Effects of forest disturbance on water yield and peak flow in low‐relief glaciated catchments assessed with Bayesian parameter estimation
Empirical assessment of how forest disturbance affects streamflow has been traditionally limited to small, experimental catchments. However, larger catchments where landscape management occurs have emergent drivers of streamflow at scale, and thus may exhibit novel responses to land cover disturbanc...
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description | Empirical assessment of how forest disturbance affects streamflow has been traditionally limited to small, experimental catchments. However, larger catchments where landscape management occurs have emergent drivers of streamflow at scale, and thus may exhibit novel responses to land cover disturbance. We used statistical models of water yield and annual maximum peak streamflow for multiple large (> 50 km2) forested catchments in the low‐relief glaciated region of central North America to investigate how forest disturbance and climatic variability affect water yield and peak flows in similar landscapes. We used linear models, linear mixed effects models, and probabilistic flood‐frequency analysis with Bayesian parameter estimation in two case studies. These included: (1) a wildfire that burned ~30% of the 650 km2 wilderness Upper Kawishiwi catchment, and (2) 11 catchments within the St. Louis River Basin ranging from 56 to 8880 km2 with a patchwork disturbance regime wherein ~0.25% to 1% of the catchment is harvested or converted to non‐forest land use each year. We also assessed the most likely hydrological recovery year after forest disturbance, and the relative importance of stationary and nonstationary drivers of streamflow. We found forest disturbance correlated with declines in water yield for low‐level disturbance regimes in some catchments, but that water yield increased in response to the large‐scale wildfire. Positive and negative associations of forest disturbance with peak flows were observed. Hydrologic recovery time ranged from 5 to 13 years for water yield and peak flows following disturbance. Despite these effects of forest disturbance on streamflow, effects of climatic variability and stationary catchment size factors were more prominent streamflow drivers. Basins larger than ~50 km2 in low‐relief glaciated regions can be impacted by forest cover change even on |
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The catchment approach has been traditionally limited to small catchments; however, larger catchments may have emergent streamflow drivers. We used statistical models of water yield and peak streamflow for forested catchments in Minnesota to investigate forest disturbance effects on streamflow at large scales. Streamflow was resilient to forest cover change, but we did observe some impacts. Stationary basin‐scale signals were the primary drivers of streamflow, explaining ~80% of the magnitude of the 50% annual exceedance probability peak flow.</description><identifier>ISSN: 0885-6087</identifier><identifier>EISSN: 1099-1085</identifier><identifier>DOI: 10.1002/hyp.14956</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Bayesian analysis ; Bayesian theory ; Catchment area ; catchment science ; Catchments ; Climate variability ; Deforestation ; Disturbance ; Empirical analysis ; forest hydrology ; Forest management ; Forests ; Frequency analysis ; Hydrology ; Land cover ; Land use ; Landscape ; Mathematical models ; northern hydrology ; Parameter estimation ; Probability theory ; Recovery ; Recovery time ; River basins ; Statistical analysis ; Statistical models ; Stream discharge ; Stream flow ; Variability ; Water ; Water yield ; Wilderness ; Wildfires</subject><ispartof>Hydrological processes, 2023-08, Vol.37 (8), p.n/a</ispartof><rights>2023 John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.</rights><rights>2023 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2576-bd8ed4a679b66daa15a01570e4dcbee70560d6671792ba5138a215d2b9da41803</cites><orcidid>0000-0002-7221-8047 ; 0000-0002-4529-0369</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fhyp.14956$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fhyp.14956$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids></links><search><creatorcontrib>McEachran, Zachary P.</creatorcontrib><creatorcontrib>Reese, Gordon C.</creatorcontrib><creatorcontrib>Karwan, Diana L.</creatorcontrib><creatorcontrib>Slesak, Robert A.</creatorcontrib><creatorcontrib>Vogeler, Jody</creatorcontrib><title>Effects of forest disturbance on water yield and peak flow in low‐relief glaciated catchments assessed with Bayesian parameter estimation</title><title>Hydrological processes</title><description>Empirical assessment of how forest disturbance affects streamflow has been traditionally limited to small, experimental catchments. However, larger catchments where landscape management occurs have emergent drivers of streamflow at scale, and thus may exhibit novel responses to land cover disturbance. We used statistical models of water yield and annual maximum peak streamflow for multiple large (> 50 km2) forested catchments in the low‐relief glaciated region of central North America to investigate how forest disturbance and climatic variability affect water yield and peak flows in similar landscapes. We used linear models, linear mixed effects models, and probabilistic flood‐frequency analysis with Bayesian parameter estimation in two case studies. These included: (1) a wildfire that burned ~30% of the 650 km2 wilderness Upper Kawishiwi catchment, and (2) 11 catchments within the St. Louis River Basin ranging from 56 to 8880 km2 with a patchwork disturbance regime wherein ~0.25% to 1% of the catchment is harvested or converted to non‐forest land use each year. We also assessed the most likely hydrological recovery year after forest disturbance, and the relative importance of stationary and nonstationary drivers of streamflow. We found forest disturbance correlated with declines in water yield for low‐level disturbance regimes in some catchments, but that water yield increased in response to the large‐scale wildfire. Positive and negative associations of forest disturbance with peak flows were observed. Hydrologic recovery time ranged from 5 to 13 years for water yield and peak flows following disturbance. Despite these effects of forest disturbance on streamflow, effects of climatic variability and stationary catchment size factors were more prominent streamflow drivers. Basins larger than ~50 km2 in low‐relief glaciated regions can be impacted by forest cover change even on <30% of basin area, but climatic variability and catchment spatial scale has a larger effect than forest disturbance.
