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Browsing by Author "Sahaar, Ahmad Shukran, author"

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    Enhancing rootzone soil moisture estimation using remote sensing, regional characteristics, and machine learning
    (Colorado State University. Libraries, 2023) Sahaar, Ahmad Shukran, author; Niemann, Jeffrey D., advisor; Chavez, Jose Luis, committee member; Green, Timothy R., committee member; Butters, Gregory, committee member
    Accurate estimation of root-zone soil moisture (θ ̄) is essential for various agricultural applications, including crop yield estimation, precision irrigation, and groundwater management. This dissertation encompasses three interconnected studies that collectively investigate different approaches for improving soil moisture estimation. The first study delves into the utilization of remote sensing methods, particularly optical and thermal satellite imagery, to estimate fine-resolution (30 m) root-zone soil moisture across diverse regions. Traditionally, these methods relied on empirical relationships with evaporative fraction Λ_SEB or evaporative index Λ_PET. However, it has been shown that a single relationship does not universally apply to all regions. This study evaluates the influence of regional soil, vegetation, and climatic conditions on the shape and strength of these relationships using global sensitivity analysis. The results highlight that soil characteristics, such as clay and silt content, and vegetation properties, like leaf area index and rooting depth, play pivotal roles in determining these relationships. Moreover, the impact of annual precipitation in defining climatic regions is crucial. Consequently, region-specific relationships are proposed, adapting to local conditions and potentially enhancing soil moisture estimates. The second study extends this investigation by applying the regionally adapted relationships for the Λ_SEB " vs." θ ̄ and Λ_PET " vs." θ ̄ to estimate rootzone soil moisture (θ ̄) from remote sensing data across four study regions. The results consistently demonstrate the superior performance of the regionally adapted relationships over a single empirical relationship, with a substantial reduction in root mean squared error. These adapted relationships are particularly effective in arid and semiarid regions. The third study explores the application of machine learning models, including XGBoost, CatBoost, RF, LightGBM, and artificial neural networks, to predict soil moisture levels across various climates and depths in the contiguous United States. The findings emphasize the high accuracy and effectiveness of machine learning models, especially XGBoost, in predicting soil moisture across diverse climate regions. XGBoost outperforms other models, making it a potentially valuable tool for soil moisture prediction in environmental monitoring and management. The study also highlights the influence of climate and soil depth on prediction accuracy, with deeper layers having improved forecasts. Additionally, feature importance analysis identifies key predictors for predicting soil moisture, such as elevation, aridity index, soil composition, and depth. These findings contribute to the advancement of soil moisture monitoring and management, with practical applications in agriculture and environmental sciences.
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    Erosion mapping and sediment yield of the Kabul River Basin, Afghanistan
    (Colorado State University. Libraries, 2013) Sahaar, Ahmad Shukran, author; Julien, Pierre Y., advisor; Arabi, Mazdak, committee member; Kampf, Stephanie K., committee member
    Soil erosion by water is a serious issue in Afghanistan. Due to the geographic landscape, soil and climatic conditions, and the latest deforestation activities, there has been intensive soil erosion which has resulted in prolonged and great impact on social and economic development of the region. In fact, recent environmental assessment shows that decades of war and continuous drought have resulted in widespread environmental degradation throughout the country; therefore, mapping of soil erosion at the basin scale is urgently needed. The Kabul River Basin was selected for the purpose of erosion and sedimentation modeling due to its great socio-economic impact. The main objectives of this study include: (1) calculations of the annual average soil loss rates at the basin level; (2) spatial distribution of soil erosion rates at the basin level; (3) predictions of deforestation effects on sediment losses under different land cover scenarios at the watershed level; and (4) calculation of sediment delivery ratios based on soil erosion rates, and sediment yields at the sub-watershed levels in the basin. This study uses the Revised Universal Soil Loss Equation (RUSLE) model combined with Geographic Information System (GIS) techniques to analyze the gross soil loss rates and the spatial distribution of soil loss rates under different land uses. Digital elevation model (DEM), average annual precipitation data, land cover map and soil type map were used to define the parameters of the RUSLE model. The annual average soil loss rate of the Kabul River Basin was estimated to be 19 tons/acre/year (4748 tons/km2/year), and the gross mean annual soil loss rate found to be 47 million tons/year. By producing 57 % of the total annual average soil loss, rangelands were the primary contributor to the basin. In case of the spatial distribution of erosion rates at the Kabul River Basin, the relationship between probability and annual average soil loss rates were analyzed. The analysis indicated that up to sixty percent of the mean annual soil loss rates are in the range of tolerable soil loss rate (0 - 5 tons/acre/year). Moreover, northern part of the basin is prone to more extensive erosion than the southern part. The study predicted that if the forest region of the Kunar watershed is completely reduced to barren lands, the watershed will produce five times more sediment than the estimated soil loss rate from 1993's UN-FAO land cover map. The annual average soil loss rate in this watershed was about 29 tons/acre/year but it will increase to 149 tons/acre/year as deforestation continues to take place in the watershed. The range of sediment delivery ratios for the basin's rivers is 2.5 -10.8 %. Based on this evaluation, the sediment delivery ratio for the sediment gauging stations in the basin are in the similar range of predicted values by the methods of Boyce, Renfro, Williams and Maner.