你能走多低?测量低流量的普遍挑战和前进的道路

IF 5.1 2区 地球科学 Q1 LIMNOLOGY
Erin C. Seybold, Anna Bergstrom, C. Nathan Jones, Amy J. Burgin, Sam Zipper, Sarah E. Godsey, Walter K. Dodds, Margaret A. Zimmer, Margaret Shanafield, Thibault Datry, Raphael D. Mazor, Mathis L. Messager, Julian D. Olden, Adam Ward, Songyan Yu, Kendra E. Kaiser, Arial Shogren, Richard H. Walker
{"title":"你能走多低?测量低流量的普遍挑战和前进的道路","authors":"Erin C. Seybold,&nbsp;Anna Bergstrom,&nbsp;C. Nathan Jones,&nbsp;Amy J. Burgin,&nbsp;Sam Zipper,&nbsp;Sarah E. Godsey,&nbsp;Walter K. Dodds,&nbsp;Margaret A. Zimmer,&nbsp;Margaret Shanafield,&nbsp;Thibault Datry,&nbsp;Raphael D. Mazor,&nbsp;Mathis L. Messager,&nbsp;Julian D. Olden,&nbsp;Adam Ward,&nbsp;Songyan Yu,&nbsp;Kendra E. Kaiser,&nbsp;Arial Shogren,&nbsp;Richard H. Walker","doi":"10.1002/lol2.10356","DOIUrl":null,"url":null,"abstract":"<p>Water resource management is facing mounting challenges associated with water scarcity, including interactive effects of a changing climate and increased water demand (Craig et al. <span>2017</span>). Climate change is increasing drought severity in many regions (Cook et al. <span>2020</span>), while demand for limited water supplies depletes water resources (de Graaf et al. <span>2019</span>). Combined, these stressors result in lower and more variable flows in streams and rivers (Zipper et al. <span>2021</span>), particularly in arid regions (Hammond et al. <span>2021</span>). Despite challenges posed by low-flow conditions, the majority of resources (e.g., time, funding) for monitoring streamflow have historically focused on high-water concerns, such as ensuring navigation and predicting floods (Vörösmarty et al. <span>2001</span>; Ruhi et al. <span>2018</span>), in larger, perennially-flowing systems (Krabbenhoft et al. <span>2022</span>).</p><p>Low-flow conditions (Mauger et al. <span>2021</span>), which we define as streams or rivers with little downstream surface water flow caused by small volumes or very low downstream velocities (i.e., slackwater), are increasingly prevalent and thus necessitate greater focus on quantification approaches. Streamflow is the underlying physical template structuring biotic and abiotic processes, biogeochemical cycling, and ecological communities in river systems; thus, inaccurate low-flow measurements can propagate to and hinder diverse analyses requiring accurate low-flow data, ranging from drought characterization (Hammond et al. <span>2022</span>), environmental flow allocations (Neachell and Petts <span>2019</span>), ecological function assessments (Leigh and Datry <span>2017</span>), species conservation plans (Lopez et al. <span>2022</span>), and streamflow forecasting (Forzieri et al. <span>2014</span>).</p><p>We posit that a lack of low-flow measurement techniques leaves monitoring networks ill-equipped to inform water management, which is a fundamental challenge that must be addressed to ensure sustainable water management in the future. Our objectives are to: (1) demonstrate the widespread challenges in low-flow measurement across an existing monitoring network in the United States, (2) discuss limitations of current streamflow measurement methods in low-flow conditions, (3) present a DST for choosing among existing measurement methods, and (4) highlight important methodological developments needed to improve low-flow measurement and monitoring. Such methodological progress is a prerequisite for understanding how low flows will respond to changing climate and human demands, thereby supporting management and policy actions seeking to avoid or minimize these impacts.</p><p>Point measurements of streamflow are essential for short- and long-term studies and monitoring, and can be made using many different methods (Turnipseed and Sauer <span>2010</span>). If conducted over a range of flow conditions, discrete streamflow measurements can be used to develop a rating curve which relates stage and discharge, allowing for long-term, continuous quantification of discharge via stage sensors (Turnipseed and Sauer <span>2010</span>). We focus our analysis and discussion on methods for point measurements of streamflow, but emphasize that limitations in these approaches have implications for the accuracy of longer-term streamflow monitoring via rating curve development.</p><p>To quantify the prevalence of substandard low-flow measurements, we examined manual point measurements of streamflow from 8008 U.S. Geological Survey (USGS) gages across the continental United States in the GAGES II dataset (Falcone <span>2011</span>), which is a dataset of sites with either 20+ years of discharge since 1950 or that were operational as of 2009 (Appendix S1). For each manual streamflow measurement, we collected the quality code assigned by USGS hydrographers immediately after making the discharge measurement: “Poor” quality is assigned when uncertainty in the discharge measurement is estimated to be above 8%, “fair” when uncertainty is estimated to be less than 8%, good when uncertainty is estimated to be less than 5%, and excellent when uncertainty is estimated to be less than 2% (Turnipseed and Sauer <span>2010</span>). These quality codes are a qualitative method for estimating the accuracy of individual discharge measurements based on suitability of the channel cross-section, flow state, and other flow conditions (Turnipseed and Sauer <span>2010</span>).</p><p>For each gage, we identified the minimum streamflow value associated with a “good” manual flow measurement and calculated the percent of each gage's daily streamflow record below the minimum “good” threshold. To ensure our results were not overly sensitive to the value of the minimum “good” threshold, we also compared the percentage of each gage's streamflow record below two additional thresholds: (1) streamflow value corresponding to the minimum “fair” measurement, and (2) average of minimum “fair” and minimum “good” thresholds (<i>see</i> Table S1 for details), and obtained comparable results. The “minimum good” metric provides a conservative estimate of the duration of flow measurements with high uncertainty for each site; it only considers uncertainty related to manual measurements and does not account for additional uncertainty in stage measurements stemming from low-flow conditions. We interrogated the USGS network because it represents a high standard that many individual investigators use as a benchmark, and because it provided a large dataset relating manual streamflow measurements with qualitative assessments of quality/uncertainty. We performed all analyses in R version 4.2.1 (R Core Team <span>2022</span>) and obtained USGS data from the National Water Information System using the DataRetrieval Package (De Cicco et al. <span>2022</span>).</p><p>Across the GAGES II network, the average percentage of flow records below the minimum good measurement was 8.4%, indicating high overall quality of the streamflow measurements. However, we found that 393 gages (~ 5.5%) had at least 50% of flow records below the minimum good flow value, 68 of which had over 95% of flow records below the minimum good flow threshold (Fig. 1A). Sites with a high percentage of streamflow below the minimum “good” threshold are widely distributed across diverse climatic zones, land uses, and hydrologic settings, although the greatest density of high uncertainty records are concentrated in the arid southwestern United States where low flows and water management issues linked to scarcity are pervasive (Brown et al. <span>2019</span>).