Landscape functioning in reservoir water quality prediction: Current use and predictive capacity

Author:

Portela Ana Paula123ORCID,Gonçalves João134,Cardoso Ana Sofia123,Vaz Ana Sofia13,de Lima Lucas Terres13,Pinto Ivo2567,Rodrigues Sara26ORCID,Antunes Sara C.26,Honrado João123

Affiliation:

1. CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, Campus de Vairão Universidade do Porto Vairão 4485‐661 Portugal

2. Departamento de Biologia, Faculdade de Ciências Universidade do Porto Porto 4169‐007 Portugal

3. BIOPOLIS Program in Genomics, Biodiversity and Land Planning CIBIO, Campus de Vairão Vairão 4485‐661 Portugal

4. proMetheus — Research Unit in Materials, Energy and Environment for Sustainability Instituto Politécnico de Viana do Castelo Viana do Castelo 4900‐347 Portugal

5. ICBAS, Instituto de Ciências Biomédicas de Abel Salazar Universidade do Porto Rua de Jorge Viterbo Ferreira 228 Porto 4050‐313 Portugal

6. CIMAR/CIIMAR, Centro Interdisciplinar de Investigação Marinha e Ambiental Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões Avenida General Norton de Matos, S/N Matosinhos 4450‐208 Portugal

7. UMIB‐ICBAS, Unidade Multidisciplinar de Investigação Biomédica—Instituto Ciências Biomédicas Abel Salazar Universidade do Porto Rua de Jorge Viterbo Ferreira 228 Porto 4050‐313 Portugal

Abstract

AbstractReservoirs fulfil several societal needs, including water storage, energy production, flood control and recreation. However, the interruption of the river continuum may cause water quality declines that compromise water use. The surrounding landscape is a key driver of water quality variation in space and time, both across and within catchments. Therefore, understanding how landscape composition, structure and functioning influence reservoir water quality can help address management challenges. Here, we aim to investigate the current use and predictive capacity of landscape functioning indicators for reservoir water quality prediction. First, we carried out a literature review to investigate which landscape factors are most frequently studied as drivers of water quality in lentic systems. Then, we tested the predictive capacity of landscape functioning indicators in four reservoirs in Portugal using linear mixed models and multi‐model inference. The literature review shows that most studies assess the effects of landscape composition while landscape functioning is rarely included. Our test using four reservoirs suggests that landscape functioning indicators, namely greenness and brightness, can complement landscape composition and structure indicators, improving the capacity to predict total suspended solids, chlorophyll‐a, and total phosphorous. Landscape functioning indicators portrayed temporal variability in ecosystem dynamics that was not encompassed by landscape composition or structure indicators and may be relevant to predict specific water quality parameters. Our results show landscape functioning indicators can improve modelling of landscape contributions to water quality and thus have great potential to contribute to monitoring, modelling and forecast systems for water quality and ecological status.

Funder

European Regional Development Fund

Fundação para a Ciência e a Tecnologia

Publisher

Wiley

Reference71 articles.

1. Agência Portuguesa do Ambiente. (2022a).Plano de Gestão de Região Hidrográfica 3° Ciclo Douro (RH3) Fichas de Massa de Água ‐ Volume I. Retrieved fromhttps://apambiente.pt/agua/3o-ciclo-de-planeamento-2022-2027

2. Agência Portuguesa do Ambiente. (2022b).Plano de Gestão de Região Hidrográfica 3° Ciclo Douro (RH3) Fichas de Massa de Água ‐ Volume II. Retrieved fromhttps://apambiente.pt/agua/3o-ciclo-de-planeamento-2022-2027

3. Agência Portuguesa do Ambiente. (2022c).Plano de Gestão de Região Hidrográfica 3° Ciclo Guadiana (RH7) Fichas de Massa de Água. Retrieved fromhttps://apambiente.pt/agua/3o-ciclo-de-planeamento-2022-2027

4. Agência Portuguesa do Ambiente. (2022d).Plano de Gestão de Região Hidrográfica 3° Ciclo Vouga Mondego e Lis (RH4A) Fichas de Massa de Água. Retrieved fromhttps://apambiente.pt/agua/3o-ciclo-de-planeamento-2022-2027

5. Agência Portuguesa do Ambiente. (2023).SNIRH: Sistema Nacional de Informação de Recursos Hídricos. (24 September 2020). Retrieved fromhttps://snirh.apambiente.pt/

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3