How are streamflow responses to the El Nino Southern Oscillation affected by watershed characteristics?

Understanding the factors that influence how global climate phenomena, such as the El-Nino Southern Oscillation (ENSO), affect streamflow behavior is an important area of research in the hydrologic sciences. While large scale patterns in ENSO-streamflow relationships have been thoroughly studied, and are relatively well-understood, information is scarce concerning factors that affect variation in ENSO responses from one watershed to another. To this end, we examined relationships between variability in ENSO activity and streamflow for 2731 watersheds across the conterminous U.S. from 1970 to 2014 using a novel approach to account for the intermediary role of precipitation. We applied an ensemble of regression techniques to describe relationships between variability in ENSO activity and streamflow as a function of watershed characteristics including: hydroclimate, topography, geomorphology, geographic location, land cover, soil characteristics, bedrock geology, and anthropogenic influences. We found that variability in watershed scale ENSO – streamflow relationships was strongly related to factors including: precipitation timing and phase, forest cover, and interactions between watershed topography and geomorphology. These, and other influential factors, share in common the ability to affect the partitioning and movement of water within watersheds. Our results demonstrate that the conceptualization of watersheds as signal filters for hydroclimate inputs, commonly applied to short-term rainfall-runoff responses, also applies to long-term hydrologic responses to sources of recurrent climate variability. These results also show that watershed processes, which are typically studied at relatively fine spatial scales, are also critical for understanding continental scale hydrologic responses to global climate.

[1]  Hoshin Vijai Gupta,et al.  Improving robustness of hydrologic parameter estimation by the use of moving block bootstrap resampling , 2010 .

[2]  C. T. Wang,et al.  A representation of an instantaneous unit hydrograph from geomorphology , 1980 .

[3]  David E. Rupp,et al.  Seasonal Climate Variability and Change in the Pacific Northwest of the United States , 2014 .

[4]  James M. Vose,et al.  The influence of watershed characteristics on spatial patterns of trends in annual scale streamflow variability in the continental U.S. , 2016 .

[5]  C. Nilsson,et al.  Fragmentation and Flow Regulation of River Systems in the Northern Third of the World , 1994, Science.

[6]  C. Ropelewski,et al.  Global and Regional Scale Precipitation Patterns Associated with the El Niño/Southern Oscillation , 1987 .

[7]  K. Beven,et al.  A physically based, variable contributing area model of basin hydrology , 1979 .

[8]  S. Margulis,et al.  Snow process estimation over the extratropical Andes using a data assimilation framework integrating MERRA data and Landsat imagery , 2016 .

[9]  J. Lenters,et al.  Interpretation of hydrologic trends from a water balance perspective: The role of groundwater storage in the Budyko hypothesis , 2012 .

[10]  Brian L. McGlynn,et al.  Landscape structure and climate influences on hydrologic response , 2011 .

[11]  G. Villarini,et al.  On the stationarity of annual flood peaks in the continental United States during the 20th century , 2009 .

[12]  Timothy J. Hoar,et al.  The 1990–1995 El Niño‐Southern Oscillation Event: Longest on Record , 1996 .

[13]  C. Revenga,et al.  Fragmentation and Flow Regulation of the World's Large River Systems , 2005, Science.

[14]  D. Lettenmaier,et al.  Variability and potential sources of predictability of North American runoff , 2004 .

[15]  Ercan Kahya,et al.  U.S. streamflow patterns in relation to the El Niño/Southern Oscillation , 1993 .

[16]  G. De’ath Boosted trees for ecological modeling and prediction. , 2007, Ecology.

[17]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[18]  C. Field,et al.  The velocity of climate change , 2009, Nature.

[19]  Kevin E. Trenberth,et al.  Indices of El Niño Evolution , 2001 .

[20]  Daniel R. Cayan,et al.  ENSO and Hydrologic Extremes in the Western United States , 1999 .

