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Conveners
Lin Lin LI, (Sun Yat-Sen University Guangzhou, China)
Djati MARDIATNO (Universitas Gadjah Mada, Yogyakarta, Indonesia)
Nelly Florida RIAMA (Indonesian Agency for Meteorology Climatology and Geophysics (BMKG)), Indonesia
Amy DONOVAN (Cambridge University, UK)

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Conveners Lin Lin LI, (Sun Yat-Sen University Guangzhou, China) Djati MARDIATNO (Universitas Gadjah Mada, Yogyakarta, Indonesia) Nelly Florida RIAMA (Indonesian Agency for Meteorology Climatology and Geophysics (BMKG)), Indonesia Amy DONOVAN (Cambridge University, UK)

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[Kyle Bradley, Singapore] - The anthropogenic nature of the 2018 Palu landslides, Indonesia

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[Kyle Bradley, Singapore] - The anthropogenic nature of the 2018 Palu landslides, Indonesia

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Remote sensing of land surface changes showed how a human activity (irrigation) exacerbated a natural hazard (earthquake-triggered liquefaction and landsliding).

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On September 28, 2018, a powerful, shallow earthquake ruptured the land surface and seafloor near Indonesia's Sulawesi island and sent a devastating tsunami into the city of Palu. While the nearby strike-slip fault was a known tsunami hazard, the magnitude 7.5 earthquake surprised scientists because it triggered large and deadly landslides in an area with a gently sloping landscape.

One year later, a team of scientists from six countries has unraveled the mystery of the landslides and discovered a new earthquake hazard in the process. Examining various types of radar and visible satellite data, the team found that mud and soil flowed most readily near irrigated rice paddies.

The practice of keeping farmland soaked for rice cultivation slowly draws the water table-the layer below ground where the soil becomes saturated-closer to the land surface. This makes the soil wetter and more prone to liquefaction -the process by which sandy soils behave like a liquid in response to strong ground shaking. As shaking overpowers the friction that normally holds particles together, soil loses its structural integrity and begins to flow like a liquid. It can act like a lubricated surface, allowing relatively solid ground to slide freely downslope under the force of gravity.

Soil liquefaction usually occurs in flat landscapes with sandy or silty ground, such as coastal plains where the water table is close to the surface. While Palu has sandy soil, the gently sloping valley around the city appeared to pose little risk as high water tables are rare on hillsides. But when researchers began to examine damage after the Palu quake, they noticed that all the landslides originated along a distinct line and near an aqueduct. "We started studying why the aqueduct so clearly defined the boundary between land sliding and not sliding," said Sang-Ho Yun, a natural disasters researcher at NASA's Jet Propulsion Laboratory.

The image above is a damage proxy map created by Yun and colleagues. They examined satellite radar data collected before and after the quake, mapping changes in the land surface and built structures. Yun's data has been overlaid on a digital elevation map to show the slope of the landscape.

The maps above were made from data provided by Kyle Bradley of Nanyang Technological University. Provoked by damage maps, Bradley and colleagues gathered land-cover maps plus visible satellite imagery taken by the commercial satellite company Planet just before and after the earthquake in order to determine where and why the land deformed and slid. They used software to calculate horizontal land displacement, particularly in areas around the aqueduct.

The Gumbasa Aqueduct was built in the early 20th century on the east side of Palu to reduce the risk of famine by providing a consistent water supply to local farmers. Only the land downhill from the aqueduct is irrigated. Farmers just below the aqueduct practice wet rice cultivation, in which fields are flooded at one point in the growing cycle. Farther downhill, near Palu, farmers grow tree crops like coconut palms, which require less irrigation and do not raise the water table as much.

Despite slopes of no more than 1.5 to 2 degrees, the earthquake caused land to slide as much as 1 kilometer. The cause was widespread liquefaction in the irrigated rice paddies below the aqueduct. (No liquefaction was identified upslope of the aqueduct, where the water table was closer to its natural level.) Because the aqueduct did not have an impermeable lining, it is likely that leakage also played a role. The slides were slowed or halted by the coconut palm plantations.

"If there hadn't been intensive irrigation, the landslides and the resulting damage and loss of life probably would not have happened," said Bradley, the lead author on the paper. "This was a human-caused hazard, and it can have a human solution. We cannot directly reduce the hazard of ground shaking on Palu, but the agricultural practices can be updated to reduce the human exposure to this hazard." For instance, farmers could plant more trees near their rice fields in order to better anchor the soil and draw down the water table.

