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The groundwater contained in aquifers is among the most important water supply resources, especially in semi-arid and arid regions worldwide. This study aims to evaluate and compare the prediction capability of two well–known models, support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS), combined with a genetic algorithm (GA), invasive weed optimization (IWO), and teaching–learning-based optimization (TLBO) algorithms in groundwater potential mapping (GPM) the Azraq Basin in Jordan. The hybridization of the SVM and ANFIS models with the GA, IWO, and TLBO algorithms results in six models: SVM–GA, SVM–IWO, SVM–TLBO, ANFIS–GA, ANFIS–IWO, and ANFIS–TLBO. A database consisting of well data containing 464 wells with 12 predictive factors was developed for the groundwater potential mapping (GPM) of the study area. Of the 464 well locations, 70% (325 locations) were assigned for the training set and the rest (139 locations) for the validation set. The correlation between the 12 predictive factors and the well locations is analyzed using the frequency ratio (FR) statistical model. An area under receiver operating characteristic (AUROC) curve was used to evaluate and compare the models. According to the results, the SVM-based hybrid models outperformed other ANFIS hybrid models in the learning (training) and validation phases. The SVM–GA and SVM–TLBO hybrid models showed AUROC values of 0.984 and 0.971, respectively, in the training and validation phases. Moreover, the ANFIS–GA and ANFIS–TLBO hybrid models showed an AUROC of 0.979 and 0.984 in the training phase and an AUROC of 0.973 and 0.984 in the validation phase, respectively. The SVM–IWO and ANFIS–IWO hybrid models showed the lowest AUROC. This study demonstrated the more efficient results of the SVM-based hybrid models in comparison with the ANFIS-based hybrid models in terms of accuracy and modeling speed.

Support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) are two well-known and powerful artificial intelligence techniques which have been frequently used for hazard mapping. So far, a plethora of hybrid models have been developed using a combination of either the SVR or ANFIS and evolutionary algorithms, but there are only a handful of studies that compare the performance of these models when integrated with evolutionary algorithms, especially in forest fire susceptibility mapping (FFSM). The aim of this study was to compare performance of ANFIS-, and SVR-based evolutionary algorithms, namely, the genetic algorithm (GA) and the shuffled frog-leaping algorithm (SFLA) in FFSM in Ajloun Governorate in Jordan. Accordingly, four hybrid models, SVR-GA, SVR-SFLA, ANFIS-GA, and ANFIS-SFLA, were developed and compared. One hundred and one forest fire locations were used in this study to assess and model susceptibility of forests to fires. The forest fire inventory data were divided into a training data subset (70%) and a testing data subset (30%). Fourteen factors affecting incidence of forest fires were employed as conditioning factors. The area under the receiver operating characteristic (AUROC) curve was used to assess performance of the models in the validation phase. The results revealed that the SVR-based hybrid algorithms had better AUROC values than the ANFIS-based algorithms. Of the four integrated models, the SVR-GA model proved to be the model with the highest accuracy and best performance. It had AUROC values of 0.97 and 0.89 in the training and the testing phases, respectively.

This study aims to develop three novel GIS-based models combining Genetic Algorithm (GA), Biogeography-Based Optimization (BBO) and Simulated Annealing (SA) with Support Vector Regression (SVR) for...

In this study, groundwater springs potentiality maps were prepared using a novel integrated model, support vector regression (SVR) with genetic algorithm (GA), for the Jerash and Ajloun region, Jor...

The significance of water harvesting in Wadi Hammad basin lies in the fact that the Jordanian government encourages the cultivation of vegetables, wheat, and barley in the country in an effort to improve food security in Jordan and create job opportunities for young people in the agricultural sector. Water harvesting in this basin will augment the water resources used for plant production and livestock watering by flash floods that involve large quantities of runoff. This study aimed to identify the best locations for water harvesting in the Wadi Hammad basin in Jordan via a Multi-Criterion Decision Analysis (MCDA) and indigenous knowledge. This study focused on consulting with indigenous knowledge where they provided information on the study area for water-collecting sites that have been used for years to provide water. In this study, site selection was based on six criteria that had been determined through a review of related literature (drainage density, rainfall depth, lineament density, soil clay content, geology, and slope). Following MCDA analysis, a water-harvesting suitability map was created. The final water-harvesting map uncovered that a large part of the basin (66.53%) has high to very high potential for water harvesting. The technique of water harvesting was subdued to statistical analysis, sensitivity analysis, and the map removal test. This study demonstrates that the selection of relevant water harvesting locations is a lengthy method that needs consultation with indigenous knowledge and the use of MCDA in the GIS environment. The study results, in general, and the final map, in particular, show the good relationship between the sites defined by the use of MCDA and the site suitability for water harvesting that was specified based on indigenous knowledge. Finally, the results of this study, which integrated indigenous knowledge with MCDA, may be employed to help in effective planning for water resource management to warrant the sustainable development of water in Jordan.

