Mapping flood susceptibility in an arid region of southern Iraq using ensemble machine learning classifiers: a comparative study

This study examined the efficacy of three machine ensemble classifiers, namely, random forest, rotation forest and AdaBoost, in assessing flood susceptibility in an arid region of southern Iraq. A dataset was created from flooded and non-flooded areas to train and validate the ensemble classifiers using a binary classification scheme (1—flood, 0—non-flood). The prepared dataset was then partitioned into two sets with a 70/30 ratio: 70% (2478 pixels) for training and 30% (1062 pixels) for testing. A total of 10 influential flood factors were selected and prepared based on data availability and a literature review. The selected factors were surface elevation, slope, plain curvature, topographic wetness index, stream power index, distance to rivers, drainage density, lithology, soil and land use/land cover. The information gain ratio was first utilised to explore the predictive abilities of the factors. The predictive performances of the three ensemble models were compared using six statistical measures: sensitivity, specificity, accuracy, kappa, root mean square error and area under the operating characteristics curve. The results revealed that the AdaBoost classifier was the best in terms of the statistical measures, followed by the random forest and rotation forest models. A flood susceptibility map was prepared based on the result of each classifier and classified into five zones: very low, low, moderate, high and very high. For the model with the best performance, i.e., the AdaBoost model, these zones were distributed over an area of 6002 km² (44%) for the very low–low zone, 2477 km² (18%) for the moderate zone and 5048 km² (40%) for the high–very high zones. This study proved the high capabilities of ensemble machine learning classifiers to decipher flood susceptibility zones in an arid region.     

GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest,northern Iran

This study presents a landslide susceptibility assessment for the Caspian forest using frequency ratio and index of entropy models within geographical information system. First, the landslide locations were identified in the study area from interpretation of aerial photographs and multiple field surveys. 72 cases (70 %) out of 103 detected landslides were randomly selected for modeling, and the remaining 31 (30 %) cases were used for the model validation. The landslide-conditioning factors, including slope degree, slope aspect, altitude, lithology, rainfall, distance to faults, distance to streams, plan curvature, topographic wetness index, stream power index, sediment transport index, normalized difference vegetation index (NDVI), forest plant community, crown density, and timber volume, were extracted from the spatial database. Using these factors, landslide susceptibility and weights of each factor were analyzed by frequency ratio and index of entropy models. Results showed that the high and very high susceptibility classes cover nearly 50 % of the study area. For verification, the receiver operating characteristic (ROC) curves were drawn and the areas under the curve (AUC) calculated. The verification results revealed that the index of entropy model (AUC = 75.59 %) is slightly better in prediction than frequency ratio model (AUC = 72.68 %). The interpretation of the susceptibility map indicated that NDVI, altitude, and rainfall play major roles in landslide occurrence and distribution in the study area. The landslide susceptibility maps produced from this study could assist planners and engineers for reorganizing and planning of future road construction and timber harvesting operations.     

GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran,Iran)

The aim of this study is to produce landslide susceptibility mapping by probabilistic likelihood ratio (PLR) and spatial multi-criteria evaluation (SMCE) models based on geographic information system (GIS) in the north of Tehran metropolitan, Iran. The landslide locations in the study area were identified by interpretation of aerial photographs, satellite images, and field surveys. In order to generate the necessary factors for the SMCE approach, remote sensing and GIS integrated techniques were applied in the study area. Conditioning factors such as slope degree, slope aspect, altitude, plan curvature, profile curvature, surface area ratio, topographic position index, topographic wetness index, stream power index, slope length, lithology, land use, normalized difference vegetation index, distance from faults, distance from rivers, distance from roads, and drainage density are used for landslide susceptibility mapping. Of 528 landslide locations, 70 % were used in landslide susceptibility mapping, and the remaining 30 % were used for validation of the maps. Using the above conditioning factors, landslide susceptibility was calculated using SMCE and PLR models, and the results were plotted in ILWIS-GIS. Finally, the two landslide susceptibility maps were validated using receiver operating characteristic curves and seed cell area index methods. The validation results showed that area under the curve for SMCE and PLR models is 76.16 and 80.98 %, respectively. The results obtained in this study also showed that the probabilistic likelihood ratio model performed slightly better than the spatial multi-criteria evaluation. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.     

