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López Moreira Mazacotte GA, Tetzlaff D, Marx C, Warter MM, Wu S, Smith AA, Soulsby C. Integrated monitoring and modeling to disentangle the complex spatio-temporal dynamics of urbanized streams under drought stress. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:560. [PMID: 38767712 PMCID: PMC11106154 DOI: 10.1007/s10661-024-12666-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 04/25/2024] [Indexed: 05/22/2024]
Abstract
We have a poor understanding of how urban drainage and other engineered components interact with more natural hydrological processes in green and blue spaces to generate stream flow. This limits the scientific evidence base for predicting and mitigating the effects of future development of the built environment and climate change on urban water resources and their ecosystem services. Here, we synthesize > 20 years of environmental monitoring data to better understand the hydrological function of the 109-km2 Wuhle catchment, an important tributary of the river Spree in Berlin, Germany. More than half (56%) of the catchment is urbanized, leading to substantial flow path alterations. Young water from storm runoff and rapid subsurface flow provided around 20% of stream flow. However, most of it was generated by older groundwater (several years old), mainly recharged through the rural headwaters and non-urban green spaces. Recent drought years since 2018 showed that this base flow component has reduced in response to decreased recharge, causing deterioration in water quality and sections of the stream network to dry out. Attempts to integrate the understanding of engineered and natural processes in a traditional rainfall-runoff model were only partly successful due to uncertainties over the catchment area, effects of sustainable urban drainage, adjacent groundwater pumping, and limited conceptualization of groundwater storage dynamics. The study highlights the need for more extensive and coordinated monitoring and data collection in complex urban catchments and the use of these data in more advanced models of urban hydrology to enhance management.
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Affiliation(s)
| | - Doerthe Tetzlaff
- Department of Ecohydrology and Biogeochemistry, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany
- Geography Institute, Humboldt University of Berlin, Berlin, Germany
- IRI THESys, Humboldt University of Berlin, Berlin, Germany
| | - Christian Marx
- MOSAIC Research Group - Modelling Surface and Groundwater With Isotopes in Urban Catchments, Technische Universität Berlin, Berlin, Germany
| | - Maria Magdalena Warter
- Department of Ecohydrology and Biogeochemistry, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany
| | - Songjun Wu
- Department of Ecohydrology and Biogeochemistry, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany
- Geography Institute, Humboldt University of Berlin, Berlin, Germany
- IRI THESys, Humboldt University of Berlin, Berlin, Germany
| | - Aaron Andrew Smith
- Department of Ecohydrology and Biogeochemistry, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany
| | - Chris Soulsby
- Department of Ecohydrology and Biogeochemistry, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany
- MOSAIC Research Group - Modelling Surface and Groundwater With Isotopes in Urban Catchments, Technische Universität Berlin, Berlin, Germany
- School of Geosciences, Northern Rivers Institute, University of Aberdeen, Aberdeen, Scotland, UK
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2
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Xiao HB, Zhou C, Hu XD, Wang J, Wang L, Huang JQ, Yang FT, Zhao JS, Shi ZH. Subsurface hydrological connectivity controls nitrate export flux in a hilly catchment. WATER RESEARCH 2024; 253:121308. [PMID: 38377925 DOI: 10.1016/j.watres.2024.121308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 01/15/2024] [Accepted: 02/11/2024] [Indexed: 02/22/2024]
Abstract
Subsurface runoff represents the main pathway of nitrate transport in hilly catchments. The magnitude of nitrate export from a source area is closely related to subsurface hydrological connectivity, which refers to the linkage of separate regions of a catchment via subsurface runoff. However, understanding of how subsurface hydrological connectivity regulates catchment nitrate export remains insufficient. This study conducted high-frequency monitoring of shallow groundwater in a hilly catchment over 17 months. Subsurface hydrological connectivity of the catchment over 38 rainfall events was analyzed by combining topography-based upscaling of shallow groundwater and graph theory. Moreover, cross-correlation analysis was used to evaluate the time-series similarity between subsurface hydrological connectivity and nitrate flux during rainfall events. The results showed that the maximum subsurface hydrological connectivity during 32 out of 38 rainfall events was below 0.5. Although subsurface flow paths (i.e., the pathways of lateral subsurface runoff) exhibited clear dynamic extension and contraction during rainfall events, most areas in the catchment did not establish subsurface hydrological connectivity with the stream. The primary pattern of nitrate export was flushing (44.7%), followed by dilution (34.2%), and chemostatic behavior (21.1%). A threshold relationship between subsurface hydrological connectivity and nitrate flux was identified, with nitrate flux rapidly increasing after the subsurface connectivity strength exceeded 0.121. Moreover, the median value of cross-correlation coefficients reached 0.67, which indicated subsurface hydrological connectivity exerts a strong control on nitrate flux. However, this control effect is not constant and it increases with rainfall amount and intensity as a power function. The results of this study provide comprehensive insights into the subsurface hydrological control of catchment nitrate export.
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Affiliation(s)
- H B Xiao
- State Environmental Protection Key Laboratory of Soil Health and Green Remediation, Huazhong Agricultural University, Wuhan 430070, PR China; Jiangxi Academy of Water Science and Engineering, Nanchang, Jiangxi 330029, PR China
| | - C Zhou
- State Environmental Protection Key Laboratory of Soil Health and Green Remediation, Huazhong Agricultural University, Wuhan 430070, PR China
| | - X D Hu
- State Environmental Protection Key Laboratory of Soil Health and Green Remediation, Huazhong Agricultural University, Wuhan 430070, PR China
| | - J Wang
- State Environmental Protection Key Laboratory of Soil Health and Green Remediation, Huazhong Agricultural University, Wuhan 430070, PR China
| | - L Wang
- State Environmental Protection Key Laboratory of Soil Health and Green Remediation, Huazhong Agricultural University, Wuhan 430070, PR China
| | - J Q Huang
- Yangtze River Scientific Research Institute of Yangtze River Water Resources Commission, Wuhan 430010, PR China
| | - F T Yang
- Qianyanzhou Ecological Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, PR China
| | - J S Zhao
- State Environmental Protection Key Laboratory of Soil Health and Green Remediation, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Z H Shi
- State Environmental Protection Key Laboratory of Soil Health and Green Remediation, Huazhong Agricultural University, Wuhan 430070, PR China; Jiangxi Academy of Water Science and Engineering, Nanchang, Jiangxi 330029, PR China.
