Percent Slope Research Articles

INDEKS STABILITAS AGREGAT TANAH PADA PERKEBUNAN TEH BERBASIS SLOPE DAN UMUR TANAMAN DI KECAMATAN GUNUNG TALANG KABUPATEN SOLOK

Land-use change from the forest into tea plantation in Solok Regency in 1983 has decreased the area of forests in Gunung Talang District. Clearing up the forest at the beginning for tea plantation could worsen the physical and chemical conditions of the soil. One of which is soil aggregate stability that is very dynamic and can influence other soil physical properties. This study was aimed to determine soil Aggregate Stability Index (ASI) at tea plantations. Soil samples was taken at a depth of 0-20 cm from five different slopes (0-8% (A), 8-15% (B), 15-25% (C), 25-45% (D), and> 45% (E)) and at two crop ages (10 and 35 years old). The results showed that the soil aggregate stability index at tea plantations ranged from stable to very stable. It tended to decrease by increasing slope percentage at each of crop age, Between the ages, it showed higher ASI at 35 years old crop age under relatively flat (0-8%) and very steep (>45%) areas.

5-HT2A receptor-mediated Gαq/11 activation in psychiatric disorders: A postmortem study

Objectives Serotonin-2A (5-HT2A) receptors play an important role in the regulation of many brain functions that are disturbed in patients with such psychiatric diseases as mood disorders and schizophrenia. The objective of this study was to evaluate 5-HT2A receptor-mediated signalling pathway through Gαq/11 activation in psychiatric patients by using post-mortem brain samples. Methods Functional activation of Gαq/11 proteins coupled to 5-HT2A receptors was determined by means of [35S]GTPγS binding/immunoprecipitation assay in post-mortem prefrontal cortex of psychiatric patients diagnosed as bipolar disorder (BP), major depressive disorder (MDD), and schizophrenia, and individually matched controls. The effects of antipsychotic treatment as well as suicide were also analysed. Results There was no significant difference in maximum percent increase (%Emax) or slope factor among the four groups. The negative logarithm of concentration eliciting the half-maximal effect (pEC50) was significantly reduced in BP and schizophrenia patients as compared to controls. These alterations were attributable to antipsychotic medication. The pEC50 values in ‘non-suicide’ group of schizophrenia, but not in ‘suicide’ group, were significantly reduced as compared with controls. Conclusions Altered 5-HT2A receptor-mediated signalling pathway through Gαq/11 proteins in prefrontal cortex might be apparently involved in pathophysiology and pharmacotherapy of BP and schizophrenia. In schizophrenic patients, these alterations as a result of successful treatment with antipsychotic agents may help in prevention of suicidal behaviour.

Development and spatial application of a submerged aquatic vegetation model for Cootes Paradise Marsh, Ontario, Canada

Cootes Paradise Marsh (CP) is an urban wetland and is part of the Hamilton Harbour Area of Concern (AOC). Anthropogenic stressors have degraded the system’s water quality. Submerged aquatic vegetation (SAV) provides critical fish habitat, and its recovery is crucial to this AOC’s delisting efforts. We developed predictive models to recommend water clarity (Secchi depth) targets that can potentially achieve a minimum SAV presence of 230 ha in CP, using macrophyte monitoring data that have been collected since 1996 by the Royal Botanical Gardens (RBG). A random forest approach was used for modelling SAV presence and SAV % cover. The final model for predicting presence of SAV consisted of Secchi depth, west wind fetch, and water level; the model had high accuracy (accuracy = 0.88, kappa = 0.77). For predicting SAV cover, the final model consisted of water depth, Secchi depth, percent slope, average fetch, water level, and substrate type; it had moderate accuracy (σ2explained = 0.66, root mean square error = 26.09, and weighted absolute percentage error = 58.96). Both models were then applied spatially using a digital elevation model to predict areas of CP where SAV would likely occur under different water level and water clarity scenarios. We recommend a delisting Secchi depth target of greater than 0.75 m to achieve the maximum potential of SAV areal extent under different water level scenarios.

Integrated GIS-based RUSLE approach for quantification of potential soil erosion under future climate change scenarios.