Exporting a Google Earth<Superscript>™</Superscript> aided earth-flow susceptibility model: a test in central Sicily

In the framework of a regional landslide susceptibility study in southern Sicily, a test has been carried out in the Tumarrano river basin (about 80 km²) aimed at characterizing its landslide susceptibility conditions by exporting a “source model”, defined and trained inside a limited (about 20 km²) representative sector (the “source area”). Also, the possibility of exploiting Google Earth^™ software and photo-images databank to produce the landslide archives has been checked. The susceptibility model was defined, according to a multivariate geostatistic approach based on the conditional analysis, using unique condition units (UCUs), which were obtained by combining four selected controlling factors: outcropping lithology, steepness, plan curvature and topographic wetness index. The prediction skill of the exported model, trained with 206 landslides, is compared with the one estimated for the whole studied area, by using a complete landslide archive (703 landslides), to see to what extent the largest time/money costs needed are accounted for. The investigated area stretches in the fore-deep sector of southern Sicily, where clayey rocks, mainly referring to the Numidian Flysch and the Terravecchia Formations, largely crop out. The results of the study confirm both the exploitability of Google Earth^™ to produce landslide archive and possibility to adopt in assessing the landslide susceptibility for large basin, a strategy based on the exportation of models trained in limited representative sectors.     

Fusain in Carboniferous shallow marine sediments, Donegal, Ireland: the sedimentological effects of wildfire

Evidence from fusain deposits in Lower Carboniferous rocks of western Ireland indicates that a catastrophic wildfire destroyed thousands of square kilometres of Carboniferous ‘forest’. In addition to yielding large quantities of charcoal, this wildfire event resulted in increased surface water runoff which affected sedimentation in an adjacent estuarine environment where the fusain is now preserved. This is the oldest documented evidence for a catastrophic palaeowildfire and a clear example of the sedimentological effects of large-scale fires. The Lower Carboniferous (Visean) rocks in the Largymore Syncline of western Donegal, Ireland, are shallow marine sandstones, mudstones and limestones. The Upper Shalwy Beds are mudstones and cross-bedded sandstones which show bi-polar cross-stratification and mud drapes on cross-bed foresets indicating deposition in a tidal environment, probably a large estuary. In three coastal exposures a bed containing up to 20% fusain is found at the same stratigraphic horizon. The fusain is interpreted as fossil charcoal produced by palaeowildfire in a land area to the north-west. The volume of fusain present in the unit can be estimated and by comparison with charcoal production in modern wildfires it has been calculated that around 95 000 km² (more than the present land area of Ireland) was burnt. Along with the fusain, other effects of the wildfire can be seen in the deposits, which are poorly sorted compared to the rest of the Upper Shalwy Beds and are characterized by inclined heterolithic stratification produced by the draping of underlying bedforms. These features are considered to be due to a considerably increased sediment load in the estuary, resulting from enhanced surface runoff and soil erosion due to the wildfire.     

Slope units-based flow susceptibility model: using validation tests to select controlling factors

A susceptibility map for an area, which is representative in terms of both geologic setting and slope instability phenomena of large sectors of the Sicilian Apennines, was produced using slope units and a multiparametric univariate model. The study area, extending for approximately 90 km², was partitioned into 774 slope units, whose expected landslide occurrence was estimated by averaging seven susceptibility values, determined for the selected controlling factors: lithology, mean slope gradient, stream power index at the foot, mean topographic wetness index and profile curvature, slope unit length, and altitude range. Each of the recognized 490 landslides was represented by its centroid point. On the basis of conditional analysis, the susceptibility function here adopted is the density of landslides, computed for each class. Univariate susceptibility models were prepared for each of the controlling factors, and their predictive performance was estimated by prediction rate curves and effectiveness ratio applied to the susceptibility classes. This procedure allowed us to discriminate between effective and non-effective factors, so that only the former was subsequently combined in a multiparametric model, which was used to produce the final susceptibility map. The validation of this map latter enabled us to verify the reliability and predictive performance of the model. Slope unit altitude range and length, lithology and, subordinately, stream power index at the foot of the slope unit demonstrated to be the main controlling factors of landslides, while mean slope gradient, profile curvature, and topographic wetness index gave unsatisfactory results.     

