GIS-based Modeling Approach Research Articles

Increasing electricity access for health facilities in Ghana through solar powered mini-grids—a GIS-based energy system modelling approach

The research aims to identify which healthcare facilities (HCFs) in Ghana are suitable for electrification using photovoltaic mini-grids to increase their energy self-sufficiency and reliability of services provided. Additionally, the study categorises the HCFs in two groups: those with and without or with poor access to electricity supply, identify settlements within their catchment area, and determine the electricity demand for identified HCF sites and their surrounding communities. The research assesses the most suitable mini-grid system setup to electrify identified HCF sites and the impact of including the demand of surrounding communities into the energy system modelling. Finally, the study aims to determine the accumulated solar mini-grid potential to electrify all identified HCF sites. The study findings highlight the importance of integrated planning between the health and energy sectors to ensure high-quality health services. Solar mini-grids are identified as a promising solution for electrifying HCFs and improving energy self-sufficiency. However, it is recommended to avoid transferring findings between different types of health facilities due to their unique characteristics. The study also emphasizes the importance of balancing the energy flow and stabilizing the energy system through the combination of HCFs and surrounding communities’ demand. It is crucial to assess the electricity demand carefully based on context-specific characteristics, such as the type of HCF and the number of households considered. Overall, the study provides valuable insights into the potential of solar mini-grids to increase energy self-sufficiency in HCFs and the importance of careful planning and context-specific assessments.

Assessment of the impact of the rise in Lake Victoria water levels on urban flooding using a GIS-based spatial flood modelling approach

ABSTRACT During the period 2018–2021, Lake Victoria experienced a rapid rise in water levels that caused significant urban flooding consequences. However, there are no studies that have extensively investigated the effect of the rising lake water levels on urban flooding. This research responds to this by developing and applying a GIS-based spatial flood modelling approach to simulate the resulting flooding impacts in Entebbe City, Uganda, corresponding to the May 2021 observed maximum lake water level of 13.49 m. Using the validated GIS-based flood model, the simulated flooded area and average flood depth were determined as 9.1 km2 and 1.52 m, respectively. Furthermore, through application of GIS overlay analysis, the total number of affected buildings and length of urban roads were quantified. The proposed methodology provides a robust and computationally efficient approach for identification of at-risk buildings along the lake shoreline and for testing the effectiveness of potential adaptation strategies.

An automatic modeling approach for the potential evaluation of CO2 geological storage in the deep saline aquifer

Geological storage of carbon dioxide is receiving more and more attention as one of the efficient carbon reduction technologies, as China’s carbon-neutral strategic plan moves forward. There is an increasing demand for more effective and thorough methodologies to assess the potential of CO2 storage in deep saline aquifers. This study proposes a method for evaluating the geological storage potential of CO2 in deep saline aquifers and constructs an automatic evaluation system for the comprehensive potential of CO2 geological storage using ArcGIS Model Builder visual modeling technology. The automatic evaluation system consists of four functional parts: information collating and database constructing, data pre-processing, model building evaluation and result validation evaluation. First, structured and unstructured data including underlying geology, tectonic geology, oil and gas geology, and drilling data are collated and established in a geodatabase. Second, pre-processing models of the deep saline reservoir-caprock data are established based on the analysis of the geological evolution history of the study area to determine the effective storage thickness, effective porosity, and the influence range of faults; kriging methods are then used to realize the spatial interpolation of the evaluation parameters. Third, the volume coefficient method is adopted to construct the underground storage space model and to establish the density distribution model of the supercritical CO2 with nonlinear function while taking into account four evaluation factors (i.e. area, effective porosity, effective thickness, effective coefficient) and two limiting factors (i.e. fault, burial depth). Finally, the geological storage potential of CO2 in the study area is evaluated with the classification of the potential level and compared with the numerical simulation results to verify the model’s accuracy. The model is first applied in this paper using a suitable target in China as a case study. The results show that this target area’s anticipated storage potential value reaches 52.557 Mt. The total precision error, according to a comparison of the numerical simulation results, is 8.20%. Based on the results obtained, it can be concluded that the automatic GIS-based modeling approach is suitable for a comparable study of potential evaluation of CO2 geological storage in deep saline aquifers.

