期刊
LANDSCAPE ECOLOGY
卷 33, 期 9, 页码 1461-1480出版社
SPRINGER
DOI: 10.1007/s10980-018-0690-4
关键词
Catchment hydrology; Landscape ecology; Landscape-based hydrological modelling; Ecohydrology; Landscape patterns
资金
- National Key R&D Program of China [2017YFE0100700]
- Key Program of National Natural Science Foundation of China [41730646]
- Key Laboratory for Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences [KLMHESP-17-02]
Landscapes and water are closely linked. Water shapes landscapes, and landscape heterogeneity in turn determines water storage, partitioning, and movement. Understanding hydrological processes from an ecological perspective is an exciting and fast-growing field of research. The motivation of this paper is to review advances in the interaction between landscape heterogeneity and hydrological processes, and propose a framework for synthesizing and moving forward. Landscape heterogeneity, mainly topography and land cover, has been widely incorporated into existing hydrological models, but not in a systematic way. Topography, as one of the most important landscape traits, has been extensively used in hydrological models, but mostly to drive water flow downhill. Land cover heterogeneity, represented mostly by vegetation, is usually linked with evaporation and transpiration rather than runoff generation. Moreover, the proportion of different land cover types is usually the only index involved in hydrological models, leaving the influence of vegetation patterns and structure on hydrologic connectivity still largely unexplored. Additionally, moving from what heterogeneity exists to why-type questions probably offers us new insights into the nexus of landscape and water. We believe that the principles of self-organization and co-evolution of landscape features shed light on the possibility to infer subsurface heterogeneity from a few observable landscapes, allowing us to simplify complexity to a few quantifiable metrics, and utilizing these metrics in models with sufficient heterogeneity but limited complexity. Landscape-based models can also be beneficial to improve our ability of prediction in ungauged basins and prediction in a changing environment (Panta Rhei, everything flows).
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