Land cover change dynamics mapping and predictions using EO data and a GIS-cellular automata model: the case of Al-Baha region,Kingdom of Saudi Arabia

The present study designed to monitor and predict land cover change (LCC) in addition to characterizing LCC and its dynamics over Al-Baha region, Kingdom of Saudi Arabia, by utilizing remote sensing and GIS-cellular automata model (Markov-CA). Moreover, to determine the effect of rainwater storage reservoirs as a driver to the expansion of irrigated cropland. Eight Landsat 5/7 TM/ETM images from 1975 to 2010 were analyzed and ultimately utilized in categorizing LC. The LC maps classified into four main classes: bare soil, sparsely vegetated, forest and shrub land, and irrigated cropland. The quantification of LCC for the analyzed categories showed that bare soil and sparsely vegetated was the largest classes throughout the study period, followed by forest, shrubland, and irrigated cropland. The processes of LCC in the study area were not constant, and varied from one class to another. There were two stages in bare soil change, an increase stage (1975–1995) and decline stage (1995–2010), and the construction of 25 rainwater-harvesting dams in the region was the turning point in bare soil change. The greatest increase was observed in irrigated cropland after 1995 in the expense of the other three categories as an effect of extensive rainwater harvesting practices. Losses were evident in forest and shrubland and sparsely vegetated land during the first stage (1975–1995) with 5.4 and 25.6 % of total area in 1995, while in 1975, they covered more than 13.8 and 32.7 % of total area. During the second stage (1995–2010), forest and shrubland witnessed a significant increase from 1569.17 km² in 1975 to 1840.87 km² in 2010. Irrigated cropland underwent the greatest growth (from 422.766 km² in 1975 to 1819.931 km² in 2010) during the entire study period, and this agriculture expansion reached its zenith in the 2000s. Markov-CA simulation in 2050 predicts a continuing upward trend in irrigated cropland and forest and shrubland areas, as well as a downward trend in bare soil and sparsely vegetated areas; the spatial distribution prediction indicates that irrigated cropland will expand around reservoirs and the mountain areas. The validation result showed that the model successfully identified the state of land cover in 2010 with 97 % agreement between the actual and projected cover. The output of this study would be useful for decision makers and LC/land use planners in Saudi Arabia and similar arid regions.     

A GIS-based approach for gully erosion susceptibility modelling: a test in Sicily, Italy

The aim of this study is to analyze the susceptibility conditions to gully erosion phenomena in the Magazzolo River basin and to test a method that allows for driving the factors selection. The study area is one of the largest (225 km²) watershed of southern Sicily and it is mostly characterized by gentle slopes carved into clayey and evaporitic sediments, except for the northern sector where carbonatic rocks give rise to steep slopes. In order to obtain a quantitative evaluation of gully erosion susceptibility, statistical relationships between the spatial distributions of gullies affecting the area and a set of twelve environmental variables were analyzed. Stereoscopic analysis of aerial photographs dated 2000, and field surveys carried out in 2006, allowed us to map about a thousand landforms produced by linear water erosion processes, classifiable as ephemeral and permanent gullies. The linear density of the gullies, computed on each of the factors classes, was assumed as the function expressing the susceptibility level of the latter. A 40-m digital elevation model (DEM) prepared from 1:10,000-scale topographic maps was used to compute the values of nine topographic attributes (primary: slope, aspect, plan curvature, profile curvature, general curvature, tangential curvature; secondary: stream power index; topographic wetness index; LS-USLE factor); from available thematic maps and field checks three other physical attributes (lithology, soil texture, land use) were derived. For each of these variables, a 40-m grid layer was generated, reclassifying the topographic variables according to their standard deviation values. In order to evaluate the controlling role of the selected predictive variables, one-variable susceptibility models, based on the spatial relationships between each single factor and gullies, were produced and submitted to a validation procedure. The latter was carried out by evaluating the predictive performance of models trained on one half of the landform archive and tested on the other. Large differences of accuracy were verified by computing geometric indexes of the validation curves (prediction and success rate curves; ROC curves) drawn for each one-variable model; in particular, soil texture, general curvature and aspect demonstrated a weak or a null influence on the spatial distribution of gullies within the studied area, while, on the contrary, tangential curvature, stream power index and plan curvature showed high predictive skills. Hence, predictive models were produced on a multi-variable basis, by variously combining the one-variable models. The validation of the multi-variables models, which generally indicated quite satisfactory results, were used as a sensitivity analysis tool to evaluate differences in the prediction results produced by changing the set of combined physical attributes. The sensitivity analysis pointed out that by increasing the number of combined environmental variables, an improvement of the susceptibility assessment is produced; this is true with the exception of adding to the multi-variables models a variable, as slope aspect, not correlated to the target variable. The addition of this attribute produces effects on the validation curves that are not distinguishable from noise and, as a consequence, the slope aspect was excluded from the final multi-variables model used to draw the gully erosion susceptibility map of the Magazzolo River basin. In conclusion, the research showed that the validation of one-variable models can be used as a tool for selecting factors to be combined to prepare the best performing multi-variables gully erosion susceptibility model.     