The catchment approach has been traditionally limited to small catchments; however, larger catchments may have emergent streamflow drivers. We used statistical models of water yield and peak streamflow for forested catchments in Minnesota to investigate forest disturbance effects on streamflow at large scales. Streamflow was resilient to forest cover change, but we did observe some impacts. Stationary basin‐scale signals were the primary drivers of streamflow, explaining ~80% of the magnitude of the 50% annual exceedance probability peak flow.</description><subject>Bayesian analysis</subject><subject>Bayesian theory</subject><subject>Catchment area</subject><subject>catchment science</subject><subject>Catchments</subject><subject>Climate variability</subject><subject>Deforestation</subject><subject>Disturbance</subject><subject>Empirical analysis</subject><subject>forest hydrology</subject><subject>Forest management</subject><subject>Forests</subject><subject>Frequency analysis</subject><subject>Hydrology</subject><subject>Land cover</subject><subject>Land use</subject><subject>Landscape</subject><subject>Mathematical models</subject><subject>northern hydrology</subject><subject>Parameter estimation</subject><subject>Probability theory</subject><subject>Recovery</subject><subject>Recovery time</subject><subject>River basins</subject><subject>Statistical analysis</subject><subject>Statistical models</subject><subject>Stream discharge</subject><subject>Stream flow</subject><subject>Variability</subject><subject>Water</subject><subject>Water yield</subject><subject>Wilderness</subject><subject>Wildfires</subject><issn>0885-6087</issn><issn>1099-1085</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp1kL1OxDAQhC0EEsdBwRtYoqIIt87FjlPC6fiRkKCAgiraxBvOkEuCnVOUjp6GZ-RJMBwt0krTfDOzGsaOBZwJgHi2GrszkWRS7bCJgCyLBGi5yyagtYwU6HSfHXj_AgAJaJiwj2VVUdl73la8ah35nhvr-40rsCmJtw0fsCfHR0u14dgY3hG-8qpuB24bHuTr_dNRbanizzWWNtCGl9iXqzU1IRe9p3CGD7Zf8QscyVtseIcO1_STHCrtGnvbNodsr8La09GfTtnj5fJhcR3d3l3dLM5vozKWqYoKo8kkqNKsUMogCokgZAqUmLIgSkEqMEqlIs3iAqWYa4yFNHGRGUyEhvmUnWxzO9e-bUJ__tJuXBMq81hLLZTKdBqo0y1VutZ7R1XeufCoG3MB-c_Wedg6_906sLMtO9iaxv_B_Prpfuv4BnVshC4</recordid><startdate>202308</startdate><enddate>202308</enddate><creator>McEachran, Zachary P.</creator><creator>Reese, Gordon C.</creator><creator>Karwan, Diana L.</creator><creator>Slesak, Robert A.</creator><creator>Vogeler, Jody</creator><general>John Wiley & Sons, Inc</general><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7ST</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0002-7221-8047</orcidid><orcidid>https://orcid.org/0000-0002-4529-0369</orcidid></search><sort><creationdate>202308</creationdate><title>Effects of forest disturbance on water yield and peak flow in low‐relief glaciated catchments assessed with Bayesian parameter estimation</title><author>McEachran, Zachary P. ; Reese, Gordon C. ; Karwan, Diana L. ; Slesak, Robert A. ; Vogeler, Jody</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2576-bd8ed4a679b66daa15a01570e4dcbee70560d6671792ba5138a215d2b9da41803</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Bayesian analysis</topic><topic>Bayesian theory</topic><topic>Catchment area</topic><topic>catchment science</topic><topic>Catchments</topic><topic>Climate variability</topic><topic>Deforestation</topic><topic>Disturbance</topic><topic>Empirical analysis</topic><topic>forest hydrology</topic><topic>Forest management</topic><topic>Forests</topic><topic>Frequency analysis</topic><topic>Hydrology</topic><topic>Land cover</topic><topic>Land use</topic><topic>Landscape</topic><topic>Mathematical models</topic><topic>northern hydrology</topic><topic>Parameter estimation</topic><topic>Probability theory</topic><topic>Recovery</topic><topic>Recovery time</topic><topic>River basins</topic><topic>Statistical analysis</topic><topic>Statistical models</topic><topic>Stream discharge</topic><topic>Stream flow</topic><topic>Variability</topic><topic>Water</topic><topic>Water yield</topic><topic>Wilderness</topic><topic>Wildfires</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>McEachran, Zachary P.</creatorcontrib><creatorcontrib>Reese, Gordon C.</creatorcontrib><creatorcontrib>Karwan, Diana L.</creatorcontrib><creatorcontrib>Slesak, Robert A.