Index Flood Research Articles

Visualisation of flooding along an unvegetated, ephemeral river using Google Earth Engine: Implications for assessment of channel-floodplain dynamics in a time of rapid environmental change

Given rapid environmental change, the development of new, data-driven, interdisciplinary approaches is essential for improving assessment and management of river systems, especially with respect to flooding. In the world's extensive drylands, difficulties in obtaining field observations of major hydrological events mean that remote sensing techniques are commonly used to map river floods and assess flood impacts. Such techniques, however, are dependent on available cloud-free imagery during or immediately after peak discharge, and single images may omit important flood-related hydrogeomorphological events. Here, we combine multiple Landsat images from Google Earth Engine (GEE) with precipitation datasets and high-resolution (<0.65 m) satellite imagery to visualise flooding and assess the associated channel-floodplain dynamics along a 25 km reach of the unvegetated, ephemeral Río Colorado, Bolivia. After cloud and shadow removal, Landsat surface reflectance data were used to calculate the Modified Normalized Difference Water Index (MNDWI) and map flood extents and patterns. From 2004 through 2016, annual flooding area along the narrow (<30 m), shallow (<1.7 m), fine-grained (dominantly silt/clay) channels was positively correlated (R2 = 0.83) with 2-day maximum precipitation totals. Rapid meander bend migration, bank erosion, and frequent overbank flooding was associated with formation of crevasse channels, splays, and headward-eroding channels, and with avulsion (shifting of flow from one channel to another). These processes demonstrate ongoing, widespread channel-floodplain dynamics despite low stream powers and cohesive sediments. Application of our study approaches to other dryland rivers will help generate comparative data on the controls, rates, patterns and timescales of channel-floodplain dynamics under scenarios of climate change and direct human impacts, with potential implications for improved river management.

Development of Flood Monitoring Index for daily flood risk evaluation: case studies in Fiji

Both fluvial and pluvial floods are a common occurrence in Fiji with fluvial floods causing significant economic consequences for island nations. To investigate flood risk and provide a mitigation tool on daily basis, the Flood Index ( $$I_{F}$$ ) is developed based on the rationale that the onset and severity of an event is based on current and antecedent day’s precipitation. This mathematical methodology considers the notion that the impact of daily cumulative precipitation on a particular flood event arising from a previous day’s precipitation, decreasing gradually over time due to the interaction of hydrological factors (e.g., evaporation, percolation, seepage, surface run-off, drainage, etc.,). These are accounted for, mathematically, by a time-reduction weighted precipitation influencing the magnitude of $$I_{F }$$ . Considering the duration, severity and intensity of all identified events, the applicability of $$I_{F}$$ is tested at 9 study sites in Fiji using 30-year precipitation datasets (1990–2019) obtained from Fiji Meteorological Services. Newly developed $$I_{F}$$ is adopted at flood prone sites, with results demonstrating that flood events were common throughout the country, mostly notable between November to April (or the wet season). Upon examining the variations in daily $$I_{F}$$ , the flood properties were determined, showing that the most severe events generally started in January. Flood events with the highest severity were recorded in Lautoka [ $$I_{F}^{acc}$$ (flood severity) $$\approx 149.14$$ , $$I_{F}^{max}$$ (peak danger) $$\approx 3.39$$ , $$D_{F}$$ (duration of flood) $$\approx 151\;{\text{days}}$$ , $$t_{onset}$$ (onset date) $$= 23{\text{rd}}\;{\text{January}}\;2012$$ ], followed by Savusavu $$(I_{F}^{acc} \approx 141.65, I_{F}^{max} \approx 1.75, D_{F} \approx 195\; {\text{days}}, t_{onset} = 27{\text{th}}\;{\text{November}}\;1999)$$ and Ba $$(I_{F}^{acc} \approx 131.57, I_{F}^{max} \approx 3.13, D_{F} \approx 113 \;{\text{days}}, t_{onset} = 9{\text{th}}\;{\text{January}}\;2009)$$ . The results clearly illustrate the practicality of daily $$I_{F}$$ in determining the duration, severity, and intensity of flood situation, as well as its potential application to small island nations. The use of daily $$I_{F}$$ to quantify flood events can therefore enable a cost-effective and innovative solution to study historical floods in both developing and first world countries. Our methodology is particularly useful to governments, private organizations, non-governmental organizations and communities to help develop community-amicable policy and strategic plans to prepare for flood impacts and undertake the necessary risk mitigation measures.

