Global patterns in base flow index and recession based on streamflow observations from 3394 catchments

Numerous previous studies have constructed models to estimate base flow characteristics from climatic and physiographic characteristics of catchments and applied these to ungauged regions. However, these studies generally used streamflow observations from a relatively small number of catchments (<200) located in small, homogeneous study areas, which may have led to less reliable models with limited applicability elsewhere. Here, we use streamflow observations from a highly heterogeneous set of 3394 catchments (<10,000 km2) worldwide to construct reliable, widely applicable models based on 18 climatic and physiographic characteristics to estimate two important base flow characteristics: (1) the base flow index (BFI), defined as the ratio of long‐term mean base flow to total streamflow; and (2) the base flow recession constant (k), defined as the rate of base flow decay. Regression analysis results revealed that BFI and k were related to several climatic and physiographic characteristics, notably mean annual potential evaporation, mean snow water equivalent depth, and abundance of surface water bodies. Ensembles of artificial neural networks (ANNs; obtained by subsampling the original set of catchments) were trained to estimate the base flow characteristics from climatic and physiographic data. The catchment‐scale estimation of the base flow characteristics demonstrated encouraging performance with R2 values of 0.82 for BFI and 0.72 for k. The connection weights of the trained ANNs indicated that climatic characteristics were more important for estimating k than BFI. Global maps of estimated BFI and k were obtained using global climatic and physiographic data as input to the derived models. The resulting global maps are available for free download at http://www.hydrology-amsterdam.nl.

[1]  Carter,et al.  Accuracy of current meter measurements , 2022 .

[2]  A. Sankarasubramanian,et al.  Seasonality of monthly runoff over the continental United States: Causality and relations to mean annual and mean monthly distributions of moisture and energy , 2012 .

[3]  Nigel W. Arnell,et al.  Simulating current global river runoff with a global hydrological model: model revisions, validation, and sensitivity analysis , 2011 .

[4]  V. Smakhtin.  Low flow hydrology: a review , 2001 .

[5]  Hubert H. G. Savenije,et al.  Is the groundwater reservoir linear? Learning from data in hydrological modelling , 2005 .

[6]  Manuel K. Schneider,et al.  Towards a hydrological classification of European soils: preliminary test of its predictive power for the base flow index using river discharge data , 2007 .

[7]  Keith Beven,et al.  TOPMODEL : a critique. , 1997 .

[8]  Douglas H. Johnson.  The Insignificance of Statistical Significance Testing , 1999 .

[9]  D. Stensrud,et al.  Evaluation of a Short-Range Multimodel Ensemble System , 2001 .

[10]  Neville Nicholls,et al.  commentary and analysis: The Insignificance of Significance Testing , 2001 .

[11]  R Govindaraju,et al.  ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY: II, HYDROLOGIC APPLICATIONS , 2000 .

[12]  null null,et al.  Artificial Neural Networks in Hydrology. II: Hydrologic Applications , 2000 .

[13]  Erik Stokstad,et al.  Scarcity of Rain, Stream Gages Threatens Forecasts , 1999, Science.

[14]  K. Eckhardt.  A comparison of baseflow indices, which were calculated with seven different baseflow separation methods , 2008 .

[15]  A. Soldati,et al.  Forecasting river flow rate during low‐flow periods using neural networks , 1999 .

[16]  Jeffrey G. Arnold,et al.  Regional estimation of base flow for the conterminous United States by hydrologic landscape regions , 2008 .

[17]  R. Farvolden.  Geologic controls on ground-water storage and base flow , 1963 .

[18]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[19]  M. Temimi,et al.  Stream recession curves and storage variability in small watersheds , 2011 .

[20]  Kerrie M. Tomkins,et al.  Uncertainty in streamflow rating curves: methods, controls and consequences , 2014 .

[21]  Charles J Vörösmarty,et al.  Widespread decline in hydrological monitoring threatens Pan-Arctic Research , 2002 .

