ALBERT

Description: Predicting event runoff and soil loss under different land covers is essential to quantitatively evaluate the hydrological responses of vegetation restoration in the Loess Plateau of China. The Soil Conservation Service curve number (SCS-CN) and Revised Universal Soil Loss Equation (RUSLE) models are widely used in this region to this end. This study incorporated antecedent moisture condition (AMC) in runoff production and initial abstraction of the SCS-CN model, and considered the direct effect of runoff on event soil loss by adopting a rainfall-runoff erosivity factor in the RUSLE model. The modified SCS-CN and RUSLE models were coupled to link rainfall-runoff-erosion modeling. The effects of AMC, slope gradient and initial abstraction ratio on curve number of SCS-CN, as well as those of vegetation cover on cover-management factor of RUSLE, were also considered. Three runoff plot groups covered by sparse young trees, native shrubs and dense tussock, respectively, were established in the Yangjuangou catchment of Loess Plateau. Rainfall, runoff and soil loss were monitored during the rainy season in 2008–2011 to test the applicability of the proposed approach. The original SCS-CN model significantly underestimated the event runoff, especially for the rainfall events that have large 5-day antecedent precipitation, whereas the modified SCS-CN model was accurate in predicting event runoff with Nash-Sutcliffe model efficiency (EF) over 0.85. The original RUSLE model overestimated low values of measured soil loss and underpredicted the high values with EF values only about 0.30. In contrast, the prediction accuracy of the modified RUSLE model improved with EF values being over 0.70. Our results indicated that the AMC should be explicitly incorporated in runoff production, and direct consideration of runoff should be included when predicting event soil loss. Coupling the modified SCS-CN and RUSLE models appeared to be appropriate for evaluating hydrological effects of restoring vegetation in the Loess Plateau. The main advantages, limitations and future study scopes of the proposed models were also discussed.

Description: The spatial discretization of watersheds is an indispensable procedure for representing landscape variations in eco-hydrological research, representing the contrast between reality and data-supported models. When discretizing a watershed, it is important to construct a scheme of a moderate number of discretized factors while adequately considering the actual eco-hydrological processes, especially in regions with unique eco-hydrological features and intense human activities. Because of their special lithological and pedologic characteristics and widespread man-made vegetation, discretization of watersheds in the Loess Plateau in Northern China is a challenge. In order to simulate the rainfall-runoff process, a watershed in the Loess Plateau, referred as Ansai, was spatially discretized into new units called land type units. These land type units were delineated under a scheme of factors including land use, vegetation condition, soil type and slope. Instead of using units delineated by overlaying land use and soil maps, the land type units were used in the Soil and Water Assessment Tool (SWAT). Curve numbers were assigned and adjusted to simulate runoff, using the US Natural Resources Conservation Service (NRCS) curve number method. The results of the runoff simulation better matched actual observations. Compared to the results that used the original units, the coefficient of determination (R2) and the Nash-Sutcliffe coefficient (ENS) for monthly flow simulation increased from 0.655–0.713 and 0.271–0.550 to 0.733–0.745 and 0.649–0.703, respectively. This method of delineating into land type units is an easy operation and suitable approach for eco-hydrological studies in the Chinese Loess Plateau and other similar regions. It can be further applied in soil erosion simulation and the eco-hydrological assessment of re-vegetation.

Description: The impact of re-vegetation on soil moisture dynamics was investigated by comparing five land cover types. Soil moisture and temperature variations under grass (Andropogon), subshrub (Artemisia scoparia), shrub (Spiraea pubescens), tree (Robinia pseudoacacia), and crop (Zea mays) vegetation were monitored in an experiment performed during the growing season of 2011. There were more than 10 soil moisture pulses during the period of data collection, and the surface soil moisture of all of the land cover types showed an increasing trend. Corn cover was associated with consistently higher soil moisture readings than the other surfaces. Grass and subshrubs showed an intermediate moisture level, with that of grass being slightly higher than that of subshrub most of the time. Shrubs and trees were characterized by lower soil moisture readings, with the shrub levels consistently being slightly higher than those of the trees. With the exception of the corn land cover type, the average soil temperature showed the same regime as the average moisture content, but exhibiting a downward trend throughout the observation period. Three typical decreasing periods were chosen to compare the differences in water losses. In periods of both relatively lower and higher water soil moisture contents, subshrubs lost the largest amount of water. The daily water loss associated with corn was most variable. The tree and shrub sites presented an intermediate level, with that of tree being slightly higher compared to shrub; the daily water loss trends of these two land cover types were similar and were more stable than those of the other types. The amount of water loss related to the grass land cover type is determined by the initial moisture content. Soil under subshrubs acquired and retained soil moisture resources more efficiently than the other cover types, representing an adaptive vegetation type in this area.

