1موسسه تحقیقات خاک و آب، سازمان تحقیقات، آموزش و ترویج کشاورزی
کرج، ایران
2موسسه تحقیقات خاک و آب کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران
3بخش تحقیقات خاک و آب، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان اصفهان، سازمان تحقیقات، آموزش و ترویج کشاورزی، اصفهان، ایران
4موسسه تحقیقات خاک و آب کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران.
چکیده
تخمین ویژگیهای هیدرولیکی خاک با استفاده از توابع انتقالی و بهرهگیری از ویژگیهای زودیافت خاک به عنوان داده ورودی امکانپذیر و امری متداول در پژوهشهای خاک و آب است. در این پژوهش ویژگیهای هیدرولیکی مدل ونگنوختن در لایههای مختلف پروفیل خاک در دشت سیستان، به منظور دادههای ورودی اولیه در مدیریت آب و ارزیابی شوری، برآورد شد. به این منظور تعداد 312 پروفیل در دشت سیستان حفر و ویژگیهای فیزیکی شامل درصد ذرات شن، رس و سیلت، وزن مخصوص ظاهری و هدایت هیدرولیکی اشباع افقهای مختلف پدوژنیکی هر پروفیل اندازهگیری شد. ویژگیهای هیدرولیکی خاک شامل هدایت هیدرولیکی اشباع (Ks) و پارامترهای مدل ونگنوختن (رطوبت اشباع (sθ)، رطوبت باقیمانده (rθ)، عکس مکش ورود هوا (α) و توزیع ذرات (n)) با کمک بسته نرمافزاری ROSETTA در زبان برنامه نویسی پایتون برآورد شد. بر اساس نتایج در هر سه لایه مورد بررسی بیشترین مقدار انحراف معیار بین ویژگیهای هیدرولیکی برآورد شده برای هدایت هیدرولیکی اشباع بهدست آمد. بیشترین مقدار Ks برآورد شده به طور معنیداری در لایه سوم و پس از لایه دوم و لایه اول بود. مقادیر sθ و rθ برآورد شده با افزایش عمق پروفیل از سطح خاک به طور معنیداری کاهش یافت درحالیکه مقدار n افزایش معنیداری با افزایش عمق از سطح پروفیلها نشان داد. تفاوت معنیداری برای α برآورد شده بین لایههای پروفیلهای مورد مطالعه مشاهده نشد. با توجه به مقادیر RMSD و NSE رابطه رگرسیونی Ks اندازهگیری شده و برآورد شده، عدم قطعیت ROSETTA در برآورد زیاد بوده و احتمالا توسعه روابط انتقالی ویزه منطقه مورد نیاز است.
1Soil and Water Research Institute (SWRI), Agricultural Research, Education and Extension Organization (AREEO),
Karaj, Iran
  
2Soil and Water Research Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran
3Department of Soil and Water Research, Isfahan Agricultural and Natural Resources Research and Training Center, AREEO, Isfahan, Iran.
4Soil and Water Research Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran;
چکیده [English]
Introduction: Estimating soil hydraulic properties using pedotransfer functions is both feasible and commonly practiced, leveraging readily measurable soil properties as input data in soil and water research. These estimated properties are essential for evaluating water and solute movement, as well as for managing water and salinity at various scales. The accurate description and estimation of soil water retention curves have been a primary focus of research for over six decades. However, estimating coefficients in empirical models remains challenging and time-consuming. Consequently, pedotransfer functions (PTFs), including neural network analysis and texture-based tables, have been employed to predict SWRC coefficients. The ROSETTA software package estimates soil hydraulic properties such as Van Genuchten model parameters using a combination of bootstrap and artificial neural networks. Despite the use of pedotransfer functions for predicting soil hydraulic parameters in Iran, no study has specifically examined the application of these functions in estimating hydraulic parameters across different soil profile layers, particularly in salt-affected soils.
Objective: The objective of this study was to evaluate the performance of the ROSETTA pedotransfer functions in estimating the hydraulic parameters of the Van Genuchten model across different layers of soil profiles in the salt-affected soils of the Sistan Plain, assessing these as preliminary input data for water management and salinity evaluation.
Materials and Methods: A total of 312 profiles was excavated in the Sistan Plain. Based on field observations and morphological characteristics (including color, texture, structure, presence of carbonates, gypsum, and roots), pedogenic (genetic) horizons were identified in each profile (ranging from 3 to 5 horizons per 100 cm profile). For statistical analysis and modeling purposes, the identified horizons were grouped into three pedogenic layers: surface (first), middle (second), and subsurface (third). physical soil characteristics, including the sand, clay, and silt content, as well as bulk density and saturated hydraulic conductivity were measured for each profile layer. Soil hydraulic properties, including Ks and the parameters of the van Genuchten model (residual (θr) and saturated (θs) soil water content, inverse of air entry pressure (α) and soil pore distribution (n)) were estimated by ROSETTA software package. In order to evaluate the results, statistical indices of root mean square error (RMSE) and Nash-Sutcliffe coefficient (NSE) were used to compare the measured saturated hydraulic conductivity and saturated moisture with their estimated values.
Results: According to the results, the highest standard deviation among the estimated hydraulic properties occurred for Ks in all three examined layers. The estimated Ks values were significantly highest in the third layer, followed by the second and first layers. In contrast, the estimated θs and θr values showed a significant decrease with increasing depth from the soil surface, while the n parameter showed a significant increase with depth. No significant difference was observed for α between the profile layers. A significant correlation was obtained between the estimated hydraulic properties and the measured soil properties, including bulk density, and the percentages of sand, silt, and clay. The measured values of saturated water content (θs), field capacity (FC), permanent wilting point (PWP), and plant available water (PAW) showed positive and significant correlations with clay and silt content. There was no significant correlation between the measured and estimated Ks values. Furthermore, the RMSD and NSE values from the regression relationship between measured and estimated Ks were 16.41 and -0.11, respectively. The RMSD and NSE values for the regression relationship between measured and estimated saturated water content were 0.06 and -1.11, respectively. The negative NSE values indicate that the mean of the measured data provides better predictions than the ROSETTA estimates, confirming the high uncertainty of the ROSETTA model in these salt-affected soils. .
Conclusion: Analysis of the RMSD and NSE values from the regression relationship between measured and estimated Ks suggested a high degree of uncertainty in ROSETTA's estimations in salt-affected soils, indicating a potential need for the development of regional pedotransfer functions that incorporate soil chemical properties (such as salinity and sodium content) as input variables. Also, more detailed considerations are suggested for soil and water management in the region.