The findings from Palu have Bradley and colleagues considering whether agricultural practices have played a role in other landslides or could be creating new seismic hazards elsewhere. Indonesia is not the only place in the world where people grow heavily irrigated crops on wet, sandy slopes. "There are a few historical cases where earthquake-triggered liquefaction seems to have been strongly correlated with irrigation, and cases where major slope failures and landslides were related in some way to irrigation," Bradley added. "This is likely to be a problem where irrigation practices are inefficient and excess groundwater exists. However, any tectonically active location with sandy soils, a surface slope, and a raised water table are of concern."
NASA Earth Observatory images by Joshua Stevens, using data courtesy of Bradley, K. et al. (2019) and topographic data from the Shuttle Radar Topography Mission (SRTM). Story by Michael Carlowicz, with reporting from Esprit Smith and Carol Rasmussen, NASA Jet Propulsion Laboratory, and Shireen Federico, Nanyang Technological University.

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Palu Landslides Linked to Rice Irrigation

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On September 28, 2018, a powerful, shallow earthquake ruptured the land surface and seafloor near Indonesia's Sulawesi island and sent a devastating tsunami into the city of Palu. While the nearby strike-slip fault was a known tsunami hazard, the magnitude 7.5 earthquake surprised scientists because it triggered large and deadly landslides in an area with a gently sloping landscape.

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please discuss the different Impact of human active on hydrogeochemicstry with an example

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How can I map/indicate anthropogenic influence to a landslide model?

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How can I map/indicate anthropogenic influence to a landslide model?

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Dam or Barrage Construction accelerate flood incident in India.

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Abstract

Flood is a natural but inevitable phenomenon occurring over the period of time. It not only damages the life, property and resources, but also hampers the economy of a nation. In this paper, an attempt has been made to delineate flood vulnerability areas for Kulik river basin through frequency ratio model. Parameters like slope, elevation, rainfall, drainage density, land use-land cover, TWI, population density, road density and household density were endorsed for understanding flood mechanism. In general, 70 (70%) flood locations from flood inventory map were randomly selected for constructing flood vulnerability map parameters and the rest 30 (30%) flood locations were used for justifying the outcomes. Flood vulnerability zone map was classified into five zones such as very low (2.02 km ²), low (2.45 km ²), moderately low (2.44 km ²), high vulnerable (2.26 km ²) and very high vulnerable (1.21 km ²) area. As the AUC value for success rate is 0.901, the constructed flood vulnerable map with FR model is very much accurate for this river basin. The outcome of this paper will help the planners and decision-makers to take some probable measure to minimize flood vulnerability in this region.

