1. SNOW SEASON FRACTIONAL FLOW 4 Trends in Eurasian Arctic runoff timing and their relationship to snow cover changes Amanda Tan 1, Jennifer C. Adam 2, Dennis P. Lettenmaier 1 1.Department of Civil and Environmental Engineering, Box 352700, University of Washington, Seattle 2.Department of Civil and Environmental Engineering, Washington State University, Pullman, WA Pronounced land surface process changes have occurred in the Arctic and sub-Arctic in recent decades. Satellite data have confirmed that average snow cover has decreased, especially in the spring and summer. Changes in timing of snowmelt and permafrost melt have been linked to increased riverine discharge to the Arctic Ocean which has the potential to affect the thermohaline circulation and in turn the formation of North Atlantic Deep Water and the northward-flowing Gulf Stream. To date, however, causality for observed hydrologic trends and changes in climatic forcings to the land surface system remains elusive, primarily because linkages between hydrologic and climatic sensitivities are not well established. We investigate timing trends in 45 streamflow gauges that are uninfluenced by anthropogenic change. Using monthly observed streamflow data and a large-scale hydrologic model applied over three major river basins (Ob’, Lena and Yenisei) in the Eurasian Arctic to develop a new daily adjusted dataset, this study explores the role of snow on the sensitivities of annual and seasonal Arctic river discharge through analysis of in-situ and satellite-based observations of snow cover. We find in general that spring streamflows are moving earlier into the year, prompted by an earlier snowmelt season. Winter flows are increasing in many parts of the Eurasian Arctic. The rapidity of snow melt and a prolonged snow free duration and may be the main contributors to spring and summer/peak flow timing trends. 1 ABSTRACT CHANGES IN STREAMFLOW TIMING 3 2 METHODOLOGY 45 gauges in the Lena, Ob and Yenisei were analyzed over the same 1958 – 1999 period of record. The 45 stations were selected to be relatively uninfluenced by land-use changes and anthropogenic factors, including upstream reservoirs. Monthly streamflow obtained from R-ArcticNet V4.0 Since only monthly streamflow data were available, the Variable Infiltration Capacity (VIC) hydrologic model was used to generate daily streamflow through use of the model’s ration of daily to monthly streamflow, which was applied to the observed monthly streamflows to produce reconstructed daily discharge time series for each gauge, which are constrained to the sum of monthly observed totals. Forcings for VIC: Temperature - Wilmott & Matsura, 2005; Precipitation - Adam et al., 2007; Groisman, 2005 Daily streamflow was analyzed for three measures outlined in Stewart et al., 2005 (a)Spring Pulse Onset (SPO) : The first day of spring or snowmelt season (b)Centroid of Timing (CT): Center mass of annual flow (c)Fractional flows: Ratio of monthly or seasonal flow to the annual flow Each measure was tested for linear trends and significance using the Seasonal Mann Kendall test. Correlation analyses between snow cover and spring pulse onset and snowmelt were conducted using both the Pearson method and Kendall’s Tau estimates. Correlation analyses between different months and interseasonal flow were also conducted. WINTER SEASON FRACTIONAL FLOW Analysis of the SPO time series for the 45 stations showed a 0 – 12 day shift towards earlier dates at 39 gauges 0-7 day shift toward later dates at the remaining 6 stations Due to the considerable interannual variability in the beginning of the spring pulse onset, only four trends are significant at the 10% significance level Of the 6 gauges with later SPO, none were statistically significant at the 0.10 level and only 3 stations had shifts towards later dates of more than 5 days Trends toward earlier spring pulse are distributed across all three basins, but they tend to be concentrated in the relatively warmer Ob’ and Yenisei basins. CT provides a time-integrated perspective of timing of snowmelt and represents the overall distribution of flow for each year (Stewart et al., 2005). 