[22]  Vazken Andréassian,et al.  Spatial proximity, physical similarity, regression and ungaged catchments: A comparison of regionalization approaches based on 913 French catchments , 2008 .

[23]  R. Royall.  The Effect of Sample Size on the Meaning of Significance Tests , 1986 .

[24]  J. M. Hollis,et al.  Hydrology of soil types: a hydrologically-based classification of the soils of United Kingdom. , 1995 .

[25]  Jens Hartmann,et al.  The new global lithological map database GLiM: A representation of rock properties at the Earth surface , 2012 .

[26]  Soroosh Sorooshian,et al.  Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops , 2006 .

[27]  M. Gevrey,et al.  Review and comparison of methods to study the contribution of variables in artificial neural network models , 2003 .

[28]  J. Townshend,et al.  Global Percent Tree Cover at a Spatial Resolution of 500 Meters: First Results of the MODIS Vegetation Continuous Fields Algorithm , 2003 .

[29]  P. Döll,et al.  Global-scale modeling of groundwater recharge , 2008 .

[30]  H. Wittenberg.  Baseflow recession and recharge as nonlinear storage processes , 1999 .

[31]  Anthony J. Jakeman,et al.  Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM) I: Model intercomparison with current land use , 2009 .

[32]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[33]  D. Brandes,et al.  BASE FLOW RECESSION RATES, LOW FLOWS, AND HYDROLOGIC FEATURES OF SMALL WATERSHEDS IN PENNSYLVANIA, USA 1 , 2005 .

[34]  M. Bierkens,et al.  Global monthly water stress: 1. Water balance and water availability , 2011 .

[35]  D. Lettenmaier,et al.  A simple hydrologically based model of land surface water and energy fluxes for general circulation models , 1994 .

[36]  N. Null.  Artificial Neural Networks in Hydrology. I: Preliminary Concepts , 2000 .

[37]  A. Dijk,et al.  Climate and terrain factors explaining streamflow response and recession in Australian catchments , 2010 .

[38]  Tom G. Chapman,et al.  A comparison of algorithms for stream flow recession and baseflow separation , 1999 .

[39]  F. R. Hall.  Base‐Flow Recessions—A Review , 1968 .

[40]  Gordon E. Grant,et al.  A geological framework for interpreting the low‐flow regimes of Cascade streams, Willamette River Basin, Oregon , 2004 .

[41]  C. Shu,et al.  Regional low‐flow frequency analysis using single and ensemble artificial neural networks , 2009 .

[42]  G. Hughes.  An analysis of baseflow recession in the Republic of South Africa. , 1997 .

[43]  A. Dijk,et al.  The role of climatic and terrain attributes in estimating baseflow recession in tropical catchments , 2010 .

[44]  Chris Derksen,et al.  Implementing hemispherical snow water equivalent product assimilating weather station observations and spaceborne microwave data , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[45]  T. Mizuyama,et al.  Seepage area and rate of bedrock groundwater discharge at a granitic unchanneled hillslope , 2003 .

[46]  Valentina Krysanova,et al.  Regionalisation of the base flow index from dynamically simulated flow components — a case study in the Elbe River Basin , 2001 .

[47]  A. Longobardi,et al.  Baseflow index regionalization analysis in a Mediterranean area and data scarcity context , 2008 .

[48]  G. Powell,et al.  Terrestrial Ecoregions of the World: A New Map of Life on Earth , 2001 .

[49]  K. Beven,et al.  The Relationship of Catchment Topography and Soil Hydraulic Characteristics to Lake Alkalinity in the , 1989 .

[50]  Rolland W. Carter,et al.  Accuracy of Current Meter Measurement , 1963 .

[51]  Warren S. Sarle,et al.  Stopped Training and Other Remedies for Overfitting , 1995 .

[52]  Chang Shu,et al.  Artificial neural network ensembles and their application in pooled flood frequency analysis , 2004 .

[53]  Chong-Yu Xu,et al.  Global water-balance modelling with WASMOD-M: Parameter estimation and regionalisation , 2007 .