Introduction

Across the globe in every year, different kinds of natural as well as man-induced hazards and disasters like floods, earthquakes and landslides are causing a large amount of life loss and property damage (Youssef et al. 2011; Du et al. 2013; WHO 2003; Tehrany et al. 2017). Floods occur when there is an overflow of huge volume of water beyond its normal limits, causing inundation of river banks and water stagnation for temporary season. Floods play a role of prime mover for damaging environmental ecosystem, transport system, agriculture and farming, and sociocultural hereditament and altering human life (Billa et al. 2006; Yu et al. 2013; Messner and Meyer 2006; Rahmati et al. 2016a, b, c, d). An immense number of research works have been done on classifying different types of floods and their short- and long-term effects. Magnitude of flood is one of the major factors that determine its impacts. In estimating all inclusive impacts, the direct or indirect damages due to floods can be stratified into materialistic and non-materialistic things (Li et al. 2012; Merz et al. 2004; Smith and Ward 1998). Throughout the globe, floods are considered the major catastrophic events that occur suddenly, causing huge money, health and wealth loss. In between 1963 and 1992, among the different kinds of hazardous events floods are causing 32% of damage to human and environment. In Orissa and Andhra Pradesh, high-catastrophic sudden flood causes huge amount of property and life loss (Dhar et al. 1981a; WMO 1994; Dhar and Nandargi 2003). Failure of dam due to sudden heavy precipitation has stimulated flood downstream. In 1979, Morvi town of Saurashtra discern uncountable loss of property, crops, livestock and about 10,000 human lives due to Machhu dam failure (Dhar et al. 1981b; Dhar and Nandargi 2003). Uncertainty in climatic condition in Iran persuades a vast number of flooding events throughout the year. The financial vandalization amount escalates to 1705 thousand dollars ascribed to flooding situation in the past decade (Norouzi and Taslimi 2012; Khosravi et al. 2016). India is situated under highly inconsistent monsoon climate, a large number of rivers flow through all over the country, and a heavy downpour of rains for a prolonged time can persuade flooding condition. According to Central Water Commission 2010, India is the second largest flood-prone country after Bangladesh with about 40 million hectares of land under flood prone. In every year in India on an average 7.6 million hectares of land is affected by floods. Statistical data provided by CWC divulge that in India flood causing around 200 billion dollars worth of capital loss and 92,000 people died in between 1953 and 2009 (CWC 2010). Every year, people died due to this calamity all over the Earth, an approximately 20% of world's people died in India. Around 1500 peoples lost their lives, and in average 3 cores of peoples are affected by flood in every rainy season in India (Gupta et al. 2003; Singh and Kumar 2013; Sing and Kumar 2017). As flooding is a terrific natural disaster and it is almost impossible to prevent it completely (Cloke and Pappenberger 2009), it is very necessary to create flood susceptibility mapping and flood vulnerability assessment through which we will be able to design some management schemes for diminishing vandalization of flood in time to come in the future and it is a utilitarian tool to direct the governments and planners to formulate proper flood management plans (Esteves 2013; Haghizadeh et al. 2017; Kia et al. 2012; Khosravi et al. 2016; Feng and Wang 2011; Tehrany et al. 2015; Samanta et al. 2018a, b). Different kinds of parameters like climate and geomorphological structure regulate the occurrence of flood and its intensity. The multi-criteria-based flood susceptibility and vulnerability estimation will much more reliable and validate instead of single criterion-based flood susceptibly analysis (Booij 2005; Minea 2013; Tehrany et al. 2014a, b; Xu et al. 2013; Poussin et al. 2014). In majority of the cases, flood management plan means strategies taken for distributing reliefs and resettling flood-affected people. Flooding prediction, preparation according to flood predictability, implementing strategies during flood, and overall damage assessment are the major four steps for flood management (Konadu and Fosu 2009). Now, in recent decades, remote sensing (RS) and GIS software techniques are intensely used as they provide a brand new dimension for vulnerability assessment with proper justification. Analysing satellite imageries on RS and GIS platform assigned satisfactory results for flood susceptibility and vulnerability mappings as it is providing flawless information about a region (Ali et al. 2019). It creates an amazingly pleasant environment where a wide variety of models can run and manipulate information to evaluate flood vulnerability, and the results become more logical and acceptable. At the present time, RS and GIS techniques are being used very successfully for flood vulnerability all over the world (Khosravi et al. 2016; Dewan et al. 2007; Haq et al. 2012). There are so many models and techniques alternatively used for flood vulnerability mapping. Frequency ratio (FR) model (Lee et al. 2012) is one of the highly acceptable techniques for hazards assessment with high accuracy rate. Frequency ratio (FR) is a one kind of bivariate statistical analysis (BSA), and this method gives value to every single class of each parameter and evaluates its impact on flood occurrence (Jebur et al. 2014; Tehrany et al. 2017). FR model is a simple process to run on GIS environment and can construct scientifically valid flood vulnerably map (Tehrany et al. 2014a, b; Liao and Carin 2009; Samanta et al. 2018a, b). RS- and GIS-like sophisticated software provides an easy way for preparing flood susceptibility mapping by FR model. The main objective of the present research is to prepare flood vulnerability map of Kulik river basin by using FR model. The objective of this work is to critically analyse flood risk potential zone of Kulik river basin.

Study area

The latitudinal and longitudinal extensions of Kulik river basin are 25°32′00″ N to 26°10′06″ N and 87°60′00″ E to 88°24′56″ E, respectively. Kulik river basin is situated in the mature alluvium of Indo-Bangladesh Barind tract. The maximum height of the Kulik river basin is 71 m which has been observed in the source segment of the Kulik River, and the minimum height is only 17 m which is found in the confluence point. The Kulik River originated in the Thakurgaon District of Bangladesh which flows 133.27 km and joins the Nagar River in Itahar CD Block of Uttar Dinajpur, India. Since the difference between the highest and lowest altitude is only 54 m, it is very easy to assume that the Kulik River flows over a flat region. Total area of the Kulik river basin is 1038.51 km ². A decrease in water carrying capacity of river due to aggradations and rainfall during monsoon is the main reason for flooding in this region. Although some of the measures are taken to prevent flood in some places, like plantation along the river side, they are less than necessary, so every year flood causes huge damage to property and life. Evidences collected from USGS toposheet (1951) show that, before 1951, Kulik River drains its water to Mahananda River directly, but due to some tectonic movement triggers now Kulik River merged with Nagar River, and after flowing to some distance Nagar River merged with Mahananda River (Sarkar and Pal 2018). About 281 numbers of oxbow lakes and paleo-channels along the both sides of Kulik River defined its migratory behaviour in nature (Sarkar 2018). Due to the fact that the river basin is in the plain region, a large number of lowlands in the form of oxbow lakes, ponds and paleo-channels scatteredly located all over the basin create an ideal condition for flooding. This region has an annual rainfall of 2280 mm and located under sub-humid monsoonal climate. This region receives about 80% of all rainfall from the month of June to October. This region is often flooded by floods due to excessive rainfall in this short period (Sarkar and Pal 2018). Figure 1 shows the flood-inundated area on 24 June 2017.

