Spherical Variogram Research Articles

A multimethod interpolation approach for mapping the spatial distribution of rainfall in southwest Iberian Peninsula

ABSTRACT Eight spatial interpolation models were used to map the spatial distribution of precipitation in the southwestern sector of the Iberian Peninsula (22330 km2) over 40 years (1980/1981–2019/2020). Rainfall data from 103 meteorological stations were used to generate the interpolation models, namely inverse distance weight (IDW) with 6, 12 and 24 points, regression spline (RS), thin spline (TS), universal kriging with spherical and Gaussian variogram (UK_Sphe and UK_gauss, respectively) and multilinear regression (MR), based on physiographic and geographic variables. Furthermore, 32 rainfall stations were used to assess the performance of the previous methods through 7 statistical metrics Ordinary Least Squares (OLS), Root Mean Square Error (RMSE), Normalized root mean square error (NRMSE), Coefficient of determination (R2), Nash-Sutcliffe efficiency coefficient (NSE), Mean Absolute Error (Bias and MAE). Based on these metrics, UK_gauss and IDW_6 provided the best adjustments, whereas MR presented the highest errors. All methods were suitable to predict the spatial distribution of rainfall, but adjustments are conditioned by the features of the study area, gauge density and gauge spatial distribution.

Salinity distribution in agricultural land by geophysical, hydrochemical and geostatistical approaches: a pilot area located in Qelabshowah–Belqas, East Nile Delta region, Egypt

The study area is situated in the Qelabshowah–Belqas region, known for its Quaternary deposits. This research aims to demonstrate the two-dimensional (2D) variation of subsurface layers and salinity distribution using geoelectrical data, hydrochemical analysis, and geostatistical analysis. DC resistivity measurements were taken at fifteen vertical electrical sounding (VES) survey points using a Schlumberger array (AB/2 = 100 m) along three profiles. In addition, an electrical resistivity tomography (ERT) survey was conducted with a dipole–dipole array across one profile. Seven surface water samples were collected in the area. From the 1D and 2D inversion of VES and ERT data sets, three-to-four geoelectric layers were identified, including unconsolidated surface deposits, saturated clayey sand, saturated sand, and a salt-rich layer. The 2D inversion of VES data revealed an ancient salt-rich layer deposited in swampy conditions over a conductive wet sand layer along profile one due to salt mineral infiltration and dissolution. The 2D inversion of ERT data showed accurate lateral geometric accuracy compared to the 2D inversion of VES data, highlighting geological features, such as caves in the second layer and a buried water canal on the ground surface. Surface water samples showed high salinity levels with sodium hazards, indicating an Na–Cl composition. Geoelectric and hydrochemical data sets were geostatistically analyzed using spherical variogram supported ordinary Kriging interpolation. The analysis indicated weak to moderate spatial dependency for true resistivity parameters, while sodium content (SC) and permeability index (PI) showed strong spatial correlation. The 2D spatial distribution resistivity maps based on the 1D inversion of VES data displayed a general decrease in resistivity with depth, likely due to clay minerals or moist soil in the second layer and saline irrigation water infiltration in the third layer. The 2D spatial distribution of SC and PI showed a high concentration zone, posing a potential risk to agricultural crops regardless of soil permeability. It is recommended to use these maps when cultivating plants that can tolerate high sodium levels during the reclamation process.

Enhancing Precision in Magnetic Map Interpolation for Regions with Sparse Data

The high-precision magnetic anomaly reference map is a prerequisite for magnetic navigation and magnetic target detection. However, it is difficult to reflect the detailed characteristics of magnetic anomaly changes by using conventional data interpolation and reconstruction in the areas where magnetic anomaly gradients vary drastically and the distribution of magnetic survey lines is sparse. To solve the problem, an improved variogram of the Kriging interpolation method is proposed to improve the spatial resolution of magnetic anomaly feature. This method selects the spherical variogram model and uses the third power of the lag distance to fit the trend of magnetic anomalies. Meanwhile, the second power of the lag distance is introduced to solve the problem of under-fitting between the lag distance and the value of the variation function near the origin of the sparse variogram graph of measured data. Hyperparameter λ is introduced to compensate for the unbalance caused by the introduction of quadratic lag in the spherical variogram model. The results of several sets of simulated and measured data show that the interpolation accuracy of the proposed method is improved by 30–50% compared with the traditional Gaussian, spherical, and exponential models in the region where the magnetic anomaly gradient changes drastically, and the proposed model provides an effective way to build a high-precision magnetic anomaly reference map of the complex magnetic background under the condition of sparse survey lines.