Human-induced agricultural and developmental activities cause substantial alteration to the natural geography of a landscape; thereby accelerates the geologic soil erosion process. This necessitates quantification of catchment-scale soil erosion under both retrospective and future scenarios for efficient conservation of soil resources. Here, we present a revised universal soil loss equation (RUSLE) based soil erosion estimation framework at an unprecedentedly high spatial resolution (30 × 30m) to quantify the average annual soil loss and sediment yield from an agriculture-dominated river basin. The input parameters were derived by using the observed rainfall data, soil characteristics (soil texture, hydraulic conductivity, organic matter content), and topographic characteristics (slope length and percent slope) derived from digital elevation model (DEM) and satellite imageries. The developed approach was evaluated in the Brahmani River basin (BRB) of eastern India, wherein the different RUSLE inputs, viz., rainfall erosivity (R factor), soil erodibility (K factor), topographic (LS factor), crop cover (C factor), and management practice (P factor) factors have the magnitude of 1937 to 4867MJmmha-1h-1year-1, 0.023 to 0.039thhaMJ-1ha-1mm-1, 0.03 to 74, 0.16 to 1, and 0 to 1, respectively. The estimated average annual soil loss over the BRB ranged from 0 to 319.55tha-1year-1, and subsequent erosion categorization revealed that 54.2% of basin area comes under extreme soil erosion zones in the baseline period. Similarly, the sediment yield estimates varied in the range of 0.96 to 133.31tha-1year-1, and 35.81% area were identified as high soil erosion potential zones. The extent of erosion under climate change scenario was assessed using the outputs of HadGEM2-ES climate model for the future time scales of 2030, 2050, 2070, and 2080 under the four representative concentration pathways (RCPs) 2.6, 4.5, 6.0, and 8.5. The severity of soil erosion under climate change is expected to have a mixed impact in the range of -25 to 25% than the baseline scenario. The outcomes of this study will serve as a valuable tool for decision-makers while implementing management policies over the BRB, and can be well extended to any global catchment-scale applications.

SPATIAL AND TEMPORAL PATTERN OF SOIL CONSERVATION IN MOUNTAIN ECOSYSTEM: CASE STUDY OF PATUHA MOUNTAIN, WEST JAVA – INDONESIA

The Patuha Mountain area located 50 km south of Bandung City and the upstream of the longest river in West Java, The Citarum River. 12% of the Patuha Mountain area categorized as the steep slope area (>25o). Agriculture and plantations on steep slope locations require appropriate soil conservation methods to reduce erosion rates and avoid the threat of landslides. Land use in The Patuha Mountain area dominated by tea plantations, paddy fields, and various agricultural commodities such as coffee, corn, scallion, and beans. The objective of this study is to identify the spatial and temporal patterns of soil conservation applied by farmers from 1990 to 2018 and project it to 2036 to understand the possible impact in the future. The scenario used in the analysis is the Business as Usual (BAU) scenario. The method used to model the changes in agricultural land use is Cellular Automata - Markov Chain by various driving factors, such as distance from the road, river, settlements, forests, and percent slope. The slope and other driving factors are then correlated with agriculture land to identify soil conservation methods and later validated by field survey. The results indicate that the spatial pattern of soil conservation methods carried out by the community is closely related to slope conditions and the changes in agricultural land caused by development pressure and type of commodities planted by farmers. Agricultural land on 40% slopes adopts mechanical, vegetative, and chemical conservation method.

Geoinformatics and geophysical survey-based estimation of best groundwater potential sites through surface and subsurface indicators

