Flood is a classic but complex issue in most regions of Indonesia. Nusantara, the future capital of Indonesia, is now undergoing construction but the site continues to experience flooding due to high intensity rainfall. To overcome this challenge, identification of flood points in those areas needs to be done. Digital Elevation Models (DEM) are the most important component in modeling flood inundation in data-sparse areas. High-accuracy DEMs have been found to give better flood estimation but the availability of such data is very limited. Therefore, open-access DEMs, such as SRTM, DEMNAS, and MERIT-Hydro, are the most common choices used in modeling flood inundation in Indonesia. Each data set may provide varying model outcomes, due to different data processing methods. These results often lead to confusion as to which result should be used in subsequent studies. In this study, flood inundation in Nusantara will be generated using different DEMs and HEC-RAS hydraulic modeling with 25-, 50-, and 100-year return period rainfall events. The return period rainfall was obtained by calibrating Global Precipitation Measurement (GPM) satellite rain data. The results provide various flood inundation conditions, MERIT-Hydro tends to present higher flood depths. Followed by DEMNAS and SRTM which have lower flood depths. On the other hand, the use of return period variation gives a linear increase in flood depth for the DEMNAS model. While a decline in increment occurred in the SRTM and MERIT-Hydro models when the two big year return periods were applied. This research can be considered for the Nusantara development planning.

Application of Tropical Rainfall Measuring Mission (TRMM) Data for Flood Estimation in Lack Data Catchment

Providing real-time hydrological data for the design, development and management of water resources is a fundamental issue that is still being faced by hydrologists and practitioners. This data limitation is not only caused by the uneven distribution of hydrological measuring instruments, but also due to the inability of measuring instruments to present the data in real time. This paper aims to evaluate hydrological data, especially spatial-based rainfall data known as Tropical Rainfall Measuring Mission (TRMM) data as an alternative to rainfall data to predict flood hydrographs in a catchment with limited data. The study was initiated by selecting pairs of automatic rainfall data (ARG) and water level (AWLR) and combined with TRMM rainfall data for the same time series. These two types of rainfall data are then used as input for the HEC-HMS hydrological model involving a number of catchment parameters, such as: basin area, stream network, land use/land cover (LULC), soil characteristics and several other parameters. Three analytical methods are assigned to determine the shape of the hydrograph which include: Snyder UH (transformation method), SCS-CN (loss method), and recession (baseflow method). Discharge data converted from water level data is input at the catchment outlet as a reference for setting calibration parameters which are evaluated with the RMSE error indicator. The simulation results show that the predicted discharge deviation from TRMM rainfall data is higher than the predicted discharge from ARG rainfall data with RMSE 0.979 and 1.731 respectively. However, basically this TRMM data can be an alternative data by validating it with ARG rain data to be applied to lack of catchment data.

Impact of Soil Moisture Dynamics and Precipitation Pattern on UK Urban Pluvial Flood Hazards Under Climate Change

AbstractThe diversity of flood‐generating mechanisms superimposed on catchment physiographic features with non‐stationary meteorological drivers makes future flood hazard assessment a grand challenge. To date, many studies have examined patterns in rainfall and streamflow, but far fewer have investigated trends in the other drivers of flooding. The complex transfer function between precipitation and flooding makes it potentially misleading to simply look at the change in rainfall to express the hazard. Furthermore, there are very few studies that have directly used output from km‐scale climate models in flood modeling. Coarse resolution climate data sets may not credibly resolve local climate and weather extremes. Changes in rainfall distribution and antecedent moisture over extended time periods due to climate change have so far been ignored when assessing urban pluvial flood risk. In this paper, an urban flood hazard assessment framework using the latest 2.2 km resolution UK Climate Projections Local is proposed. Global warming induced changes in pluvial flood risks under RCP8.5 are projected, focusing on the impact of changing precipitation patterns and soil moisture dynamics on flood generation. Results indicate a strong increase in the frequency of occurrence of extreme floods, and the resultant future (2060–2080) annual flood volume is expected to increase up to 52.6% relative to 1980–2000 over a major UK urban region, and these patterns are likely to hold more generally elsewhere in the UK. Shifts to a later occurrence of extreme flooding is identified under global warming. Previous studies that have neglected soil moisture dynamics are unlikely to give accurate flood estimates.