The present study was carried out in the central part of the Azraq Basin of Jordan to delineate the various groundwater potential zones within this region. This study includes eight criteria; land use, soil, slope, rainfall, elevation, lithology , lineament density and drainage density. Data and information regarding these criteria were collated and ascertained through the employment and utilisation of satellite imagery and digital mapping. The weighted linear combination (WLC) technique was then employed to integrate these criteria within the GIS so that the area concerned could be evaluated regarding its various hydrogeomorphic characteristics. A review of the literature was then used to assess each of the criteria. Subsequently the criteria were assigned to various parameters per their significance and rating regarding groundwater potentiality. The delineated groundwater potential map was classified into four distinct zones, namely, low, moderate, high, and very high potential zones. From the study findings it can be seen that approximately17% of the area concerned in this study was shown to have very high groundwater potential, while 2.181% has a low potential and 32.612%is categorised as having high groundwater potential; the largest region was that of moderate groundwater potential, with 48.207% of the land being categorised as such. Furthermore, the groundwater potential zones map was verified against a map of existing wells to calculate the percentage of wells in each potential zone. The results showed that approximately 2% of the existing wells were within low potential zones, 66% were within moderate potential zones, 15% were within high potential zones, and only 17% were within very high potential zones.

In this study, an attempt was made to develop a new simplified groundwater vulnerability to contamination index (SGVI). Nine experts in the fields of groundwater, surface water, soil, landuse and GIS were interviewed to develop the new index. They were asked to agree on new parameters that could be used to investigate groundwater vulnerability. Data about such parameters must be affordable and inexpensive. Subsurface parameters were excluded due to the fact that most researchers might not have adequate data about them. The experts agreed that depth to groundwater, soil texture, lineament density, rainfall, topographic slope, drainage density and landuse/land cover parameters should be included in the new vulnerability index. The experts were also asked to give a weight and the ratings for each parameter. The weights given by the experts were subjected to AHP analysis to determine the exact weight for each parameter. An area of 3200 km2 in the northern part of Jordan was selected to test the SGVI. The final map of the SGVI showed that most of the area (more than 96%) had moderate-low and moderate-high vulnerability to contamination. The new index was also subjected to statistical analysis, map removal test and map removal sensitivity analysis. The outcomes of these analyses showed that the new index was applicable and could be used in areas where subsurface data was limited or not available.

This research is an attempt to develop a new GIS index to investigate the surface water susceptibility to pollution (SWSi). In this index, the Weighted Linear Combination techniques within GIS environment was used to calculate the overall surface water susceptibility to pollution scores based on using 6 factors. The model includes 3 natural factors: gradient slope, distance to surface water and soil. Also, it includes 3 man-made activities: urban, agriculture and roads. Each factor was given the appropriate weight and ratings and then the final index was calculated using GIS techniques. The final results showed that the study area (1235 km2) could be classified into low susceptibility with an area of 250 km2 (20.2%), moderate susceptibility with an area of 815.5 km2 (66%), high susceptibility with an area of 166.2 km2 (13.5%) and very high susceptibility with an area of 3.3 km2 (0.3%).

The generation of reality-based 3D models of archeological sites is the future of representing existing ancient structures. Such approach requires substantial economic and logistical costs which limit this application. In this context, this paper presents the use of photogrammetric workflow, based on Structure from Motion techniques (SfM) to derive 3D metric information from Structure-and-Motion images. The interdisciplinary 3D modeling framework consists of fusion of multi resolution images from both overlapped low-altitude aerial and multi-view terrestrial imagery. The acquisition of aerial photographs survey was based on archived oblique aerial stereo pair photos acquired from the Aerial Photographic Archive for Archaeology in the Middle East (APAAME) project, while terrestrial of close range photos covering the castle walls has been acquired using portable camera. Camera calibration and orientation were carried out by VisualSFM, CMPMVS (Multi-View Reconstruction) and SURE (Photogrammetric Surface Reconstruction from Imagery) software. The resulted cloud points were processed using cloud Compare, MeshLab, Agisoft, and Skethup Softwares. A complete 3D digital geometric recording of the site was accomplished based on dense 3D point clouds with realistic metric accuracy and photorealistic performance to meet all the surveying and archaeological needs. The final Ajloun castle’s digital model geometry was added as a 3D building layer on Google Earth.