Regional frequency analysis of extreme precipitation based on a nonstationary population index flood method

Anthropogenic climate change has led to nonstationarity in hydrological data and their statistical characteristics. To consider nonstationarity in regional frequency analysis, several nonstationary index flood (NS-IF) methods comprising a time-dependent site-specific scaling factor or nonstationary regional growth curves have been suggested. However, these methods have limitations related to underestimation from using sample statistics as a site-specific scaling factor or considering nonstationarity only in regional parameters. To overcome these drawbacks, this study developed a nonstationary population index flood (NS-PIF) method that considers nonstationarity in the statistical characteristics at each site in a region based on nonstationary generalized extreme value distributions. Monte Carlo simulations were conducted for synthetic regions under various nonstationary conditions to compare the performance of the NS-PIF method with those of existing NS-IF methods. Then the applicability of the NS-PIF method to real-world data was assessed via Monte Carlo simulations of regions with annual maximum rainfall data in South Korea. The results indicated that the NS-PIF method can solve the underestimation problem inherent in existing NS-IF methods. Moreover, the NS-PIF method yielded the best performance and provided more reliable and reasonable quantile estimates considering site-specific trends. In addition, the heterogeneity measure based on L-skewness and L-kurtosis was identified as a suitable test of homogeneity for application of the proposed method.

Estimating Flood Characteristics Using Geomorphologic Flood Index with Regards to Rainfall Intensity-Duration-Frequency-Area Curves and CADDIES-2D Model in Three Iranian Basins

There is not enough data and computational power for conventional flood mapping methods in many parts of the world, thus fast and low-data-demanding methods are very useful in facing the disaster. This paper presents an innovative procedure for estimating flood extent and depth using only DEM SRTM 30 m and the Geomorphic Flood Index (GFI). The Geomorphologic Flood Assessment (GFA) tool which is the corresponding application of the GFI in QGIS is implemented to achieved the results in three basins in Iran. Moreover, the novel concept of Intensity-Duration-Frequency-Area (IDFA) curves is introduced to modify the GFI model by imposing a constraint on the maximum hydrologically contributing area of a basin. The GFA model implements the linear binary classification algorithm to classify a watershed into flooded and non-flooded areas using an optimized GFI threshold that minimizes the errors with a standard flood map of a small region in the study area. The standard hydraulic model envisaged for this study is the Cellular Automata Dual-DraInagE Simulation (CADDIES) 2D model which employs simple transition rules and a weight-based system rather than complex shallow water equations allowing fast flood modelling for large-scale problems. The results revealed that the floodplains generated by the GFI has a good agreement with the standard maps, especially in the fluvial rivers. However, the performance of the GFI decreases in the less steep and alluvial rivers. With some overestimation, the GFI model is also able to capture the general trend of water depth variations in comparison with the CADDIES-2D flood depth map. The modifications made in the GFI model, to confine the maximum precipitable area through implementing the IDFAs, improved the classification of flooded area and estimation of water depth in all study areas. Finally, the calibrated GFI thresholds were used to achieve the complete 100-year floodplain maps of the study areas.

