GC11A-­‐0971 Collabora�ve Research: Mul�-­‐Century Perspec�ves on Current and Future Streamflow in the Missouri River Basin Gregory T. Pedersona, Jay Alderb, Edward Cookc, Naresh Devinenid, Jonathan Friedmane, Steven Hostetlerb, Upmanu Lallc, Caroline Lelandc, Jus�n Mar�nf, Gregory McCabeg, Parker Nortonh, Sco� St. Georgei, Jeannine St. Jacquesj, Dave Sauchynj, John Stammh, Erika Wisek, and Connie Woodhousel ABSTRACT The Missouri River Basin (MRB) is the only major river headwaters in the western U.S. for which hydrologic reconstruc�ons from tree rings have not been generated in any systema�c way. This knowledge gap is cri�cal given that the region is facing an array of water resource issues that are challenged by hydrologic variability – experiencing both severe floods and droughts in the recent past. For example, the 2011 floods along the Missouri River mainstream were the largest documented floods since record keeping began in the late nineteenth century (Haj et al. 2014) and occurred against a complex backdrop of differing regional trends and variability in annual flow volumes (Norton et al. 2014) (Figure 1). Providing a longer context for understanding past variability of flow and the clima�c controls on it, par�cularly at decadal and longer �me scales, is cri�cal for an�cipa�ng and managing future water supplies. Historical discharge records are too short for such assessments; hence, the value of tree-­‐ring based hydrologic reconstruc�ons that span the past several centuries to millennium. Here we present early progress in a collabora�ve research project that seeks to address this data and knowledge gap by developing Hierarchical Bayesian Models (HBM) for streamflow reconstruc�on on the MRB drainage network using exis�ng, new, and updated tree-­‐ring collec�ons (Figure 2). The HBMs will be constructed to directly consider the spa�al dependence structure of flows in the drainage network leading to improved regional assessments of poten�al non-­‐
sta�onarity in past, present, and projected future changes in streamflow. A variety of hydrologic, clima�c, and land-­‐use modeling experiments will also provide informa�on on the likely drivers of historic and future drought and pluvial events. Flow reconstruc�ons developed using the HBM methods will be compared to results obtained from a suite of more tradi�onal to novel reconstruc�on methods that are less complex, but offer other poten�al advantages such as enhanced reten�on of low-­‐frequency climate informa�on. Reconstruc�on of annual (water year) total flows and seasonal low flows will be targeted due to their importance for naviga�on, reservoir opera�on, and environmental regula�on and ecology. Figure 1. Sta�s�cally significant trends in annual streamflow for w a t e r y e a r s 1 9 6 0 -­‐ 2 0 1 1 . Reproduced from Norton et al. (2014). Figure 3. “Proof-­‐of-­‐concept” reconstruc�on of Yellowstone River discharge at Billings MT (C.E. 986-­‐1989; the data a�er 1989 are instrumental) from a network of 30 annual tree-­‐
ring chronologies. Annual values are in red, 20 yr low-­‐
pass filtered values are in blue, and gray bars are 90% uncertain�es. Streamflow Reconstruc�on Methods (con�nued): Figure 2. Chronologies available as poten�al proxies for streamflow reconstruc�ons of major tributaries and along the mainstream of the Missouri River Basin. DATA & METHODS Streamflow Data: o
The primary MRB streamflow dataset used in screening and model calibra�on will extend from the Hydroclima�c Data Network (HCDN) which has been updated to present by Norton et al. (2014). The HCDN watersheds are basins whose streamflow records are minimally affected by anthropogenic influences such as upstream storage and groundwater pumping or land use changes. (Figure 1). o
Addi�onally, naturalized flow records from the Army Corps of Engineers and the Bureau of Reclama�on, available on the main stem and for headwaters basins, will be used when available since these records are directly relevant to opera�onal decisions. o
