| چکیده انگلیسی مقاله |
1- Introduction
Soil erosion is a serious environmental issue. This issue is especially significant when it comes the use and management of land. It is a complex and dynamic phenomenon that has detrimental effects on the physical, chemical, and biological properties—or the quality—of soil and, water while being strongly influenced by soil characteristics, land use, and land management practices. Sediments originate from different parts of the watershed, and the spatial-temporal variability in the contribution of sources (land use, geology, and topography) to sediment yield is related to the localized and dynamic nature of erosion processes. The purpose of sediment transfer studies is often to identify the sediment source, its ultimate fate, and the sediment transport process within the watershed. However, many variables, such as climate, vegetation cover, topography, soil type, and human interventions, can affect the dynamics of source distribution, the fate of suspended sediment, and the sediment transport process in rivers. Therefore, modeling and predicting sediment sources is complex and uncertain due to the variability of environmental factors. Quantifying and identifying sediment sources and associated pollutants is a critical step in soil and water management. Consequently, quantifying soil erosion and sediment production using conventional techniques is challenging, time-consuming, and costly.
In recent decades, sediment fingerprinting has been developed to address this problem and has been successfully employed as a powerful tool in sediment and pollutant source tracing studies. Over the past 20 years, sediment fingerprinting has emerged as an essential approach to quantifying the relative contributions of different land use sources to organic matter loads in waterways. Using multiple source group classifications (e.g., agricultural land, pasture, forest, wetlands) can lead to better spatial differentiation of sediment sources, with each source needing to be robustly distinguished by at least one tracer. Currently, examining soil erosion mechanisms and weathering processes is essential to help prevent soil degradation in the region. The weathering process is one of the primary mechanisms controlling the cycling of materials on the Earth’s surface and involves a combination of physical processes and chemical reactions that transform primary minerals into more stable forms. Weathering indices are based on the elemental composition of rocks and soils. Most of these indices are molecular ratios and weight percentages among different groups of major oxides. Therefore, weathering indices have the potential to provide useful tracers for identifying and allocating sediment sources, as they reflect the susceptibility of potential source areas to erosion driven by interactions among climate, geology, soil science, tectonic processes, vegetation cover, and human activities. Accordingly, the main aim of this study is to examine the role of different land uses in sediment production in the Alvand watershed, using geochemical properties and weathering indices as tracers and applying the Bayesian mixing model MixSIR.
2- Materials and methods
The study area in this research includes the Alvand watershed, located in the southwest of Kermanshah province. The study area ranges between latitudes 33 degrees 57 minutes to 34 degrees 34 minutes and longitudes 45 degrees 32 minutes to 46 degrees 28 minutes, situated between the Tigris and Iran Plateau. The four main sub-catchments of the Alvand watershed include Chelh-Gilan Gharb, Koferavor-Sagan, Sar Pol Zohab, and Qasr-e Shirin. Due to the vastness of the Alvand watershed, this study focused on the Sar Pol Zohab sub-catchment, which includes three sub-catchments: Qale Shahin, Reijab, and Patagh. This watershed, with an area of 1671.93 square kilometers, follows the general direction of the Zagros, running from northwest to southeast. Its maximum elevation is 2474 meters, and its minimum elevation is 486 meters at the watershed outlet. The study area has a Mediterranean climate with a rainy season corresponding to the cold season. The average annual precipitation in the watershed is about 600 millimeters, with an average annual temperature of 13 degrees Celsius. Generally, in the eastern mountainous regions of the watershed, there is more precipitation and lower temperatures, while in the lower western regions, precipitation is less and temperatures are higher.
Sampling is done in two ways, one based on sediment sources and the other based on the target sediment or basin output. In other words, in sediment provenance, samples should be taken from sediment sources and sediment outputs. All sediment and source samples were dried at 60 degrees for 24 hours. They were sieved using a sieve smaller than 125 micrometers. XRF analysis was performed on 36 sediment samples. 20 grams of each sample were taken for this purpose, and the concentrations of important geochemical elements, including Al, Ca, Fe, K, Na, Mg, Si, Ti, P, and LOI, were measured using X-ray fluorescence (XRF) equipment. In order to assess the efficiency of weathering indices computable using the measured geochemical elements, 42 air pollution indices (Table 1) were calculated to provide useful tracers for inclusion in sediment provenance. All elements were converted to oxide percentages, and then the molecular weights of the oxide forms were calculated using molar mass.
