Vegetation restoration has significant effects on soil properties and vegetation cover and thus affects soil detachment by overland flow. Few studies have been conducted to evaluate this effect in the Loess Plateau where a Great Green Project was implemented in the past decade. This study was carried out to quantify the effects of age of abandoned farmland under natural vegetation restoration on soil detachment by overland flow and soil resistance to erosion as reflected by soil erodibility and critical shear stress. The undisturbed soil samples were collected from five abandoned farmlands with natural restoration age varying from 3 to 37 years. The samples were subjected to flow scouring in a 4.0 m long by 0.35 m wide hydraulic flume under six different shear stresses ranging from 5.60 to 18.15 Pa. The results showed that the measured soil detachment capacities in currently cultivated farmland were 24.1 to 35.4 times greater than those of the abandoned farmlands. For the abandoned farmlands, soil detachment capacities fluctuated greatly due to the complex effects of root density and biological crust thickness, and could be simulated well by flow shear stress and biological crust thickness with a power function (NSE = 0.851). Soil erodibility of abandoned farmlands decreased gradually with restoration age and reached a steady stage when restoration age was greater than 28 years. The critical shear stress of the natural abandoned farmlands declined when restoration age was less than 18 years and then increased due to the episodic influences of vegetation recovery and biological crust development. More studies in the Loess Plateau are necessary to quantify the relationship between soil detachment capacity and biological crust thickness for better understanding the mechanism of soil detachment under natural vegetation restoration. Copyright © 2013 John Wiley & Sons, Ltd.

Information System (LREIS), the Institute of Geographic Sciences and Natural Resources Research (IGSNRR), the Chinese Academy of Sciences; the University of Chinese Academy of Sciences (UCAS) in Beijing, China; and the Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application in Nanjing, China. Hui-Ran Gao is a graduate student at the LREIS, the IGSNRR, the Chinese Academy of Sciences; and the UCAS in Beijing, China. Liang-Jun Zhu (corresponding author) is a doctoral student at the LREIS, the IGSNRR, the Chinese Academy of Sciences; and the UCAS in Beijing, China. AXing Zhu is a professor at the Key Laboratory of Virtual Geographic Environment (VGE),

Different spatial configurations (or scenarios) of multiple best management practices (BMPs) at the watershed scale may have significantly different environmental effectiveness, economic efficiency, and practicality for integrated watershed management. Several types of spatial configuration units, which have resulted from the spatial discretization of a watershed at different levels and used to allocate BMPs spatially to form an individual BMP scenario, have been proposed for BMP scenarios optimization, such as the hydrologic response unit (HRU) etc. However, a comparison among the main types of spatial configuration units for BMP scenarios optimization based on the same one watershed model for an area is still lacking. This paper investigated and compared the effects of four main types of spatial configuration units for BMP scenarios optimization, i.e., HRUs, spatially explicit HRUs, hydrologically connected fields, and slope position units (i.e., landform positions at hillslope scale). The BMP scenarios optimization was conducted based on a fully distributed watershed modeling framework named the Spatially Explicit Integrated Modeling System (SEIMS) and an intelligent optimization algorithm (i.e., NSGA-II, short for Non-dominated Sorting Genetic Algorithm II). Different kinds of expert knowledge were considered during the BMP scenarios optimization, including without any knowledge used, using knowledge on suitable landuse types/slope positions of individual BMPs, knowledge of upstream-downstream relationships, and knowledge on the spatial relationships between BMPs and spatial positions along the hillslope. The results showed that the more expert knowledge considered, the better the comprehensive cost-effectiveness and practicality of the optimized BMP scenarios, and the better the optimizing efficiency. Thus, the spatial configuration units that support the representation of expert knowledge on the spatial relationships between BMPs and spatial positions (i.e., hydrologically connected fields and slope position units) are considered to be the most effective spatial configuration units for BMP scenarios optimization, especially when slope position units are adopted together with knowledge on the spatial relationships between BMPs and slope positions along a hillslope.

