Estimating effective soil hydraulic properties for largescale is an outstanding challenge in hydrologic modelling. The aim of this study was to provide effective van Genuchten-Mualem hydraulic parameters at localscale through topography-based Upscaling Pedotransfer functions (PTFs) and spectrotransfer functions (STFs, SPTFs). The Zanjanrood sub-watershed with an area about 250 Km2 was selected as the study area for this research. hereby, a topography-based aggregation scheme, the so-called power average operator, PAO, was used to upscale (1Km×1Km) point scale parameters of, , and. With respect to the results, considerable and significant correlations (R>0. 50) were obtained between measured and estimated hydraulic parameters. Upscaled STFs showed the largest and significant correlations with upscaled measurements of (0. 65), (0. 70), (0. 86), while upscaled PTFs performed best for (0. 55). The PAO performed best in the pixels which have the highest topographic index (CTI). The biggest R values were obtained between shape parameters (i. e., and ) andthe standard deviation CTI as well as between and with the average CTI. The ASAR estimated soil moisture values appeared to be significantly (p<0. 005) correlated (-0. 453) with the average CTI. The results of this study indicate that the topography is an important factor in characterization ofeffective soil hydraulic properties at localscale.

A general multiresolution approach is developed and applied to upscaling of multiscale 2D heterogeneous reservoirs. The method uses the wavelet transformations to the original detailed description of the reservoir, with finer resolution introduced in region of potentially high flow rate (high permeability) and coarser, homogenized property descriptions applied throughout the bulk of the model. Wavelet transformations are currently recognized as the most efficient method of data compression. The method is applied to flow problem (steady single-phase flow) and transport problem (miscible displacement process). In this way, pressure is computed on the coarse-grid using upscaled properties. These pressures are then used to compute the pressure at the small scale within each coarse block. Thus, an approximation for the pressure is obtained without ever having to solve the full fine-grid problem, saving CPU time and memory. Finally, velocity field, the most important key component of fluid flow process, is calculated using obtained pressure field. The results of two problems show good agreement with full fine-grid solution.

In this paper, a new method called adaptive bandwidth in the kernel function has been used for two-dimensional upscaling of reservoir data. Bandwidth in the kernel can be considered as a variability parameter in porous media. Given that the variability of the reservoir characteristics depends on the complexity of the system, either in terms of geological structure or the specific feature distribution, variations must be considered differently for upscaling from a fine model to a coarse one. The upscaling algorithm, introduced in this paper, is based on the kernel function bandwidth, written in combination with the A* search algorithm and the first-depth search algorithm. In this algorithm, each cell in its x and y neighborhoods as well as the optimal bandwidth, obtained in two directions will be able to be merged with its adjacent cells. The upscaling process is performed on artificial data with 30×30 grid dimensions and SPE-10 model as real data. Four modes are used to start the point of upscaling and the process is performed according to the desired pattern, and in each case, the upscaling error and the number of final upscaled blocks are obtained. Based on the number of coarsen cells as well as the upscaling error, the first pattern is selected as the optimal pattern for synthetic data and the second pattern is selected as the optimal simulator model for real data. In this model, the number of cells was 236 and 3600, and the upscaling errors for synthetic and real data were 0. 4183 and 12. 2, respectively. The results of the upscaling in the real data were compared with the normalization method and showed that the upscaling error of the normalization method was 15 times the upscaling error of the kernel bandwidth algorithm.

We use a multi-resolution analysis based on a wavelet transform to upscale a 3D fractured reservoir. This paper describes a 3D, single-phase, and black-oil geological model (GM) that is used to simulate naturally-fractured reservoirs. The absolute permeability and porosity of GM is upscaled by all the possible combinations of Haar, Bior1.3, and Db4 wavelets in three levels of coarsening. The applied upscaling method creates a non-uniform computational grid, which preserves its resolved structure in the near-well zones as well as in the high-permeability sectors but the data are scaled up in the other regions. To demonstrate the accuracy and efficiency of the method, the values for the oil production rate, mean reservoir pressure, water cut, and total amount of water production are studied, and their mean error is estimated for the upscaled models. Finally, the optimized model is selected based on the computation time and accuracy value.