Predictive Modeling of Envelope Flood Extents Using Geomorphic and Climatic‐Hydrologic Catchment Characteristics

AbstractA topographic index (flood descriptor) that combines the scaling of bankfull depth with morphology was shown to describe the tendency of an area to be flooded. However, this approach depends on the quality and availability of flood maps and assumes that outcomes can be directly extrapolated and downscaled. This work attempts to relax these problems and answer two questions: (1) Can functional relationships be established between a flood descriptor and geomorphic and climatic‐hydrologic catchment characteristics? (2) If so, can they be used for low‐complexity predictive modeling of envelope flood extents? Linear stepwise and random forest regressions are developed based on classification outcomes of a flood descriptor, using high‐resolution flood modeling results as training benchmarks, and on catchment characteristics. Elementary catchments of four river basins in Europe (Thames, Weser, Rhine, and Danube) serve as training data set, while those of the Rhône river basin in Europe serve as testing data set. Two return periods are considered, the 10‐ and 10,000‐year. Prediction of envelope flood extents and flood‐prone areas show that both models achieve high hit rates with respect to testing benchmarks. Average values were found to be above 60% and 80% for the 10‐ and the 10,000‐year return periods, respectively. In spite of a moderate to high false discovery rate, the critical success index value was also found to be moderate to high. It is shown that by relating classification outcomes to catchment characteristics, the prediction of envelope flood extents may be achieved for a given region, including ungauged basins.

An integrated approach for investigating geomorphic changes due to flash flooding in two small stream channels (Western Polish Carpathians)

Study regionPoland, Carpathians, Gorce Mountains, Jamne and Jaszcze catchments. Study focusThe hydrological response to, and geomorphic impacts of, a 18 July 2018 flood involving two small catchments (each covering less than 12 km2) that were not hydrologically controlled were examined. A peak flood discharge was simulated with two hydrological models: SCS-CN and SCS-UH. Hydrological modelling was performed in order to complement the post-flooding investigations and to verify the hydrological data via cross-check comparison. Geodetic measurements of the channels and ALS and TLS data were used to analyse the geomorphological changes. In addition, the flood magnitude recorded for the studied streams was compared with the other local flash floods in the Carpathians using the unit K flood index (a non-dimensional measure). New hydrological insights for the regionThe maximum unit flow (4.1–4.8 m3 s−1 km-2) placed this flood event among the 10 largest flash floods in the Polish Carpathians (based on the K index). The peak discharge simulated with the SCS-CN and SCS-UH hydrological models was similar to the data obtained during the post-flooding investigation, indicating that this method can be used for catchments in the Polish Carpathians that are not managed hydrologically. The high unit stream powers estimated for the analysed channel cross sections (>750 W m-2) were associated with narrow channels and steep slopes. The most significant geomorphological changes were observed in unmanaged channel reaches. Moreover, the channel transformations in the Jamne catchment were greater than those in the Jaszcze catchment (in a more forested area). This analysis can be useful in the evaluation of flood risk management processes associated with flash floods in small, unmanaged catchments.

Comparison of two approaches for an estimation of the mean annual flood at ungauged sites in Slovakia

AbstractRegional flood frequency analysis is considered to be an important and popular method for estimating different hydrological variables at ungauged sites. The estimation of the index flood is the essential problem when this method is applied. The objective of the study is a comparison of the estimation of the mean annual flood (or index flood) by using two approaches based on the ‘so-called’ index flood method and top-kriging. The concept behind these methods permits estimating the mean annual flood at ungauged locations using information taken from gauged sites located within the same homogeneous pooling groups. The study area comprises 104 gauging stations on the whole territory of Slovakia. The observation period of the annual maximum discharges of the selected stations was from 1961-2010. The identification of the homogeneous pooling group was performed using a non-hierarchical k-means clustering algorithm. The optimal number of clusters is determined by the Silhouette method. As a result, eight homogeneous pooling group clusters were identified. Finally, the results of the estimated mean annual floods using the index flood method and top-kriging were compared with the observed data. Top-kriging provided better results than the classical index flood method for estimating the mean annual flood at ungauged sites.

