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Drought-Driven Changes in Lake Areas and Their Effects on the Surface Energy Balance of Minnesota’s Lake-Dotted Landscape COLIN PLANK Department of Geology and Geophysics, University of Minnesota, Minneapolis, Minnesota
BRYAN SHUMAN Department of Geology and Geophysics, University of Wyoming, Laramie, Wyoming (Manuscript received 30 March 2007, in final form 21 February 2009) ABSTRACT Open water within Minnesota constitutes 11 830 km2 and represents a significant proportion of the moisture available on the landscape. Because lakes absorb and store heat in the spring and summer, and release heat in the autumn and winter, they also play a significant role in surface energy budgets. Lake areas fluctuate significantly over time, and thus alter surface albedo and energy partitioning in the expanding and contracting lake margin. Observations of lake areas from the 1930s ‘‘Dust Bowl’’ drought were used to approximate the impact of changes in lake area and volume on surface energy budgets. A statewide map of lake area change constructed from historical aerial photos of 620 lakes shows trends in percent area changes that mimic trends in moisture balance. Based on the aerial photos and an analysis of lake bathymetries, the water volume removed from the land exposed around the lakes during the drought was 9.1 6 3.0 3 109 m3. The total area of exposed land was 3300 6 800 km2. Based on a balanced energy budget estimate, the transition from standing water to exposed land at lake margins accounted for 1.12 6 0.31 3 1011 W of energy storage across the state. The decreased heat storage resulted in a statewide average storage flux anomaly of 0.50 6 0.14 W m22 with localized anomalies as high as 33.9 6 7.1 W m22. Large uncertainties exist, however, regarding the partitioning of the energy because of the wide range of potential albedo values for both land and water. As conditions warmed during the twentieth century, lake volumes have increased. Therefore, the assessment herein of the change in heat storage is relevant to comparisons of high regional temperatures in the 1930s and 2000s.
1. Introduction The surface water contained in lakes and wetlands plays an important role in regional climates. The albedo, heat capacity, and roughness of surface waters generally differ significantly from those of the surrounding landscape (Bonan 1995; Coe and Bonan 1997; Rouse et al. 2005; Stull 2000). These differences affect the proportions and rates of moisture and energy exchange between the land surface and atmosphere, and thereby impact climate conditions at local and regional scales. ‘‘Lake effects’’ include intensified local precipitation and mediated summer high or winter low tem-
Corresponding author address: Bryan Shuman, Department of Geology and Geophysics, University of Wyoming, Dept. 3006, 1000 University Ave., Laramie, WY 82071. E-mail:
[email protected] DOI: 10.1175/2009JCLI1978.1 Ó 2009 American Meteorological Society
peratures. Much of the investigation into the climatic effect of surface waters (via energy and water budgets) has focused on the impact of very large lakes of the past or present (Kutzbach 1980; Bates et al. 1993; Hostetler et al. 1994). Downing et al. (2006), however, show that 43% of the 4 200 000 km2 of the total surface area of freshwater on the globe is likely stored in lakes of an area ,1 km2, and 63% is likely in lakes ,100 km2 in area. These small water bodies respond volumetrically and areally to mesoscale climate (Fig. 1), and thus affect the capacity of the landscapes that contain them to store and partition energy. Several recent papers have focused on the effect of lakes and wetlands at the synoptic scale (Pitman 1991; Bonan 1995; Coe and Bonan 1997; Delire et al. 2002; Krinner 2003; Nagarajan et al. 2004). These studies find that coupled land surface–general circulation models (GCMs) that account for surface water cover predict
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FIG. 1. Summer (June–August) temperature anomalies in Minnesota climate division 5 (NCDC 1994), which is representative of the records in all Minnesota climate divisions, and the records of three long-term lake-level monitoring sites (see http://www.dnr.state.mn.us). The thin trend line plotted on top of the time series of temperature anomalies shows a general warming trend over the twentieth century. Over the same time period mean lake levels have been stable to slightly increasing.