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3
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Zhi W, Appling AP, Golden HE, Podgorski J, Li L. Deep learning for water quality. NATURE WATER 2024; 2:228-241. [PMID: 38846520 PMCID: PMC11151732 DOI: 10.1038/s44221-024-00202-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 01/10/2024] [Indexed: 06/09/2024]
Abstract
Understanding and predicting the quality of inland waters are challenging, particularly in the context of intensifying climate extremes expected in the future. These challenges arise partly due to complex processes that regulate water quality, and arduous and expensive data collection that exacerbate the issue of data scarcity. Traditional process-based and statistical models often fall short in predicting water quality. In this Review, we posit that deep learning represents an underutilized yet promising approach that can unravel intricate structures and relationships in high-dimensional data. We demonstrate that deep learning methods can help address data scarcity by filling temporal and spatial gaps and aid in formulating and testing hypotheses via identifying influential drivers of water quality. This Review highlights the strengths and limitations of deep learning methods relative to traditional approaches, and underscores its potential as an emerging and indispensable approach in overcoming challenges and discovering new knowledge in water-quality sciences.
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Affiliation(s)
- Wei Zhi
- The National Key Laboratory of Water Disaster Prevention, Yangtze Institute for Conservation and Development, Key Laboratory of Hydrologic-Cycle and Hydrodynamic-System of Ministry of Water Resources, Hohai University, Nanjing, China
- Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA
| | | | - Heather E Golden
- Office of Research and Development, US Environmental Protection Agency, Cincinnati, OH, USA
| | - Joel Podgorski
- Department of Water Resources and Drinking Water, Swiss Federal Institute of Aquatic Science and Technology (EAWAG), Dübendorf, Switzerland
| | - Li Li
- Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA
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4
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Stenger R, Park J, Clague J. Routine stream monitoring data enables the unravelling of hydrological pathways and transfers of agricultural contaminants through catchments. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169370. [PMID: 38104825 DOI: 10.1016/j.scitotenv.2023.169370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 12/11/2023] [Accepted: 12/12/2023] [Indexed: 12/19/2023]
Abstract
Catchment-scale understanding of water and contaminant fluxes through all pathways is essential to address land use and climate change impacts on freshwater. However, few options exist to obtain this understanding for the many catchments worldwide for which streamflow and low-frequency water chemistry, but little other data exists. We applied the Bayesian chemistry-assisted hydrograph separation and load partitioning model (BACH) to 47 catchments with widely differing characteristics. As BACH relies on concentration differences between pathways, chemodynamic behaviour of a water constituent indicates its likely suitability as tracer. Typical tracers (e.g. silica, chloride) were unavailable, but Electrical Conductivity and a few monitored nutrients proved chemodynamic in most catchments. Using one of two tracer combinations (Total Nitrogen + Electrical Conductivity, Total Nitrogen + Total Phosphorus) allowed in 85 % of the catchments to estimate streamflow contributions by near-surface (NS), shallow groundwater (SGW), and deep groundwater (DGW) pathways and pathway-specific tracer concentrations and yields with acceptable confidence. In 46 catchments, at least two pathways contributed ≥20 % of the streamflow, and all three ≥20 % in 12 catchments, cautioning against the notion of a single 'dominant' pathway. In contrast to hydrometric hydrograph separation, BACH allows differentiation between 'young' (NS + SGW) and 'old' (DGW) water, which is crucial for the understanding of pollution in catchments with strong temporal gradients in land use intensity. Consistent with generally increasing land use intensity, and groundwater denitrification occurring in some catchments, Total Nitrogen (TN) concentrations were in most catchments higher in NS and SGW compared to DGW. In most catchments, the greatest fraction of the TN yield was conveyed by SGW (≈ 40-90 %). Exceptions were wet and hilly catchments under bush, where the NS transferred most of the very low yields, and three young volcanic catchments where the DGW transferred the majority of the yield due to particularly high DGW flow contributions.
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Affiliation(s)
- Roland Stenger
- Lincoln Agritech Ltd, Ruakura Research Centre, Hamilton 3214, New Zealand
| | - Jungho Park
- Lincoln Agritech Ltd, Ruakura Research Centre, Hamilton 3214, New Zealand
| | - Juliet Clague
- Lincoln Agritech Ltd, Ruakura Research Centre, Hamilton 3214, New Zealand.
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5
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Dao PU, Heuzard AG, Le TXH, Zhao J, Yin R, Shang C, Fan C. The impacts of climate change on groundwater quality: A review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169241. [PMID: 38072271 DOI: 10.1016/j.scitotenv.2023.169241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/02/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023]
Abstract
Groundwater has been known as the second largest freshwater storage in the world, following surface water. Over the years, groundwater has already been under overwhelming pressure to satisfy human needs for artificial activities around the world. Meanwhile, the most noticeable footprint of human activities is the impact of climate change. Climate change has the potential to change the physical and chemical properties of groundwater, thereby affecting its ecological functions. This study summarizes existing research affiliated with the possible effects of a changing climate on the quality of groundwater, including changes in water availability, increased salinity and pollution from extreme weather events, and the potentiality of seawater intrusion into coastal aquifers. Previous works dealing with groundwater-induced responses to the climate system and climate impacts on groundwater quality through natural and anthropogenic processes have been reviewed. The climate-induced changes in groundwater quality including pH, dissolved oxygen level, salinity, and concentrations of organic and inorganic compounds were assessed. Some future research directions are proposed, including exploring the potential changes in the occurrences and fate of micropollutants in groundwater, examining the relationship between the increase of microcystin in groundwater and climate change, studying the changes in the stability of metals and metal complexation, and completing studies across different regional climate regions.
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Affiliation(s)
- Phuong Uyen Dao
- Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Arnaud Guillaume Heuzard
- Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Thi Xuan Hoa Le
- Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Jing Zhao
- Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong.
| | - Ran Yin
- Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Chii Shang
- Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong; Hong Kong Branch of Chinese National Engineering Research Center for Control & Treatment of Heavy Metal Pollution, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Chihhao Fan
- Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan.
