Integrated Watershed Management

Integrated Watershed Management

Assessment of Various Kriging Models for Interpolating Soil Moisture Data in the Zagros Forests (Case Study: Shalam Region, Ilam)

Document Type : Original Article

Authors
Nargess Pordel 1
Jaafar Hosseinzadeh 1
Mehdi Heydari 1
Reza Omidipour 2
1 Department of Forest Sciences, Faculty of Agriculture, Ilam University, Ilam, Iran
2 Department of Range and Watershed Management, Faculty of Agriculture, Ilam University, Ilam, Iran
10.22034/iwm.2024.2045354.1185
Abstract
Extended Abstract
Introduction: The Zagros forests, as one of Iran's most important forest ecosystems, play a crucial role in preserving water resources, carbon storage, and biodiversity. Soil moisture in these forests is vital for ecosystem stability, groundwater recharge, vegetation growth, and erosion control. However, accurately measuring soil moisture across this vast and challenging terrain is difficult due to its extent, inaccessibility, and high costs. Consequently, advanced interpolation methods like Kriging have become essential for creating soil moisture maps and managing environmental resources. Nevertheless, selecting the optimal Kriging method under varying topographic and climatic conditions remains a research challenge that requires further investigation.
Materials and Methods: This study involved collecting 60 soil samples from depths of 0 to 15 cm across different forest zones with varying canopy densities (open and closed), in both northern and southern aspects, and at three elevation classes (1750-1850 m, 1850-1950 m, and 1950-2050 m) within the Zagros forests. Various Kriging methods, including Ordinary Kriging, Simple Kriging, and Universal Kriging, were utilized to estimate and map soil moisture distribution across the region. The primary objective of employing these three Kriging methods was to compare and determine the most accurate approach, aiming to achieve precise soil moisture distribution estimates based on limited sampling data and to minimize estimation error. Model accuracy was evaluated using statistical metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²). In this evaluation, models with lower RMSE values were considered more accurate for estimating soil moisture spatial distribution.
Results and Discussion: The results indicated that slope aspect and canopy density significantly influenced soil moisture content (P < 0.05). The highest soil moisture content, approximately 12.46%, was observed in areas with closed canopy and northern slopes, which was significantly greater than that in open canopy areas and southern slopes, suggesting the protective effect of closed canopies in retaining soil moisture. Furthermore, comparison of interpolation models revealed that Simple Kriging with a linear variogram had the best performance on northern slopes, achieving the lowest RMSE value (0.79), indicating the effectiveness of this model in conditions where soil moisture distribution exhibits a linear dependency on spatial location. Conversely, on southern slopes, Ordinary Kriging with an exponential variogram provided higher accuracy, with an RMSE value of 0.44. This finding suggest that in conditions with greater moisture complexity and nonlinear variation, employing an exponential variogram and Ordinary Kriging can yield improved interpolation results.
Conclusion: This study strongly emphasizes the critical importance of selecting the correct variogram models and interpolation techniques, which are specifically suited to the unique characteristics and features of a given region. The results clearly demonstrate that employing a single, fixed interpolation method across all areas does not necessarily yield the most optimal results. The choice of the appropriate interpolation method can have a significant impact on improving the accuracy of soil moisture predictions and estimations. For example, in areas with more uniform variations and relatively stable conditions, simple Kriging with a linear variogram proves to perform better, while in regions with more rapid and significant changes over shorter distances, ordinary Kriging with an exponential variogram results in higher accuracy and better performance. Therefore, it is highly recommended that researchers take into account a combination of various optimal interpolation methods, carefully considering the climatic, topographical, and vegetation characteristics of the region. This methodical approach can greatly assist in reducing estimation errors, improving the precision and reliability of soil moisture maps, and ultimately enhancing the management and preservation of natural resources and forest ecosystems. Additionally, this study underscores the importance of conducting a thorough and precise analysis, along with the careful selection of suitable statistical methods, in order to achieve more accurate predictions of soil moisture conditions, which play an essential role in sustainable natural resource management and environmental conservation, particularly in forested and ecologically sensitive regions.
Keywords
Soil
Geostatistics
Remote Sensing
GIS
Variogram

Subjects

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Integrated Watershed Management
Volume 5, Issue 2 - Serial Number 16
Summer 2025
Pages 113-128

  • Receive Date 11 November 2024
  • Revise Date 09 December 2024
  • Accept Date 26 December 2024