lower sensible heat fluxes, higher latent heat fluxes, and decreased average annual air temperatures than GCMs that do not incorporate surface water. Summer air temperatures in model experiments with surface water effects are 1.78–3.08C cooler than in those without surface water (Delire et al. 2002; Bonan 1995). Other recent work has focused on the quantification of local and regional effects of small lakes via micrometeorological measurements of surface energy fluxes (Sturrock et al. 1992; Spence et al. 2003; Stannard et al. 2004; Oswald and Rouse 2004; Rouse et al. 2005; Binyamin et al. 2006). These studies have evaluated the seasonal energy cycle of lakes and lake-dotted landscapes. In particular, Rouse et al. (2005) studied the Mackenzie River Valley of western Canada and found that the presence of surface water over 37% of the landscape increased heat storage flux (Qs) by 500%,
increased latent heat flux (Qe) by 32%, and decreased sensible heat fluxes (Qh) by 19% compared to the fluxes associated with uplands alone. Climatic feedbacks caused by coherent changes in the area and volumes of numerous lakes over time, however, have not been studied, but they may represent a component of the surface–atmosphere interactions that influence climatic trends in a given region. Indeed, in many parts of the United States, such as Minnesota, one of the warmest intervals of the past century occurred in the 1930s (DeGaetano and Allen 2002), and coincided with drought and reduced surface water in soils, wetlands, and lakes (Fig. 1). Reduced regional energy storage and increased sensible heating likely contributed to the high temperatures. By contrast, the recent absence of cool years has occurred when surface water levels, and the capacity for energy storage and latent heating, have
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been high, except for brief intervals in 1978 and the early 1990s (when warming was greatest). Here, we examine the effects of numerous lake area and volume changes during the 1930s ‘‘Dust Bowl’’ drought at the regional scale in Minnesota. To do so, we combine regional air photography analysis with energy budget calculations that use the measurements and calculations of energy fluxes reported in micrometerological studies (e.g., Rouse et al. 2005). We hypothesize that the volume of water lost from the landscape was large enough to significantly impact surface energy budgets. Our work is a case study of the cumulative importance of many surface water bodies for a regional climate. The landscape of the north-central United States provides an ideal setting for our study. There are approximately 11 842 lakes in Minnesota alone (see the Minnesota Department of Natural Resources information online at http://www.dnr.state.mn.us). We use historic aerial photography, lake-level records, and bathymetry data from Minnesota to document regional-scale trends in surface water area and generate a conservative estimate of the volume of water removed from lake margins as a result of the 1930s drought (Fig. 2). The volume estimate is used to calculate the effect of the lake changes on regional energy budgets. Finally, a discussion is provided about the magnitude of the regional energy storage changes relative to other forcings (i.e., greenhouse gases).
2. Data and methods In our approach, we manipulate and analyze data at three basic scales: the individual lake basin, major watershed, and climate division. At the lake scale, we use aerial photographs to study the amount of area change per lake. At the scale of Minnesota’s 84 major watersheds, defined by the Minnesota Department of Natural Resources Division of Waters (information online at http://deli.dnr.state.mn.us/), based on topographic boundaries between surface drainages, we combine the results from individual study lakes to calculate the total cumulative lake area change. The major watersheds contain within their boundaries numerous (sometimes .1000) individual lake basins, and we used the watershed boundaries to guide our random sampling of lake photographs to ensure representative coverage of the state. Finally, total lake volume change and associated energy portioning changes are calculated within each of the Minnesota’s climate divisions (NCDC 1994) to enable comparison with climate data. The state is divided into nine climate divisions (NCDC 1994), the boundaries of which generally encompass or intersect 10s of watersheds. In most cases watershed boundaries have no physical relationship with the climate division boundaries.