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6
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Marx C, Tetzlaff D, Hinkelmann R, Soulsby C. Effects of 66 years of water management and hydroclimatic change on the urban hydrology and water quality of the Panke catchment, Berlin, Germany. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 900:165764. [PMID: 37516173 DOI: 10.1016/j.scitotenv.2023.165764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 07/21/2023] [Accepted: 07/22/2023] [Indexed: 07/31/2023]
Abstract
Long-term records of combined stream flow and water chemistry can be an invaluable source of information on changes in the quantity and quality of water resources. To understand the effect of hydroclimate and water management on the heavily urbanized Panke catchment in Berlin, Germany, an extensive search, collation and digitization of historic data from various sources was undertaken. This integrated a unique 66-year spatially distributed record of stream water quality, a 21-year record of groundwater quality and a 31-year stream flow record. These data were analysed in the context of hydroclimatic variability, as well as the history and technological evolution of water resource management in the catchment. To contextualize the effect of droughts, "average" and wet years the Standard Precipitation Index (SPI) was applied. As upstream sites have been less regulated by human impacts, the flow regime is most sensitive to changes in hydroclimatic conditions, while downstream sites are more influenced by wastewater effluents, urban storm drains and inter-basin transfers for flood alleviation. However, at all sites, a general increase in maximum event discharge was observed until a recent drought, starting in 2018. In general, water quality in the catchment has gradually improved as a result of management change and increasingly effective wastewater treatment, though in some places legacy and/or contemporary urban and rural groundwater contamination may be affecting the stream. Hydroclimatic changes, particularly drought years can affect water quality classes, and alter the chemostatic/dynamic behaviour of catchment export patterns. These insights from the Panke catchment underline the importance of strategic adaptation and improvement of water treatment and water resource management in order to enhance the quality of urban water courses. It also demonstrates the importance of long-term integrated data sets.
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Affiliation(s)
- C Marx
- Chair of Water Resources Management and Modeling of Hydrosystems, Technische Universit at Berlin, Gustav-Meyer-Allee 25, 13355 Berlin, Germany.
| | - D Tetzlaff
- Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587 Berlin, Germany; Department of Geography, Humboldt-Universit¨at zu Berlin, Rudower Chaussee 16, 12489 Berlin, Germany; Northern Rivers Institute, University of Aberdeen, St. Mary's Building, Kings College, Old Aberdeen AB24 3UE, United Kingdom
| | - R Hinkelmann
- Chair of Water Resources Management and Modeling of Hydrosystems, Technische Universit at Berlin, Gustav-Meyer-Allee 25, 13355 Berlin, Germany
| | - C Soulsby
- Chair of Water Resources Management and Modeling of Hydrosystems, Technische Universit at Berlin, Gustav-Meyer-Allee 25, 13355 Berlin, Germany; Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587 Berlin, Germany; Northern Rivers Institute, University of Aberdeen, St. Mary's Building, Kings College, Old Aberdeen AB24 3UE, United Kingdom
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7
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Saha GK, Rahmani F, Shen C, Li L, Cibin R. A deep learning-based novel approach to generate continuous daily stream nitrate concentration for nitrate data-sparse watersheds. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 878:162930. [PMID: 36934914 DOI: 10.1016/j.scitotenv.2023.162930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 03/08/2023] [Accepted: 03/14/2023] [Indexed: 05/13/2023]
Abstract
High-frequency stream nitrate concentration provides critical insights into nutrient dynamics and can help to improve the effectiveness of management decisions to maintain a sustainable ecosystem. However, nitrate monitoring is conventionally conducted through lab analysis using in situ water samples and is typically at coarse temporal resolution. In the last decade, many agencies started collecting high-frequency (5-60 min intervals) nitrate data using optical sensors. The hypothesis of the study is that the data-driven models can learn the trend and temporal variability in nitrate concentration from high-frequency sensor-based nitrate data in the region and generate continuous nitrate data for unavailable data periods and data-limited locations. A Long Short-Term Memory (LSTM) model-based framework was developed to estimate continuous daily stream nitrate for dozens of gauge locations in Iowa, USA. The promising results supported the hypothesis; the LSTM model demonstrated median test-period Nash-Sutcliffe efficiency (NSE) = 0.75 and RMSE = 1.53 mg/L for estimating continuous daily nitrate concentration in 42 sites, which are unprecedented performance levels. Twenty-one sites (50 % of all sites) and thirty-four sites (76 % of all sites) demonstrated NSE > 0.75 and 0.50, respectively. The average nitrate concentration of neighboring sites was identified as a crucial determinant of continuous daily nitrate concentration. Seasonal model performance evaluation showed that the model performed effectively in the summer and fall seasons. About 26 sites showed correlations >0.60 between estimated nitrate concentration and discharge. The concentration-discharge (c-Q) relationship analysis showed that the study watersheds had four dominant nitrate transport patterns from landscapes to streams with increasing discharge, including the flushing pattern being the most dominant one. Stream nitrate estimation impedes due to data inadequacy. The modeling framework can be used to generate temporally continuous nitrate at nitrate data-limited regions with a nearby sensor-based nitrate gauge. Watershed planners and policymakers could utilize the continuous nitrate data to gain more information on the regional nitrate status and design conservation practices accordingly.
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Affiliation(s)
- Gourab Kumer Saha
- Department of Agricultural and Biological Engineering, The Pennsylvania State University, United States of America
| | - Farshid Rahmani
- Department of Civil and Environmental Engineering, The Pennsylvania State University, United States of America
| | - Chaopeng Shen
- Department of Civil and Environmental Engineering, The Pennsylvania State University, United States of America
| | - Li Li
- Department of Civil and Environmental Engineering, The Pennsylvania State University, United States of America
| | - Raj Cibin
- Department of Agricultural and Biological Engineering, The Pennsylvania State University, United States of America; Department of Civil and Environmental Engineering, The Pennsylvania State University, United States of America.