</p><p>To provide an example of the difficulties in making low-flow measurements, we focused on the gage for Kings Creek near Manhattan KS (USGS Gage 06879650), a well-studied, grassland stream with a long continuous record (1979–present). Only 73 of the 238 manual flow measurements (~ 31%) were considered “good” or “excellent” (Fig. 1B). The relatively low incidence of “good” manual flow measurements at Kings Creek resulted in over 58.6% of the daily flow record (from 1980 to 2021) being below the lowest “good” flow measurement, with the proportion below that threshold in a given water year ranging from 2.5% to 100%. This underscores that even for a given site, the relative importance of accurate low-flow measurements will vary from year-to-year, with greatest impact during dry years (Fig. 1C). Furthermore, uncertainties in low-flow measurements may propagate into subsequent estimates of nutrient export, which may lead to some annual load estimates to be much less certain than others. Systems with frequent low flows and flashy high flows may also face highly uncertain streamflow measurements at the high flow end of the rating curve, leading to additional sources of uncertainty. While a sensitivity analysis of uncertainty propagation in streamflow is beyond the scope of this paper, our analysis highlights many areas in the United States where current methods are poorly suited to capture low-flow conditions.</p><p>Three general categories of methods comprise the toolbox available to most practitioners. These include: (1) velocity-area methods; (2) tracer-based methods using salt or dye; and (3) measuring stage at a known streambed geometry (e.g., flume or weir) or capturing flow at a channel constriction (WMO <span>2010</span>). Most methods tend to be inaccurate or unusable under low-flow conditions (Hamilton <span>2008</span>) for three reasons: (1) low water velocities and/or shallow water depths (Fig. 2A,B), (2) mobile streambeds and/or irregular channels (Fig. 2D,E), and (3) high proportions of flow in the subsurface (Fig. 2C–E).</p><p>Many streams transition from visible surface water flow to very slow or imperceptible movement of water, which is sometimes spatially discontinuous or pooled. Low velocities can lead to poor tracer mixing and recovery when using dilution gaging methods (Fig. 2A). High channel width-to-depth ratios (i.e., very wide channels with shallow water) can also lead to poor tracer mixing and the inability to fully submerge velocimeters (Fig. 2E). Furthermore, highly variable bed elevations (e.g., rocks and boulders) or emergent vegetation can further reduce the accuracy of velocity measurements and even render them impossible (Fig. 2D). Finally, estimates of discharge based on velocity-area methods only measure surface-water flow and therefore are not directly comparable to tracer-based estimates, which capture some subsurface flow. This is particularly relevant in low-flow conditions which often exhibit a greater proportion of hyporheic flow. These general problems are not mutually exclusive; indeed, multiple issues can arise in low-flow settings, leaving practitioners unsure about which method to use and leading to considerable uncertainty in low-flow measurements.</p><p>Given these challenges, we present a DST that reflects our collective experience working in low-flow systems, and describes how we approach applying existing discharge methods given the complicating factors that dominate low-flow systems (Fig. 3). The aim of the DST is to offer guidance on a systematic way to apply consistent methods to complex systems. This tool assumes the chosen location is the best available site (i.e., there are no better sites within a reasonable distance upstream or downstream) and highlights what conditions should be avoided in site selection. The DST is not intended to be a data-driven study on the optimal way to measure low flows, rather it is offering informed opinions on what methods tend to work best in specific contexts from experts who frequently attempt flow measurements under non-ideal conditions. In compiling the DST, we also highlight conditions where method development should be prioritized, which we hope catalyzes further discussion and method advances within the water resource community.</p><p>The initial bifurcation in this DST separates sites by whether water is visibly flowing or not (Fig. 3). We define visible flow as whether material in the water (e.g., leaves) can be observed moving downstream. If there is no visible movement, fewer options exist to measure flow. If streamflow is visible, the DST prompts a series of questions regarding channel cross-section and water depth to help practitioners identify the most suitable flow measurement for their site (Fig. 3). We acknowledge that the pathways and nodes are not equally likely to be encountered. For example, very few locations have natural constriction points for which the bucket method is suitable (Fig. 2F), even though it appears twice (Fig. 3). Furthermore, three nodes terminate in “no widely used methods.” In our experience, the majority of sites where we work (numbering in the dozens, examples in Fig. 2) fall into nodes characterized by “no widely used method” for at least part of the year, leaving us unable to accurately measure hydrologic fluxes and limiting subsequent analyses like long-term nutrient flux estimates. While this DST can be used to help practitioners identify the best possible methods, we acknowledge that under many low-flow conditions, even a recommended method can lead to suboptimal discharge measurements with relatively high error.</p><p>Selecting a method to measure discharge requires practitioners to identify the degree of precision needed for their study and consider trade-offs between precision and resource costs. For some studies, hydrologic parameters that are easier to measure—like depth, wetted width/area, or approximate flow state—may be sufficient (Jaeger et al. <span>2023</span>). In contrast, biogeochemistry studies for which water movement is a key variable for calculating nutrient loads (Gómez-Gener et al. <span>2021</span>) may require greater precision than studies focused on aquatic habitat. Other trade-offs, including personnel costs, measurement frequency, and available time to conduct a measurement may outweigh the scientific considerations given in Fig. 3. At low but visible flows, dilution gaging can be used but may take hours to days, rather than minutes to an hour required at moderate to high flow conditions. In addition, dilution gaging at low flows often results in non-optimal breakthrough curves from incomplete mixing that are not suitable for discharge estimates. Portable flumes/weirs are faster to implement but require modifying the channel, for example manually creating berms to concentrate flow through a flume (Fig. 2C), which may not be possible for many reasons. While the DST provides recommendations for general categories of measurement methods, further modifications of each method can help accommodate specific flow conditions (e.g., different variations on the application of dilution gaging; Table S1). We provide suggestions for situations where modifications of standard methods may be desirable, and further challenges in applying those modifications in Table S1.</p><p>In streams and rivers, streamflow is the underlying physical template structuring biotic and abiotic processes, biogeochemical cycling, and ecological communities. Discharge is used to assess the degree of connectivity between tributaries and quantify movement of solutes through a stream network. Time series of discharge are key inputs for models of aquatic ecosystem function and biogeochemical cycling, and the desired output of hydrologic models identifying factors driving streamflow and predicting responses to anthropogenic change. All of these applications require accurate discharge measurements across the full range of flow variability.</p><p>While there is no universal answer to the question of “what percent error is acceptable when measuring low flows,” we argue the general need for a high degree of accuracy is clear. Although absolute changes in streamflow in low-flow systems may be small (e.g., changes from 0.01 to 0.02 m<sup>3</sup>/s), this represents a large relative change within the system (100%). Small changes in discharge at low flows can have substantial consequences for habitat extent and suitability (Rolls et al. <span>2012</span>). Detection of long-term trends is hampered by imprecise or uncertain data, which may cause trends in vulnerable low-flow systems to go unquantified (Whitfield and Hendrata <span>2006</span>). Environmental flow regulations require precise data for enforcement (Neachell and Petts <span>2019</span>), and uncertain low-flow data can complicate implementation and enforcement. Finally, there are many systems ranging from large, arid rivers to small streams where difficult-to-measure low-flow conditions are the norm and thus prevent accurate streamflow measurements across the flow-duration curve, leaving sites with minimal data for research and management purposes. Although low flows may represent a smaller component of annual water or solute fluxes than high flows in many systems, they are critical for understanding and predicting hydrological, ecological, and biogeochemical dynamics in river systems. This is not possible without robust low-flow discharge measurements.