Estimated Peak Discharge Research Articles

Determining an optimal run-off coefficient method for estimating peak discharge in the Lesti River catchment

The run-off coefficients provide vital hydrological data used for river discharge forecasts and flood risk management. Selecting an appropriate method to determine this coefficient is essential for accurately estimating peak discharge. This study compared the effectiveness of the Hassing, Cook, and U.S. Forest Service methods integrating GIS in estimating run-off coefficients in the Lesti River catchment area from 2013 to 2019. The findings revealed that the run-off coefficient was determined to be 0.188–0.243 using the U.S. Forest Service method, 0.194–0.213 using the Hassing method, and 0.466–0.480 using the Cook method. These results showed a rapid increase in the run-off coefficient within the Lesti River catchment area, signifying a heightened susceptibility to flooding. This is particularly concerning as the Lesti River is a primary tributary to the Brantas River. The comparison of estimated versus observed peak discharge emphasised the superiority of the runoff coefficient associated with the Hassing method over alternative methodologies when utilised as input data for peak discharge estimation. This was evident by the notable measurement error values of 11% for MAPE and 0.58 for MAE. The Hassing method emerged as the most appropriate and reliable for reflecting run-off characteristics in the Lesti River catchment area. Additionally, it proved to be the most accurate for estimating run-off coefficients in the Nakayasu process for peak discharge estimation. Consequently, applying the Hassing method offers a viable strategy for effectively mitigating flood risks in the Lesti catchment area.

Study of Flooding Behavior and Discharge from Karot Dam in the Event of a Possible Breach by Using the Hydrodynamic Model

This study utilizes the MIKE 11 hydrodynamic model developed by the Danish Hydraulic Institute to simulate flood behavior downstream of Karot Dam under multi-year in-flow conditions. The key parameters analyzed include breach characteristics, flood duration, water depth, flow velocity, discharge rate, and downstream distance. After dam failure, the peak discharge reaches 33,171 m3/s, exceeding the 10,000-year recurrence peak flow of 32,300 m3/s, with a breach duration of 2 h. The estimated peak discharge after simulation using empirical equations and comparative analyses showed maximum flood discharges of 28,187 m3/s, 28,922 m3/s, and 29,769 m3/s, with breach widths of 181 m, 256 m, and 331 m, respectively. The peak discharge predicted to reach the outlet with travel time ranging from 4 h 25 min to 4 h 40 min. Under multi-year average inflow conditions, Mangla Dam faces no risk of failure, with a maximum outflow of 12,097 m3/s and a spillway capacity of 30,147 m3/s. The model accurately predicted discharge values, with a strong correlation coefficient of R2 = 0.9653, indicating strong agreement between the actual water level data and predicted discharge. These insights are essential for developing effective emergency response strategies to mitigate the risks associated with dam failure.

Forecasting the design maxima water discharges of floods on the Latorica river according to the data of the Mukachevo gauging station using plotting position formulas

This article presents the results of forecasting design maxima discharges on the Latorica River within Mukachevo town based on hydrological observation data at the “Mukachevo” gauging station using plotting position formulas. While solving the task, a novel non-parametric method of forecasting using observation data is applied. The method includes extrapolating the discrepancy (divergence, disagreement) between the estimates of the statistical annual probabilities of exceedance obtained by different plotting position formulas. The task is considered in the frame of the stationarity hypothesis of the maximum river flow employing a time series of maximal discharges of the Latorica River observed at the “Mukachevo” gauging station from 1947 to 1999.We involved the thirteen plotting position formulas. There was no specific criterion for choosing them to solve the task. All applied formulas were considered admissible options, and results obtained after using them – expert judgments reflecting decision-makers’ predisposition to more cautious or less expensive decision options in flood management strategies.The epistemic uncertainty of the different plotting positions was reduced by employing the Fishburn rule. According to this rule, the significance of various plotting positions was given by arranging their estimates in descending order of importance of their values under decision-making. Depending on the selected significance option assignment of the different plotting position formulas, such rank-weighted estimates of the design peak discharges (each of them for annual exceedance probability 1%, 0.5%, and 0.2%) were computed: (1) the rank-weighted upper bound estimate (sup-estimate) corresponding to the predisposition to more cautious decision options; (2) the rank-weighted lower bound estimate (inf-estimate) corresponding to the predisposition to less expensive decision options. As possible control theoretical alternatives for forecasting design maximal discharges considered were five parametric probability distributions: 1) the Kritskyi-Menkel three-parameter gamma distribution; 2) Pearson’s type III distribution; 3) the Extreme value type I distribution (Gumbell’s type I distribution); 4) the Logarithmic Pearson type III distribution; and 5) the Two-parameters logarithmic-normal distribution. The population statistical parameters for these parametric probability distributions were estimated from the sample statistics by the method of moments.