Mapping soil property variability in oil palm (Elaeis guineensis jacq.) plantationsof Krishna basin of Andhra Pradesh through geospatial technologies

Sixty seven soil samples were collected from oil palm growing regions of NTR District of Andhra Pradesh and were analysed for different soil properties. The data was subjected to descriptive analysis to find out the variability and then to geostatistical analysis to design best fit models having minimal error for developing prediction maps. Highest variation was observed in exch. Mg and the lowest in pH. The mean values of soil pH, EC (dS/m), OC (g/kg), Olsen-P (mg/kg), NH4OAc-K (mg/kg), Exch. Ca (mg/kg), Exch. Mg (mg/kg), CaCl2-S (mg/kg) and HWB (mg/kg) were 7.32 ± 0.08, 0.25 ± 0.02, 0.87 ± 0.03, 101.47 ± 6.95, 566.14 ± 42.97, 4.72 ± 0.24, 2.46 ± 0.27, 60.86 ± 2.6 and 5.98 ± 0.25 respectively in the surface layer (0-20 cm) of the soil. Geostatistical analysis revealed wider spatial variability in surface soil properties and they had circular, spherical, stable and exponential best fit semi variogram models for evaluating dependency. The wide spatial variability of soil properties warrants site specific nutrient management for higher oil palm production. Nutrient distribution maps could be developed through kriging interpolation to interpolate the nutrient levels in unsampled areas.

Fertilization Mapping Based on the Soil Properties of Paddy Fields in Korea

The purpose of this study was to construct a map of expected fertilization rates for nitrogen (N) and phosphorus (P2O5) based on measurements of components in soil samples and to identify the spatial variabilities of four lots of a salt-affected paddy field in Korea. Four salt-affected paddy field lots in Korea were divided into 30 sectors for collecting soil samples. They were then analyzed for soil organic matter (SOM), silicon dioxide (SiO2), total nitrogen (TN), and available phosphorus (Av.P2O5) in accordance with international standards. Expected fertilization rates of N and P2O5 were developed as prescription standards for the application of fertilizer to paddy fields. They were derived using a model of the fertilization rates of N and P2O5. To determine the presence of spatial correlation and continuity in the given fields, a spherical variogram was used. Based on the spherical model with the application of a regular kriging interpolation, maps of the contents of TN and Av.P2O5 as well as the expected fertilization rates of N and P2O5 at each sector of 1×1 m2 were developed. The expected fertilization rate of N at each sector appeared in the range of min. 10.0 g to max. 25.7 g, while that of P2O5 appeared in the range of min. 0.68 g to max. 8.46 g.

Evaluation and optimization of hydrometric locations using entropy theory and Bat algorithm (case study: Karkheh Basin, Iran)

Hydrometric stations are important in most countries because of the application and importance of the data obtained from these stations. It is necessary to choose the best place for their establishment according to the cost of constructing hydrometric stations. The aim and innovation of this research are to optimize the location of hydrometric stations using Bat's meta-heuristic algorithm and interpolation methods, which information transfer entropy theory and Bat's algorithm were used to maximize the average amount of information transfer entropy. For this purpose, the data of 43 hydrometric stations of Karkheh basin in western Iran in period of 1991–2015 were used. In this research, two scenarios were investigated in order to improve the entropy of information transmission between stations. In the first scenario, using the kriging method to prepare the flow distribution map in the region and choosing normal kriging with spherical variogram as the best model to fit the average annual flow data and using the Bat algorithm to increase the correlation coefficient between the data and assuming no none of the available stations, 43 points were used to redeploy stations with higher average entropy in the region. The results of this scenario showed the concentration of new stations in the central and eastern areas of the basin. In the second scenario, the amount of entropy of information transfer at the regional level was calculated and 18 potential points were recommended for the establishment of new stations. The obtained variogram for the discharge of the basin showed that the range of influence is low and it is necessary to establish the stations at a close distance.