Data source and methodology

For flood vulnerability mapping of Kulik river basin, nine individual parameters were taken into consideration. Table 1 shows the detail specification of different data sets and their characteristics. All the layers have been constructed and combined in ArcGIS (v.10.3.1) environment and Fig. 2 showing the flowchart and methodology for preparing flood vulnerability mapping of Kulik river basin. Slope map, elevation map and TWI map have been constructed from SRTM DEM data (Table 1). The land use-land cover (LULC) map has been prepared using maximum likelihood classifier method. All stream segments have been digitized from Google Earth image for construction of drainage density map in ArcGIS (v.10.3.1) platform. Road density map also is extracted from the same source using hand-held GPS tracking system which can be used wherever it is needed. Population and household data have been collected from official census website (Census 2011) of both countries. A field inspection during flood and post-flood study has been conducted (June-August 2017) for assessing probable causes, damages due to flooding and people's perception about this threatening calamity.

Flood inventory map

For analysing flood vulnerability, it is extremely important of having a scientifically justified past flood occurrence data for estimating future flood (Manandhar 2010). The correctness and reliability of the future flood vulnerability hinge on the precision of the previously recorded flooding events (Merz et al. 2007). For Kulik river basin, flood inventory map has been prepared based on 2017 extreme flood event (geoapps.icimod.org/BD2017) on ArcGIS 10.3.1 environment (Fig. 3). A total of 100 flood points are randomly divided into two groups of training and validation data sets for preparing and justifying the result of flood vulnerability.

Though there are no specific rules for defining how flood point will be allocated into training and validation data sets (Pradhan and Lee 2010), usually research work has been done by using 70% of floods events as training data set for preparing flood vulnerability model and the rest 30% have been used for validation of the output model (Ohlmacher and Davis 2003; Tunusluoglu et al. 2008; Pourghasemi et al. 2012; Pourghasemi and Beheshtirad 2014). In this research work, 70 (70%) training points were randomly nominated for flood modelling and 30 (30%) validation points have been used for authentication of the model (Rahmati et al. 2016a, c).

Flood conditioning factors

There are so many geo-environmental and man-made independent factors controlling flood occurrences in a region (Tehrany et al. 2015). So for investigating flood risk assessment it is strategically important to select appropriate factors to get reliable outcomes because an individual flood controlling factor may be very much functional in a specific catchment, but in other areas it may not be necessary (Kia et al. 2012). For the present research work, totally nine conditioning factors such as (1) slope, (2) elevation, (3) drainage density, (4) rainfall, (5) LULC, (6) topographical wetness index (TWI), (7) road density, (8) population density and (9) household frequency are taken into consideration.

Slope and elevation maps (Fig. 4a, b) of the study area have been prepared from DEM by using ArcGIS software (Khosravi et al. 2016; Tehrany et al. 2017). Slope and elevation are most important factors for flood vulnerability assessment. Land elevation always plays an important and effective role for contriving flood vulnerability mapping (Rahmati et al. 2016a, c; Fernandez and Lutz 2010). The climate of the region is regulated by the variability of the height of the land in that region (Samanta et al. 2012). Different altitudinal zones receive different amounts of rainfall and temperature; as a result, it creates different types of vegetation cover and soil forms (Aniya 1985). Slope is one of the principle components of flooding (Mishra 2013; Haghizadeh et al. 2017). Land slope controls the surface water flow, vertical infiltration process (Youssef et al. 2011) and rate of soil erosion (Adiat et al. 2012) of a region.

a- i Spatial data layers used for preparing flood vulnerability mapping: a slope, b elevation, c drainage density, d rainfall, e LULC, f TWI, g road density, h population density, i household frequency

Drainage density has a great significant impact on flood occurrence. It has a positive correlation with flooding as a large number of streams raise the flood potentiality multiplied by it. For preparing rainfall map (Fig. 4 d), rainfall values put against seven weather stations, i.e. Slilguri, Islampur, Dalkhola, Raiganj, Raghunathpur (India) and Thakurgaon and Dinajpur (Bangladesh), and by using IDW (inverse distance weighting) in ArcGIS environment rainfall map of the study area has been constructed.

Different types of LULC of a region control flood vulnerability differently. Vegetation cover has a negative relation with flood as it slows down surface water flow and recharging groundwater by promoting infiltration process (Rahmati et al. 2016a, b, c, d; Tehrany et al. 2013a, b). On the other hand, built-up area with high surface run-off creates flooding condition. LULC map was constructed from Landsat 8 OLI imageries. Supervised image classification is done by using maximum likelihood classifier algorithm of ERDAS Imagine (v.9.1) (Samanta 2013).

