Flood Water Level Research Articles

Global sensitivity analysis in hydrodynamic modeling and flood inundation mapping

Flood simulation with two-dimensional hydrodynamic models is subject to different sources of uncertainties in model configuration, boundary conditions, and model parametrization. HEC-RAS 2D is a widely used hydrodynamic model. Here, we assess the sensitivity of the HEC-RAS 2D to its configuration factors and parameters. We evaluate the impacts of different model configuration factors, including the floodplain and channel roughness coefficients, terrain and mesh size, as well as river boundary conditions on the dynamic of water levels, maximum water level, and flood extent, and determine the importance of these contributing factors for reliable flood inundation modeling using both variogram and variance-based analyses. For our case study, we found that depending on the perturbation scale, the performance measure, and the predicted output, the sensitivity to input factors changes. While for simulation of water level dynamics, the DEM and mesh resolutions are the most important factors, for flood extent mapping the floodplain roughness coefficient and the upstream boundary condition are the key factors. In addition, our analyses indicated that for a reliable simulation of maximum flood water level, modelers need to spend resources on the calibration of floodplain roughness coefficient while using fine DEM and mesh resolutions. Here, we also investigate the role of computational time interval and mesh resolution on the model's run time and accuracy. Our results suggested that reducing the computational time interval has minimum impact, as it increases the model run time without much improving the model accuracy.

Fragility functions for highway RC bridge under various flood scenarios

Bridges are a country's lifeline infrastructure. As a result of climate change and global warming, floods are becoming more frequent and unpredictable. Even well-designed bridges are vulnerable to extreme flooding, as recent flood events have demonstrated. Flood fragility curves are developed for an existing RC rail over bridge considering various loading scenarios such as flood level up to the top of the pier, flood level up to the top of the girder, the impact due to log and debris, the effect of providing shear key and the effect due to scouring. The threshold mean flood velocities for the initiation of serviceability limit state for the above flood scenarios are found out. When there is no shear key, the exceedance probability for the deck displacement is found to increase with the flood water level. This increase is found to be about 92% at a particular mean flood velocity of 8.6 m/s, when the floodwater level increases from 3/4th the height of the girder to the full height of the girder. Typical log impact and debris impact increase the vulnerability of the bridge by about 45% and 80%, respectively, for a particular flood velocity of 6.8 m/s and when the flood level is up to the full height of the girder. The fragility of the bridge increases by about 80% for a scour depth of about 1.5 m at a mean flood velocity of 8.6 m/s.

The importance of peak attenuation for flood risk management, exemplified on the Meuse River, the Netherlands

Peak attenuation results in a gradual decrease of the peak discharge when a flood propagates in downstream direction. Peak attenuation occurs along all rivers, but is not equally strong on all rivers. The rate of peak attenuation depends on the river geometry, such as river slope, floodplain width and available storage areas (floodplain, wetlands and lakes). Along many rivers worldwide, measures have been taken that decrease peak attenuation and hence resulted in higher flood water levels and increased flood risk downstream. This paper shows the importance of considering peak attenuation in flood risk management. It also shows that construction of embankments may, contrary to what generally is expected, increase peak attenuation, but that this strongly depends on the height of the flood defences in combination with the discharges that pose the greatest threat to areas downstream. This is exemplified using the Meuse River in the Netherlands as a case study.

Pengaruh Pasang Surut Terhadap Profil Muka Air Banjir Bantaran Sungai Musi Kota Palembang

Palembang is famous for the Musi River with a length of 460 km. The area along the river border is vulnerable to the impact of floods, this is the same thing that occurs in the Gandus District, which is mostly located on the banks of the Musi River. Analysis of the flood water level profile along the banks of the Musi River, which is affected by the tides is needed to find out how big the impact of flooding in the study area is. It aims to determine the type of tide, the amount of flow discharge, and simulation of river water level. Primary data was taken using a current meter to determine the speed of river flow. Some of the secondary data needed are river channel topographic data, rainfall data for 10 years, channel cross-section data and Musi River level data. The analysis was carried out in the form of a simulation of flood water level with the help of the HEC-RAS 5.0.7 program. The tidal type of the Musi River is based on the results of the study in the form of Diurnal Tide, with a watershed area of 2.2 km2 in Gandus District, the runoff discharge is 280.34 m3/s. The simulation results with the help of the HEC-RAS program obtained an average runoff height on the Musi River border of 2.7 m. The high runoff from the simulation can have a negative impact on the surrounding community, so there needs to be serious handling for flood control.

