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United States Geological Survey, MS964, Denver Federal Center,. Lakewood, Colorado, 80225, USA. Abstract. Climate change is one possible external driver of ...
CLIMATE CHANGE EFFECTS ON ECOSYSTEM SERVICES IN THE UNITED STATES – ISSUES OF NATIONAL AND GLOBAL SECURITY SHORT TITLE: CLIMATE CHANGE EFFECTS ON ECOSYSTEM SERVICES

MICHAEL J. FRIEDEL* United States Geological Survey, MS964, Denver Federal Center, Lakewood, Colorado, 80225, USA Abstract. Climate change is one possible external driver of ecosystem services. In the tropical Pacific, short-term climate change is influenced by oceanic Kelvin waves that induce remote temperatures to rise (El Niño event) or decrease (La Niña event). This teleconnection is not globally uniform; in the United States (U.S.) drought conditions induced by El Niño commonly appear in the northern latitudes, whereas drought induced by La Niña occurs in the southern latitudes. Should natural or anthropogenic climate forcing influence the frequency or intensity of drought, there is a potential for catastrophic events to occur placing our national and global security at risk. Because climate forcing interacts with ecosystems characterized by nonlinear and multivariate processes over local-to-global and immediate-to-long-term scales, their assessment and prediction are challenging. This study demonstrates the efficacy of an alternative modeling paradigm based on using a self-organizing map to examine and predict the role that climatic change has on water-resource related ecosystem services. Examples include: (1) hindcasting 2000 years of temperature and precipitation across states in the south-central and southwestern U.S.; (2) forecasting climate-induced groundwater recharge variability across subbasins in mid-western U.S.; and, (3) forecasting climatechange effects on post-fire hydrology and geomorphology in the western U.S..

Keywords: climate change; ecosystem services, El Niño, La Niña, groundwater recharge; self-organizing map; hindcasting; forecasting; uncertainty

1 In Baba et al., 2011, Climate Change and its Effects on Water Resources – Issues of National and Global Security, NATO Science for Peace and Security Series C, Environmental Security, 3, Springer, The Netherlands DOI 10.1007/78-94-007-143_1 .

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1. Introduction Humans benefit from a multitude of resources and processes supplied by natural ecosystems (Randhir and Ekness, 2009). These benefits (collectively known as ecosystem services) include water resources suitable for supporting various sectors of society, such as agriculture, construction, daily living, energy, fishing, forestry, manufacturing, public health, recreation, and transportation. Climate change is frequently cited as one possible external driver of ecosystem services (Furnis, 2010). Because climate is temporally and spatially dependent, change at a global scale differs from regional or local scales. Some considerations when discussing temporal climate change include amplitude, duration, and gradient (Woodhouse and Overpeck, 1998). In many studies, the duration of climate change is considered short-term (years to decades) and long-term (hundreds to thousands of years) variability (Woodhouse and Overpeck, 1998). Short-term climate variability is attributed to oscillations in the sea surface temperature (SST) that alter ocean currents and overlying air pressure resulting in a redistribution of temperature and precipitation (Smith and Reynolds, 2003). Long-term climate variability is attributed to alterations in external processes leading to a redistribution of heat at depth in the world's oceans (Smith and Reynolds, 2003). Some long-term processes (Woodhouse and Overpeck, 1998) are (1) the changing solar radiation due to sunspot activity, (2) the addition of carbon dioxide from volcanic activity, and (3) the reversal of the earth’s magnetic field. The El Niño Southern Oscillation (ENSO) is considered the strongest shortterm periodic fluctuation (2-7 years) with a rise (El Niño) or decrease (La Niña) of SST in the equatorial Pacific Ocean (Blade et al., 2008). This teleconnection is not globally uniform in the U.S., and drought conditions induced by an El Niño event commonly affect the northern latitudes, and a La Niña event affects the southern latitudes (Woodhouse and Overpeck, 1998). Droughts have tremendous consequences on the physical, economic, social, and political elements of our environment (Wilhite, 2000). They affect surface and groundwater resources by diminishing the water supply, water quality, riparian habitat, power generation, and range productivity. Other consequences often include crop failure, debris flows, insect infestations, pestilence, violent conflict, wildfires, and disruptions to economic and social activities (Riebsame et al., 1991). Should natural or anthropogenic forcing influence the frequency or intensity of climate change, there is an increased likelihood for drought hazards placing national and global security at risk (Riebsame et al., 1991). For this reason, accurate and timely climate-change information and related predictions could benefit many sectors of society, but the scale-dependent complexities render it a challenge to model. Specifically, climate forcing is known to interact with