Assessment of the Spatial Variability and Uncertainty of Shreddable Pruning Biomass in an Olive Grove Based on Canopy Volume and Tree Projected Area

Olive pruning residues are a by-product that can be applied to soil or used for energy production in a circular economy model. Its benefits depend on the amount of pruning, which varies greatly within farms. This study aimed to investigate the spatial variability of shreddable olive pruning in a traditional olive grove in Córdoba (Spain) with an area of 15 ha and trees distanced 12.5 m from each other. To model the spatial variability of shreddable olive pruning, geostatistical methods of stochastic simulation were applied to three correlated variables measured on sampled trees: the crown projected area (n = 928 trees), the crown volume (n = 167) and the amount of shreddable pruning (n = 59). Pearson’s correlation between pairs of variables varied from 0.71 to 0.76. The amount of pruning showed great variability, ranging from 7.6 to 76 kg tree−1, with a mean value of 37 kg tree−1. Using exponential and spherical variogram models, the spatial continuity of the variables under study was established. Shreddable dry pruning weight values showed spatial autocorrelation up to 180 m. The spatial uncertainty of the estimation was obtained using sequential simulation algorithms. Stochastic simulation algorithms provided 150 possible images of the amount of shreddable pruning on the farm, using tree projected area and crown volume as secondary information. The interquartile range and 90% prediction interval were used as indicators of the uncertainty around the mean value. Uncertainty validation was performed using accuracy plots and the associated G-statistic. Results indicate with high confidence (i.e., low uncertainty) that shreddable dry pruning weight in the mid-western area of the farm will be much lower than the rest of the farm. In the same way, results show with high confidence that dry pruning weight will be much higher in a small area in the middle east of the farm. The values of the G-statistic ranged between 0.89 and 0.90 in the tests performed. The joint use of crown volume and projected areas is valuable in estimating the spatial variability of the amount of pruning. The study shows that the use of prediction intervals enables the evaluation of farm areas and informed management decisions with a low level of risk. The methodology proposed in this work can be extrapolated to other 3D crops without requiring modifications. On a larger scale, it can be useful for predicting optimal locations for biomass plants, areas with high potential as carbon sinks or areas requiring special soil protection measures.

Spatial variability of the granulometric composition within arable field on soddy-podzolic soil

T e spatial variability of the content of granulometric fraction d>0,25 mm in the arable layer of soddy-podzolic cultivated soil on an area of 18 hectares was studied. T e soil formed in the loess-like loam, underlain by moraine deposits. Samples were taken from the upper (0-10 cm) and lower parts of the arable layer (10-20 cm) following a random-stratif ed sampling scheme. T e total number of samples was 350 cores. T e average fraction content was about 22%, the coef cient of variation was 39-41%, the distributions were approximated by a logarithmically normal distribution. T e Spearman correlation coef cient between values at dif erent depths equaled to 0,87. T e spatial distribution cartograms were constructed by the ordinary kriging method using a spherical variogram model. It is shown that preliminary censoring of high sample values (quantile 0,95) gives better results when constructing a cartogram than removing a linear spatial trend and logarithm of the original data. Spatial structures with reduced and increased values were found on the site, the average linear sizes of which was about 100 m. Presumably, they were associated with the heterogeneity of soil-forming material.

Major Changes in Spatiotemporal Trends of US Rotavirus Laboratory Detections After Rotavirus Vaccine Introduction-2009-2021.

