Flood Inundation Area Research Articles

PEMODELAN MIKE21 DALAM KEJADIAN BANJIR ROB MENJELANG GERHANA BULAN DI PESISIR SEMARANG

Pesisir Semarang merupakan wilayah yang rentan terkena dampak bencana banjir rob. Hampir setiap bulan wilayah tersebut tergenang akibat pasang air laut. Peristiwa banjir rob yang paling diingat publik adalah saat terjadi menjelang gerhana bulan tanggal 31 Januari 2018. Peringatan dini informasi banjir rob perlu dibuat dengan tingkat akurasi tinggi dan dalam bentuk yang mudah dipahami masyarakat untuk kepentingan mitigasi. Tipe pasang surut (pasut) di Pesisir Semarang perlu diidentifikasi untuk mengetahui kecenderungan waktu kejadian banjir rob. Model Hidrodinamika Mike 21 digunakan untuk mengolah data pengamatan pasut sepanjang tahun 2017 menjadi informasi prakiraan pasut sepanjang bulan Januari 2018. Nilai akurasi prakiraan pasut Mike 21 diukur dengan Root Mean Square Error (RMSE), serta dibandingkan dengan keakurasian prakiraan pasut dari Pushidrosal (Pusat Hidrografi dan Oseanografi TNI Angkatan Laut) dan BIG (Badan Informasi Geospasial). Hasil prakiraan pasut terbaik selanjutnya diolah pada aplikasi ArcGIS, dengan mengambil nilai prakiraan pasang tertinggi pada hari kejadian sebagai data input untuk pembuatan peta luas genangan banjir rob. Puncak kejadian banjir rob cenderung terjadi pada pukul 06.00 WIB dan 17.00 WIB, yang mana berdasarkan hasil identifikasi di Pesisir Semarang termasuk tipe pasang surut harian ganda. Prakiraan pasut dari Mike 21 memiliki nilai RMSE terbaik. Nilai RMSE prakiraan pasut Mike 21, Pushidrosal dan BIG berturut-turut bernilai 10 cm, 30 cm dan 22 cm. Nilai pasut tertinggi baik model Mike 21 maupun hasil pengamatan terjadi pukul 20.00 WIB. Pemodelan nilai pasut tertinggi dari Mike21 sebesar 122 cm menghasilkan peta luas genangan banjir rob sebesar 1753.35 ha, sedangkan nilai pasang tertinggi hasil pengamatan sebesar 133 cm menghasilkan 185.31 ha lebih luas.

Residential Flood Loss Assessment and Risk Mapping from High-Resolution Simulation

Since the patterns of residential buildings in the urban area are small-sized and dispersed, this study proposes a high-resolution flood loss and risk assessment model to analyze the direct loss and risk impacts caused by floods. The flood inundation simulation with a fine digital elevation model (DEM) provides detailed estimations of flood-inundated areas and their corresponding inundation depths during the 2016 Typhoon Megi and 2017 Typhoon Haitang. The flood loss assessment identifies the impacts of both events on residential areas. The depth-damage table from surveys in the impacted area was applied. Results indicated that the flood simulation with the depth-damage table is an effective way to assess the direct loss of a flood disaster. The study also showed the effects of spatial resolution on the residential loss. The results indicated that the low-resolution model easily caused the estimated error of loss in dispersed residential areas when compared with the high-resolution model. The analytic hierarchy process (AHP), as a multi-criteria decision-making method, was used to identify the weight factor for each vulnerability factor. The flood-vulnerable area was mapped using natural and social vulnerability factors, such as high-resolution DEM, distance to river, distance to fire station, and population density. Eventually, the flood risk map was derived from the vulnerability and flood hazard maps to present the risk level of the flood disaster in the residential areas.