</p><p>In addition to providing guidance for systematically deciding which methods to employ in determining low-flow discharge, our DST (Fig. 3) highlights areas of critical need for method development and uncertainty assessment. In some cases, further modification and optimization of existing methods may be sufficient (e.g., Table S1). However, there are conditions for which entirely new methods need to be developed or refined, such as: (1) slackwater pools (Fig. 2A); (2) wide, shallow, irregular, or threaded channels (Fig. 2E), particularly in locations with no opportunity for channel modification; (3) reaches with dense emergent vegetation; and (4) reaches where wind strongly affects water surface velocities. These conditions are commonly found in freshwaters but share similarities with coastal settings, opening up the potential for method transfer to/from coastal hydrology (e.g., Birgand et al. <span>2022</span>).</p><p>There are promising recent technological advances including micro velocity sensors (Osorno et al. <span>2018</span>), time-lapse imagery analysis from trail cameras and videos (Birgand et al. <span>2022</span>; Chapman et al. <span>2022</span>; Dolcetti et al. <span>2022</span>) or radar altimetry (Bandini et al. <span>2020</span>), and presence/absence sensors for measuring water surface extent (Chapin et al. <span>2014</span>;). Emerging tools like time-lapse imagery analysis and water presence/absence sensors may improve our understanding of the spatiotemporal variation in the hydrologic state of low-flow systems by providing an assessment of surface water presence at the time of streamflow measurements, or in the absence of suitable discharge measurement approaches. However, more work must be done to advance these methods because as of now they only estimate stage or water presence/absence, leaving the difficulties of estimating discharge unresolved. Finally, there may be settings in which modeling or mathematical relationship development may be the best option (Gao et al. <span>2021</span>). We suggest a concentrated effort on uncertainty assessment and method development is urgently needed, as there are numerous settings for which there is no current viable method for measuring streamflow.</p><p>Methods development for accurate low-flow measurements will be critical as environmental change accelerates, leading to increased hydrologic variability and shifts to low flows around the world. To better manage future trade-offs among water uses, managers will require accurate data on streamflow under low-flow conditions. To achieve this, we need methodological flexibility to capture extreme flow conditions, including at low flows. Without improvements, we will not be able to sustain existing long-term streamflow records that can help us predict the continuing trajectories of environmental change. Understanding and managing shifts in water resources will be critical for ensuring habitat integrity, promoting good water quality, and safeguarding sustainable water access. The first step is ensuring consistent high-quality flow measurements in these vulnerable systems.</p>","PeriodicalId":18128,"journal":{"name":"Limnology and Oceanography Letters","volume":null,"pages":null},"PeriodicalIF":5.1000,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aslopubs.onlinelibrary.wiley.com/doi/epdf/10.1002/lol2.10356","citationCount":"0","resultStr":"{\"title\":\"How low can you go? Widespread challenges in measuring low stream discharge and a path forward\",\"authors\":\"Erin C. Seybold,&nbsp;Anna Bergstrom,&nbsp;C. Nathan Jones,&nbsp;Amy J. Burgin,&nbsp;Sam Zipper,&nbsp;Sarah E. Godsey,&nbsp;Walter K. Dodds,&nbsp;Margaret A. Zimmer,&nbsp;Margaret Shanafield,&nbsp;Thibault Datry,&nbsp;Raphael D. Mazor,&nbsp;Mathis L. Messager,&nbsp;Julian D. Olden,&nbsp;Adam Ward,&nbsp;Songyan Yu,&nbsp;Kendra E. Kaiser,&nbsp;Arial Shogren,&nbsp;Richard H. Walker\",\"doi\":\"10.1002/lol2.10356\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Water resource management is facing mounting challenges associated with water scarcity, including interactive effects of a changing climate and increased water demand (Craig et al. <span>2017</span>). Climate change is increasing drought severity in many regions (Cook et al. <span>2020</span>), while demand for limited water supplies depletes water resources (de Graaf et al. <span>2019</span>). Combined, these stressors result in lower and more variable flows in streams and rivers (Zipper et al. <span>2021</span>), particularly in arid regions (Hammond et al. <span>2021</span>). Despite challenges posed by low-flow conditions, the majority of resources (e.g., time, funding) for monitoring streamflow have historically focused on high-water concerns, such as ensuring navigation and predicting floods (Vörösmarty et al. <span>2001</span>; Ruhi et al. <span>2018</span>), in larger, perennially-flowing systems (Krabbenhoft et al. <span>2022</span>).</p><p>Low-flow conditions (Mauger et al. <span>2021</span>), which we define as streams or rivers with little downstream surface water flow caused by small volumes or very low downstream velocities (i.e., slackwater), are increasingly prevalent and thus necessitate greater focus on quantification approaches. Streamflow is the underlying physical template structuring biotic and abiotic processes, biogeochemical cycling, and ecological communities in river systems; thus, inaccurate low-flow measurements can propagate to and hinder diverse analyses requiring accurate low-flow data, ranging from drought characterization (Hammond et al. <span>2022</span>), environmental flow allocations (Neachell and Petts <span>2019</span>), ecological function assessments (Leigh and Datry <span>2017</span>), species conservation plans (Lopez et al. <span>2022</span>), and streamflow forecasting (Forzieri et al. <span>2014</span>).</p><p>We posit that a lack of low-flow measurement techniques leaves monitoring networks ill-equipped to inform water management, which is a fundamental challenge that must be addressed to ensure sustainable water management in the future. Our objectives are to: (1) demonstrate the widespread challenges in low-flow measurement across an existing monitoring network in the United States, (2) discuss limitations of current streamflow measurement methods in low-flow conditions, (3) present a DST for choosing among existing measurement methods, and (4) highlight important methodological developments needed to improve low-flow measurement and monitoring. Such methodological progress is a prerequisite for understanding how low flows will respond to changing climate and human demands, thereby supporting management and policy actions seeking to avoid or minimize these impacts.</p><p>Point measurements of streamflow are essential for short- and long-term studies and monitoring, and can be made using many different methods (Turnipseed and Sauer <span>2010</span>). If conducted over a range of flow conditions, discrete streamflow measurements can be used to develop a rating curve which relates stage and discharge, allowing for long-term, continuous quantification of discharge via stage sensors (Turnipseed and Sauer <span>2010</span>). We focus our analysis and discussion on methods for point measurements of streamflow, but emphasize that limitations in these approaches have implications for the accuracy of longer-term streamflow monitoring via rating curve development.