Groundwater is the most important natural resource around the world which needs new and advanced technologies for control and monitoring. This investigation was to check the groundwater potential zones by integrated techniques of geoinformatics and geophysical investigation through a vertical electrical sounding in Rawalakot, Azad Kashmir, Pakistan. The study area being a sedimentary basin, sandstone is the major rock type with good porosity and permeability. Satellite data were used to generate different thematic maps such as slope percent, lineament density, land use/cover, drainage density, geology, roughness index, curvature, topographic wetness index, and rainfall. Subsequently suitable rank and weightage was assinged for final weight overlay analysis to identify the groundwater potential zones. Final potential map was generated with good, moderate, and poor zones. This analysis revealed that 92.60% of the area is categorized to be having good to moderate groundwater potential. Then, an electrical resistivity survey was done by vertical electrical sounding (VES) using Schlumberger electrode configuration, at 32 locations to check the subsurface condition. The predominant curve types were Q type, A type, H type, and K type. The apparent resistivity was calculated at 20-m, 40-m, and 60-m depths. Dar-Zarrouk parameters were calculated to identify the subsurface aquifers and protective capacity rating. Integrating the outcome of GIS and geophysical studies, the region with good groundwater potential was delineated with more accuracy.

Mitigation of floodwaters inundation due to land subsidence in the coastal area of Semarang City

Semarang city is the capital of Central Java province, which is located on the cost. The Semarang coastal topography is flat, with slope percentage around 0-2%. Most area is almost the same as height as the sea level and in some places are lower. Environmetal problems faced by Semarang related to this topography area tidal inundation, land subsidence and flooding during rainy season. The research was conducted to model locations that area vulnerable to tide inundation. The data of spot height, countour line and the rate of subsidence were analyzed by using Geographic Information System (GIS). The results showed that the Semarang coastal had a high level of vulnerability to inundated area. Tidal flood inundation area was about 2,400 ha with depths varying from 1 to 66 cm. Mitigation of tide inundation impact must be adjusted to land use and social condition of the local community. The house on stilts or lightweight houses that are easy to dismantle pairs is one of the solutions that is suitable for fisherman residential areas. Whereas the trade and service areas must be carried out by raising of the building floor and making protective buildings from sea water.

Mapping depth to the argillic horizon on historically farmed soil currently under forests

The Piedmont region of the southeastern United States experienced a period of accelerated erosion in the 1800s. Clear-cutting of the forests coupled with soil tilling and inadequate erosion control practices led to substantial soil redistribution and loss. This redistribution exposed the subsoil clay (argillic) horizon in many locations and altered hydrologic processes (i.e., infiltration or interflow) across the landscape. Describing the current hydrologic and biogeochemical processes in this landscape requires a spatially explicit mapping of soil re-distribution and the current depth to the argillic horizon. To achieve this mapping, we measured the depth to the argillic horizon in highly eroded (historically farmed) and undisturbed hillslopes (reference areas) by direct measures—soil augur and tile push probe—and by electromagnetic induction (EMI) combined with ancillary data (i.e., terrain features). Direct measures indicated that the depth to the argillic horizon in the historically farmed watersheds ranged from 26 ± 2 to 87 ± 12 cm in ridge-top and toe-slope, respectively, while in the reference hillslopes depths from ridge-top to toe-slope only ranged from 30 ± 3 to 48 ± 12 cm. Using remotely-sensed data, the best predictive models of the depth to the argillic horizon were based on measurements of the site topographic characteristics (percent slope and hillslope position), along with the following EMI data products: the 2 m horizontal coplanar (HCP) EMI measurement and some of its derivatives (i.e., 2 m HCP – 2 m perpendicular, 2 m HCP – 1 m HCP). Our data suggest that in this previously farmed and reforested landscape, geophysical sensing is an efficient means of predicting depth-to-clay when combined with landscape feature characteristics (R2 = 0.69).

A Modeling Comparison of Groundwater Potential Mapping in a Mountain Bedrock Aquifer: QUEST, GARP, and RF Models

In some arid regions, groundwater is the only source of water for human needs, so understanding groundwater potential is essential to ensure its sustainable use. In this study, three machine learning models (Genetic Algorithm for Rule-Set Production (GARP), Quick Unbiased Efficient Statistical Tree (QUEST), and Random Forest (RF)) were applied and verified for spatial prediction of groundwater in a mountain bedrock aquifer in Piranshahr Watershed, Iran. A spring location dataset consisting of 141 springs was prepared by field surveys, and from this three different sample datasets (S1–S3) were randomly generated (70% for training and 30% for validation). A total of 10 groundwater conditioning factors were prepared for modeling, namely slope percent, relative slope position (RSP), plan curvature, altitude, drainage density, slope aspect, topographic wetness index (TWI), terrain ruggedness index (TRI), land use, and lithology. The area under the receiver operating characteristic curve (AUC) and true skill statistic (TSS) were used to evaluate the accuracy of models. The results indicated that all models had excellent goodness-of-fit and predictive performance, but that RF (AUCmean = 0.995, TSSmean = 0.89) and GARP (AUCmean = 0.957, TSSmean = 0.82) outperformed QUEST (AUCmean = 0.949, TSSmean = 0.74). In robustness analysis, RF was slightly more sensitive than GARP and QUEST, making it necessary to consider several random partitioning options for preparing training and validation groups. The outcomes of this study can be useful in sustainable management of groundwater resources in the study region.