Topographical wetness index (TWI) is also known as compound topographical index (CTI). TWI technique is used to understand the topographical influence on the rate of wetness of a catchment area and to show the rate of over land flow (Samanta et al. 2018a, b). Amount of flow accumulation at any point in the catchment area can be calculated by TWI technique, and it shows the flow of water under the influence of gravity (Moore et al. 1991). TWI is a very effective technique for analysing flood potentiality (Rahmati et al. 2016a, b, c, d; Haghizadeh et al. 2017; Samanta et al. 2018a, b; Khosravi et al. 2016; Ali et al. 2019; Tehrany et al. 2015, 2017). TWI map has been constructed from DEM by running flowing equation on Raster Calculator in ArcGIS environment (Regmi et al. 2010; Moore et al. 1991; Jaafari et al. 2014; Nampak et al. 2014; Ali et al. 2019):

where FLOWACC is the flow accumulation derived from DEM in ArcGIS environment and β is the slope (in °).

For preparing road density map, all major roads of Kulik river basin have been extracted by manual digitization process from Google Earth image and imported to ArcGIS (v10.3.1). Road has a significant effect on flooding as it works as an artificial barrier in the way of flood. To some extent, high road density reduces the destruction process of flood. The horrors of floods were measured by judging environmental and societal aspects. Extreme flood events leave the evidence of huge loss of property, houses and death every year around the world. In densely populated areas, the number of deaths increases in multiples by flooding.

Bivariate statistical analysis (frequency ratio model)

Assessment of flood vulnerability mapping is very much essential to identify the factors affecting flood. The relationship between flood and associated conditioning factors that can trigger flood could be derived from the past flooding events and their causing parameters. In the present study, frequency ratio (FR) model has been used to construct flood vulnerability mapping of Kulik river basin. FR is a BSA method that critically analyses the contribution of every class of each conditioning factor on future flooding (Lee et al. 2012). FR is calculated by analysing the relationship between flood occurrences and the attributed factors. So FR of every class of each conditioning factor has been calculated with respect to previous flood event as shown in Table 2. FR values were calculated by using the following formula:

After calculating FR values of every class, each controlling factor summed up all the values for generating final flood vulnerability map. The formula of flood vulnerability map given below:

$${\text{FVI}} = \sum\limits_{j = 1}^{n} {\text{FR}} ,$$

where N pix(SXi) is the number of flood points in class i of variable X, N pix( X j) is the number of pixels in variable X j, m is the total classes in the variable X i, and n is the total factors of the study area (Regmi et al. 2013; Jaafari et al. 2014).

FR is one of the simplest and authentic methods frequently applied for flood vulnerability mapping all over the world (Yilmaz 2007; Khosravi et al. 2016; Rahmati et al. 2016a, b, c, d; Samanta et al. 2018a, b). If the FR value is greater than 1, it means parameters are strongly influencing flooding, and if it is below 1 it shows the negative relationship between flood occurrence and controlling variables (Lee and Talib 2005; Pradhan 2010; Sujatha et al. 2013; Akgun et al. 2008; Mondal and Maiti 2013).

Results and discussion

Tables 2 and 3 show the weights of different sub-classes of each of the nine conditioning factors in reference to flood vulnerability mapping. Flood occurrence parameters like slope, elevation, drainage density, rainfall, LULC and TWI have been calculated based on the FR model (Table 2), and road density, population density and household frequency layers are constructed in reference to five-point rating scale (Table 3). High frequency ratio (FR) value is stipulating high flood probability. So a high positive correlation exists in between frequency ratio (FR) and flooding phenomena (Tehrany et al. 2013a, b).

The slope of a region regulates the flood occurrence as low flat plain area has a strongly association with flooding condition in the rainy season. A high number of floods occur in lower slope area as the water cannot discharge swiftly. Slope between 0° and 10.59° supports the fact of flooding as the FR value is considerably more than one (Tehrany et al. 2017). High slope indirectly enhances the flood probability as it fosters speedy water and the area with high slope has the less time to regulate and percolate water under the ground (Jaiswal et al. 2003). In Kulik river basin, the outcome exhibits that the FR value is more than 1 in the first two slope sub-classes 0°-1.18° and 1.18°-2.12° and the FR value is less than 1 where slope is greater than 2.12°, respectively. About 60% of flooding occurred in the area having less than 2.12° of slope (Table 2).