Geospatial modelling of flood susceptibility pattern in a subtropical area of West Bengal,India

Flood hazards are the most destructive among all natural disasters and are a constant threat to human’s life and property. Effective disaster risk reduction strategies can be improved by geospatial approach in the way of producing information and knowledge that are useful to plan truly effective actions for the protection from floods. This research aims to develop a quantified predictive model of flood susceptibility in the Ghatal and Tamluk subdivision of Medinipur district of West Bengal, India, by means of empirically selected and weighted spatial predictors of flood. The weighted prediction model is used to quantify the spatial associations between individual geospatial factors within the flood inundated study area. Yule’s coefficient and distance distribution analysis are used to assign weights to individual geo-factors, and finally weighted spatial predictors are integrated to a multi-class index overlay analysis to derive the spatially explicit predictive model of flood susceptibility. The resultant susceptibility model reveals that approximately 32.35 and 52.99% of the total study areas (3261.45 km²) are under the category of high-to-moderate flood susceptible zone. Quantitative results of this study could be integrated into the policy process in the formulation of local and national government plans for the future flood mitigation management and also to develop appropriate infrastructure in order to protect the lives and properties of the common people of the Medinipur district.     

Spatial distribution analysis and susceptibility mapping of landslides triggered before and after Mw7.8 Gorkha earthquake along Upper Bhote Koshi,Nepal

The 2015 Mw7.8 Gorkha earthquake triggered thousands of landslides of various types scattered over a large area. In the current study, we utilized pre- and post-earthquake high-resolution satellite imagery to compile two landslide inventories before and after earthquake and prepared three landslide susceptibility maps within 404 km² area using frequency ratio (FR) model. From the study, we could map about 519 landslides including 178 pre-earthquake slides and 341 coseismic slides were identified. This study investigated the relationship between landslide occurrence and landslide causative factors, i.e., slope, aspect, altitude, plan curvature, lithology, land use, distance from streams, distance from road, distance from faults, and peak ground acceleration. The analysis showed that the majority of landslides both pre-earthquake and coseismic occurred at slope >30°, preferably in S, SE, and SW directions and within altitude ranging from 1000 to 1500 m and 1500 to 3500 m. Scatter plots between number of landslides per km^?2 (LN) and percentage of landslide area (LA) and causative factors indicate that slope is the most influencing factor followed by lithology and PGA for the landslide formation. Higher landslide susceptibility before earthquake is observed along the road and rivers, whereas landslides after earthquake are triggered at steeper slopes and at higher altitudes. Combined susceptibility map indicates the effect of topography, geology, and land cover in the triggering of landslides in the entire basin. The resultant landslide susceptibility maps are verified through AUC showing success rates of 78, 81, and 77%, respectively. These susceptibility maps are helpful for engineers and planners for future development work in the landslide prone area.     

Radar remote sensing for monitoring of dynamic ecosystem processes related to biogeochemical exchanges in tropical peatlands

Peatlands are an important multipurpose ecosystem, supporting huge quantities of biomass and peat soil carbon. A time series of Japanese Earth Resource Satellite-1 (JERS-1) L-band Synthetic Aperture Radar (SAR) data was employed to monitor two dynamic ecosystem processes; deforestation and inundation patterns. Using a change detection analysis for three images acquired during dry seasons of 1994, 1997 and 1998, we detected the deforestation that has occurred in the region due to the anthropogenic and natural causes. At a threshold of ±2 dB change in backscattering response, an area of about 98 km² of these forests was found to have been cleared during 1994–1997 for conversion to cultivatable lands. However, the agricultural crops miserably failed to grow on these cleared lands because of the adverse water and soil chemistry conditions. The deliberate draining of these lands, by laying and extension of a huge network of canals, created congenial ecological conditions for the spread of forest fires, particularly during the 1997 El Niño period. An area of 250 km² of forests was thus detected to have been destroyed by these fires between September 1997 and January 1998. These deforested lands are rapidly regenerating since their abandonment and the regenerating carbon stocks were simulated using the CENTURY ecosystem dynamics model. Furthermore, the L-band SAR was able to detect the pixel-wise seasonal and spatial inundation information for particular forest types where the transmissivity of the L-band SAR signals was quite significant. These forest types corresponded to comparatively low biomass areas. The SAR derived information about these two important dynamic processes would be useful for improving the accuracy of modelling the spatial and temporal distribution of the carbon and other trace gases in these ecosystems.Edited by D. Boyd     