</p><p>To quantify the prevalence of substandard low-flow measurements, we examined manual point measurements of streamflow from 8008 U.S. Geological Survey (USGS) gages across the continental United States in the GAGES II dataset (Falcone <span>2011</span>), which is a dataset of sites with either 20+ years of discharge since 1950 or that were operational as of 2009 (Appendix S1). For each manual streamflow measurement, we collected the quality code assigned by USGS hydrographers immediately after making the discharge measurement: “Poor” quality is assigned when uncertainty in the discharge measurement is estimated to be above 8%, “fair” when uncertainty is estimated to be less than 8%, good when uncertainty is estimated to be less than 5%, and excellent when uncertainty is estimated to be less than 2% (Turnipseed and Sauer <span>2010</span>). These quality codes are a qualitative method for estimating the accuracy of individual discharge measurements based on suitability of the channel cross-section, flow state, and other flow conditions (Turnipseed and Sauer <span>2010</span>).</p><p>For each gage, we identified the minimum streamflow value associated with a “good” manual flow measurement and calculated the percent of each gage's daily streamflow record below the minimum “good” threshold. To ensure our results were not overly sensitive to the value of the minimum “good” threshold, we also compared the percentage of each gage's streamflow record below two additional thresholds: (1) streamflow value corresponding to the minimum “fair” measurement, and (2) average of minimum “fair” and minimum “good” thresholds (<i>see</i> Table S1 for details), and obtained comparable results. The “minimum good” metric provides a conservative estimate of the duration of flow measurements with high uncertainty for each site; it only considers uncertainty related to manual measurements and does not account for additional uncertainty in stage measurements stemming from low-flow conditions. We interrogated the USGS network because it represents a high standard that many individual investigators use as a benchmark, and because it provided a large dataset relating manual streamflow measurements with qualitative assessments of quality/uncertainty. We performed all analyses in R version 4.2.1 (R Core Team <span>2022</span>) and obtained USGS data from the National Water Information System using the DataRetrieval Package (De Cicco et al. <span>2022</span>).</p><p>Across the GAGES II network, the average percentage of flow records below the minimum good measurement was 8.4%, indicating high overall quality of the streamflow measurements. However, we found that 393 gages (~ 5.5%) had at least 50% of flow records below the minimum good flow value, 68 of which had over 95% of flow records below the minimum good flow threshold (Fig. 1A). Sites with a high percentage of streamflow below the minimum “good” threshold are widely distributed across diverse climatic zones, land uses, and hydrologic settings, although the greatest density of high uncertainty records are concentrated in the arid southwestern United States where low flows and water management issues linked to scarcity are pervasive (Brown et al. <span>2019</span>).</p><p>To provide an example of the difficulties in making low-flow measurements, we focused on the gage for Kings Creek near Manhattan KS (USGS Gage 06879650), a well-studied, grassland stream with a long continuous record (1979–present). Only 73 of the 238 manual flow measurements (~ 31%) were considered “good” or “excellent” (Fig. 1B). The relatively low incidence of “good” manual flow measurements at Kings Creek resulted in over 58.6% of the daily flow record (from 1980 to 2021) being below the lowest “good” flow measurement, with the proportion below that threshold in a given water year ranging from 2.5% to 100%. This underscores that even for a given site, the relative importance of accurate low-flow measurements will vary from year-to-year, with greatest impact during dry years (Fig. 1C). Furthermore, uncertainties in low-flow measurements may propagate into subsequent estimates of nutrient export, which may lead to some annual load estimates to be much less certain than others. Systems with frequent low flows and flashy high flows may also face highly uncertain streamflow measurements at the high flow end of the rating curve, leading to additional sources of uncertainty. While a sensitivity analysis of uncertainty propagation in streamflow is beyond the scope of this paper, our analysis highlights many areas in the United States where current methods are poorly suited to capture low-flow conditions.</p><p>Three general categories of methods comprise the toolbox available to most practitioners. These include: (1) velocity-area methods; (2) tracer-based methods using salt or dye; and (3) measuring stage at a known streambed geometry (e.g., flume or weir) or capturing flow at a channel constriction (WMO <span>2010</span>). Most methods tend to be inaccurate or unusable under low-flow conditions (Hamilton <span>2008</span>) for three reasons: (1) low water velocities and/or shallow water depths (Fig. 2A,B), (2) mobile streambeds and/or irregular channels (Fig. 2D,E), and (3) high proportions of flow in the subsurface (Fig. 2C–E).</p><p>Many streams transition from visible surface water flow to very slow or imperceptible movement of water, which is sometimes spatially discontinuous or pooled. Low velocities can lead to poor tracer mixing and recovery when using dilution gaging methods (Fig. 2A). High channel width-to-depth ratios (i.e., very wide channels with shallow water) can also lead to poor tracer mixing and the inability to fully submerge velocimeters (Fig. 2E). Furthermore, highly variable bed elevations (e.g., rocks and boulders) or emergent vegetation can further reduce the accuracy of velocity measurements and even render them impossible (Fig. 2D). Finally, estimates of discharge based on velocity-area methods only measure surface-water flow and therefore are not directly comparable to tracer-based estimates, which capture some subsurface flow. This is particularly relevant in low-flow conditions which often exhibit a greater proportion of hyporheic flow. These general problems are not mutually exclusive; indeed, multiple issues can arise in low-flow settings, leaving practitioners unsure about which method to use and leading to considerable uncertainty in low-flow measurements.</p><p>Given these challenges, we present a DST that reflects our collective experience working in low-flow systems, and describes how we approach applying existing discharge methods given the complicating factors that dominate low-flow systems (Fig. 3). The aim of the DST is to offer guidance on a systematic way to apply consistent methods to complex systems. This tool assumes the chosen location is the best available site (i.e., there are no better sites within a reasonable distance upstream or downstream) and highlights what conditions should be avoided in site selection. The DST is not intended to be a data-driven study on the optimal way to measure low flows, rather it is offering informed opinions on what methods tend to work best in specific contexts from experts who frequently attempt flow measurements under non-ideal conditions. In compiling the DST, we also highlight conditions where method development should be prioritized, which we hope catalyzes further discussion and method advances within the water resource community.</p><p>The initial bifurcation in this DST separates sites by whether water is visibly flowing or not (Fig. 3). We define visible flow as whether material in the water (e.g., leaves) can be observed moving downstream. If there is no visible movement, fewer options exist to measure flow. If streamflow is visible, the DST prompts a series of questions regarding channel cross-section and water depth to help practitioners identify the most suitable flow measurement for their site (Fig. 3). We acknowledge that the pathways and nodes are not equally likely to be encountered. For example, very few locations have natural constriction points for which the bucket method is suitable (Fig. 2F), even though it appears twice (Fig. 3). Furthermore, three nodes terminate in “no widely used methods.” In our experience, the majority of sites where we work (numbering in the dozens, examples in Fig. 2) fall into nodes characterized by “no widely used method” for at least part of the year, leaving us unable to accurately measure hydrologic fluxes and limiting subsequent analyses like long-term nutrient flux estimates. While this DST can be used to help practitioners identify the best possible methods, we acknowledge that under many low-flow conditions, even a recommended method can lead to suboptimal discharge measurements with relatively high error.