Effects of slope and flow depth on the roughness coefficient of lodged vegetation

Various vegetations are often grown on floodplains, and it has a significant influence on the movement of water flow and the protection of river slopes. In the experiments performed in this study, a cylindrical aluminum column with a diameter of 4 mm was selected to simulate natural vegetation and 7 classes of slopes (i = 0%, 0.5%, 1.0%, 1.5%, 2.0%, 2.5%, and 3.0%, where i is the percentage of slope) and four categories of lodging angles (θ = 20°, 40°, 60°, and 80°) were assigned. The experimental results show that when i > 0%, the curves of Manning’s roughness coefficient (n) and flow depth (h) converge, and the degree of convergence gradually increases with the slope. In addition, Manning's roughness coefficient increases with the increase in slope at shallow flow depths, and decreases with the increase in slope at deeper flow depths. Exploration of the relationship between slope and vegetation roughness not only provides a theoretical support for flood control, but also has practical significance for river ecological environment management.

The possibility of preparing soil texture class map by artificial neural networks, inverse distance weighting, and geostatistical methods in Gavoshan dam basin, Kurdistan Province, Iran

Soil texture is one of the most important variables in crop production that can influence agriculture plans. Preparing a map showing the different zones of soil texture classes is very important in agricultural planning. The map also serves as a basic map in many soil studies and land suitability. In the present research, the efficiency of different methods of interpolation of soil particle size distribution such as inverse distance weighting (IDW) and geostatistical methods such as kriging, co-kriging, and artificial neural networks (ANNs) was evaluated to predict the soil texture. We collected 105 soil samples from depths of 0–15 cm from Gavoshan dam basin in Kurdistan Province, Iran, and measured their texture by hydrometer method. Next, the map of soil particles groups was prepared using different methods of interpolation in ArcGIS environment. Afterward, the longitude and latitude, height, slope percent, and soil texture particles of training points were introduced to an ANN to estimate soil texture particles by MATLAB software. The accuracy of each method was assessed by statistical indicators such as root mean square error (RMSE), geometric mean error ratios (GMER), and the correlation coefficient (R) with the scoring method. Results showed that the accuracy of the co-kriging was greater than that of kriging in estimating the clay percentage. Although the efficiency and accuracy of ANNs in the estimation of particle size spatial distribution in the sand, silt, and clay was greater than three inverse distance weighting, kriging, and co-kriging methods, the correlation of the model was less than 50% using the ANNs. In this regard, the IDW had a lower accuracy among other methods.

Land suitability evaluation for agricultural use using GIS and remote sensing techniques: The case study of Ma’an Governorate, Jordan

The present study illustrates the Spatial-temporal dynamics of Land use/Land cover (LULC), and locate land suitability for agricultural use. Landsat satellite images of three different years 1990, 2000 and 2018 over 28 years were acquired from Global Land Cover Facility Site (GLCF) and earth explorer. Land suitability for agriculture use was assessed on five criteria; rainfall, temperature, slope percentage, soil types and water Wells distribution. The different geospatial layers were transferred using suitability coding system to produce the suitability maps for rainfed and irrigated areas using weighted overlay method. Ma’an Governorate covers approximately 37% of the total area of Jordan, and consists mostly of uncultivated land 33304.66 km2 (99.49%). Three main LULC classes were identified of the three years; 1990, 2000 and 2018; forest and irrigated areas have been fluctuated between 86.00 km2 (0.26%), 108.06 km2 (0.32) and 102.68 km2 (0.31%), built-up area increased by 24.00 km2, (0.07%), 41.88 km2 (0.13%) and 68.66 km2 (0.21%), while the bare land has been decreased from 33366 km2 (99.67%),33476 km2 (99.55%) and 33304.66 km2 (99.49%) during 1990, 2000 and 2018 respectively. land suitability for the agricultural use (rainfed areas and irrigated crops); Data from the five parameters (mentioned above) are used for spatial analysis applying weighted overlay method. The results showed that only about (0.2%) from the total area of Ma’an is highly suitable for agriculture dependent on rainfall. While about (1.4%) is high suitable for the irrigation crops. The main reason of the low levels of land suitability for agriculture is very low soil fertility, and the water scarcity.