For the 15 years before rotavirus vaccine introduction in 2006, annual rotavirus activity in the United States showed a distinct spatiotemporal pattern, peaking first in the Southwest and last in the Northeast. We modeled spatiotemporal trends in rotavirus laboratory detections from 2009 to 2021. Laboratories reporting to the National Respiratory and Enteric Virus Surveillance System were eligible for inclusion in a given surveillance year (July to June) if ≥1 polymerase chain reaction or enzyme immunoassay rotavirus test per week was reported during ≥26 weeks and totaling ≥100 annual tests. For each laboratory, the season peak was the week with the highest 7-week moving average of the number of rotavirus positive tests during the national season, defined as the period with a 3-week moving average of >10% rotavirus positivity lasting ≥2 consecutive weeks. We input peak week as a continuous variable and the geospatial coordinates of each laboratory into a spherical variogram model for Kriging spatial interpolation. We also created a state-level bivariate choropleth map using tertiles of the 2010-2019 average birth rates and rotavirus vaccine coverage. Following the established biennial trend, the 2010-2011, 2012-2013, 2014-2015, 2016-2017, and 2018-2019 surveillance years had >10% rotavirus positivity for ≥2 weeks and were included in the geospatial analysis. During all 5 seasons included in the geospatial analysis, the earliest peak week occurred in Oklahoma, Arkansas, and the western Gulf coast, a pattern markedly different from prevaccine seasons. These states also had the average lowest rotavirus vaccine coverage and highest birth rate, suggesting that more rapid accumulation of susceptible children drives annual rotavirus season activity. Increasing vaccine coverage remains a key tool in reducing rotavirus burden.

Geospatial variability mapping of soil nutrients for site specific input optimization in a part of central India

AbstractThe ability to define site‐specific variability affects the acceptability of precision farming. A study was conducted to determine the soil nutrient spatial variability in the Nagpur Rural block of Nagpur district, Maharashtra, India. A total of 1707 surface soil samples (0‐to‐15‐cm soil depth) were collected from a 65,386‐ha region. Soil samples were analyzed for pH, electrical conductivity (EC), soil organic carbon (SOC), and available macronutrients (N, P, K, and S) and micronutrients (Fe, Mn, Cu, and Zn). Geostatistical modeling (kriging) was run on soil geodatabase to obtain the best‐fit experimental semivariogram having the lowest RMSE. Exponential (for pH, SOC, and N) and spherical (for P, K, and S) variogram model of ordinary kriging type as well as exponential variogram of simple kriging (for EC) were found to be best fitted to the soil parameters studied. The spatial distribution of maps showed deficiency of available N, P, and Zn, whereas soil test K, Mn, and Cu was high in most of the study area. Spatial dependence was moderate for all soil fertility parameters (N/S ratio 0.25–0.75), whereas soil test P exhibited strong spatial dependency (N/S ratio 0.19). The soil fertility maps developed through geostatistical techniques can be successfully used for managing site‐specific nutrients, enhancing fertilizer use efficiency, minimizing cost of cultivation, improving socioeconomic status of farmers, and increasing farm income without adverse effects on soil environment.

PEMODELAN GEOSTATISTIKA DATA VERTICAL ELECTRICAL SOUNDING UNTUK MENENTUKAN DISTRIBUSI AKUIFER AIR TANAH (STUDI KASUS: INSTITUT TEKNOLOGI SUMATERA)

Geostatistical modeling is carried out to determine the spatial correlation of aquifer distribution with Vertical Electrical Sounding data. Geostatistical modeling is done using variogram analysis. Data Vertical Electrical Sounding used 24 point soundings using the Schlumberger configuration. The length of the VES measurement range is varied, with a minimum AB/2 of 2 m and a maximum AB/2 of 150 m and MN/2 changes made three times (0.5, 2, 10 m). VES data processing results are correlated with binding data in the form of Well Logging (resistivity log and SP logs). Based on the results of VES data processing and Well Logging, it was found that the ITERA campus has 2 aquifer layers, namely unconfined aquifers with resistivity values>80 μm which are suspected as tuff and confined aquifers with resistivity values of 20-80 80m which is thought to be the tuffaceous sandstone. In the geostatistical modeling, a model that is suitable for the distribution of VES data is obtained, which is a spherical variogram model with a 500 m lag. The results are used for gridding the iso resistivity map with kriging variogram correlated with the 3D model, it is obtained that the aquifer distribution in the western part is close to the surface (shallow) with a fairly thick layer that is around ± 48,865 m and increasingly towards the east the aquifer layer gets deeper and tend to thin out.
 Keyword : Aquifer, VES, Geostatistics, Well Logging, ITERA