Water Level Forecasting Using Spatiotemporal Attention-Based Long Short-Term Memory Network

Bangladesh is in the floodplains of the Ganges, Brahmaputra, and Meghna River delta, crisscrossed by an intricate web of rivers. Although the country is highly prone to flooding, the use of state-of-the-art deep learning models in predicting river water levels to aid flood forecasting is underexplored. Deep learning and attention-based models have shown high potential for accurately forecasting floods over space and time. The present study aims to develop a long short-term memory (LSTM) network and its attention-based architectures to predict flood water levels in the rivers of Bangladesh. The models developed in this study incorporated gauge-based water level data over 7 days for flood prediction at Dhaka and Sylhet stations. This study developed five models: artificial neural network (ANN), LSTM, spatial attention LSTM (SALSTM), temporal attention LSTM (TALSTM), and spatiotemporal attention LSTM (STALSTM). The multiple imputation by chained equations (MICE) method was applied to address missing data in the time series analysis. The results showed that the use of both spatial and temporal attention together increases the predictive performance of the LSTM model, which outperforms other attention-based LSTM models. The STALSTM-based flood forecasting system, developed in this study, could inform flood management plans to accurately predict floods in Bangladesh and elsewhere.

Reservoir Sediment Management and Downstream River Impacts for Sustainable Water Resources—Case Study of Shihmen Reservoir

Sustainable water resources of reservoirs depend on preserving the valuable storage capacity. Sediment management is a crucial task in reservoir operations. Extreme floods caused by typhoon events have brought a massive amount of sediments from the watershed of Shihmen Reservoir in Taiwan. In the case study of Shihmen Reservoir, the primary purpose of the sediment management strategies is to minimize sediment deposition and recover reservoir capacity. Two assessment indexes, the capacity–inflow ratio (CIR) and the capacity–sediment ratio (CSR) are investigated to provide a feasible assessment of desilting techniques. Three desilting projects have been planned and implemented in progress, including one modified power plant penstock and two desilting tunnels. Without effective sediment management strategies, the projected storage capacity may drop to 32.9% of the initial storage capacity in the next fifty years. On the other hand, if sediment management is implemented as per the project schedule, 70.3% of the initial storage capacity may be retained, enabling the provision of sustainable water supplies to meet projected water demands. In investigating the impacts caused by the desilting operations, the floodwater level and riverbed variations along the downstream river of the dam are simulated by the hydromorphological model. From simulated results, the downstream river morphology may adjust toward the equilibrium state under the long-term desilting operations of Shihmen Reservoir. Although relatively high sediment concentration is released during the desilting operation, it decays gradually along the downstream river. In addition, riverbed deposition does not significantly affect the floodwater level caused by the increment of riverbed elevation in desilting operations.

Early River Flood-Warning System Based on Embedded Systems

This paper details the design and construction of a Flood Warning System (FWS) for River Nyamwamba that has been prone to floods of a greater magnitude. The idea was developed on the principle that floods are a meteorological event that develops over time, and thus a need for sufficient time for people to evacuate, and to protect their lives and property. However, the range of existing FWSs have a tangle of conflicting requirements in terms of cost and reliability and have challenges from factors as diverse as technological and social. Built on Computer Embedded Systems, this study provides a cheaper and reliable FWS for a country like Uganda. River Nyamwamba flow was modelled with DEM, Topography sheets, river map, imageries, flow data, stage data, land use maps, and rainfall data. The data sets were conditioned and processed in a GIS environment using ArcGIS software and exported to the HECRAS program to perform a steady flow simulation of the river. High-risk areas were visualized that provided reliable river flow parameters that were used as input values for the design of the FWS. An Arduino programmed microcontrollers were used to control all input and output values regarding the modelled river. An ultrasonic sensor was used to monitor the normal flow, intermediate flow, and peak flood water levels. From this, the river stage was displayed onto an LCD screen at all times, an electronic SMS is sent to operators at intermediate flow, while an alarm is sounded at flood level.