Investigation the Impact of the Climate Change on Intensity Duration Frequency (IDF) Curve Development: Case Study at Hulu Terengganu, Malaysia

The intensity-duration-frequency (IDF) curves are the most common form of design rainfall data used for peak discharge estimation. Thus, the IDF curve needs to be improved with the expectation that rainfall intensity and frequency have increased as a result of climate change. The main purpose of this study was to investigate the changes of IDF curves considering the climate change impacts on Hulu Terengganu. The climate projection from MRI-ESM2-0, CMCC-CM2-SR5, and GFDL-ESM4 under 3 different scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were used to provide the climate changes pattern in the future year (∆2050). In order to downscale the climate projection, the statistical downscaling method (SD-LS) was employed to correct the biases of these three GCMs. The IDF curves for the return periods of 2, 5, 10, 20, 50, 100, and 200 year were then developed based on the maximum rainfall intensity that were projected by the SD-LS model. The results clearly indicated that there are possibilities for increasing patterns in the projected annual and monthly rainfall for both time periods compared to historical data. Thus, the future extreme rainfall events for various durations with different return periods are all likely to increase over time. The largest potential increase is predicted at Sg. Gawi (+2.0% to +86.0%) based on the different return periods and rainfall durations. It could change the pattern of IDF curve that been developed based on projected rainfall by various SSPs. The developed IDF curves shows higher rainfall intensities in a shorter duration under the same return periods. Therefore, comprehensive action must be taken immediately to regulate and manage the effects of climate change.

Prediction of flash flood peak discharge in hilly areas with ungauged basins based on machine learning

ABSTRACT Peak discharge is an essential element of hydrological forecasting. Due to rapid outbreaks of flash floods in hilly areas and the lack of measured data, the fast and accurate estimation of peak discharge is crucial for flash flood hazard management. Three machine learning algorithms were applied to estimate peak discharge; this estimation was compared with the results of hydrological–hydraulic models, and the results were verified with measured watershed data. In this paper, 10 hydrological and geomorphological parameters were selected to predict the flood peak discharge in 103 watersheds in Taiyi Mountain North District. The results show that the particle swarm optimization backpropagation (PSO-BP) neural network model outperforms the BP neural network and random forest regression in prediction performance. PSO-BP has a lower mean absolute error (2.51%), root mean square error (3.74%), and mean absolute percentage error (2.74%) than the other models, which indicates that PSO-BP has high prediction accuracy. Importance analysis revealed that rainfall, early impact rainfall, catchment area, and rain intensity are the key input parameters of PSO-BP. The proposed method was confirmed to be a fast and relatively accurate algorithm for estimating the peak discharge of flash floods in ungauged basins.