Using CYGNSS Data to Monitor China’s Flood Inundation during Typhoon and Extreme Precipitation Events in 2017

NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission, launched in 2016, is a small satellite constellation designed to measure the ocean surface wind speed in hurricanes and tropical cyclones. To explore its additional capabilities for applications on the land surface, this study investigated the advantages and limitations of using CYGNSS data to monitor flood inundation during typhoon and extreme precipitation events in southeast China in 2017. The results showed that despite the lack of quantitative evaluation, the CYGNSS-derived surface reflectivity (SR) and flood inundation area was qualitatively consistent with the Global Precipitation Measurement (GPM)-derived precipitation and Soil Moisture Active Passive (SMAP)/Soil Moisture and Ocean Salinity (SMOS)-derived total brightness temperature at circular polarization ( T b C ). The results provide supporting evidence for further designation of Global Navigation Satellite System (GNSS) reflectometry (GNSS-R) constellations to monitor land surface hydrology.

Flood damage assessment on rice crop in the Stung Sen River Basin of Cambodia

In Cambodia, rice production is important for the majority of rural populations as the main source of income. However, historical extreme flood events, especially the 2011 flood, caused significant damage to rice production due to insufficient flood preparedness and mitigation. To reduce the risk of damage, flood damage assessment is critical for providing essential information about potential risks to support the decision-making process and policy development in flood risk management. In this study, a physically based rainfall–runoff–inundation (RRI) model was used to simulate flood inundation in the Stung Sen River Basin of Cambodia, and then damage to rice crops was estimated based on the simulated inundation depth and duration that resulted from the RRI model. The simulated river discharges were calibrated and validated for 2011 and 2009 flood events, respectively. Indeed, the simulated flood inundation areas were compared with flood maps derived from satellite imagery to check their accuracy. As a result, it was confirmed that these results were reasonable and adequate. In the case of the 2011 flood, a distribution map of rice crop damage was created to represent the potentially affected rice areas at various levels of damage. Furthermore, the total of estimated affected rice areas was found to be close to the reported damage data, with an overlap of 89%. Overall, the preliminary results from this study are expected to be useful for key stakeholders at all levels, especially decision-makers, in planning better risk reduction strategies, for instance, when choosing the appropriate flood control option to cope with future extreme flood events.

Flood hazard mapping using geospatial techniques and satellite images-a case study of coastal district of Tamil Nadu.

In recent years, extreme rainfall events have resulted in several devastating floods especially in the east coast of Tamil Nadu, India. The main aim of this study is to identify the flood hazard zones in Cuddalore district by integrating spatial distribution of extreme rainfall events, slope, drainage density, soil, and geomorphology. Moderate Resolution Imaging Spectroradiometer satellite data was used to delineate the flood inundation area for the flood event which occurred in 2010 to validate the derived flood hazard zones by using geographical information system (GIS) and satellite images. The ground truth points were collected from the field to validate flood hazard zones, and it was identified that 92% of results were matched with hazard zones. Highly vulnerable blocks including Cuddalore, Kattumannarkoil, Keerapalayam, Kumaratchi, Kurinjipadi, Melbhunagiri, and Parangipettai were identified in Cuddalore district. From this study, it was also identified that nearly 45% of the total area of 3678km2 were inundated during the flood time. This present study will be a very useful tool and a resource for the policy planners and coastal planners to make effective decisions towards mitigation measures in flood-prone areas in the coastal districts.

Automatic extraction of flood inundation areas from SAR images: a case study of Jilin, China during the 2017 flood disaster

ABSTRACTFlood is one of the most frequent and widespread natural hazards globally, which can cause tremendous economic damage and human casualties. As such, flood event monitoring is essential, for which Synthetic Aperture Radar (SAR), with high spatial resolution as well as all-weather and all-time capabilities, can provide high-quality data support. However, algorithms for automatic flood inundation mapping that do not require ancillary data are limited. In this study, we propose a hybrid methodology that combines automatic thresholds selection, pixel- and object-based classification, and bidirectional region growing method for extracting flood inundation areas. This is a fully automatic approach that does not require the assistance of ancillary data. Firstly, the gamma distribution is used to estimate the probability density function (PDF) of ‘open water’ and to set thresholds. Then, we introduce a two-step classification approach, applying the pixel- and object-based classifications; the former is easy to implement with low computational complexity and stable performance, whereas the latter can reduce noise pixels and is less sensitive to SAR intrinsic speckle. The two-step classification is employed to yield core flooded and non-flooded regions that are used as seeds for region growing. Furthermore, we propose a bidirectional region growing approach that grows regions for flooded and non-flooded regions simultaneously to eliminate areas of uncertainty, while minimizing under- and over-detection. We verified the proposed approach by applying it to real flood events that occurred in Jilin, China on 13 July 2017 and 20 July 2017. The experimental results demonstrate the effectiveness and reliability of the proposed approach.