Automated extraction of watershed boundary and drainage network from SRTM and comparison with Survey of India toposheet

The recent development of digital representation has stimulated the development of automatic extraction of topographic and hydrologic information from digital elevation model input, using geographic information system (GIS) and hydrologic models that integrate multiple databases within a minimal time. The objective of this investigation is to compare the drainage extracted from Shuttle Radar Topography Mission (SRTM) data with the drainage digitized from topographic data (1:50,000) and also to draw attention to the functions of an add-on tool in ArcGIS 9.2 (Arc Hydro v.2) of Kuttiyadi River basin. The analysis reveals that the watershed extracted from the SRTM digital elevation model (DEM) (90 m resolution) is having an area of 668 km² and that from toposheet is 676 km². The river mouth in the drainage network from the SRTM DEM is found to be shifted to the northern side from where it actually exists. The drainage network from SRTM DEM at stream threshold 15 (0.0002 % of maximum flow accumulation) is delivering best results than the other threshold value in comparison with the drainage pattern derived from toposheets. The study reveals the importance, reliability, and quaintness of drainage network and watershed derived from the SRTM using the Arc Hydro Tool, an extension for Environmental Systems Research Institute ArcGIS. The advantage of the Arc Hydro Tool is that it would help a novice with little GIS knowledge to run the model to obtain watershed and drainage network.     

Numerical Modeling of the Seasonal Dynamic Characteristics of the Koxkar Glacier,in West Tianshan,China

Landslides commonly occurs in hilly areas and causes an enormous loss iof life and property every year. National highway-1D (NH-1D) is the only road link between the two districts (Kargil and Leh) of Ladakh region that connects these districts with Kashmir valley. The landslide failure record of the recent past along this sector of the highway is not available. The present study documents landslide susceptible zones and records occurrence of 60 landslides during the last 4 years showing an increasing trend in the occurrence of landslides over these years in this sector. The landslide susceptibility zonation map has been prepared based on the numerical rating of ten major factors viz. slope morphometry, lithology, structure, relative relief, land cover, landuse, rainfall, hydrological conditions, landslide incidences and Slope Erosion, categorised the area in different zones of instability based on the intensity of susceptibility. The landslide susceptibility map of the area encompassing 73.03 km² is divided into 150 facets. Out of the total of 150 facets, 85 facets fall in low susceptibility zone covering 43.56 km2 which constitute about 59.65% of the total area under investigation with a record of 5 landslides; 40 facets fall in the moderate susceptibility zone covering 16.94km² which constitutes about 23.19% of the study area with a record of 20 landslides; and 25 facets fall in the high susceptibility zone covering 12.53 km² which constitute about 17.15% of the study area with a record of 35 landslides. Most of the facets which fall in HSZ are attributed to slope modification for road widening.     

LiDAR-supported prediction of slope failures using an integrated ensemble weights-of-evidence and analytical hierarchy process

The present study investigates a potential application of different resolution topographic data obtained from airborne LiDAR and an integrated ensemble weight-of-evidence and analytic hierarchy process (WoE–AHP) model to spatially predict slope failures. Previously failed slopes of the Pellizzano (Italy) were remotely mapped and divided into two subsets for training and testing purposes. 1, 2, 5, 10, 15, and 20 m topographic data were processed to extract nine terrain attributes identified as conditioning factors for landslides: slope degree, aspect, altitude, plan curvature, profile curvature, stream power index, topographic wetness index, sediment transport index, and topographic roughness index. Landslide (slope failure) susceptibility maps were produced using a single WoE (Model 1), an ensemble WoE–AHP model that used all conditioning factors (Model 2), and an ensemble WoE–AHP model that only used highly nominated conditioning factors (Model 3). The validation results proved the efficiency of high-resolution (≤ 5 m) topographic data and the ensemble model, particularly when all factors were used in the modeling process (Model 2). The average success rates and prediction rates for Model 2 that used ≤ 5 m resolution datasets were 84.26 and 82.78%, respectively. The finding presented in this paper can aid in planning more efficient LiDAR surveys and the handling of large datasets, and in gaining a better understanding of the nature of the predictive models.     