</p><p>Selecting a method to measure discharge requires practitioners to identify the degree of precision needed for their study and consider trade-offs between precision and resource costs. For some studies, hydrologic parameters that are easier to measure—like depth, wetted width/area, or approximate flow state—may be sufficient (Jaeger et al. <span>2023</span>). In contrast, biogeochemistry studies for which water movement is a key variable for calculating nutrient loads (Gómez-Gener et al. <span>2021</span>) may require greater precision than studies focused on aquatic habitat. Other trade-offs, including personnel costs, measurement frequency, and available time to conduct a measurement may outweigh the scientific considerations given in Fig. 3. At low but visible flows, dilution gaging can be used but may take hours to days, rather than minutes to an hour required at moderate to high flow conditions. In addition, dilution gaging at low flows often results in non-optimal breakthrough curves from incomplete mixing that are not suitable for discharge estimates. Portable flumes/weirs are faster to implement but require modifying the channel, for example manually creating berms to concentrate flow through a flume (Fig. 2C), which may not be possible for many reasons. While the DST provides recommendations for general categories of measurement methods, further modifications of each method can help accommodate specific flow conditions (e.g., different variations on the application of dilution gaging; Table S1). We provide suggestions for situations where modifications of standard methods may be desirable, and further challenges in applying those modifications in Table S1.</p><p>In streams and rivers, streamflow is the underlying physical template structuring biotic and abiotic processes, biogeochemical cycling, and ecological communities. Discharge is used to assess the degree of connectivity between tributaries and quantify movement of solutes through a stream network. Time series of discharge are key inputs for models of aquatic ecosystem function and biogeochemical cycling, and the desired output of hydrologic models identifying factors driving streamflow and predicting responses to anthropogenic change. All of these applications require accurate discharge measurements across the full range of flow variability.</p><p>While there is no universal answer to the question of “what percent error is acceptable when measuring low flows,” we argue the general need for a high degree of accuracy is clear. Although absolute changes in streamflow in low-flow systems may be small (e.g., changes from 0.01 to 0.02 m<sup>3</sup>/s), this represents a large relative change within the system (100%). Small changes in discharge at low flows can have substantial consequences for habitat extent and suitability (Rolls et al. <span>2012</span>). Detection of long-term trends is hampered by imprecise or uncertain data, which may cause trends in vulnerable low-flow systems to go unquantified (Whitfield and Hendrata <span>2006</span>). Environmental flow regulations require precise data for enforcement (Neachell and Petts <span>2019</span>), and uncertain low-flow data can complicate implementation and enforcement. Finally, there are many systems ranging from large, arid rivers to small streams where difficult-to-measure low-flow conditions are the norm and thus prevent accurate streamflow measurements across the flow-duration curve, leaving sites with minimal data for research and management purposes. Although low flows may represent a smaller component of annual water or solute fluxes than high flows in many systems, they are critical for understanding and predicting hydrological, ecological, and biogeochemical dynamics in river systems. This is not possible without robust low-flow discharge measurements.</p><p>In addition to providing guidance for systematically deciding which methods to employ in determining low-flow discharge, our DST (Fig. 3) highlights areas of critical need for method development and uncertainty assessment. In some cases, further modification and optimization of existing methods may be sufficient (e.g., Table S1). However, there are conditions for which entirely new methods need to be developed or refined, such as: (1) slackwater pools (Fig. 2A); (2) wide, shallow, irregular, or threaded channels (Fig. 2E), particularly in locations with no opportunity for channel modification; (3) reaches with dense emergent vegetation; and (4) reaches where wind strongly affects water surface velocities. These conditions are commonly found in freshwaters but share similarities with coastal settings, opening up the potential for method transfer to/from coastal hydrology (e.g., Birgand et al. <span>2022</span>).</p><p>There are promising recent technological advances including micro velocity sensors (Osorno et al. <span>2018</span>), time-lapse imagery analysis from trail cameras and videos (Birgand et al. <span>2022</span>; Chapman et al. <span>2022</span>; Dolcetti et al. <span>2022</span>) or radar altimetry (Bandini et al. <span>2020</span>), and presence/absence sensors for measuring water surface extent (Chapin et al. <span>2014</span>;). Emerging tools like time-lapse imagery analysis and water presence/absence sensors may improve our understanding of the spatiotemporal variation in the hydrologic state of low-flow systems by providing an assessment of surface water presence at the time of streamflow measurements, or in the absence of suitable discharge measurement approaches. However, more work must be done to advance these methods because as of now they only estimate stage or water presence/absence, leaving the difficulties of estimating discharge unresolved. Finally, there may be settings in which modeling or mathematical relationship development may be the best option (Gao et al. <span>2021</span>). We suggest a concentrated effort on uncertainty assessment and method development is urgently needed, as there are numerous settings for which there is no current viable method for measuring streamflow.</p><p>Methods development for accurate low-flow measurements will be critical as environmental change accelerates, leading to increased hydrologic variability and shifts to low flows around the world. To better manage future trade-offs among water uses, managers will require accurate data on streamflow under low-flow conditions. To achieve this, we need methodological flexibility to capture extreme flow conditions, including at low flows. Without improvements, we will not be able to sustain existing long-term streamflow records that can help us predict the continuing trajectories of environmental change. Understanding and managing shifts in water resources will be critical for ensuring habitat integrity, promoting good water quality, and safeguarding sustainable water access. The first step is ensuring consistent high-quality flow measurements in these vulnerable systems.</p>\",\"PeriodicalId\":18128,\"journal\":{\"name\":\"Limnology and Oceanography Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.1000,\"publicationDate\":\"2023-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://aslopubs.onlinelibrary.wiley.com/doi/epdf/10.1002/lol2.10356\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Limnology and Oceanography Letters\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/lol2.10356\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"LIMNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Limnology and Oceanography Letters","FirstCategoryId":"93","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/lol2.10356","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"LIMNOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