Value of the gadoxetic acid-enhanced magnetic resonance imaging on oxaliplatin-induced liver function injury in C57BL/6 mice

Objective To investigate the value of gadoxetic acid-enhanced magnetic resonance imaging (MRI) on oxaliplatin-induced liver function injury in C57BL/6 mice. Methods Forty male and six weeks old C57BL/6 mice without specific pathogens were included and the body weght ranged from 19 to 23 g. They were randomly assigned into control group, experimental group A, experimental group B and experimental group C (10 mice/group). The mice in the control group was intraperitoneally injected with saline solution. The mice in the experimental groups were intraperitoneally injected with oxaliplatin twice a week. The experimental group A, B and C were administered for 2 weeks, 4 weeks and 6 weeks, respectively. The T1 relaxation time on the hepatobiliary phase and the first rapid enhancement slope percentage (ESP) in liver parenchyma were measured and calculated. Serum albumin and bilirubin values were measured and albumin and bilirubin (ALBI) scores were calculated. Pathological staining was used to observe liver tissue damage and fibrosis. The receiver operating characteristic (ROC) curve evaluated the ALBI score, ESP and T1 relaxation time on hepatobiliary phase for the diagnosis of liver function. Results Sixteen mice in the experimental groups (including group A, B and C) were included in the hepatic degeneration group (hepatocyte degeneration without fibrosis). Fourteen mice were included in the hepatic fibrosis group. T1 relaxation time on hepatobiliary phase in the hepatic fibrosis group was higher than that in the control group and in the hepatic degeneration group. The differences were statistically significant (P<0.05). The ESP of the control group, the hepatic degeneration group and the hepatic fibrosis group was increased, with statistically significant differences (all P<0.05). Compared with the control group, ALBI scores of the hepatic degeneration group and the hepatic fibrosis group were both decreased, with statistically significant differences (P<0.05). In the hepatic fibrosis group, the areas under the curve of ALBI scores, the T1 relaxation time on hepatobiliary phase and the ESP were 0.734, 0.962 and 0.989, respectively. Conclusion The T1 relaxation time on hepatobiliary phase and the ESP of gadoxetic acid-enhanced MRI can effectively evaluate the hepatic function reduction induced by oxaliplatin-induced hepatic tissue injury in C57BL/6 mice. Key words: Magnetic resonance imaging; Gadoxetic acid; Albumin-bilirubin; Liver function

Reformulating and testing Temesgen-Melesse's temperature-based evapotranspiration estimation method

The use of FAO-56 Penman–Monteith (PM) equation is the recommended equation to estimate potential evapotranspiration. However, when data that satisfy the PM equation is not available or incomplete, the use of PM equation is not an option. In this study, one such method known as Temesgen-Melesse's (TM) method was assessed in relation to the PM equation using data of eight class-I meteorological stations in Ethiopia. In the study, first the problems with this method were identified and the TM equation was modified. The modifications made were replacement of the average maximum temperature at the denominator of the equation varying with time with the average of Tmax for each location (which is a constant for a given location). The Second consideration was calibrating the power of the maximum temperature at the numerator using PM data instead of taking it as a constant 2.5 suggested by the authors in their original equation. Then the three (the original TM, the modified TM with constant power of 2.5 and the modified TM with the power calibrated) methods were fitted against PM equation. Thereafter tests using statistical parameters, model tendency parameters and model performances were carried out. The results indicate the modified TM equation to be better than the original TM equation in terms of percent slope (0.8–12.3 against 1.3–15.1) and the correlation coefficient (R2) and the slope (100% good or satisfactory against 25%). The modified and calibrated equation gave best results in terms of percent error by slope (0.5–2.3), by coefficient of efficiency (100% good or satisfactory), by R2 and slope (100% good or satisfactory) and by mean percent error (5.7–13.6%). Therefore, whenever data that satisfy PM equation are available (even if for limited years), it is better to calibrate the power of the maximum temperature and to consider more decimal places rather than taking 2.5 as suggested by the authors. When data is not available it is better to use the modified TM equation rather than using the original TM equation. The study would benefit those who want to study long-term climate changes and drought patterns, which involve the use of evapotranspiration with limited data that satisfy the PM equation, but have long-term data of temperature.