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To begin the process of estimating the statewide change in lake area, the modern and historic shorelines of 620 individual lakes were digitized as a means to calculate lake area change through time. Percent change in area was calculated for each lake relative to the 2003 lake level. Lakes that completely dried out in the 1930s are indicated as an area change of 100%. We used a stratified random sampling approach to ensure that lakes were chosen from all watersheds within the state, and that the lakes were comparable with respect to surface hydrology. Digital orthorectified countywide composites of 2003 color aerial photography (Fig. 3) from the National Agricultural Imagery Program (NAIP) were obtained from the Minnesota Land Management Information Center (LMIC). For comparison, black and white aerial photography from the U.S. Department of Agriculture’s Agricultural Stabilization and Conservation Service (now the Farm Service Agency), obtained from the University of Minnesota’s Borchert Map Library, form the basis of our study of lake areas at the end of the 1930s drought (Fig. 3). Because the photography was originally obtained for assessment of agricultural resources, the majority of the aerial photos were taken during the middle to late summer prior to harvest. However, some variability exists in the timing of photo acquisition. Of the 233 photos used (some photos contain three or more lakes), 5.6% are from 1937, 22.7% are from 1938, 55.4% from 1939, 15% are from 1940, and 1.3% are from 1941. Given that many lakes began to rise by 1940 (Fig. 1), approximately 16.3% of our photos represent a minimum estimate of lake-area change resulting from the Dust Bowl drought. We scaled upward from the changes observed in the aerial photography to an approximation of statewide lake area change. To do so, we divided the sampled lakes within each of the climate divisions into five classes of percent area change (0%–20%, 20%–40%, 40%–60%, 60%–80%, and 80%–100%; see Table 1) and applied the fraction of the sampled lakes in each class to the total area of lakes in each of the watersheds within that climate division. To estimate the change in energy storage associated with the lake area changes, we first estimate the volume of water lost or gained at the lake margin (Fig. 2). The volume of water removed from a lake as a whole is not as important as the volume of water removed from the lake’s mixed layer (epilimnion), where the warming of the lake water in the spring and cooling in autumn (i.e., storage and then loss of energy) primarily takes place. As a lake loses volume, the epilimnion only changes volume (and thus energy storage) at its margins (Fig. 2b). To approximate the changed volume of the epilimnion,
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FIG. 2. (a) A summary of the key relationships and variables in our estimation of the effect of lake-level change on energy budgets. For the purpose of estimating the energy storage and partitioning changes, we consider the volume of water that forms a ring around the margin of a given lake. Shaded areas denote the cross section of this ring. (b) A hypothetical cross section showing the relationship between average calculated lake-level drop (1–3 m) and the average thickness of the epilimnion, which warms most during the summer. (c) An overlay of the hypothetical epilimnion cross-sectional areas from 1930s and 2003. Unshaded areas represent the volume of water that is seasonally heated and cooled during both intervals, and illustrates why we only focus on water lost from the lake margin (i.e., the unshaded volumes are similar in both the 1930s and 2003 scenarios).
we calculate the volume of water required to fill the area between the 2004 and 1930s shorelines (Figs. 2a,b), but we recognize that this approach underestimates decreases in epilimnion volume to varying amounts controlled by each lake’s bathymetry (Fig. 2c). To do so, we must convert percent area change data to an estimate of the volumetric change in water at the lake margin in the absence of equally extensive bathymetric (water depth) data. Therefore, we 1) analyze available lake depth and shape data to determine the range of possible area–volume relationships, 2) approximate the
actual (rather than percent) total lake area changes, and 3) calculate water volumes by combining the total lake area change estimates with the lake area–volume relationships. For the first step, we conducted an analysis of statewide trends in lake morphology similar to that of Hondzo and Stefan (1993). Fifty-one lakes with detailed bathymetric data were randomly chosen from across the entire state, and were used to generate three generalized area–depth relationships for convex, concave, and linear basin types (Fig. 4). These curves were then applied
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FIG. 3. Examples from the aerial photo survey of over 600 lakes in Minnesota. (a)–(c) The observed gradient in lake-level response to the 1930s drought, with little change in the east and pronounced change in the west. All 2003 imagery is color and was collected between June and August 2003. The inset map shows Minnesota’s climate divisions and the locations of the three lakes.
to lakes across the state. For each watershed, we distributed the total lake area among the three morphometric classes in the same proportions as observed in our statewide analysis. The application of generalized curves is a simplification of the true natural variety of lake shapes, but is a step better than assigning a single morphology to all lakes. For the second step, we generated an estimate of the total land area exposed at the lake margins. To do so, we applied the following equation: Exposed land area per watershed, Aw 5
åA1
15
5
åWMsF x (Dx /100).
(1)
For each watershed, W equals the sum of the areas of all individual lakes, Ms equals the fraction of the total number of lakes in each of the three morphometric shape classes (s 5 1, 2, 3), Fx equals the fraction of the total number of lakes in each of the five classes of percent area change (x 5 1, 2, . . . 5), and Dx equals the magnitude of the percent change in each class (i.e., for
change class x 5 1, D1 equaled 10 6 10%). Given the five change classes and the three area–depth relationships, 15 area estimates (A1–15) represent the possible combinations. The total area change within a given watershed Aw is the sum of all of these areas (A1–15) and the total area change over the state (As) is in turn the sum of all Aw values. Finally, using our idealized area–depth relationships (Fig. 4), we then converted the exposed areas (A1–15) to an estimate of volumetric changes, V1–15, by multiplying the area values by their attendant depth estimates, Z1–15 (from the equations in Fig. 4). These volumes were then summed to create watershed-scale estimates Vw and a statewide volume estimate Vs. We estimate the effect of the lake-level changes on the regional surface energy storage based on Eq. (2): QG 5 ([V w ]Cliq T)/([Aw ]t) 5 QTG /(Aw t).
