Optimal selection of number and location of rain gauge stations for areal estimation of annual rainfall using a procedure based on inverse distance weighting estimator

2018 ◽  
Vol 16 (3) ◽  
pp. 617-629 ◽  
Author(s):  
M. Shahidi ◽  
M. J. Abedini
Keyword(s):  
Rain Gauge ◽  
Inverse Distance Weighting ◽  
Annual Rainfall ◽  
Optimal Selection ◽  
Distance Weighting ◽  
Inverse Distance ◽  
Selection Of

scholarly journals Interpolation of Small Datasets in the Sandstone Hydrocarbon Reservoirs, Case Study of the Sava Depression, Croatia

Geosciences ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 201 ◽  
Author(s):  
Tomislav Malvić ◽  
Josip Ivšinović ◽  
Josipa Velić ◽  
Rajna Rajić
Keyword(s):  
Inverse Distance Weighting ◽  
Challenging Problem ◽  
Interpolation Methods ◽  
Distance Weighting ◽  
Hydrocarbon Fields ◽  
Northern Croatia ◽  
First Time ◽  
Inverse Distance ◽  
Selection Of

The interpolation of small datasets is challenging problem regarding the selection of interpolation methods and type of datasets. Here, for such analysis, the analysed data was taken in two hydrocarbon fields (“A” and “B”), located in the western part of the Sava Depression (in Northern Croatia). The selected reservoirs “L” (in the “A” Field) and “K” (“B”) are of Lower Pontian (Upper Miocene) age and belong to the Kloštar-Ivanić Formation. Due to strong tectonics, there are numerous tectonic blocks, each sampled with only a few wells. We selected two variables for interpolation—reservoirs permeabilities and injected volumes of field water. The following interpolation methods are described, compared and applied: Nearest Neighbourhood, Natural Neighbour (for the first time in the Sava Depression) and Inverse Distance Weighting. The last one has been recommended as the most appropriate in this study. Also, the presented research can be repeated in similar clastic environments at the same level hydrocarbon of exploration.

scholarly journals Distance in spatial interpolation of daily rain gauge data

2006 ◽  
Vol 10 (2) ◽  
pp. 197-208 ◽  
Author(s):  
B. Ahrens
Keyword(s):  
Spatial Interpolation ◽  
Rain Gauge ◽  
Inverse Distance Weighting ◽  
Mountainous Terrain ◽  
Geographical Distance ◽  
Rain Gauge Data ◽  
Distance Weighting ◽  
Gauge Data ◽  
Statistical Distance ◽  
Inverse Distance

Abstract. Spatial interpolation of rain gauge data is important in forcing of hydrological simulations or evaluation of weather predictions, for example. This paper investigates the application of statistical distance, like one minus common variance of observation time series, between data sites instead of geographical distance in interpolation. Here, as a typical representative of interpolation methods the inverse distance weighting interpolation is applied and the test data is daily precipitation observed in Austria. Choosing statistical distance instead of geographical distance in interpolation of available coarse network observations to sites of a denser network, which is not reporting for the interpolation date, yields more robust interpolation results. The most distinct performance enhancement is in or close to mountainous terrain. Therefore, application of statistical distance in the inverse distance weighting interpolation or in similar methods can parsimoniously densify the currently available observation network. Additionally, the success further motivates search for conceptual rain-orography interaction models as components of spatial rain interpolation algorithms in mountainous terrain.

scholarly journals A Multimethod Analysis for Average Annual Precipitation Mapping in the Khorasan Razavi Province (Northeastern Iran)

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 592
Author(s):  
Mehdi Aalijahan ◽  
Azra Khosravichenar
Keyword(s):  
Regional Planning ◽  
Interpolation Method ◽  
Absolute Error ◽  
Rain Gauge ◽  
Annual Precipitation ◽  
Coefficient Of Determination ◽  
Distance Weighting ◽  
Average Annual Precipitation ◽  
Inverse Distance

The spatial distribution of precipitation is one of the most important climatic variables used in geographic and environmental studies. However, when there is a lack of full coverage of meteorological stations, precipitation estimations are necessary to interpolate precipitation for larger areas. The purpose of this research was to find the best interpolation method for precipitation mapping in the partly densely populated Khorasan Razavi province of northeastern Iran. To achieve this, we compared five methods by applying average precipitation data from 97 rain gauge stations in that province for a period of 20 years (1994–2014): Inverse Distance Weighting, Radial Basis Functions (Completely Regularized Spline, Spline with Tension, Multiquadric, Inverse Multiquadric, Thin Plate Spline), Kriging (Simple, Ordinary, Universal), Co-Kriging (Simple, Ordinary, Universal) with an auxiliary elevation parameter, and non-linear Regression. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2) were used to determine the best-performing method of precipitation interpolation. Our study shows that Ordinary Co-Kriging with an auxiliary elevation parameter was the best method for determining the distribution of annual precipitation for this region, showing the highest coefficient of determination of 0.46% between estimated and observed values. Therefore, the application of this method of precipitation mapping would form a mandatory base for regional planning and policy making in the arid to semi-arid Khorasan Razavi province during the future.

scholarly journals Evaluation of an inverse distance weighting method for patching daily and dekadal rainfall over the Free State Province, South Africa