Elevation is one of the major controlling factors of flooding. Generally, the FR value will decrease as the height of the area increases. It is exhibited in Table 2 that the two lower elevated areas (17-27.8 m and 27.08-38.06 m) in Kulik river basin have been recorded high FR values of 4.98 and 2.04, respectively, which illustrate the high probability of flooding in low elevated area (Khosravi et al. 2016). Area with low FR value with high altitude has the less probability of flooding. About 63% of flood (2017) occurred in these two low elevation zones of Kulik River.

Area with low drainage density has the greater chances for flooding; due to low number of drainage systems, water cannot be drained very quickly in a short period of time. In Kulik river basin, the lower drainage density (< 45) area recorded almost 61% of past flooding as the FR value is 0.67 (Table 2).

The amount of rainfall is the most important reason for flooding in any area. No region expects that the rain can be flooded. It is to be observed that the FR value (1.59) is high in places where rainfall is high (294.96-321.33 mm) (Samanta et al. 2018a, b).

The land use-land cover (LULC) in the region can be used as a very important tool to identify flood-prone areas in a river basin. For example if there is no natural. Forest area acts as a natural resistance to flood prevention in any area. On the other hand, the open field enhances flood trends many times. So there is a positive relationship with the flood in the open wilderness and negative relations with forest cover area (Samanta et al. 2018a, b; Shabani et al. 2014). Again, in built-up area due to concretization the amount of underground water percolation is very negligible but surface run-off of very high. In Kulik river basin, the high FR value recorded in fallow land and agricultural land is 1.49 and 0.78, respectively (Table 2).The outcome supports the fact that unprotected area is highly susceptible for flooding.

The TWI factor is one of the extremely necessary factors for future flood prediction. In the case of Kulik river basin, the topographical wetness index (TWI) is divided into five sub-classes. Normally, it is safe to say that the flood probability is high in the places where the TWI is more (Khosravi et al. 2016). The highest FR value of 1.81 is observed in the class of 5.40-17.31, and the lowest FR value in the class of − 5.7-0.53.

For preparing flood vulnerability mapping, here road density, population density and household frequency layers are divided into five main categories and given rating from 1 to 5 (Table 3) towards vulnerability. The area where the concentration of roads is the highest is given 1 and given 5 where the lowest road concentration found. The road works in an area as an artificial barrier to prevent flooding. Therefore, the more the density of roads is in those areas, the lesser the tendency to flood in those areas. If there is more population density in any area, then the chances of damage to floods in those areas are greatly increased. Here is existing a positive relationship in between population density and flood vulnerability. For preparing flood vulnerability map of Kulik River, maximum class (> 5086) is given a maximum of 5 points and the lowest class (< 602) is given 1 point. Following the same way of population density, household frequency layer has been prepared and given point to five different sub-classes, respectively. As the number of homesteads in flood-prone area is high, the probability of damage due to floods increases.

Flood vulnerability mapping of Kulik river basin

After preparing individual layers of nine conditioning parameters and given weights to those parameters, all the parameters are combined to construct the final flood vulnerability map of Kulik river basin on ArcGIS 10.3.1 environment by overlapping process (Fig. 5). The final flood map has been divided into very low (244-433), low (433-562), moderate (562-693), high (693-819) and very high (819-1327) vulnerable zone (Table 4). The result has shown that 2.02 (19.48%) km ² of land is categorized under low flood vulnerability. The low flood vulnerable zone was situated mostly in the high altitude above 50 m from the mean sea level. Around 2.45 (23.64%) km ² of land is categorized under low vulnerability zone, 2.44 (23.45%) km ² of land under moderately vulnerable, 2.26 (21.74%) km ² of land under high flood zone and 1.21 (11.69%) km ² of land under very high flood vulnerability zone in future. Very high and high flood vulnerable areas are located in the lower segment of Kulik river basin. From the final flood vulnerability map, it is clearly identified that peoples living on the banks of Kulik River are most likely to be affected by future floods as this area is situated under very high flood vulnerability zone.

Validation of flood vulnerability map

After creating a model, it is not the scientific way to use it for governmental or non-government purpose without checking the accuracy and reliability of that model (Chung and Fabbri 2003). In the study area, the receiver operating characteristic (ROC) curve was used to verify flood vulnerability map. This ROC curve system is a simple and universal technique based on scientific basis through which we can accurately assess a test. In ROC curve, false positive rate is shown along the X-axis and true positive rate along Y-axis. To substantiate the prediction of any model, area under curve (AUC) is taken into consideration as if the AUC value is 0.5 or less than that the model is inappropriate for flood vulnerability mapping and if the value is perfectly 1 then this model is the most suitable for estimating flood trends and if the AOC value is > 0.75 then flood predictability is also well accepted (Saha 2017; Ozdemir and Altural 2013; Akgun et al. 2008; Egan 1975; Pradhan et al. 2010). Based on AOC curve, prediction of model has been classified into five categories as 0.5-0.6 (poor), 0.6-0.7 (average), 0.7-0.8 (good), 0.8-0.9 (very good) and 0.9-1.0 (excellent) (Al-Abadi 2015; Saha 2017). In the present research work, 70 (70%) flood locations are used for flood vulnerability mapping and 30 (30%) of total data set, which is not incorporated with FVM, are used for justifying the flood vulnerability. The AUC value is 0.901 (Fig. 6). It is very easy to establish that the model used for preparing flood vulnerability mapping of Kulik river basin is quite accurate and suitable for determining flood trends. Consequently, it can be concluded that the FR model is very useful technique for determining flood vulnerability mapping.

