Spatiotemporal Dimensions of Water Stress Accounting: Incorporating

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Spatiotemporal Dimensions of Water Stress Accounting: Incorporating Groundwater-Surface Water Interactions and Ecological Thresholds Sara Alian, Alex S. Mayer, Ann Maclean, David Watkins, and Ali Mirchi Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b04804 • Publication Date (Web): 12 Feb 2019 Downloaded from http://pubs.acs.org on February 13, 2019

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Environmental Science & Technology

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Spatiotemporal Dimensions of Water Stress Accounting: Incorporating

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Groundwater-Surface Water Interactions and Ecological Thresholds

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Sara Alian1, 3*, Alex Mayer2, Ann Maclean1, David Watkins2, Ali Mirchi3

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1 School

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University, 1400 Townsend Drive, Houghton, Michigan 49931.

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2 Department

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University, 1400 Townsend Drive, Houghton, Michigan 49931.

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3 Department

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111 Agricultural Hall, Stillwater, Oklahoma 74078.

of Forest Resources and Environmental Science, Michigan Technological

of Civil and Environmental Engineering, Michigan Technological

of Biosystems and Agricultural Engineering, Oklahoma State University,

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*Corresponding Author: Sara Alian, Department of Biosystems and Agricultural

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Engineering, Oklahoma State University, 111 Agricultural Hall, Stillwater, Oklahoma

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74078. Email: [email protected]

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Abstract

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Coarse temporal (i.e., annual) and

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spatial

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camouflage water stress associated

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with withdrawals from surface water

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and groundwater sources. To address

(i.e.,

TOC Art

watershed)

scales

1

ACS Paragon Plus Environment


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this “curse of scale”, we developed a framework to characterize water stress at different

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time scales and at fine spatial scales that have not been explored before. Our framework

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incorporates surface water-groundwater interactions by accounting for spatially cumulative

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consumptive and non-consumptive use impacts and associated changes in flow due to

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depletion and return flow along stream networks. We apply the framework using a rich

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data set of water withdrawals from more than 6,800 principal facilities (i.e., withdrawal

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capacity > 380,000 liters/day) across the U.S. Great Lakes Basin. Results underscore the

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importance of spatiotemporal scale and return flows when characterizing water stress.

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Although the majority of catchments in this water-rich region do not experience large

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stress, a number of small headwater catchments with sensitive streams are vulnerable to

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flow depletion caused by surface water and shallow groundwater withdrawals, especially

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in a high-withdrawal, low-flow month (e.g., August). The return flow from deep

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groundwater withdrawals compensates for the stream flow depletion to the extent that

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excess flow is likely in many catchments. The improved ability to pinpoint the imbalance

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between natural water supply and withdrawals based on stream-specific ecological water

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stress thresholds facilitates protecting fragile aquatic ecosystems in vulnerable catchments.

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Keywords: Water stress, spatiotemporal scale, geospatial analysis, water withdrawal,

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flow depletion, return flow

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1. Introduction

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Water stress is a complex concept for which there is no universally accepted definition.1-2

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It has been conceptualized from the perspectives of human needs and activities,3-7as well 2


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as ecological impacts.8-11 Two widely used water stress indices in terms of human water

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needs are the national-scale water availability per capita per year3 and the ratio between

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total annual withdrawals and annual water supply. 2,4,12 Seckler et al.13 developed physical

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and economic scarcity indicators that account for evapotranspiration and return flow on a

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national scale, as well as the impact of improved water management policies on society’s

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adaptive capacity to cope with water stress. From an ecological perspective, water stress

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caused naturally by droughts or artificially by water withdrawals and flow alterations

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affects aquatic habitats directly through changing flow regimes and connectivity and

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indirect food chain dynamics.9-11,14,15 The adverse ecological impacts have motivated

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minimum environmental flows16 and more holistic guidelines and indicators to maintain

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streamflow variability (e.g., refs 8 and 17).