(2)
The equation is based on the observation that surface waters warm by 208C over approximately 3 months from
TABLE 1. The fraction of lakes per climate division within each of the five percent area change classes. Precipitation (P) minus evaporation (E) calculated for 1934–39 is also given for each division. Climate division
P 2 E (mm)
0%–20%
20%–40%
40%–60%
60%–80%
80%–100%
1 2 3 4 5 6 7 8 9
2796 29 2 2811 2330 256 2668 2489 107
0.303 0.733 0.967 0.139 0.270 0.877 0.300 0.292 0.000
0.193 0.160 0.033 0.107 0.190 0.088 0.100 0.083 0.000
0.143 0.069 0.000 0.074 0.160 0.018 0.100 0.208 0.000
0.118 0.023 0.000 0.156 0.130 0.018 0.000 0.042 0.200
0.244 0.015 0.000 0.525 0.250 0.000 0.500 0.375 0.800
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FIG. 4. Generalized hypsometric curves [depth (Z) vs area (A)] for Minnesota. A subset of 51 lakes (locations shown in inset map of Minnesota climate divisions) was examined. Graphs show the area contained within each depth contour on the digitized bathymetric maps of the lakes plotted vs the depth of the contour as a percent of the maximum depth. The lakes are grouped according to their volume development (Vd) values (e.g., Hakanson and Jansson 2002), which are divided into three classes: concave (44% of the lakes), linear (40%), and convex (16%). All data points from the lakes in each class were plotted to create a generalized hypsometric curve for the class. The general shape of each basin type is shown above.
mid-April to mid-July, and assumes that all energy that went into storage in the spring comes out in the fall/ early winter. The stored energy (QG) may be partitioned to latent or sensible heating in the absence of surface water during a drought. Equation (2) estimates the storage flux [QG (W m22)] based on the estimate of water volume change [Vw (m3)], the observed increase in temperature (T) of 208C, the time span of the temperature increase [t (s)], the heat capacity of liquid water [Cliq (J m23 8C21)], and the surface-area exposed [A from Eq. (1) (m2)]. To calculate total energy stored (QTG) (W), we do not divide by the area and time terms. To calculate statewide averages of storage and energy fluxes, we use the statewide sum of volumetric change, Vs [for Vw in Eq. (2)] and replace the area exposed term (Aw) with the total state area (2.25 3 1011 m2). Thus, we examine the statewide energy fluxes rather than the sum of fluxes over the exposed margins of the lakes alone. After estimating the change in energy storage (QG), we used the Bowen ratio (b) to solve for the sensible heat flux (QH) and the latent energy flux (QE), respectively. We therefore use the following equations to make estimates for both the 1930s (drought) and 2003 (wet) scenarios (see Fig. 2): QH 5 b(QG QE 5 (QG
Q*)/(b 1 1), Q*)/(b 1 1).
(3) (4)
We use Bowen ratios of 0.7 for exposed land (during the drought), and 0.2 for water (in 2003) based on data from North Dakota upland and wetland sites (Stannard et al. 2004). The Bowen ratio of 0.2 for water is consistent with the calculated values from Sturrock et al. (1992) from Williams Lake, Minnesota. For the 1930s calculation, we use a value of QG for soil equal to 0.06Q*, based on field measurements from Rouse et al. (2005) and comparable to a rule of thumb for soil storage flux on the order of 0.1Q* (Stull 2000). Here, Q* represents the net radiation at the surface (W m22), based on the following equation: Q* 5 KY(1
a) 1 L;
(5)
KY is the incoming short wave radiation, a is the surface albedo, and L is net longwave radiation. To calculate the surface radiation budget using Eq. (5), we use a value of 231 W m22 for incoming radiation (KY) based on the May through July average of the compilation of mean daily solar radiation data from 1969 to 1985 of Baker et al. (1987) from an observation station in St. Paul, Minnesota. We assign the exposed land area (in the 1930s calculation) a net longwave radiation value of 270 W m22 based on Baker et al. (1987). We use an average net longwave value of 290 W m22 for water, which is consistent with the measurements of Sturrock et al. (1992) from Williams Lake, Minnesota.