COMPARISON AND SUITABILITY ASSESSMENT OF SLOPE CALCULATION ALGORITHMS ON DIFFERENT TERRAINS USING AERIAL SURVEY

Abstract. Digital Elevation Models are one of the important datasets of any Geographic Information System (GIS) and so are the parameters derived from them. One such parameter is slope, whose accuracy can have a significant effect on many engineering and construction works. This paper addresses the eight-slope calculation methods that are currently available to calculate slope value from a DEM and compares how these methods works on different slope range and values. These methods were applied to calculate slope from DEM of 30 m. To determine the method that calculates the most accurate slope value for a particular slope range by comparing them with actual slope value is the main objective of this paper. The methods 2FD, 3FD, 3FDWRD, Average Neighborhood, Constrained Quadratic Surface and FFD has given similar results across all slope range while the algorithms that appears to yield the most varying results are Maximum Max and Simple D. In addition, it is observed that the choice of algorithms is more important when grade slope is less than 10 percent. However, for terrains with above 10 percent slope, the choice of algorithms seems less important with only a difference of approximately 0.5 gradient.

The Impacts of Topography on Travel Behaviors - Walking and Cycling to Commute to Work in Southern California -

This study aims to examine the relationship between topography, particularly slopes of land, and travel behaviors, specifically mode choice. In this study, we are seeking to identify mode choice and neighborhood topographic feature, such as average percentage of land slope, with other drivers to mode choice such as built environment and socioeconomic status. Keeping built environment, distance to job centers, socioeconomic status, and spatial effect constatnt, we found that steeper neighborhoods tended to have a higher percentage of people who rode public transit or walked to work, whereas there were negative associations of the average percentage of land slope with the percentage of residents who drove private cars or rode bicycles to work. Unlike our assumption, walking trips were positively associated with the average percentage of land slope in neighborhoods.

Urban flood risk mapping using data-driven geospatial techniques for a flood-prone case area in Iran

AbstractIn an effort to improve tools for effective flood risk assessment, we applied machine learning algorithms to predict flood-prone areas in Amol city (Iran), a site with recent floods (2017–2018). An ensemble approach was then implemented to predict hazard probabilities using the best machine learning algorithms (boosted regression tree, multivariate adaptive regression spline, generalized linear model, and generalized additive model) based on a receiver operator characteristic-area under the curve (ROC-AUC) assessment. The algorithms were all trained and tested on 92 randomly selected points, information from a flood inundation survey, and geospatial predictor variables (precipitation, land use, elevation, slope percent, curve number, distance to river, distance to channel, and depth to groundwater). The ensemble model had 0.925 and 0.892 accuracy for training and testing data, respectively. We then created a vulnerability map from data on building density, building age, population density, and socio-economic conditions and assessed risk as a product of hazard and vulnerability. The results indicated that distance to channel, land use, and runoff generation were the most important factors associated with flood hazard, while population density and building density were the most important factors determining vulnerability. Areas of highest and lowest flood risks were identified, leading to recommendations on where to implement flood risk reduction measures to guide flood governance in Amol city.