Use of convolutional neural networks with encoder-decoder structure for predicting the inverse operator in hydraulic tomography

In this manuscript, we discuss the capabilities of a deep learning algorithm implemented with the Conventional Neural Network concept to characterize the hydraulic properties of aquifers. The algorithm called CNN-HT is designed to predict the inverse operator of hydraulic tomography using a synthetic training dataset in which the hydraulic head data associated with pumping tests are linked to hydraulic transmissivity field. This approach relies on an adaptation of the SegNet network that was initially developed to process image segmentation. The SegNet is composed of encoders and decoders networks. In the encoder, sequential operations with multiple filters, as convolution, batch normalization, max-pooling are performed to identify feature maps of the input data. In the decoder, the up-sampling, convolution, batch normalization and regression operations are used to prepare the output by recovering the loss of spatial resolution that occurred in the encoder process. In this adaptation, we used the least-square iterative formulation at the initial iteration with Jacobian matrix to resize the hydraulic head data to match the size of the output (transmissivity field). This protocol was applied to the hydraulic head data computed numerically by solving the groundwater flow equation for a given transmissivity field, generated geostatistically with Gaussian and spherical variograms. A part of this data was used for training the network and the other part to test its performance. The test step confirmed the effectiveness of this tool in reconstructing the main heterogeneities of the hydraulic properties, and its effectiveness is related to the nature and quantity of the training data. Moreover, the CNN-HT method provided inversion results of the same quality than those obtained with the Gauss-Newton algorithm using the finite difference or adjoint state method in the computation of the Jacobian matrix. However, the computational time is longer in CNN-HT but this time can be less or of the same order as that of Gauss-Newton using finite difference method.

Optimal prediction in isotropic spatial process under spherical type variogram model with application to corn plant data

A central problem in modelling of spatial data is the construction of an optimal prediction map of a numerical quantity or variable under study. In geostatistics this study is commonly called universal kriging. In this paper we study weighted universal kriging method in which a weight function is used for improving the efficiency of the predictor. We consider isotropic spatial process under a variogram function belongs to the parametric family of spherical type. The parameters of the postulated variogram model are estimated by applying ordinary least squares method in that the squared distance between the variogram model and the associated variogram sample is minimized. Numerical approximation for finding the solution of the least squares equations is conducted by using graphical approach. The application of the method to a corn plant data results in the kriging map of the maximum height that can be achieved by the corn plants planted over a rectangular farm land. This investigation result can in advance describe the map of the fertility level of the farm land where the corn have been planted.

Spatial analysis of harsh driving behavior events in urban networks using high-resolution smartphone and geometric data

The aim of the present study is to conduct spatial analysis of harsh events of driving behavior across road segments of an urban road network. The adopted approach involved automating the segment characteristic extraction process for the urban network study area. Subsequently, naturalistic driving big data from an innovative smartphone application were map-matched to the segments that each driver traversed, and thus geometrical, road network and driver behavior spatial data frames were obtained per road segment. Global and local Moran's I coefficients were calculated based on a nearest-neighbour scheme, and indicated the presence of a certain degree of positive spatial autocorrelation both for harsh brakings (HBs) and for harsh accelerations (HAs). Furthermore, the creation of empirical and theoretical spherical variograms indicated that on average, about 190 m from each road segment centroid there is no observable spatial autocorrelation for HBs; the respective distance is 200 m for HAs. Geographically Weighted Poisson Regression (GWPR) models were used to model harsh event frequencies. Segment length and pass count are positively correlated with HB frequencies, while gradient and neighbourhood complexity are negatively correlated with HB frequencies. Curvature, segment length, pass count and the presence of traffic lights are positively correlated with HA frequencies. Road type and lane number were found to have a more circumstantial effect overall.