86% of gauges have trends toward earlier CT (between 1 – 24 days), 12 are significant 5 Combined seasonal fractional flow for the winter season (December, January and February) Increasing trends: December: 68% January: 72% February: 76% Winter season: 75% Trends are statistically significant at roughly half of the gauges (p=0.10) The correlation between SPO and winter discharge is significant at 20% of the gauges Correlation between snowmelt season fractional flow and winter fractional flow is significant at 36% of gauges. (c) (d) \ April July June May SummerSnowmelt (a) (b) January December WinterFebruary Summary There is modest evidence that snowmelt is shifting earlier in the water year (about 60% of the gauges) Winter season fractional flow is increasing in 75% of the gauges in the basin Summer season flow is decreasing at most stations The most significant changes occur during the months of May, July and all through winter The rapidity of snow melt and a prolonged snow free duration and may be the main contributors to spring and summer/peak flow timing trends. −Adam, J.C., and D.P. Lettenmaier, 2008: Application of new precipitation and reconstructed streamflow products to streamflow trend attribution in Northern Eurasia J. Climate 21(8): 1807-1828 −Stewart I., D. R. Cayan, and M. Dettinger, 2005: Changes toward earlier streamflow timing across western North America. J. Climate, 18, 1136– 1155. Contact: amanda@hydro.washington.edu Summary of variables used in Snow Cover analysis VariableSCOSCDSMPSFD Measure Snow Cover Onset Snow Cover Disappearance Snow Melt Period Snow Free Duration Weeks31 – 4613 – 269 – 2813 – 31 Snow Cover Fraction0.030.980.98 – 0.010.00 Linear trends of snow cover timing for the Lena, Yenisei and Ob’’ basins. Trends in bold are significant at p =0.10. BasinSCOSCDSMPSFD Lena-0.28-0.880.78-0.96 Yenisei-1.01-1.151.16-0.86 Ob’-0.47-0.591.40-1.44 BasinSCDSMP Lena0.0780.348 Ob’0.0840.600 Yenisei-0.0880.501 Snowmelt in the three basins occurs mostly during May and June, with peak discharge usually occurring mid-June. Analysis of fractional streamflow for the May-June snowmelt period shows: 1) Increase in May monthly fractional flows at 57% of the gauges. 2) June discharge is decreasing for 72% of the gauges. Overall, May-June fractional flow decreased at 54% of the gauges with some of the stations experiencing up to 30% decreases in streamflow, particularly at the eastern-most Aldan basin, which is tributary to the Lena and which is also underlain by continuous permafrost. Although 46% stations that show increasing May-June streamflow trends none of these are statistically significant; the statistically significant trends are only for decreasing snowmelt season streamflow(2 gauges). These findings can be linked to both the SP and CT Streamflow timing results correspond with predominant trends toward an earlier snowmelt season (increase in May fractional flow, SP and CT moving earlier into the water year, decreased JJA flows). We also found that April flows were decreasing at 75% of the stations (13% are significant) in the Lena basin which may suggest an overall snowpack decrease leading to lower April SWE. Summer flow (July and August) is decreasing with some gauges registering as much as a 20% decrease in discharge. The most regionally cohesive trends are in the Lena basin, the coldest of the three major drainage basins. All of the gauges in the Lena showed a decrease in July streamflow, of which 87% were statistically significant (p = 0.10). The trend of decreasing flows in June and July appears to be a compensation for the increase of fractional flow in May. As suggested in Yang et al. (2002), the increase in May and decrease in June flows suggest a hydrologic shift towards earlier snowmelt and early summer peak flow. Correlation between springpulse onset with SCD and SMP. Trends that are significant at p=0.1 are highlighted in bold. STREAMFLOW-SNOW COVER RELATIONSHIP 6 SPO and SMP are significantly correlated for all three basins Highest correlation occurring in the Ob’ basin Pronounced changes in timing of the SMP (i.e. the rapidity of the snowmelt season) and changes in May fractional flow may account for the timing changes in springpulse and CT, where the bulk of annual flow is now occurring earlier in the year due to the prompt melting of snow cover. The cold and threshold basins generally have positive temperature correlation coefficients, indicating that as temperature increases, streamflow also increases, possibly due to the melting of ground ice (Adam et al.,2007).