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The spatiotemporal dimensions of water stress and accounting for interactions

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between components of the hydrologic cycle, e.g. surface and groundwater, are important

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for water and environmental management at local to regional scales to safeguard vulnerable

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aquatic habitats. Global assessments of annual water stress have been carried out at spatial

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scales ranging from country-level to major watersheds and 1ᵒ×1ᵒ and 0.5ᵒ×0.5ᵒ grid cells.18-

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24

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distribution of water scarcity, particularly in arid and semi-arid regions. However, the

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“curse of scale” in the high-level water stress analyses hinders detecting localized

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imbalances between natural water supply and human activities (withdrawals and

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diversions) that could threaten fragile aquatic ecosystems. Seasonal and monthly water

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stress analyses on 0.5ᵒ×0.5ᵒ global grid cells,25 user-delineated subbasins,26 and 8-digit and

These water stress analyses have provided a high-level understanding of the global

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12-digit Hydrologic Unit Code watersheds 27-30 confirm that water shortages occur only as

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occasional regional or local deficits, at certain times of the year, as previously speculated

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by Meigh et al.31 Furthermore, differentiation of water supply sources and their dynamic

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interactions has been recognized as an important research gap in water stress analysis

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frameworks.26 By considering surface and groundwater separately, previous efforts in

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water stress accounting have often ignored stream depletions caused by pumping from

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shallow aquifers, as well as the return flow to streams from non-consumptive groundwater

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use (e.g., ref 29).

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Using a rich data set of water withdrawals from more than 6,800 principal facilities (i.e.,

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facilities with a withdrawal capacity >380,000 liters/day) across the U.S. Great Lakes Basin

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(U.S. GLB; Figure 1), we characterize water stress at fine spatial scales which have not

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been explored before. The five Laurentian Great Lakes (GL) cover an area of

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approximately 243,460 km2 (slightly larger than the United Kingdom) and hold about 18%

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of the global freshwater,32 creating a water-abundant region that has little to no water stress

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based on the Falkenmark water scarcity indicator.33 The basin is networked with thousands

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of streams ordered from 1 to 7 based on the Strahler stream order classification,34 with

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average annual flows ranging from 0.01 cms to more than 5,000 cms. The GLB supports a

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$5 trillion regional economy and is home to about 34 million people, with more than 80%

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living in the U.S. portion of the basin.35 Mapping water stress in the U.S. GLB is especially

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relevant from an ecological standpoint28,36 to develop sound water management policies

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that consider the connection between water use, withdrawals, and biodiversity.37 4


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This paper demonstrates the implications of spatiotemporal scales and surface water-

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groundwater interactions in pinpointing vulnerable catchments with sensitive aquatic

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habitats in order to mitigate adverse impacts of excessive localized water withdrawal. We

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describe the water stress characterization framework and present annual and warm season

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results from across the U.S. GLB to illustrate the effects of temporal resolution and

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catchment size on water stress. We emphasize the critical importance of accounting for

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return flows to streams, especially from pumped groundwater, when characterizing

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localized water stress. Further, we highlight ecological water stress in Michigan, a state

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where ecological-based guidelines have been adopted for water withdrawal permitting. Our

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framework can potentially improve the permitting process and guide monitoring

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campaigns with the aim of mitigating ecological water stress.