“最小良好”度量提供了对每个站点流量测量持续时间的保守估计,具有很高的不确定性;它只考虑与人工测量相关的不确定性,而不考虑由低流量条件引起的级测量的附加不确定性。我们询问USGS网络,因为它代表了许多个人调查员作为基准的高标准,并且因为它提供了与人工流量测量相关的大型数据集,并对质量/不确定性进行了定性评估。我们使用R 4.2.1版本(R Core Team 2022)进行所有分析,并使用数据检索包(De Cicco et al. 2022)从国家水信息系统获得USGS数据。在GAGES II网络中,低于最低良好测量值的流量记录的平均百分比为8.4%,表明流量测量的整体质量较高。然而,我们发现393个(~ 5.5%)的流量计至少有50%的流量记录低于最小良好流量值,其中68个流量计超过95%的流量记录低于最小良好流量阈值(图1A)。流量低于最低“良好”阈值的高百分比地点广泛分布在不同的气候带、土地利用和水文环境中,尽管高不确定性记录的最大密度集中在干旱的美国西南部,在那里与稀缺相关的低流量和水管理问题普遍存在(Brown et al. 2019)。为了提供一个进行低流量测量困难的例子,我们将重点放在曼哈顿KS附近的国王溪(USGS gage 06879650)的测量上,这是一条经过充分研究的草地溪流,具有长时间连续记录(1979年至今)。238个手动流量测量中,只有73个(约31%)被认为是“良好”或“优秀”(图1B)。king Creek人工流量测量“良好”的发生率相对较低,导致超过58.6%的日流量记录(1980年至2021年)低于最低“良好”流量测量值,在给定水年中低于该阈值的比例从2.5%到100%不等。这表明,即使在给定的地点,精确的低流量测量的相对重要性也会逐年变化,在干旱年份影响最大(图1C)。此外,低流量测量的不确定性可能会影响到随后对养分输出的估计,这可能导致一些年负荷估计比其他估计不确定得多。频繁低流量和快速高流量的系统在额定值曲线的高流量端也可能面临高度不确定的流量测量,从而导致额外的不确定性来源。虽然流中不确定性传播的敏感性分析超出了本文的范围,但我们的分析强调了美国的许多地区,其中当前的方法不适合捕捉低流量条件。大多数实践者可以使用的工具箱由三大类方法组成。这些方法包括:(1)速度面积法;(2)基于示踪剂的盐或染料法;(3)在已知的河床几何形状(如水槽或堰)上进行测量,或在河道狭窄处捕捉水流(WMO 2010)。大多数方法在低流量条件下往往是不准确或不可用的(Hamilton 2008),原因有三:(1)低流速和/或浅水深(图2A,B),(2)流动河床和/或不规则河道(图2D,E),(3)地下流量比例高(图2C-E)。许多溪流从可见的地表水流动转变为非常缓慢或难以察觉的水运动,有时在空间上不连续或汇集。使用稀释测定方法时,低流速会导致示踪剂混合和回收率差(图2A)。高通道宽深比(即,非常宽的通道和浅水)也会导致示踪剂混合不良,无法完全淹没测速仪(图2E)。此外,高度变化的地层高度(如岩石和巨石)或新兴植被会进一步降低速度测量的准确性,甚至使其无法实现(图2D)。最后,基于速度面积法的流量估计只能测量地表水流量,因此不能直接与基于示踪剂的估计进行比较,因为示踪剂可以捕获一些地下流量。这在低流量条件下尤其重要,因为低流量条件下经常出现较大比例的低循环流动。这些一般性问题并不相互排斥;事实上,在低流量环境中可能会出现多种问题,使从业者不确定使用哪种方法,并导致低流量测量中的相当大的不确定性。考虑到这些挑战,我们提出了一个反映我们在低流量系统中工作的集体经验的DST,并描述了我们如何在考虑到控制低流量系统的复杂因素的情况下应用现有的排放方法(图3)。 DST的目的是提供对复杂系统应用一致方法的系统方法的指导。此工具假定所选位置是最佳可用站点(即,在上游或下游的合理距离内没有更好的站点),并突出显示在站点选择中应避免的条件。DST并不是一项以数据为导向的研究,旨在研究测量低流量的最佳方法,而是为经常尝试在非理想条件下进行流量测量的专家提供在特定情况下哪种方法最有效的明智意见。在编制DST时,我们还强调了方法开发应优先考虑的条件,我们希望这能促进水资源界的进一步讨论和方法进步。这个DST的初始分岔是通过水是否可见流动来区分站点的(图3)。我们将可见流动定义为水中的物质(如树叶)是否可以观察到向下游移动。如果没有可见的运动,那么测量流的选择就会更少。如果水流可见,DST会提示一系列关于通道横截面和水深的问题,以帮助从业者确定最适合其站点的流量测量(图3)。我们承认,路径和节点不可能同样地遇到。例如,很少有位置具有适合bucket方法的自然收缩点(图2F),尽管它出现了两次(图3)。此外,有三个节点以“没有广泛使用的方法”终止。根据我们的经验,我们工作的大多数地点(数量为数十个,如图2所示)至少在一年中有部分时间处于“没有广泛使用方法”的节点,这使我们无法准确测量水文通量,并限制了后续分析,如长期营养通量估计。虽然该DST可用于帮助从业者确定最佳方法,但我们承认,在许多低流量条件下,即使推荐的方法也可能导致不理想的放电测量,并且误差相对较高。选择一种测量排放的方法需要从业者确定他们的研究所需的精度程度,并考虑精度和资源成本之间的权衡。对于一些研究,更容易测量的水文参数——如深度、湿润宽度/面积或近似流动状态——可能就足够了(Jaeger et al. 2023)。相比之下,将水运动作为计算营养负荷的关键变量的生物地球化学研究(Gómez-Gener et al. 2021)可能需要比专注于水生栖息地的研究更高的精度。其他权衡,包括人员成本、测量频率和进行测量的可用时间,可能超过图3中给出的科学考虑。在低但可见的流量下,可以使用稀释测量,但可能需要数小时到数天,而不是在中等到高流量条件下所需的几分钟到一小时。此外,在低流量下的稀释测量通常会由于不完全混合而导致非最佳突破曲线,这不适用于流量估计。便携式水槽/堰的实施速度更快,但需要修改通道,例如手动创建护堤以集中通过水槽的水流(图2C),这可能由于许多原因而不可能实现。虽然DST为测量方法的一般类别提供了建议,但每种方法的进一步修改可以帮助适应特定的流动条件(例如,稀释测量应用的不同变化;表S1)。我们在表S1中提供了对标准方法可能需要修改的情况的建议,以及应用这些修改时面临的进一步挑战。在溪流和河流中,水流是构成生物和非生物过程、生物地球化学循环和生态群落的潜在物理模板。流量用于评估支流之间的连通性程度,并量化溶质通过流网络的运动。流量时间序列是水生生态系统功能和生物地球化学循环模型的关键输入,也是识别河流驱动因子和预测对人为变化响应的水文模型的期望输出。所有这些应用都需要在整个流量变化范围内进行精确的流量测量。虽然对于“在测量低流量时可以接受多少百分比的误差”这个问题没有普遍的答案,但我们认为对高度精度的普遍需求是明确的。虽然低流量系统中流量的绝对变化可能很小(例如,从0.01到0.02 m3/s的变化),但这代表了系统内较大的相对变化(100%)。低流量时流量的微小变化会对栖息地的范围和适宜性产生重大影响(Rolls et al. 2012)。 不精确或不确定的数据阻碍了长期趋势的检测,这可能导致脆弱的低流量系统的趋势无法量化(Whitfield和Hendrata 2006)。环境流量法规需要精确的执行数据(Neachell和Petts 2019),而不确定的低流量数据可能会使实施和执行复杂化。最后,有许多系统,从干旱的大河到小溪,在这些系统中,难以测量的低流量条件是常态,因此阻碍了流量持续时间曲线上的准确流量测量,留下了用于研究和管理目的的最小数据。虽然在许多系统中,低流量可能比高流量在年水或溶质通量中所占的比例更小,但它们对于理解和预测河流系统的水文、生态和生物地球化学动力学至关重要。如果没有可靠的低流量排放测量,这是不可能的。除了为系统地决定在确定低流量排放时使用哪些方法提供指导之外,我们的DST(图3)强调了方法开发和不确定性评估的关键需求领域。在某些情况下,进一步修改和优化现有方法可能就足够了(例如,表S1)。然而,在某些情况下,需要开发或改进全新的方法,例如:(1)静水池(图2A);(2)宽、浅、不规则或螺纹通道(图2E),特别是在没有机会修改通道的位置;(3)植被密集的河段;(4)风对水面速度影响较大的区域。这些条件通常在淡水中发现,但与沿海环境有相似之处,这为沿海水文学的方法转移开辟了潜力(例如,Birgand等人,2022)。最近的技术进步很有希望,包括微速度传感器(Osorno等人,2018),跟踪摄像机和视频的延时图像分析(Birgand等人,2022;Chapman et al. 2022;Dolcetti et al. 2022)或雷达测高(Bandini et al. 2020),以及用于测量水面范围的存在/不存在传感器(Chapin et al. 2014;)。新兴的工具,如延时图像分析和水存在/不存在传感器,可以提高我们对低流量系统水文状态的时空变化的理解,通过在流量测量时提供地表水存在的评估,或者在没有合适的流量测量方法的情况下。然而,需要做更多的工作来推进这些方法,因为到目前为止,它们只能估计阶段或水的存在/不存在,因此估计排放量的困难尚未解决。最后,在某些情况下,建模或数学关系发展可能是最佳选择(Gao et al. 2021)。我们建议集中精力进行不确定度评估和方法开发是迫切需要的,因为有许多设置,目前没有可行的方法来测量流量。随着环境变化加速,导致全球水文变异性增加和向低流量转移,精确的低流量测量方法的发展将至关重要。为了更好地管理未来用水之间的权衡,管理人员将需要关于低流量条件下的流量的准确数据。为了实现这一目标,我们需要灵活的方法来捕捉极端的流量条件,包括低流量。如果没有改进,我们将无法维持现有的长期流量记录,而这些记录可以帮助我们预测环境变化的持续轨迹。了解和管理水资源的变化对于确保栖息地的完整性、促进良好的水质和保障可持续用水至关重要。第一步是确保在这些脆弱的系统中进行一致的高质量流量测量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