Developing Non-Stationary Frequency Relationships for Greater Pamba River basin, Kerala India incorporating dominant climatic precursors

Abstract. Global climate changes significantly contribute to increased frequency of hydrologic extremes. This significantly underestimates the hydrologic design parameters, bringing of hydro systems to increased failure risk. In order to address this concern, the current practice of development of hydrologic frequency tools need to be updated accounting for non-stationarity. This study first considered a diverse set of statistical tests to examine the trend, change points, non-stationarity and randomness of streamflow, rainfall and temperature time series of scales ranging from daily to annual. The annual maxima time series indicated non stationarity against the stationary behaviour of daily series of hydro-meteorological datasets of the basin. Subsequently, this study developed the Temperature Duration Frequency (TDF), Rainfall Intensity Duration Frequency (IDF) and Flood Frequency (FF) curves of Greater Pamba river basin in Kerala India, the part of which was most severely affected by the near century return period flood event of 2018. The analysis was performed for a multitude of combinations of variations in distribution parameters with time and climatic drivers as physical covariates in the extreme value formulations. The study proposed a novel wavelet coherence (WC) based driver selection of most dominant combination of climatic precursors in developing FF and IDF relations of three locations of Kalloopara, Malakkara and Thumpamon and TDF curve of Kuttanad region in the basin, considering data of 1985–2015 period. The proposed WC framework considers bi-multi-and partial effects of climatic oscillations (COs) like El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO) in identifying potential drivers. The different WC formulations captured in-phase relationships of streamflows and rainfall with COs at intra-annual, annual and inter annual scales up to 4 years. The methods showed that addition of climatic precursors improved the NS estimates of flood and rainfall quantiles by more accurately capturing the magnitudes of extreme streamflows and rainfalls of 2018, 2021 than the time covariate formulations. However, the role of COs on extreme temperature is not found to be influential in developing TDF relationships, which needs further investigation.

Gauged and historical abrupt wave front floods (‘walls of water’) in Pennine rivers, northern England

AbstractExtremely rapid rates of rise in level and discharge in a subset of flash floods (‘abrupt wave front floods’, AWF) are separate hazards from peak level. Such flood events are investigated for Pennine catchments in northern England using both gauged and historical information. Gauged level and flow digital records at 15‐min intervals provide recent data. Historical information for 122 AWF events is extracted from a chronology of flash floods for Britain. Historical AWF events are mapped and found to occur on every major Pennine catchment; catchment descriptors are derived as a basis for assessing catchment vulnerability. We discuss the disputed origin of AWF. Using gauged data, we contrast the rising limb of AWF and ‘normal’ floods. We investigate time series of historical AWF, noting a puzzling peak in the late 19th century. Current rainfall and river monitoring does not provide a reliable basis for understanding AWF processes or for operational response and we suggest improvements. Similarly, current models for design flood estimation and forecasting do not generate the observed rapid increase in level in AWF floods.

A hydro-climatic approach for extreme flood estimation in mountainous catchments

Prediction of rainfall-runoff process, peak discharges, and finally flood hydrograph is essential for flood risk management and river engineering projects. In most previous studies in this field, the precipitation rates have been entered into the models without considering seasonal and monthly distribution. In this study, the daily precipitation data of 144 climatology stations in Iran were used to evaluate the seasonal and monthly pattern of flood-causing precipitation. Then, by determining the rainy seasons and seasonal fit of precipitation with a probabilistic model and using regional precipitation, a semi-distributed conceptual model of rainfall-runoff (MORDOR-SD) was trained and validated using the observed discharge data. Flood prediction was performed using climatic data, modeling of hydrological conditions, and extreme flow data with high performance. According to the results, the Nash–Sutcliffe and Kling–Gupta coefficients were 0.69 and 0.82 for the mean daily streamflow, 0.98 and 0.98 for the seasonal streamflow, 0.98 and 0.94 for the maximum discharges, and 0.57 and 0.78 for low flows, respectively. Moreover, the maximum daily discharges in different return periods were estimated using the results of the MORDOR-SD model, considering the probability distribution function of the probabilistic model of central precipitation (MEWP), the probabilistic model of adjacent precipitation, and probability distribution function of the previous precipitation. Finally, the extreme flows were predicted and compared using different methods including the SCHADEX, regional flood analysis, GRADEX, and AGREGEE. The results showed that the methods GRADEX, AGREGEE, and SCHADEX have the highest performance in predicting extreme floods, respectively.