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Figure 1. The U.S. portion of the Great Lakes drainage basin. 5



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2. Methods

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We apply an integrative water accounting framework to quantify catchment-scale water

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stress at annual and monthly time intervals. The approach resolves several limitations in

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geospatially explicit water stress mapping. First, surface-groundwater interactions are

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incorporated in the analysis by estimating stream depletion from shallow groundwater

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withdrawals and accounting for return flows from non-consumptive water use. Second,

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cumulative impacts of water withdrawals from different sources and return flow are

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accounted for by aggregating consumptive and non-consumptive use impacts from

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upstream to downstream along stream networks. Third, spatially variable water stress

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thresholds are considered based on ecological characteristics of streams. The framework is

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based on comparing surface water stress index (SWSI), defined here as the ratio of flow

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disturbance to adjusted streamflow--similar to the Modified Water Exploitation Index--12

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with specified stress thresholds. It uses a comprehensive, logically structured geodatabase

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of major water withdrawals for different use categories in the U.S. GL states, including

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domestic and public, commercial and industrial, agricultural and golf course irrigation,

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livestock, mining, and thermoelectric power generation. Figure 2 illustrates the analysis

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framework.

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Figure 2. Water stress analysis framework.

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Required data include georeferenced surface water and groundwater withdrawals (Table

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S1 in Supporting Information lists water withdrawal data sources) based on water use

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categories (Figure S1 in Supporting Information), consumptive use coefficients by water

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use category (Table S2), and hydrologic data inputs (e.g., stream network, catchment areas,

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and flows). Geospatial data, including monthly water withdrawal magnitudes, water use

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categories and catchment boundaries and associated flows, were compiled in an ArcMap

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file geodatabase (GDB), providing a systematic data repository. The water withdrawal data

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set is composed of 6,805 georeferenced water withdrawals for 2010 from facilities with

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capacities greater than or equal to 380,000 liters/day, scattered over 106,000 catchments.38

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The consumptive use coefficients are USGS-published values based on synthesis of 7



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consumptive water use data in Indiana and Ohio, with additional data from Wisconsin.39

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We converted annual water withdrawal data into monthly values using the arithmetic

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average of the median monthly fraction (%) of water withdrawals for different use

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categories in Indiana and Ohio (Table S3 in Supporting Information).39

148 149 150

The catchments defined in the National Hydrography Dataset Plus Version 2 (NHDPlus

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V2) are generally very small (less than 2 km2), although a few hundred catchments are 20

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km2 or larger (Figure S2). The NHDPlus V2 database provides hydrographic, hydrologic,

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and spatial attributes at the catchment scale and at different temporal scales for the

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conterminous U.S.40 The hydrologic data include mean annual and mean monthly

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unimpaired flows derived from the Enhanced Runoff Method.41 EROM uses temperature

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and precipitation data, simulated runoff, and gauge flow measurements within a grid-based

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(900m×900m) water balance framework to estimate the hydrologic response at ungauged

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sites for the period 1971 to 2000.

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We only consider withdrawals within the tributary watersheds of the Great Lakes, rather

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than the Great Lakes themselves, since SWSIs are unlikely to be substantial at the scale of

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the Great Lakes volume (23,000 km3).42 Illinois is excluded from the analysis as it occupies

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a small portion of the U.S. GLB, with Lake Michigan being the dominant water source.

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Water withdrawal sources include tributary surface water, shallow groundwater, and deep

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groundwater. Direct surface water withdrawals and groundwater extractions from shallow 8


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aquifers that are hydraulically connected to the streams both potentially deplete

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streamflow. Thus, the magnitude of streamflow depletion (𝑄𝑑) due to withdrawal depends

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on the withdrawal source:

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𝑄𝑑𝑖,𝑡,𝑠 = 𝑆𝐷𝐹𝑖,𝑡,𝑠𝑊𝑖,𝑡,𝑠

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where 𝑄𝑑 = stream depletion (volume/time); 𝑆𝐷𝐹 = streamflow depletion fraction

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(unitless); W = withdrawal (volume/time); i = location index (catchment); t = time index

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(annual or monthly); and s = source index.

