Flood Depth Maps Research Articles

DEM-based pluvial flood inundation modeling at a metropolitan scale

The global increase in urban flooding presents a substantial challenge that affects communities across the globe. This study introduces a post-flood inundation modeling framework tailored to pluvial floods on a metropolitan scale. We employ a dual drainage modeling approach for enhanced accuracy. The framework comprises two primary components: a Storm Water Management Model (SWMM) for simulating water movement within the storm drain system, and an advanced DEM-based flood inundation module that leverages SWMM results to predict flood inundation areas. Compared to the conventional flood spreading models commonly used in dual drainage modeling, this module incorporates high-resolution DEM cells into the surface flow routing processes, allowing to capture intricate surface terrain complexities. Additionally, the proposed module considers direct rainfall impacts on pluvial flood inundation mapping, resulting in enhanced accuracy. To evaluate the proposed model's accuracy, a two-step validation approach has been implemented. This includes comparing the proposed model results with both the high water marks measured in the aftermath of Hurricane Harvey and the flood depth map generated by a hydrodynamic model. The findings affirm the effectiveness of our proposed framework for post-flood inundation mapping within metropolitan settings. This method provides a dependable approach to urban flood modeling and represents an advancement in addressing the challenges associated with urban flooding.

A framework for flood depth using hydrodynamic modeling and machine learning in the coastal province of Vietnam

Flood models based on traditional hydrodynamic modeling encounter significant difficulties with real-time predictions, require enormous computational resources, and perform poorly in data-limited regions. The difficulties are compounded as flooding worldwide worsens due to the increasing frequency of short-term torrential rain events, making it more challenging to predict floods over the long term. This study aims to address these challenges by developing a rapid flood forecasting model combining machine learning algorithms (support vector regression, XGBoost regression, CatBoost regression, and decision tree regression) with hydrodynamic modeling in Quang Tri province in Vietnam. 560 flood depth locations were obtained by hydrodynamic modeling, and several locations measured in the field were used as input data for the machine learning models to build a flood depth map for the study area. The statistical indices used to evaluate the performance of the four proposed models were the receiver operating characteristic (ROC) curve, area under the ROC curve, root mean square error, mean absolute error, and coefficient of determination (R2). The results showed that all four models successfully constructed a flood depth map for the study area. Among the four proposed models, CatBoost regression performed best, with an R² value of 0.86. This was followed by XGBoost regression (R²=0.84), decision tree regression (R²=0.72), and then support vector regression (R2=0.7). This integration of hydrodynamic modeling and machine learning complements the framework in much of the existing literature. It can provide decision-makers and local authorities with an advanced flood warning tool and contribute to improving sustainable development strategies in this and similar regions.

Probabilistic modeling framework for flood risk assessment: A case study of Poldokhtar city

Study regionPoldokhtar City is located on the bank of the Kashkan river in Iran. Study focusThis study presents a probabilistic modeling framework for flood risk assessment using Monte Carlo simulations. We developed a Machine Learning (ML)-based flood depth prediction model for Poldokhtar city using the Least Squares Support Vector Machine. HEC-RAS was utilized for 2D flood modeling, and its performance was evaluated by comparing simulated and remote sensing-derived flood extent maps. The simulated results were used to develop a surrogate ML-based model that predicts flood depth maps. Finally, we used this model to estimate the flood risk of Poldokhtar city as a combination of hazard, exposure, and vulnerability for 10000 flood scenarios. New hydrological insights for the regionThe mean annual flood damage of the city based on the proposed framework is US$ 1177034, which is about three times lower than that calculated using the simplified method used in the classical risk analysis (US$ 3455400). Buildings near the floodwalls of the river and in the southwestern parts of the city have higher mean flood losses than those in other areas. Risk index frequencies of buildings reveal the most at-risk zones in the city, where there have been building constructions without considering flood hazards. The proposed framework would be of use to stakeholders such as policymakers to develop effective flood management strategies

