Mapping the organic carbon stocks of surface soils using local spatial interpolator

The largest uncertainties are associated with estimating the soil organic carbon (SOC) stock because of natural soil variability and data scarcity. Thus, a local spatial geostatistical hybrid approach, the geographically weighted regression kriging (GWRK), was used in the present study to overcome some of these uncertainties. This study was designed to estimate the SOC stock (kg C m(-2)) for the surface 0 to 15 cm depth using the state of Pennsylvania as the study region. A total of 920 soil profiles were extracted from the National Soil Survey Center database and were divided into calibration (80%) and validation (20%) periods. Some soil parameters that include clay content, bulk density (ρ(b)), total nitrogen (TN) content, pH, Ca(2+), Na(+), extractable acidity (EXACID), and cation exchange capacity (CEC) were used as covariates for estimating the SOC stock. These covariates exhibited spatial autocorrelation (Moran's Index, I = 0.62 to 0.89). Further, residuals of geographically weighted regression were spatially autocorrelated, and hence support the use of the GWRK approach. Validation results concluded that the performance of the GWRK approach was the best with the lowest values of root mean square error, mean estimation error and mean absolute estimation error. The estimated SOC stock for the surface 0 to 15 cm depth ranged from 1.41 to 3.94 kg m(-2). Results from this study show that the GWRK captures spatial dependent relationships, and addresses spatial non-stationarity issues, hence this approach improves the estimations of SOC stock.     

Conversion of grazing land into Grevillea robusta plantation and exclosure: impacts on soil nutrients and soil organic carbon

Different studies have shown that the effect of land use conversion on soil nutrients and soil organic carbon (SOC) is variable, which indicates that more investigations that focus on different specific geographical locations and land use types are required. The objectives of this study were (1) to evaluate the effect of grazing land (GL) conversion into Grevillea robusta plantation and exclosure (EX) on soil nutrients and soil organic carbon (SOC) and (2) to examine the impact of soil organic matter (SOM) on soil nutrients. To achieve these objectives, soil samples were taken from a soil depth of 20 cm (n?=?4) in each of the studied land areas. Each soil sample was analysed in a soil laboratory following a standard procedure. Analysis of variance (ANOVA) and Pearson’s correlation coefficient were used for the data analysis. The result indicated that conversion of GL into EX improved the soil electrical conductivity (EC), exchangeable K, cation exchange capacity (CEC), total N and available P (p?<?0.05), while the exchangeable Mg, SOC, available K and SOM were decreased (p?<?0.05). Conversion of GL into G. robusta improved the soil EC, exchangeable (K, Ca, Mg), CEC, SOC, total N, available K and SOM (p?<?0.05). There was a significant relationship between SOM and available P, total N, SOC and EC. There were no significant relationships between SOM and pH, available K and CEC. Finally, the results indicate that both land uses, established in acidic Nitosols, have variable impacts on soil chemical properties and that G. robusta plantation improved most of the soil nutrients and SOC much better than the EX land use.     

Soil carbon storage as influenced by tree cover in the Dehesa cork oak silvopasture of central-western Spain

The extent of carbon (C) stored in soils depends on a number of factors including soil characteristics, climatic and other environmental conditions, and management practices. Such information, however, is lacking for silvopastoral systems in Spain. This study quantified the amounts of soil C stored at various depths (0-25, 25-50, 50-75, and 75-100 cm) under a Dehesa cork oak (Quercus suber L.) silvopasture at varying distances (2, 5, and 15 m) to trees. Soil C in the whole soil and three soil fractions (<53, 53-250, and 250-2000 μm) was determined. Results showed soil depth to be a significant factor in soil C stocks in all soil particle sizes. Distance to tree was a significant factor determining soil C stocks in the whole soil and the 250-2000 μm soil fraction. To 1 m depth, mean total C storage at 2, 5, and 15 m from cork oak was 50.2, 37, and 26.5 Mg ha(-1), respectively. Taking into account proportions of land surface area containing these C stocks at varying distances to trees to 1 m depth, with a tree density of 35 stems ha(-1), estimated landscape soil C is 29.9 Mg ha(-1). Greater soil C stocks directly underneath the tree canopy suggest that maintaining or increasing tree cover, where lost from disease or management, may increase long term storage of soil C in Mediterranean silvopastoral systems. The results also demonstrate the use of soil aggregate characteristics as better indicators of soil C sequestration potential and thus a tool for environmental monitoring.     