Geophysical study and watershed hydrological delineation have been integrated at downstream of Alasra dam site Norh Azraq area to investigate their potential for artificial groundwater recharge. The total surface area of the watershed was found to be about 195 square kilometers. The estimated annual runoff volumes for the Alasra watershed ranged between 1.2 and 1.8 MCM. Moreover, the interpretation of Ten Time Domain Electromagnetic (TDEM) soundings suggested three principal subsurface layers. The top surface layer has an intermediate resistivity (90 - 110 Ohm·m) with a thickness ranging from a few meters to around 50 m. This layer was interpreted as superficial deposits. The second subsurface layer with variably high resistivity values is composed of unsaturated massive basalt layer and probably belongs to Madhala Olivine Phyric Basalt Formation (MOB). The large variations in resistivity could be ascribed to the degree of water saturation (as a result of groundwater recharge from the nearby harvested water dam), or lithological variations (clay content) and/or due to structural control. The third subsurface layer has low resistivity values (

The aim of this study was to identify potential sites for wind turbine in the North West of Jordan. The Analytic Hierarchy Process (AHP) was used in a novel approach to identify the potential sites for the wind turbine in the study area based on five physical criteria (Wind Speed, Rainfall, Slope, Altitude and Land use) that affect the wind turbine sites. The importance of each criterion was based on experts’ opinions. The ratings for each criterion were based on the available literature review. The consistency ratio between the experts’ opinions was evaluated using the pairwise comparison method and a final weight was computed for each criterion. A wind turbine suitability map was generated using the weighted linear combination (WLC) method within GIS environment. It was found that 45% of the study area has high and very high suitability for wind turbine. In conclusion, this research will contribute to the enhancement of the available renewable energy resources in Jordan if the selected sites will be utilized for wind turbine.

Za’atari camp is the largest refugee camp in Jordan. It was first established in 2012 to host Syrian refugees. Currently the camp hosts more than 81,000 refugees, with no proper sanitary system which might pose a major threat to surface resources in the area. An investigation was done at Za’atari refugees’ camp to find the impact of refugees settling on surface and groundwater quality. Surface water quality of surface runoff generated from thirty rain fall events were collected during the winter season of 2013/2014 from the major Wadi that passes through the camp and small ponds within the camp after the rainfall event. The collected samples were analyzed for acidity (pH), the electrical connectivity (EC), total dissolved solids (TDS), nutrients (NO3- and PO43-) and selected heavy metals (Mn, Cd, Zn, Pb and Ni) in addition to biological oxygen demand (BOD5), chemical oxygen demand (COD) and intestinal worms (Total Coliform, E. cali). The results showed that there are significant variations in the EC as well as with TDS between the sites due to fluctuating amounts of water used for different activities within the camp as it was highest in the center of the camp where most of the refugees settle decreasing away from the center. The pH values were within the specifications of the World Health Organization and the Jordanian Standards. For nutrients, nitrate concentration was low with high phosphate ions which are most probably from detergents origin.

EIA are needed to avoid adverse impacts and to ensure long term benefits which lead to sustainable development. There are several techniques to conduct an EIA. Among these techniques include checklists, Matrices and overlay. DRASTIC index is considered an important methodology for studying groundwater vulnerability. In this research study, a simple matrix technique was adopted to investigate the Environmental Impacts of Akaider dumpsite on the surrounding environment. Nine environmental parameters (Groundwater, surface water, air, soil, land use, industry, public health, heritage and historical sites and socio-economic situation) were studies to see the impact of the dumpsite on their quality. It was found that the dumpsite might pose a major threat to these parameters. Also, DRASTIC index was used to investigate groundwater vulnerability to contamination in the area. It was found that the dumpsite is located within a moderate vulnerability zone. This means that groundwater in the underlying groundwater basins in not completely save and might be contaminated in the future.