A Novel GIS-Based Modeling Approach for Evaluating Aquifer Susceptibility to Anthropogenic Contamination

Population growth, increasing urbanization and industrialization, mismanagement, and climate change are accountable for the rising depletion and pollution of groundwater worldwide. Consequently, water security, food security, and environmental security are in jeopardy, leading to a severe concern for the sustainable water supply on the Earth. The contamination of groundwater, a complex and hidden resource, is difficult to detect and treat. Therefore, it is essential to evaluate aquifer’s propensity for contamination to protect this precious resource. In this paper, a novel approach integrating the GWQI (Groundwater Quality Index), AVI (Aquifer Vulnerability Index), and geospatial modeling is proposed to explore aquifer susceptibility to contamination, applied to an unconfined aquifer. The Groundwater Quality Index (GWQI) was developed by the conventional method and the Analytic Hierarchy Process (AHP), whereas the Aquifer Vulnerability Index (AVI) was developed using a modified DRASTIC model. It was found that the spherical semi-variogram along with simple Kriging is suitable for interpolating concentrations of groundwater quality parameters. Geospatial modeling indicated that the AHP-based GWQI map is more accurate than the conventional method. The integration of the best GWQI and AVI resulted in an Aquifer Susceptibility Index (ASI) map, which revealed that >80% of the study area falls under ‘severe’ to ‘very severe’ susceptible zones, while about 20% of the area falls under ‘moderate’ or ‘minimum’ susceptible zones. The validation results confirmed that the developed ASI map is reliable. The ASI map can serve as a useful tool for planners and decision makers to devise sustainable aquifer management programs to protect vital groundwater resources from contamination and ensure a safe and reliable water supply under climate change.

Habitat Classification Predictions on an Undeveloped Barrier Island Using a GIS-Based Landscape Modeling Approach

Landscape models are essential tools that link landscape patterns to ecological processes. Barrier island vegetation communities are strongly correlated with geomorphology, which makes elevation-based metrics suitable for developing a predictive habitat classification model in these systems. In this study, multinomial logistic regression is used to predict herbaceous, sparse, and woody habitat distributions on the North End of Assateague Island from slope, distance to shore, and elevation change, all of which are derived from digital elevation models (DEMs). Sparse habitats, which were generally found closest to shore in areas that are exposed to harsh conditions, had the highest predictive accuracy. Herbaceous and woody habitats occupied more protected inland settings and had lower predictive accuracies. A majority of woody cells were misclassified as herbaceous likely because of the similarity in the predictive parameter distributions. This relatively simple model is transparent and does not rely on subjective interpretations. This makes it an effective tool that can directly aid practitioners making coastal management decisions surrounding storm response planning and conservation management. The model results were used in a nutrient sequestration application to quantify carbon and nitrogen stored in barrier island vegetation. This represents an example of how the model results can be used to assign economic value of ecosystem services in a coastal system to justify different management and conservation initiatives.

A GIS-Based Modeling Approach for Determining the Efficiency of the Traffic System between Ancient Military Castles

Ancient Chinese military castles were equipped with rapid transportation routes for mutual aid, and this was an essential indicator of regional defense capability. However, since the sites of these transportation routes have mostly disappeared, it is not easy to examine the actual distribution of these routes. It is necessary to speculate the trend and position of military routes on the basis of the castle locations. In this study, the geographic features of each castle location were extracted as factors affecting the efficiency of the intercastle transportation system using the ArcGIS cost path function. By analyzing the fit of each factor for screening and weight assignment, a time cost path was established, and a model was generated for calculating the efficiency of this transportation system. The Weihai area, a typical representation of sea defense during the Chinese Ming Dynasty, was taken as an example for simulation. Overall, five ancient military transportation routes were restored. The establishment of the Ming Dynasty Wendengying transformed the linear defense layout of the Weihai region into a longitudinal network layout, and its site selection was of great benefit to the overall defense of the coastal citadel of Weihai. This model breaks the traditional limitations of relying on subjective speculation for ancient road restoration and dramatically improves its accuracy and credibility. Moreover, it makes a significant contribution to judging the road systems of ancient cities in different regions and provides a new idea to quantify the efficiency of ancient castle defenses.