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To further estimate the potential effects of lake change on surface energy budgets, we also consider three possible levels of albedo contrast between water and land. We let the albedo (a) of land vary from 0.2 to 0.5, a range of values that approximate sparse vegetation on soil to brighter sand and gravel surfaces (Bonan 2002). Albedos of lake water were allowed to vary from 0.05 to 0.2 to approximate the wide ranges of water clarity and color that exist in different trophic states. Scenario 1 is the extreme minimum contrast that might be the case if all lakes were eutrophic (a green color resulting from algal growth, with an albedo of 0.2) and all land exposed by the low lake-level was covered with opportunistic, weedy vegetation or decaying aquatics (also an albedo of 0.2). Scenario 3 represents the extreme maximum contrast in albedo as might be the case if all lakes were oligotrophic (clear and thus almost black from above, with an albedo of 0.05) and all land exposed by the low lake-level was a bright sand (albedo of 0.5). Scenario 2 uses intermediate albedo values and is qualitatively the most realistic of the three. Using these scenarios, we generated estimates of energy flux changes at 1) the lake margins and 2) on average across the state.
3. Results a. Comparison of spatial trends in lake area change and moisture balance Figure 5 shows that lake area trends mimicked the divisional-scale trends in moisture balance and that the trends were not obscured by regional geomorphologic (i.e., a flattening of the landscape westward) or local hydrologic factors. Small amounts of lake area change are found in regions where the moisture balance remained positive during drought (northeast Minnesota) or was only modestly negative (east-central Minnesota), but large amounts of change occurred in the driest climate divisions. The scatterplots (Fig. 5b) show that variability in lake area response increased westward in northern and central areas of the state with highly negative moisture balance. The frequency of small and large lake area changes is related to estimated moisture balance at the scale of climate divisions; large changes are commonly documented in dry areas and small changes in wet areas (Fig. 6). Greater than 70% of the lakes recorded minimal change (0%–20% change in area) where precipitation (P) minus evaporation (E) remained near or above zero (climate divisions 2, 3, and 6 in Table 1), but 30.3% or fewer of the lakes showed minimal changes in area where P 2 E dropped below 2200 mm yr21 (Fig. 6a; Table 1). Likewise, the fraction of lakes that recorded large changes
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(100%–80% change in area; Fig. 6c) was highest where P 2 E was most negative (i.e., climate divisions 4 and 7 in Table 1). The intermediate change classes (Fig. 6b) also tended to be most frequent in the driest areas, but their distribution shows the most pronounced influence of nonclimatic factors. For example, different lake bathymetries (e.g., steep versus shallow lake margins) can dramatically alter the response of lake area to a reduction in water volume, and differences in local geology (e.g., impermeable bedrock versus unconsolidated glacial tills) and hydrology [e.g., depth of groundwater and proximity to rivers; see Almendinger (1990)] can alter the responsiveness of a lake to changes in precipitation. Even where P 2 E was most negative, the areas of lakes with steep sides or groundwater buffering of the response to the drought changed less than pan-like lakes or those with minimal inputs from groundwater.
b. Volume and surface area change Based on the range of possible volumes resulting from the combination of area change classes and morphometries, we estimate that 9.1 6 3.0 3 109 m3 of surface water was lost from the margins of lakes in Minnesota in response to the 1930s drought. The volume was associated with a loss of surface water area of 3300 6 800 km2. The water coverage of Minnesota would have dropped by 21%–35% from 5.2% to 4.1%–3.4% of the total state area. Uncertainties reflect the range of magnitudes [Dx in Eq. (1)] within each percent area change class (e.g., 10 6 10%, 20 6 10%).
