Assessment of climate change impact on floodplain and hydrologic ecotones.

Summary Current modeling efforts continue to indicate that the effects of climate change will be both global and local in scale, and that ecohydrologic factors including vegetation pattern, altered precipitation events, reduced system yields due to streamflow changes, increased flooding and changes to current floodplain characteristics will be affected. Therefore, using technology such as light detection and ranging (LiDAR) data, using future general circulation model (GCM) data, and conducting floodplain analyses to predict the changes to ecohydrologic factors are critical for cataloging existing ecosystem resources and for understanding the effects that different climate change scenarios may have on these resources at the basin scale. This study considers the effects of three different GCM climate change emissions scenarios (high from the IPSL GCM’s A2 scenario, middle from the ECHAM5 GCM’s A2 scenario and low from the GISS GCM’s B1 scenario) using daily downscaled precipitation and temperature data over the Lower Tualatin basin in the Pacific Northwest US. The Tualatin River basin is a dynamic watershed that supports urban and agricultural uses and is also 50% forested. Its economic drivers include agricultural and forest products, as well as other consumer products including high-tech software and hardware industries. The Soil and Water Assessment Tool (SWAT) software was used as a distributed hydrologic model to predict the daily flows in the basin. It is predicted the 50-year recurrence interval (RI) flow will decrease significantly for the low and middle emissions scenarios (to between approximately 18,000–19,000 cfs compared to the observed 50-year RI of near 26,000 cfs) and will increase significantly under the high emissions scenario to nearly 33,000 cfs. Floodplain extents for the various climate scenarios and timeframes were delineated using the HEC-RAS model. A geo-processing procedure was employed to delineate hydrologic ecotones to evaluate the condition of riparian areas and streams. A current and future conditions analysis was performed using the combination of aerial imagery and LiDAR data for the vegetation within the ecotones in order to: (1) provide an existing inventory of vegetation within the basin and (2) to predict the effects that climate change may have on vegetation within the basin.

[1]  R. Huth.  Sensitivity of Local Daily Temperature Change Estimates to the Selection of Downscaling Models and Predictors , 2004 .

[2]  Ashish Sharma,et al.  Measurement of GCM Skill in Predicting Variables Relevant for Hydroclimatological Assessments , 2009 .

[3]  D. Randall,et al.  Convective Precipitation Variability as a Tool for General Circulation Model Analysis , 2007 .

[4]  P. Mote,et al.  Review of scenario selection and downscaling methods for the assessment of climate change impacts on hydrology in the United States pacific northwest , 2007 .

[5]  Jay R. Lund,et al.  Climate change, urbanization, and optimal long‐term floodplain protection , 2007 .

[6]  C. Bretherton,et al.  Statistical Precipitation Downscaling over the Northwestern United States Using Numerically Simulated Precipitation as a Predictor , 2003 .

[7]  Robert C. Harney,et al.  Military Applications Of Coherent Infrared Radar , 1982, Optics & Photonics.

[8]  Robert J. Moore,et al.  Use of a grid‐based hydrological model and regional climate model outputs to assess changing flood risk , 2007 .

[9]  Jeffrey G. Arnold,et al.  Soil and Water Assessment Tool Theoretical Documentation Version 2009 , 2011 .

[10]  S. S. Niemiec.  HARDWOODS OF THE PACIFIC NORTHWEST , 1995 .

[11]  Mohammad Najafi,et al.  Statistical Downscaling of Precipitation Using Machine Learning with Optimal Predictor Selection , 2011 .

[12]  N. Snyder.  Studying Stream Morphology With Airborne Laser Elevation Data , 2009 .

[13]  H. Kwon,et al.  Accounting for Uncertainty Propagation: A Streamflow Forecasting Framework using Multiple Climate and Hydrological Models , 2009 .

[14]  J. Arnold,et al.  AUTOMATED METHODS FOR ESTIMATING BASEFLOW AND GROUND WATER RECHARGE FROM STREAMFLOW RECORDS 1 , 1999 .

[15]  D. Lettenmaier,et al.  Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs , 2004 .

[16]  R. Srinivasan,et al.  A global sensitivity analysis tool for the parameters of multi-variable catchment models , 2006 .

[17]  R. Wilby,et al.  A framework for assessing uncertainties in climate change impacts: Low‐flow scenarios for the River Thames, UK , 2006 .

[18]  Thomas Kleinen,et al.  Integrated assessment of changes in flooding probabilities due to climate change , 2007 .

[19]  R. B. Jackson,et al.  Methods in Ecosystem Science , 2000, Springer New York.

[20]  Susan Diaz-Ramos,et al.  EMAP Statistical Methods Manual , 1996 .

[21]  H. Moradkhani,et al.  Assessing the uncertainties of hydrologic model selection in climate change impact studies , 2011 .

[22]  R. Mehrotra,et al.  Development and Application of a Multisite Rainfall Stochastic Downscaling Framework for Climate Change Impact Assessment , 2010 .

[23]  Alison L. Kay,et al.  Comparison of uncertainty sources for climate change impacts: flood frequency in England , 2009 .

[24]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[25]  Philip W. Mote,et al.  Scenarios of Future Climate for the Pacific Northwest , 2005 .

[26]  R. Schmid,et al.  Forest Giants of the Pacific Coast , 2001 .

[27]  R. Neilson,et al.  Climate change effects on vegetation distribution, carbon, and fire in California , 2003 .

[28]  D. Kelsall.  Physics and Technology of Coherent Infrared Radar , 1983 .

[29]  Richard N. Palmer,et al.  The Impacts of Climate Change on the Tualatin River Basin Water Supply: An Investigation into Projected Hydrologic and Management Impacts , 2004 .

[30]  H. Moradkhani,et al.  Long-Lead Water Supply Forecast Using Large-Scale Climate Predictors and Independent Component Analysis , 2010 .

[31]  E. S. Verry,et al.  Riparian ecotone: A functional definition and delineation for resource assessment , 2004 .

[32]  Hayley J. Fowler,et al.  Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling , 2007 .

[33]  Hans-Erik Andersen,et al.  Using airborne light detection and ranging (LIDAR) to characterize forest stand condition on the Kenai Peninsula of Alaska. , 2009 .

[34]  E. Salathe,et al.  Downscaling simulations of future global climate with application to hydrologic modelling , 2005 .