Carbon sequestration in reclaimed manganese mine land at Gumgaon, India

Carbon emission is supposed to be the strongest factor for global warming. Removing atmospheric carbon and storing it in the terrestrial biosphere is one of the cost-effective options, to compensate greenhouse gas emission. Millions of acres of abandoned mine land throughout the world, if restored and converted into vegetative land, would solve two major problems of global warming and generation of degraded wasteland. In this study, a manganese spoil dump at Gumgaon, Nagpur in India was reclaimed, using an integrated biotechnological approach (IBA). The physicochemical and microbiological status of the mine land improved after reclamation. Soil organic carbon (SOC) pool increased from 0.104% to 0.69% after 20 years of reclamation in 0–15 cm spoil depth. Soil organic carbon level of reclaimed site was also compared with a native forestland and agricultural land. Forest soil showed highest SOC level of 1.11% followed by reclaimed land and agriculture land of 0.70% and 0.40%, respectively. Soil profile studies of all three sites showed that SOC pool decreased from 0–15, 15–30, and 30–45 cm depths. Although reclaimed land showed less carbon than forestland, it showed better SOC accumulation rate. Reclamation of mine lands by using IBA is an effective method for mitigating CO₂ emissions.     

The effects of land use changes on some soil properties in İndağı Mountain Pass – Çankırı, Turkey

Understanding spatial variability of dynamic soil attributes provides information for suitably using land and avoiding environmental degradation. In this paper, we examined five neighboring land use types in Indagi Mountain Pass - Cankiri, Turkey to spatially predict variability of the soil organic carbon (SOC), bulk density (BD), textural composition, and soil reaction (pH) as affected by land use changes. Plantation, recreational land, and cropland were the lands converted from the woodland and grassland which were original lands in the study area. Total of 578 disturbed and undisturbed soil samples were taken with irregular intervals from five sites and represented the depths of 0-10 and 10-20 cm. Soil pH and BD had the lower coefficient of variations (CV) while SOC had the highest value for topsoil. Clay content showed greater CV than silt and sand contents. The geostatistics indicated that the soil properties examined were spatially dependent to the different degrees and interpolations using kriging showed the dynamic relationships between soil properties and land use types. The topsoil spatial distribution of SOC highly reflected the changes in the land use types, and kriging anticipated significant decreases of SOC in the recreational land and cropland. Accordingly, BD varied depending on the land use types, and also, the topsoil spatial distribution of BD differed significantly from that of the subsoil. Generally, BD greatly decreased in places where the SOC was relatively higher except in the grassland where overgrazing was the more important factor than SOC to determine BD. The topsoil spatial distributions of clay, silt, and sand contents were rather similar to those of the subsoil. The cropland and grassland were located on the very fine textured soils whereas the woodland and plantation were on the coarse textured soils. Although it was observed a clear pattern for the spatial distributions of the clay and sand changing with land uses, this was not the case for the silt content, which was attributed to the differences of dynamic erosional processes in the area. The spatial distribution of the soil pH agreed with that of the clay content. Soils of the cropland and grassland with higher amounts of clay characteristically binding more cations and having higher buffering capacities had the greater pH values when compared to the soils of other land uses with higher amounts of sand naturally inclined to be washed from the base cations by the rainwater.     