[54]  Gregory J. McCabe,et al.  Explaining spatial variability in mean annual runoff in the conterminous United States , 1999 .

[55]  D. Tetzlaff,et al.  Towards simple approaches for mean residence time estimation in ungauged basins using tracers and soil distributions , 2008 .

[56]  D. Lawler,et al.  RAINFALL SEASONALITY: DESCRIPTION, SPATIAL PATTERNS AND CHANGE THROUGH TIME , 1981 .

[57]  B. Clausen,et al.  Streamflow recession in basins with multiple water storages , 1997 .

[58]  I. Chaubey,et al.  Estimation of annual baseflow at ungauged sites in Indiana USA , 2013 .

[59]  R. S. Govindaraju,et al.  Artificial Neural Networks in Hydrology , 2010 .

[60]  Kazuaki Hiramatsu,et al.  A comparison of techniques for hydrograph recession analysis , 2004 .

[61]  D. Altman,et al.  Statistic Notes: Regression towards the mean , 1994, BMJ.

[62]  Igor V. Tetko,et al.  Neural network studies, 1. Comparison of overfitting and overtraining , 1995, J. Chem. Inf. Comput. Sci..

[63]  B. Neff,et al.  Base flow in the Great Lakes Basin , 2005 .

[64]  David M. Wolock,et al.  Base-Flow Index Grid for the Conterminous United States , 2003 .

[65]  G. Daily,et al.  The Nature and Value of Ecosystem Services: An Overview Highlighting Hydrologic Services , 2007 .

[66]  Günter Blöschl,et al.  A comparison of regionalisation methods for catchment model parameters , 2005 .

[67]  Yves Gagnon,et al.  Methodology for the large-scale assessment of small hydroelectric potential: Application to the Province of New Brunswick (Canada) , 2011 .

[68]  Antonio Trabucco,et al.  Climate change mitigation through afforestation/reforestation: A global analysis of hydrologic impacts with four case studies , 2008 .

[69]  R. Vogel,et al.  Estimation of baseflow recession constants , 1996 .

[70]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

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

[72]  H. Velthuizen,et al.  Harmonized World Soil Database (version 1.2) , 2008 .

[73]  Anthony J. Jakeman,et al.  Effects of rainfall seasonality and soil moisture capacity on mean annual water balance for Australian catchments , 2005 .

[74]  Kenneth Levenberg.  A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[75]  Michel Meybeck,et al.  Lithologic composition of the Earth's continental surfaces derived from a new digital map emphasizing riverine material transfer , 2005 .

[76]  Anthony J. Jakeman,et al.  RELATIONSHIPS BETWEEN CATCHMENT ATTRIBUTES AND HYDROLOGICAL RESPONSE CHARACTERISTICS IN SMALL AUSTRALIAN MOUNTAIN ASH CATCHMENTS , 1996 .

[77]  N. Batjes.  ISRIC-WISE derived soil properties on a 5 by 5 arc-minutes global grid (ver. 1.2) , 2006 .

[78]  John C. Schaake,et al.  A Priori Estimation of Land Surface Model Parameters , 2013 .

[79]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[80]  Alfred Stein,et al.  Prediction of flow characteristics using multiple regression and neural networks: A case study in Zimbabwe , 2005 .

[81]  Cheng-Haw Lee,et al.  Estimation of groundwater recharge using water balance coupled with base-flow-record estimation and stable-base-flow analysis , 2006 .

[82]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[83]  Julian D. Olden,et al.  Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks , 2002 .

[84]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[85]  O. Planchon,et al.  Soil crusting and infiltration on steep slopes in northern Thailand , 2003 .

[86]  R. Grayson,et al.  Relating baseflow to catchment properties in south-eastern Australia , 1998 .

[87]  Patrick M. Reed,et al.  Reducing uncertainty in predictions in ungauged basins by combining hydrologic indices regionalization and multiobjective optimization , 2008, Water Resources Research.

[88]  David S. Leigh,et al.  An expanded role for river networks , 2012 .

