In arid and semi-arid regions freshwater resources have become scarcer with increasing demands from socio-economic development and population growth. Until recently, water research and management in these has mainly focused on blue water but ignored green water. Here we report on spatial and temporal patterns of both blue and green water flows simulated by the Soil and Water Assessment Tool (SWAT) for the Heihe river basin, the second largest inland river basin in Northwest China. Calibration and validation at two hydrological stations show good performance of the SWAT model in modelling hydrological processes. The total green and blue water flows were 22.09 billion m<sup>3</sup> in the 2000s for the Heihe river basin. Blue water flows are larger in upstream sub-basins than in downstream sub-basins mainly due to high precipitation and large areas of glaciers in upstream. Green water flows are distributed more homogeneously among different sub-basins. The green water coefficient was 88.0% in the 2000s for the entire river basin, varying from around 80–90% in up- and mid-stream sub-basins to above 95% in downstream sub-basins. This is much higher than reported green water coefficient in many other river basins. The spatial patterns of green water coefficient were closely linked to dominant land covers (e.g. glaciers in upstream and desert in downstream) and climate conditions (e.g. high precipitation in upstream and low precipitation in downstream). There are no clear consistent historical trends of change in green and blue water flows and green water coefficient at both the river basin and sub-basin levels. This study provides insights into green and blue water endowments for the entire Heihe river basin at sub-basin level. The results are helpful for formulating reasonable water policies to improve water resources management in the inland river basins of China
In order to develop a better model for quantifying aquatic community using environmental factors that are easy to get, we construct quantitative aquatic community models that utilize the different relationships between water environmental impact factors and aquatic biodiversity as follows: a multi-factor linear-based (MLE) model and a black box-based ‘Genetic algorithm-BP artificial neural networks’ (GA-BP) model. A comparison of the model efficiency and their outputs is conducted by applying the models to real-life cases, referring to the 49 groups of seasonal data observed over seven field sampling campaigns in Shaying River, China, and then performing model to reproduce the seasonal and inter-annual variation of the water ecological characteristics in the Huaidian (HD) site over 10 years. The results show that (1) the MLE and GA-BP models constructed in this paper are effective in quantifying aquatic communities in dam-controlled rivers; and (2) the performance of GA-BP models based on black-box relationships in predicting the aquatic community is better, more stable, and reliable; (3) reproducing the seasonal and inter-annual aquatic biodiversity in the HD site of Shaying River shows that the seasonal variation of species diversity for phytoplankton, zooplankton, and zoobenthos are inconsistent, and the inter-annual levels of diversity are low due to the negative impact of dam control. Our models can be used as a tool for aquatic community prediction and can become a contribution to showing how quantitative models in other dam-controlled rivers to assisting in dam management strategies.
To study the spatial variability of water surface fluxes, turbulence measurements on a moving platform are useful. However, such measurements have only been carried out with large research vessels over the ocean. We tested the feasibility of flux measurements with a small excursion ship over Lake Kasumigaura, the second largest lake in Japan. After the formal application of coordinate rotations to account for the ship's movements, we derived mean wind velocities as well as latent and sensible heat fluxes. They were compared with spatially interpolated wind velocities from meteorological stations and with fluxes estimated from the bulk method. Equally good agreements were found with those reported in previous studies over the ocean, indicating the feasibility of ship measurements in a lake. Possible error sources were identified for the improvement of the accuracy of flux estimation.
The spatiotemporal patterns of key hydrological variables across China were illustrated based on the developed Water and Energy Transfer Processes model in China (WEP-CN model). Time series of four key hydrological variables, namely, precipitation (P), runoff (R), infiltration (Inf), and actual evapotranspiration (ETa), were obtained over 60 years. Then, the temporal trends and spatial differences of these variables were analyzed using the Mann-Kendall and linear methods on a national scale and on the water resource regional scale. Moreover, we explored the drivers and constraints for changes in R, Inf, and ETa. The results showed: (1) Based on the coefficient of variations of P (5.24%), R (11.80%), Inf (2.57%), and ETa (3.77%), R was more fluctuating than the other variables. (2) These variables followed a similar trend of gradually decreasing from the southeast coast to the northwest inland. (3) Changes in R and Inf were caused mainly by P, having correlation coefficients with precipitation of 0.74 and 0.73, respectively. The ETa was constrained by a combination of P and energy. The results improved the refined and quantitative research on hydrological processes in China, identified the differences in hydrological variables between water-resource regions, and provided a useful supplement to the research of the large-scale hydrological process.
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