A globally applicable framework for compound flood hazard modeling

Abstract. Coastal river deltas are susceptible to flooding from pluvial, fluvial, and coastal flood drivers. Compound floods, which result from the co-occurrence of two or more of these drivers, typically exacerbate impacts compared to floods from a single driver. While several global flood models have been developed, these do not account for compound flooding. Local-scale compound flood models provide state-of-the-art analyses but are hard to scale to other regions as these typically are based on local datasets. Hence, there is a need for globally applicable compound flood hazard modeling. We develop, validate, and apply a framework for compound flood hazard modeling that accounts for interactions between all drivers. It consists of the high-resolution 2D hydrodynamic Super-Fast INundation of CoastS (SFINCS) model, which is automatically set up from global datasets and coupled with a global hydrodynamic river routing model and a global surge and tide model. To test the framework, we simulate two historical compound flood events, Tropical Cyclone Idai and Tropical Cyclone Eloise in the Sofala province of Mozambique, and compare the simulated flood extents to satellite-derived extents on multiple days for both events. Compared to the global CaMa-Flood model, the globally applicable model generally performs better in terms of the critical success index (−0.01–0.09) and hit rate (0.11–0.22) but worse in terms of the false-alarm ratio (0.04–0.14). Furthermore, the simulated flood depth maps are more realistic due to better floodplain connectivity and provide a more comprehensive picture as direct coastal flooding and pluvial flooding are simulated. Using the new framework, we determine the dominant flood drivers and transition zones between flood drivers. These vary significantly between both events because of differences in the magnitude of and time lag between the flood drivers. We argue that a wide range of plausible events should be investigated to obtain a robust understanding of compound flood interactions, which is important to understand for flood adaptation, preparedness, and response. As the model setup and coupling is automated, reproducible, and globally applicable, the presented framework is a promising step forward towards large-scale compound flood hazard modeling.

ANALYZING THE EFFECTS OF LAND USE / COVER CHANGE (LULCC) SIMULATION ON FLOODING: A CASE STUDY IN LAS NIEVES, AGUSAN DEL NORTE, MINDANAO, PHILIPPINES

Abstract. The municipality of Las Nieves, located in Agusan del Norte, Mindanao, Philippines, has experienced floods and flood-related damages in the floodplain region in recent years. Despite the best intentions of planning and implementing projects anchored in its comprehensive land use plans, recurring flooding significantly impacts its residents. Due to its growing population, changes in land cover are inevitable. The changes can result in increased overland flow and decreased infiltration rates. This study was conducted to determine and quantify the effect of changing the land use/land cover (LULC) on the flooding condition in the municipality. Base LULC map was generated using Sentinel-2 image captured in 2021. Support Vector Machine classifier was used in the classification resulting in an accuracy of 94.07%. The 2021 LULC map was the reference for predicting future LULCs for 2025 and 2029 implemented in MOLUSCE and was utilized for flood simulation. HEC-HMS and HEC-RAS were used to generate flood depth maps of different extreme rainfall scenarios. The results showed that as the rainfall event increased, the extent of affected LULC areas also increased. Based on the rainfall impact assessment results, the annual cropland was the most impacted LULC class across the various LULC classes. The class open forest is the least affected class for 2021, 2025, and 2029. However, the barren was the least affected in the scenario-based build-up increase. These assessments are especially pertinent because storm-related rains have been increasingly severe recently and will continue due to climate change. The simulations and flood mapping knowledge can inform and empower the Local Government Unit of Las Nieves. It will guide them in developing informed decision frameworks for mitigating significant land surface variabilities and adapting effective future land use plans to reduce the adverse effects of flooding.

Hydrological impacts of land use - land cover change on urban flood hazard: A case study of the Jukskei River in Alexandra Township, Johannesburg, South Africa.