c. Impact on energy balance Overall, the water lost from lake margins during the drought stored 33.9 6 7.1 W m22 [QG, based on Eq. (2)]. The reduction in lake areas, therefore, caused an estimated reduction of 1.12 6 0.31 3 1011 W of total storage across the state (QTG). The total storage loss equals a potential statewide increase in the combination of latent and sensible heating of 0.50 6 0.14 W m22. We find, however, that changes in energy partitioning to latent and sensible heating are extremely sensitive to the range of possible albedo changes (Table 2). In our first albedo scenario (Table 2), where highly eutrophic lakes margins are replaced by vegetated land, the surface albedo does not change. The exposed land has a higher net radiation at the surface (Q*upland) than the preexisting lake water (Q*water) because vegetated sediment loses less longwave radiation than water [upward fluxes of 70 and 90 W m22, respectively, based on measurements by Baker et al. (1987)]. Because of the high net radiation available over the exposed land, both latent heat QE and sensible heat QH fluxes are higher during the drought (1930s) than in wet conditions (2003;
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FIG. 5. (a) Mapped circles represent the location and magnitude of lake area changes observed in historic aerial photography, and are plotted over average difference between precipitation (P) and evaporation (E) rates for each of the nine climate divisions for a 6-yr period at the end of the 1930s drought. P 2 E values are calculated using climate division precipitation data (NCDC 1994) and the Thornthwaite (1948) method for calculating evaporation rates, which was applied to climate division data. (b) Plots of area change with respect to longitude (UTM x coordinate) are divided into subsets by latitude, which are bounded by the approximate limits of the northern, central, and southern climate divisions.
see Table 2). The local increase in QH is quite large (34.2 6 1.2 W m22) over the desiccated lake margins and corresponds to a statewide sensible heating anomaly of 0.50 6 0.11 W m22, with the uncertainty arising from the volumetric uncertainty. In the second albedo scenario, we use the midrange values of albedo for both land and water. This scenario may be qualitatively the most realistic for an investigation of statewide average energy budgets because not all lakes are highly eutrophic (albedo scenario 1), not all lakes are oligotrophic and highly transparent (albedo
scenario 3), and not all exposed substrates are the same. Albedos of water bodies will also vary depending on the time of day. Here, because the dry land albedo is larger than that for water, Q* is lower during the drought than during wet conditions, and, thus, QE(drought) is also lower than QE(wet) by 20.9 6 5.9 W m22. As a result, QH(drought) is 18 6 1.2 W m22 higher than QH(wet) and the change produces a statewide sensible heating anomaly of 0.26 6 0.06 W m22 during the drought. In the third albedo scenario, which uses the highest possible albedo for land (i.e., a bright sand) and the
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FIG. 6. The frequency of small, intermediate, and large lake area changes compared to moisture balance. The percentages of lakes in each climate division that fall within the three change classes are plotted vs the climate division moisture balance (P 2 E) values for 1934– 39 shown in Fig. 5. Gray circles represent two groups of climate divisions: one with near-zero moisture balance values in the 1930s and few lake area changes, and one with highly negative moisture balance and many lake area changes. The open circle in the upper-right portion of (c) is an outlier data point generated in climate division 9, which is a nonglaciated portion of the state characterized by few lakes and complex karst hydrology.
lowest possible albedo for water (i.e., dark, clear water), net radiation (Q*) on the exposed substrate during drought is extremely low because much of the radiation is reflected and latent heat fluxes are low. The low available net radiation translates into a muted increase in sensible heat flux [QH(drought)] compared to wet conditions when more energy is stored but less is reflected. In this case, QH(drought) is only 1.7 6 1.2 W m22 higher than QH(wet), resulting in a negligible statewide sensible heating anomaly of 0.03 6 0.02 W m22 (Table 2).