Geological Modeling for Nahr Umr Formation in Subba Oil Field, Southern Iraq

Geological modeling is very important in reservoir study, where it is described the framework of the structure and provided a distribution of petrophysical properties, the geological model helps to comprehend the fluid flow behavior affected by the heterogeneity of reservoir, also it is used to calculate oil initially in place and to choose the new well location in reservoir management. In this study, a geological model is built for Nahr Umr Formation based on data of 11 wells (SU-2, SU-3, SU-4, SU-5, SU-6, SU-7, SU-9, SU-10, SU-11, SU-13, and SU-14), including the location of wells, well tops, also contour map of the Nahr Umr Formation in Subba oil field is utilized. The structural model is constructed for the Nahr Umr Formation, which consists of two domes. The formation is composed of sandstone mainly interbedded with shale. The formation is divided into four units (B1, B2, C1, and C2) depending on well log data, each unit is divided into layers for getting better distribution of petrophysical properties, where unit B2 is divided to the number of layers is higher than other units due to its heterogeneity. The variogram is used as a geostatistical method to predict the petrophysical properties at unsampled locations, experimental variogram is calculated from well log data in a horizontal and vertical direction. The spherical variogram model shows a good fitting with the experimental variogram. Sequential Gaussian algorithm is used in distribution of petrophysical properties in a geological model which shows good petrophysical properties in unit B2 and unit B1, while units C1 and C2 are highly saturated with water.

Geostatistical features of streambed vertical hydraulic conductivities in Frenchman Creek Watershed in Western Nebraska

AbstractThis study evaluated the spatial variability of streambed vertical hydraulic conductivity (Kv) in different stream morphologies in the Frenchman Creek Watershed, Western Nebraska, using different variogram models. Streambed Kv values were determined in situ using permeameter tests at 10 sites in Frenchman, Stinking Water and Spring Creeks during the dry season at baseflow conditions. Measurements were taken both in straight and meandering stream channels during a 5 day period at similar flow conditions. Each test site comprised of at least three transects and each transect comprised of at least three Kv measurements. Linear, Gaussian, exponential and spherical variogram models were used with Kriging gridding method for the 10 sites. As a goodness‐of‐fit statistic for the variogram models, cross‐validation results showed differences in the median absolute deviation and the standard deviation of the cross‐validation residuals. Results show that using the geometric means of the 10 sites for gridding performs better than using either all the Kv values from the 93 permeameter tests or 10 Kv values from the middle transects and centre permeameters. Incorporating both the spatial variability and the uncertainty involved in the measurement at a reach segment can yield more accurate grid results that can be useful in calibrating Kv at watershed or sub‐watershed scales in distributed hydrological models.

Mapping the spatial variability of the field distribution of sterile Anastrepha ludens over a release area

AbstractThe sterile insect technique (SIT) is an environmental‐friendly method used against Anastrepha ludens Loew (Diptera: Tephritidae) populations. This study aimed to perform an analysis of the spatial variability of the field distribution of sterile A. ludens using a geostatistical approach along with Geographic Information Systems (GIS). Field data on captures of sterile A. ludens during a Valencia orange season over a release area were analysed using spherical, exponential and Gaussian variograms. Such variograms were evaluated by criteria such as the mean absolute error, average standard error, root mean square error and the coefficient of determination. Results revealed a spatially structured distribution of sterile A. ludens across the release area. Interpolated models by Ordinary Kriging technique exhibited continuous surfaces evidencing spatial heterogeneity of the distribution of flies. Such a result evidenced that the spatial dynamics of flies significantly varied despite the planned uniform coverage of the release. The GIS led to integrating spatial information of the spatial dynamics through one single model. The release activity should be improved westward of the studied area, as the final model suggested that the ratio sterile: wild is lower than that in the east. This study provides insights into the spatial analysis of the distribution of sterile flies further than one single geographical point. Moreover, it highlights geostatistical techniques and GIS as useful tools for the assessment of the impact and quality of the release activity over fruit‐growing areas subjected to an area‐wide integrated pest management approach.