(Eq. 1)

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A streamflow depletion fraction 𝑆𝐷𝐹𝑖,𝑡,𝑠 = 1 is assigned to surface water withdrawals prior

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to accounting for return flows because these withdrawals are directly extracted from the

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streams. Deep wells (≥30 m) or wells open to consolidated materials are assumed not in

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hydraulic connection with streams and are assigned 𝑆𝐷𝐹𝑖,𝑡,𝑠 = 0, meaning the withdrawals

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from these sources have only a recharge effect on the streamflow. Shallow wells (≤30 m)

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in coarse-grained drift material (e.g., sand and gravel) are considered hydraulically

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connected to the nearby stream, as is typically the case in the U.S. GLB.43 We obtained

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quantitative and qualitative information about well depth and aquifer material by linking

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the georeferenced water withdrawal data set to the Wellogic GIS layers.44 Wellogic is a

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database of water well records that provides information about well depth and

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hydrogeologic characteristics of the aquifers.44 Where georeferenced coordinates did not

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match between the Wellogic system and the withdrawal locations, the properties of the

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nearest well in the Wellogic system within a 1000-m radius of the withdrawal point were

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utilized. Wells outside the coverage of the Wellogic database were considered connected 9



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to streams if they were located in surficial glacial and alluvial deposits based on a map of

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surficial materials for the eastern and central U.S.45 Values of SDFi,t,s for shallow

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groundwater range from 0 to 1 and are estimated using the Hunt analytical solution46 as a

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function of withdrawal and hydrogeological properties (Eq. 1), as described in Watson et

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al.: 47 𝑆𝐷𝐹𝑖,𝑡,𝑠𝑔𝑤 =

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Qdi,t,sgw QGWi,t,sgw

[ ( ) (

= erfc

λ2tpw λd Sd2 ― exp + erfc 4ST 2T 4Ttpw

) (

Eq.2)

λ2tpw

Sd2 + 4Ttpw 4ST

)]

( 𝑖,𝑡,𝑠𝑔𝑤

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where QGW = pumping rate for shallow groundwater (sgw) (volume/time); tpw = average

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length of pumping in a given year (time); i = location index (catchment); and t = time index

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(annual or monthly). The hydrogeologic properties in each catchment include S = storage

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coefficient (dimensionless) of the shallow aquifer underlying catchment i; d = distance

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from each pumping well to the stream segment in catchment i (length); T = transmissivity

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of the shallow aquifer underlying catchment i (length/time); and λ = streambed

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conductance for the stream segment in catchment i. The spatial dependence of equation (2)

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corresponds to the variable d, which is the distance from the point locations of each

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pumping well location to the nearest stream segments, estimated using Arc GIS tools. For

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the temporal dependence in equation (2), we assume that the depletion, Qdi,t,sgw, reaches

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steady state instantaneously as the pumping rate changes each time step, t, avoiding the

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necessity to assign arbitrary values for the variable tpw. Following the approach of Watson

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et al.47 we use tpw = 100 days to assure that equation (2) has reached steady state for all

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pumping locations. 10


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Aggregated streamflow disturbance (ASD) is determined as the collective effect of return

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flow and flow depletion (i.e., sum of gains and losses) at the catchment scale, which can

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be positive or negative. Return flows are the converse of the consumptive proportion of

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water withdrawal, i.e., the amount of water removed locally due to evapotranspiration,

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incorporation in exported products, and consumption by humans and livestock.48

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ArcHydro49 is used to propagate depletions and return flows from upstream to downstream

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by accumulating the ASDs from one catchment to the next along the stream network.

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Streamflows are adjusted (𝑄𝑎𝑖,𝑡) in each catchment by adding the cumulative aggregated

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streamflow disturbance (𝐶𝐴𝑆𝐷𝑖,𝑡) to the NHDPlus V2 unimpaired streamflow (Eqs. 3 and

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4), i.e., flow (𝑄𝑖,𝑡) that would occur at a given catchment if unaffected by human activities

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(NHD, 2016). The SWSI is calculated as the ratio of the cumulative aggregated streamflow

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disturbance and adjusted streamflow (Eq. 5).