A web-based urban hydrology model for municipal scale applications

Extensive data and computational requirements limit the application of existing urban hydrology models at municipal scales. Community-enabled Lifecycle Analysis of Stormwater Infrastructure Costs (CLASIC) is a web-based deployment of the SWMM model with decoupled hydrologic and hydraulic components to enable hydrologic assessment at the municipal and larger scales. This study comprehensively evaluates the performance validity of CLASIC for characterization of hydrologic responses against SWMM and observed data. Furthermore, global sensitivity analysis is used to explore the significance of hydrologic and hydraulic model parameters across spatial and temporal scales. CLASIC reliably represents the urban hydrological processes and accurately quantifies stream discharge at the municipal scale and temporal scales greater than the catchment's time of concentration. Notably, the computational requirements of CLASIC are substantially lower than those of SWMM as the catchment drainage area increases. The application of CLASIC for flood assessment may be conducted with careful examination of the estimated peak discharge at sub-daily timescales.

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

Prediction of Peak Discharge Using the SCS Curve Number Method in the Manikin Watershed

Surface runoff is a crucial hydrological variable in the analysis of water infrastructure planning. A reliable method for predicting surface runoff resulting from rainfall in an ungauged watershed is the SCS CN method. This research aims to represent the effectiveness of the Curve Number (CN) method in calculating peak discharge in the Manikin Watershed. The data used for this analysis includes rainfall data from three rain stations, each with a 25-year dataset, water level data of 11 years, digital elevation model (DEM) data, land use maps, and hydrogeological maps. The SCS curve number method is the most commonly used method for the estimation of peak discharge in a watershed. The calculated flood discharge values for the Manikin River Basin, with return periods of 5, 10, 20, 25, 50, 100, 500, and 1000 years, are as follows: 60.32 m3/s, 84.74 m3/s, 111.98 m3/s, 121.41 m3/s, 153.25 m3/s, 188.79 m3/s, 287.24 m3/s, and 337.30 m3/s, respectively. The reliability testing of the method in the Manikin Watershed was determined by a Nash–Sutcliffe efficiencies (NSE) value of 0.93 and root mean square error (RMSE) value of 19.70. As a result, the Curve Number Method proves to be highly reliable in representing the peak discharge in the Manikin Watershed.

An Advanced Empirical NRCS-CN Model Estimation for Ungauged Catchment Insufficient Data

The calculation of runoff is continuously considered a difficult analysis to define specific prediction methods in ungauged catchments. This leads to the implementation of an observed model to determine curve number (CN) and estimate peak discharge from ungauged basins. Therefore, this study aimed to modify an experimental NRCS model through fieldwork and conduct sensitivity analysis for relevant modeling procedures. In the analysis, the assessment of CN required land use, soil, infiltration in situ measurement, and Hydrological Soil Group (HSG) parameters for the catchment area. The determination of infiltration rate was also initially carried out using the Horton method (double-ring infiltrometer), accompanied by the evaluation of CN through soil classes and land use parameters. Based on infiltration rate and soil classification, HSG was significantly defined for the catchment area. The results showed that the analytical parameters in rainfall-runoff modeling included the Composite Curve Number (SCS-CN ( ⁄S) with a ratio value of 0.2. This was accompanied by the potential retention maximum (S) of 264.37 mm, with the initial proportional abstraction ( ) being 52.87 at an assumed preliminary coefficient of 0.2. Therefore, the CN composite estimation was 65.5, and the correlation between P and Q was evaluated using graph analysis. The trial CN ranging from 39-74 were also significantly considered to optimize the development models of HEC HMS for the best performance, proving that the improvement of CN was interrelated with discharge.

Analysis of scaling relationships for flood parameters and peak discharge estimation in a tropical region

Abstract Relationships between peak discharges and catchment size (e.g., flood scaling) in a catchment have the potential to support new river flood forecasting approaches but have not been tested in tropical regions. This study determined flood scaling relationships between peak discharge and nested drainage areas in the La Sierra catchment (Mexico). A statistical power law equation was applied to selected rainfall–runoff events that occurred between 2012 and 2015. Variations in flood scaling parameters were determined in relation to catchment descriptors and processes for peak downstream discharge estimation. Similar to studies in humid temperate regions, the results reveal the existence of log-linear relationships between the intercept (α) and exponent (θ) parameter values and the log–log power–law relationships between (α) and the peak discharge observed from the smallest headwater catchments. The flood parameter values obtained were then factored into the scaling equation (QP = αAθ) and successfully predicted downstream flood peaks, especially highly recurrent flood events. The findings contribute to a better understanding of the nature of flood wave generation and support the development of new flood forecasting approaches in unregulated catchments suitable for non-stationarity in hydrological processes with climate change.

