Humans and Hydrology at High Latitudes (H 3 L) Richard B. Lammers Water Systems Analysis Group, UNH Dan WhiteUniversity of Alaska, Fairbanks Lawrence C. HamiltonDepartment of Sociology, UNH Lilian AlessaUniversity of Alaska, Anchorage Alexander I. ShiklomanovWater Systems Analysis Group, UNH Charles J. Vorosmarty Water Systems Analysis Group, UNH Rasmus O. Rasmussen University of Roskilde, Denmark Igor A. ShiklomanovDirector, State Hydrological Institute, St. Petersburg, Russia Cynthia M. DuncanUniversity of New Hampshire Sponsored by NSF - Synthesis of Arctic System Science - OPP

Major Challenges (1)Integrating biogeophysical and human dimension data sets at the pan-arctic scale (2)Ranking the major forcings on the water system (3)Drawing conclusions on current system state and future trajectories at scales relevant to human activities (i.e. local and intermediate)

Hypotheses H1: At local scales, direct human impacts (such as changes in land use and land cover, or water management) most often drive hydrologic change. At larger regional and pan-arctic scales, climate change drives hydrologic change. H2: Climate change affects human activities in the pan-Arctic in ways that will, in turn, impact both the humans and hydrology of the pan-arctic system. H3: A more temperate pan-arctic basin will lead to conditions under which direct human impacts on the hydrologic cycle will intensify and expand in scale.

GOAL 1 Retrospective: To analyze the major forces and trajectories shaping the pan-arctic water system and to understand their interactions with humans. GOAL 2 Contemporary: To advance our knowledge of relationships linking broad scales of change to local societal impacts. GOAL 3 Future: To forecast the range of potential future statistics of the pan-arctic hydrosphere, societal impacts, and response at multiple scales.

The proposed work involves a 200 year time range made up of the historical ( ), contemporary (2000), and prognostic ( ) periods. These periods can be viewed from the perspective of a variety of scales; large (continental), regional, and local (human) scales. Goal 1 Goal 2 Goal 3

ArcticRIMS -

Okrug = Administrative District

Aging Population Therefore a Declining Population

Alaska counties mapped into EASE-Grid Cells

How do we handle human data and do we handle it at the local or regional scales?

Identify typologies: Economy Subsistence Resource Public Military

Within typologies, consider population growth, resource use, climate change, and values to estimate future reliance on freshwater Photo courtesy of Bryan Collver

Ice Road Construction Snow birds flooding sea ice Point of withdrawal Photos courtesy of AIC

Access to subsistence resources

(Larry Smith et al., 2005)

How Will People Respond to Change?

Multi-Agent Simulation (MAS) Modeling of the Human-Freshwater Network

Broader ARCSS Synthesis

Runoff Precipitation/evaporation Atmospheric circulation Photo: Malcolm Ford

Outreach

Matrix view: Indicator Database of Arctic Community Change (INDACC). Rows in this human- dimensions database are place/years; variables could be any place/year attributes.

Choropleth map of Alaska regions

Point map showing population of settlements in northern Alaska

Observed & modeled population in 12 Alaska regions, 1969–2003

Multilevel models, also called mixed models, can include both “fixed effects,” which at the top level are analogous to coefficients of one-level models, and also “random effects,” which vary within levels. We can have multiple nested levels of random effects. The red curves in Figure 1 depict a simple two-level model of the form pop i j = β0 + β1 year i j + β2 year 2 i j + ζ0 j + ζ1 j year i j + ζ2 j year 2 i j + ε i j where pop i j is the population in year i for region j. The β (beta) parameters describe growth trends for all 12 regions considered together. ζ j (zeta) parameters represent effects that are unique for each region. Estimates of β’s and the standard deviations of ζ’s (all of which differ significantly from zero) are summarized in the following table.

. xtmixed pop year0 year2 || fips: year0 year2 Performing EM optimization: Performing gradient-based optimization: Iteration 0: log restricted-likelihood = Iteration 1: log restricted-likelihood = Computing standard errors: Mixed-effects REML regression Number of obs = 376 Group variable: fips Number of groups = 12 Obs per group: min = 24 avg = 31.3 max = 35 Wald chi2(2) = Log restricted-likelihood = Prob > chi2 = pop | Coef. Std. Err. z P>|z| [95% Conf. Interval] year0 | year2 | _cons | Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] fips: Independent | sd(year0) | sd(year2) | sd(_cons) | sd(Residual) | LR test vs. linear regression: chi2(3) = Prob > chi2 =

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