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ecosystems which are characterized by coupled, nonlinear, and multivariate processes. Data associated with these systems are typically sparsely populated ranging spatially from local (1000s km2) to global and temporally from immediate (1-10’s years) to long-term (100’s to 1000’s years). This makes the construction of process-based models difficult. One critical issue is the lack of essential calibration data which results in large inaccuracies (Loke et al., 1999). Also, process-based modeling schemes are commonly too rigid with respect to detecting unexpected features like the onset of trends, non-linear relations, or patterns restricted to sub-samples of a data set. These shortcomings created the need for an alternate modeling approach capable of using available data. This paper demonstrates the efficacy of using data mining to understand the effects of climate change on water-resource dependent ecosystem services. The objectives are: (1) hindcasting 2000 years of temperature and precipitation across states in the south-central and southwest U.S.; (2) forecasting climateinduced groundwater recharge variability across subbasins in mid-western U.S.; and (3) forecasting climate-change effects on post-fire hydrology and geomorphology in the western U.S.. 2. Methodology 2.1. CONCEPTUAL MODEL

A conceptual model is defined to facilitate understanding of the climate-change effects on water-resource dependent ecosystem services. In this model, the response can be climatic (annual temperature and precipitation), hydrologic (debris flows, flooding, groundwater recharge, water quality), or ecologic (fish and macroinvertebrate index of biotic integrity). It is functionally related to natural and anthropogenic stresses on coupled and nonlinear processes induced by drought, urbanization, and wildfire across land segments and sub-basins. A self-organizing map (SOM) is used to evaluate the various conceptual models. 2.2. SELF ORGANZING MAP

A self-organizing map (SOM) is a type of unsupervised artificial neural network (Kohonen, 2001). The method projects sparse, coupled, nonlinear, and multidimensional data into a lower dimensional space using vector quantization. Following the initial distribution of random seed vectors (weights) and numerous iterations, the competitive learning process results in a network of information in which topological relationships within the training set are maintained by a neighborhood function. Unlike other types of artificial neural networks, the SOM does not need target output to be specified, and its neighborhood function can be used to impute values based on the

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organized data vector relations. It is this imputation process that facilitates hindcasting and forecasting in this study. 2.3. DATA

This study uses readily available data published by various authors (Cook et al., 1999; Cherkauer and Ansari, 2005; Gartner et al., 2005). These data are sparsely populated with numerical and categorical observations sampled at different scales and across natural and anthropogenic gradients. Gradients relate one (or more) dependent responses, such as biological, physical, and chemical variables, as a function of space or time changes in multiple environmental variables that include climate observations (precipitation, temperature); or derived metrics, such as urban intensity, Palmer drought severity index (PDSI), and others. 3. Results 3.1. HINDCASTING 2000 YEARS OF CLIMATE DATA ACROSS STATES IN THE SOUTH-CENTRAL AND SOUTHWESTERN U.S.

The effective planning of water resources requires accurate information about climate variability. The short time period for which instrument records exist, however, limits our knowledge of long-term temperature and precipitation variability. To overcome this limitation, a simultaneous reconstruction of annual temperature and precipitation values was conducted for eight states across a gradient of modern climate zones: Arizona (Desert), California (Mediterranean), Colorado (Semiarid to Alpine), Kansas (Semiarid to Humid Continental), Nevada (Semiarid to Arid), New Mexico (Semiarid), Texas (Semiarid to Humid Subtropical), and Utah (Semiarid). The reconstruction involved imputation of values based on the self-organized nonlinear data vector relations among 2000 years (0 to 2000 AD) of reconstructed warmseason (June–August) Palmer drought severity index data (PDSI; Cook et al., 1999), and 114 years (1895 – 2009) of annual state precipitation and temperature data (National Climatic Data Center, 2010). The reconstruction was verified against independent precipitation and temperature data for the years: 1896, 1900, 1911, 1919, 1923, 135, 1940, 1952, 1960, 1966, 1968 (La Niña), 1986, 1993, 1998 (La Niña), and 2005 (El Niño) using split- and crossvalidation (leave one out) approaches. The Spearman Rho correlation among observed and imputed values was greater than 95% with a p-value of 0.001. Quantile modeling (Cade and Noon, 2003) of the reconstructed temperature change data (annual temperature minus 2000 year median) revealed that the long-term global climate was interrupted by short-term changes. For example, the so-called Medieval Warm Period (~900 to ~1250) and Little Ice Age (~1400