Conclusion

In recent times, among many geo-environmental hazard and disasters, floods are considered the most catastrophic disaster all over the globe. Analysing flood vulnerability mapping is very much significant for diminishing ruinous flood by implementation of fide approaches. Flood vulnerability-related information is a principle asset for planners for implementation of right land use in flood-prone areas. In the present study, a BSA-based FR model espouses to comprehend the interconnection of flood occurrence and factor variables of Kulik river basin. Nine conditioning factors, i.e. slope, elevation, drainage density, rainfall, LULC, topographical wetness index (TWI), road density, population density and household frequency, were taken, and based on the flood inventory map individual layers have been generated with 30 × 30 m ² resolution. A random sampling technique has been used for selecting 70% total flood point for layers construction and 30% for validation purposes. The authentication of flood vulnerability mapping relies on how precisely factor layers were prepared. The final flood vulnerability map was classified into five zones, very low, low, moderate, high and very high zone, respectively. Figure 5 shows that very high vulnerable areas were delineated in lower segment adjacent to the river with 1.21 km ² of lands. The ROC curve was used to evaluate the effectiveness of the present FR model for vulnerability mapping and appraise the outcome. The product divulges that the method applied in the present work produces reliable and accurate result with 90.10% success rate. So it can be concluded that the perfections of the conditioning factors have a noteworthy impact on flood vulnerability mapping as if the standard level of parameters strengthens the performance of the model also increases. The most important parameters and the class for flood vulnerability mapping of Kulik river basin are low slope (0°-1.18°), low elevation (17-27.8 m) and high TWI (5.40-17.32). This model can assist the government, planners and decision-makers to implement proper management schemes in this study area and confined the developmental processes by demarcating flood vulnerable places.

References

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Acknowledgements

This research work would never be completed without the contribution of a great few peoples to whom we would like to express our deepest gratitude. Authors would like to acknowledge Dr. Sunil Saha (Assistant Professor, University of Gour Banga) and Gopal Ch. Paul (Research Scholar, University of Gour Banga) for helping and supporting in every step of this work. We would also like to thank peoples who contributed their knowledge and information during field survey.

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Department of Geography, Raiganj University, Raiganj, West Bengal, 733134, India

Debabrata Sarkar & Prolay Mondal

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Correspondence to Debabrata Sarkar.

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# data[].attributes.attachment_link.title
Flood vulnerability mapping using frequency ratio (FR) model: a case study on Kulik river basin, Indo-Bangladesh Barind region

# data[].attributes.attachment_link.description
Flood is a natural but inevitable phenomenon occurring over the period of time. It not only damages the life, property and resources, but also hampers the economy of a nation. In this paper, an attempt has been made to delineate flood vulnerability areas for Kulik river basin through frequency ratio model.

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[Malamud, Kenya Example] Drainage networks getting clogged by plastic bags used for water that women carry. Enhance risk of flooding.

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[Malamud, Kenya Example] Drainage networks getting clogged by plastic bags used for water that women carry. Enhance risk of flooding.

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Are there examples of participatory methods with communities or the public (citizen science) to support these approaches?

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Lack of progress in science communication is a bigger issue in the developing countries. This single factor is influencing towards many natural hazards, its risk factor, management and the geopolitical action.

-Dr. R. Arun Prasath

# data[].attributes.headline
In risk assessment human influence is influencing the three hazard components (H,E,V). How can we take these into account in developing future scenarios? 

# data[].attributes.subject
In risk assessment human influence is influencing the three hazard components (H,E,V). How can we take these into account in developing future scenarios? 

# data[].attributes.body
In my view, Food preparation and consumption behaviour significantly affects environmental pollution/hazards

# data[].attributes.headline
How can we use the interactions between anthropogenic processes and natural hazards to determine a potential root cause of a specific natural hazard?

# data[].attributes.subject
How can we use the interactions between anthropogenic processes and natural hazards to determine a potential root cause of a specific natural hazard?