A GIS-Based Hydrological Modeling Approach for Rapid Urban Flood Hazard Assessment

Urban floods are detrimental to societies, and flood mapping techniques provide essential support for decision-making on the better management of flood risks. This study presents a GIS-based flood characterization methodology for the rapid and efficient identification of urban flood-prone areas, which is especially relevant for large-scale flood hazards and emergency assessments for data-scarce studies. The results suggested that optimal flood mapping was achieved by adopting the median values of the thresholds for local depression extraction, the topographic wetness index (TWI) and aggregation analyses. This study showed the constraints of the depression extraction and TWI methods and proposed a methodology to improve the performance. A new performance indicator was further introduced to improve the evaluation ability of hazard mapping. It was shown that the developed methodology has a much lower demand on the data and computation efforts in comparison to the traditional two-dimensional models and, meanwhile, provides relatively accurate and robust assessments of flood hazards.

Identification of risk factors contributing to COVID-19 incidence rates in Bangladesh: A GIS-based spatial modeling approach

BackgroundCOVID-19 pandemic outbreak is an unprecedented shock throughout the world, which has generated a massive social, human, and economic crisis. Identification of risk factors is crucial to prevent the COVID-19 spread by taking appropriate countermeasures effectively. Therefore, this study aimed to identify the potential risk factors contributing to the COVID-19 incidence rates at the district-level in Bangladesh. MethodSpatial regression methods were applied in this study to fulfill the aim. Data related to 28 demographic, economic, built environment, health, and facilities related factors were collected from secondary sources and analyzed to explain the spatial variability of this disease incidence. Three global (ordinary least squares (OLS), spatial lag model (SLM), and spatial error model (SEM)) and one local (geographically weighted regression (GWR)) regression models were developed in this study. ResultsThe results of the models identified four factors: percentage of the urban population, monthly consumption, number of health workers, and distance from the capital city, as significant risk factors affecting the COVID-19 incidence rates in Bangladesh. Among the four developed models, the GWR model performed the best in explaining the variation of COVID-19 incidence rates across Bangladesh, with an R2 value of 78.6%. ConclusionFindings and discussions from this research offer a better insight into the COVID-19 situation, which helped discuss policy implications to negotiate the future epidemic crisis. The primary policy response would be to decentralize the urban population and economic activities from and around the capital city, Dhaka, to create self-sufficient regions throughout the country, especially in the north-western region.

AquaZone: An Integrative Tool for Sustainable Fish Farm Zoning

Aquaculture is the fastest-growing animal food production sector and is expected to become increasingly important to meet future food demands. As a landlocked country, Austria’s self-sufficiency rate for fish is rather low with 6% in total and 48% for freshwater fish. Therefore, and in order to enable sustainable growth of the sector while avoiding negative impacts on the aquatic ecosystem and other uses, we developed aquaZone, a decision support tool for sustainable trout farm zoning. Thereby, 30 spatially explicit criteria related to environmental prerequisites, land use, legal constraints and water quality/quantity were defined, collected and classified according to their suitability for sustainable trout production. Criteria were combined in an integrative GIS-based modelling approach in order to perform the first countrywide and spatially-explicit zonation of suitable areas for aquaculture in Austria. Thereby, 7920 suitable areas with a mean size of 8.2 ha located in 1129 out of 5011 sub-basins (23%) were identified. The decision tree assigned the highest variable importance to water temperature, slope, agricultural unit, geology, nitrate retention capacity, fish region, minimum flow and pollution risk. These results should support decision making of investors and authorities in order to avoid conflicts and stranded investments at an early stage.