On the Use of Weather Generators for the Estimation of Low-Frequency Floods under a Changing Climate

The present work presents a methodology based on the use of stochastic weather generators (WGs) for the estimation of high-return-period floods under climate change scenarios. Applying the proposed methodology in a case study, Rambla de la Viuda (Spain), satisfactory results were obtained through the regionalization of the bias-corrected EUROCORDEX climate projections and the integration of this information into the parameterization of the WG. The generated synthetic data series fed a fully distributed hydrological model to obtain the future flood quantiles. The results obtained show a clear increase in the precipitation extreme quantiles for the two analyzed projections. Although slightly reducing the annual amount of precipitation, variations between 4.3% for a return period of 5 years in the mid-term projection and 19.7% for a return period of 100 years in the long-term projection have been observed. In terms of temperatures, the results point to clear increases in the maximum and minimum temperatures for both projections (up to 3.6 °C), these increases being greater for the long-term projection, where the heat waves intensify significantly in both magnitude and frequency. Finally, although rivers may present, in general, with lower flows during the year, flood quantiles experience an increase of 53–58% for high return periods, which reach values of up to 145% when we move to smaller catchments. All this combined translates into substantial shifts in the river flow regimes, increasing the frequency and magnitude of extreme flood events.

Compounding effects in flood drivers challenge estimates of extreme river floods.

Estimating river flood risks under climate change is challenging, largely due to the interacting and combined influences of various flood-generating drivers. However, a more detailed quantitative analysis of such compounding effects and the implications of their interplay remains underexplored on a large scale. Here, we use explainable machine learning to disentangle compounding effects between drivers and quantify their importance for different flood magnitudes across thousands of catchments worldwide. Our findings demonstrate the ubiquity of compounding effects in many floods. Their importance often increases with flood magnitude, but the strength of this increase varies on the basis of catchment conditions. Traditional flood analysis might underestimate extreme flood hazards in catchments where the contribution of compounding effects strongly varies with flood magnitude. Overall, our study highlights the need to carefully incorporate compounding effects in flood risk assessment to improve estimates of extreme floods.

A systematic review of climate change science relevant to Australian design flood estimation

Abstract. In response to flood risk, design flood estimation is a cornerstone of planning, infrastructure design, setting of insurance premiums, and emergency response planning. Under stationary assumptions, flood guidance and the methods used in design flood estimation are firmly established in practice and mature in their theoretical foundations, but under climate change, guidance is still in its infancy. Human-caused climate change is influencing factors that contribute to flood risk such as rainfall extremes and soil moisture, and there is a need for updated flood guidance. However, a barrier to updating flood guidance is the translation of the science into practical application. For example, most science pertaining to historical changes to flood risk focuses on examining trends in annual maximum flood events or the application of non-stationary flood frequency analysis. Although this science is valuable, in practice, design flood estimation focuses on exceedance probabilities much rarer than annual maximum events, such as the 1 % annual exceedance probability event or even rarer, using rainfall-based procedures, at locations where there are few to no observations of streamflow. Here, we perform a systematic review to summarize the state-of-the-art understanding of the impact of climate change on design flood estimation in the Australian context, while also drawing on international literature. In addition, a meta-analysis, whereby results from multiple studies are combined, is conducted for extreme rainfall to provide quantitative estimates of possible future changes. This information is described in the context of contemporary design flood estimation practice to facilitate the inclusion of climate science into design flood estimation practice.