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8
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Kerins D, Li L. High Dissolved Carbon Concentration in Arid Rocky Mountain Streams. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:4656-4667. [PMID: 36897171 DOI: 10.1021/acs.est.2c06675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Warming in mountains is known to intensify aridity and threaten water availability globally. Its impacts on water quality, however, have remained poorly understood. Here we collate long-term (multi-year to decadal mean), baseline stream concentrations and fluxes of dissolved organic and inorganic carbon, two essential indicators of water quality and soil carbon response to warming, across more than 100 streams in the United States Rocky Mountains. Results show a universal pattern of higher mean concentrations in more arid mountain streams with lower mean discharge, a long-term climate measure. A watershed reactor model revealed less lateral export of dissolved carbon (via less water flow) out of the watersheds in more arid sites, leading to more accumulation and higher concentrations. Lower concentrations typically occur in cold, steep, and compact mountains with higher snow fraction and lower vegetation cover, which generally have higher discharge and carbon fluxes. Inferring from a space-for-time perspective, the results indicate that as warming intensifies, lateral fluxes of dissolved carbon will decrease but concentrations will increase in these mountain streams. This indicates deteriorating water quality and potentially elevated CO2 emission directly from the land (instead of streams) in the Rockies and other mountain areas in the future climate.
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Affiliation(s)
- Devon Kerins
- Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park 16802-1204, Pennsylvania, United States
| | - Li Li
- Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park 16802-1204, Pennsylvania, United States
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9
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Sadayappan K, Kerins D, Shen C, Li L. Nitrate concentrations predominantly driven by human, climate, and soil properties in US rivers. WATER RESEARCH 2022; 226:119295. [PMID: 36323218 DOI: 10.1016/j.watres.2022.119295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 10/11/2022] [Accepted: 10/22/2022] [Indexed: 06/16/2023]
Abstract
Nitrate is one of the most widespread and persistent pollutants in our time. Our understanding of nitrate dynamics has advanced substantially in the past decades, although its predominant drivers across gradients of climate, land use, and geology have remained elusive. Here we collated nitrate data from 2061 rivers along with 32 watershed characteristic indexes and developed machine learning models to reconstruct long-term mean (multi-year average) nitrate concentrations in the contiguous United States (CONUS). The trained models show similarly satisfactory model performance and can predict nitrate concentrations in chemically-ungauged places with about 70% accuracy. Further analysis revealed that five (out of 32) indexes (drivers) can explain about 70% of spatial variations in mean nitrate concentrations. The five influential drivers are nitrogen application rates Nrate and urban area Aurban% (human drivers), mean annual precipitation and temperature (climate drivers), and sand percent Sand% (soil property driver). Nitrate concentrations in undeveloped sites are primarily modulated by climate and soil property; they decrease with increasing mean discharge and Sand%. Nitrate concentrations in agriculture and urban sites increase with Nrate and Aurban% until reaching their apparent maxima around 10,000 kg/km2/yr and around 25%, respectively. Results indicate that nitrate concentrations may remain similar or increase with growing human population. In addition, nitrate concentrations can increase even without human input, as warming escalates water demand and reduces mean discharge in many places. These results allude to a conceptual model that highlights the impacts of distinct drivers: while human drivers predominate nitrogen input to land and rivers, climate drivers and soil properties modulate its transport and transformation, the balance of which determine long-term mean concentrations. Such mechanism-based insights and forecasting capabilities are essential for water management as we expect changing climate and growing agriculture and urbanization.
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Affiliation(s)
- Kayalvizhi Sadayappan
- Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Devon Kerins
- Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Chaopeng Shen
- Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Li Li
- Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA.
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10
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Li X, Wang J, Lin J, Yin W, Shi YY, Wang L, Xiao HB, Zhong ZM, Jiang H, Shi ZH. Hysteresis analysis reveals dissolved carbon concentration - discharge relationships during and between storm events. WATER RESEARCH 2022; 226:119220. [PMID: 36242935 DOI: 10.1016/j.watres.2022.119220] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 09/30/2022] [Accepted: 10/05/2022] [Indexed: 06/16/2023]
Abstract
The dissolved carbon concentration, which is responsible for aquatic ecosystem productivity and water quality, is tightly coupled with hydrological processes. Excess dissolved carbon may exacerbate eutrophication and hypoxia in aquatic ecosystems and lead to deterioration of water quality. Storm events dominate the dynamics of dissolved carbon concentrations, and this nonlinear behavior exhibits significant time scale dependence. Here, we identified inter- and intra-event variability in the dissolved carbon concentration-discharge (C-Q) relationship in an agriculture-intensive catchment. The driving factors of C-Q hysteresis patterns for dissolved inorganic carbon (DIC) and organic carbon (DOC) were quantified by redundancy analysis combined with hierarchical partitioning. At the inter-event scale, DIC exhibited mainly clockwise hysteresis, indicating an exhaustible, proximal source (e.g., groundwater). However, DOC hysteresis was generally counter-clockwise, indicating distal and plentiful sources (e.g., soil water) in the agricultural catchment. Hierarchical partitioning showed that total rainfall, peak discharge and flood intensity explained 28.38% of the total variation in C-Q hysteresis for DIC and 39.87% for DOC at the inter-event scale. At the intra-event scale, time series analysis of dissolved carbon concentration and discharge indicated the interconversion of supply limitation to transport limitation, which depends on the activation of the specific DIC or DOC source zones. These findings provide significant insights into understanding the dynamics of dissolved carbon during storm periods and are important for targeted watershed management practices aimed at reducing carbon loading to surface waters.
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Affiliation(s)
- X Li
- State Environmental Protection Key Laboratory of Soil Health and Green Remediation, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
| | - J Wang
- State Environmental Protection Key Laboratory of Soil Health and Green Remediation, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China.
| | - J Lin
- Nanjing Forestry University, Nanjing 210037, China
| | - W Yin
- Changjiang Water Resources Protection Institute, Wuhan 430051, China
| | - Y Y Shi
- State Environmental Protection Key Laboratory of Soil Health and Green Remediation, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
| | - L Wang
- State Environmental Protection Key Laboratory of Soil Health and Green Remediation, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
| | - H B Xiao
- State Environmental Protection Key Laboratory of Soil Health and Green Remediation, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
| | - Z M Zhong
- State Environmental Protection Key Laboratory of Soil Health and Green Remediation, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
| | - H Jiang
- Soil and Water Conservation Monitoring Centre, Danjiangkou 442700, China
| | - Z H Shi
- State Environmental Protection Key Laboratory of Soil Health and Green Remediation, College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China.