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𝑄𝑎𝑖,𝑡 = 𝐶𝐴𝑆𝐷𝑖,𝑡 + 𝑄𝑖,𝑡 = [(∑𝑖∑𝑡((1 ― CUi,t, wu )𝑊𝑖,𝑡,𝑤𝑢 ― Qdi,t)] + 𝑄𝑖,𝑡

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𝑘 𝐶𝐴𝑆𝐷𝑖,𝑡 = ∑𝑖 = 𝐴𝑆𝐷𝑖,𝑡 1

(Eq. 4)

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𝑆𝑊𝑆𝐼𝑖,𝑡 = (CASDi,t/Qai,t) × 100

(Eq. 5)

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where 𝑄𝑎 = adjusted (index) streamflow (volume/time); 𝐶𝐴𝑆𝐷 = cumulative aggregated

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streamflow disturbance (volume/time); 𝑄 = unimpaired streamflow (from NHD Plus V2);

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𝑆𝑊𝑆𝐼 = surface water stress index (%); CU = consumptive use coefficient (fraction);39 i =

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catchment index; nk = number of stream segments in a given aggregated set of catchments,

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k; t = time index (annual or monthly); wu = water use category index; and 𝑄𝑑 = streamflow

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depletion (volume/time).

𝑛

11


(Eq. 3)


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Note that, on the one hand, in equation 5, if streamflow depletions exceed return flows in

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a given catchment, the SWSI has a negative value, implying depleted flow; and, on the

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other hand, the SWSI has a positive value if the return flow exceeds the stream depletion,

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implying excess flow, as in the water use regime analyses of Weiskel et al.50 Accounting

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for net increased streamflow due to return flows is important in terms of impacts on the

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streams’ thermal regime, and water quality, especially in the context of non-point source

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nutrient loading. It is assumed that return flows occur within the same catchment as the

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withdrawal. August is designated as the warm low-flow month, since 93% of the lowest

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monthly stress (stream depletion) occurs in this month, due to a combination of lower

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streamflows and higher water withdrawals and consumptive use coefficients.

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In the U.S. GLB, Michigan is currently the only state that has implemented geographically-

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specific ecological flow thresholds into policy. The Michigan thresholds are based on field

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studies of relationships between target fish populations and streamflow and are classified

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by stream thermal regime and size.51 The Michigan policy also classifies depletion

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thresholds as low, intermediate or high risk, according to the level of stream depletion

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(Table 1); and the policy uses average monthly flows in the low-flow month (e.g., August)

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flows, rather than average annual flows, to assess stream depletions. We use the Michigan

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flow criteria, which provide specific stream depletion thresholds for each stream segment

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in the network, to assess the potential ecological impacts of stream depletions across the

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state. These flow criteria are temporally and spatially commensurate with our SWSI

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analyses. 12


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3. Results and Discussion

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Accounting for return flows from groundwater pumping substantially reduces and even

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reverses estimated stream depletions at annual time scales. Water in the U.S. GLB is

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mostly withdrawn from surface waters, with the exception of Pennsylvania, which relies

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predominantly on shallow groundwater (Figure S3 in Supporting Information). The overall

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fractions of withdrawals from tributary surface water, shallow groundwater, and deep

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groundwater are 81%, 10%, and 9% respectively. Thus, in the majority of catchments,

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streamflow depletion occurs due to direct surface water extractions (93%), rather than

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stream depletions associated with shallow groundwater pumping (7%). Figure 3a shows

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the distribution of annual SWSI for the case where groundwater return flows are ignored.

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The majority of stressed catchments (86%) have SWSIs between 0% and -5%. Of the

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catchments that have lower annual SWSI, 60% occur within cultivated lands, 16% in

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developed areas, and 24% in non-developed non-agricultural land use types. In these

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catchments, approximately 40% of total water withdrawal is supplied from shallow

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groundwater sources, while surface water provides 60%. Clusters of low SWSIs are

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detected in areas of more intensive irrigated agriculture (e.g. central Wisconsin, southern

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Michigan, northeastern Indiana, northwestern Ohio, and northern New York) (Figure 3a).