Flooding in urban areas is a major natural disaster causing damage to infrastructure, properties and loss of life. In urban areas the major causes behind the changing hydrological processes (i.e., floods) include topography, increase in precipitation due to climate change and change in land-use/land-cover (LULC) over time. The objective of this study is to evaluate the spatial and temporal LULC change impacts on flooding along the Jukskei River in Alexandra Township, Johannesburg, South Africa. The LULC images of 1987 MSS and 2015 OLI derived from Landsat satellite were pre-processed and classified using a supervised classification method. The analysis of LULC revealed that, there is an increase in built-up area from 934,2 ha to 1277,2 ha and reduction in intact and sparse vegetation from 190,5 ha to 62,4 ha and 380,8 ha to 142,1 ha, respectively, between the years 1987 and 2015. The flood depth map, velocity map and flood depth-velocity for different return periods and LULC scenarios have been developed by using an integrated approach of the Hydrological Engineering Centre-River Analysis System (HEC-RAS) and the HEC-GeoRAS with the geographic information system (GIS) and remote sensing data. From the analysis, it is observed that there is an increase in flood depth and flood velocity from 2,3 m to 3,0 m and 1,4 m/s to 3,4 m/s, whereas the depth-velocity for the last 28-years increased by 3,4 m2/s from 2,9 m2/s to 6,3 m2/s for the 1987 LULC and the 2015 LULC conditions, respectively. The flood hazard maps generated in this study can be used by local authorities and municipalities for flood disaster management.

Building level flood exposure analysis using a hydrodynamic model

The advent of detailed hydrodynamic model simulations of urban flooding has not been matched by improved capabilities in flood exposure analysis which rely on validation against observed data. This work introduces a generic, building-level flood exposure analysis tool applying high resolution flood data and building geometries derived from hydrodynamic simulations performed with the 2D hydrodynamic flood modelling software CityCAT. Validation data were obtained from a survey of affected residents following a large pluvial flood event in Newcastle upon Tyne, UK. Sensitivity testing was carried out for different hydrodynamic model and exposure tool settings and between 68% and 75% of the surveyed buildings were correctly modelled as either flooded or not flooded. The tool tends to underrepresent flooding with a better performance in identifying true negatives (i.e. no flooding observed with no flooding modelled) compared to true positives. As higher true positive rates were accompanied by higher false positive rates, no single scenario could be identified as the optimal solution. However, the results suggest a greater sensitivity of the results to the classification scheme than to the buffer distance applied. Overall, if applied to high resolution flood depth maps, the method is efficient and suitable for application to large urban areas for flood risk management and insurance analysis purposes.

FloodStroem: A fast dynamic GIS-based urban flood and damage model

Due to climate change and urbanization, urban flood modelling has become an increasingly important tool in assessing flooding and associated damage costs. However, large computational demands of state-of-the art hydrodynamic flood models makes multiple and real-time simulations unfeasible. This study presents a fast-dynamic GIS-based flood model, FloodStroem. FloodStroem generates a surface network of depressions (bluespots) and flow paths, and routes surcharged water from a subsurface drainage model through the network resulting in flood depth maps and associated damage costs. FloodStroem is tested on three sub-catchments in Elster Creek Catchment, Melbourne, Australia and benchmarked against the 2D distributed hydrodynamic model MIKE 21 and two other simplified models, RUFIDAM and CA-ffé. FloodStroem is robust to the number of bluespots included. For the three sub-catchments, FloodStroem can reproduce flooding time, pattern, depth, and damage costs sufficiently, but has a tendency to underestimate flooding upstream and overestimate flooding downstream. Performance is best for the large, steep sub-catchments and largest rainstorms, where FloodStroem performs better than the two other simplified models. The Critical Success Index (CSI) ranges from 23% for a 5-year storm event in a flat catchment to 65% for a 100-year return period for a steeper catchment. With respect to simulation time, FloodStroem is five orders of magnitude faster than the 2D hydrodynamic model, and 33 times faster when including the entire model setup time, which has potential for further reduction by optimization of the workflow.

Rapid monitoring of cyclone induced flood through an automated approach using multi–temporal Earth Observation (EO) images in RSS CloudToolbox platform