How low can you go? Widespread challenges in measuring low stream discharge and a path forward

How low can you go? Widespread challenges in measuring low stream discharge and a path forward

How low can you go? Widespread challenges in measuring low stream discharge and a path forward

Water resource management is facing mounting challenges associated with water scarcity, including interactive effects of a changing climate and increased water demand (Craig et al. 2017). Climate change is increasing drought severity in many regions (Cook et al. 2020), while demand for limited water supplies depletes water resources (de Graaf et al. 2019). Combined, these stressors result in lower and more variable flows in streams and rivers (Zipper et al. 2021), particularly in arid regions (Hammond et al. 2021). Despite challenges posed by low-flow conditions, the majority of resources (e.g., time, funding) for monitoring streamflow have historically focused on high-water concerns, such as ensuring navigation and predicting floods (Vörösmarty et al. 2001; Ruhi et al. 2018), in larger, perennially-flowing systems (Krabbenhoft et al. 2022).

Low-flow conditions (Mauger et al. 2021), which we define as streams or rivers with little downstream surface water flow caused by small volumes or very low downstream velocities (i.e., slackwater), are increasingly prevalent and thus necessitate greater focus on quantification approaches. Streamflow is the underlying physical template structuring biotic and abiotic processes, biogeochemical cycling, and ecological communities in river systems; thus, inaccurate low-flow measurements can propagate to and hinder diverse analyses requiring accurate low-flow data, ranging from drought characterization (Hammond et al. 2022), environmental flow allocations (Neachell and Petts 2019), ecological function assessments (Leigh and Datry 2017), species conservation plans (Lopez et al. 2022), and streamflow forecasting (Forzieri et al. 2014).

We posit that a lack of low-flow measurement techniques leaves monitoring networks ill-equipped to inform water management, which is a fundamental challenge that must be addressed to ensure sustainable water management in the future. Our objectives are to: (1) demonstrate the widespread challenges in low-flow measurement across an existing monitoring network in the United States, (2) discuss limitations of current streamflow measurement methods in low-flow conditions, (3) present a DST for choosing among existing measurement methods, and (4) highlight important methodological developments needed to improve low-flow measurement and monitoring. Such methodological progress is a prerequisite for understanding how low flows will respond to changing climate and human demands, thereby supporting management and policy actions seeking to avoid or minimize these impacts.

Point measurements of streamflow are essential for short- and long-term studies and monitoring, and can be made using many different methods (Turnipseed and Sauer 2010). If conducted over a range of flow conditions, discrete streamflow measurements can be used to develop a rating curve which relates stage and discharge, allowing for long-term, continuous quantification of discharge via stage sensors (Turnipseed and Sauer 2010). We focus our analysis and discussion on methods for point measurements of streamflow, but emphasize that limitations in these approaches have implications for the accuracy of longer-term streamflow monitoring via rating curve development.

To quantify the prevalence of substandard low-flow measurements, we examined manual point measurements of streamflow from 8008 U.S. Geological Survey (USGS) gages across the continental United States in the GAGES II dataset (Falcone 2011), which is a dataset of sites with either 20+ years of discharge since 1950 or that were operational as of 2009 (Appendix S1). For each manual streamflow measurement, we collected the quality code assigned by USGS hydrographers immediately after making the discharge measurement: “Poor” quality is assigned when uncertainty in the discharge measurement is estimated to be above 8%, “fair” when uncertainty is estimated to be less than 8%, good when uncertainty is estimated to be less than 5%, and excellent when uncertainty is estimated to be less than 2% (Turnipseed and Sauer 2010). These quality codes are a qualitative method for estimating the accuracy of individual discharge measurements based on suitability of the channel cross-section, flow state, and other flow conditions (Turnipseed and Sauer 2010).

For each gage, we identified the minimum streamflow value associated with a “good” manual flow measurement and calculated the percent of each gage's daily streamflow record below the minimum “good” threshold. To ensure our results were not overly sensitive to the value of the minimum “good” threshold, we also compared the percentage of each gage's streamflow record below two additional thresholds: (1) streamflow value corresponding to the minimum “fair” measurement, and (2) average of minimum “fair” and minimum “good” thresholds (see Table S1 for details), and obtained comparable results. The “minimum good” metric provides a conservative estimate of the duration of flow measurements with high uncertainty for each site; it only considers uncertainty related to manual measurements and does not account for additional uncertainty in stage measurements stemming from low-flow conditions. We interrogated the USGS network because it represents a high standard that many individual investigators use as a benchmark, and because it provided a large dataset relating manual streamflow measurements with qualitative assessments of quality/uncertainty. We performed all analyses in R version 4.2.1 (R Core Team 2022) and obtained USGS data from the National Water Information System using the DataRetrieval Package (De Cicco et al. 2022).

Across the GAGES II network, the average percentage of flow records below the minimum good measurement was 8.4%, indicating high overall quality of the streamflow measurements. However, we found that 393 gages (~ 5.5%) had at least 50% of flow records below the minimum good flow value, 68 of which had over 95% of flow records below the minimum good flow threshold (Fig. 1A). Sites with a high percentage of streamflow below the minimum “good” threshold are widely distributed across diverse climatic zones, land uses, and hydrologic settings, although the greatest density of high uncertainty records are concentrated in the arid southwestern United States where low flows and water management issues linked to scarcity are pervasive (Brown et al. 2019).

To provide an example of the difficulties in making low-flow measurements, we focused on the gage for Kings Creek near Manhattan KS (USGS Gage 06879650), a well-studied, grassland stream with a long continuous record (1979–present). Only 73 of the 238 manual flow measurements (~ 31%) were considered “good” or “excellent” (Fig. 1B). The relatively low incidence of “good” manual flow measurements at Kings Creek resulted in over 58.6% of the daily flow record (from 1980 to 2021) being below the lowest “good” flow measurement, with the proportion below that threshold in a given water year ranging from 2.5% to 100%. This underscores that even for a given site, the relative importance of accurate low-flow measurements will vary from year-to-year, with greatest impact during dry years (Fig. 1C). Furthermore, uncertainties in low-flow measurements may propagate into subsequent estimates of nutrient export, which may lead to some annual load estimates to be much less certain than others. Systems with frequent low flows and flashy high flows may also face highly uncertain streamflow measurements at the high flow end of the rating curve, leading to additional sources of uncertainty. While a sensitivity analysis of uncertainty propagation in streamflow is beyond the scope of this paper, our analysis highlights many areas in the United States where current methods are poorly suited to capture low-flow conditions.

Three general categories of methods comprise the toolbox available to most practitioners. These include: (1) velocity-area methods; (2) tracer-based methods using salt or dye; and (3) measuring stage at a known streambed geometry (e.g., flume or weir) or capturing flow at a channel constriction (WMO 2010). Most methods tend to be inaccurate or unusable under low-flow conditions (Hamilton 2008) for three reasons: (1) low water velocities and/or shallow water depths (Fig. 2A,B), (2) mobile streambeds and/or irregular channels (Fig. 2D,E), and (3) high proportions of flow in the subsurface (Fig. 2C–E).

Many streams transition from visible surface water flow to very slow or imperceptible movement of water, which is sometimes spatially discontinuous or pooled. Low velocities can lead to poor tracer mixing and recovery when using dilution gaging methods (Fig. 2A). High channel width-to-depth ratios (i.e., very wide channels with shallow water) can also lead to poor tracer mixing and the inability to fully submerge velocimeters (Fig. 2E). Furthermore, highly variable bed elevations (e.g., rocks and boulders) or emergent vegetation can further reduce the accuracy of velocity measurements and even render them impossible (Fig. 2D). Finally, estimates of discharge based on velocity-area methods only measure surface-water flow and therefore are not directly comparable to tracer-based estimates, which capture some subsurface flow. This is particularly relevant in low-flow conditions which often exhibit a greater proportion of hyporheic flow. These general problems are not mutually exclusive; indeed, multiple issues can arise in low-flow settings, leaving practitioners unsure about which method to use and leading to considerable uncertainty in low-flow measurements.

