El Jeitany Jerome, Nussbaum Madlene, Pacetti Tommaso, Schröder Boris, Caporali Enrica
Department of Civil and Environmental Engineering (DICEA), Univpersità degli Studi di, Firenze, Via di S. Marta 3, Firenze 50139, Italy; Landscape Ecology and Environmental Systems Analysis, Institute of Geoecology, Technische Universität Braunschweig, Langer Kamp 19c, Braunschweig 38092, Germany.
University of Utrecht, Faculty of Geosciences, Physical Geography, Netherlands.
Sci Total Environ. 2024 Dec 1;954:176567. doi: 10.1016/j.scitotenv.2024.176567. Epub 2024 Sep 29.
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 <75), and reduced connectivity (cohesion index under 80). Similarly, groundwater variation is minimized with decreased boundary length of vegetation patches (perimeter-to-area ratio <0.8), agricultural lands (perimeter-to-area ratio under 1), and the presence of low core agricultural areas (core area index above 8). The identified thresholds could inform land-use policies, such as targeted afforestation or crop diversification strategies, to optimize WES provision.
该研究应对了将复杂的景观 - 水文相互作用纳入预测模型以改善水资源管理这一挑战。其目的是调查景观指标（衡量景观组成和配置的定量指标）作为意大利阿尔诺河流域水资源服务预测指标的有效性。我们利用两个水文模型以及随机森林算法，评估了产水量、径流和地下水补给的时空变化。研究结果表明，从高分辨率土地利用数据得出的景观指标对水资源服务结果有显著影响。具体而言，这些模型显示，对于径流的空间预测，景观指标的平均重要性为16.8%，时间预测为17.8%。对于产水量，空间平均重要性为32.9%，时间平均重要性为43.5%，而地下水模拟显示空间重要性为14.09%，时间重要性为33.8%。确定的关键景观指标包括阔叶林的核心面积指数和非灌溉农业区的周长面积比，它们是产水量和地下水补给的关键时空预测指标。观察到了阈值，表明了使水文变异性最小化的景观配置。例如，当景观呈现高森林破碎化（针叶林斑块超过1000个）、低聚集度（聚集指数<75）和低连通性（凝聚指数<80）时，径流变化最小。同样，随着植被斑块（周长面积比<0.8）、农田（周长面积比<1）边界长度的减少以及低核心农业区（核心面积指数>8）的存在，地下水变化最小化。确定的阈值可为土地利用政策提供参考，如定向造林或作物多样化策略，以优化水资源服务的供给。