# data[].attributes.body
My impression is that as communities become more developed and/or more urbanised their direct exposure to natural hazards reduces, but they become increasingly vulnerable to infrastructure failure

# data[].attributes.headline
Merapi 2010, influence of sabo dams (concrete barriers to contain lahars) may have promoted the overspilling of volcanic flows into villages

# data[].attributes.subject
Merapi 2010, influence of sabo dams (concrete barriers to contain lahars) may have promoted the overspilling of volcanic flows into villages

# data[].attributes.body
The development of Jakarta year by year make an urbanization happen. Beside that flood being a natural hazard and the impact from urbanization.

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A clear example of human's influencing hazard is the haze that causes air pollution across southeast Asia following peat fires in the region. If people didn't start the fires, there would be little to no haze (although there are socio-political issues with that)

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How can we map the human activity impact on hydrogeochemistry

# data[].attributes.subject
How can we map the human activity impact on hydrogeochemistry

# data[].attributes.body
The urbanization is already take place everywhere, how to reduce its impact on natural hazard in that place such as landslide and flooding?

# data[].attributes.body
With a lot of models being churned out to predict how people react or respond proactively or concurrently to hazards, there few studies considering cultural influence, particular in asia and africa where this remains strong,  in the way people respond hazards?

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Recent days, sub-surface (continental and sea floor) drilling is at peak to uncover deep life and it might affect many natural processes and influence natural hazards

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are we ready to prepare our country if the VEI 7 volcano eruption occurs in the future? how to minimize it? is the community ready?

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Ram Krishna Tiwari,nepalpeople constructing road in hilly region without hazard suvey

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Ram Krishna Tiwari,nepalpeople constructing road in hilly region without hazard suvey

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Question: How do you think we should address the assessment of humans on Natural hazards impacts while we know there are several agenda (Sendai Framework, UNFCCC Paris Agreement, UNCBD, UN SDGs, and many many others) and Human behaviors and impacts might also be affected by those? Another point is how do you think we can address the Root Causes of Natural hazards/Disasters which are perceived (assessed) differently?(Explanations related to the above Questions:)Although Anthropogenic process and impacts may seem different or affect natural disasters (or affect the society) differently, they may be led by similar root causes. 1.	We need to address the Root causes more comprehensively! 2.	We need to cross-check those Root causes with Policies, Institutional settings, etc.

# data[].attributes.subject
Question: How do you think we should address the assessment of humans on Natural hazards impacts while we know there are several agenda (Sendai Framework, UNFCCC Paris Agreement, UNCBD, UN SDGs, and many many others) and Human behaviors and impacts might also be affected by those? Another point is how do you think we can address the Root Causes of Natural hazards/Disasters which are perceived (assessed) differently?(Explanations related to the above Questions:)Although Anthropogenic process and impacts may seem different or affect natural disasters (or affect the society) differently, they may be led by similar root causes. 1.	We need to address the Root causes more comprehensively! 2.	We need to cross-check those Root causes with Policies, Institutional settings, etc.

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How do you see the role of Citizen science, IoT in 𝘀𝘆𝘀𝘁𝗲𝗺𝗮𝘁𝗶𝗰lly  𝘀𝘁𝘂𝗱𝘆𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻 𝗶𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲𝘀 𝗼𝗻 𝗻𝗮𝘁𝘂𝗿𝗮𝗹 𝗵𝗮𝘇𝗮𝗿𝗱 processes and hazard 𝗶𝗺𝗽𝗮𝗰𝘁𝘀

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How useful is it to identify a few hundred potential hazards that are currently not high risk? Climate Change due to carbon dioxide emission was known as a potential hazard in the mid 19th Century, but it was very low risk at that time, so did that help us?

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Do we have any studies comparing the bad land use practice to the natural hazard impact in the 19-20-21 century? this might indicates the correlation of how fast the growth of human influence to the impact of natural hazard related to bad land use practice

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What about the discharge of industrial wastewater into ocean and river and what kind of hazard it can bring?

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Were there any documented cases where a natural hazard was traced back to a geopolitical activity involving another administrative region or country?

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Were there any documented cases where a natural hazard was traced back to a geopolitical activity involving another administrative region or country?

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During the 2006 lahars at Mayon, we learned that dikes (built to withstand floods but not lahars) can give a false sense of security and increase the number of casualties. People tend to build their houses nearest to dikes. Unfortunately, the November 2006 lahars overtopped dikes, breaching them and creating new paths, and burying the downstream communities.

# data[].attributes.body
During the 2006 lahars at Mayon, we learned that dikes (built to withstand floods but not lahars) can give a false sense of security and increase the number of casualties. People tend to build their houses nearest to dikes. Unfortunately, the November 2006 lahars overtopped dikes, breaching them and creating new paths, and burrying the downstream communities.