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11
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Sarkar S, Mukherjee A, Senapati B, Duttagupta S. Predicting Potential Climate Change Impacts on Groundwater Nitrate Pollution and Risk in an Intensely Cultivated Area of South Asia. ACS ENVIRONMENTAL AU 2022; 2:556-576. [PMID: 37101727 PMCID: PMC10125289 DOI: 10.1021/acsenvironau.2c00042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/22/2022] [Accepted: 08/22/2022] [Indexed: 11/28/2022]
Abstract
One of the potential impacts of climate change is enhanced groundwater contamination by geogenic and anthropogenic contaminants. Such impacts should be most evident in areas with high land-use change footprint. Here, we provide a novel documentation of the impact on groundwater nitrate (GWNO3 ) pollution with and without climate change in one of the most intensely groundwater-irrigated areas of South Asia (northwest India) as a consequence of changes in land use and agricultural practices at present and predicted future times. We assessed the probabilistic risk of GWNO3 pollution considering climate changes under two representative concentration pathways (RCPs), i.e., RCP 4.5 and 8.5 for 2030 and 2040, using a machine learning (Random Forest) framework. We also evaluated variations in GWNO3 distribution against a no climate change (NCC) scenario considering 2020 status quo climate conditions. The climate change projections showed that the annual temperatures would rise under both RCPs. The precipitation is predicted to rise by 5% under RCP 8.5 by 2040, while it would decline under RCP 4.5. The predicted scenarios indicate that the areas at high risk of GWNO3 pollution will increase to 49 and 50% in 2030 and 66 and 65% in 2040 under RCP 4.5 and 8.5, respectively. These predictions are higher compared to the NCC condition (43% in 2030 and 60% in 2040). However, the areas at high risk can decrease significantly by 2040 with restricted fertilizer usage, especially under the RCP 8.5 scenario. The risk maps identified the central, south, and southeastern parts of the study area to be at persistent high risk of GWNO3 pollution. The outcomes show that the climate factors may impose a significant influence on the GWNO3 pollution, and if fertilizer inputs and land uses are not managed properly, future climate change scenarios can critically impact the groundwater quality in highly agrarian areas.
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Affiliation(s)
- Soumyajit Sarkar
- School of Environmental Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
| | - Abhijit Mukherjee
- School of Environmental Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
- Department of Geology and Geophysics, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
| | - Balaji Senapati
- Centre For Oceans, Rivers, Atmosphere and Land Science (CORAL), Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India
| | - Srimanti Duttagupta
- Graduate School of Public Health, San Diego State University, San Diego, California 92182, United States
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12
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Tesoriero AJ, Stratton LE, Miller MP. Influence of redox gradients on nitrate transport from the landscape to groundwater and streams. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 800:150200. [PMID: 34625279 DOI: 10.1016/j.scitotenv.2021.150200] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 09/02/2021] [Accepted: 09/03/2021] [Indexed: 06/13/2023]
Abstract
Increases in nitrogen applications to the land surface since the 1950s have led to a cascade of negative environmental impacts, including degradation of drinking water supplies, nutrient enrichment of aquatic ecosystems and contributions to global climate change. In this study, groundwater, streambed porewater, and stream sampling were used to establish trends in nitrate concentrations and how redox gradients influence nitrate transport across diverse glacial terranes. Decadal sampling has found that elevated nitrate concentrations in shallow groundwater beneath cropland have been sustained for decades. Redox gradients established in the saturated zone using dissolved O2, iron, nitrate and excess N2 from denitrification suggest that nitrate-bearing zones are thin in glacial terranes dominated by fine materials. These thin nitrate-bearing zones lead to suboxic, low nitrate streambed porewater and limit the contributions of nitrate to streams from slow-flow groundwater. In contrast, thick oxic zones in more coarse-grained glacial terranes allow nitrate to reach deeper groundwater, resulting in streambed porewater with elevated nitrate concentrations and causing a large portion of stream nitrate to be derived from slow-flow groundwater. Groundwater age tracer data indicate that denitrification occurs more quickly in the terrane dominated by fine material than in the more coarse-grained terrane. The quicker depletion of nitrate in the more fine-grained terrane suggests that the thinner oxic zone in this terrane is due, in part, to the greater availability and reactivity of electron donors in this terrane than in the more coarse-grained terrane. Groundwater age tracer data and hydrograph separation analysis suggest that saturated zone lag times between when changes in land use practices occur and when changes in stream water are fully observed may vary widely across hydrogeologic settings.
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Affiliation(s)
- Anthony J Tesoriero
- U.S. Geological Survey, 2130 S.W. 5th Avenue, Portland, OR 97201, United States of America.
| | - Laurel E Stratton
- U.S. Geological Survey, 2130 S.W. 5th Avenue, Portland, OR 97201, United States of America
| | - Matthew P Miller
- U.S. Geological Survey, 3215 Marine St., Boulder, CO 80303, United States of America
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13
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Lu ZH, Abdelhai Senosy I, Zhou DD, Yang ZH, Guo HM, Liu X. Synthesis and adsorption properties investigation of Fe3O4@ZnAl-LDH@MIL-53(Al) for azole fungicides removal from environmental water. Sep Purif Technol 2021. [DOI: 10.1016/j.seppur.2021.119282] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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14
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Hood RR, Shenk GW, Dixon RL, Smith SMC, Ball WP, Bash JO, Batiuk R, Boomer K, Brady DC, Cerco C, Claggett P, de Mutsert K, Easton ZM, Elmore AJ, Friedrichs MAM, Harris LA, Ihde TF, Lacher I, Li L, Linker LC, Miller A, Moriarty J, Noe GB, Onyullo G, Rose K, Skalak K, Tian R, Veith TL, Wainger L, Weller D, Zhang YJ. The Chesapeake Bay Program Modeling System: Overview and Recommendations for Future Development. Ecol Modell 2021; 465:1-109635. [PMID: 34675451 PMCID: PMC8525429 DOI: 10.1016/j.ecolmodel.2021.109635] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
The Chesapeake Bay is the largest, most productive, and most biologically diverse estuary in the continental United States providing crucial habitat and natural resources for culturally and economically important species. Pressures from human population growth and associated development and agricultural intensification have led to excessive nutrient and sediment inputs entering the Bay, negatively affecting the health of the Bay ecosystem and the economic services it provides. The Chesapeake Bay Program (CBP) is a unique program formally created in 1983 as a multi-stakeholder partnership to guide and foster restoration of the Chesapeake Bay and its watershed. Since its inception, the CBP Partnership has been developing, updating, and applying a complex linked modeling system of watershed, airshed, and estuary models as a planning tool to inform strategic management decisions and Bay restoration efforts. This paper provides a description of the 2017 CBP Modeling System and the higher trophic level models developed by the NOAA Chesapeake Bay Office, along with specific recommendations that emerged from a 2018 workshop designed to inform future model development. Recommendations highlight the need for simulation of watershed inputs, conditions, processes, and practices at higher resolution to provide improved information to guide local nutrient and sediment management plans. More explicit and extensive modeling of connectivity between watershed landforms and estuary sub-areas, estuarine hydrodynamics, watershed and estuarine water quality, the estuarine-watershed socioecological system, and living resources will be important to broaden and improve characterization of responses to targeted nutrient and sediment load reductions. Finally, the value and importance of maintaining effective collaborations among jurisdictional managers, scientists, modelers, support staff, and stakeholder communities is emphasized. An open collaborative and transparent process has been a key element of successes to date and is vitally important as the CBP Partnership moves forward with modeling system improvements that help stakeholders evolve new knowledge, improve management strategies, and better communicate outcomes.