4. Discussion and conclusions Regional moisture balance became increasingly negative westward across Minnesota during the 1930s, and lake areas observed in aerial photography show correlated declines (Figs. 5,6). A westward increase in the variability of lake area change likely reflects the com-
plexity of lake morphologies, the subsurface hydrology, and the influence of increasing amounts of agricultural land use westward. However, the coherent regional trends from moist areas with little lake area change to dry areas with substantial lake desiccation could provide one mechanism for surface moisture trends to feedback and amplify drought conditions. The spatially coherent loss of heat storage could have amplified sensible heating and contributed to the prolonged anomalous warmth of the 1930s (Fig. 1). Our study shows the potential impact of changing surface water volumes and areas on surface energy budgets. The ;30% reduction in water coverage calculated here is associated with localized changes in latent and sensible heat fluxes on the order of 20 W m22 (Table 2) over 1%–2% of the state area. Depending on surface albedo, changes in surface water of the magnitude presented here may result in either negligible or
TABLE 2. Summary of energy balance calculations for the albedo scenarios. All fluxes are local values (i.e., over the lake margins only, not statewide averages; W m22). Values in italics were assumed; values with standard deviations were calculated using Eqs. (2)–(4). Uncertainties arise from calculations of QG(wet) based on magnitude ranges within the percent area change classes in Eq. (1). Respectively, DQE and DQH, change in latent and sensible heat, are land values minus water values. In scenario 1, the albedo (a) of dry land and water were both assumed to be 0.2; in scenario 2, aland and awater were assumed to be 0.350 and 0.125, respectively; in scenario 3, they were 0.5 and 0.05, respectively. Magnitude of change Q*(dry) Q*(wet) QG(dry) Scenario 1 Scenario 2 Scenario 3 Avg of all scenarios Std dev
114.8 80.2 45.5 80.2 34.7
94.8 112.1 129.5 112.1 17.4
6.9 4.8 2.7 4.8 2.1
QG(wet)
QE(dry)
QE(wet)
DQE
QH(dry)
QH(wet)
DQH
33.9 6 7.1 63.5 6 0.0 50.8 6 5.9 12.7 6 5.9 44.4 6 0.0 10.2 6 1.2 34.2 6 1.2 33.9 6 7.1 44.3 6 0.0 65.2 6 5.9 220.9 6 5.9 31.0 6 0.0 13.0 6 1.2 18.0 6 1.2 33.9 6 7.1 25.2 6 0.0 79.7 6 5.9 254.5 6 5.9 17.6 6 0.0 15.9 6 1.2 1.7 6 1.2 33.9 44.3 65.2 220.9 31.0 13.0 18.0 7.1 16.6 13.5 29.5 11.6 2.7 14.1
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large changes in sensible heating. Statewide changes in sensible heat fluxes of 0.26 W m22 are qualitatively, most likely, based on albedo scenario 2. Our calculations are consistent with the existing literature. The magnitude of our calculated storage flux for lake water (QG; 33.9 6 7.1 W m22) is comparable to the measurements and calculations of Sturrock et al. (1992), Spence et al. (2003), and Rouse et al. (2005). Sturrock et al. (1992) report an average storage term from late April to July of 37.2 W m22 at Williams Lake in north-central Minnesota. Spence et al. (2003) observed storage fluxes of ;40 W m22 (0.3Q*) in a small lake in northern Canada. Rouse et al. (2005) report that, depending on lake size, storage terms may be as low as 0.26Q* or as high as 0.76Q* for lakes in northwestern Canada. In general, our storage term is about 0.30Q* at the water’s surface (Table 2). The magnitude of our estimated changes in sensible and latent heat fluxes can also be compared to the flux changes generated by the more dynamically complex global-scale modeling of Bonan (1995), which examined the sensitivity of general circulation models (GCMs) to the inclusion of inland water bodies. The inclusion of water bodies in the model resulted in a sensible heat flux decreased by 5–30 W m22, and latent heat fluxes increased by 10–45 W m22 (Bonan 1995). Here, we find that the removal of water from the margins of the lakes may have increased sensible heat fluxes (based on DQH; Table 2) by 1.7 6 1.2 to 34.2 6 1.2 W m22 and changed latent heat fluxes (based on DQE; Table 2) by 254.5 6 5.9 to 112.7 6 5.9 W m22 over the desiccated lake margins; but, when viewed as a statewide average comparable to the spatial resolution of a GCM, our values are much lower than estimated by Bonan (1995). Bonan (1995), however, considers the presence versus absence of lakes (including large lakes, such as Lake Superior) that can cover .40% of 2.88 3 2.88 grid cells, whereas we address the removal of only 21%–35% of the state lake area (covering 1.1%–1.8% of the total area of the state). In our treatment of lake energy budgets, we have also made several simplifications and assumptions. Most importantly, we based our calculations on the generalized energy cycle of a dimictic lake (which has a distinct epilimnion), but acknowledge that a percentage of lakes will not stratify or may stratify and mix multiple times through the year (in both cases serving as larger heat sinks than a consistently stratified lake because energy is stored throughout the water column). Additionally, each lake will undergo multiple phases of heating, cooling, and increased mixing controlled by the temperature of the surrounding air masses and the passage of weather fronts (Ford and Stefan 1980; Spence et al. 2003), and these phases affect whether a lake is an energy source or sink,
