Evaluating the suitability of large-scale datasets to estimate nitrogen loads and yields across different spatial scales.

Autor: Suárez-Castro AF; Australian Rivers Institute, School of Environment and Science, Griffith University, 170 Kessels Rd, Nathan, Queensland 4111, Australia. Electronic address: a.suarezcastro@griffith.edu.au., Robertson DM; U.S. Geological Survey, 1 Gifford Pinchot Drive, Madison, WI 53726, USA., Lehner B; Department of Geography, McGill University, Montreal, Québec H3A 0B9, Canada., de Souza ML; National Water and Sanitation Agency, Setor Policial, Área 5, Quadra 3, Bloco M, Brasilia 7010-200, Brazil., Kittridge M; Headwaters Hydrology, Wellington, New Zealand., Saad DA; U.S. Geological Survey, 1 Gifford Pinchot Drive, Madison, WI 53726, USA., Linke S; CSIRO Land & Water, Dutton Park, Queensland, Australia., McDowell RW; AgResearch, Lincoln Science Centre, Private Bag 4749, Christchurch 8140, New Zealand; Faculty of Agriculture and Life Sciences, Lincoln University, P O Box 84, Christchurch, Lincoln 7647, New Zealand., Ranjbar MH; Australian Rivers Institute, School of Environment and Science, Griffith University, 170 Kessels Rd, Nathan, Queensland 4111, Australia., Ausseil O; Australian Rivers Institute, School of Environment and Science, Griffith University, 170 Kessels Rd, Nathan, Queensland 4111, Australia; Traverse Environmental, Wellington, New Zealand., Hamilton DP; Australian Rivers Institute, School of Environment and Science, Griffith University, 170 Kessels Rd, Nathan, Queensland 4111, Australia.
Jazyk: angličtina
Zdroj: Water research [Water Res] 2025 Jan 01; Vol. 268 (Pt A), pp. 122520. Date of Electronic Publication: 2024 Oct 05.
DOI: 10.1016/j.watres.2024.122520
Abstrakt: Decision makers are often confronted with inadequate information to predict nutrient loads and yields in freshwater ecosystems at large spatial scales. We evaluate the potential of using data mapped at large spatial scales (regional to global) and often coarse resolution to predict nitrogen yields at varying smaller scales (e.g., at the catchment and stream reach level). We applied the SPAtially Referenced Regression On Watershed attributes (SPARROW) model in three regions: the Upper Midwest part of the United States, New Zealand, and the Grande River Basin in southeastern Brazil. For each region, we compared predictions of nitrogen delivery between models developed using novel large-scale datasets and those developed using local-scale datasets. Large-scale models tended to underperform the local-scale models in poorly monitored areas. Despite this, large-scale models are well suited to generate hypotheses about relative effects of different nutrient source categories (point and urban, agricultural, native vegetation) and to identify knowledge gaps across spatial scales when data are scarce. Regardless of the spatial resolution of the predictors used in the models, a representative network of water quality monitoring stations is key to improve the performance of large-scale models used to estimate loads and yields. We discuss avenues of research to understand how this large-scale modelling approach can improve decision making for managing catchments at local scales, particularly in data poor regions.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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