Strategic expansion of existing forest monitoring plots: a case study using a stratified GIS-based modelling approach

Background: Understanding the relationship between sites and the plant species they support is essential for effective vegetation management. Site-species matching requires knowledge of the growth response of a given species to the full range of environmental conditions in potential planting sites. This can be achieved by repeatedly measuring species growth at a comprehensive network of sample plots that cover a range of environmental conditions, including topography, climate, and soil factors. The New Zealand Dryland Forests Initiative has established permanent sample plots (PSPs) of a plantation species, Eucalyptus bosistoana F.Muell., across New Zealand. However, these PSPs do not cover the entire range of environmental conditions available for the species and hence there is a need to expand the network of sites. The aim of this study was to determine optimal locations for new PSPs to provide more unique information to support site-species matching studies for Eucalyptus bosistoana in New Zealand.Methods: A geographic information system (GIS) and stratified random sampling method were used to generate a model to identify optimal locations for E. bosistoana PSP establishment. The variables used in this study included topography, climate, and soil data. Redundancy between the initial set of potential explanatory variables was reduced by a multi-collinearity analysis. The potential habitat for the species was restricted to land with environmental conditions that could support E. bosistoana. All environmental variables were stratified and an initial priority index for each stratum in each variable was calculated. Then a weighted-overlay analysis was conducted to create the final priority index, which was mapped to identify high-priority areas for targeted PSP expansion.Results: The existing PSP network for E. bosistoana generally covers the environmental conditions in low-elevation New Zealand dry lands, which are located alongside the east coast of the South Island, and the southern part of the North Island. The model identified high priority areas for PSP expansion, including several large regions in the North Island, especially in Rangitikei and Taupo Districts.Conclusions: The model successfully allowed identification of areas for a strategic expansion of permanent sample plots for E. bosistoana. Newly identified areas expand upon the topographic, climatic, and soil conditions represented by the existing PSP network. The new area for PSP expansion has potential to provide valuable information for further site-species matching studies. The methodology in this paper has potential to be used for other plot networks of a different species, or even natural forests.

Evaluation of groundwater prolific zones in the unconfined basaltic aquifers of Western India using geospatial modeling and MIF technique

A study on sub-watershed scale covering an area of 593 sq km representing basaltic terrain was performed with the aim of finding the areas with groundwater potential using geospatial modeling and multi-influential (MIF) method. Various thematic layers of influential parameters were generated with GIS-based modeling approach. ASTER-GDEM image having 30-m pixel resolution was acquired to construct the digital elevation model (DEM); further, the drainage network and slopes were generated using DEM. The published district resource map of geology was referred to prepare the geology and geomorphology layers. Totally, 57 dug wells were inventoried and groundwater level fluctuation map was created using pre- and post-monsoon water levels. The ranks were allotted to specific factor of each influential layer, and weights were given to subclass, based on the degree of influence using MIF. Superimposition of all the thematic layers in ArcGIS was carried out to obtain the potential zonation map. Further, it has been observed that the excellent to very good category covers 29.11%, very good to good 20.96%, good to moderate 22.31%, moderate to moderately poor 23.07% and moderately poor to poor 4.55% of the total area. The resultant zonation map was validated with groundwater level fluctuation values, and it clearly showed that the high-yielding dug wells were located in the high potential zones. Thus, the evaluation of groundwater prolific zones would play a key role in sustainable development and judicial planning of this resource in the study area.

A GIS-based linear regression modeling approach to assess the impact of geologic rock types on groundwater recharge and its hydrological implication

In this study, the efficacy of the linear regression modeling technique in the company of recharge rate-induced geophysical parameters was implemented through geographical information system to establish the essentiality of geology factor in recharge rate estimate and prediction. The study empirically assessed the in situ and the spatiotemporal variability in the recharge rate in response to the multi-faceted geologic settings in the Perak province, Malaysia. The used data sets were 2D resistivity data, climate data and the ancillary data (surface geologic rock types; [quaternary (QUA); Devonian (DEV); Silurian (SIL); igneous(ING)]. The acquired 2D data were interpreted for delineating vadose zone units having varying geoelectrical parameters consisting of resistivity (ρ) and its thickness (D) (depth to aquifer top). The combined GIS and linear regression techniques were applied to relate the vadose zone geoelectrical parameters with the spatially modeled rainfall–recharge estimate. The obtained results were processed in R statistical software environment to develop GIS linear regression (GLR)-based recharge models for the area underlain geologic rock types. The developed GIS linear regression (GLR)-based recharge models were validated using the models’ parameter significance evaluation and models’ forecasting accuracy assessment approaches. The results of the applied GLR-based recharge models at each 2D location were processed to produce groundwater recharge maps for the rock types. Applying the concept of GIS-based geoelectrical parameters recharge estimate and rainfall–recharge rate–subsurface media estimate approaches, the output of the GLR-based recharge models was quantitatively evaluated to give expected recharge estimates of 242 mm/yr, 248.mm/yr, 268 mm/yr and 224 mm/y and total recharge water values of 377.9 × 106 m3/year, 319 × 106 m3/year, 161.6 × 106 m3/year, 227.3 × 106 m3/year, for the area underlain rock types, namely QUA, DEV, SIL and ING, respectively. Based on these results, the impact of geologic rocks’ factor on the area recharge rate estimates is established. Thus, the consideration of the geology factor is very important in accurate estimation and prediction of the groundwater recharge rate. The developed GLR-based recharge models and the produced groundwater recharge maps are viable decision tools for efficient management of groundwater resources in the study area and any area of similar geologic settings.