Spatio-temporal mapping of salinity in the heterogeneous coastal aquifer

The interface between freshwater and saltwater in the coastal aquifer is a main factor that is affected by environmental threat and excessive pumping. In this study, a density-dependent numerical model was utilized to simulate the salt distribution of a coastal aquifer in the Talar region near the Caspian Sea, Mazandaran, Iran. To simulate the aquifer salinity and head contour over 2-year stress period, SEAWAT model in groundwater modeling system (GMS) package was used. Moreover, different variogram models based on 21 observation points were tested to find a best fit between observed and simulated total dissolved solids (TDS). A genetic algorithm was coupled with GMS model to identify the optimal sill ratio, range, and nugget value of each variogram. The interface between saltwater and freshwater estimated by the optimal variogram model and compared with the result of sharp interface and variable density flow assumption. The result indicates that the optimal Gaussian variogram as best estimation of spatial TDS can be used for estimation of temporal salinity with a coefficient determination about 0.91. Also, an integration of spherical and exponential variogram shows the high performance for mapping spatial TDS and neutralizes the underestimation behavior of single variogram. A comparison between variogram model and analytical solution indicates that the interface length in the eastern part of aquifer for Gaussian model is relatively small than sharp interface relation. The maximum saltwater penetration based on sharp interface and SEAWAT results is about 1600 m and 1750 m, respectively. Additionally, maximum saltwater intrusion is observed in the western part of aquifer which has a large number of active wells.

Development of an Uncertainty Based Model to Predict Land Subsidence Caused by Groundwater Extraction (Case Study: Tehran Basin)

Excessive extraction from groundwater supplies in recent years has caused substantial land subsidence over large area. Therefore, the groundwater exploration from aquifer needs information about maximum subsidence of basin. The aim of this study is to use the ability of both numerical model and data driven algorithms for robust and fast prediction of land subsidence. In this way, the subsidence package in framework of MODFLOW is used to determine effect of groundwater fluctuant on subsidence rate through the Tehran basin. To evaluate the permeability (T) effect on uncertainty simulation model, four realization of T generated using different variogram based on borehole information. Regarding to the high run time for simulation of each realization and evaluate uncertainty of parameters, two type of model tree algorithm was trained with information of deformation rates. The non-dimensional geological and hydrogeological parameters of the aquifer were used to train the model tree. Result of numerical model show that deformation rate generated by spherical variogram show the high correlation (0.96) with low SD value (13.04) between simulated and observed data. Also, pruned M5′ tree model with depth of 7 shows the high value of correlation coefficient (0.965) between simulated and predicted subsidence values.

Adjustment of a spherical variogram of the annual precipitation of the Climatological Normals 1951-2010

En este trabajo se analizo la precipitacion media anual de las normales climatologicas de 1951-2010, para determinar su estructura espacial y para que pueda ser utilizada en el metodo de interpolacion de kriging, que permite no solo estimar los valores de esta variable, sino tambien la varianza de estimacion. Para ello se genero un semivariograma (que en el presente trabajo denominaremos “variograma”) experimental con la precipitacion media anual de 2 003 estaciones con 30 o mas anos de informacion de precipitacion, tomadas de un total de 4 385 estaciones. Al incio, el variograma se construyo con 2 005 003 pares de estaciones (combinaciones de 2 003 estaciones tomadas de dos en dos), que fue preciso depurar. Para este proposito se calculo un “indice de variacion espacial”, igual a la diferencia de precipitacion dividida entre la distancia que separa las estaciones, indice que permitio identificar 20 046 pares atipicos, como aquellos que tenian un valor superior a 9.85. Ademas se consideraron solo los pares de valores con distancia entre estaciones no mayores de 1 900 km. El variograma experimental finalmente obtenido consta de 380 clases a intervalos de 5 km y fue ajustado a un modelo esferico. En este proceso se utilizo el metodo de los multiplicadores de Lagrange, para asegurar continuidad del variograma en el punto donde la parte curva del variograma se encuentra con la parte horizontal del mismo. El mejor variograma esferico tiene un radio de influencia = 1 455 km, un efecto pepita C 0 = 18 082 y umbral C 1 = 602 452.