ABSTRACT The new era of cloud platform technologies opens up many opportunities for near real-time dissemination of disaster information to the end-users. The present study utilizes the European Space Agency (ESA) Research and Service Support (RSS) CloudToolbox platform to monitor the spatio-temporal dynamics of a flood event. A collective flood monitoring framework is formulated to rapidly assess cyclone-induced flood in the CloudToolbox platform. The outputs of the framework are spatio-temporal maps of flood extent, depth, and hot spot zones. The framework utilizes Earth Observation (EO) images such as optical and C-band Synthetic Aperture Radar (SAR) images and an automatic Kittler and Illingworth thresholding algorithm for rapid flood mapping. The temporal flood depth maps are created with the Floodwater Depth Estimation Tool (FwDET) which requires only two input parameters, viz. flood extent, and Digital Elevation Model (DEM). Subsequently, flood hotspot zones are also identified. We tested the flood monitoring framework on Amphan cyclone-induced flood event at both regional and local levels. The spatio-temporal flood extent, depth, and hot spot maps are generated for the Amphan cyclone event and a 97% overall accuracy is achieved at the local level. The entire process took less than one hour for regional and local level analysis.

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.

Indonesia: Threats to physical urban water problems

The world population percentage placed Indonesia in fourth place of the highest populated countries in the world. The majority of Indonesians live in an urban area. Java Island is the highest populated island in Indonesia, especially in Jakarta, Semarang, and Surabaya. This study showed the flood depth in these three cities, which has become a yearly threat during the rainy season. At the same time, another problem due to water withdrawal was analyzed. Land subsidence has occurred due to simultaneous water withdrawal and overload of alluvial soil due to land-use conversion. A remote sensing data and geographical information system (GIS) took account in this analysis to achieve the aim of this study. The hydraulic model was created to produce a flood depth map, and differential interferometric synthetic aperture radar (DInSAR) technique generated a land subsidence map. This study aims to show the problem in these three cities related to water in urban areas. This study found that all study areas had a severe risk of flood and land subsidence. Semarang received more threats to flood and land subsidence comparing to the other cities.

FLOOD EXTENT MAPPING USING DUAL-POLARIMETRIC SENTINEL-1 SYNTHETIC APERTURE RADAR IMAGERY

Abstract. Rapid generation of synthetic aperture radar (SAR) based flood extent maps provide valuable data in disaster response efforts thanks to the cloud penetrating ability of microwaves. We present a method using dual-polarimetric SAR imagery acquired on Sentinel-1a/b satellites. A false-colour map is generated using pre- and post- disaster imagery, allowing operators to distinguish between existing standing water pre-flooding, and recently flooded areas. The method works best in areas of standing water and provides mixed results in urban areas. A flood depth map is also estimated by using an external DEM. We will present the methodology, it’s estimated accuracy as well as investigations into improving the response in urban areas.

Evaluation of web maps for the communication of flood risks to the public in Europe

ABSTRACTFloods are one of the most significant hazards in Europe. This paper describes an iterative approach consisting of designing and evaluating static flood risk maps. In the first step, the use of different colour schemes and background types (e.g. topographic map, street map, and satellite images) is evaluated in an online survey. Next the design is optimized based on the outcomes of dedicated focus groups. The usability tests include the users’ preference, also considering their characteristics (age, gender, and expertise). The obtained results of each step are integrated to optimize the design of evacuation route maps and flood depth maps.

A Probabilistic Analysis of Surface Water Flood Risk in London.

Flooding in urban areas during heavy rainfall, often characterized by short duration and high-intensity events, is known as "surface water flooding." Analyzing surface water flood risk is complex as it requires understanding of biophysical and human factors, such as the localized scale and nature of heavy precipitation events, characteristics of the urban area affected (including detailed topography and drainage networks), and the spatial distribution of economic and social vulnerability. Climate change is recognized as having the potential to enhance the intensity and frequency of heavy rainfall events. This study develops a methodology to link high spatial resolution probabilistic projections of hourly precipitation with detailed surface water flood depth maps and characterization of urban vulnerability to estimate surface water flood risk. It incorporates probabilistic information on the range of uncertainties in future precipitation in a changing climate. The method is applied to a case study of Greater London and highlights that both the frequency and spatial extent of surface water flood events are set to increase under future climate change. The expected annual damage from surface water flooding is estimated to be to be £171 million, £343 million, and £390 million/year under the baseline, 2030 high, and 2050 high climate change scenarios, respectively.