Usability of water surface reflectance for the determination of riverine dissolved methane during extreme flooding in northeastern Siberia

An extreme flooding event occurred from June to July 2017 in the Indigirka River lowland of northeastern Siberia. We used Landsat 8 satellite surface reflectance data to detect the flood inundation area and extract water color for delineating different water sources. We also took direct samples of dissolved methane concentrations in the river water. Relatively high concentrations of dissolved methane (0.7–1.1 μmol l−1, or μM) were observed in four tributary areas in 2017 during the flood's recession, while the values remained low in the main channel (0.2–0.3 μM). In contrast, the concentrations of dissolved methane were low in both the main channel and tributaries during the non-flood period of 2016 (0.1–0.2 μM). We then used 2017 satellite reflectance data with an empirical model to estimate the spatial differences of dissolved methane concentration for water sources contributing to the methane-poor main channel and methane-rich tributaries and applied the results to the calculation of riverine methane in the study region (approx. 200 × 300 km). This approach to estimating dissolved methane concentrations using satellite reflectance can provide a new tool for environmental monitoring of flood events in remote areas.

Flood hazard zones using 2d hydrodynamic modeling and remote sensing approaches

Increasing frequency and severity of flooding demands identification of flood hazard zones in Kalilangan, Bukidnon in response to the echoing need of better disaster preparedness via enhancing the understanding and awareness of the public on flood characteristics by integrating the use of two-dimensional hydrodynamic modeling and remote sensing. Flood simulation was carried out in a two-dimensional hydrodynamic model using hydrologic engineering center-river analysis system to derive the flood inundation area and flood depth of Kalilangan, Bukidnon. Thus, it was preceded by pre-processing of the model using software packages of hydrologic engineering center-hydrologic modeling system and ArcGIS along with interferometric synthetic aperture radar–digital elevation model, Manning’s roughness coefficient and precipitation data. Five different rain return flooding scenarios were simulated using rainfall intensity duration frequency data. Three zones of flood hazard were then set as low, medium and high. The result shows that most areas of Kalilangan are within the zones of medium to high hazard with residential buildings as the most flooded type of built-up structures. Flood hazard zone areas could be mapped at an accuracy of 79.51%. Thus, harnessing this potential approach offers cost-effective way of flood preparedness viewing hazard-prone areas with special attention and utmost importance.

Quantification of potential flood inundation areas in the marsh wetland of Honghe National Natural Reserve, Northeast China

Wetland protection and recovery needs scientific foundations for a better understanding of wetland floods, since the flooding existence and its dynamics are the key aspects in the formation and evolution of wetland ecosystems. The Honghe National Natural Reserve (HNNR) in the Northeast China has been listed in the Ramsar Convention on Wetlands since 2002. The specific dates for determining of an average inundation boundary caused by a regular flooding were firstly fixed from multiple rainfall events by a hydro-meteorological analysis based on the long-term rainfall data. The flood inundation areas in the HNNR marshes were mapped according to the correlation between hydro-geomorphology and regular flood progress. The mapping of the flood inundation areas were supported technically from the application of remote sensing images, combining the hydro-geomorphologic analysis with meteorological statistical data. The final map with the spatial-temporal content of an inundation created by hydro-geomorphological analysis and remote sensing data was validated by the long-term rainfall, soil moisture and surface water level data in the HNNR. The results indicated that the flood inundation areas were mainly distributed in large patches along the two main river courses of Nongjiang River and its branch of Woyalan River in the northern and central HNNR. The flood inundation patches were scattered as seasonal marshes and permanent ponds in the southern and eastern HNNR. The extracted flood inundation area accounted for more than a quarter of the total area of HNNR. The flood inundation area of Nongjiang was much larger than that of Wuyalan.

