Comparison of SWAT and DLBRM for Hydrological Modeling of a Mountainous Watershed in Arid Northwest China

et al. 2017

Land Degrad Dev

Self Cite

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.

Section: Introductionmentioning

confidence: 99%

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

Tian

et al. 2017

Land Degrad Dev

Self Cite

“…The model has been applied extensively to riverine watersheds draining into the North America's Laurentian Great Lakes for use in both simulation and forecasting (Croley and He, , ; He and Croley, ). Recently, DLBRM has been successfully adapted to the Heihe River Watershed for modelling the hydrological processes in both the upper reach mountainous area and the lower oasis reaches (He et al, ; Zhang et al, ). Distributed large basin runoff model is used to assess the impacts of both the IDW and the PBIDEW methods on modelling the runoff in the study area because it has demonstrated better performance than other similar hydrological models in the study watershed (Zhang et al, ).…”

Section: Methodsmentioning

confidence: 99%

Comparison of IDW and Physically Based IDEW Method in Hydrological Modelling for a Large Mountainous Watershed, Northwest China

et al. 2017

River Research & Apps

Topography and spatial patterns of landscape significantly affect spatial distribution of precipitation and, in turn, hydrological modelling, especially in high elevation, mountainous watersheds of arid regions. This study incorporates a physically based inverse distance and elevation weighted (PBIDEW) method into a distributed conceptual hydrological model, distributed large basin runoff model, and compared with an inverse distance weighted (IDW) method to assess the performances of both methods in precipitation estimation for hydrological modelling at watershed scale. The PBIDEW method considers the impacts of topography using month‐dependent parameters in its interpolation of meteorological variables while the IDW method does not. Both the IDW and the PBIDEW methods are evaluated and compared in hydrological modelling at different spatial resolutions in the upper reach of the Heihe River Watershed, Northwest China. Results show that the IDW method underestimated the areal precipitation, and the PBIDEW method produced more realistic precipitation estimations in the study area. Both methods have some limitations, the performance of the IDW method was mainly influenced by the availability of observation data, while that of the PBIDEW method was mainly influenced by the representation of topographical information. Considering more detailed information for precipitation estimates, the PBIDEW method performed better at finer spatial resolution. Overall, the PBIDEW method, using month‐dependent physical interpolation parameters, seems more suitable for precipitation estimation in hydrological simulations in data‐scarce, high elevation and topographically complex mountainous watersheds in arid area. Copyright © 2017 John Wiley & Sons, Ltd.

“…The upstream of the Heihe River Watershed is located in the Qilian mountain ranges and it produces almost all of the water resource for the entire watershed [42,43]. The elevation of the upstream of the Heihe River Watershed ranges from 1674 to 5584 m above sea level (m a.s.l.)…”

Section: The Study Areamentioning

confidence: 99%

“…In this study, we used the mean absolute error (MAE) and the relative mean absolute error (RMAE) to evaluate the soil texture type predictions that are derived using the MCRF and OK methods [43,59]. MAE and RMAE are computed while using the following formulas:…”

Section: Evaluation Criteriamentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2018

Sustainability

Self Cite

Accurate mapping the spatial distribution of different soil textures is important for eco-hydrological studies and water resource management. However, it is quite a challenge to map the soil texture in data scarce, hard to access mountainous watersheds. This paper compares a nonlinear method, the Markov chain random field (MCRF) with a classical linear method, ordinary kriging (OK) for calculating the soil texture at different search radiuses in the upstream region of the Heihe River Watershed. Results show that soil texture values that were calculated by the OK method tends to predict soil texture values within a certain range (sand (12.098~40.317), silt (47.847~71.231), and clay (12.781~19.420)) because of the smoothing effect, thus leading to greater accuracy in predicting the major soil texture type (silt loam). Nonetheless, the MCRF method considers the interclass relationships between sampling points, leading to greater accuracy in predicting minor types (loam and sandy loam). Meanwhile, the OK method performed best for all the types at the radius of 65 km influenced by the densities of all the sampling points, while the best performance of the MCRF method differs with radiuses as the largest densities varying for different soil types. For loam and sandy loam, the OK method ignored them, thus the MCRF method is more suitable in mountainous areas with high soil heterogeneity.