Integrated GIS-based modelling approach for irrigation water quality suitability zonation in parts of Satluj River Basin, Bist Doab region, North India

The groundwater is the main source for irrigation and other purposes in the alluvial plains of Punjab. The poor irrigational water quality affects the soil quality which interrupts the growth of plants impacting the agricultural production and can cause risk to human health, and thus, it is important to evaluate the water quality for irrigation uses in the agrarian regions. An integrated GIS-based modelling approach along with conventional water quality indices such as sodium adsorption ratio, per cent sodium, residual sodium carbonate, residual sodium bicarbonate, magnesium hazard, permeability index, potential salinity, Kelly’s ratio, electrical conductivity, total hardness, pH and total dissolved solids was used to delineate the irrigation water quality suitability zones in parts of agricultural prolific Satluj River Basin. Analytical results revealed that groundwater of the study area is very hard and slightly alkaline in nature and the relative abundance order of major ions is Mg2+ > Na+ + K+ > Ca2+ and HCO3− + CO32− > Cl− > SO42−, respectively, for cations and anions. The weighted overlay technique was applied to generate the irrigation water quality zonation map, and results revealed that nearly 3.2% (117 sq. km) of the total area was not suitable for the irrigation water quality, whereas 85.2% (3076 sq. km) and 11.6% (421 sq. km) area fall under moderately suitable and suitable classes, respectively. The elevated level of some ions found in the shallower zone as compared to the deeper zones indicates that in addition to the geogenic factors (weathering and dissolution of aquifer materials) anthropogenic inputs such as ill agricultural practices, leaching from septic tanks and municipal waste are adversely affecting the groundwater system in the region. The results of US Salinity Laboratory’s diagram showed that 42% groundwater samples fall in C3S1 (high salinity with low sodium hazard) category indicating the unsuitability for irrigation purpose due to high salinity hazard and cannot be used in soils with restricted drainage. The results of Piper and Durov diagrams revealed that Ca2+–Mg2+–HCO3− is the dominant hydrochemical facies with Mg2+–HCO3− water type in the groundwater of the study area and hydrochemistry is controlled by both natural and anthropogenic factors. The bottom-up approach and involvement of locals can boost the management of water resources in the study area.

GIS-based modeling of forest soil moisture regime classes: Using Rinker Lake in northwestern Ontario, Canada as a case study

There is a need to develop geospatial technological approaches to predict and map relative soil moisture (soil moisture regime) classes on forested landscapes. This is because soil moisture regime (SMR) maps are continuously being sought by forest managers for application in planning and operations. The aim of the study was to develop a GIS-based modeling approach to predict and map relative soil moisture classes on forested landscape. A rule-based geographic information system (GIS) modeling technique was developed to predict SMR classes (dry, fresh, moist and wet) using: 1) soil textural classes derived from Quaternary geology maps, and 2) water receiving areas derived from topographic attributes generated from 15 m digital elevation model. The rule-based GIS modeling approach produced a SMR map with an overall accuracy of about 65% relative to 54% generated from soil wetness index reclassification approach when compared to observed ground plot data. This novel geospatial technique could be easily applied by forest managers for enhanced forest resource inventory.