A Novel Hybrid Swarm Optimized Multilayer Neural Network for Spatial Prediction of Flash Floods in Tropical Areas Using Sentinel-1 SAR Imagery and Geospatial Data.

Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg–Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility.

Analysis of Flood Inundation in Ungauged Mountainous River Basins: A Case Study of an Extreme Rain Event on 5–6 July 2017 in Northern Kyushu, Japan

The heavy rainfall event that occurred on 5–6 July 2017 in Northern Kyushu, Japan, caused extensive flooding across several mountainous river basins and resulted in fatalities and extensive damage to infrastructure along those rivers. For the periods before and during the extreme event, there are no hydrological observations for many of the flooded river basins, most of which are small and located in mountainous regions. We used the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model, a physically based model, to acquire more detailed information about the hydrological processes in the flood-affected ungauged mountain basins. We calibrated the GSSHA model using data from an adjacent gauged river basin, and then applied it to several small ungauged basins without changing the parameters of the model. We simulated the gridded flow and generated a map of the possible maximum flood depth across the basins. By comparing the extent of flood-affected areas from the model with data of the Japanese Geospatial Information Authority (GSI), we found that the maximum flood inundation areas of the river networks estimated by the GSSHA model are sometimes less than those estimated by the GSI, as the influence of landslides and erosion was not considered in the modeling. The model accuracy could be improved by taking these factors into account, although this task would be challenging. The results indicated that simulations of flood inundation in ungauged mountain river basins could contribute to disaster management during extreme rain events.

Hydrological assessment of basin development scenarios: Impacts on the Tonle Sap Lake in Cambodia

The study aimed to assess the hydrological impacts of the basin development scenarios—which are BS (baseline scenario), DFS (definite future scenario), and FFS (foreseeable future scenario)—by the Mekong River Commission (MRC) on the hydrological regime between the Mekong River and Tonle Sap Lake. The proposed CAESAR-LISFLOOD system was applied to analyze flood inundation patterns of the Tonle Sap Lake using satellite images, as well as to compare the simulated inundation maps. The flow duration variability was also analyzed at the outlet point of the Mekong River basin based on the analysis results of CAESAR-LISFLOOD, according to the basin development. The results show that the CAESAR-LISFLOOD model produces acceptable flooding areas (8607 km2) compared with the satellite observations (9615 km2), and the flood inundation areas decrease gradually from 3.69 (103 km2) in the BS scenario to 3.54 (103 km2) in the FFS scenario. However, it was found that the annual average flow duration variability scenario at the Kratie point, presented in the MRC, does not significantly affect the flow duration downstream from the Kratie point.

Risk decision-making model for reservoir floodwater resources utilization

Floodwater resources utilization (FRU) can alleviate the shortage of water resources, but there are risks. To safely and efficiently utilize the floodwater resources, it is necessary to study the risk of reservoir FRU. In this paper, the risk rate of exceeding the design flood water level and the risk rate of exceeding safety discharge are estimated. Based on the principle of the minimum risk and the maximum benefit of FRU, a multi-objective risk decision-making model for FRU is constructed. Probability theory and mathematical statistics method is selected to calculate the risk rate; C–D production function method and emergy analysis method is selected to calculate the risk benefit; the risk loss is related to flood inundation area and unit area loss; the multi-objective decision-making problem of the model is solved by the constraint method. Taking the Shilianghe reservoir in Jiangsu province as an example, the optimal equilibrium solution of FRU of the Shilianghe reservoir is found using the risk decision-making model, and the validity and applicability of the model are verified.

