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Exploring the multiscale hydrologic regulation of multipond systems in a humid agricultural catchment.
Chen, Wenjun; Nover, Daniel; Yen, Haw; Xia, Yongqiu; He, Bin; Sun, Wei; Viers, Joshua.
Afiliação
  • Chen W; Jinling Institute of Technology, 99 Hongjing Road, Nanjing, 211169, China; Key Laboratory of Watershed Geographic Science, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China. Electronic address: chenwenjun@niglas.ac.cn.
  • Nover D; School of Engineering, University of California Merced, Merced, CA, 95343, USA.
  • Yen H; Blackland Research and Extension Center, Texas A&M University, Temple, TX, 76502, USA.
  • Xia Y; State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, 210008, China.
  • He B; Guangdong Institute of Eco-environmental Science & Technology, Guangdong Academy of Sciences, Guangzhou, 510650, China.
  • Sun W; Key Laboratory of Watershed Geographic Science, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China.
  • Viers J; School of Engineering, University of California Merced, Merced, CA, 95343, USA.
Water Res ; 184: 115987, 2020 Oct 01.
Article em En | MEDLINE | ID: mdl-32688156
ABSTRACT
Assessing the hydrologic processes over scales ranging from single wetland to regional is critical to understand the hydrologically-driven ecosystem services especially nutrient buffering of wetlands. Here, we present a novel approach to quantify the multiscale hydrologic regulation of multipond systems (MPSs), a common type of small, scattered wetland in humid agricultural regions, because previous studies have stopped in commending the catchment scale flood and drought resilience of these waters, and contemporary models do not adequately represent the corresponding intra-catchment fill-spill relationships. A new version of Soil and Water Assessment Tool (SWAT) was developed to incorporate improved representation of (1) perennial or intermittent spillage connections of pond-to-pond and pond-to-stream, and (2) bidirectional exchange between pond surface water and shallow groundwater. We present SWAT-MPS, which adopts rule-based artificial intelligence to model the possibilities of different spillage directions and GA-based parameter optimization over the two simulation years (June 2017 to May 2019), with successfully replicated streamflow and pond water-level variations in a 4.8 km2 test catchment, southern China. Water balance analysis and scenario simulations were then executed to assess the hydrologic regulation at single pond, single MPS, and entire catchment scales. Results revealed (1) the presence of 9 series- or series-parallel connected MPSs, in which pond overflow accounted for as much as 59% of the catchment water yield; (2) seasonally- and MPS-independent baseflow support and quickflow attenuation, with ranked level of pond water storage for baseflow support across different landuse types forest > farm > village, and inversed correlation of pond spillage to baseflow and quickflow variations in the farmland; and (3) MPS-aggregated catchment flood peak reduction (>20%) and baseflow increment (26%) in the following dry days. Meteorological data analysis and simulated average daily values indicated these hydrologic patterns are credible even if extending to a 5-year period. As a first modelling attempt to explore the intra-catchment details of MPSs, our study underscores the water storage and connectivity in their hydrologic regulation, and suggests inventories, long-term field monitoring, and several research directions of the new model for integrated pond management in watersheds and river basins. These findings can inform refined assessment of similar small, scattered wetlands elsewhere, where restoration efforts are required.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Ecossistema Tipo de estudo: Prognostic_studies País/Região como assunto: Asia Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Ecossistema Tipo de estudo: Prognostic_studies País/Região como assunto: Asia Idioma: En Ano de publicação: 2020 Tipo de documento: Article