Comparative analysis of long short-term memory and storage function model for flood water level forecasting of Bokha stream in NamHan River, Korea

In this study, the applicability of machine learning models was investigated for real-time flood forecasting of a small river basin with a short time of concentration and the modes were compared with the storage function model (SFM), a rainfall-runoff model. Bokha stream basin located in the NamHan river, Korea was selected as the study area. Flood water level forecasting was performed for Heungcheon bridge station which is located in the downstream of Bokha stream using hydrological data observed at Bokha bridge station located in the upstream of the stream. For each of the upstream and downstream basins, Rainfall, water level, and discharge data from 2005 to 2020 were collected at two stations and especially the collected rainfall data were classified into 53 rainfall events using Interevent Time Definition (IETD) analysis of rainfall data. In addition, flood water level forecasting at downstream point was performed using machine learning models such as GB (Gradient Boosting), SVM (Support Vector Model), and LSTM (Long Short-Term Memory). Also the SFM which is a rainfall-runoff model was used for the forecasting. For the application of machine learning models, 33 rainfall events were used for learning and 23 rainfall events were used for evaluation. With the SFM, the flood discharge was forecasted first and then the flood water level was forecasted through the rating curve. The flood water level forecasted by each model was compared with the observed flood water level and predictive power for each model was evaluated by calculating NRMSE (Normalized Root Mean Squared Error). The NRMSEs for the models were ranged from 0.18 to 0.27, and the predictive power was good in the order of the LSTM model at 0.18 followed by the SFM at 0.21. Therefore, the LSTM model showed the best predictive power and was selected as the optimal model for real-time flood water level forecasting in this study. However, the SFM is currently employed in Korea for flood forecasting and warning, and the model well incorporates the basin characteristics, showing relatively good predictive power. Based on the models presented in this study, an optimal model suitable for real-time flood water level forecasting can be selected for flood forecasting and warning points of a small river basin, and it is expected that the forecasting results can be used as base data for decision making.

Correlation of water consumption during irrigation of cotton with the dynamics of flood water levels mathematical model

This article provides information on the creation of a mathematical model of water consumption in the irrigation of cotton, depending on the change in the level of seepage water during the movement of salts and soil moisture. The world has 1,500 million hectares of drylands and 932 million hectares of saline soils, of which 32 million hectares are directly affected by salts.. A similar situation was observed in the Jizzakh and Khorezm regions, and it was noted that, respectively, the increase in the level of flood waters has changed.

Abnormal fluctuation of water level in the Chenglingji reach of the middle Yangtze River during flood seasons, 2016-2020: Influencing factors and filtering correction