Deriving forest fire probability maps from the fusion of visible/infrared satellite data and geospatial data mining

Information on fire probability is of vital importance to environmental and ecological studies as well as to fire management. This study aimed at comparing two forest fire probability mapping techniques, one based primarily on freely distributed EO (Earth observation) data from Landsat imagery, and another one based purely on GIS modeling. The Normalized Burn Ratio (NBR) computed from Landsat data was used to detect the high fire severity and probability area based on the NBR difference between pre- and post-fire conditions. The GIS-based modeling was based on a multi criterion evaluation technique, into which other attributes like anthropogenic and natural sources were also incorporated. The ability of both techniques to map forest fire probability was evaluated for a region in India, for which suitable ancillary data had been previously acquired to support a rigorous validation. Subsequently, a conceptual framework for the prediction of high fire probability zones in an area based on a newly introduced herein data fusion technique was constructed. Overall, the EO-based technique was found to be the most suitable option, since it required less computational time and resources in comparison to the GIS-based modeling approach. Furthermore, the fusion approach offered an appropriate path for developing a forest fire probability identification model for long-term pragmatic conservation of forests. The potential fusion of these two modeling approaches may provide information that can be useful to forest fire mitigation policy makers, and assist at conservation and resilience practices.

Shaping pre-modern digital terrain models: The former topography at Charlemagne's canal construction site.

The use of remote sensing techniques to identify (geo)archaeological features is wide spread. For archaeological prospection and geomorphological mapping, Digital Terrain Models (DTMs) on based LiDAR (Light Detection And Ranging) are mainly used to detect surface and subsurface features. LiDAR is a remote sensing tool that scans the surface with high spatial resolution and allows for the removal of vegetation cover with special data filters. Archaeological publications with LiDAR data in issues have been rising exponentially since the mid-2000s. The methodology of DTM analyses within geoarchaeological contexts is usually based on “bare-earth” LiDAR data, although the terrain is often significantly affected by human activities. However, “bare-earth” LiDAR data analyses are very restricted in the case of historic hydro-engineering such as irrigation systems, mills, or canals because modern roads, railway tracks, buildings, and earth lynchets influence surface water flows and may dissect the terrain. Consequently, a "natural" pre-modern DTM with high depth accuracy is required for palaeohydrological analyses. In this study, we present a GIS-based modelling approach to generate a pre-modern and topographically purged DTM. The case study focuses on the landscape around the Early Medieval Fossa Carolina, a canal constructed by Charlemagne and one of the major medieval engineering projects in Europe. Our aim is to reconstruct the pre-modern relief around the Fossa Carolina for a better understanding and interpretation of the alignment of the Carolingian canal. Our input data are LiDAR-derived DTMs and a comprehensive vector layer of anthropogenic structures that affect the modern relief. We interpolated the residual points with a spline algorithm and smoothed the result with a low pass filter. The purged DTM reflects the pre-modern shape of the landscape. To validate and ground-truth the model, we used the levels of recovered pre-modern soils and surfaces that have been buried by floodplain deposits, colluvial layers, or dam material of the Carolingian canal. We compared pre-modern soil and surface levels with the modelled pre-modern terrain levels and calculated the overall error. The modelled pre-modern surface fits with the levels of the buried soils and surfaces. Furthermore, the pre-modern DTM allows us to model the most favourable course of the canal with minimal earth volume to dig out. This modelled pathway corresponds significantly with the alignment of the Carolingian canal. Our method offers various new opportunities for geoarchaeological terrain analysis, for which an undisturbed high-precision pre-modern surface is crucial.

Flood risk assessment in metro systems of mega-cities using a GIS-based modeling approach

Metro system is a vital component of mass transportation infrastructure, providing crucial social and economic service in urban area. Flood events may cause functional disruptions to metro systems; therefore, a better understanding of their vulnerability would enhance their resilience. A comparative study of flood risk in metro systems is presented using the analytic hierarchy process (AHP) and the interval AHP (I-AHP) methods. The flood risk in the Guangzhou metro system is evaluated according to recorded data. Evaluated results are validated using the flood event occurred in Guangzhou on May 10, 2016 (hereinafter called “May 10th event”), which inundated several metro stations. The flood risk is assessed within a range of 500 m around the metro line. The results show that >50% of metro lines are highly exposed to flood risk, indicating that the Guangzhou metro system is vulnerable to flood events. Comparisons between results from AHP and I-AHP show that the latter yields a wider range of high flooding risk than the former.