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Affiliation(s)
- Raleigh R Hood
- Horn Point Laboratory, University of Maryland Center for Environmental Science, P.O. Box 775, Cambridge, MD 21613, USA
| | - Gary W Shenk
- USGS Chesapeake Bay Program Office, 410 Severn Avenue, Suite 109, Annapolis, MD, 21403, USA
| | - Rachel L Dixon
- Chesapeake Research Consortium, 645 Contees Wharf Road, Edgewater, MD 21037, USA
| | - Sean M C Smith
- University of Maine, School of Earth and Climate Sciences, Bryand Global Science Center, Orono, ME 04469, USA
| | - William P Ball
- Chesapeake Research Consortium, 645 Contees Wharf Road, Edgewater, MD 21037, USA
| | - Jesse O Bash
- Environmental Protection Agency, Center for Environmental Measurement and Modeling, 109 T.W. Alexander Drive, Durham, NC 27709, USA
| | - Rich Batiuk
- U.S. Environmental Protection Agency, Chesapeake Bay Program Office, 410 Severn Avenue, Suite 109, Annapolis, MD, 21403, USA
| | - Kathy Boomer
- The Nature Conservancy, 114 South Washington Street, Easton, MD 21601, USA
| | - Damian C Brady
- Darling Marine Center, University of Maine, 193 Clarks Cove Rd, Walpole, ME 04573, USA
| | - Carl Cerco
- #U.S. Army Corps of Engineers Waterways Experiment Station, P.O. Box 631, Vicksburg, MS 39180, USA
| | - Peter Claggett
- USGS Chesapeake Bay Program Office, 410 Severn Avenue, Suite 109, Annapolis, MD, 21403, USA
| | - Kim de Mutsert
- University of Southern Mississippi, Gulf Coast Research Laboratory, 703 East Beach Drive, Ocean Springs, MS 39564, USA
| | | | - Andrew J Elmore
- Appalachian Laboratory, University of Maryland Center for Environmental Science, 301 Braddock Rd, Frostburg, MD 21532, USA
| | - Marjorie A M Friedrichs
- Virginia Institute of Marine Science, William & Mary, 1375 Greate Rd, Gloucester Point, VA 23062, USA
| | - Lora A Harris
- Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science, P.O. Box 38, Solomons, MD 20688, USA
| | - Thomas F Ihde
- Patuxent Environmental & Aquatic Research Laboratory, Morgan State University, 10545 Mackall Road, St. Leonard, MD 20685, USA
| | - Iara Lacher
- Smithsonian Conservation Biology Institute, 1500 Remount Rd, Front Royal, VA 22630 USA
| | - Li Li
- Department of Civil and Environmental Engineering, Penn State University, University Park, PA 16802, USA
| | - Lewis C Linker
- U.S. Environmental Protection Agency, Chesapeake Bay Program Office, 410 Severn Avenue, Suite 109, Annapolis, MD, 21403, USA
| | - Andrew Miller
- Department of Geography and Environmental Systems, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
| | - Julia Moriarty
- Institute for Arctic and Alpine Research, Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder CO 80309, USA
| | - Gregory B Noe
- Florence Bascom Geoscience Center, U.S. Geological Survey, 12201 Sunrise Valley Drive, MS926A, Reston, VA 20192, USA
| | - George Onyullo
- District of Columbia Department of Energy and Environment, 1200 First Street NE, Washington DC 20002, USA
| | - Kenneth Rose
- Horn Point Laboratory, University of Maryland Center for Environmental Science, P.O. Box 775, Cambridge, MD 21613, USA
| | - Katie Skalak
- National Research Program, U.S. Geological Survey, 12201Sunrise Valley Drive, Reston, VA 20192, USA
| | - Richard Tian
- USGS Chesapeake Bay Program Office, 410 Severn Avenue, Suite 109, Annapolis, MD, 21403, USA
| | - Tamie L Veith
- U.S. Department of Agriculture Agricultural Research Service, Pasture Systems and Watershed Management Research Unit, Building 3702, Curtin Road, University Park, PA 16802, USA
| | - Lisa Wainger
- Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science, P.O. Box 38, Solomons, MD 20688, USA
| | - Donald Weller
- Smithsonian Environmental Research Center, 647 Contees Wharf Road, Edgewater, MD 21037, USA
| | - Yinglong Joseph Zhang
- Virginia Institute of Marine Science, William & Mary, 1375 Greate Rd, Gloucester Point, VA 23062, USA
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15
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Liu W, Birgand F, Tian S, Chen C. Event-scale hysteresis metrics to reveal processes and mechanisms controlling constituent export from watersheds: A review ✰. WATER RESEARCH 2021; 200:117254. [PMID: 34107427 DOI: 10.1016/j.watres.2021.117254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 06/12/2023]
Abstract
Due to the increased availability of high-frequency measurements of stream chemistry provided by in situ sensors, researchers have gained more access to relationships between stream discharge and constituent concentrations (C-Q relationships) at event-scales. Existing studies reveal that event-scale C-Q relationships are mostly non-linear and exhibit temporal lags between peaks (or troughs) of hydrographs and chemographs, resulting in apparent hysteresis effects. In this paper, we summarize and introduce tools and methods in hysteresis analysis, especially the history and progresses of metrics to quantify hysteresis patterns. In addition, this paper provides a typical workflow to conduct event-scale hysteresis analysis, such as how to obtain the access to high-frequency measurements, existing methods to delineate storm events, approaches to classify and quantify hysteresis patterns, possible features/properties controlling hysteresis patterns, statistical methods to identify features at play, and strategies to deliver the inferences from hysteresis analysis. Lastly, we discuss some potential limitations that arise in the workflow and possible future work to address the challenges, including the development of advanced quantitative hysteresis metrics, generalized and standardized tools to delineate events and the integration of hysteresis analysis with numerical modeling. This paper aims to provide a critical overview of technical approaches for hysteresis analysis for researchers and hopefully foster their interests to advance our understanding of complex mechanisms in event-scale hydro-biogeochemical processes.