Using LiDAR surveys to document floods: A case study of the 2008 Iowa flood

Can we use Light Detection and Ranging (LiDAR), an emergent remote sensing technology with wide applications, to document floods with high accuracy? To explore the feasibility of this application, we propose a method to extract distributed inundation depths from a LiDAR survey conducted during flooding. This method consists of three steps: (1) collecting LiDAR data during flooding; (2) classifying the LiDAR observational points as flooded water surface points and non-flooded points, and generating a floodwater surface elevation model; and (3) subtracting the bare earth Digital Terrain Model (DTM) from the flood surface elevation model to obtain a flood depth map. We applied this method to the 2008 Iowa flood in the United States and evaluated the results using the high-water mark measurements, flood extent extracted from SPOT (Small Programmable Object Technology) imagery, and the near-simultaneously acquired aerial photography. The root mean squared error of the LiDAR-derived floodwater surface profile to high-water marks was 30cm, the consistency between the two flooded areas derived from LiDAR and SPOT imagery was 72% (81% if suspicious isolated ponds in the SPOT-derived extent were removed), and LiDAR-derived flood extent had a horizontal resolution of ∼3m. This work demonstrates that LiDAR technology has the potential to provide calibration and validation reference data with appreciable accuracy for improved flood inundation modeling.

Vulnerability of flash flooding in Riyadh, Saudi Arabia

In recent years, hazardous flash flooding has caused human deaths and damages to urban infrastructures in Saudi Arabian cities. This study has computed, mapped, and analyzed the physical vulnerability, social vulnerability, and overall composite flash flood vulnerability (CFVI) indices using a simulated flood and 2006 census data on seven social variables for 153 neighborhoods of the City of Riyadh, the capital of the Kingdom of Saudi Arabia. The physical vulnerability index was computed by ranking a potential flood depth map created by simulating a 6 h of high-intense rainfall in the city. The social vulnerability index was constructed by standardizing the social data variables. Finally, the two indices were multiplied to create the CFVI. The CFVI map revealed that the low-lying central and southern half of the city is highly vulnerable; northern and northeastern peripheral neighborhoods are moderately to highly vulnerable; and the mountainous western neighborhoods are the least vulnerable to flash flooding. Low-income and unemployed expatriate families living in densely populated central, south, and southeastern neighborhoods are more vulnerable to flooding than rich Saudi families living in the sparsely populated northern half of the central city, western, northwestern, and southwestern neighborhoods. The CFVI map will help city planners to formulate effective flood control measures to protect the city residents and urban infrastructures from future flood damage. This flood vulnerability research can be expanded to other Saudi cities by incorporating more physical and social variables.

ASSESSING THE IMPACTS OF FLOODING CAUSED BY EXTREME RAINFALL EVENTS THROUGH A COMBINED GEOSPATIAL AND NUMERICAL MODELING APPROACH

In this paper, we present a combined geospatial and two dimensional (2D) flood modeling approach to assess the impacts of flooding due to extreme rainfall events. We developed and implemented this approach to the Tago River Basin in the province of Surigao del Sur in Mindanao, Philippines, an area which suffered great damage due to flooding caused by Tropical Storms Lingling and Jangmi in the year 2014. The geospatial component of the approach involves extraction of several layers of information such as detailed topography/terrain, man-made features (buildings, roads, bridges) from 1-m spatial resolution LiDAR Digital Surface and Terrain Models (DTM/DSMs), and recent land-cover from Landsat 7 ETM+ and Landsat 8 OLI images. We then used these layers as inputs in developing a Hydrologic Engineering Center Hydrologic Modeling System (HEC HMS)-based hydrologic model, and a hydraulic model based on the 2D module of the latest version of HEC River Analysis System (RAS) to dynamically simulate and map the depth and extent of flooding due to extreme rainfall events. The extreme rainfall events used in the simulation represent 6 hypothetical rainfall events with return periods of 2, 5, 10, 25, 50, and 100 years. For each event, maximum flood depth maps were generated from the simulations, and these maps were further transformed into hazard maps by categorizing the flood depth into low, medium and high hazard levels. Using both the flood hazard maps and the layers of information extracted from remotely-sensed datasets in spatial overlay analysis, we were then able to estimate and assess the impacts of these flooding events to buildings, roads, bridges and landcover. Results of the assessments revealed increase in number of buildings, roads and bridges; and increase in areas of land-cover exposed to various flood hazards as rainfall events become more extreme. The wealth of information generated from the flood impact assessment using the approach can be very useful to the local government units and the concerned communities within Tago River Basin as an aid in determining in an advance manner all those infrastructures (buildings, roads and bridges) and land-cover that can be affected by different extreme rainfall event flood scenarios.