长江经济带的人口和经济总量均超过全国的40%，长江中游城市群受地理位置与水文条件的综合影响更易遭受洪涝灾害的威胁.莲花塘水位站作为长江中游城陵矶河段蓄滞洪区启用指标控制站，当三峡水库水位达155.0 m后，如莲花塘站水位达到34.40 m并继续上涨，则城陵矶附近区蓄滞洪区需采取分洪措施.然而受河流形态和江湖关系（长江与洞庭湖）的双重影响，莲花塘水位站水位呈现出潮汐式不规则周期性波动变化（俗称假潮），给蓄滞洪区启用等工作带来一定决策困扰.为了研究洞庭湖出口长江城陵矶河段高水期洪水位波动现象相关因子和水位滤波方法，本文对莲花塘水位站近5年18个月（2016年6-8月、2017年7-10月、2018年5-8月、2019年6-8月、2020年6-9月）5 min水位自记数据进行分析，首先明确了单日水位波动值D>0.05 m为发生假潮现象的判别标准；通过二元Logistic回归分析表明，在监利站高水位（Z_j ≥ 34.00 m）期间，当监利至莲花塘水位落差（ΔZ_jl）大于2.75 m时，假潮发生比例为100%，模型预测结果综合百分比校正值为96.4%，表明ΔZ_jl为发生假潮的主要影响因子；然后通过快速傅里叶变换法、局部加权回归算法两种数据滤波方法对水位波动序列进行处理，研究表明，两种方法均适用于不同振幅的波形态数据分析，不受短时间数据丢失影响，不受水位瞬时值的影响，具有较大的应用价值；从误差值的波动情况来看，局部加权回归算法误差值波动更小，更适用于对波动水位数据的处理.;The Yangtze economic belt accounts for more than 40% of China's total populaton and economy, and the urban agglomeration in the middle reaches of the Yangtze River is more vulnerable to the threat of flood disasters due to the comprehensive influence of geographical location and hydrological conditions. The water level of the Lianhuatang station is served as the index control station of the flood storage and detention area in the middle reaches of the Yangtze River. When the water level of the Three Gorges Reservoir reaches 155.0 m, if the water level of the Lianhuatang station reaches 34.40 m and continues to rise, flood diversion measures should be taken in the flood storage and detention area near Chenglingji. While under the dual influence of the river form and the relationship between the river and the lake (the Yangtze River and Lake Dongting), the water level of the Lianhuatang station presents tidal irregular periodic fluctuation (commonly known as false tide), which brings decision-making difficulties to the flood storage and detention area. To study the related factors of flood level fluctuation phenomenon and water level filtering method in the Chenglingji reach of the Yangtze River at the outlet of Lake Dongting, this paper analyzed the self-recorded water level data of Lianhuatang water station in the last 5 years and 18 months (June-August 2016, July-October 2017, May-August 2018, June-August 2019, and June-September 2020). Firstly, the single-day water level fluctuation value D>0.05 m is defined as the criterion for the occurrence of false tide. And the Binary Logistic regression analysis showed that during the period of high water level (Z_j ≥ 34.00 m) at Jianli station, when the water level drop from Jianli to Lianhuatang (ΔZ_jl) is greater than 2.75 m, the proportion of false tide is 100%. The comprehensive correction value of model prediction results was 96.4%, indicating that ΔZ_jl was the main factor influencing the false tide. Then, two data filtering methods, fast fourier transform and local regression, are used to process the water level fluctuation sequence, which shows that both of the two methods are applicable to the analysis of wave shape data with different amplitudes, and are not affected by short-term data loss and instantaneous water level value, which may have great application value. From the perspective of the fluctuation of the error value, the locally weighted regression algorithm has a smaller fluctuation of the error value and is more suitable for the processing of the fluctuating water level data.

ANALISIS REMBESAN TERHADAP KEAMANAN BENDUNGAN KEDUNG OMBO DI GROBOGAN, JAWA TENGAH

A dam, besides having a great benefits to meet human needs, it also can be a big disaster in addition to the dam collapsing. One of the main causes of failure of an embankment dam is the occurrence of excessive seepage which triggers piping events that can disturb the stability and safety of the dam. In general, the body of the Kedung Ombo Dam is in good condition, but there are several problems, such as the drain holes that are overgrown with dense grass which indicates that seepage has occurred. Therefore, it is necessary to evaluate the seepage to determine the safety level of the Kedung Ombo Dam. This study aims to analyze the condition of pore water pressure and seepage that occurs in the body of the Kedung Ombo Dam and to determine the level of safety of the dam body. The analyze was carried out using seepage monitoring instruments installed on the dam, namely the Piezometer and V-Notch at the Kedung Ombo Dam in 2021. Based on the results of the analysis, it was found that the pore water pressure and seepage discharge that occurred in the Kedung Ombo Dam were generally still within the permissible limits. According to the analysis results of the seepage index, the highest QI value is 0.09 at the maximum flood water level of +95 m, where the safety criteria for the seepage index is QI <1. Therefore it indicates that the seepage condition index at the Kedung Ombo Dam are still in a safe condition.Keywords: pore water pressure, seepage, piezometer, V-Notch, seepage index

Physical Characteristics Of The Krueng Seunagan Watershed And River Storage Capacity Against Peak Discharge

