To prevent desertification, countries all over the world have made diversified efforts and vegetation restoration has been proved to be an effective approach. However, for sandy land that has limited water resources, measures such as artificial vegetation, may lead to the increase risk of drought. While affirming the achievements of sand utilization, there are many controversies exist regarding the advantages of turning deserts green, especially considering the water scarcity. Therefore, the long-run and causal relationships between sandy land, water consumption and vegetation coverage are necessary for explorations. Taken the southern margin of the Mu Us Sandy Land as the study area, this study explored the interactions between sandy land, water consumption and NDVI over a period of 2000–2018 with a VAR model approach. In the study area, various revegetation projects have made great achievements, resulting in a significant reduction of the sandy land area. In addition, the NDVI has ascend from 0.196 in 2000 to 0.371 in 2018 with a ratio of 89.3%. Results showed that there exist long-term stable equilibrium and causal relationships between water consumption with sandy land and NDVI. The increase of NDVI is relatively the direct factor causes the increase of water consumption. It could be inferred that those artificial vegetation measures may be based on large amount of water consumption, which may aggravate further water shortage and ecological damage. More scientific and stronger water resources management measures need to be implemented locally to achieve a balance between water resources and revegetation.
Climate change refers to a statistically significant change in the average state of the climate or a climate alteration that lasts for a long period of time. Runoff (R) is as a measure of the interaction between climate change and human activities and plays an important role in the hydrological cycle, as it is directly related to the development of agricultural water management. Therefore, it is a requirement to correctly simulate R and have the ability to separate the impacts due to climate change and human activities. In this paper, five single-type simulation models (Back Propagation Neural Network (BP), Non-Autoregressive (NAR), Radial Basis Function (RBF), Support Vector Machine (SVM) and TOPMODEL Hydrological Model (TOPMODEL)) were adopted to simulate the R to analyze the simulating quality by comparing the evaluation indexes like relative error (RE), relative mean squared error (RMSE) and Nash–Sutcliff Efficiency (NSE) with the combined hierarchical structure hydrological (CHSH) simulation model. In traditional studies, only the relative contribution of the impacts of human activities and climate change on R are considered; however, in this study, the relative contribution of each meteorological factor affecting R is included. To quantitatively analyze the impact of human activities and climate change on R, we used a CHSH simulation model to calculate runoff values for the Lancang River of China for a period of nine years (2005–2013). Our objective was to use this type of model to improve both the accuracy and stability of calculated values of R. For example, the RE, RMSE and NSE of simulated monthly R calculated with the CHSH model were 6.41%, 6.67 × 108 m3 and 0.94, respectively. These values substantiate the improved accuracy and stability of calculated values of R obtained with single-type simulation models (the SVM model, for instance, widely used in runoff simulations, and the RE, RMSE and NSE were 14.1%, 12.19 × 108 m3 and 0.87, respectively). The total contribution of human activities and climate change to R, respectively, accounted for 34% and 66% for the nine-year period based on the CHSH model. Furthermore, we adopted a vector autoregressive (VAR) model to analyze the impacts of the meteorological factors on R. The results from this analysis showed that R has a strong fluctuation response to the changes in precipitation (P) and potential water evaporation (Ep). The contribution rates of Ep, P and air temperature (Ta) to R were 15%, 14% and 2%, respectively. Based on the total climate change contribution, the corresponding contribution rates of Ep, Ta and P in the Lancang River of China were 32%, 30% and 5%, respectively. The values of R calculated with the CHSH model are more accurate and stable compared to values obtained with single-type simulation model. Further, they have the advantage of avoiding drawbacks associated when using a single-type simulation model. Moreover, moving away from the traditional method of separating the impact of meteorological factors on R, the vector autoregressive model proposed in this paper can describe the contribution of different meteorological factors on R in more detail and with precision.
The impact of development on the ecological environment is a matter of concern, especially in the ecologically fragile agro-pastoral ecotone. The agro-pastoral ecotone of northern Shaanxi (ANS) is a representative region of the agro-pastoral ecotone in northern China. The agricultural use of sand land (AUSL) is an effective way to respond to the development needs of this region. The AUSL will certainly have an impact on water yield services in the ANS, where water resources are already scarce. In this study, the Integration Valuation of Ecosystem Services and Tradeoffs Tool (InVEST) model was used to simulate the water yield of the ANS under different intensity scenarios of AUSL. The results showed that AUSL reduces the regional water yield, but the impact is limited. At the maximum scale of development, the total regional water yield decreased by 1.35% compared to the base year of 2020. In terms of distribution pattern, water yield decreased the most in Yuyang District and the least in Fugu County due to the uneven distribution of sand land. At the land use and land cover (LULC) scale, the AUSL has a diminishing effect on the water yield of both cultivated land and sand land due to the more intense evapotranspiration from cropland. This study can provide data and decision support for the ANS region for building the resource-economical and environment-friendly society.
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