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to ~1850) were two changes over the last two millennia that appeared independently in the northern hemisphere (Loehle, 2007), and our Arizona, Colorado but not Kansas (Fig. 1) reconstructions. The muted peaks and increased uncertainty (0.055 and 0.95 quantile models) in these reconstructions corroborates previous findings that PDSI data are best suited to spatial rather than temporal reproduction of peaks (Woodhouse and Overpeck, 1998). Regionally, our reconstructions revealed 2000 year trends with increasing temperature for Arizona, constant temperatures for Colorado, and decreasing temperatures for Kansas. These findings are attributed to a strong ENSO teleconnection with Arizona, mixed ENSO signals in Colorado because it is a region between El Niño and La Niña latitudes, and Kansas because it is in continental interior and influenced by the Gulf of Mexico. These findings suggest that the natural multicentennial and regional climate variability may be larger than commonly believed.

a

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d e Figure 1 Temperature change reconstruction: (a) northern hemisphere (after Loehle, 2007); (b) Arizona (desert); (c) Colorado (alpine to semiarid); and Kansas (semiarid to humid). Colored lines are quantile regression models with b-spline smoothing (green 0.5; red upper 0.05; red lower 0.95).

3.2. FORECASTING CLIMATE-INDUCED GROUND WATER RECHARGE VARIABILITY ACROSS SUBBASINS IN THE MID-WESTERN U.S.

Optimal groundwater resource management under changing climate requires knowledge of the rates and spatial distribution of recharge to aquifers. The SOM technique was used to estimate groundwater recharge from available and

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uncertain hydrologic, land use, and topographic information without long-term monitoring (Cherkauer and Ansari, 2005). The technique was applied to twelve basins in southeastern Wisconsin where recharge observations were determined using a recession-curve-displacement technique and normalized by annual precipitation. Uncertainty was introduced and nonlinear correlation preserved among these explanatory and response variables using a Monte Carlo (MC) technique. Common patterns among the MC realizations were identified and mapped onto a two-dimensional torroid. Fitted data vectors in the SOM were then used to impute normalized recharge ratios that compared well with the observations and published results (Fig. 2). The effects of climate change on spatial groundwater recharge were evaluated using the model and precipitation extremes associated with the ENSO.

Figure 2 Comparison of observed and forecasted annual recharge as a function of short-term ENSO climatic events.

3.3. FORECASTING CLIMATE-CHANGE EFFECTS ON POST-FIRE HYDROLOGY AND GEOMORPHOLOGY IN THE WESTERN U.S.

Few studies attempt to model the range of possible hydrologic and geomorphic responses following rainfall on burned basins because of the sparseness of data, and the coupled, nonlinear, spatial, and temporal relationships among post-fire landscape variables. This study used an unsupervised artificial neural network (ANN) to project data from 540 burned basins in the western United States (Gartner et al., 2005) onto a SOM. The sparsely populated data set included independent numerical landscape categories (climate, land surface form, geologic texture, and post-fire condition), independent landscape classes (bedrock geology and state), and dependent initiation processes (runoff, landslide, and runoff-and-landslide combination)

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and responses (debris flows, floods, and no events). Clustering of the SOM topography identified eight conceptual models of regional post-fire hydrologic and geomorphic landscape interaction. Stochastic cross-validation of the ANN model demonstrated that initiation process and response predictions were globally unbiased. A split-sample validation on 60 basins (not included in the training set) revealed that the simultaneous predictions of initiation process and response events were 78% accurate. Using this model, forecasts across post-fire landscapes revealed a decrease in the total number of debris flow, flood, and runoff events as climate shifted from wet (El Niño) to dry (La Niña) conditions (Fig. 3). Insight on individual basin changes and variability with respect to initiation process and response events also was revealed. Climate - Change Forecast Wet Basin State 1 MT 2 CA 3 MT 4 CA 5 CO 6 CA 7 MT 8 MT 9 CO 10 CA 11 CO 12 CO 13 UT 14 CA 15 CO … 20 ID 21 CO 22 NM 23 MT 24 CO 25 CA 26 MT 27 CA 28 CO 29 CO 30 CO … 57 CO 58 MT 59 ID 60 ID Number of events =

Dry

Debris flow Yes Yes Yes Yes Yes

Response Wet Dry Flooding

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None

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Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

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Figure 3 The forecast effects of short-term climate variability on post-fire initiation processes and associated responses were evaluated using the self-organizing map. Wet and dry conditions were characterized by El Nino and La Nina associated precipitation events recorded in last 100 years.