Description: The Temporal stability of soil moisture (TSSM) is an important factor to evaluate the value of available water resources in a water-controlled ecosystem. In this study we used the evapotranspiration-TSSM (ET-TSSM) model and a new sampling design to examine the soil water dynamics and water balance of different land uses/cover types in a hilly landscape of the Loess Plateau under a finer spatiotemporal scale. Our primary focus is to examine the difference among soil water processes, including the wet-to-dry (WTD) process triggered by precipitation and the dry-to-wet (DTW) process caused by radiation among varied land uses/cover types. Three vegetation types and bare land were selected in the sampling scheme. For each land uses/cover type, four microplots (60 cm × 60cm) were established, and the soil moisture was measured at the central point (CP) and four ambient points (AP). The results indicated that (1) the bare land (plot1) was sensitive to the influence of rainfall and radiation compared with other land uses types; (2) Andropogon (plot2) and Spiraea pubescens (plot4) more efficiently represented the average soil moisture of the different land uses/cover in the WTD and DTW processes, respectively, in the CP position. In contrast, the bare land and Artemisia coparia (plot3) seemed to be more representative of the average soil water content in the AP position; (3) the ET-TSSM model demonstrated that, in the WTD processes, although Spiraea pubescens land use reached the net deficit of the soil water storage condition was longest, the vegetated land uses have a higher capacity of water consumption than bare land and more easily affected the serious condition of the soil water deficiency at the end of WTD processes. We concluded that a finer spatiotemporal scale in the TSSM study could be a new method to describe the effect of plant on soil moisture dynamics triggered by precipitation or radiation and that the improvement of the application of the TSSM-based model to hydrological processes could be a promising research subject in the future.

Description: We studied the impacts of re-vegetation on soil moisture dynamics and evapotranspiration (ET) of five land cover types in the Loess Plateau in northern China. Soil moisture and temperature variations under grass (Andropogon), subshrub (Artemisia scoparia), shrub (Spiraea pubescens), plantation forest (Robinia pseudoacacia), and crop (Zea mays) vegetation were continuously monitored during the growing season of 2011. There were more than 10 soil moisture pulses during the period of data collection. Surface soil moisture of all of the land cover types showed an increasing trend in the rainy season. Soil moisture under the corn crop was consistently higher than the other surfaces. Grass and subshrubs showed an intermediate moisture level. Grass had slightly higher readings than those of subshrub most of the time. Shrubs and plantation forests were characterized by lower soil moisture readings, with the shrub levels consistently being slightly higher than those of the forests. Despite the greater post-rainfall loss of moisture under subshrub and grass vegetation than forests and shrubs, subshrub and grass sites exhibit a higher soil moisture content due to their greater soil retention capacity in the dry period. The daily ET trends of the forests and shrub sites were similar and were more stable than those of the other types. Soils under subshrubs acquired and retained soil moisture resources more efficiently than the other cover types, with a competitive advantage in the long term, representing an adaptive vegetation type in the study watershed. The interactions between vegetation and soil moisture dynamics contribute to structure and function of the ecosystems studied.

Description: The spatial discretization of watersheds is an indispensable procedure for representing landscape variations in eco-hydrological research, representing the contrast between reality and data-supported models. When discretizing a watershed, it is important to construct a scheme of a moderate number of discretized factors while adequately considering the actual eco-hydrological processes, especially in regions with unique eco-hydrological features and intense human activities. Because of their special lithological and pedologic characteristics and widespread man-made vegetation, discretization of watersheds in the Loess Plateau in Northern China is a challenge. In order to simulate the rainfall-runoff process, a watershed in the Loess Plateau, referred as Ansai, was spatially discretized into new units called land type units. These land type units were delineated under a scheme of factors including land use, vegetation condition, soil type and slope. Instead of using units delineated by overlaying land use and soil maps, the land type units were used in the Soil and Water Assessment Tool (SWAT). Curve numbers were assigned and adjusted to simulate runoff, using the US Natural Resources Conservation Service (NRCS) curve number method. The results of the runoff simulation better matched actual observations. Compared to the results that used the original units, the coefficient of determination (R2) and the Nash–Sutcliffe coefficient (ENS) for monthly flow simulation increased from 0.710–0.721 and 0.581–0.656 to 0.726–0.731 and 0.692–0.703, respectively. This method of delineating into land type units is an easy operation and suitable approach for eco-hydrological studies in the Chinese Loess Plateau and other similar regions. It can be further applied in soil erosion simulation and the eco-hydrological assessment of re-vegetation.

Description: Predicting event runoff and soil loss under different land covers is essential to quantitatively evaluate the hydrological responses of vegetation restoration in the Loess Plateau of China. The Soil Conservation Service Curve Number (SCS-CN) and Revised Universal Soil Loss Equation (RUSLE) models are widely used in this region to this end. This study incorporated antecedent moisture condition (AMC) in runoff production and initial abstraction of the SCS-CN model, and considered the direct effect of runoff on event soil loss by adopting a rainfall-runoff erosivity factor in the RUSLE model. The modified SCS-CN and RUSLE models were coupled to link rainfall-runoff-erosion modeling. The effects of AMC, slope gradient and initial abstraction ratio on curve number of SCS-CN, as well as those of vegetation cover on cover-management factor of RUSLE were also considered. Three runoff plot groups covered by sparse young trees, native shrubs and dense tussock, respectively, were established in the Yangjuangou catchment of Loess Plateau. Rainfall, runoff and soil loss were monitored during the rainy season in 2008–2011 to test the applicability of the proposed approach. The original SCS-CN model significantly underestimated the event runoff, especially for the rainfall events that have large 5-day antecedent precipitation, whereas the modified SCS-CN model could predict event runoff well with Nash-Sutcliffe model efficiency (EF) over 0.85. The original RUSLE model overestimated low values of measured soil loss and under-predicted the high values with EF only about 0.30. In contrast to it, the prediction accuracy of the modified RUSLE model improved satisfactorily with EF over 0.70. Our results indicated that the AMC should be explicitly incorporated in runoff production, and direct consideration of runoff should be included in predicting event soil loss. Coupling the modified SCS-CN and RUSLE models appeared to be appropriate for runoff and soil loss simulation at plot scale in the Loess Plateau. The limitations and future study scopes of the proposed models were also indicated.