[89]  John P. Bloomfield,et al.  Examining geological controls on baseflow index (BFI) using regression analysis: An illustration from the Thames Basin, UK , 2009 .

[90]  Jens Hartmann,et al.  Mapping permeability over the surface of the Earth , 2011 .

[91]  Reto Knutti,et al.  The use of the multi-model ensemble in probabilistic climate projections , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[92]  T. McMahon,et al.  Evaluation of automated techniques for base flow and recession analyses , 1990 .

[93]  Yinghai Ke,et al.  Evaluating runoff simulations from the Community Land Model 4.0 using observations from flux towers and a mountainous watershed , 2011 .

[94]  P. Milly.  Climate, soil water storage, and the average annual water balance , 1994 .

[95]  Cheng-Haw Lee,et al.  Estimation of groundwater recharge using water balance coupled with base-flow-record estimation and stable-base-flow analysis , 2007 .

[96]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[97]  Anthony J. Jakeman,et al.  Assessing the impact of land use change on hydrology by ensemble modelling(LUCHEM) II: ensemble combinations and predictions , 2009 .

[98]  J. McDonnell,et al.  Effect of bedrock permeability on subsurface stormflow and the water balance of a trenched hillslope at the Panola Mountain Research Watershed, Georgia, USA , 2007 .

[99]  Bicheron Patrice,et al.  GlobCover - Products Description and Validation Report , 2008 .

[100]  J. Randerson,et al.  Technical Description of version 4.0 of the Community Land Model (CLM) , 2010 .

[101]  J. Shao.  Linear Model Selection by Cross-validation , 1993 .

[102]  Eric F. Wood,et al.  Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide , 2013 .

[103]  Lennart Ljung,et al.  Nonlinear Black Box Modeling in System Identification , 1995 .

[104]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .

[105]  Thomas C Winter,et al.  Delineation and Evaluation of Hydrologic-Landscape Regions in the United States Using Geographic Information System Tools and Multivariate Statistical Analyses , 2004, Environmental management.

[106]  Hoshin Vijai Gupta,et al.  Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins , 2007 .

[107]  Jeffrey J. McDonnell,et al.  Scale effects on headwater catchment runoff timing, flow sources, and groundwater‐streamflow relations , 2004 .

[108]  T. McMahon,et al.  Updated world map of the Köppen-Geiger climate classification , 2007 .

[109]  F. Galton.  Regression Towards Mediocrity in Hereditary Stature. , 1886 .

[110]  Wilfried Brutsaert,et al.  The influence of basin morphology on groundwater outflow , 1988 .

[111]  K. Price.  Effects of watershed topography, soils, land use, and climate on baseflow hydrology in humid regions: A review , 2011 .

[112]  Lennart Ljung,et al.  Nonlinear black-box modeling in system identification: a unified overview , 1995, Autom..

[113]  Chris Moran,et al.  ASRIS: the database , 2003 .

[114]  J. Feyen,et al.  The influence of physical catchment properties on baseflow in semi-arid environments , 2002 .

[115]  Kevin W. Manning,et al.  The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements , 2011 .

[116]  Russell G. Death,et al.  An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data , 2004 .

[117]  John L. Nieber,et al.  Regionalized drought flow hydrographs from a mature glaciated plateau , 1977 .

[118]  D. Fitzjarrald,et al.  Evidence of Seasonal Changes in Evapotranspiration in Eastern U.S. Hydrological Records , 2004 .

[119]  Charles J Vörösmarty,et al.  The current status of global river discharge monitoring and potential new technologies complementing traditional discharge measurements , 2007 .

[120]  Eric F. Wood,et al.  Predicting the Discharge of Global Rivers , 2001, Journal of Climate.

[121]  T. W. Simpson,et al.  Distribution and genesis of soils of the northeastern United States , 1989 .

[122]  Richard M. Vogel,et al.  Regional geohydrologic‐geomorphic relationships for the estimation of low‐flow statistics , 1992 .