Landscape metrics as predictors of water-related ecosystem services: Insights from hydrological modeling and data-based approaches applied on the Arno River Basin, Italy

被引：1

作者：

el Jeitany, Jerome ^{[1
,2
]}

Nussbaum, Madlene ^{[3
]}

Pacetti, Tommaso ^{[1
]}

Schröder, Boris ^{[4
,5
]}

Caporali, Enrica ^{[1
]}

机构：

[1] Department of Civil and Environmental Engineering (DICEA), Univpersità degli Studi di, Firenze, Via di S. Marta 3, Firenze,50139, Italy

[2] Landscape Ecology and Environmental Systems Analysis, Institute of Geoecology, Technische Universität Braunschweig, Langer Kamp 19c, Braunschweig,38092, Germany

[3] University of Utrecht, Faculty of Geosciences, Physical Geography, Netherlands

[4] Plant Ecology, Institute of Ecology, Technische Universität Berlin, Rothenburgstr. 12, Berlin,12165, Germany

[5] Berlin-Brandenburg Institute of Advanced Biodiversity Research, Berlin,14195, Germany

来源：

Science of the Total Environment | 2024年 / 954卷

关键词：

The study addresses the challenge of integrating complex landscape-hydrological interactions into predictive models for improved water resource management. The aim is to investigate the effectiveness of landscape metrics—quantitative indices measuring landscape composition and configuration—as predictors of WES in the Arno River Basin; Italy. Utilizing two hydrological models alongside a random forest algorithm; we assessed spatial and temporal variations in water yield; runoff; and groundwater recharge. The findings indicate that landscape metrics derived from high-resolution land use data significantly impact WES outcomes. Specifically; the models demonstrated average landscape metric importances of 16.8 % for spatial and 17.8 % for temporal predictions concerning runoff. For water yield; these averages were 32.9 % spatially and 43.5 % temporally; while groundwater modeling showed importances of 14.09 % spatially and 33.8 % temporally. Key landscape metrics identified include the core area index for broad-leaved forests and the perimeter-to-area ratio for non-irrigated agricultural areas as critical spatial and temporal predictors of water yield and groundwater recharge. Thresholds were observed; indicating landscape configurations that minimize hydrological variability. For instance; runoff variation is minimal when the landscape exhibits high forest fragmentation (over 1000 coniferous patches); low aggregation (aggregation index © 2024 Elsevier B.V;

D O I：

10.1016/j.scitotenv.2024.176567

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学科分类号：

摘要：

引用

共 2 条

[1] Temporal-Spatial Characteristics and Trade-off-Synergy Relationships of Water-Related Ecosystem Services in the Yangtze River Basin from 2001 to 2021
Du, Hongbo
Wu, Jianping
Li, Wenjie
Wan, Yu
Yang, Ming
Feng, Peng
SUSTAINABILITY, 2024, 16 (09)
[2] How Do Spatial Patterns Impact Regulation of Water-Related Ecosystem Services? Insights from a New Town Development in the Yangtze River Delta, China
Wang, Jieqiong
Chen, Siqing
Wang, Min
SUSTAINABILITY, 2019, 11 (07)

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