Water SA ◽  
2016 ◽  
Vol 42 (3) ◽  
pp. 466 ◽  
Author(s):  
Mokhele Edmond Moeletsi ◽  
Zakhele Phumlani Shabalala ◽  
Gert De Nysschen ◽  
Sue Walker
Keyword(s):  
South Africa ◽  
Inverse Distance Weighting ◽  
Free State ◽  
Weighting Method ◽  
Free State Province ◽  
Distance Weighting ◽  
Inverse Distance

Spatial interpolation of climatic variables using land surface temperature and modified inverse distance weighting

2015 ◽  
Vol 36 (4) ◽  
pp. 1000-1025 ◽  
Author(s):  
Emre Ozelkan ◽  
Serdar Bagis ◽  
Ertunga Cem Ozelkan ◽  
Burak Berk Ustundag ◽  
Meric Yucel ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Spatial Interpolation ◽  
Inverse Distance Weighting ◽  
Climatic Variables ◽  
Distance Weighting ◽  
Inverse Distance

ÁP DỤNG PHƯƠNG PHÁP NỘI SUY NGHỊCH ĐẢO KHOẢNG CÁCH CÓ TRỌNG SỐ ĐÁNH GIÁ PHÂN BỐ KHÔNG GIAN TÀI SẢN SINH KẾ TẠI TỈNH ĐỒNG THÁP

2020 ◽  
Vol 17 (12) ◽  
pp. 2229
Author(s):  
Huỳnh Song Nhựt ◽  
Nguyễn An Bình ◽  
Nguyễn Ngọc Ẩn ◽  
Trần Anh Phương ◽  
Phạm Việt Hòa ◽  
...  
Keyword(s):  
Inverse Distance Weighting ◽  
Asset Index ◽  
Distance Weighting ◽  
Inverse Distance

  Việc tính toán các chỉ số sinh kế góp phần nắm bắt sự khác biệt về sinh kế của các hộ nông dân trên một khu vực nghiên cứu nhất định. Tuy nhiên, công tác điều tra sinh kế sẽ bị giới hạn bởi nhiều yếu tố như chi phí, nhân công, khoảng cách khiến cho các điểm điều tra không thể bao trọn cả vùng nghiên cứu. Các phương pháp thống kê không gian mà cụ thể là phương pháp nội suy cho phép tính toán giá trị tại một vị trí thông qua các giá trị tại những vị trí đã biết bao quanh nó. Nghiên cứu áp dụng phương pháp IDW (Inverse Distance Weighting) để tính toán chỉ số tài sản sinh kế LAI (Livelihood Asset Index) cho toàn bộ khu vực gồm 3 huyện Tam Nông, Tháp Mười và Tân Hồng. Kết quả cho thấy, có sự phân bố không đồng đều về các nguồn vốn và chỉ số tài sản sinh kế giữa các xã cũng như các huyện trong khu vực nghiên cứul; đồng thời, còn chứng minh rằng, phương pháp IDW là một công cụ hữu hiệu trong thống kê không gian với độ chính xác cao. Hơn nữa, kết quả của nghiên cứu có thể được dùng để đánh giá hiện trạng sinh kế, góp phần tạo sự liên kết giữa các vùng trong khu vực nghiên cứu và hướng đến phát triển bền vững.

Static Reservoir Modeling Comparing Inverse Distance Weighting to Kriging Interpolation Algorithm in Volumetric Estimation. Case Study: Gullfaks Field

2021 ◽  
Author(s):  
Daniel Asante Otchere ◽  
David Hodgetts ◽  
Tarek Arbi Omar Ganat ◽  
Najeeb Ullah ◽  
Alidu Rashid
Keyword(s):  
Power Index ◽  
Inverse Distance Weighting ◽  
Kriging Interpolation ◽  
Reservoir Modeling ◽  
Interpolation Algorithm ◽  
Significant Difference ◽  
Distance Weighting ◽  
Porosity And Permeability ◽  
Well Data ◽  
Inverse Distance

Abstract Understanding and characterizing the behaviour of the subsurface by combining it with a suitable statistical method gives a higher level of confidence in the reservoir model produced. Interpolation of porosity and permeability data with minimum error and high accuracy is, therefore, essential in reservoir modeling. The most widely used interpolation algorithm, kriging, with enough well data is the best linear unbiased estimator. This research sought to compare the applicability and competitiveness of inverse distance weighting (IDW) method using power index of 1, 2 and 4 to kriging when there is sparse data, due to time and budget constraints, to calculate hydrocarbon volumes in a fluvial-deltaic reservoir. Interpolation results, estimated from descriptive statistics, were insignificant and showed similar prediction accuracy and consistency but IDW with power index of 1 indicated the least error estimation and higher accuracy. The assessment of hydrocarbon volume calculations also showed a marginal difference below 0.08 between IDW power index of 1 and kriging in the reservoir zones. Reservoir segments cross-validation and correlation analysis results indicate IDW to have no significant difference to kriging with absolute errors of 3% for recoverable oil and 0.7% for recoverable gas. Grid upscaling, which usually causes a loss of geological features and extreme porosity values, did not impact the results but rather complemented the robustness of IDW in both fine and coarse grid upscale. With IDW exhibiting least errors and higher accuracy, the volumetric and statistical results confirm that when there are fewer well data in a fluvial-deltaic reservoir, the suitable spatial interpolation choice should be IDW method with a power index of 1.

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