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Lower SWSIs from agricultural withdrawals are also due to high consumptive use

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coeffients for this use category (~80%, see Table S2). For the SWSIs less than -10%, almost

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all (98%) of the withdrawals are for irrigation, while a small number of withdrawals are

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attributed to the industrial and mining sectors (1% each).

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The neglect of groundwater return flow generally leads to an overestimation of water stress

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levels (Figure 4). When groundwater return flow is accounted for (Figure 3b and Figure

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4), the results are substantially different. Thirteen percent of the stressed catchments now

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have SWSI values greater than 5%, and 2% have SWSIs less than -5%. The clusters of

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higher SWSIs are associated with larger cities that use deep groundwater for water

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supplies, which are scattered around the study area but tend to be at least 10s of km from

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the Great Lakes shoreline. For annual SWSIs greater than 10%, more than 50% of

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withdrawals belong to domestic and public use categories. The remaining withdrawals are

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split between industrial (30%), mining (10%) and agricultural (9%) purposes. Higher SWSI

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for public water supplies is explained by the relatively low consumptive use coeffients for

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this use category (~10%, see Table S2).

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Warm season withdrawals and streamflows produce much greater stresses that are more

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sensitive to groundwater return flows. For the case where groundwater return flows are

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ignored, Figure 3c shows that a substantially larger number of catchments have low SWSIs

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in the warm month (August), when water withdrawals and consumptive use coefficients

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are larger and streamflows are smaller, compared to annual averages (Figure 3a). In this

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case, SWSIs less than -10% are detected in 23% of the stressed catchments (Figure 4), and

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16% of the warm month SWSIs are less than -20%. This is a very different picture than

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the annual scale analysis which detects water stress levels less than -10% in only 8% of the

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catchments that experience water scarcity (Figure 4). Approximately 60% of the total water

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withdrawal in catchments with water stress levels less than -10% in August is from shallow 14


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groundwater. When accounting for groundwater return flow, the proportion of stressed

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catchments with SWSIs greater than +5% reaches 13% in the warm month (Figure 4). In

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this case, 10% of the catchments have SWSIs less than -5%. Further, 8% and 5% of the

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catchments have SWSIs below -10% and -20%, respectively, in the warm month, which

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are significantly greater than the corresponding SWSI distributions at the annual time scale.

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The results indicate that offsets of flow depletions from adding groundwater return flow

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have an almost negligible effect in many catchments during the warm month. Highly

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stressed catchments due to streamflow depletion (i.e., SWSI less than -10%) are generally

303

more sensitive to warm season withdrawals.

15



304 305 306 307 308 309 310 311 312 313 314 315

Figure 3. Spatial distribution of annual and warm month (August) water stress in the U.S. portion of the Great Lakes Basin: (a) annual

316

SWSI without GW return flow, (b) annual SWSI with GW return flow, (c) warm month SWSI without GW return flow, and (d) warm

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month SWSI with GW return flow. Groundwater return flows compensate for surface water depletions resulting in positive SWSI

318

values (excess flow) in many catchments. Flow depletion is generally more pronounced and widespread in the high-withdrawal, low-

319

flow month of August, creating clusters of catchments with low SWSI even after considering the effect of groundwater return flows.

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Figure 4. Summary of stressed catchments for the four cases: annual and warm month water

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stress with and without groundwater return flows (GW). Ignoring groundwater return flow

324

causes an overestimation of streamflow depletion, especially in the warm month.