Modeled flow records from the Precipita�on-­‐Runoff Modeling System (PRMS) model (Haj et al. 2014) and/or a Monthly Water Balance Model (Wolock and McCabe 1999) will be used to es�mate flows at ungaged loca�ons, at sites that have flows substan�ally modified by dams and diversions, and to provided es�mates of future flows. Tree-­‐Ring Data: KEY RESEARCH QUESTIONS 1.  How can one develop mul�-­‐site paleo-­‐reconstruc�ons of the streamflow in the MRB using data from mul�ple tree-­‐ring proxies? Specifically, how can the high-­‐dimensional se�ng be reliably modeled with a finite data set such that spa�al dependence structure across the river network and across the tree-­‐ring data is properly represented, and parameter uncertain�es are iden�fied? 2.  How can such a model be extended to explicitly consider stochas�c simula�on of flows on the network and reflect epochal or quasi-­‐oscillatory low-­‐frequency climate variability? 3.  What can be learned about the low-­‐frequency variability of drought and wet epochs in the MRB, and their spa�al organiza�on from paleo-­‐reconstruc�ons of the streamflow network? 4.  What are the large-­‐scale atmospheric circula�on controls on high and low frequency hydroclima�c variability over the MRB and how does this fit into larger-­‐scale pa�erns of variability in the West? 5.  How can exis�ng management systems and stakeholders use the reconstructed and stochas�cally simulated informa�on on low-­‐frequency streamflow variability on this river system? U.S. Department of the Interior
U.S. Geological Survey
Any use of trade, firm, or product names is for descrip�ve purposes only and does not imply endorsement by the U.S. Government. PACLIM 2015 Mee�ng Droughts: Reconstruc�ng the past, monitoring the present, modeling the future o
This project will primarily draw upon the 140+ exis�ng ring-­‐width chronologies collected within the MRB and around its margins (Figure 2). o
Non-­‐clima�c noise from the tree-­‐ring measurement records for each site will be removed using signal-­‐free detrending, a cu�ng-­‐edge data-­‐processing approach that corrects for distor�ons caused by curve fi�ng that maximizes the preserva�on of common low-­‐to-­‐medium frequency signals among trees in each final site chronology. Streamflow Reconstruc�on Methods: 1.  Hierarchical Bayesian Regression Modeling (HBM) approach with various pooling op�ons for an explora�on of the appropriate scale of modeling (see Devineni et al. 2013) o  Specifically, the effec�veness of pooling informa�on across sites under an aggrega�on hypothesis vs. the ability to be�er reconstruct flows at a larger site where the signal-­‐
to-­‐noise ra�o may already be be�er followed by disaggrega�on will be inves�gated. 2.  The efficacy of either of these formula�ons will be compared with the point-­‐by-­‐point regression (PPR) approach (e.g. see Cook et al. 2004). o
o
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Here, the PPR applica�on will correspond to the reconstruc�on of flows at specific streamflow sites, possibly followed by disaggrega�on to smaller basins. This version of PRR includes enhancements that allow for direct comparison of results against HBM results as well as the ability deal with skewed streamflow data in a regression based reconstruc�on framework. An example reconstruc�on of the Yellowstone River at Billings MT (USGS 06214500) using 30 tree-­‐ring chronologies in and around the catchment area (sub-­‐region) as predictors in PPR is shown in Figure 3. 3.  Non-­‐sta�onary Stochas�c Simula�on Model is a mul�-­‐level approach for the stochas�c modeling of non-­‐sta�onary, low-­‐frequency clima�c variability and high-­‐frequency phenomena (see Kwon et al. 2007) that will be used to develop simulated flows. o  Here, a wavelet autoregressive moving average procedure serves as an effec�ve low-­‐