Groundwater is considered to be the major water resource for many areas and the only source of water in some areas in Jordan. Some of Jordan groundwater resources are presently exploited at maximum capacity and in some cases are exploited beyond there safe yield. One of the efficient ways to fight the deficit in groundwater resources is through recharging the water tables naturally or artificially. This research aimed to select the optimum sites for groundwater recharge in the Azraq Oasis area/Jordan through the use of GIS techniques. The selection criteria were based on slope, drainage density, lineament density within the study area. The adopted selection technique was the Boolean techniques (Multiplication) within a Raster GIS. Thirty five sites were selected within the study area with areas vary between 5.2 ha to 273.5 ha. The total area that has the potential for groundwater recharge is 1659.5 ha which represents ca. 3.55% of the study area.

The spatial, temporal and spectral characteristics of the remote sensing data are effectively used in land use and land cover change mapping, hence helping in decision making for sustainable land resource management. The aim of the study is to map urbanization growth using satellite imagery, Google imagery and GIS in Mafraq city/North Jordan. Landsat imageries of 1987, 2005 and Google Earth (GeoEye-1) imagery of 2010 were used in GIS environment to map the change in the urbanization at Mafraq city. Maximum likelihood algorithm of supervised classification was used to delineate two land use and land cover classes for the study area, namely: populated areas and non-populated areas from 1987 and 2005 imageries. On-Screen digitizing was adopted on Google Earth (GeoEye-1) imagery of 2010 to map the populated areas. The main change observed for the time period of 1987-2010 was that the urbanized areas have increased approximately by 7.14 km2 (approximately 23% of the study area). The population density within the study area has increased from approximately 965 inhabitants per sq.km in 1987 to 1808 inhabitants per sq·km in 2005 and reached 2146 inhabitants per sq·km in 2010. The increase in the populated area within Mafraq city has impacted the surface hydrology runoff which leads to diverting some Wadis to avoid passing through the city centre. Also, the increase in urbanization in Mafraq city has put more pressures on the waste water treatment plant and solid waste dumpsite that serve Mafraq city.

Jordan is located in an arid to semi arid region where around 90% of its land receives an average annual precipitation of less than 100 mm while only 3% of the land receives an average annual precipitation of 300 mm or more. Jordan is characterised as a “water scarce” country because the current per capita share of water is estimated to be of the order of 140 m3 per year which is well below the 1000 m3 threshold. Rainwater harvesting is the accumulating and storing, of rainwater. It has been used to provide drinking water, water for livestock, water for irrigation or to refill aquifers as a groundwater recharge. GIS has been widely used in selecting the best sites for water harvesting schemes. This research aims at selecting optimum sites for water harvesting schemes in the Jordan arid lands (Badia) using indigenous knowledge and geo-informatics. To achieve this aim, a community-based research and desktop investigating is applied. The community-based research focused on consulting with 200 stakeholders form local communities where they provided knowledge on opportunities and constrains form their experience on water management in the arid lands where they live and interact. Also they provided information on potential location for water collecting sites that has been used for ages to provide water to humans and livestock. On the other hand, desktop research is conducted on sitting criteria for water harvesting based on physical and socio-economic characteristics. The physical criteria include rainfall volumes, slope, distance to water courses (wadis), distance form geologic faults and soil texture, where socio-economic criteria include distance to groundwater wells, distance to urban area, distance to agriculture activities and distance to international brooders. This selecting criterion in combination with indigenous knowledge is used within GIS environment to identify optimum sites for water harvesting. GIS analysis resulted in identifying 118 potential sites. Of those, 30 sites had already recommended by the community consultations.

Jordan is characterised as a ‘water scarce’ country. It is considered as one of the lowest countries in the world on a per capita basis. The available water resources per capita are falling as a result of population growth and are projected to fall from less than 160 m3 per capita per year at present to about 90 m3 per capita per year by 2025. Water harvesting has been practiced in Jordan for thousands of years. There are indications of early water harvesting structures believed to have been constructed over 9000 years ago. There is a need to establish water harvesting projects at various places in Jordan to collect more surface water for domestic, agricultural and livestock watering usages. The selection of best sites for water harvesting schemes must be based on certain criteria that take into consideration the socio-economic and the physical characteristics of the targeted area. This research aimed to use both the Weighted Linear Combination and the Boolean techniques within GIS environment to select suitable areas in Northern Jordan for establishing water harvesting ponds. This resulted in having ca. 25% of the study area (64,184.8 ha) with high potential for constructing water harvesting ponds.