Land use impact on soil quality in eastern Himalayan region of India

Quantitative assessment of soil quality is required to determine the sustainability of land uses in terms of environmental quality and plant productivity. Our objective was to identify the most appropriate soil quality indicators and to evaluate the impact of six most prevalent land use types (natural forestland, cultivated lowland, cultivated upland terrace, shifting cultivation, plantation land, and grassland) on soil quality in eastern Himalayan region of India. We collected 120 soil samples (20 cm depth) and analyzed them for 29 physical, chemical, and biological soil attributes. For selection of soil quality indicators, principal component analysis (PCA) was performed on the measured attributes, which provided four principal components (PC) with eigenvalues >1 and explaining at least 5 % of the variance in dataset. The four PCs together explained 92.6 % of the total variance. Based on rotated factor loadings of soil attributes, selected indicators were: soil organic carbon (SOC) from PC-1, exchangeable Al from PC-2, silt content from PC-3, and available P and Mn from PC-4. Indicators were transformed into scores (linear scoring method) and soil quality index (SQI) was determined, on a scale of 0–1, using the weighting factors obtained from PCA. SQI rating was the highest for the least-disturbed sites, i.e., natural forestland (0.93) and grassland (0.87), and the lowest for the most intensively cultivated site, i.e., cultivated upland terrace (0.44). Ratings for the other land uses were shifting cultivation (0.60)?>?cultivated low land (0.57)?>?plantation land (0.54). Overall contribution (in percent) of the indicators in determination of SQI was in the order: SOC (58 %)?>?exch. Al (17.1 %)?>?available P (8.9 %)?>?available Mn (8.2 %)?>?silt content (7.8 %). Results of this study suggest SOC and exch. Al as the two most powerful indicators of soil quality in study area. Thus, organic C and soil acidity management holds the key to improve soil quality under many exploitatively cultivated land use systems in eastern Himalayan region of India.     

Spatial Variability of Soil Organic Carbon in Relation to Environmental Factors of a Typical Small Watershed in the Black Soil Region, Northeast China

A total of 292 soil samples were taken from surface soil (0–20 cm) of a typical small watershed–Tongshuang in the black soil region of Heilongjiang province, northeast China in June 2005 for examining the concentration of soil organic carbon (SOC). Spatial variability of SOC in relation to topography and land use was evaluated using classical statistics, geostatistics and geographic information system (GIS) analyses. The objective of this study was to provide a scientific basis for land management targeting at improving soil quality in this region. Classical statistical analysis results indicated that the variability of SOC was moderate (C _V = 0.30). Slope position and land use types were discriminating factors for its spatial variability. Geostatistics analyses showed that SOC had a strong spatial autocorrelation, which was mainly induced by structural factors. Mean concentration of SOC in surface soil was 2.27% in this watershed, which was a very low level in the northern black soil region of northeast China. In this small watershed, present soil and water conservation measures played an important role in controlling soil loss. But SOC's restoration was unsatisfactory. Nearly three-quarters of the area had worrisome productivity. How to improve SOC concentration targeting at soil fertility is a pressing need in the future.     

Spatial distribution characteristics of soil organic carbon in subtropical forests of mountain Lushan,China

The study on the spatial distribution of forest soil organic carbon (SOC) is of great significance for accurate assessment of carbon storage in forest ecosystems. In the present study, by taking eight kinds of forest soils of Mountain Lushan in the subtropical area as the research object, we studied the spatial distribution characteristics of SOC in this mountainous area. The results showed that the SOC content and SOC density (SOCD) of main forest types in the Mountain Lushan were lower than the national and the world average. The soil layer of Lushan forest was thinner, and the SOC and active SOC (ASOC) contents of different forest types and SOCDs are the highest in the surface soil. SOCD of the topsoil accounts for 32.64–54.03% of the total SOCD in the whole soil profile. Surface litter is an important source of SOC, and the different vegetation types are the important reason for the different spatial distribution of SOC in this area. Soil SOC contents in the high-altitude forest (bamboo forest, deciduous broadleaf forest, Pinus taiwanensis forest, evergreen-deciduous forest, and coniferous-broadleaved mixed forest) were higher than those in the low-altitude forest (evergreen broadleaf forest, shrub, and Pinus massoniana forest). However, the difference in SOC content exhibited at the altitude gradient is significantly lower than that in SOC in the soil profile. This indicates that both soil depth and elevation are the important factors that affected SOC distribution. However, the influence of soil depth on spatial distribution of SOC may be more complex than that of altitude. Vegetation types and soil properties are the main reasons for the large differences of reduction rate in the contents of SOC and ASOC.     