For investigating the proportion of each source of sediment in a tributary, three main steps were used. First, the non-conservative behavior of tracers and a mass conservation test were performed. Second, a two-stage statistical procedure identified the optimum set of source material properties to use as composite fingerprints. The abilities of individual properties to discriminate among sources were tested via the Kruskal-Wallis rank sum test, and those properties that return a P value >0.05 were excluded. Then, a stepwise discriminant function analysis (DFA) was performed to determine the proportion of samples that were accurately classified into the correct source groups. Third, the mixture sampling-Importance-Resampling (Mix SIR) Bayesian model was used to estimate source proportion.
Modeling source proportions using statistically validated composite tracers was assessed through virtual sediment samples with known source contributions (Haddadchi et al., 2014). Specifically, model predictions were evaluated for land-use sediment source proportions using 14 sets of virtual sediment mixtures. To generate these virtual sediment samples, known source proportions (details of the source proportions used in the virtual samples are provided in Table 5) were multiplied by selected tracer values, which serve as composite tracer components. The resulting values were then used as inputs for the un-mixing model. The predicted source proportions were subsequently compared with the actual proportions to evaluate the accuracy of the Bayesian model’s predictions. The results from the virtual sediment sample tests were assessed using root mean square error (RMSE), mean absolute error (MAE), and the index of agreement (d).
3- Results
The results of the standard Bracket test indicated that 10 tracers (WIP, ALK, MWPI, IR, WI-1, Chittleborough, ST, SF, CIW, LOI) were non-conservative and were thus excluded from further analysis in different erosional land-use units. To select tracers, the Kruskal-Wallis test was initially applied. Out of 9 geochemical elements and 42 weathering indices used as tracers, the element sodium (Na) and the weathering index (FENG) were found effective in differentiating sediment units.
Comparing the known contributions of sediment sources to the estimated contributions across different land uses (rainfed cultivation, irrigated cultivation, and pasture) using virtual sediment mixtures demonstrated that the root mean square error (RMSE) ranged from 4.2% to 35.5%, the mean absolute error (MAE) ranged from 3.9% to 30.7%, and the index of agreement (d) ranged from 0.060 to 0.907. The overall mean RMSE, MAE, and d for spatial predictions generated using virtual mixtures were 19.6%, 17.5%, and 0.711, respectively.
4- Discussion & Conclusions
In this study, land use was considered as sediment sources, the samples collected from various land uses can serve as indicators of different geomorphological processes (erosion and sedimentation), geological changes, and more. This may explain why the optimal combination of tracers aligns with findings from other studies. It is important to remember that source estimates are scale-dependent, as they may vary across sampling sites along a channel network. Regarding land use in the studied watershed, irrigated agriculture is more prevalent than other cultivation methods. However, rainfed cultivation, due to improper land use practices and poor agricultural methods (such as plowing along the slope), contributes the most to erosion and sediment production. This research comprehensively examines weathering indices in various land uses as sediment sources within the Alvand watershed, assessing 42 indices as new tracers, in contrast to previous studies (Derakhshan et al., 2024; Mohammadi Rayegani et al., 2019) that evaluated only a limited number of weathering indices. Geochemical properties of soil generally differentiate the surface soil based on weathering differences, which are also influenced by land use and management practices. With the high erosion rates (Derakhshan Babaei et al., 2020), approximately three-quarters of the nine geochemical elements and 42 weathering indices initially listed passed the two range-based bracket tests.
This study investigates the contributions of sediment sources from various land uses based on sediment fingerprinting techniques, using weathering indices along with geochemical elements as tracers to identify and quantify the contribution of each source to sediment yield in the Alvand watershed. A total of 28 soil samples were collected from different land uses (irrigated cultivation, rainfed cultivation, and pasture) to quantify each land use’s contribution using sediment fingerprinting. The results indicate that although rainfed lands cover the smallest area, they appear to produce the highest sediment yield at 53.7%, likely due to poor agricultural practices. Among the 9 geochemical elements and 42 weathering indices analyzed, the chemical element Na and the weathering index FENG were identified as the best tracers, with the strongest differentiation power for sediment sources. These findings suggest that poor land management practices, such as slope-based plowing, cultivation on steep slopes, and improper methods of preparing the land for re-cultivation, are influential factors in erosion and sediment production, which was also observed during field surveys in the study area.
|