Characteristics and Estimation of the Time of Concentration for Small Forested Catchments in Steep Mountainous Terrain

Accurate modeling of flood flow hydrographs for small forested catchments in steep mountainous terrain is challenging because of large errors in the estimation of response time using existing empirical equations. The time of concentration (TC) for a catchment is a widely used time parameter for estimating peak discharges in hydrological designs. In this study, we developed an estimated TC using readily available mountain catchment variables, a small catchment, steep slope, and narrow valley, using empirical equations. For our approach, we used directly measured data from 39 forested catchments (area: 0.02–9.69 km2) during 3648 observed rainfall events over a 10-year observation period. Based on the uncertainties inherent in the empirical equation, the estimated TC values were compared and analyzed through multiple regression and two different modified empirical modelling equations using our observed catchment parameters. The mean TC was significantly correlated with catchment size and stream length but negatively correlated with stream slope (p < 0.01). As a result, the mean TC estimated using the three modelling equations with catchment variables was qualitatively similar and had relative differences ranging from −12.5 to 15.5 min (−49 to 56%). Therefore, the models (particularly modeling equations with multiple regression, a modified empirical formula, and modified SCS Lag) can efficiently determine the TC and can be used in any small forested catchment in steep mountainous terrain.

Selecting the accurate hydrological method for estimating peak discharge in the Lesti River Catchment Area, Malang Regency, East Java Province, Indonesia

Estimating peak discharge in the catchment poses a challenge for hydrologists due to the potential overflow risks. The present study examined various methods, including Synthetic unit hydrograph, Melchior, and Rational, to determine the peak discharge value in the Lesti River Catchment Area. Accurate hydrological input data is essential for effectively estimating peak discharge and mitigating potential damage or failure. Optimized physical parameters using geographic information systems are critical in generating peak discharge values, emphasizing their importance in the estimation process. The mean absolute percentage error (MAPE) and mean average error (MAE) were used to determine the accurate method for estimating peak discharge. Analysis results showed varied peak discharge values across different methods, and it found that the MAPE values ranged from 15.15% to 7.48% and the MAE values ranged from 1.47% to 0.63%, respectively. The Nakayasu synthetic unit hydrograph obtained the minimum error measure value compared to other methods by 7.48% in MAPE and 0.63% in MAE. Therefore, the MAPE and MAE performance considered that the Nakayasu was closely approximating the observed peak discharge and a relatively accurate method for estimating peak discharge in the Lesti River catchment area.

Development of decomposition-based model using Copula-GARCH approach to simulate instantaneous peak discharge

Estimation of instantaneous peak discharge is important in the design of hydraulic structures and reservoir management. In this study, a new approach called CEEMD-Copula-GARCH is presented for simulating instantaneous peak discharge in the Qale Shahrokh basin, upstream of Zayanderood Dam, Iran. In the developed method, the Complementary ensemble empirical mode decomposition (CEEMD) algorithm was used to analyze the observed values and generate the intrinsic mode function values and residual series. For this purpose, the intrinsic mode function values were simulated based on vine copula and its tree sequence (C-vine, D-vine, R-vine and their independent and Gaussian modes), and the residual series of the CEEMD algorithm were simulated by the GARCH model. The results of simulating instantaneous peak discharge values (m3/s) using the CEEMD-Copula-GARCH approach in the study area showed that the amount of simulation error based on the RMSE statistic compared to the CEEMD-Copula model and simulation without decomposition has improved by about 20 and 70%, respectively. The model’s efficiency was also estimated based on the Nash–Sutcliffe efficiency in the proposed approach of 0.99, and the certainty of the proposed approach was also confirmed based on the presented violin plot. According to the presented results, the proposed approach has high accuracy and efficiency in the simulation of instantaneous peak discharge (m3/s), which can be used in the flood control system design and flood management. Using the methodology proposed in this study, multivariable models can be used in simulating univariate series with high accuracy.