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Affiliation(s)
- Wenlong Liu
- College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, 225009, China.
| | - François Birgand
- Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC 27606 USA
| | - Shiying Tian
- Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC 27606 USA
| | - Cheng Chen
- College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, 225009, China
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16
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Brookfield AE, Hansen AT, Sullivan PL, Czuba JA, Kirk MF, Li L, Newcomer ME, Wilkinson G. Predicting algal blooms: Are we overlooking groundwater? THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 769:144442. [PMID: 33482544 DOI: 10.1016/j.scitotenv.2020.144442] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 12/04/2020] [Accepted: 12/07/2020] [Indexed: 06/12/2023]
Abstract
Significant advances in understanding and predicting freshwater algal bloom dynamics have emerged in response to both increased occurrence and financial burden of nuisance and harmful blooms. Several factors have been highlighted as key controls of bloom occurrence, including nutrient dynamics, local hydrology, climatic perturbations, watershed geomorphology, biogeochemistry, food-web control, and algal competition. However, a major research gap continues to be the degree to which groundwater inputs modulate microbial biomass production and food-web dynamics at the terrestrial-aquatic interface. We present a synthesis of groundwater related algal bloom literature, upon which we derive a foundational hypothesis: long residence times cause groundwater to be geochemically and biologically distinct from surface water, allowing groundwater inputs to modulate algal bloom dynamics (growth, decline, toxicity) through its control over in-stream water chemistry. Distinct groundwater chemistry can support or prevent algal blooms, depending on specific local conditions. We highlight three mechanisms that influence the impact of groundwater discharge on algal growth: 1) redox state of the subsurface, 2) extent of water-rock interactions, and 3) stability of groundwater discharge. We underscore that in testing hypotheses related to groundwater control over algal blooms, it is critical to understand how changes in land use, water management, and climate will influence groundwater dynamics and, thus, algal bloom probabilities. Given this challenge, we argue that advances in both modeling and data integration, including genomics data and integrated process-based models that capture groundwater dynamics, are needed to illuminate mechanistic controls and improve predictions of algal blooms.
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Affiliation(s)
- Andrea E Brookfield
- Department of Earth and Environmental Sciences, University of Waterloo, Waterloo, ON, Canada.
| | - Amy T Hansen
- Civil, Environmental & Architectural Engineering, University of Kansas, Lawrence, KS, USA
| | - Pamela L Sullivan
- College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR, USA
| | - Jonathan A Czuba
- Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA, USA
| | - Matthew F Kirk
- Department of Geology, Kansas State University, Manhattan, KS, USA
| | - Li Li
- Department of Civil and Environmental Engineering, Penn State, University Park, PA, USA
| | - Michelle E Newcomer
- Climate & Ecosystems Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Grace Wilkinson
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA; Center for Limnology, University of Wisconsin-Madison, Wisconsin, USA
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17
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Hare DK, Helton AM, Johnson ZC, Lane JW, Briggs MA. Continental-scale analysis of shallow and deep groundwater contributions to streams. Nat Commun 2021; 12:1450. [PMID: 33664258 PMCID: PMC7933412 DOI: 10.1038/s41467-021-21651-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 01/21/2021] [Indexed: 11/24/2022] Open
Abstract
Groundwater discharge generates streamflow and influences stream thermal regimes. However, the water quality and thermal buffering capacity of groundwater depends on the aquifer source-depth. Here, we pair multi-year air and stream temperature signals to categorize 1729 sites across the continental United States as having major dam influence, shallow or deep groundwater signatures, or lack of pronounced groundwater (atmospheric) signatures. Approximately 40% of non-dam stream sites have substantial groundwater contributions as indicated by characteristic paired air and stream temperature signal metrics. Streams with shallow groundwater signatures account for half of all groundwater signature sites and show reduced baseflow and a higher proportion of warming trends compared to sites with deep groundwater signatures. These findings align with theory that shallow groundwater is more vulnerable to temperature increase and depletion. Streams with atmospheric signatures tend to drain watersheds with low slope and greater human disturbance, indicating reduced stream-groundwater connectivity in populated valley settings.
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Affiliation(s)
- Danielle K Hare
- Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT, USA.
- Volunteer, U.S. Geological Survey, Earth Systems Processes Division, Hydrogeophysics Branch, Storrs, CT, USA.