Investigating regional distribution for maximum daily rainfall in arid regions: case study in Saudi Arabia

The hydrologists need effective procedures to assist them in predicting the implications related to high rainfall risks, both at gauged and ungauged areas. The first step for any assessment is estimating the rainfall values associated with various return periods in years. This information is obtained using rainfall frequency analysis techniques based on observed maximum daily rainfall values. The existence of a regional distribution for the rainfall frequency within a certain region is considered as a precious information for the hydrologists to estimate—with confidence—the expected rainfall at high return periods. The aim of this research is to determine the regional statistical distribution for various regions in Saudi Arabia. Saudi Arabia was selected to represent arid regions as it covers a large area of the Arabian Peninsula in addition to the availability of rainfall data compared with other similar countries. The data of 394 rainfall gauging stations were included in the study, which covers all Saudi Arabia in its 13 main regions. The data was analyzed using multiple frequency analysis methods and was subjected to various statistical tests including Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Anderson-Darling Criterion (AD), Station-Year Analysis, and Index Flood Method, in order to determine the best regional statistical distribution and define the homogenous regions using regional analysis. The study concluded that the Log-Pearson type III distribution was the best model to describe the distribution of the daily maximum rainfall in the region. Saudi Arabia was sub-divided into seven homogeneous regions. Contour maps for the 2-, 5-, 10-, 25-, 50-, and 100-year rainfall were produced to predict the rainfall at any point within the Kingdom of Saudi Arabia.

Estimating the UK Index Flood: an Improved Spatial Flooding Analysis

Flooding is one of the major natural hazards in the UK. Accurate flood estimation at ungauged catchment is an important component to understand and mitigate flood hazards, but still a difficult issue. This study therefore attempts to explore and improve an index flood estimation model, known as the FEH-QMED model, popular in the UK. It was developed under the assumption that the index flood of QMED, i.e., the median of the set of annual maximum (AMAX) flood data, standing for a flooding level of 2-year return period, can be explained by catchment descriptors. In this study, two fundamentals are empirically explored, including assessing reliability of the nonlinear functional impacts of the catchment descriptors on the logarithmic transformation of QMED, specified by the FEH-QMED model, and the potential to improve the model for more accurate index flood estimation, based on the flooding data of 586 gauged stations across the UK. Through a spatial additive regression analysis, we empirically find that the nonlinear impacts of the catchment descriptors in an updated FEH-QMED model appear reliable. However, spatial correlation tests including Moran’s I and Lagrange multiplier tests show that strong spatial dependence exists in the residuals of, but was not fully taken into account by, the QMED type models. We have therefore empirically established new spatial index flood estimation models by proposing spatial autoregressive models to model the impacts of the neighboring sites. Cross-validation assessments demonstrate that the suggested spatial error-based index flood model outperforms the updated FEH-QMED model with a significant improvement, which is robust in the sense of different error measures, say by a reduction of 13.8% of the mean squared error of prediction, for the UK index flood estimation.

Penalized quasi-maximum likelihood estimation for extreme value models with application to flood frequency analysis

A common statistical problem in hydrology is the estimation of annual maximal river flow distributions and their quantiles, with the objective of evaluating flood protection systems. Typically, record lengths are short and estimators imprecise, so that it is advisable to exploit additional sources of information. However, there is often uncertainty about the adequacy of such information, and a strict decision on whether to use it is difficult. We propose penalized quasi-maximum likelihood estimators to overcome this dilemma, allowing one to push the model towards a reasonable direction defined a priori. We are particularly interested in regional settings, with river flow observations collected at multiple stations. To account for regional information, we introduce a penalization term inspired by the popular Index Flood assumption. Unlike in standard approaches, the degree of regionalization can be controlled gradually instead of deciding between a local or a regional estimator. Theoretical results on the consistency of the estimator are provided and extensive simulations are performed for the reason of comparison with other local and regional estimators. The proposed procedure yields very good results, both for homogeneous as well as for heterogeneous groups of sites. A case study consisting of sites in Saxony, Germany, illustrates the applicability to real data.

