人类活动明显干扰了流域水循环相关过程，其中水利工程（水库和水闸，简称闸坝）调控、城市化是最具代表性的人类活动方式。流域水循环多过程演变与人类活动影响机制是人类活动效应研究领域重点关注的难点之一，也是流域开发亟待回答的资源环境问题。张永勇副研究员与合作者在国家自然科学基金(41671024, 41730645)、所秉维优秀青年人才计划（2015RC201）、中科院青年创新促进会等项目资助下，在水循环系统模型不确定性及其传递、闸坝调控的动态变化识别和人类活动效应量化等方面取得了如下进展：

（1）提出流域水循环系统模型各过程不确定性及其传递的量化方法。不确定性是数学模型研究中必须回答的关键问题之一。在已构建的流域水循环系统模型（Zhang et al., 2016a）的基础上，提出各过程不确定性及其传递的量化方法；并以水量水质耦合模拟为例，探索各模块不确定性对参数分布和模拟过程的影响，以及参数不确定性在各模块之间传递影响（Zhang et al., 2018a）。由此，也提出了水循环多过程多指标均衡率定方法，以减少不确定性在各模块之间的传递（Zhang et al., 2016b, 2017b）。 

 

（2）揭示闸坝调控对完整径流过程影响的年际变化。闸坝调节影响研究往往关注多年平均径流量的变化，忽视了闸坝调度规则的年际波动及其对完整径流过程的影响。研究筛选径流情势特征指标（平均流量、低流量和高流量、变率、历时、频率和时间累计等）用于刻画完整径流过程，辨识了我国南北方典型水库调节的年际变化特征及其可能影响（Zhang et al., 2017a, 2018b）；在此基础上，分析了调控对河流浮游植物群落的时空影响（Zhou et al., 2019）。

 

（3） 量化城市化、闸坝调控等人类活动的水文水环境效应。城市化和闸坝调控水文水环境效应主要体现在前者改变水文气象条件、产流产污过程，而后者改变汇流和污染物的迁移过程等。利用城区长期下垫面数据和站点降水观测，采用成因分析和区域气候模式，揭示了北京市域极端降水变化及其与城市硬化的影响（Zhang et al., 2018c）；构建考虑土地利用动态变化和闸坝动态调控的水文水动力-水质耦合模型，分别量化北京城市化对典型流域径流情势的影响（Zhang et al., 2018d）、深圳市饮用水源地梯级水库和淮河流域闸坝调控对径流和水环境改善的影响等（Hua and Zhang, 2017; Zhai et al., 2017）。 

 

相关成果列表：

[1]     Zhang Y.Y.*, Shao Q.X., Zhao T.T.G.. Comprehensive assessment of dam impacts on flow regimes with consideration of interannual variations. Journal of Hydrology 2017a, 552:447-459.

https://www.sciencedirect.com/science/article/pii/S0022169417304523

 

[2]     Zhang Y.Y.*, Gao Y., Yu Q.. Diffuse nitrogen loss simulation and impact assessment of stereoscopic agriculture pattern by integrated water system model and consideration of multiple existence forms. Journal of Hydrology 2017b, 552:660-673.

https://www.sciencedirect.com/science/article/pii/S0022169417304845

 

[3]     Hua R.X., Zhang Y.Y.* Assessment of water quality improvements by hydrodynamic simulation approach in regulated cascade reservoirs: case study in the drinking water source of Shenzhen City, China. Water 2017,9,825; doi:10.3390/w9110825.

https://www.mdpi.com/2073-4441/9/11/825

 

[4]     Zhai X.Y., Xia J., Zhang Y.Y.*. Integrated approach of hydrological and water quality dynamic simulation for anthropogenic disturbance assessment in the Huai River Basin, China. Science of the Total Environment 2017, 598 (15):749–764.

https://www.sciencedirect.com/science/article/pii/S0048969717309282

 

[5]     Zhang Y.Y.*, Shao Q.X.. Uncertainty and its propagation estimation for an integrated water system model: An experiment from water quantity to quality simulations. Journal of Hydrology 2018a, 565:623-635.

https://www.sciencedirect.com/science/article/pii/S0022169418306747

 

[6]     Zhang Y.Y.*, Zhai X.Y., Zhao T.T.G.. Annual shifts of flow regime alteration: new insights from the Chaishitan Reservoir in China. Scientific Reports, 2018b,8:1414. DOI : 10.1038/s41598-018-19717-z

https://www.nature.com/articles/s41598-018-19717-z

 

[7]     Zhang Y.Y.*, Pang X., Xia J., Shao Q.X., Yu E.T., Zhao T.T.G., She D.X., Sun J.Q., Yu J.J., Pan X.Y., Zhai X.Y.. Regional patterns of extreme precipitation and urban signatures in metropolitan areas. Journal of Geophysical Research: Atmosphere, 2018c (accepted).

 

[8]     Zhang Y.Y.*, Xia J., Yu J.J., Randall M., Zhang Y.C., Zhao T.T.G., Pan X.Y., Zhai X.Y., Shao Q.X.. Simulation and assessment of urbanization impacts on runoff metrics: insights from landuse changes. Journal of Hydrology 2018d, 520:247-258.

https://www.sciencedirect.com/science/article/pii/S0022169418301975

 

[9]     Zhou Y.J., Zhang Y.Y.*, Liang T., Wang L.Q.. Shifting of phytoplankton assemblages in a regulated Chinese river basin after streamflow and water quality changes. Science of the Total Environment, 2019, 654:948-959.

    https://www.sciencedirect.com/science/article/pii/S0048969718342499