325 326

Largest stresses occur primarily in catchments with sizes less than 25 km2, with stream

327

orders of 2 or less. Catchment-level geospatial analysis provides an opportunity to

328

systematically investigate SWSI at much finer spatial scales than previous watershed-scale

329

analyses (e.g., refs 28 and 29), as shown in Figure 5a and b. In line with the outcomes of

330

similar attempts to quantify water stress at coarser scales (e.g., refs 26-29) our findings

331

demonstrate that the ability to detect vulnerable catchments is sensitive to the

332

spatiotemporal scale of analysis. As expected, larger (absolute) SWSIs are estimated for

333

smaller catchments (e.g., smaller than 10 km2), and water stress index values generally

334

recede towards zero as catchments become larger. This effect is observed in both annual

335

and warm month water stress analyses (Figure 5a and b). A significant number of larger

336

catchments (e.g., 10-20 km2) that were not identified as highly stressed (in excess of +/-

337

10%) in the annual scale analysis do exceed the 10% threshold in the warm month. In 17



338

general, catchment sizes greater than 25 km2 rarely exhibit stresses less than - 10% or

339

greater than +10%.

340 341

Clusters of catchments with negative SWSI values in the warm month tend to be found in

342

headwater catchments (i.e., Strahler stream order 1) in southwestern and southeastern

343

Michigan, northwestern and northeastern Ohio, and western New York (Figure S4 in

344

Supporting Information). Seventy eight percent of catchments with streamflow depletion

345

in the warm month are found to have stream order type 1. Stream order 2 is the second

346

most vulnerable, where 16% of the negative warm month SWSIs occur, while only 7% of

347

catchments with stream orders 3 or higher are found to have negative SWSIs in August.

348

Furthermore, small versus large catchments results imply that water stress assessments of

349

small catchments are particularly sensitive to the neglect of groundwater-surface water

350

interactions and higher temporal resolution.

351

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Figure 5. Distribution of water stress relative to catchment size: (a) annual and (b) warm

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month. Small catchments are more vulnerable to localized water stress, especially in the

355

warm month. Excluding groundwater return flows will lead to overestimating stream flow

356

depletion (i.e., negative water stress) and failing to pinpoint catchments that may

357

experience excess flow (i.e., positive stress) due to large groundwater return flows,

358

especially in small catchments.

359 360

Incorporation of ecological thresholds facilitates detecting vulnerable stream segments.

361

The results of the spatially-explicit ecological surface water stress characterization in

362

Michigan are shown in Table 1. The ecological thresholds were applied in a post-process

363

SWSI analysis phase to evaluate the stressed stream segments in Michigan in terms of

364

ecological impact. The results reveal that while most catchments are not currently

365

threatened, approximately 650 km of streams fall under the low, intermediate, or high-risk

366

category, when accounting for groundwater return flow. More than 20% of stream

367

segments with negative SWSI values in Michigan are prone to intermediate to high

368

ecological risk due to flow depletion. In the high-risk category, most of the streams 19



Page 20 of 31

369

exceeding depletion thresholds are small warm or small cold-transitional, generally due to

370

high withdrawals in small warm streams and stringent thresholds in the case of the cold-

371

transitional streams. Excluding groundwater return flows results in significant

372

overestimation of vulnerable streams (i.e., 2918 km).

373 374 375

Table 1- Ecological vulnerability of Michigan streams to streamflow depletion in the warm month.

Stream type

Stress range (%)

Cold stream

Low risk* Stream length in range (km)

Intermediate risk* Stream length in range (km) Stress range With GW Without (%) return GW return flow flow

With GW return flow

Without GW return flow

24

25

77

17

1

1

22

--

22

--

511

2242

--

76

506

--

57

170

376

*Risk

377 378 379

**Streamflow

380

The framework developed herein facilitates identification of locales where site-specific

381

evaluations should be performed to ensure existing or new water withdrawals will not put

382

aquatic habitats at risk (Figure S5). Identifying the areas of concern is critical for

intervals are based on absolute streamflow depletion thresholds in

MDNR.51

depletion range threshold not available

20


Page 21 of 31


383

developing campaigns to monitor adverse ecological impacts related to excessive water

384

withdrawals, and for devising strategies to maintain ecological integrity. Ecological

385

guidelines should be developed for streams in all GL states to facilitate the spatially-

386

explicit analysis of the potential adverse impacts of anthropocentric water use on aquatic

387

habitats. Furthermore, ecological impacts of return flows in terms of discharge location,

388

magnitude, thermal regime, and water quality should be better understood.