Estimation of expected peak discharge and flood volume of the Heliopolis basin, East Cairo, Egypt, using RS and WMS program

In recent years, rainfall-induced floods in various Egyptian towns affected several regions, and the area in east Cairo was the worst. The present study aims to detect the rainfall-runoff relationships of the Heliopolis watershed and its sub-basins, to estimate peak discharge and flood volume for the storm event (12 March 2020) and predicting the runoff volume and peak discharge of the flows for various return intervals. This was achieved by applying the Hydrological models (HEC-1and HMS) program using a unit Hydrograph namely SCS-CN. The drainage system and the identification of sub-basinswere established using SRTM DEM, 90 m resolution. The maximum daily rainfall per year was investigated and determined for various return times using the statistical analysis tool Hyfran. Results reveal that the peak discharge (Qp) and flood volume value (Qv) of the Heliopolis watershed are 686.8 m3/s and 48039.8 m3, respectively. The obtained results for the various recurrence times during the regression periods of 5 and 100 years, reveal that the peak flood inflow of the Heliopolis basin ranged between 19.47 and 742.16 m3/s and the flood volume ranged between 1,295,110 and 53,752,968 m3. This shows that these valleys move a significant volume of water to be released into the mainstream. The recognition and forecasting of the degree of dangerous rain events can help planners and decision-makers in finding flooded areas before the future development of the new settlements.

Estimation of Runoff in Data Scare Watershed of Middle Gujarat, India Using HEC-HMS Model

The accurate estimation of the hydrologic response in water resources planning and development is crucial. In this study, the rainfall-runoff relationship and hydrologic response of the Devgadh Baria watershed in the Panam river catchment were investigated. Due to limited availability and accuracy of runoff data, Clark UH, and SCS UH models were explored for runoff estimation. Remote sensing and GIS techniques were integrated to assess the ungauged watershed in the Middle Gujarat region.
 Rainfall and discharge data, Sentinel-2 data, 12.5m ALOS PALSAR DEM, base maps, and soil maps were collected from various sources. The study area's five different events were randomly selected, and a Thiessen map was prepared to calculate weighted rainfall. Geomorphological parameters and Horton's ratios were estimated using remote sensing and GIS software ILWIS.
 The composite curve number (CN) for the watershed was found to be 77.69. Excess-rainfall hyetographs were separated using the SCS-loss method, and the time to peak estimated by the models matched well with observed data, except for a one-day delay in the HEC-HMS based Clark UH model.
 Four performance indices, namely NSE, PBIAS, RMSE, and d, were used to compare model performance. The SCS UH model demonstrated the highest NSE value (-0.28) for event-1, while the HEC-HMS based SCS UH model showed the lowest NSE value (-0.28) for event-1. The SCS model outperformed the other models based on performance indicators NSE, RMSE, and d. Overall, the SCS UH approach provided the best estimation of peak discharge and time to peak discharge. The direct surface runoff hydrographs estimated using.

