Climate change and land use/cover change (LUCC) are two factors that produce major impacts on hydrological processes. Understanding and quantifying their respective influence is of great importance for water resources management and socioeconomic activities as well as policy and planning for sustainable development. In this study, the Soil and Water Assessment Tool (SWAT) was calibrated and validated in upper stream of the Heihe River in Northwest China. The reliability of the SWAT model was corroborated in terms of the Nash–Sutcliffe efficiency (NSE), the correlation coefficient (R), and the relative bias error (BIAS). The findings proposed a new method employing statistical separation procedures using a physically based modeling system for identifying the individual impacts of climate change and LUCC on hydrology processes, in particular on the aspects of runoff and evapotranspiration (ET). The results confirmed that SWAT was a powerful and accurate model for diagnosis of a key challenge facing the Heihe River Basin. The model assessment metrics, NSE, R, and BIAS, in the data were 0.91%, 0.95%, and 1.14%, respectively, for the calibration period and 0.90%, 0.96%, and −0.15%, respectively, for the validation period. An assessment of climate change possibility showed that precipitation, runoff, and air temperature exhibited upward trends with a rate of 15.7 mm, 6.1 mm, and 0.38 °C per decade for the 1980 to 2010 period, respectively. Evaluation of LUCC showed that the changes in growth of vegetation, including forestland, grassland, and the shrub area have increased gradually while the barren area has decreased. The integrated effects of LUCC and climate change increased runoff and ET values by 3.2% and 6.6% of the total runoff and ET, respectively. Climate change outweighed the impact of LUCC, thus showing respective increases in runoff and ET of about 107.3% and 81.2% of the total changes. The LUCC influence appeared to be modest by comparison and showed about −7.3% and 18.8% changes relative to the totals, respectively. The increase in runoff caused by climate change factors is more than the offsetting decreases resulting from LUCC. The outcomes of this study show that the climate factors accounted for the notable effects more significantly than LUCC on hydrological processes in the upper stream of the Heihe River.

Abstract:Quantification of the changes of water balance components is significant for water resource assessment and management. This paper employed the Soil and Water Assessment Tool (SWAT) model to estimate the water balance in a mountainous watershed in northwest China at different spatial scales over the past half century. The results showed that both Nash-Sutcliffe efficiency (NSE) and determination coefficient (R 2 ) were over 0.90 for the calibration and validation periods. The water balance components presented rising trends at the watershed scale, and the total runoff increased by 30.5% during 1964 to 2013 period. Rising surface runoff and rising groundwater flow contributed 42.7% and 57.3% of the total rising runoff, respectively. The runoff coefficient was sensitive to increasing precipitation and was not significant to the increase of temperature. The alpine meadow was the main landscape which occupied 51.1% of the watershed and contributed 55.5% of the total runoff. Grass land, forest land, bare land, and glacier covered 14.2%, 18.8%, 15.4%, and 0.5% of the watershed and contributed 8.5%, 16.9%, 15.9%, and 3.2% of the total runoff, respectively. The elevation zone from 3500 to 4500 m occupied 66.5% of the watershed area, and contributed the majority of the total runoff (70.7%). The runoff coefficients in the elevation zone from 1637 to 2800 m, 2800 to 3500 m, 3500 to 4000 m, 4000 to 4500 m, and 4500 to 5062 m were 0.20, 0.27, 0.32, 0.43, and 0.78, respectively, which tend to be larger along with the elevation increase. The quantities and change trends of the water balance components at the watershed scale were calculated by the results of the sub-watersheds. Furthermore, we characterized the spatial distribution of quantities and changes in trends of water balance components at the sub-watershed scale analysis. This study provides some references for water resource management and planning in inland river basins.

The 30-year normalized-difference vegetation index (NDVI) time series from AVHRR/MODIS satellite sensors was used in this study to assess the regional vegetation dynamic changes in the Tao River Basin, which cuts across the Eastern Tibetan Plateau (ETP) and the Southwestern Loess Plateau (SLP). First, principal component and correlation analyses were carried out to determine the key climatic variables driving ecological change in the region. Then, regression models were tested to correlate NDVI with the selected climatic variables to determine their predictive power. Finally, Sen's slope method was used to determine how terrestrial vegetation has responded to regional climate change in the region. The results indicated an average winter season NDVI value of 0.14 in the ETP but only 0.04 in the SLP. Primarily driven by increasing temperature, vegetation growth has generally been enhanced since 1981; spring NDVI increased by 0.03 every 10 years in the ETP and 0.02 in the SLP. Further, results from trend analyses suggest vegetation growth in the ETP shifted to earlier-start and earlier-end dates, however in the SLP, the growing season has been extended with an earlier-start and later-end date. The precipitation threshold for vegetation germination, measured by the cumulative spring rainfall, was found to be 44 mm for both the ETP and SLP.

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Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface soil moisture (SSM) forecasts, employing the gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and convolutional neural network (CNN). To establish the objective model’s viability for SSM forecasting at multi-step daily horizons, the hybrid CEEMDAN-CNN-GRU model is tested at 1st, 5th, 7th, 14th, 21st, and 30th day ahead period by assimilating a comprehensive pool of 52 predictor dataset obtained from three distinct data sources. Data comprise satellite-derived Global Land Data Assimilation System (GLDAS) repository a global, high-temporal resolution, unique terrestrial modelling system, and ground-based variables from Scientific Information Landowners (SILO) and synoptic-scale climate indices. The results demonstrate the forecasting capability of the hybrid CEEMDAN-CNN-GRU model with respect to the counterpart comparative models. This is supported by a relatively lower value of the mean absolute percentage and root mean square error. In terms of the statistical score metrics and infographics employed to test the final model’s utility, the proposed CEEMDAN-CNN-GRU models are considerably superior compared to a standalone and other hybrid method tested on independent SSM data developed through feature selection approaches. Thus, the proposed approach can be successfully implemented in hydrology and agriculture management.

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