[21]  H. Kao Eastern Pacific and central Pacific types of ENSO , 2009 .

[22]  Lawrence E. Band,et al.  Ecosystem processes at the watershed scale: Hydrologic vegetation gradient as an indicator for lateral hydrologic connectivity of headwater catchments , 2012 .

[23]  F. Chapin,et al.  A safe operating space for humanity , 2009, Nature.

[24]  K. Trenberth,et al.  The changing character of precipitation , 2003 .

[25]  D. Vimont,et al.  Utilizing the state of ENSO as a means for season‐ahead predictor selection , 2016 .

[26]  Michael D. Dettinger,et al.  Global Characteristics of Stream Flow Seasonality and Variability , 2000 .

[27]  G. Meehl,et al.  Will There Be a Significant Change to El Niño in the Twenty-First Century? , 2012 .

[28]  K. Trenberth,et al.  A Global Dataset of Palmer Drought Severity Index for 1870–2002: Relationship with Soil Moisture and Effects of Surface Warming , 2004 .

[29]  Daren M. Carlisle,et al.  GAGES: A stream gage database for evaluating natural and altered flow conditions in the conterminous United States , 2010 .

[30]  C. Daly,et al.  A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain , 1994 .

[31]  C. Daly,et al.  Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States , 2008 .

[32]  J. Vose,et al.  Continental U.S. streamflow trends from 1940 to 2009 and their relationships with watershed spatial characteristics , 2015 .

[33]  Andrew W. Western,et al.  A rational function approach for estimating mean annual evapotranspiration , 2004 .

[34]  Richard P. Hooper,et al.  Moving beyond heterogeneity and process complexity: A new vision for watershed hydrology , 2007 .

[35]  Jeffrey J. McDonnell,et al.  How does rainfall become runoff? A combined tracer and runoff transfer function approach , 2003 .

[36]  Robert B. Jackson,et al.  Effects of afforestation on water yield: a global synthesis with implications for policy , 2005 .

[37]  S. Lahiri Theoretical comparisons of block bootstrap methods , 1999 .

[38]  Leo Breiman,et al.  Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001, Statistical Science.

[39]  J. Vose,et al.  Watershed memory at the Coweeta Hydrologic Laboratory: The effect of past precipitation and storage on hydrologic response , 2016 .

[40]  R. Davies‐Colley,et al.  Planted Riparian Buffer Zones in New Zealand: Do They Live Up to Expectations? , 2003 .

[41]  Paolo D'Odorico,et al.  Spatial and temporal controls on watershed ecohydrology in the northern Rocky Mountains , 2010 .

[42]  Leo Breiman,et al.  Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001 .

[43]  M. Kummu,et al.  Strong influence of El Niño Southern Oscillation on flood risk around the world , 2014, Proceedings of the National Academy of Sciences.

[44]  Brian L. McGlynn,et al.  Vegetation and topographic influences on the connectivity of shallow groundwater between hillslopes and streams , 2014 .

[45]  Thomas C. Piechota,et al.  Drought and Regional Hydrologic Variation in the United States: Associations with the El Niño-Southern Oscillation , 1996 .

[46]  J. Dracup,et al.  The influences of Type 1 El Nino and La Nina events on streamflows in the Pacific Southwest of the United States , 1994 .

[47]  Lauren A. Patterson,et al.  Streamflow Changes in the South Atlantic, United States During the Mid‐ and Late 20th Century 1 , 2012 .

[48]  Eran Stark,et al.  Partial cross-correlation analysis resolves ambiguity in the encoding of multiple movement features. , 2006, Journal of neurophysiology.

[49]  Glenn A. Hodgkins,et al.  Changes in the Proportion of Precipitation Occurring as Snow in New England (1949–2000) , 2004 .

[50]  David A. Kovacic,et al.  Riparian vegetated buffer strips in water‐quality restoration and stream management , 1993 .