Peak discharge information is indispensable for flood control planning by taking into account the physiographic factors of the watershed. Flood occurs because the capacity of the watershed and river channels is smaller than the flood discharge. One of the causes of rivers' reduced flood flow capacity is vegetation cover and land use that cannot store rainfall. Thus, this paper aims to determine the peak discharge from the watershed's physical characteristics and land-use changes by comparing the river storage capacity in the Krueng Seunagan watershed. The rational method is used to calculate the peak discharge. Some of the data used in this study are the shapefile map provided by Geospatial Information Agency, land use, rainfall, soil types, and Landsat 8 OLI/TIRS digital imagery. Analysis of the physical characteristics of the watershed consists of morphology and morphometry. Land use in the area of the Krueng Seunagan watershed increased the peak discharge during the observation year (2003-2017). Besides the land-use factor, discharge is also influenced by high rainfall intensity and runoff coefficient obtained from land use analysis. In the Krueng Seunagan watershed area, the C value obtained was 0.0505 – 0.0720, indicating that the Krueng Seunagan watershed area was classified as good or harmless. The river flow density of 0.53 belongs to the medium category. The river flow increases and decreases in the flood water level, which is neither too fast nor too slow. The roundness index of the Seunagan watershed of 1.0004 indicates that the basins are widened or circular and, consequently, the rate and volume of surface runoff are fast. Krueng Seunagan watershed runoff coefficient increased by 42.51% and rainfall intensity by 37.05%, while discharge increased by 95.31%. The discharge capacity that the Krueng Seunagan River Basin can accommodate from the measurement results in the downstream watershed is 158.47 m3/sec. While the peak discharge using the rational method gets a value that varies in each year of observation from (2003-2017). The peak discharge value in 2003 amounted to 183.52 m3/sec, and the highest in 2017 amounted to 358.44 m3/sec. This shows that the river holding capacity in the Krueng Seunagan watershed will not accept the amount of peak discharge, and consequently, the flood will always occur.

DIFFERENTIAL MODEL OF FLOOD PHENOMENA DEVELOPMENT

In the work on the basis of long-term observations of the development of flood phenomena in the region, a mathematical model of the development of flood phenomena based on the apparatus of linear inhomogeneous ordinary systems is proposed. Such model allows us to estimate the level of flood waters, the ability of the environment to absorb water - namely the permeability of the soil, the level of greenery, hydraulic support, etc., the level of precipitation in the region, the effectiveness of funds allocated to measures and measures to combat floods/ Heterogeneities in the system of equations describe the precipitation regime in the region and the amount of funds allocated for flood control measures. The system is supplemented by appropriate initial conditions. The coefficients of the model are determined by the method of expert estimates using the criterion of Kendall's impartiality/ When constructing a computational scheme, the fourth-order Runge-Kutta method and the corresponding software for its implementation are used.The parametric identification of the model is carried out in order to select the coefficients of the model, which would ensure the adequacy of the model to the real system. All calculations were performed in dimensionless form. The results of modeling are given, the directions of further researches which will be devoted to introduction of model at the real enterprises and services which task is the prevention of occurrence of the flood phenomena, especially in a zone of possible flooding of objects of an oil and gas complex are defined.

Application of image processing and convolutional neural networks for flood image classification and semantic segmentation

Deep learning algorithms are exceptionally valuable tools for collecting and analyzing the catastrophic readiness and countless actionable flood data. Convolutional neural networks (CNNs) are one form of deep learning algorithms widely used in computer vision which can be used to study flood images and assign learnable weights to various objects in the image. Here, we leveraged and discussed how connected vision systems can be used to embed cameras, image processing, CNNs, and data connectivity capabilities for flood label detection. We built a training database service of >9000 images (image annotation service) including the image geolocation information by streaming relevant images from social media platforms, Department of Transportation (DOT) 511 traffic cameras, the US Geological Survey (USGS) live river cameras, and images downloaded from search engines. We then developed a new python package called “FloodImageClassifier” to classify and detect objects within the collected flood images. “FloodImageClassifier” includes various CNNs architectures such as YOLOv3 (You look only once version 3), Fast R–CNN (Region-based CNN), Mask R–CNN, SSD MobileNet (Single Shot MultiBox Detector MobileNet), and EfficientDet (Efficient Object Detection) to perform both object detection and segmentation simultaneously. Canny Edge Detection and aspect ratio concepts are also included in the package for flood water level estimation and classification. The pipeline is smartly designed to train a large number of images and calculate flood water levels and inundation areas which can be used to identify flood depth, severity, and risk. “FloodImageClassifier” can be embedded with the USGS live river cameras and 511 traffic cameras to monitor river and road flooding conditions and provide early intelligence to emergency response authorities in real-time.