Observed trends of climate and land cover changes in Lake Baikal basin

Land cover and vegetation in Lake Baikal basin (LBB) are considered to be highly susceptible to climate change. However, there is less information on the change trends in both climate and land cover in LBB and thus less understanding of the watershed sensitivity and adaptability to climate change. Here we identified the spatial and temporal patterns of changes in climate (from 1979 to 2016), land cover, and vegetation (from 2000 to 2010) in the LBB. During the past 40 years, there was a little increase in precipitation while air temperature has increased by 1.4 °C. During the past 10 years, land cover has changed significantly. Herein grassland, water bodies, permanent snow, and ice decreased by 485.40 km², 161.55 km² and 2.83 km², respectively. However, forest and wetland increased by 111.40 km² and 202.90 km², respectively. About 83.67 km² area of water bodies has been converted into the wetland. Also, there was a significant change in Normalized Difference Vegetation Index (NDVI), the NDVI maximum value was 1 in 2000, decreased to 0.9 in 2010. Evidently, it was in the mountainous areas and in the river basin that the vegetation shifted. Our findings have implications for predicting the safety of water resources and water eco-environment in LBB under global change.     

A quantitative analysis of relative tectonic activity classes of Kali river basin, southwest coast of India

The Kali river basin of the southwest coast of India has been investigated to examine the influence of tectonic activity through an analysis of the geomorphic indices that were computed using geographical information system (GIS). Five geomorphic indices—stream length–gradient index, asymmetry factor, hypsometric integral, valley floor width-to-height ratio, and elongation ratio—were used for identification of evidences of tectonic activity. The results obtained from these indices were combined to develop an index of relative tectonic activity classes using GIS. The average of the five measured geomorphic indices was used to evaluate the distribution of relative tectonic activity in the study area. About 18 % of the study area (～726 km²) belongs to class?1; 37 % (～1506 km²) to class?2; and 44 % (～1,762 km²) to class 3. Sub-basins found at the Western Ghat region, relative tectonic activity classes show low and medium values which indicate the higher degree of tectonic activity compared to the upland plateau regions.     

Integration of hydrological factors and demarcation of groundwater prospect zones: insights from remote sensing and GIS techniques

The study demonstrates the potential of geographical information system and statistical-based approaches to identify the hydrological processes and demarcate the groundwater prospect zones of the Gangolli basin, Karnataka State, India. The basin is situated in humid tropical climate and influenced by three major rivers viz. Kollur (6th order stream), Chakra (6th order stream) and Haladi (7th order stream) which cover an area of ~1,512 km² and cumulative length of ~84 km. Various thematic maps—drainage, geomorphology, geology, slope, soil, lineament and lineament density—were prepared using Survey of India topographic maps, Indian remote sensing (IRS-P6) images and other published maps. Hydrogeomorphologic characteristics were correlated with different morphometric parameters to identify the hydrological processes and demarcate the groundwater potential zones of the basin. All the hydrological units and morphometric parameters were assigned suitable weightages according to their relative importance to groundwater potentiality to identify the most deficit/surplus zones of groundwater. Based on hydrological characteristics, integrated thematic maps reveal that ~14 % (~217 km²) of basin area falls under very good, ~32 % (~486 km²) under good, ~23 % (~353 km²) under moderate, and 30 % (~443 km²) under poor zones for groundwater potential. From the sub-basin-wise prioritisation, it has been inferred that SB-III scored highest groundwater potential, followed by SB-X. Result of morphometric analyses with the hydrologic parameters indicates that ~99 % area of SB-III and SB-X are under very good to moderate groundwater potential zone. This study clearly demonstrates that hydrological parameters in relation with morphometric analyses are useful to demarcate the prospect zones of groundwater.     