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4. Conclusions The self-organizing map (SOM) was useful for evaluating climate change effects on water-resource dependent ecosystem services. In case 1, it was possible to simultaneously reconstruct temperature and precipitation change over 2000 years from which short-term breaks similar to global results for the northern hemisphere were observed in some states. The regional differences in long-term trends were attributed to variations in the ENSO teleconnection. In case 2, the SOM was deemed useful for forecasting the effects of ENSO events on ground-water resources in basins with perennial streamflow. In case 3, the SOM made it possible to forecast the simultaneous and probable effects of ENSO events on multiple post-fire response variables including runoff, landslides, flooding, and debris flows. 5. References Blade, I., Newman, M., Alexander, M.A., Scott,J.D. (2008). "The Late Fall Extratropical Response to ENSO: Sensitivity to Coupling and Convection in the Tropical West Pacific." J. Climate 21(23): 6101-6118. Cade, B.S., Noon, B.R. (2003). “A gentle introduction to quantile regression for ecologists.” Frontiers in Ecology and Enviornomnent 1: 412-420. Cherkauer, D.S, Ansari, S.A. (2005). “Estimating Ground Water Recharge from Topography, Hydrogeology, and Land Cover.” Groundwater, 43(1): 102-112. Cook, E.R., Meko, D.M., Stahle, D.W. and Cleaveland, M.K. (1999). “Drought reconstructions for the continental United States.” Journal of Climate 12: 1145-1162. Furnis, M.J., (2010). “Water, climate change, and forests.” U.S. Forest service, General Technical Report, PNW-GTR-812, pp. 75. Gartner J.E., Cannon S.H., Bigio E.R., Davis N.K., Parrett C., Pierce K.L., Rupert M.G., Thurston B.L., Trebish M.J., Garcia S.P., Rea A.H. (2005). “Compilation of data relating to the erosive response of 606 recently burned basins in the western U.S..” U.S. Geological Survey Open-File Report 2005-1218. Kohonen, T. (2001). “Self-Organizing Maps.” Third Extended Edition, Springer Series in Information Sciences, Vol. 30, Springer, Berlin, Heidelberg, New York, 253 p. Loke, E., Arnbjerg-Nielsen, K., Harremoes, P. (1999). “Artificial neural networks and grey-box modelling: a comparison.” In: Joliffe, I.B., Ball, J.E. (Eds.), Eighth International Conference: Urban Storm Drainage Proceedings, vol. 1. The Institution of Engineers Australia, Australia. Loehle, C. (2007). “A 2000-year global temperature reconstruction based on non-treering proxies.” Energy & Environment 18(7-8): 1049-1058. National Climatic Data Center, 2010. WEB Palmer, W.S. (1965). ”Meteorological drought,” U.S. Weather Bureau Res. Pap. 45(58). Randhir, T., Ekness, P. (2009), “Urbanization on watershed habitat potential: a multivariate assessment of thresholds and interactions.” Ecohydrology 2: 88-101. Riebsame, W. E., Changnon, S.A., Karl, T.R. (1991). “Drought and Natural Resources Management in the United States: Impacts and Implications of the 1987–89 Drought. Westview Press, 174 pp. Smith, T.M., and Reynolds, R.W. (2003). “Extended Reconstruction of Global Sea Surface Temperatures Based on COADS Data (1854-1997).” Journal of Climate, 16, 1495-1510.

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Woodhouse, C.A., Overpeck, J.T. (1998). “2000 years of drought variability in the Central United States.” Bulletin of the American Meteorological Society 79(12): 2693-2714. Wilhite, D.A., 2000. “Drought as a natural hazard: concepts and definitions.” In: Donald, A., Wilhite, (Eds.), Drought: A Global Assessment, vol. I, Routledge, New York, pp. 3-18 (Chapter 1).