Analysis of large-scale flood inundation area using optimal topographic factors

Recently, the spatiotemporal patterns of flood disasters have become more complex and unpredictable due to climate change. Flood hazard map including information on flood risk level has been widely used as an unstructured measure against flooding damages. In order to product a high-precision flood hazard map by combination of hydrologic and hydraulic modeling, huge digital information such as topography, geology, climate, landuse and various database related to social economic are required. However, in some areas, especially in developing countries, flood hazard mapping is difficult or impossible and its accuracy is insufficient because such data is lacking or inaccessible. Therefore, this study suggests a method to delineate large scale flood-prone area based on topographic factors produced by linear binary classifier and ROC (Receiver Operation Characteristics) using globally-available geographic data such as ASTER or SRTM. We applied the proposed methodology to five different countries: North Korea Bangladesh, Indonesia, Thailand and Myanmar. The results show that model performances on flood area detection ranges from 38% (Bangladesh) to 78% (Thailand). The flood-prone area detection based on the topographical factors has a great advantage in order to easily distinguish the large-scale inundation-potent area using only digital elevation model (DEM) for ungauged watersheds.

Investigating the role of model structure and surface roughness in generating flood inundation extents using one‐ and two‐dimensional hydraulic models

Hydraulic models play an important role in determining flood inundation areas. When considering a wide array of one‐ (1D) and two‐dimensional (2D) hydraulic models, selecting an appropriate model and its calibration are crucial in an accurate prediction of flood inundation. This study compares the performance of four commonly used 1D and 2D hydraulic models, including HEC‐RAS 1D, HEC‐RAS 2D, LISFLOOD‐FP diffusive, and LISFLOOD‐FP subgrid, with respect to their model structure and their sensitivity to surface roughness characterisation. Application of these models to four study reaches with different river geometry and roughness characterisation shows that for a given set of roughness condition, the geometry, including the sinuosity, reach length and floodplain width, does not affect the performance of a 1D or 2D model. Overall, the performance of a 1D model is comparable to the 2D models used in the study, with the 2D models showing slightly better results. The performance of 2D models is affected by low channel roughness, and it improves with increasing channel roughness that enables more water to enter into the floodplain. On the contrary, the performance of 1D model is positively affected with increasing floodplain roughness. When the models are evaluated for uniform versus distributed roughness characterisation in the floodplain, the uniform surface characterisation provides the best results compared to the distributed roughness characterisation.

Flood Inundation Modelling in the Kuantan River Basin using 1D-2D Flood Modeller coupled with ASTER-GDEM

Topography dataset is an important input in performing flood inundation modelling. However, it is always difficult to obtain high resolution topography that provide accurate elevation information. Fortunately, there are some open source topography datasets available with reasonable resolution such as SRTM and ASTER-GDEM. In Malaysia particularly in Kuantan, the modelling research on the floodplain area is still lacking. This research aims to: a) to investigate the suitability of ASTER-GDEM to be applied in the 1D-2D flood inundation modelling for the Kuantan River Basin; b) to generate flood inundation map for Kuantan river basin. The topography dataset used in this study is ASTER-GDEM to generate physical characteristics of watershed in the basin. It is used to perform rainfall runoff modelling for hydrological studies and to delineate flood inundation area in the Flood Modeller. The results obtained have shown that a 30m resolution ASTER-GDEM is applicable as an input for the 1D-2D flood modelling. The simulated water level in 2013 has NSE of 0.644 and RSME of 1.259. As a conclusion, ASTER-GDEM can be used as one alternative topography datasets for flood inundation modelling. However, the flood level obtained from the hydraulic modelling shows low accuracy at flat urban areas.

Analysis of flood vulnerability in urban area; a case study in deli watershed

Based on the National Disaster Management Agency of Indonesia, the distribution of disasters and victims died until the year 2016 is the largest flood disaster. Deli River is a river that has the greatest flood potential through Medan City. In Deli Watershed, flow discharge affected by the discharge from its tributaries, the high rainfall intensity and human activity. We should anticipate reducing and preventing the occurrence of losses due to flood damage. One of the ways to anticipate flood disaster is to predict which part of urban area is would flood. The objective of this study is to analyze the flood inundation areas due to overflow of Deli River through Medan city. Two-dimensional modeling by HEC-RAS 5.0.3 is a widely used hydraulic software tool developed by the U.S Army Corps of Engineers, which combined with the HEC-HMS for hydrological modeling. The result shows flood vulnerability in Medan by a map to present the spot that vulnerable about flood. The flooded area due to the overflowing of Deli River consists of seven sub districts, namely Medan Johor, Medan Selayang, Medan Kota, Medan Petisah, Medan Maimun, Medan Perjuangan and Medan Barat.

