Variability in Soil Hydraulic Conductivity and Soil Hydrological Response Under Different Land Covers in the Mountainous Area of the Heihe River Watershed, Northwest China

Abstract

Understanding the variability in soil hydraulic conductivity in the mountainous headwaters is critical to the modeling of mountainous runoff and the water resources management of river basins, especially in the arid and semiarid areas. In this study, a total of 32 soil profiles with five layers within 0–70 cm were sampled under different land cover types: forest, meadow, high coverage grassland (HCG), medium coverage grassland (MCG), and barren land in the upper stream of the Heihe river watershed, Northwest China. Saturated hydraulic conductivity (KS) was measured for each sample. The vertical variation of KS and soil hydrological response under different land covers were analyzed. Results show that KS value in layer 5 was significantly lower than the values of above four layers. KS decreased in the order of forest, meadow, HCG, MCG, and barren land, corresponding to the degree of vegetation degradation. The KS decreased with depth under forest, HCG, and barren land, but increased first and then decreased under meadow and MCG. The dominant stormflow paths for different land covers were different: forest was dominated by deep percolation, HCG was dominated by subsurface flow (SSF), meadow was prevailed by Hortonian overland flow and had no SSF, while MCG and barren land were also dominated by Hortonian overland flow, but still formed SSF. This result provides important information for improving the accuracy of mountainous hydrological modeling and, in turn, leading to sustainable management of water resources in the study watershed. Copyright © 2016 John Wiley & Sons, Ltd.

Hydraulic Properties in Different Soil Architectures of a Small Agricultural Watershed: Implications for Runoff Generation

“…The soil properties for croplands are modified regularly by agricultural activities such as tilling, plowing, leveling, sowing, and irrigating [55][56][57][58]. Vertical variation of Ksat combined with rainfall intensity has been shown to affect the dominant runoff pathways [59][60][61][62], including overland flow (ORF, appears when rainfall intensity exceeds the soil infiltration capacity), subsurface flow (SSF, occurs at lateral flow structures), and deep-water infiltration (DF, water penetrates into the soil profile). In this study, the mean I30 (maximum rainfall intensity in 30 min, 6.3 mm h −1 ) of all erosive rainfalls during 2016 (data were from the collection of the Zigui Soil and Water Conservation Experiment Station, not shown here) was chosen to predict the dominant runoff pathways under different soil architectures.…”

Multi-Scale Evaluation of the SMAP Product Using Sparse In-Situ Network over a High Mountainous Watershed, Northwest China

“…For each node, soil moisture at depths of 5, 15, 25, 40 and 60 cm was measured at 30-min intervals by soil moisture sensor (Decagon's 5TE), and the data at depths of 5 cm were used as the surface soil moisture data in this study. The measurements by the soil moisture sensor were calibrated using field collected soil samples [32,33]. Soil samples were first weighed in the field and then weighed again after oven dried for 24 h at 105 • C. The details of the field sampling and analysis can be found in [33,34].…”

“…The measurements by the soil moisture sensor were calibrated using field collected soil samples [32,33]. Soil samples were first weighed in the field and then weighed again after oven dried for 24 h at 105 • C. The details of the field sampling and analysis can be found in [33,34]. According to the manual provided by Decagon Company [35], the soil moisture at each in situ point was subsequently calculated based on the weight difference between the wet and dry soil and volume of the container.…”

A Comparison of Markov Chain Random Field and Ordinary Kriging Methods for Calculating Soil Texture in a Mountainous Watershed, Northwest China

“…We used a Mastersizer 2000 (Malvern Instruments Ltd., Malvern, UK; the measurement range is 0.02-2000 µm) laser diffraction particle size analyzer at the Key Laboratory of West China's Environmental System (Ministry of Education) in the College of Earth and Environmental Sciences at Lanzhou University to analyze the proportions of sand, silt, and clay in the samples. We used the United States Department of Agriculture (USDA) soil texture classification scheme [52,53] Environmental System (Ministry of Education) in the College of Earth and Environmental Sciences at Lanzhou University to analyze the proportions of sand, silt, and clay in the samples. We used the United States Department of Agriculture (USDA) soil texture classification scheme [52,53] to determine the soil textures of the 178 soil samples.…”

“…We used the United States Department of Agriculture (USDA) soil texture classification scheme [52,53] Environmental System (Ministry of Education) in the College of Earth and Environmental Sciences at Lanzhou University to analyze the proportions of sand, silt, and clay in the samples. We used the United States Department of Agriculture (USDA) soil texture classification scheme [52,53] to determine the soil textures of the 178 soil samples. Five soil texture types were identified: silt loam, clay loam, loam, sandy loam, and loamy sand.…”