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and how deeply energy is stored in the lake. The timing, depth, and maximum temperature of the stratification also varies depending on lake size, water clarity, and dissolved organic carbon, lake depth, and exposure (Ford and Stefan 1980; Rouse et al. 2005). These observations reveal weakness in our assumptions that each epilimnion warms steadily by 208C over approximately 3 months in the spring, and then does not cool until autumn, and indicate that the annual synchronization of the energy cycles of all lakes may vary. These possibilities affect our assumption that lakes across the state coherently affect regional energy budgets, although these problems are probably more important during spring and fall than in midsummer. With these caveats about our assumptions, we speculate that by decreasing Minnesota’s lake cover by 3300 6 800 km2 the surface energy storage may influence temperatures in the state. For example, the loss of heat storage may have contributed to high summer temperatures in the 1930s (Fig. 1), although changes in wetland extent and soil moisture levels were likely as significant or more than lake fluctuations. At a finer scale, the temperature gradient between a climate division with few lakes and a division with many lakes may decrease during drought because more energy would be redistributed from storage and latent heating to sensible heating in the division with numerous lakes than in the division with fewer lakes. The potential impact of changing surface water area and volume is significant for lake-dotted landscapes. Our estimate of a statewide energy flux anomaly (0.50 6 0.14 W m22), only resulting from fluctuations in surface water area and volume, is similar to the net radiative forcing of atmospheric carbon dioxide (1.66 W m22) and methane (0.48 W m22; Solomon et al. 2007). During the 1930s, greenhouse forcing would have been reduced relative to today, but with reduced surface water, much of the net radiation over the state would be been partitioned to sensible heat. Thus, the warmth in the 1930s and 2000s occurred during significantly different radiative energy scenarios. Given the similar magnitudes of the greenhouse gas radiative forcing and our approximated energy anomaly, statewide increases in lake area and volume (and the associated sensible heating anomaly of about 20.3 W m22) since the 1930s may have helped dampen long-term (twentieth century) temperature increases relative to other regions. Acknowledgments. We thank Drs. J. McFadden and E. Ito for their input, and M. Ferreira for her considerable help in digitizing aerial photos and interpreting indicators of shoreline changes. We also acknowledge the critiques of our three anonymous reviewers and thank them for
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helping us improve our research and manuscript. Funding and support for this project were provided by the University of Minnesota’s Departments of Geography and Geology and Geophysics, the National Science Foundation Behavioral and Cognitive Sciences (Doctoral Dissertation Improvement Grant BCS- 0526314) and Earth System History (ATM-0402308) programs. M. Ferreira participated as an undergraduate researcher through the NSF’s Research Experiences for Undergraduates Summer program. REFERENCES Almendinger, J. E., 1990: Groundwater control of closed-basin lake levels under steady-state conditions. J. Hydrol., 112, 293–318. Baker, D. G., D. L. Ruschy, and R. H. Skaggs, 1987: Climate of Minnesota, Part XVI: Incoming and reflected solar radiation at St. Paul. University of Minnesota Agricultural Experiment Station Bulletin, AD-SB-3276, 580-1987, 73 pp. Bates, G. T., F. Giorgi, and S. W. Hostetler, 1993: Toward the simulation of the effects of the Great Lakes on regional climate. Mon. Wea. Rev., 121, 1373–1387. Binyamin, J., W. Rouse, J. Davies, C. Oswald, and W. Schertzer, 2006: Surface energy balance calculations for small northern lakes. Int. J. Climatol., 26, 2261–2273. Bonan, G. B., 1995: Sensitivity of a GCM simulation to inclusion of inland water surfaces. J. Climate, 8, 2691–2704. ——, 2002: Ecological Climatology: Concepts and Applications. Cambridge University Press, 678 pp. Coe, M. T., and G. B. Bonan, 1997: Feedbacks between climate and surface water in northern Africa during the middle Holocene. J. Geophys. Res., 102 (D10), 11 087–11 101. DeGaetano, A. T., and R. J. Allen, 2002: Trends in twentiethcentury temperature extremes across the United States. J. Climate, 15, 3188–3205. Delire, C., S. Levis, G. Bonan, J. A. Foley, M. Coe, and S. Vavrus, 2002: Comparison of the climate simulated by the CCM3 coupled to two different land-surface models. Climate Dyn., 19, 657–669. Downing, J. A., and Coauthors, 2006: The global abundance and size distribution of lakes, ponds and impoundments. Limnol. Oceanogr., 51, 2388–2397. Ford, D. E., and H. Stefan, 1980: Stratification variability in three morphometrically different lakes under identical meteorological forcing. Water Resour. Bull., 16, 243–247.
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