Can Atmospheric Reanalysis Data Sets Be Used to Reproduce Flooding Over Large Scales?

AbstractFloods are costly to global economies and can be exceptionally lethal. The ability to produce consistent flood hazard maps over large areas could provide a significant contribution to reducing such losses, as the lack of knowledge concerning flood risk is a major factor in the transformation of river floods into flood disasters. In order to accurately reproduce flooding in river channels and floodplains, high spatial resolution hydrodynamic models are needed. Despite being computationally expensive, recent advances have made their continental to global implementation feasible, although inputs for long‐term simulations may require the use of reanalysis meteorological products especially in data‐poor regions. We employ a coupled hydrologic/hydrodynamic model cascade forced by the 20CRv2 reanalysis data set and evaluate its ability to reproduce flood inundation area and volume for Australia during the 1973–2012 period. Ensemble simulations using the reanalysis data were performed to account for uncertainty in the meteorology and compared with a validated benchmark simulation. Results show that the reanalysis ensemble capture the inundated areas and volumes relatively well, with correlations for the ensemble mean of 0.82 and 0.85 for area and volume, respectively, although the meteorological ensemble spread propagates in large uncertainty of the simulated flood characteristics.

Coupling a global hydrodynamic algorithm and a regional hydrological model for large-scale flood inundation simulations

Abstract To bridge the gap between 1D and 2D hydraulic models for regional scale assessment and global river routing models, we coupled the CaMa-Flood (Catchment-based Macro-scale Floodplain) model and the regional hydrological model SWIM (Soil and Water Integrated Model) as a tool for large-scale flood risk assessments. As a proof-of-concept study, we tested the coupled models in a meso-scale catchment in Germany. The Mulde River has a catchment area of ca. 6,171 km2 and is a sub-catchment of the Elbe River. The modified CaMa-Flood model routes the sub-basin-based daily runoff generated by SWIM along the river network and estimates the river discharge as well as flood inundation areas. The results show that the CaMa-Flood hydrodynamic algorithm can reproduce the daily discharges from 1991 to 2003 well. It outperforms the Muskingum flow routing method (the default routing method in the SWIM) for the 2002 extreme flood event. The simulated flood inundation area in August 2002 is comparable with the observations along the main river. However, problems may occur in upstream areas. The results presented here show the potential of the coupled models for flood risk assessments along large rivers.

Assessment of future flood inundations under climate and land use change scenarios in the Ciliwung River Basin, Jakarta

This study assessed flood inundation of the Ciliwung River Basin, Greater Jakarta to improve the urban water environment under climate change and unplanned urbanisation. The 1‐day maximum precipitation data for 50‐ and 100‐year return period under current and future climate conditions were used to assess the impact of climate change. Precipitation output of the MRI‐CGCM3, MIROC5, and HadGEM2‐ES General Circulation Models (GCMs) with RCP 4.5 and 8.5 emission scenario over periods 1985–2004 and 2020–2039 representing current and future climate conditions, respectively, were used. Similarly, land use data of 2009 and 2030 were used to represent the current and future conditions, respectively. The HEC‐HMS model was used to simulate the river discharge at Katulampa, which represents the outlet location for the hydrologic modelling and the inlet location for the flood inundation modelling. FLO‐2D, a two‐dimensional hydrodynamic model, was used to simulate current and future flood inundation simulations. Increasing flood inundation areas and depths (6% to 31% for different GCMs) in the future reveal the need to improve flood management tools for the sustainable development of urban water environments.