A global database of historic glacier lake outburst floods

Abstract. Ongoing atmospheric warming has accelerated glacier mass loss in many mountain regions worldwide. Glacier lakes trap part of the glacial meltwater and have increased by about 50 % in number and area since the 1990s. Some of these glacier lakes may empty catastrophically and pose hazards to mountain communities, infrastructure, and habitats. Such glacier lake outburst floods (GLOFs) have caused millions of dollars of damages and fatalities and are one of many concerns about future changes in the magnitude, frequency, and impacts of processes of a shrinking mountain cryosphere. Consistently compiled inventories are thus vital to assess regional and local trends in GLOF occurrence, hazard, and risk. To this end, we studied 769 literature and internet sources and developed a standardized database with 57 attributes that describe and quantify the location, dam type, size, timing, and impacts of GLOFs in nine glaciated mountain regions. Our GLOF inventory also includes details about the lake area before and after the outburst for 391 cases that we manually mapped from optical satellite images since 1984. In total, we compiled 3151 reported GLOFs that occurred in 27 countries between 850 and 2022 CE. Most GLOFs have been reported in NW North America (26 %) and Iceland (19 %). However, the reporting density in our inventory varies. During the 20th century alone, the number of yearly documented GLOFs increased 6-fold. Less than one-quarter of all reported cases feature hydrodynamic characteristics such as flood peak discharge or volume or estimates of loss and damage. Our inventory more than doubles the number of reported GLOFs in a previous global inventory, though gaps in attributes remain. Our data collection process emphasizes the support of local experts in contributing previously undocumented cases, and we recommend applying protocols when reporting new cases. The global database on historic GLOFs is archived at https://doi.org/10.5281/zenodo.7330344 (Lützow and Veh, 2023a) and regularly updated at http://glofs.geoecology.uni-potsdam.de/ (last access: 9 May 2023).

Estimates of persistent inward currents in lower limb motoneurons are larger in females than in males.

Noninvasive recordings of motor unit (MU) spike trains help us understand how the nervous system controls movement and how it adapts to various physiological conditions. The majority of participants in human and nonhuman animal physiology studies are male, and it is assumed that mechanisms uncovered in these studies are shared between males and females. However, sex differences in neurological impairment and physical performance warrant the study of sex as a biological variable in human physiology and performance. To begin addressing this gap in the study of biophysical properties of human motoneurons, we quantified MU discharge rates and estimates of persistent inward current (PIC) magnitude in both sexes. We decomposed MU spike trains from the tibialis anterior (TA), medial gastrocnemius (MG), and soleus (SOL) using high-density surface electromyography and blind source separation algorithms. Ten participants of each sex performed slow triangular (10 s up and down) isometric contractions to a peak of 30% of their maximum voluntary contraction. We then used linear mixed-effects models to determine if peak discharge rate and estimates of PICs were predicted by the fixed effects of sex, muscle, and their interaction. Despite a lack of sex-differences in peak discharge rates across all muscles, estimates of PICs were larger [χ2(1) = 6.26, P = 0.012] in females [4.73 ± 0.242 pulses per second (pps)] than in males (3.81 ± 0.240 pps). These findings suggest that neuromodulatory drive, inhibitory input, and/or biophysical properties of motoneurons differ between the sexes and may contribute to differences in MU discharge patterns.NEW & NOTEWORTHY Sex-related differences in motoneuron analyses have emerged with greater inclusion of female participants, however, mechanisms for these differences remain unclear. Estimates of persistent inward currents (i.e., ΔF) in motoneurons of the lower limb muscles were larger in females than in males. This suggests neuromodulatory drive, monoaminergic signaling, intrinsic motoneuron properties, and/or descending motor commands may differ between the sexes, which provides a potential mechanism underlying previously reported sex-related differences in motoneuron discharge patterns.

Intercomparison and Assessment of Stand-Alone and Wavelet-Coupled Machine Learning Models for Simulating Rainfall-Runoff Process in Four Basins of Pothohar Region, Pakistan

