Long-term trends of streamflow, sediment load and nutrient fluxes from the Mississippi River Basin: Impacts of climate change and human activities

Yin, Shihua; Gao, Guangyao; Li, Yanjiao; Xu, Y. Jun; Turner, R. Eugene; Ran, Lishan; Fu, Bojie

doi:10.1016/j.jhydrol.2022.128822

Cited by 44 publications

(14 citation statements)

References 70 publications

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“…The lower Mississippi region is projected to have a significant increase in wetlands habitats by about 10% (Fig. 7f ), which can be caused by the increased streamflow 56 . Notably, the Everglades wetland region in Florida exhibits different sensitivities under the two emission scenarios with a 30% increase in wetland habitats under SSP126 and a 14% decrease in wetland habitats under SSP585 (Fig.…”

Section: Resultsmentioning

confidence: 99%

Climate change will reduce North American inland wetland areas and disrupt their seasonal regimes

Xu,

Bisht,

Tan

et al. 2024

Nat Commun

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Climate change can alter wetland extent and function, but such impacts are perplexing. Here, changes in wetland characteristics over North America from 25° to 53° North are projected under two climate scenarios using a state-of-the-science Earth system model. At the continental scale, annual wetland area decreases by ~10% (6%-14%) under the high emission scenario, but spatiotemporal changes vary, reaching up to ±50%. As the dominant driver of these changes shifts from precipitation to temperature in the higher emission scenario, wetlands undergo substantial drying during summer season when biotic processes peak. The projected disruptions to wetland seasonality cycles imply further impacts on biodiversity in major wetland habitats of upper Mississippi, Southeast Canada, and the Everglades. Furthermore, wetlands are projected to significantly shrink in cold regions due to the increased infiltration as warmer temperature reduces soil ice. The large dependence of the projections on climate change scenarios underscores the importance of emission mitigation to sustaining wetland ecosystems in the future.

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Section: Resultsmentioning

confidence: 99%

Climate change will reduce North American inland wetland areas and disrupt their seasonal regimes

Xu,

Bisht,

Tan

et al. 2024

Nat Commun

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“…Lower P is thought to be a result of a broader regional “oligotrophication” trend that includes greater demand from forest and wetland ecosystems (Huser et al., 2018) and less leaching from farmed land. The Mississippi river in the United States has seen a decreasing N: P trend since the 1970s (Yin et al., 2023). A slight reduction in N was attributed in part to sedimentation and denitrification during transport, as well as policy interventions such as the clean air/water acts.…”

Section: Discussionmentioning

confidence: 99%

“…A slight reduction in N was attributed in part to sedimentation and denitrification during transport, as well as policy interventions such as the clean air/water acts. The P increase was primarily a result of higher fertilizer and manure use, despite some retention of particulate P by engineered structures such as dams (Yin et al., 2023). In brief, historical changes in ecosystem stoichiometry are regionally differently influenced by climate and human activities, including interventions aimed at reducing nutrient exports from the landscape.…”

Section: Discussionmentioning

confidence: 99%

Decadal Changes in Anthropogenic Inputs and Precipitation Influence Riverine Exports of Carbon, Nitrogen, and Phosphorus, and Alter Ecosystem Level Stoichiometry

Shousha,

Maranger,

Lapierre

2023

Global Biogeochemical Cycles

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Changes in precipitation and land use influence carbon (C), nitrogen (N) and phosphorus (P) exports from land to receiving waters. However, how these drivers differentially alter elemental inputs and impact subsequent ecosystem stoichiometry over time remains poorly understood. Here we quantified long‐term (1979‐2020) trends in C, N, and P exports at three sites along the mainstem of a north temperate river in Québec, Canada, that successively drains forested, urban, and more agriculturally impacted land‐use areas. Riverine N and to a lesser degree C exports tended to increase over time, with major inter‐annual variation largely resolved by changes in precipitation. Historical increases in net anthropogenic N inputs on land (NANI) also explained increases in riverine N exports, with about 35% of NANI reaching the river annually. Despite higher Net anthropogenic P inputs, NAPI, over time, P exports tended to decrease at all riverine sites. This decrease in P at the forested site was more gradual, whereas a precipitous drop was observed at the downstream urban site, following legislated P removal in municipal wastewater. Changes in historical ecosystem stoichiometry reflected the differential elemental exports due to natural and anthropogenic drivers and ranged from 174: 23: 1 to 547: 76: 1 over the years. Our work shows how C, N, and P have responded to different drivers in the same catchment over the last four decades, and how their differential riverine exports have influenced ecosystem stoichiometry.This article is protected by copyright. All rights reserved.

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“…Human activity has affected carbon fate in water environments by changing hydrological flow, agriculture, urbanization, reclamation projects, mariculture, etc. , The temperature rise caused by climate change has significant impacts on carbon fixation, transport, and emissions in water environments by affecting the organic matter degradation rate and microbial communities. , Quantifying the contribution of human activity and climate change to carbon fate in aquatic environments is necessary to address future challenges.…”

Section: Human Activity and Climate Change In Ghg Emissions Modelingmentioning

confidence: 99%

Machine Learning Provides Opportunities to Recognize Greenhouse Gas Emissions from Water at a Large Scale

Deng,

Hu,

2023

ACS EST Water

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Water environments (e.g., oceans, lakes, and rivers) are important carbon sinks and sources and contribute to the carbon cycle of the earth's ecosystem. Machine learning provides a potential solution for recognizing greenhouse gas (GHG) emissions from water based on big data analysis. Data-driven machine learning can comprehensively recognize the key environmental drivers that affect GHG emissions. However, several urgent issues should be addressed to guarantee machine learning recognition of GHG emissions from water. For example, matching in situ data and databases is the greatest challenge in conducting large-scale machine learning research. It is imperative to unify in situ data collection methods and improve database quality (e.g., spatiotemporal matching and high resolution). Quantifying the contributions of human activity and climate change to GHG emissions from water is urgently needed to resolve future challenges. Beyond providing recognition and prediction, machine learning, due to its interpretability, can optimize the carbon cycle model; thus, empirical formulas for machine learning deserve attention. Overall, machine learning can manage large-scale and complicated data for climate change regarding GHG emissions from water.

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Long-term trends of streamflow, sediment load and nutrient fluxes from the Mississippi River Basin: Impacts of climate change and human activities

Cited by 44 publications

References 70 publications

Climate change will reduce North American inland wetland areas and disrupt their seasonal regimes

Climate change will reduce North American inland wetland areas and disrupt their seasonal regimes

Decadal Changes in Anthropogenic Inputs and Precipitation Influence Riverine Exports of Carbon, Nitrogen, and Phosphorus, and Alter Ecosystem Level Stoichiometry

Machine Learning Provides Opportunities to Recognize Greenhouse Gas Emissions from Water at a Large Scale

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