Identification of sprinkling storage facilities for windblown timber using a GIS-based modeling approach

Description of the subject. After catastrophic storm events, the storage of windblown timber is an effective measure for mitigating economic losses in the forest sector by preventing wood decay, protecting stands from secondary damage, and regulating the timber supply in the middle and long term. Objectives. In this study, we first propose a GIS-based methodology for identifying suitable sprinkling storage terminals in Wallonia (Belgium). In addition, we suggest an approach for building a coherent regional network as well as methods for selecting and activating terminals within this network after a storm, depending on the severity and distribution of the damage. Method. The GIS-based approach was used to crosscheck technical requirements related to sprinkling storage according to operational and environmental constraints in the Ardenne, which is the most forested sub-region of Wallonia. A three-step process was employed to identify suitable areas. Nine procurement areas were also delineated according to the regional forest inventory plots as a reference for choosing the terminals that should be included in the regional network and activated after the storm. Results. We generated and evaluated 96 scenarios. In the second step, a network of 30 terminals was suggested, which corresponded to a storage capacity of 4 million m³. This network could facilitate the flexibility in the strategic management of storage after huge storms. The procurement area approach also helps addressing routing and transportation issues in a simple way. Conclusions. The GIS approach facilitates the selection of sprinkling storage terminals, but field validation and enhanced collaboration between public and private landowners and forest owners would still be needed.

Combining geophysical techniques and multi-criteria GIS-based application modeling approach for groundwater potential assessment in southwestern Nigeria

A new approach of modeling very low frequency–electromagnetic (VLF–EM) and vertical electrical sounding (VES) data with a view of evaluating groundwater resources potential via application of GIS-based multi-criteria technique is investigated in this study. On eight VLF–EM traverses established in the site, 40 VES locations were combed. The acquired geophysical data (VLF–EM and VES) were processed applying Fraser/Karous–Hjelt filter and Win-Resist program geophysical software to determine the area subsurface geophysical parameters. Five hydrogeologic maps were produced based on the results of the interpreted geophysical parameters. The produced hydrogeologic maps were assigned suitable weights and different rankings to the individual classes boundary within the maps using the standard Saaty’s scale principle in the context of analytical hierarchy process (AHP) data mining technique. A raster-based empirical GIS model was developed for integrating the hydrogeologic maps to compute the groundwater potential index (GWPI) values in the range of 1.02–2.82 for the study area. Based on the estimated GWPI results, a final map zoning the area into low (0.0930–1.3922), medium (1.3922–1.9109) and high (1.9109–2.8173) groundwater potential classes was produced in GIS environment. The prediction accuracy of the produced potential map was established via cross-validation and in situ well correlation analysis. The results of the study established a new approach of modeling geophysical data for exploring groundwater productivity potential in the study area.

Assessing water erosion in Mediterranean tree crops using GIS techniques and field measurements: the effect of climate change

In this work, a dynamic GIS modeling approach is presented that incorporates: a) geoinformatic techniques, b) 55-year historical meteorological data, and c) field measurements, in order to estimate soil erosion risk in intensively cultivated regions. The proposed GIS-based modeling approach includes the estimation of soil erosion rates due to surface water flow under current and future climate change scenarios A2 and B1 for the years 2030 and 2050. The soil erosion was estimated using the Universal Soil Loss Equation (USLE). The proposed soil erosion model was validated using field measurements at different sites of the study area. The results show that an extended part of the study area is under intense erosion with the mean annual loss to be 4.85 t/ha year−1. Moreover, an increase in rainfall intensity, especially for scenario B1, can generate a significant increase (32.44 %) in soil loss for the year 2030 and a much more (50.77 %) for the year 2050 in comparison with the current conditions. Regarding the scenario A2, a slight decrease (1.85 %) in soil loss was observed for the year 2030, while for 2050 the results show an adequate increase (7.31 %) in comparison with the present. All these approaches were implemented at one of the most productive agricultural areas of Crete in Greece dominated by olive and citrus crops.