Surface water is one of the major water resources for drinking and agricultural purposes in Jordan. It is the largest contributor to the irrigation sector and it is the second largest source for domestic consumption. Jordan also has a huge amount of oil shale that exists in the Southern and Eastern parts of the country. It is estimated that Jordan has a reserve of 50 billion tons of oil shale. The oil shale deposits in these locations are shallow and near the surface and can be utilized by an open cut mining method. The ash is considered one of the most important factors in selecting suitable and more economical utilization technology for Jordanian oil shale. Oil shale ash is considered one of the main environmental challenges and barriers, which stand in the way of developing oil shale industry in Jordan. The main concern in this case is that ash might reach nearby surface water resources in the area. This study aimed to evaluate the risk of pollution of surface water resources in Lajjoun area/S...

Water harvesting is a must and not an option for Jordan in the near future. The available water resources are limited and barely satisfy the current growing demands. In the basalt aquifer/NE of Jordan, there is a possibility to construct water harvesting ponds to assist the livestock owners in villages and towns in watering their livestock which will ease the pressure on the existing water resources. This research was based on the use of GIS techniques to select the optimum sites for water harvesting ponds within the basalt aquifer. Buffering, Union and arithmetic operations in GIS were all used in analysing the data based on different socio-economic and physical criteria. The outcomes of the GIS analysis resulted in selecting 72 suitable sites within the study area. These sites could be used as a guideline to the decision makers in Jordan when they decided to establish a water harvesting pond in the area. This will save time and money which could be directed to the construction of the actual water harvesting ponds.

National and sub-national scale estimates of soil organic carbon (SOC) stocks and changes can provide information land degradation risk, C sequestration possibilities and the potential sustainability of proposed land management plans. Under a GEF co-financed project, ‘The GEFSOC Modelling System’ was used to determine SOC stocks and projected stock change rates for four case study areas; The Brazilian Amazon, The Indo-Gangetic Plains of India, Kenya and Jordan. Each case study represented soil and vegetation types, climates and land management systems that are under represented globally, in terms of an understanding of land use and land management systems and the effects these systems have on SOC stocks. The stocks and stock change rates produced were based on detailed geo-referenced datasets of soils, climate, land use and management information. These datasets are unique as they bring together national and regional scale data on the main variables determining SOC, for four contrasting non-temperate eco-regions. They are also unique, as they include information on land management practices used in subsistence agriculture in tropical and arid areas. Implications of a greater understanding of SOC stocks and stock change rates in non-temperate areas are considered. Relevance to national land use plans are explored for each of the four case studies, in terms of sustainability, land degradation and greenhouse gas mitigation potential. Ways in which such information will aid the case study countries in fulfilling obligations under the United Nations Conventions on Climate Change, Biodiversity and Land Degradation are also considered. The need for more detailed land management data to improve SOC stock estimates in non-temperate areas is discussed.

The common GIS-based approach to regional analyses of soil organic carbon (SOC) stocks and changes is to define geographic layers for which unique sets of driving variables are derived, which include land use, climate, and soils. These GIS layers, with their associated attribute data, can then be fed into a range of empirical and dynamic models. Common methodologies for collating and formatting regional data sets on land use, climate, and soils were adopted for the project Assessment of Soil Organic Carbon Stocks and Changes at National Scale (GEFSOC). This permitted the development of a uniform protocol for handling the various input for the dynamic GEFSOC Modelling System. Consistent soil data sets for Amazon-Brazil, the Indo-Gangetic Plains (IGP) of India, Jordan and Kenya, the case study areas considered in the GEFSOC project, were prepared using methodologies developed for the World Soils and Terrain Database (SOTER). The approach involved three main stages: (1) compiling new soil geographic and attribute data in SOTER format; (2) using expert estimates and common sense to fill selected gaps in the measured or primary data; (3) using a scheme of taxonomy-based pedotransfer rules and expert-rules to derive soil parameter estimates for similar soil units with missing soil analytical data. The most appropriate approach varied from country to country, depending largely on the overall accessibility and quality of the primary soil data available in the case study areas. The secondary SOTER data sets discussed here are appropriate for a wide range of environmental applications at national scale. These include agro-ecological zoning, land evaluation, modelling of soil C stocks and changes, and studies of soil vulnerability to pollution. Estimates of national-scale stocks of SOC, calculated using SOTER methods, are presented as a first example of database application. Independent estimates of SOC stocks are needed to evaluate the outcome of the GEFSOC Modelling System for current conditions of land use and climate.