Predicting climate change effects on surface soil organic carbon of Louisiana,USA

This study aimed to assess the degree of potential temperature and precipitation change as predicted by the HadCM3 (Hadley Centre Coupled Model, version 3) climate model for Louisiana, and to investigate the effects of potential climate change on surface soil organic carbon (SOC) across Louisiana using the Rothamsted Carbon Model (RothC) and GIS techniques at the watershed scale. Climate data sets at a grid cell of 0.5°?×?0.5° for the entire state of Louisiana were collected from the HadCM3 model output for three climate change scenarios: B2, A2, and A1F1, that represent low, higher, and even higher greenhouse gas emissions, respectively. Geo-referenced datasets including USDA-NRCS Soil Geographic Database (STATSGO), USGS Land Cover Dataset (NLCD), and the Louisiana watershed boundary data were gathered for SOC calculation at the watershed scale. A soil carbon turnover model, RothC, was used to simulate monthly changes in SOC from 2001 to 2100 under the projected temperature and precipitation changes. The simulated SOC changes in 253 watersheds from three time periods, 2001–2010, 2041–2050, and 2091–2100, were tested for the influence of the land covers and emissions scenarios using SAS PROC GLIMMIX and PDMIX800 macro to separate Tukey-Kramer (p?p?p?p?相似文献   

Dynamics of Organic Carbon and Microbial Biomass in Alluvial Soil With Tillage and Amendments in Rice-wheat Systems

Rice-wheat cropping systems of the Indo-Gangetic plains (IGP) occupying 12 million ha of productive land are important for the food security of South Asia. There are, however, concerns that yield and factor productivity trends in these systems are declining/stagnating in recent years. Decrease in soil organic carbon is often suggested as a reason for such trends. A field experiment was conducted to study the soil organic carbon (SOC) and soil microbial biomass carbon (MBC) dynamics in the rice-wheat systems. Use of organic amendments and puddling of soil before rice transplanting increased SOC and MBC contents. Microbial biomass carbon showed a seasonal pattern. It was low initially, reached its peak during the flowering stages in both rice and wheat and declined thereafter. Microbial biomass carbon was linearly related to SOC in both rice and wheat indicating that SOC could be used as a proxy for MBC.     

Land degradation assessment by geo-spatially modeling different soil erodibility equations in a semi-arid catchment

Land degradation by soil erosion is one of the most serious problems and environmental issues in many ecosystems of arid and semi-arid regions. Especially, the disturbed areas have greater soil detachability and transportability capacity. Evaluation of land degradation in terms of soil erodibility, by using geostatistical modeling, is vital to protect and reclaim susceptible areas. Soil erodibility, described as the ability of soils to resist erosion, can be measured either directly under natural or simulated rainfall conditions, or indirectly estimated by empirical regression models. This study compares three empirical equations used to determine the soil erodibility factor of revised universal soil loss equation prediction technology based on their geospatial performances in the semi-arid catchment of the Saraykoy II Irrigation Dam located in Cankiri, Turkey. A total of 311 geo-referenced soil samples were collected with irregular intervals from the top soil layer (0-10?cm). Geostatistical analysis was performed with the point values of each equation to determine its spatial pattern. Results showed that equations that used soil organic matter in combination with the soil particle size better agreed with the variations in land use and topography of the catchment than the one using only the particle size distribution. It is recommended that the equations which dynamically integrate soil intrinsic properties with land use, topography, and its influences on the local microclimates, could be successfully used to geospatially determine sites highly susceptible to water erosion, and therefore, to select the agricultural and bio-engineering control measures needed.     