Abstract:The Loess Plateau of China has experienced extensive vegetation restoration in the past several decades, which leads to great changes in soil properties such as soil bulk, porosity, and organic matter with the vegetation restoration age. And these soil properties have great effect on the soil infiltration and soil hydraulic conductivity. However, the potential changes in soil hydraulic conductivity caused by vegetation restoration age have not been well understood. This study was conducted to investigate the changes in soil hydraulic conductivity under five grasslands with different vegetation restoration ages (3, 10, 18, 28 and 37 years) compared to a slope farmland, and further to identify the factors responsible for these changes on the Loess Plateau of China. At each site, accumulative infiltration amount and soil hydraulic conductivity were determined using a disc permeameter with a water supply pressure of -20 mm. Soil properties were measured for analyzing their potential factors influencing soil hydraulic conductivity. The results showed that the soil bulk had no significant changes over the initial 20 years of restoration (P>0.05); the total porosity, capillary porosity and field capacity decreased significantly in the grass land with 28 and 37 restoration ages compared to the slope farmland; accumulative infiltration amount and soil hydraulic conductivity were significantly enhanced after 18 years of vegetation restoration. However, accumulative infiltration amount and soil hydraulic conductivity fluctuated over the initial 10 years of restoration. The increase in soil hydraulic conductivity with vegetation restoration was closely related to the changes in soil texture and structure. Soil sand and clay contents were the most influential factors on soil hydraulic conductivity, followed by bulk density, soil porosity, root density and crust thickness. The Pearson correlation coefficients indicated that the soil hydraulic conductivity was affected by multiply factors. These results are helpful to understand the changes in hydrological and erosion processes response to vegetation succession on the Loess Plateau.

Abstract. The management and conservation of lakes should be conducted in the context of catchments because lakes collect water and materials from their upstream catchments. Thus, the datasets of catchment-level characteristics are essential for limnology studies. Lakes are widely spread on the Tibetan Plateau (TP), with a total lake area exceeding 50 000 km2, accounting for more than half of the total lake area in China. However, there has been no dataset of lake-catchment characteristics in this region to date. This study constructed the first dataset of lake-catchment characteristics for 1525 lakes with areas from 0.2 to 4503 km2 on the TP. Considering that large lakes block the transport of materials from upstream to downstream, lake catchments are delineated in two ways: the full catchment, which refers to the full upstream-contributing area of each lake, and the inter-lake catchments, which are obtained by excluding the contributing areas of upstream lakes larger than 0.2 km2 from the full catchment. There are six categories (i.e., lake body, topography, climate, land cover/use, soil and geology, and anthropogenic activity) and a total of 721 attributes in the dataset. Besides multi-year average attributes, the time series of 16 hydrological and meteorological variables are extracted, which can be used to drive or validate lumped hydrological models and machine learning models for hydrological simulation. The dataset contains fundamental information for analyzing the impact of catchment-level characteristics on lake properties, which on the one hand, can deepen our understanding of the drivers of lake environment change, and on the other hand can be used to predict the water and sediment properties in unsampled lakes based on limited samples. This provides exciting opportunities for lake studies in a spatially explicit context and promotes the development of landscape limnology on the TP. The dataset of lake-catchment characteristics for the Tibetan Plateau (LCC-TP v1.0) is accessible at the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Terre.tpdc.272026, Liu, 2022).

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Spatial optimization of watershed best management practice (BMP) scenarios based on watershed modeling is an effective decision support tool for watershed management. During such optimization, existing types of BMP configuration units for configuring BMPs (or BMP configuration units, e.g. subbasins, hydrologic response units, farms) remain fixed boundaries once they have been created through spatial discretization prior to BMP scenario optimization. This sort of “boundary-fixed” method does not allow for adjustments to the spatial characteristics of BMP configuration units. Hence, it runs the risk of missing superior BMP scenarios that could have been obtained by adjusting unit boundaries and may produce less effective spatial optimization. In this article, we propose a new approach to the spatial optimization of BMP scenarios based on boundary-adaptive configuration units. The proposed optimization approach adopts slope positions (basic landform units along hillslopes inherently related to physical hillslope processes) as BMP configuration units and dynamically adjusts their boundaries by using quantitative information about their spatial gradation (i.e. fuzzy slope positions) during the optimization. A case study conducted in the Youwuzhen watershed in Fujian, China, showed that the proposed optimization approach can significantly enlarge the search space and obtain optimal BMP scenarios with better cost-effectiveness and higher optimization efficiency than with boundary-fixed units. The proposed optimization approach provides a new alternative framework for spatial optimization of BMP scenarios, in which other watershed models, intelligent optimization algorithms, and BMP configuration units available for boundary adjustment can be applied to BMP scenario optimization in a boundary-adaptive manner. This study also exemplifies the potential for transforming qualitative, vague, and empirical geographical knowledge about slope position units related to physical hillslope processes and BMPs into quantitative, explicit, and automated geospatial algorithms for effectively resolving environmental management problems in a more geographically meaningful way.

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