Modeling of Flooding of Settlements During the Spring Flood

The problem of flooding of territories with flood waters for settlements of the Volgograd region is considered. A numerical model of flood water dynamics is constructed, taking into account the topography of the terrain. The simulation is based on two-dimensional shallow water equations. For computational experiments, a parallel implementation of the numerical scheme CSPH-TVD for NVIDIA graphics accelerators with CUDA technology is used. The digital model of the river-bed and floodplain relief is based on spatial data SRTM3 and SRTMGL, topographic maps of the area, longitudinal profiles of rivers. The water flow rates for the Buzuluk and Perevozinka rivers are determined and a hydrograph of the Volga hydroelectric power station is constructed for the given probabilities of exceeding the water level. Flood maps were obtained for the following localities: Novoannisky, Berezovka 1st, kh. Vyazovka. The maximum values of the depths for 1 %, 3 %, 5 %, 10 %, 25 % and 50 % of water security are presented. An analysis of the flood situation was carried out and appropriate engineering and protective measures for settlements were proposed. At maximum flood water levels, residential buildings of kh. Berezovka 1-I are not subject to flooding. In order to minimize the negative impact for the city of Novoanninsky, it is proposed to carry out timely clearing of the river-bed of the Perevozinka river from congestion. To protect from flooding ch. Vyazovka it is recommended to use diversion dyke.

Study on the Contribution of Normalization to Reducing Flood Risk in the Ciliwung River, Tebet District, Jakarta

Jakarta is the capital city of the State of Indonesia and fast economy and population growth rate. With these, urbanization continues to increase every year. In this study, we analyse the effect of river normalization on reducing flood risk on the MT. Haryono - Manggarai section based on the Hec-RAS hydraulic model. Boundary Condition applied in upstream river is flow hydrograph with a peak discharge of 561.48 m3/s. In the upstream part, a rating curve is applied from the water level measurement data for the Manggarai Sluice Gate. While in the middle, the lateral flow from the urban drainage channels is inserted. The simulation results show that normalizing the channel can increase the drainage capacity as implied by the decrease in the flood water level. However, downstream there is backwater due to the lack of capacity of the Manggarai floodgate.

Emulation of 2D Hydrodynamic Flood Simulations at Catchment Scale Using ANN and SVR

Two-dimensional (2D) hydrodynamic models are one of the most widely used tools for flood modeling practices and risk estimation. The 2D models provide accurate results; however, they are computationally costly and therefore unsuitable for many real time applications and uncertainty analysis that requires a large number of model realizations. Therefore, the present study aims to (i) develop emulators based on SVR and ANN as an alternative for predicting the 100-year flood water level, (ii) improve the performance of the emulators through dimensionality reduction techniques, and (iii) assess the required training sample size to develop an accurate emulator. Our results indicate that SVR based emulator is a fast and reliable alternative that can predict the water level accurately. Moreover, the performance of the models can improve by identifying the most influencing input variables and eliminating redundant inputs from the training process. The findings in this study suggest that the training data size equal to 70% (or more) of data results in reliable and accurate predictions.

Evaluation of the Groundsill’s stability at downstream of “Citorek” Bridge in Cimadur River, Banten Province