Revised Dicken’s method for flood frequency estimation of Upper Ganga basin

Flood discharge and its frequency is an important input to many infrastructure development, management and decision making models. However, for a number of potential sites, the data used for statistical analysis of flood discharge and its frequency, are either insufficient or unavailable. In such a situation, empirical formulae are the only alternative method to provide an estimate of design flood. Dicken’s formula is one of such empirical formulae used by practicing field engineers for flood estimation. In spite of its simplicity and applicability, Dicken’s formula is not capable of yielding flood volume at different desired frequency. In the present study an attempt has been made to modify the Dicken’s coefficient so as to accommodate the frequency component in flood estimation. For this purpose, observed flood discharge data sixteen gauging points of Ganga basin have been used to develop regional flood frequency curve, using probability weighted method, Index flood method, General extreme value and Wakeby distribution. Modified Dicken’s coefficient CT shows a sharp gradient for sub-catchments draining up to 5000 Km2. Further, CT value, ranges from 0.834 to 6.924. The CT –values are lower when calculated by Index Flood method while these are higher by Wakeby method. In all of these methods, CT –values do not approach the values of 11 or 14 as suggested for northern Indian catchments. An analysis of flood volume corresponding to CT –values of 11 or 14, reveals that these values yield high flood volume that correspond to return period of more than 1000 years and 3000 years respectively. These values are certainly very high for any rational design. Best fit distribution is decided, based on annual maximum discharge peak (ADF), Efficiency (EFF) and Standard Error (SE) parameters. Prediction accuracy of flood frequency analysis using revised Dicken’s formula is found to be above 85% for the best-fit distribution. General Extreme Value is found to be the best fit distribution for the Upper Ganga basin.

Urban flash flood index based on historical rainfall events

Urban flash flood poses significant hazards on urbanised area, in particular to buildings and infrastructure due to its fast occurrence and high magnitude in financial loss. Risk assessment of the flash flood identify the critically flood-prone areas and provide assistance in improving the resiliency of mitigation plans. In this study, we developed an assessment of flood susceptibility, vulnerability, the impact of socio-economic, and integrated flash flood index based on the historical data of flood events recorded in Kuala Lumpur. Data of rainfall characteristics, inundated location, and areas are extracted from the reports of flood events from the year 2005-2015. Each event is then segregated according to the place of incidence, providing point-based recurrence of flood at each identified location. Indicators of assessment include frequency and month of occurrence, rainfall characteristics (of intensity, duration, and depth), and land use categories. A total of 137 (point) locations have been identified, where each location is colour-coded based on a 5-point rating scale. The point-based flood-prone locations are validated with the watershed based of 50-ARI rainfall modelling, providing comprehensive hotspot maps. Developed interactive colour-coded flood prone maps facilitate relevant agencies for improved coordination in flash flood mitigation, response and early warning.

Evaluation of coupled ANN-GA model to prioritize flood source areas in ungauged watersheds

Abstract An important step in flood control planning is identification of flood source areas (FSAs). This study presents a methodology for identifying FSAs. Unit flood response (UFR) approach has been proposed to quantify FSAs at subwatershed and/or cell scale. In this study, a distributed ModClark model linked with Muskingum flow routing was used for hydrological simulations. Furthermore, a fuzzy hybrid clustering method was adopted to identify hydrological homogenous regions (HHRs) resulting in clusters involving the most effective variables in runoff generation as selected through factor analysis (FA). The selected variables along with 50-year rainfall were entered into an artificial neural network (ANN) model optimized via genetic algorithm (GA) to predict flood index (FI) at cell scale. The case studies were two semi-arid watersheds, Tangrah in northeastern Iran and Walnut Gulch Experimental Watershed in Arizona. The results revealed that the predicted values of FI via ANN-GA were slightly different from those derived via UFR in terms of mean squared error (MSE), mean absolute error (MAE), and relative error (RE). Also, the prioritized FSAs via ANN-GA were almost similar to those of UFR. The proposed methodology may be applicable in prioritization of HHRs with respect to flood generation in ungauged semi-arid watersheds.

Regional flood frequency analysis for flood index estimation in hydrologic regions with limited flood data

The objective of this study is to establish a regional relationship between mean annual peak flood and the catchment area based on the annual peak flood frequency analysis. Annual peak flood time series for various gauging sites of six basins in north part of Iraq are used in analysis. The procedure of analysis is approached by scaling a regional flood frequency distribution (index-flood of catchment) which is defined here as the mean annual maximum flood discharge and estimated by linear regression using physiographic and climatic catchment properties. The index-flood method was found to have slightly better prediction accuracies over the direct-regression method. The coefficient of determination R2 between reference and estimated index-floods is relatively high in most cases for all the gauging stations. All regions give very high R 2 correlation and the best model is the model (2), which is also associated with minimum value of RMSE.