The Jordanian government has encouraged irrigated agriculture since the early 1990s by providing low cost loans to drill private wells. The impacts of this practice on land use change were examined within a small (362 km2) area of the Jordanian Badia using Geoinformatics. Aerial photographs, SPOT and Landsat TM imageries were used, in a GIS environment, to map and examine changes in the farming patterns since 1990. Field surveys and questionnaires were used to ground truth the remotely sensed data. The results indicate that the cultivated area has increased from just 28.5 ha in 1990 to over 1000 ha in 2000 and that the number of farms has increased from 2 to 32 over the same time period. Furthermore, farm locations are moved annually restricted by the position of the well and land availability; this also causes land use change.

Water consumption in Jordan already exceeds renewable freshwater resources by more than 20% and, after the year 2005, freshwater resources are likely to be fully utilised. Over 50% of supply derives from groundwater and this paper focuses on a small part of the northern Badia region of Jordan that is underlain by the Azraq groundwater basin where it has been estimated that annual abstraction stands at over 100% of the projected safe yield. While water supply is a crucial issue, there is also evidence to suggest that the quality of groundwater supplies is also under threat as a result of salinisation and an increase in the use of agrochemicals. Focusing on this area, this paper attempts to produce groundwater vulnerability and risk maps. These maps are designed to show areas of greatest potential for groundwater contamination on the basis of hydro-geological conditions and human impacts. All of the major geological and hydro-geological factors that affect and control groundwater movement into, through, and out of the study area were incorporated into the DRASTIC model. Parameters included; depth to groundwater, recharge, aquifer media, soil media, topography, and impact of the vadose zone. The hydraulic conductivity of the aquifer was not included in calculating the final DRASTIC index for potential contamination due to a lack of sufficient quantitative data. A Geographical Information System (GIS) was used to create a groundwater vulnerability map by overlaying the available hydro-geological data. The resulting vulnerability map was then integrated with a land use map as an additional parameter in the DRASTIC model to assess the potential risk of groundwater to pollution in the study area. The final DRASTIC model was tested using hydrochemical data from the aquifer. Around 84% of the study area was classified as being at moderate risk while the re mainder was classified as low risk. While the analysis of groundwater chemistry was not conclusive, it was encouraging to find that no well with high nitrate levels was found in the areas classified as being of low risk suggesting that the DRASTIC model for this area provided a conservative estimate of low risk areas. It is recognised that the approach adopted to produce the DRASTIC index was limited by the availability of data. However, in areas with limited secondary data, this index provides important objective information that could be used to inform local decision making.

Sama Monastery is one of the best preserved architecture built in the third century AD at Jordan’s eastern desert. Before the twentieth century, the roofing materials of Monastery, even most of the arch stones, were carried off for the construction of bridges and ferries above the valleys and water channels in the region. Documenting the Monastery which remains in scientific and precise ways is an important goal in order to protect and preserve this important part of the cultural heritage of Jordan. In this study, laser scanner and digital photogrammetry have been used for this purpose. In spite of the potential of each single approach, the integration aims at supporting the visual quality as well as the geometric accuracy of the collected textured 3D models. The purpose of such visual information will serve as a tool for the identification of the nature, extent and severity of the

Estimates of soil organic carbon (SOC) stocks and changes under different land use systems can help determine vulnerability to land degradation. Such information is important for countries in arid areas with high susceptibility to desertification. SOC stocks, and predicted changes between 2000 and 2030, were determined at the national scale for Jordan using The Global Environment Facility Soil Organic Carbon (GEFSOC) Modelling System. For the purpose of this study, Jordan was divided into three natural regions (The Jordan Valley, the Uplands and the Badia) and three developmental regions (North, Middle and South). Based on this division, Jordan was divided into five zones (based on the dominant land use): the Jordan Valley, the North Uplands, the Middle Uplands, the South Uplands and the Badia. This information was merged using GIS, along with a map of rainfall isohyets, to produce a map with 498 polygons. Each of these was given a unique ID, a land management unit identifier and was characterized in terms of its dominant soil type. Historical land use data, current land use and future land use change scenarios were also assembled, forming major inputs of the modelling system. The GEFSOC Modelling System was then run to produce C stocks in Jordan for the years 1990, 2000 and 2030. The results were compared with conventional methods of estimating carbon stocks, such as the mapping based SOTER method. The results of these comparisons showed that the model runs are acceptable, taking into consideration the limited availability of long-term experimental soil data that can be used to validate them. The main findings of this research show that between 2000 and 2030, SOC may increase in heavily used areas under irrigation and will likely decrease in grazed rangelands that cover most of Jordan giving an overall decrease in total SOC over time if the land is indeed used under the estimated forms of land use.