Use of USLE/GIS Methodology for Predicting Soil Loss in a Semiarid Agricultural Watershed

The Universal Soil Loss Equation (USLE) is an erosion model to estimate average soil loss that would generally result from splash, sheet, and rill erosion from agricultural plots. Recently, use of USLE has been extended as a useful tool predicting soil losses and planning control practices in agricultural watersheds by the effective integration of the GIS-based procedures to estimate the factor values in a grid cell basis. This study was performed in the Kazan Watershed located in the central Anatolia, Turkey, to predict soil erosion risk by the USLE/GIS methodology for planning conservation measures in the site. Rain erosivity (R), soil erodibility (K), and cover management factor (C) values of the model were calculated from erosivity map, soil map, and land use map of Turkey, respectively. R values were site-specifically corrected using DEM and climatic data. The topographical and hydrological effects on the soil loss were characterized by LS factor evaluated by the flow accumulation tool using DEM and watershed delineation techniques. From resulting soil loss map of the watershed, the magnitude of the soil erosion was estimated in terms of the different soil units and land uses and the most erosion-prone areas where irreversible soil losses occurred were reasonably located in the Kazan watershed. This could be very useful for deciding restoration practices to control the soil erosion of the sites to be severely influenced.     

Integrating in-situ, Landsat, and MODIS data for mapping in Southern African savannas: experiences of LCCS-based land-cover mapping in the Kalahari in Namibia

Integrated ecosystem assessment initiatives are important steps towards a global biodiversity observing system. Reliable earth observation data are key information for tracking biodiversity change on various scales. Regarding the establishment of standardized environmental observation systems, a key question is: What can be observed on each scale and how can land cover information be transferred? In this study, a land cover map from a dry semi-arid savanna ecosystem in Namibia was obtained based on the UN LCCS, in-situ data, and MODIS and Landsat satellite imagery. In situ botanical relevé samples were used as baseline data for the definition of a standardized LCCS legend. A standard LCCS code for savanna vegetation types is introduced. An object-oriented segmentation of Landsat imagery was used as intermediate stage for downscaling in-situ training data on a coarse MODIS resolution. MODIS time series metrics of the growing season 2004/2005 were used to classify Kalahari vegetation types using a tree-based ensemble classifier (Random Forest). The prevailing Kalahari vegetation types based on LCCS was open broadleaved deciduous shrubland with an herbaceous layer which differs from the class assignments of the global and regional land-cover maps. The separability analysis based on Bhattacharya distance measurements applied on two LCCS levels indicated a relationship of spectral mapping dependencies of annual MODIS time series features due to the thematic detail of the classification scheme. The analysis of LCCS classifiers showed an increased significance of life-form composition and soil conditions to the mapping accuracy. An overall accuracy of 92.48% was achieved. Woody plant associations proved to be most stable due to small omission and commission errors. The case study comprised a first suitability assessment of the LCCS classifier approach for a southern African savanna ecosystem.     

Spatial and temporal variation of soil organic carbon in the North China Plain

The organic carbon, permeability test, grain size, chemical composition, and mineral composition were analyzed for 147 samples collected from the Luan River catchment, Hebei province, China, to quantitatively characterize the effects of land use, climate change, sedimentary environment, mineral composition, and chemical composition on the spatial and temporal variation of soil organic carbon (SOC). The results indicate that there was higher SOC content and stronger variation in the south plain than in the northern low mountain. The effects of land use, climate change, and sedimentary environment on SOC distribution were greater than the effects of mineral composition and chemical composition. The cropping systems in the Luan River catchment resulted in significant difference in SOC concentration between the south plain and north mountain. The precipitation mainly transmitted its effects through the sedimentary environment to SOC, which caused the stronger temporal variation in SOC from June to October in the south plain. The north mountain did not have significant temporal variation because of the lower hydraulic conductivity of the sedimentary sequence. The spatial variation of SOC was correlated with land use, and their temporal variation was attributed to climate change and sedimentary environment. Apart from land use, the decision maker can also affect the organic carbon mineral and sequence through the sedimentary environment.     