Hydrological impact of forest fires and climate change in a Mediterranean basin

Forest fire can modify and accelerate the hydrological response of Mediterranean basins submitted to intense rainfall: during the years following a fire, the effects on the hydrological response may be similar to those produced by the growth of impervious areas. Moreover, climate change and global warming in Mediterranean areas can imply consequences on both flash flood and fire hazards, by amplifying these phenomena. Based on historical events and post-fire experience, a methodology to interpret the impacts of forest fire in terms of rainfall-runoff model parameters has been proposed. It allows to estimate the consequences of forest fire at the watershed scale depending on the considered burned area. In a second stage, the combined effect of forest fire and climate change has been analysed to map the future risk of forest fire and their consequence on flood occurrence. This study has been conducted on the Llobregat river basin (Spain), a catchment of approximately 5,000 km² frequently affected by flash floods and forest fires. The results show that forest fire can modify the hydrological response at the watershed scale when the burned area is significant. Moreover, it has been shown that climate change may increase the occurrence of both hazards, and hence, more frequent severe flash floods may appear.     

Analyzing spatiotemporal changes in wildfire regime and exposure across a Mediterranean fire-prone area

We evaluated the spatiotemporal changes in wildfire regime and exposure in a fire-prone Mediterranean area (Sardinia, Italy) in relation to changes in ignition patterns, weather, suppression activities, and land uses. We also used wildfire simulations to identify fine-scale changes in wildfire exposure of important features on the island. Sardinia experienced a sharp reduction in fire number and area burned between the periods 1980–1994 and 1995–2009. Despite this decrease, losses and fatalities from wildfires continue. This suggests that localized areas and seasons of high wildfire risk persist on the island. Our analysis showed (1) a reduction in area burned (60,000–20,000 ha/year) and ignitions (3,700–2,600 fires/year), (2) an advance of 15 days for the fire season peak, (3) an increase in spring temperatures, and (4) an increase in fire exposure for WUI areas. Little change was noted for land use types and associated fuels. Most likely the reduction in fire activity may be due to a combination of social factors and suppression capabilities. On the other hand, simulation modeling suggested pockets of high wildfire exposure in specific places. The combined empirical analyses and simulation modeling provided a robust approach to understanding the spatiotemporal dynamics of wildfire risk on the island.     

Controlling parameter analyses and hazard mapping for earthquake-triggered landslides: an example from a square region in Beichuan County, Sichuan Province, China

On May 12, 2008, at 1428 hours (Beijing time), a catastrophic earthquake, with a magnitude of Ms 8.0, struck the Sichuan Province, China. About 200,000 landslides, as a secondary geological hazard associated with the earthquake, were triggered over a broad area. These landslides were of almost all types such as shallow, disrupted landslides, rock falls, deep-seated landslides, and rock avalanches. Some of these landslides damaged and destroyed large part of some towns, blocked roads, dammed rivers, and caused other serious damages. The purpose of this study is to detect correlations between landslide occurrence and the surface rupture plane, ground shaking conditions (measured by peak ground acceleration, PGA), lithology, slope gradient, slope aspect, topographic position, and distance from drainages by using two indices, landslide area percentage (LAP) and the landslide number density (LND), based on geographic information system (GIS) technology and statistical analysis method in a square region (study area) of Beichuan County, Sichuan Province, China. There were 5,096 landslides related with the earthquake which were delineated by visual interpretation and selected field checking throughout the study area. The total area (horizontal projection) of the 5,096 landslides is about 41.103 km². The LAP, which is defined as the percentage of the plane area affected by landslides, was 10.276 %, and the LND, means the number of landslides per square kilometers, was 12.74 landslides/km². Statistical analysis results show that both LAP and LND have a positive correlation with slope gradient and a negative correlation with distance from the surface rupture. However, the correlation between the occurrence of landslides with PGA, topographic position, and distance from drainages are uncertain, or has just a little positive correlation. The correlation between landslide and slope aspect also shows the effect of the directivity of the seismic wave. The Zbq formation had the most concentrated landslide activity with the LND value of 21.78 landslides/km _, ² and the ∈₁ q Gr. geological units had the highest LAP value. Furthermore, weight index (W _i) model is performed with a GIS platform to derive landslide hazard index map. The success rate of the model was 71.615 % and, thus, it was valid. In addition, comparison of five landslide controlling parameters’ influence on landslide occurrences was also carried out.