ASSESSING THE IMPACTS OF FLOODING CAUSED BY EXTREME RAINFALL EVENTS THROUGH A COMBINED GEOSPATIAL AND NUMERICAL MODELING APPROACH

Abstract. In this paper, we present a combined geospatial and two dimensional (2D) flood modeling approach to assess the impacts of flooding due to extreme rainfall events. We developed and implemented this approach to the Tago River Basin in the province of Surigao del Sur in Mindanao, Philippines, an area which suffered great damage due to flooding caused by Tropical Storms Lingling and Jangmi in the year 2014. The geospatial component of the approach involves extraction of several layers of information such as detailed topography/terrain, man-made features (buildings, roads, bridges) from 1-m spatial resolution LiDAR Digital Surface and Terrain Models (DTM/DSMs), and recent land-cover from Landsat 7 ETM+ and Landsat 8 OLI images. We then used these layers as inputs in developing a Hydrologic Engineering Center Hydrologic Modeling System (HEC HMS)-based hydrologic model, and a hydraulic model based on the 2D module of the latest version of HEC River Analysis System (RAS) to dynamically simulate and map the depth and extent of flooding due to extreme rainfall events. The extreme rainfall events used in the simulation represent 6 hypothetical rainfall events with return periods of 2, 5, 10, 25, 50, and 100 years. For each event, maximum flood depth maps were generated from the simulations, and these maps were further transformed into hazard maps by categorizing the flood depth into low, medium and high hazard levels. Using both the flood hazard maps and the layers of information extracted from remotely-sensed datasets in spatial overlay analysis, we were then able to estimate and assess the impacts of these flooding events to buildings, roads, bridges and landcover. Results of the assessments revealed increase in number of buildings, roads and bridges; and increase in areas of land-cover exposed to various flood hazards as rainfall events become more extreme. The wealth of information generated from the flood impact assessment using the approach can be very useful to the local government units and the concerned communities within Tago River Basin as an aid in determining in an advance manner all those infrastructures (buildings, roads and bridges) and land-cover that can be affected by different extreme rainfall event flood scenarios.

A method for generating floodplain maps using IKONOS images and DEMs

Flooding is a natural hazard that takes millions of lives each year. Remote sensing and geographic information systems (GIS), two fields of surveying engineering, can play an important role in flood management by generating floodplain maps. This paper proposes a model to generate a floodplain map for a flood event. A digital elevation model (DEM) is used to compute a flood-depth map for a given flood event. The flood-depth map is overlaid over the classified high-resolution image (IKONOS) to determine the inundated classes after flooding. This model is applied on a part of the river that is located in the northwest of Iran in Khoram-Abad city. Applying this model on this study area shows that the accuracy classification is improved by about 15% using a multi-layer perceptron neural network. This improvement could play an important role in flood risk assessment. A nonlinear regression model is also used for modelling the flood-water line.

LAND USE PLANNING FOR SETTLEMENTS AREA IN CONSIDERATION OF FLOOD AND LANDSLIDE HAZARDS IN BAGELEN SUB-DISTRICT, PURWOREJO INDONESIA

The objective of this research is determining alternative land use for settlement development considering its hazards susceptibility. Data were obtained by interviews, participatory GIS, direct-observation, sampling of soil and secondary data analysis. The flood hazard map was produced using Kriging interpolation techniques; flood depth map for the largest flood in the year 2004 was created. The area with high landslide hazard is located on the hilly area in the eastern part of the area of study. The result from overlaying two hazard maps indicates that the area which considered as having less hazards is located on the colluvial plain. The result of suitability analysis including hazard and people perception-based criteria showed that the area suitable is mostly located in Bagelen and Krendetan Villages. The result of suitability analysis of the non-hazard criteria showed that the area suitable of which 338.1 Ha is an existing settlement.