Upscaling based on the bandwidth of the kernel function is a flexible approach to upscale the data because the cells will be coarse-based on variability. The intensity of the coarsening of cells in this method can be controlled with bandwidth. In a smooth variability region, a large number of cells will be merged, and vice versa, they will remain fine with severe variability. Bandwidth variation can be effective in upscaling results. Therefore, determining the optimal bandwidth in this method is essential. For each bandwidth, the upscaled model has a number of upscaled blocks and an upscaling error. Obviously, higher thresholds or bandwidths cause a lower number of upscaled blocks and a higher sum of squares error (SSE). On the other hand, using the smallest bandwidth, the upscaled model will remain in a fine scale, and there will be practically no upscaling. In this work, different approaches are used to determine the optimal bandwidth or threshold for upscaling. Investigation of SSE changes, the intersection of two charts, namely SSE and the number of upscaled block charts, and the changes of SSE values versus bandwidths, are among these approaches. In this particular case, if the goal of upscaling is to minimize the upscaling error, the intersection method will obtain a better result. Conversely, if the purpose of upscaling is computational cost reduction, the SSE variation approach will be more appropriate for the threshold setting.

Introduction and Objective: Silt is one the most important constituents of soil texture that directly influence the soil erosion process and should take into account in many projects of soil erosion management and conservation. The study of this fraction using the traditional and prevalent lab methods, especially on large scales, is time-consuming, laborious and costly. Today, this can be done in a quick and cost-effective method applying new high-techs such as the spectroscopy technology. The present work intends to investigate the spectral behaviours of the soil silt fraction using the reflectance spectroscopy technology in Mazandaran province. Material and Methods: Accordingly, 128 soil samples were collected from 20 cm of soil surface using the SRS method and auxiliary info-layers like as geology, pedology, landuse and road map of Mazandaran province. First, the sample set was sub-divided into two subsets: calibration and validation. Spectral signatures and domains specific to the silt components were detected and specified utilizing the PLSR and Cross-Validation techniques, as well, the hyperspectral pre-processing methods such as averaging, smoothing and 1st derivative algorithms based on the Savitzky-Golay Algorithm were done. Results: Modeling process was done based on the PLS technique to investigate the spectral signatures and behaviours of silt constituents. The final model with 4 latent factors (LFs) was calibrated with these specs: Rc: 0.55, RMSEc: 8.31 %, RPDc: 1.20 and RPIQc: 1.71 and was eventually selected as the best model for studying the soil silt of Mazandaran province. Results showed the model potentiality in prediction of soil silt of the study area, as well, the most influential spectral domains and ranges were detected and recognized. The correlation coefficients of silt contents with the influential spectral ranges and wavebands were also defined as follows, UV-390 nm: 0.27, Vis-680 nm: 0.31, NIR-970 to 990 nm: 0.32, SWIR- 1400 to 1410 nm wavebands: 0.34, 1910-1930 nm: 0.38, 2200-2210 nm: 0.39, 2340-2350 nm: 0.41 and finally, for 2430-2460 wavebands calculated as 0.43. The obtained spectral wavebands with the highest correlation coefficients (R(CCmax)) indicate the high impact as the independent predictor variables in the processes of soil silt modeling of Mazandaran province. Finally, the capability of the proximal sensing of diffuse reflectance spectroscopy technology (VNIR-PS) was demonstrated in the study of silt contents of Mazandaran province. Conclusion: In this approach, the spectral ranges and bands affected by the silt components were defined, in addition to the predictive modeling processes. That can be used as a basis for studying silt contents at large scales applying the upscaling operation via airborne/satellite hyperspectral data. Also, it indicates the importance of soil reflectance spectroscopy technology as a fundament for detecting and recognizing the useful and effective spectral wavelengths as well as creating the optimized model for the utilization by remotely sensed satellite data. Moreover, the use of data with higher coefficient of variation and greater amplitude is highly recommended to improve and boost the model preciseness so that, the PLS algorithm can process better.