Flood frequency analysis

The recurring flooding causes loss of life and damage to buildings and other structures, including bridges, sewerage systems, roadways and canals. It also frequently damages power transmission and sometimes power generation, which then has knock-on effects caused by the loss of power. The present study compares the relative performance of flood frequency methods to estimate design flood, using available data of 18 small catchments in the Mahanadi River basin (India). The primary objective of the referred approach was for design flood estimation at gauge sites; however, the main focus is referred to ungauged catchments by an interpolation method. In this regard, three interpolation methods are used: (1) inverse distance weighing method, (2) ordinary kriging and (3) area weighted method. As per the recent trends, flood frequency analysis methods are used specifically for two data types, i.e., at-site analysis and for regionalizing the available data within the homogeneous region. In this study, an attempt has been made to categorize the interpolation properties, where the first two approaches belong to site analysis and the third one uses the regional analysis. In the first approach, the output results in terms of flood quantiles are interpolated for the intermediate results, which is generally termed as direct interpolation of flood quantile, and the second approach uses the linear interpolating or L-moments in flood estimation. The above one refers to the interpolation of L-moments, while flood index is interpolated in the third approach, which is named as ‘flood index procedure.’ In the study, it was observed that the designed flood quantile results were better by using the flood index approach at lower return periods at 2 and 5 years, and the direct interpolation method gave a better estimation for higher return periods. Further, it was found that the difference in prediction error of direct interpolation of flood quantiles and the flood index procedure is negligible.

Floods and Hydrograms of Floods of Rivers in Arid Zones of the Mediterranean, Case of the Kingdom of Morocco

The Moroccan territory is often exposed to brutal and destructive floods. The latter is triggered by intense rains and sudden duration (rain showers, thunderstorms). The human and material damage caused by these floods has always been very high. The violence of the flood (1000 m3/s) of Oued Ourika on Aug 17, 1995, killed 730 people. The material damage caused by torrential rain only for the years 2008 and 2009 on the road network (roads and civil engineering works) represents more than 2.9 billion dirhams. The development of flood protection studies requires the values of the instantaneous frequency flow of a certain probability and the corresponding flood hydrogram, generally, these values are not available in the watersheds studied. The objective of this study is to propose for the Moroccan territory a method of calculating flood flows and hydrographs of floods for stream and wadis ungauged for the benefit of engineers and planners for possible studies of flood protection. The proposed method is based on the delimitation of homogeneous regions of Moroccan territory (three homogeneous regions). The frequency analysis carried out for these three zones made it possible to identify the climatic parameters necessary to calculate the flood index and consequently the frequency floods. The proposed Galton type flood hydrograph includes three parameters namely the rise time, the shape parameter and the flood index. These latter parameters are easily calculated by the formulas proposed.

Local and regional flood frequency analysis based on hierarchical Bayesian model in Dongting Lake Basin, China

This study developed a hierarchical Bayesian (HB) model for local and regional flood frequency analysis in the Dongting Lake Basin, in China. The annual maximum daily flows from 15 streamflow-gauged sites in the study area were analyzed with the HB model. The generalized extreme value (GEV) distribution was selected as the extreme flood distribution, and the GEV distribution location and scale parameters were spatially modeled through a regression approach with the drainage area as a covariate. The Markov chain Monte Carlo (MCMC) method with Gibbs sampling was employed to calculate the posterior distribution in the HB model. The results showed that the proposed HB model provided satisfactory Bayesian credible intervals for flood quantiles, while the traditional delta method could not provide reliable uncertainty estimations for large flood quantiles, due to the fact that the lower confidence bounds tended to decrease as the return periods increased. Furthermore, the HB model for regional analysis allowed for a reduction in the value of some restrictive assumptions in the traditional index flood method, such as the homogeneity region assumption and the scale invariance assumption. The HB model can also provide an uncertainty band of flood quantile prediction at a poorly gauged or ungauged site, but the index flood method with L-moments does not demonstrate this uncertainty directly. Therefore, the HB model is an effective method of implementing the flexible local and regional frequency analysis scheme, and of quantifying the associated predictive uncertainty.

Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network

Abstract. In many countries, urban flooding due to local, intense rainfall is expected to become more frequent because of climate change and urbanization. Cities trying to adapt to this growing risk are challenged by a chronic lack of surface flooding data that are needed for flood risk assessment and planning. In this work, we propose a new approach that exploits existing surveillance camera systems to provide qualitative flood level trend information at scale. The approach uses a deep convolutional neural network (DCNN) to detect floodwater in surveillance footage and a novel qualitative flood index (namely, the static observer flooding index – SOFI) as a proxy for water level fluctuations visible from a surveillance camera's viewpoint. To demonstrate the approach, we trained the DCNN on 1218 flooding images collected from the Internet and applied it to six surveillance videos representing different flooding and lighting conditions. The SOFI signal obtained from the videos had a 75 % correlation to the actual water level fluctuation on average. By retraining the DCNN with a few frames from a given video, the correlation is increased to 85 % on average. The results confirm that the approach is versatile, with the potential to be applied to a variety of surveillance camera models and flooding situations without the need for on-site camera calibration. Thanks to this flexibility, this approach could be a cheap and highly scalable alternative to conventional sensing methods.

Using Physically Based Synthetic Peak Flows to Assess Local and Regional Flood Frequency Analysis Methods

AbstractAlthough prior studies have evaluated the role of sampling errors associated with local and regional methods to estimate peak flow quantiles, the investigation of epistemic errors is more difficult because the underlying properties of the random variable have been prescribed using ad‐hoc characterizations of the regional distributions of peak flows. This study addresses this challenge using representations of regional peak flow distributions derived from a combined framework of stochastic storm transposition, radar rainfall observations, and distributed hydrologic modeling. The authors evaluated four commonly used peak flow quantile estimation methods using synthetic peak flows at 5,000 sites in the Turkey River watershed in Iowa, USA. They first used at‐site flood frequency analysis using the Pearson Type III distribution with L‐moments. The authors then pooled regional information using (1) the index flood method, (2) the quantile regression technique, and (3) the parameter regression. This approach allowed quantification of error components stemming from epistemic assumptions, parameter estimation method, sample size, and, in the regional approaches, the number of pooled sites. The results demonstrate that the inability to capture the spatial variability of the skewness of the peak flows dominates epistemic error for regional methods. We concluded that, in the study basin, this variability could be partially explained by river network structure and the predominant orientation of the watershed. The general approach used in this study is promising in that it brings new tools and sources of data to the study of the old hydrologic problem of flood frequency analysis.

Flood Hazard Assessment Based on Analysis of Geomorphic Flood Index and History of Flood Events (Case Study in Kemuning Watershed, Sampang)

Sampang is one of regency in Madura which always experiences floods every year. The frequency of flood events reach the number of 15 to 18 Sampang regency is divided into 6 watersheds that consist of Kemuning, Nedung, Blega, Samajid, Tambengan, and Tamberu watersheds events annually. From all watersheds, Kemuning watershed has the biggest area and become the priority in management of critical land management due to the impact of floods. The Length of Kemuning watershed is 54.11 kilometres with a watershed area of 354.50 km square consisting of 6 sub-districts, namely Sampang, Omben, Kedungdung, Robata, Torjun, and Camplong. The objective of the paper is to analyse the hazard index in Kemuning watershed, Sampang. The disaster hazard level is divided into 3 classes; low (hazard index <0.333), medium (hazard index: 0.333 - 0.666), high (hazard index> 0.666). The method used is a analysis which combines/overlays parameters of flood-prone areas and History of Flood Events data. The result is a flood hazard map that with details of 3476,18 hectare (51,71%) high level, 2614,37 hectare (38,89%) medium level, 631,61 hectare (9,39%) low level.