order addi�ve model for iden�fying and simula�ng low-­‐frequency variability from a long �me series. o  The approach is to a) perform a wavelet decomposi�on of the �me series; b) iden�fy frequency bands over which the global wavelet spectrum has sta�s�cally significant power; c) reconstruct the �me series for each of these bands by projec�ng on the wavelet basis and the sum of the wavelet reconstructed series; d) for each of these series fit the best Autoregressive-­‐Moving Average model; e) simulate stochas�c series from each of these models and add them up to generate a simula�on that preserves the proper�es of the �me series including the �me-­‐
varying amplitudes of the low-­‐
Figure 4. Illustra�on of the Wavelet Auto frequency modes. Regressive Moving Average procedure for o  This method is illustrated in Figure 4. developing stochas�c simula�ons as in Kwon et al. (2007). REFERENCES Cook, E., C. Woodhouse, C. Eakin, D. Meko, and D. Stahle (2004), Long-­‐term aridity changes in the western United States, Science, 306(5698), 1015. Devineni, N., U. Lall, N. Pederson, and E. Cook (2013), A Tree-­‐Ring-­‐Based Reconstruc�on of Delaware River Basin Streamflow Using Hierarchical Bayesian Regression, J Climate, 26(12), 4357–4374, doi:10.1175/JCLI-­‐D-­‐11-­‐00675.1. Haj, A. E., D. E. Chris�ansen, and A. R. J. Viger (2014), The Effects of Missouri River Mainstem Reservoir System Opera�ons on 2011 Flooding Using a Precipita�on-­‐Runoff Modeling System Model, U.S. Geological Survery Professional Paper 1798-­‐K, 1–
44. Kwon, H.-­‐H., U. Lall, and A. F. Khalil (2007), Stochas�c simula�on model for nonsta�onary �me series using anautoregressive wavelet decomposi�on: Applica�ons to rainfall and temperature, Water Resour. Res. 43, W05407. Norton, P. A., M. T. Anderson, and A. J. F. Stamm (2014), Trends in Annual, Seasonal, and Monthly Streamflow Characteris�cs at 227 Streamgages in the Missouri River Watershed, Water Years 1960–2011, U.S. Geological Survey Scien�fic Inves�ga�ons Report 2014-­‐5053, 1–138. Wolock, D. M., and G. J. McCabe (1999), Effects of poten�al clima�c change on annual runoff in the conterminous United States, J. Am. Water Resour. Assoc., 35, 1341–1350. INSTITUTIONAL AFFILIATIONS & CONTACT INFORMATION aU.S. Geological Survey, 2327 University Way (Suite 2), Bozeman, MT 59715 (gpederson@usgs.gov); bU.S. Geological Survey, 104 Wilkenson Hall, Oregon State University Corvallis OR 97331 (jalder@usgs.gov, swhostet@usgs.gov); cLamont-­‐Doherty Earth Observatory, Columbia University, 61 Route 9w, Palisades, NY (drdendro@ldeo.columbia.edu, upmanulall@gmail.com, cleland@ldeo.columbia.edu); dDepartment of Civil Engineering, Convent Ave. and 140th St., The City College of New York (CUNY) New York, NY, 10031 (ndevineni@ccny.cuny.edu); eU.S. Geological Survey, 2150c Centre Avenue Fort Collins, CO 80526 (friedmanj@usgs.gov); fDepartment of Ecology, 106 AJM Johnson Hall, Montana State University, Bozeman, MT 59717 (mar�njus�n�mothy@gmail.com); gU.S. Geological Survey, Denver Federal Center, MS 412, Denver, CO 80225 (gmccabe@usgs.gov); hU.S. Geological Survey, South Dakota Water Science Center, 1608 Mountain View, Rapid City, SD 57702 (pnorton@usgs.gov, jstamm@usgs.gov); iUniversity of Minnesota, 414 Social Sciences, 267 19th Ave South Minneapolis, MN 55455 (stgeorge@umn.edu); jPrairie Adapta�on Research Collabora�ve 3737 Wascana Pkwy, Regina, SK S4S 0A2 (David.Sauchyn@uregina.ca, Jeannine.St.Jacques@uregina.ca); kDepartment of Geography, University of North Carolina at Chapel Hill, Saunders Hall, Campus Box 3220 Chapel Hill, NC 27599 (ekwise@email.unc.edu); lSchool of Geography and Development 453E Harvill Building, Box #2 University of Arizona Tucson, Arizona 85721 (conniew1@email.arizona.edu) More informa�on and resources available at: h�p://www.nrmsc.usgs.gov/MissouriRiverWater March, 2015