Scouring problems faced in the Cimadur River especially near to the Citorek bridge abutments have become the major discussion by the local researchers and the local water resources manager in Banten Province. As an effort in reducing the scouring problem around the abutment of the Citorek bridge, a groundsill structure with specific design is going to be installed in Cimadur River at downstream of Citorek bridge. To make sure the optimal function of the structure, the stability of the groundsill structure in Cimadur River need to be evaluated. This study attempt to evaluate the stability of a groundsill structure from the occurrences of rolling and sliding at both normal and flood conditions. The eccentricity of the groundsill structure is also checked during normal and flood conditions to make sure the stability of the structure. The required data (which are consisting of detail description of the groundsill structure, cross section of the river, rainfall data, topography data and sediment/soil data) are observed in the field and obtained from P.T. Saeba Konsulindo. The data are further analysed to determine: 1) design water discharges for several return period, 2) forces acting to the groundsill structure and 3) stability of the groundsill structure in the river. The results showed that the groundsill structure are stable and safety again rolling and sliding occurrences where the safety factor (SF) for rolling and sliding are higher than critical coefficients of rolling and sliding (1.5). At normal water level, safety factors (SF) for rolling and sliding are 8.07>1.5 and 2.7>1.5, respectively, while at flood water level, SF for rolling and sliding are 5.61>1.5 and 1.88>1.5, respectively. Besides, the results also found that the groundsill is safety from eccentricity at both normal and flood conditions where the calculated coefficients of eccentricities are lower than critical coefficient of eccentricity which could cause rolling and sliding.

Analysis of Cross-Sectional Capacity of the Jambi River in the Muaro Jambi Temple About Various Times of Flooding Using HEC-RAS Software

Jambi River is one of the rivers located in the Muaro Jambi Temple Complex Area, Muaro Jambi Regency, Jambi Province. Muaro Jambi Temple is one of the tourist attractions in Jambi Province. This study aims to find the capacity of Jambi River tested by planned flood discharge utilizing (synthetic unit hydrograph) HSS Nakayasu method for a return period of two, five, ten, twenty-five, fifty and hundred years. HEC-RAS software used to analyse the water level in the Jambi River towards the flood potential that causes the submerging of the Kedaton Temple building. This research used the log Pearson type III method to calculate the planned rain return period and used the Nakayasu synthetic unit method to calculate the planned flood discharge. The analysis showed that the Jambi River could not load the flood discharge in the five, ten, twenty-five, fifty, and one hundred years return period at several measurement points: river sta-1, river sta-2 and river sta-5. The floodwater level did not cause the Kedaton Temple building to be flooded from the simulation result

Geological framework for assessing variability in subsurface piping parameters underneath dikes in the Rhine-Meuse delta, the Netherlands

Dike failure due to piping – concentrated flow of seepage water underneath the dike during periods of high flood water levels – has been recognized as a major component of flood risk. Simulation models to predict piping in risk assessments require detailed information on subsurface characteristics such as sediment grain size and thickness of overburden layers. Quantitative local determination of these characteristics poses a major challenge in natural environments with a heterogeneous substrate. Geological knowledge on the natural genesis and resulting structure of the subsurface may provide useful information on the spatial variability of substrate characteristics. When correctly implemented, available subsurface geological information can provide for a-priori identification of dike sections of which the subsurface is susceptible to piping (strategic to new data collection), and a-posteriori screening of new collected field data (identifying unexpected values). In these two ways it has potential to reduce the uncertainty in parameter estimates for calculations that determine the potential occurrence of piping at a specific site. Here we describe a framework for using geological subsurface information for these assessments. Based on existing digital geological mapping products and knowledge of the geological development of the Rhine-Meuse delta, we first compiled a map that distinguishes primary hydrologically relevant units: upper main aquifer sands (pre-deltaic subunits), topped by the deltaic wedge aquitard that is dissected by channel belt sand bodies (deltaic subunits). We then used these spatial divisions in an analysis of digital borehole data (>130.000 locations, UU-LLG dataset), to provide quantitative information on grain sizes of the very top of sand bodies and non-sand overburden thicknesses, split per subunit and summarized for sub regions (lower delta, central delta, upper delta, delta rim sectors). This framework enables us to demonstrate to what extent the median grain size of the top of sand deposits varies within the delta. We quantitatively determined (1) a delta-scale longitudinal downstream fining trend due to drops in specific stream power through changes in gradient and substrate erodibility, (2) pulsed variations associated with local uptake and reworking of pre-deltaic sediments within channel belts. Although tailored for the Rhine-Meuse delta, utilizing geological knowledge as a substantiation for grouping of subsurface data underneath local dike sections can be transposed for such applications in delta regions elsewhere. This regionalization of the delta plain can greatly help streamline data acquisition, anticipating growing availability of medium to high density subsurface datasets in increasingly urbanized deltas with managed rivers around the world.