The GEFSOC soil carbon modelling system was built to provide interdisciplinary teams of scientists, natural resource managers and policy analysts (who have the appropriate computing skills) with the necessary tools to conduct regional-scale soil carbon (C) inventories. It allows users to assess the effects of land use change on soil organic C (SOC) stocks, soil fertility and the potential for soil C sequestration. The tool was developed in conjunction with case-studies of land use and management impacts on SOC in Brazil, Jordan, Kenya and India, which represent a diversity of land use and land management patterns and are countries where sustaining soil organic matter and fertility for food security is an on-going problem. The tool was designed to run using two common desktop computers, connected via a local area network. It utilizes open-source software that is freely available. All new software and user interfaces developed for the tool are available in an open source environment allowing users to examine system details, suggest improvements or write additional modules to interface with the system. The tool incorporates three widely used models for estimating soil C dynamics: (1) the Century ecosystem model; (2) the RothC soil C decomposition model; and (3) the Intergovernmental Panel on Climate Change (IPCC) method for assessing soil C at regional scales. The tool interacts with a Soil and Terrain Digital Database (SOTER) built for the specific country or region the user intends to model. A demonstration of the tool and results from an assessment of land use change in a sample region of North America are presented.

Groundwater resources are vitally important in arid and semi-arid areas meaning that spatial planning tools are required for their exploration and mapping. Accordingly, this research compared the predictive powers of five machine learning models for groundwater potential spatial mapping in Wadi az-Zarqa watershed in Jordan. The five models were random forest (RF), boosted regression tree (BRT), support vector machine (SVM), mixture discriminant analysis (MDA), and multivariate adaptive regression spline (MARS). These algorithms explored spatial distributions of 12 hydrological-geological-physiographical (HGP) conditioning factors (slope, altitude, profile curvature, plan curvature, slope aspect, slope length (SL), lithology, soil texture, average annual rainfall, topographic wetness index (TWI), distance to drainage network, and distance to faults) that determine where groundwater springs are located. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was employed to evaluate the prediction accuracies of the five individual models. Here the results were ranked in descending order as MDA (83.2%), RF (80.6%), SVM (80.2%), BRT (78.0%), and MARS (75.5%).The results show good potential for further use of machine learning techniques for mapping groundwater spring potential in other places where the use and management of groundwater resources is essential for sustaining rural or urban life.

The terrestrial biosphere is an important global carbon (C) sink, with the potential to drive large positive climate feedbacks. Thus a better understanding of interactions between land use change, climate change and the terrestrial biosphere is crucial in planning future land management options. Climate change has the potential to alter terrestrial C storage since changes in temperature, precipitation and carbon dioxide (CO 2 ) concentrations could affect net primary production (NPP), C inputs to soil, and soil C decomposition rates. Climate change could also act as a driver for land use change, thus further altering terrestrial C fluxes. The net balance of these different effects varies considerably between regions and hence the case studies presented in this paper (the GEFSOC project countries Kenya, Jordan, Brazil, and India) provide a unique opportunity to study climate impacts on terrestrial C storage. This paper first presents predicted changes in climate for the four case study countries from a coupled climate-C cycle Global Circulation Model (HadCM3LC), followed by predicted changes in vegetation type, NPP and soil C storage. These very coarse assessments provide an initial estimate of large-scale effects. A more detailed study of climate impacts on soil C storage in the Brazilian Amazon is provided as an example application of the GEFSOC system. Interestingly in the four cases studied here precipitation seems to control the sign of the soil C changes under climate change with wetter conditions resulting in higher soil C stocks and drier conditions in lower soil C stocks, presumably because increased NPP in wetter conditions here will override any increase in respiration. In contrast, globally, it seems to be temperature that controls changes in C stocks under climate change. Even if there is a slight increase in precipitation globally, a decrease in C stocks is predicted—in other words, the regional response to precipitation differs from the global response. The reason for this may be that whilst temperature increases under climate change were predicted everywhere, the nature of precipitation changes varies greatly between regions.