[51]  R. Vogel,et al.  Trends in precipitation and streamflow in the eastern U.S.: Paradox or perception? , 2006 .

[52]  G. Villarini,et al.  Flood peak distributions for the eastern United States , 2009 .

[53]  I. Rodríguez‐Iturbe,et al.  The geomorphologic structure of hydrologic response , 1979 .

[54]  L. Breiman Stacked Regressions , 1996, Machine Learning.

[55]  David H. Wolpert,et al.  Stacked generalization , 1992, Neural Networks.

[56]  T. Piechota,et al.  Relationships between Pacific and Atlantic ocean sea surface temperatures and U.S. streamflow variability , 2006 .

[57]  Effect of interannual and interdecadal climate oscillations on groundwater in North Carolina , 2008 .

[58]  R. Emanuel,et al.  Effect of interannual climate oscillations on rates of submarine groundwater discharge , 2010 .

[59]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[60]  J. Holden,et al.  The storage and aging of continental runoff in large reservoir systems of the world , 1997 .

[61]  A. Rinaldo,et al.  Can One Gauge the Shape of a Basin , 1995 .

[62]  Douglas W. Nychka,et al.  Statistical significance of trends and trend differences in layer-average atmospheric temperature time series , 2000 .

[63]  T. Barnett,et al.  Potential impacts of a warming climate on water availability in snow-dominated regions , 2005, Nature.

[64]  Andrew J. Patton,et al.  Correction to “Automatic Block-Length Selection for the Dependent Bootstrap” by D. Politis and H. White , 2009 .

[65]  B. Scanlon,et al.  El Niño–Southern Oscillation and Pacific Decadal Oscillation impacts on precipitation in the southern and central United States: Evaluation of spatial distribution and predictions , 2007 .

[66]  P. Troch,et al.  Hillslope subsurface flow similarity: Real‐world tests of the hillslope Péclet number , 2007 .

[67]  J. Friedman Greedy function approximation: A gradient boosting machine. , 2001 .

[68]  J. Friedman Stochastic gradient boosting , 2002 .

[69]  S. Swenson,et al.  Satellites measure recent rates of groundwater depletion in California's Central Valley , 2011 .

[70]  J Elith,et al.  A working guide to boosted regression trees. , 2008, The Journal of animal ecology.

[71]  W. Brutsaert The unit response of groundwater outflow from a hillslope , 1994 .

[72]  Michael D. Dettinger,et al.  Trends in Snowfall versus Rainfall in the Western United States , 2006 .

[73]  B. McGlynn,et al.  Deep Impact: Effects of Mountaintop Mining on Surface Topography, Bedrock Structure, and Downstream Waters. , 2016, Environmental science & technology.

[74]  A. Timmermann,et al.  ENSO and greenhouse warming , 2015 .

[75]  K. Wolter,et al.  El Niño/Southern Oscillation behaviour since 1871 as diagnosed in an extended multivariate ENSO index (MEI.ext) , 2011 .

[76]  Mathew Barlow,et al.  ENSO, Pacific Decadal Variability, and U.S. Summertime Precipitation, Drought, and Stream Flow , 2001 .

[77]  Gregory J. McCabe,et al.  A step increase in streamflow in the conterminous United States , 2002 .

[78]  J. Vose,et al.  Long-term temperature and precipitation trends at the Coweeta Hydrologic Laboratory, Otto, North Carolina, USA , 2012 .

[79]  V. V. Srinivas,et al.  Hybrid moving block bootstrap for stochastic simulation of multi-site multi-season streamflows , 2005 .

[80]  Naiming Yuan,et al.  Detrended Partial-Cross-Correlation Analysis: A New Method for Analyzing Correlations in Complex System , 2015, Scientific Reports.

[81]  J. Tomasella,et al.  The water balance of an Amazonian micro‐catchment: the effect of interannual variability of rainfall on hydrological behaviour , 2008 .