Spatial and temporal variation of moisture content in the soil profiles of two different agricultural fields of semi-arid region

Modeling spatio-temporal variation of soil moisture with depth in the soil profile plays an important role for semi-arid crop production from an agro-hydrological perspective. This study was performed in Guvenc Catchment. Two soil series that were called Tabyabayir (TaS) and Kervanpinari (KeS) and classified as Leptosol and Vertisol Soil Groups were used in this research. The TeS has a much shallower (0–34 cm) than the KeS (0–134 cm). At every sampling time, a total of geo-referenced 100 soil moisture samples were taken based on horizon depths. The results indicated that soil moisture content changed spatially and temporally with soil texture and profile depth significantly. In addition, land use was to be important factor when soil was shallow. When the soil conditions were towards to dry, higher values for the coefficient of variation (CV) were observed for TaS (58 and 43 % for A and C horizons, respectively); however, the profile CV values were rather stable at the KeS. Spatial variability range of TaS was always higher at both dry and wet soil conditions when compared to that of KeS. Excessive drying of soil prevented to describe any spatial model for surface horizon, additionally resulting in a high nugget variance in the subsurface horizon for the TaS. On the contrary to TaS, distribution maps were formed all horizons for the KeS at any measurement times. These maps, depicting both dry and wet soil conditions through the profile depth, are highly expected to reduce the uncertainty associated with spatially and temporally determining the hydraulic responses of the catchment soils.     

Estimation of soil specific surface area using some mechanical properties of soil by artificial neural networks

Soil specific surface area (SSA) is an important property of soil. Depending on the measurement techniques, determination of the SSA is costly and time consuming. Hence, a limited number of studies have been conducted to predict the SSA from the soil variables. In this study, the soil samples were taken from the literature. Fractal parameters (FP) were calculated by the model of Bird et al. (European Journal of Soil Science 51, 55–63, 2000) used as the input variables to predict the SSA. Some studies have been carried out on the prediction capability of the different parameters using the artificial neural networks (ANNs). The ANNs were further used and 20 models were developed to investigate the value of input variables to predict the SSA. The results showed that the PTF13 (RMSE?=?0.13) and PTF18 (RMSE?=?0.13) with the input variables of particle-size distribution and Atterberg limits revealed better performance than the other PTFs (in the training step). It is because of the fact that free swelling index (FSI) and Atterberg limits were closely correlated to the soil clay mineralogy as one of the important factors controlling the SSA. In general, this results demonstrated that the PTF9 with the variables of sand, clay, plastic limit (PL), liquid limit (LL), and FSI showed the best (RMSE?=?0.37) results in the estimation of the SSA. In conclusion, there was not a strong correlation between the soil mechanical properties and SSA but also ANNs were a suitable method to predict the SSA from the soil variables.     

The effectiveness of digital soil mapping to predict soil properties over low-relief areas

This study investigates the ability of different digital soil mapping (DSM) approaches to predict some of physical and chemical topsoil properties in the Shahrekord plain of Chaharmahal-Va-Bakhtiari province, Iran. According to a semi-detailed soil survey, 120 soil samples were collected from 0 to 30 cm depth with approximate distance of 750 m. Particle size distribution, coarse fragments (CFs), electrical conductivity (EC), pH, organic carbon (OC), and calcium carbonate equivalent (CCE) were determined. Four machine learning techniques, namely, artificial neural networks (ANNs), boosted regression tree (BRT), generalized linear model (GLM), and multiple linear regression (MLR), were used to identify the relationship between soil properties and auxiliary information (terrain attributes, remote sensing indices, geology map, existing soil map, and geomorphology map). Root-mean-square error (RMSE) and mean error (ME) were considered to determine the performance of the models. Among the studied models, GLM showed the highest performance to predict pH, EC, clay, silt, sand, and CCE, whereas the best model is not necessarily able to make accurate estimation. According to RMSE%, DSM has a good efficiency to predict soil properties with low and moderate variabilities. Terrain attributes were the main predictors among different studied auxiliary information. The accuracy of the estimations with more observations is recommended to give a better understanding about the performance of DSM approach over low-relief areas.     