Given these challenges, we present a DST that reflects our collective experience working in low-flow systems, and describes how we approach applying existing discharge methods given the complicating factors that dominate low-flow systems (Fig. 3). The aim of the DST is to offer guidance on a systematic way to apply consistent methods to complex systems. This tool assumes the chosen location is the best available site (i.e., there are no better sites within a reasonable distance upstream or downstream) and highlights what conditions should be avoided in site selection. The DST is not intended to be a data-driven study on the optimal way to measure low flows, rather it is offering informed opinions on what methods tend to work best in specific contexts from experts who frequently attempt flow measurements under non-ideal conditions. In compiling the DST, we also highlight conditions where method development should be prioritized, which we hope catalyzes further discussion and method advances within the water resource community.

The initial bifurcation in this DST separates sites by whether water is visibly flowing or not (Fig. 3). We define visible flow as whether material in the water (e.g., leaves) can be observed moving downstream. If there is no visible movement, fewer options exist to measure flow. If streamflow is visible, the DST prompts a series of questions regarding channel cross-section and water depth to help practitioners identify the most suitable flow measurement for their site (Fig. 3). We acknowledge that the pathways and nodes are not equally likely to be encountered. For example, very few locations have natural constriction points for which the bucket method is suitable (Fig. 2F), even though it appears twice (Fig. 3). Furthermore, three nodes terminate in “no widely used methods.” In our experience, the majority of sites where we work (numbering in the dozens, examples in Fig. 2) fall into nodes characterized by “no widely used method” for at least part of the year, leaving us unable to accurately measure hydrologic fluxes and limiting subsequent analyses like long-term nutrient flux estimates. While this DST can be used to help practitioners identify the best possible methods, we acknowledge that under many low-flow conditions, even a recommended method can lead to suboptimal discharge measurements with relatively high error.

Selecting a method to measure discharge requires practitioners to identify the degree of precision needed for their study and consider trade-offs between precision and resource costs. For some studies, hydrologic parameters that are easier to measure—like depth, wetted width/area, or approximate flow state—may be sufficient (Jaeger et al. 2023). In contrast, biogeochemistry studies for which water movement is a key variable for calculating nutrient loads (Gómez-Gener et al. 2021) may require greater precision than studies focused on aquatic habitat. Other trade-offs, including personnel costs, measurement frequency, and available time to conduct a measurement may outweigh the scientific considerations given in Fig. 3. At low but visible flows, dilution gaging can be used but may take hours to days, rather than minutes to an hour required at moderate to high flow conditions. In addition, dilution gaging at low flows often results in non-optimal breakthrough curves from incomplete mixing that are not suitable for discharge estimates. Portable flumes/weirs are faster to implement but require modifying the channel, for example manually creating berms to concentrate flow through a flume (Fig. 2C), which may not be possible for many reasons. While the DST provides recommendations for general categories of measurement methods, further modifications of each method can help accommodate specific flow conditions (e.g., different variations on the application of dilution gaging; Table S1). We provide suggestions for situations where modifications of standard methods may be desirable, and further challenges in applying those modifications in Table S1.

In streams and rivers, streamflow is the underlying physical template structuring biotic and abiotic processes, biogeochemical cycling, and ecological communities. Discharge is used to assess the degree of connectivity between tributaries and quantify movement of solutes through a stream network. Time series of discharge are key inputs for models of aquatic ecosystem function and biogeochemical cycling, and the desired output of hydrologic models identifying factors driving streamflow and predicting responses to anthropogenic change. All of these applications require accurate discharge measurements across the full range of flow variability.

While there is no universal answer to the question of “what percent error is acceptable when measuring low flows,” we argue the general need for a high degree of accuracy is clear. Although absolute changes in streamflow in low-flow systems may be small (e.g., changes from 0.01 to 0.02 m3/s), this represents a large relative change within the system (100%). Small changes in discharge at low flows can have substantial consequences for habitat extent and suitability (Rolls et al. 2012). Detection of long-term trends is hampered by imprecise or uncertain data, which may cause trends in vulnerable low-flow systems to go unquantified (Whitfield and Hendrata 2006). Environmental flow regulations require precise data for enforcement (Neachell and Petts 2019), and uncertain low-flow data can complicate implementation and enforcement. Finally, there are many systems ranging from large, arid rivers to small streams where difficult-to-measure low-flow conditions are the norm and thus prevent accurate streamflow measurements across the flow-duration curve, leaving sites with minimal data for research and management purposes. Although low flows may represent a smaller component of annual water or solute fluxes than high flows in many systems, they are critical for understanding and predicting hydrological, ecological, and biogeochemical dynamics in river systems. This is not possible without robust low-flow discharge measurements.

In addition to providing guidance for systematically deciding which methods to employ in determining low-flow discharge, our DST (Fig. 3) highlights areas of critical need for method development and uncertainty assessment. In some cases, further modification and optimization of existing methods may be sufficient (e.g., Table S1). However, there are conditions for which entirely new methods need to be developed or refined, such as: (1) slackwater pools (Fig. 2A); (2) wide, shallow, irregular, or threaded channels (Fig. 2E), particularly in locations with no opportunity for channel modification; (3) reaches with dense emergent vegetation; and (4) reaches where wind strongly affects water surface velocities. These conditions are commonly found in freshwaters but share similarities with coastal settings, opening up the potential for method transfer to/from coastal hydrology (e.g., Birgand et al. 2022).

There are promising recent technological advances including micro velocity sensors (Osorno et al. 2018), time-lapse imagery analysis from trail cameras and videos (Birgand et al. 2022; Chapman et al. 2022; Dolcetti et al. 2022) or radar altimetry (Bandini et al. 2020), and presence/absence sensors for measuring water surface extent (Chapin et al. 2014;). Emerging tools like time-lapse imagery analysis and water presence/absence sensors may improve our understanding of the spatiotemporal variation in the hydrologic state of low-flow systems by providing an assessment of surface water presence at the time of streamflow measurements, or in the absence of suitable discharge measurement approaches. However, more work must be done to advance these methods because as of now they only estimate stage or water presence/absence, leaving the difficulties of estimating discharge unresolved. Finally, there may be settings in which modeling or mathematical relationship development may be the best option (Gao et al. 2021). We suggest a concentrated effort on uncertainty assessment and method development is urgently needed, as there are numerous settings for which there is no current viable method for measuring streamflow.

Methods development for accurate low-flow measurements will be critical as environmental change accelerates, leading to increased hydrologic variability and shifts to low flows around the world. To better manage future trade-offs among water uses, managers will require accurate data on streamflow under low-flow conditions. To achieve this, we need methodological flexibility to capture extreme flow conditions, including at low flows. Without improvements, we will not be able to sustain existing long-term streamflow records that can help us predict the continuing trajectories of environmental change. Understanding and managing shifts in water resources will be critical for ensuring habitat integrity, promoting good water quality, and safeguarding sustainable water access. The first step is ensuring consistent high-quality flow measurements in these vulnerable systems.

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来源期刊
CiteScore
10.00
自引率
3.80%
发文量
63
审稿时长
25 weeks
期刊介绍: Limnology and Oceanography Letters (LO-Letters) serves as a platform for communicating the latest innovative and trend-setting research in the aquatic sciences. Manuscripts submitted to LO-Letters are expected to present high-impact, cutting-edge results, discoveries, or conceptual developments across all areas of limnology and oceanography, including their integration. Selection criteria for manuscripts include their broad relevance to the field, strong empirical and conceptual foundations, succinct and elegant conclusions, and potential to advance knowledge in aquatic sciences.
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