The science of hydrological modeling has continuously evolved under the influence of rapid advancements in software and hardware technologies. Starting from simple rational formulae for estimating peak discharge and developing into sophisticated univariate predictive models, accurate conversion of rainfall into runoff and the assessment of inherent uncertainty has been a prime focus for researchers. Therefore, alternative data-driven methods have gained widespread attention in hydrology. Moreover, scientists often couple conventional machine learning models with data pre-processing techniques, i.e., wavelet transformation (WT), to enhance modelling accuracy. In this context, this research work attempts to explore the latent linkage between rainfall and runoff in Pothohar region of Pakistan by developing a novel linkage of five streamline techniques of machine learning, including single decision tree (SDT), decision tree forest (DTF), tree boost (TB), multilayer perceptron (MLP), and gene expression modeling (GEP), with a more sophisticated variant of WT, i.e., maximal overlap discrete wavelet transformation (MODWT), for boundary correction of the transformed components of timeseries data. This study also implements these machine learning models in a stand-alone mode for a more comprehensive comparative analysis of performances. Furthermore, the study uses a combined-basin approach that divides Pothohar region into two basins to compensate for the complex topographic division of the study area. The results indicate that MODWT-based DTF outperformed other stand-alone and hybrid models in terms of modeling accuracy. In the first scenario, considering the Bunha-Kahan River basin, MODWT-DTF yielded the highest NSE (0.86) and the lowest RMSE (220.45 mm) and R2 (0.92 at lag order 3 (Lo3)) when transformed with daubechies4 (db4) at level three. While in the Soan-Haro River basin, MODWT-DTF produced the highest accuracy modeling at lag order 4 (Lo4) (NSE = 0.88, RMSE = 21.72 m3/s, and R2 = 0.91). The highly accurate performance of 3- and 4-days lagged models reflects the temporal consistency in hydrological response of the study area. The comparison of simple and hybrid model performance indicates up to a 55% increase in modeling accuracy due to data pre-processing with wavelet transformation.

Flood Hazard Assessment of Water-Front Geosynthetic Reinforced Soil Wall for Dam Regulation Rule Level

The flood hazard parameter of peak discharge and prolonged inundation due to change in dam regulation is estimated for a water front Geosynthetic Reinforced Soil (GRS) wall on the downstream of large dam. Dam regulation rule level is pre-emptying the reservoir with lower peak discharge to protect the expanding downstream city intruding the flood plain. Present study is to test the hypothesis that rule level has significantly changed peak discharge and inundation duration and also to estimate these two important parameters of flood hazard for different flood return periods. The methodology consists of three parts: first is the categorization of flood data according to major event of flood regulation, second is the distribution test and third is the estimation of design maximum peak discharge and duration of flood. The estimated value of peak discharge and flood duration clearly indicate the implication of dam regulation rule level; the estimated value of peak discharge for 200 years return period is 13.9 lakh Cusecs, which is lower than the flood discharge for year 1968, which was 15 lakh Cusecs, and the estimated flood duration for 10 years return period is 41 days. The most important finding of this study is the substantial increase in duration of flood due to implementation of rule level which will add one more flood hazard parameter for water front geotechnical structure that is prolonged inundation.

Study of debris flow peak discharge at Kamikamihorizawa Creek

Prediction of the peak discharge of debris flow is one of the most important factors in mitigating debris flow disasters. Currently, empirical methods based on the relationship between the peak discharge and total debris flow volume (magnitude) for many debris flow events are used to estimate peak discharge for planning or designing debris flow countermeasures in Japan. In order to estimate the peak discharge of debris flow with high accuracy, the debris flow monitoring sensors with load cell and pressure sensor (hereafter referred to as a DFLP system), was installed to evaluate various characteristics during floods involving debris flows at Kamikamihorizawa Creek. During the debris flow that occurred on August 29, 2019 at Kamikamihorizawa Creek, seven surges corresponding to debris flow events were monitored by the DFLP system for a 20-minute period. Based on the relationship between the peak discharge and magnitude, the observation data of Kamikamihorizawa Creek, Illgraben and Schipfenbach has been added to previous data from the literature and the relationship has been updated. The peak discharge and magnitude of debris flow on August 29, 2019 at Kamikamihorizawa Creek exhibited distributions similar to past observation data, which shows that the relationship between peak discharge and magnitude follows a similar trend.