| | - Ashley M Helton
- Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT, USA
- Center for Environmental Sciences & Engineering, University of Connecticut, Storrs, CT, USA
| | - Zachary C Johnson
- U.S. Geological Survey, Washington Water Science Center, Tacoma, WA, USA
| | - John W Lane
- U.S. Geological Survey, Earth System Processes Division, Hydrogeophysics Branch, Storrs, CT, USA
| | - Martin A Briggs
- U.S. Geological Survey, Earth System Processes Division, Hydrogeophysics Branch, Storrs, CT, USA
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18
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Zhi W, Feng D, Tsai WP, Sterle G, Harpold A, Shen C, Li L. From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale? ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:2357-2368. [PMID: 33533608 DOI: 10.1021/acs.est.0c06783] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Dissolved oxygen (DO) reflects river metabolic pulses and is an essential water quality measure. Our capabilities of forecasting DO however remain elusive. Water quality data, specifically DO data here, often have large gaps and sparse areal and temporal coverage. Earth surface and hydrometeorology data, on the other hand, have become largely available. Here we ask: can a Long Short-Term Memory (LSTM) model learn about river DO dynamics from sparse DO and intensive (daily) hydrometeorology data? We used CAMELS-chem, a new data set with DO concentrations from 236 minimally disturbed watersheds across the U.S. The model generally learns the theory of DO solubility and captures its decreasing trend with increasing water temperature. It exhibits the potential of predicting DO in "chemically ungauged basins", defined as basins without any measurements of DO and broadly water quality in general. The model however misses some DO peaks and troughs when in-stream biogeochemical processes become important. Surprisingly, the model does not perform better where more data are available. Instead, it performs better in basins with low variations of streamflow and DO, high runoff-ratio (>0.45), and winter precipitation peaks. Results here suggest that more data collections at DO peaks and troughs and in sparsely monitored areas are essential to overcome the issue of data scarcity, an outstanding challenge in the water quality community.
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Affiliation(s)
- Wei Zhi
- Department of Civil and Environmental Engineering, The Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Dapeng Feng
- Department of Civil and Environmental Engineering, The Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Wen-Ping Tsai
- Department of Civil and Environmental Engineering, The Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Gary Sterle
- Department of Natural Resources & Environmental Science, The University of Nevada, Reno, Nevada 89557, United States
| | - Adrian Harpold
- Department of Natural Resources & Environmental Science, The University of Nevada, Reno, Nevada 89557, United States
| | - Chaopeng Shen
- Department of Civil and Environmental Engineering, The Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Li Li
- Department of Civil and Environmental Engineering, The Pennsylvania State University, State College, Pennsylvania 16802, United States
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19
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Zhang Q, Webber JS, Moyer DL, Chanat JG. An approach for decomposing river water-quality trends into different flow classes. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 755:143562. [PMID: 33199002 DOI: 10.1016/j.scitotenv.2020.143562] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 10/29/2020] [Accepted: 11/03/2020] [Indexed: 06/11/2023]
Abstract
A number of statistical approaches have been developed to quantify the overall trend in river water quality, but most approaches are not intended for reporting separate trends for different flow conditions. We propose an approach called FN2Q, which is an extension of the flow-normalization (FN) procedure of the well-established WRTDS ("Weighted Regressions on Time, Discharge, and Season") method. The FN2Q approach provides a daily time series of low-flow and high-flow FN flux estimates that represent the lower and upper half of daily riverflow observations that occurred on each calendar day across the period of record. These daily estimates can be summarized into any time period of interest (e.g., monthly, seasonal, or annual) for quantifying trends. The proposed approach is illustrated with an application to a record of total nitrogen concentration (632 samples) collected between 1985 and 2018 from the South Fork Shenandoah River at Front Royal, Virginia (USA). Results show that the overall FN flux of total nitrogen has declined in the period of 1985-2018, which is mainly attributable to FN flux decline in the low-flow class. Furthermore, the decline in the low-flow class was highly correlated with wastewater effluent loads, indicating that the upgrades of treatment technology at wastewater treatment facilities have likely led to water-quality improvement under low-flow conditions. The high-flow FN flux showed a spike around 2007, which was likely caused by increased delivery of particulate nitrogen associated with sediment transport. The case study demonstrates the utility of the FN2Q approach toward not only characterizing the changes in river water quality but also guiding the direction of additional analysis for capturing the underlying drivers. The FN2Q approach (and the published code) can easily be applied to widely available river monitoring records to quantify water-quality trends under different flow conditions to enhance understanding of river water-quality dynamics.
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Affiliation(s)
- Qian Zhang
- University of Maryland Center for Environmental Science, Chesapeake Bay Program Office, Annapolis, MD, USA.
| | - James S Webber
- U.S. Geological Survey, Virginia and West Virginia Water Science Center, Richmond, VA, USA
| | - Douglas L Moyer
- U.S. Geological Survey, Virginia and West Virginia Water Science Center, Richmond, VA, USA
| | - Jeffrey G Chanat
- U.S. Geological Survey, Virginia and West Virginia Water Science Center, Richmond, VA, USA
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20
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Wherry SA, Tesoriero AJ, Terziotti S. Factors Affecting Nitrate Concentrations in Stream Base Flow. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:902-911. [PMID: 33356185 DOI: 10.1021/acs.est.0c02495] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Elevated nitrogen concentrations in streams and rivers in the Chesapeake Bay watershed have adversely affected the ecosystem health of the bay. Much of this nitrogen is derived as nitrate from groundwater that discharges to streams as base flow. In this study, boosted regression trees (BRTs) were used to relate nitrate concentrations in base flow (n = 156) to explanatory variables describing nitrogen sources, geology, and soil and catchment characteristics. From these relations, a BRT model was developed to predict base flow nitrate concentrations in streams throughout the Chesapeake Bay watershed. The highest base flow nitrate concentrations were associated with intensive agricultural land use, carbonate geology, and sparse riparian canopy, which suggested that reduced nitrogen inputs, particularly over carbonate terrane, are critical for limiting nitrate concentrations. The lowest nitrate concentrations in the BRT model were associated with extensive riparian canopy, high levels of organic carbon in soils, and suboxic conditions at shallow depths, which suggested that denitrification in the subsurface, particularly in the riparian zone, is limiting base flow nitrate concentrations. Nitrate transport from aquifers to streams can take decades to occur, resulting in decades-long lag times between the time when a land-use activity is implemented and when its effects are fully observed in streams. Predictive models of base flow nitrate concentrations in streams will help identify which portions of a watershed are likely to have large fractions of total stream nitrogen load derived from pathways with significant lag times.
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Affiliation(s)
- Susan A Wherry
- U.S. Geological Survey, 2130 SW 5th Avenue, Portland, Oregon 97201, United States
| | - Anthony J Tesoriero
- U.S. Geological Survey, 2130 SW 5th Avenue, Portland, Oregon 97201, United States
| | - Silvia Terziotti
- U.S. Geological Survey, 3916 Sunset Ridge Road, Raleigh, North Carolina 27607, United States
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