Urban Flood Hazard Modeling Using Self-Organizing Map Neural Network

Floods are the most common natural disaster globally and lead to severe damage, especially in urban environments. This study evaluated the efficiency of a self-organizing map neural network (SOMN) algorithm for urban flood hazard mapping in the case of Amol city, Iran. First, a flood inventory database was prepared using field survey data covering 118 flooded points. A 70:30 data ratio was applied for training and validation purposes. Six factors (elevation, slope percent, distance from river, distance from channel, curve number, and precipitation) were selected as predictor variables. After building the model, the odds ratio skill score (ORSS), efficiency (E), true skill statistic (TSS), and the area under the receiver operating characteristic curve (AUC-ROC) were used as evaluation metrics to scrutinize the goodness-of-fit and predictive performance of the model. The results indicated that the SOMN model performed excellently in modeling flood hazard in both the training (AUC = 0.946, E = 0.849, TSS = 0.716, ORSS = 0.954) and validation (AUC = 0.924, E = 0.857, TSS = 0.714, ORSS = 0.945) steps. The model identified around 23% of the Amol city area as being in high or very high flood risk classes that need to be carefully managed. Overall, the results demonstrate that the SOMN model can be used for flood hazard mapping in urban environments and can provide valuable insights about flood risk management.

Landfill site selection via integrating multi-criteria decision techniques with geographic information systems: a case study in Naqadeh, Iran.

Sanitary landfill is still the most significant and reasonable and the least expensive waste disposal method. The process of selecting a landfill site with minimal adverse effects on the environment is a complex task, in which numerous criteria need to be taken into account. In the present research, the AHP (analytical hierarchy process) and TOPSIS (technique for order of preference by similarity to ideal solution) models were used to weigh the layers and prioritize the identified regions, respectively. In the present study, the following criteria were considered: (1) environmental criteria consisting of groundwater depth and distance from rivers, streams, and wetland/dam/lake; (2) physical criteria consisting of geology, slope percentage, distance from faults, heights, soil depth, and aspect; (3) social-economic criteria including the subcriteria land uses and distance from urban centers and villages; and finally, (4) access factors with the subcriteria distance from roads and power lines. After the preparation phase, the maps corresponding to each subscale were weighed in GIS by means of AHP. To weigh the layers and evaluate the inconsistency rate of pairwise comparisons, the Expert Choice software, in which the AHP model can be run, was used. On the output map, 11 highly suitable areas for landfill were identified via the integration of layers and the identified options and were ranked using the TOPSIS technique and five secondary criteria based on experts' views. Ultimately, area no. 12 was selected as the proposed site for the landfill in Naqadeh County. The results indicated that the combination of multicriteria decision-making models including AHP and TOPSIS can be properly utilized for the purpose of locating.

Controls on depression-focused recharge during spring snowmelt on the Oak Ridges Moraine, southern Ontario, Canada

The Oak Ridges Moraine (ORM) is a key hydrogeologic feature in southern Ontario. Previous work has emphasized the value of groundwater recharge (R) estimates and information on the timing and location of R to incorporating groundwater protection into land-use planning on the ORM, and has suggested that ponding and depression-focused recharge (DFR) occurs in closed topographic depressions on the ORM during winter and spring. However, the influence of land cover, topography, and soil type on the occurrence and extent of DFR is unclear. Sixteen closed topographic depressions on the crest and flanks of the ORM were chosen for study. Observations of ponding and measurements of pond extent during the snowmelt period were related to depression land cover (percent coverage of open vs. forested cover), depression geometry (i.e., contributing area, average slope, and relief ratio) and soil texture (% sand, silt and clay). Ponding occurred at 10 of 16 depressions, all of which were in predominantly open agricultural cover. Ponded and non-ponded depressions differed significantly in terms of percentage of open area and average slope. Ponded depressions were gently-sloped with high proportions (>85%) of open coverage, while non-ponded depressions were steep and largely forested. Extent of ponding was not related to any of the depression characteristics. However, pond volume showed a significant positive, non-linear relationship with average slope for a subset of sites with similar land cover and soil type. Results suggest that ponding and DFR during winter and spring on the ORM is restricted to open, agricultural areas and that this phenomenon may be more pronounced in steeper depressions. The relationship between land cover and DFR presented here could have implications for the quantity and quality of water recharging the ORM's aquifers, and further work is recommended to better define this relationship.