Modeling Runoff Response to Land Cover and Rainfall Spatial Variability in Semi-Arid Watersheds

Hydrologic response is an integrated indicator of watershed condition, and significant changes in land cover may affect the overall health and function of a watershed. This paper describes a procedure for evaluating the effects of land cover change and rainfall spatial variability on watershed response. Two hydrologic models were applied on a small semi-arid watershed; one model is event-based with a one-minute time step (KINEROS), and the second is a continuous model with a daily time step (SWAT). The inputs to the models were derived from Geographic Information System (GIS) theme layers of USGS digital elevation models, the State Soil Geographic Database (STATSGO) and the Landsat-based North American Landscape Characterization classification (NALC) in conjunction with available literature and look up tables. Rainfall data from a network of 10 raingauges and historical stream flow data were used to calibrate runoff depth using the continuous hydrologic model from 1966 to 1974. No calibration was carried out for the event-based model, in which six storms from the same period were used in the calculation of runoff depth and peak runoff. The assumption on which much of this study is based is that land cover change and rainfall spatial variability affect the rainfall-runoff relationships on the watershed. To validate this assumption, simulations were carried out wherein the entire watershed was transformed from the 1972 NALC land cover, which consisted of a mixture of desertscrub and grassland, to a single uniform land cover type such as riparian, forest, oak woodland, mesquite woodland, desertscrub, grassland, urban, agriculture, and barren. This study demonstrates the feasibility of using widely available data sets for parameterizing hydrologic simulation models. The simulation results show that both models were able to characterize the runoff response of the watershed due to changes of land cover.     

Application and evaluation of kriging and cokriging methods on groundwater depth mapping

Groundwater and water resources management play a key role in conserving the sustainable conditions in arid and semi-arid regions. Applying some techniques that can reveal the critical and hot conditions of water resources seem necessary. In this study, kriging and cokriging methods were evaluated for mapping the groundwater depth across a plain in which has experienced different climatic conditions (dry, wet, and normal) and consequently high variations in groundwater depth in a 12 year led in maximum, minimum, and mean depths. During this period groundwater depth has considerable fluctuations. Results obtained from geostatistical analysis showed that groundwater depth varies spatially in different climatic conditions. Furthermore, the calculated RMSE showed that cokriging approach was more accurate than kriging in mapping the groundwater depth though there was not a distinct difference. As a whole, kriging underestimated the real groundwater depth for dry, wet, and normal conditions by 5.5, 2.2, and 5.3%, while cokriging underestimations were 3.3, 2, and 2.2%, respectively; which showed the unbiasedness in estimations. Results implied that in the study area farming and cultivation in dry conditions needs more attention due to higher variability in groundwater depth in short distances compared to the other climate conditions. It is believed that geostatistical approaches are reliable tools for water resources managers and water authorities to allocate groundwater resources in different environmental conditions.     

Assessment of the potential hazards of nitrate contamination in surface and groundwater in a heavily fertilized and intensively cultivated district of India

We made an inventory of nitrate (NO3-N) enrichment in surface and groundwater systems in the Hooghly district of India owing to intensive farming with high fertilizer doses as a function of quantity of fertilizers use, soil characteristics, types of crop grown, depth of groundwater sampling and also N-load in soil profiles. Water samples were collected from different sources at 412 odd sites spread over in 17 blocks of the district along with representative soil profiles. On average, the study area had high clay and NO3-N in soil profiles with an increasing and decreasing trends along depth, respectively. The NO3-N content both in surface and groundwater varied from 0.01 microg mL(-1) to 4.56 microg mL(-1), being well below the threshold limit of 10 microg mL(-1) fixed by WHO for drinking purpose. The content decreased with increasing depth of wells (r = -0.39**) and clay content of soil profiles (r = -0.31**). It, however, increased with increasing rate of fertilizer application (r = 0.72**), NO3-N load in soil profiles (r = 0.85**) and was higher in areas where shallow--rather than deep-rooted crops are grown. Results indicated even under fairly high quantity of fertilizer use, groundwater of the study area is safe for drinking purpose.