389 390

Sources of uncertainty in the input data and modeling framework. Interpreting the results

391

to derive management insights requires recognition of sources of uncertainty and caveats

392

of the presented SWSI analysis framework. Our water-accounting framework uses

393

withdrawal data collected from seven Great Lakes states, all of which use different

394

protocols for collecting data on water withdrawal magnitude and location for individual

395

users. As such, verifying the magnitude and location of the water withdrawal data over

396

such a large geographic extent is a challenge. On an aggregate level, water withdrawals

397

across sectors from our database compare well with aggregated withdrawals reported by

398

the Great Lakes Commission.52 Overall, the Great Lakes Commission’s estimate of total

399

withdrawals in the study area is about 10% larger than our independent estimate, which,

400

although significant in terms of volume (i.e., 60 million m3/day), indicates reasonable

401

agreement between the two databases. The difference is most likely due to our refinement

402

of the raw data to assemble a consistent database, excluding some water withdrawal data

403

due to concerns about accurate location and reported withdrawal source. For example,

404

withdrawal points whose coordinates fell on inland lakes were excluded in order to 21



405

consider only surface water withdrawals from streams, and wells that are very close (i.e.,

406

less than 1 km) to the Great Lakes were assumed to have minimal impact on inland

407

streamflow disturbance due to hydraulic connection to the lakes. Accurate spatial locations

408

for the withdrawal points are also important to estimate the fraction of streamflow depletion

409

in areas where shallow aquifers hydraulically connected to nearby streams are the main

410

water source.47

411 412

The presented surface water stress characterization framework and reported findings about

413

spatiotemporal dimensions and limitations of water stress accounting are generally

414

applicable for catchment level waters stress analysis in other settings. This study highlights

415

a need for an extensive, unified water withdrawal, transfer, and discharge reporting

416

protocol. The process of cross-walking the water withdrawal data from different

417

management jurisdictions (e.g., U.S. Great Lakes States) to a common classification

418

scheme is time-consuming and potentially involves subjective interpretation of water use

419

classes. While water withdrawal locations with capacities less than 380,000 liters/day could

420

affect SWSI calculations in small catchments, obtaining the required data to extend the

421

SWSI calculations to these locations is difficult. Further, we assume the return flows

422

remain in the same catchment as the withdrawals, although in reality there may be cases in

423

which water utilities transfer water to users across catchment boundaries. Cross-catchment

424

water transfers can also occur due to irrigation of large agricultural areas. Finally, a better

425

understanding of the spatial variation of consumptive water use for different use categories

426

and seasons across the GL states will improve SWSI calculations. Monthly consumptive

427

use values for Indiana and Ohio were used for all the GL states based on climatic 22


Page 22 of 31

Page 23 of 31


428

similarities.39,53 Better estimates of consumptive use coefficients are especially critical for

429

headwater catchments with large agricultural and livestock farm operations, which are

430

found to be most sensitive to streamflow disturbances.

431 432

Acknowledgements

433

We acknowledge funding from the U.S. National Science Foundation (NSF award No.

434

CBET 0725636) and the Great Lakes Protection Fund (award No. 946). We also appreciate

435

technical and data support from Katelyn Watson, Meredith Ballard LaBeau, and Rabi

436

Gyawali, as well as helpful comments from three anonymous reviewers. Any opinions,

437

findings, and conclusions or recommendations expressed in this material are those of the

438

authors.

439 440

Supporting Information. Input data, additional results, and maps of water stress indices.

441 442 443

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