</creatorcontrib><creatorcontrib>Vogeler, Jody</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environment Abstracts</collection><jtitle>Hydrological processes</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>McEachran, Zachary P.</au><au>Reese, Gordon C.</au><au>Karwan, Diana L.</au><au>Slesak, Robert A.</au><au>Vogeler, Jody</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Effects of forest disturbance on water yield and peak flow in low‐relief glaciated catchments assessed with Bayesian parameter estimation</atitle><jtitle>Hydrological processes</jtitle><date>2023-08</date><risdate>2023</risdate><volume>37</volume><issue>8</issue><epage>n/a</epage><issn>0885-6087</issn><eissn>1099-1085</eissn><abstract>Empirical assessment of how forest disturbance affects streamflow has been traditionally limited to small, experimental catchments. However, larger catchments where landscape management occurs have emergent drivers of streamflow at scale, and thus may exhibit novel responses to land cover disturbance. We used statistical models of water yield and annual maximum peak streamflow for multiple large (> 50 km2) forested catchments in the low‐relief glaciated region of central North America to investigate how forest disturbance and climatic variability affect water yield and peak flows in similar landscapes. We used linear models, linear mixed effects models, and probabilistic flood‐frequency analysis with Bayesian parameter estimation in two case studies. These included: (1) a wildfire that burned ~30% of the 650 km2 wilderness Upper Kawishiwi catchment, and (2) 11 catchments within the St. Louis River Basin ranging from 56 to 8880 km2 with a patchwork disturbance regime wherein ~0.25% to 1% of the catchment is harvested or converted to non‐forest land use each year. We also assessed the most likely hydrological recovery year after forest disturbance, and the relative importance of stationary and nonstationary drivers of streamflow. We found forest disturbance correlated with declines in water yield for low‐level disturbance regimes in some catchments, but that water yield increased in response to the large‐scale wildfire. Positive and negative associations of forest disturbance with peak flows were observed. Hydrologic recovery time ranged from 5 to 13 years for water yield and peak flows following disturbance. Despite these effects of forest disturbance on streamflow, effects of climatic variability and stationary catchment size factors were more prominent streamflow drivers. Basins larger than ~50 km2 in low‐relief glaciated regions can be impacted by forest cover change even on <30% of basin area, but climatic variability and catchment spatial scale has a larger effect than forest disturbance.
The catchment approach has been traditionally limited to small catchments; however, larger catchments may have emergent streamflow drivers. We used statistical models of water yield and peak streamflow for forested catchments in Minnesota to investigate forest disturbance effects on streamflow at large scales. Streamflow was resilient to forest cover change, but we did observe some impacts. Stationary basin‐scale signals were the primary drivers of streamflow, explaining ~80% of the magnitude of the 50% annual exceedance probability peak flow.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1002/hyp.14956</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0002-7221-8047</orcidid><orcidid>https://orcid.org/0000-0002-4529-0369</orcidid></addata></record> |
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subjects | Bayesian analysis Bayesian theory Catchment area catchment science Catchments Climate variability Deforestation Disturbance Empirical analysis forest hydrology Forest management Forests Frequency analysis Hydrology Land cover Land use Landscape Mathematical models northern hydrology Parameter estimation Probability theory Recovery Recovery time River basins Statistical analysis Statistical models Stream discharge Stream flow Variability Water Water yield Wilderness Wildfires |
title | Effects of forest disturbance on water yield and peak flow in low‐relief glaciated catchments assessed with Bayesian parameter estimation |
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