Assessing the impact of climate variability on catchment water balance and vegetation cover

Understanding the interactions among climate, vegetation cover and the water cycle lies at the heart of the study of watershed ecohydrology. Recently, considerable attention is being paid to the effect of climate variability on catchment water balance and also associated vegetation cover. In this paper, we investigate the general pattern of long-term water balance and vegetation cover (as reflected by fPAR) among 193 study catchments in Australia through statistical analysis. We then employ the elasticity analysis approach for quantifying the effects of climate variability on hydrologic partitioning (including total, surface and subsurface runoff) and on vegetation cover (including total, woody and non-woody vegetation cover). Based on the results of statistical analysis, we conclude that annual runoff ( R ), evapotranspiration ( E ) and runoff coefficient ( R/P ) increase with vegetation cover for catchments in which woody vegetation is dominant and annual precipitation is relatively high. Control of water available on annual evapotranspiration in non-woody dominated catchments is relatively stronger compared to woody dominated ones. The ratio of subsurface runoff to total runoff ( R g / R ) also increases with woody vegetation cover. Through the elasticity analysis of catchment runoff, it is shown that precipitation ( P ) in current year is the most important factor affecting the change in annual total runoff ( R ), surface runoff ( R s ) and subsurface runoff ( R g ). The significance of other controlling factors is in the order of annual precipitation in previous years ( P −1 and P −2 ), which represents the net effect of soil moisture and annual mean temperature ( T ) in current year. Change of P by +1% causes a +3.35% change of R , a +3.47% change of R s and a +2.89% change of R g , on average. Results of elasticity analysis on the maximum monthly vegetation cover indicate that incoming shortwave radiation during the growing season ( R sd,grow ) is the most important factor affecting the change in vegetation cover. Change of R sd,grow by +1% produces a −1.08% change of total vegetation cover ( F t ) on average. The significance of other causative factors is in the order of precipitation during growing season, mean temperature during growing season and precipitation during non-growing season. Growing season precipitation is more significant than non-growing season precipitation to non-woody vegetation cover, but both have equivalent effects to woody vegetation cover.

[1]  Peter A. Troch,et al.  Functional model of water balance variability at the catchment scale: 2. Elasticity of fast and slow runoff components to precipitation change in the continental United States , 2011, Water Resources Research.

[2]  Dawen Yang,et al.  Impact of climate variability and human activity on streamflow decrease in the Miyun Reservoir catchment , 2010 .

[3]  Tim R. McVicar,et al.  Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate , 2010 .

[4]  Dominik Bänninger,et al.  Plant-compositional effects on surface runoff and sediment yield in subalpine grassland , 2009 .

[5]  D. Jones,et al.  High-quality spatial climate data-sets for Australia , 2009 .

[6]  Peter A. Troch,et al.  Climate and vegetation water use efficiency at catchment scales , 2009 .

[7]  S. Kanae,et al.  Impact of vegetation coverage on regional water balance in the nonhumid regions of China , 2009 .

[8]  Tim R. McVicar,et al.  Climate‐related trends in Australian vegetation cover as inferred from satellite observations, 1981–2006 , 2009 .

[9]  Stuart R. Phinn,et al.  Estimating tree‐cover change in Australia: challenges of using the MODIS vegetation index product , 2009 .

[10]  T. Oki,et al.  Investigating the roles of climate seasonality and landscape characteristics on mean annual and monthly water balances , 2008 .

[11]  Tim R. McVicar,et al.  Deriving consistent long-term vegetation information from AVHRR reflectance data using a cover-triangle-based framework , 2008 .

[12]  Michael Notaro,et al.  Response of the mean global vegetation distribution to interannual climate variability , 2008 .

[13]  A. Provenzale,et al.  Vegetation response to rainfall intermittency in drylands: Results from a simple ecohydrological box model , 2007 .

[14]  Francis H. S. Chiew,et al.  Estimating the sensitivity of mean annual runoff to climate change using selected hydrological models , 2006 .

[15]  Dawen Yang,et al.  Interpreting the complementary relationship in non‐humid environments based on the Budyko and Penman hypotheses , 2006 .

[16]  F. Chiew,et al.  Estimation of rainfall elasticity of streamflow in Australia , 2006 .

[17]  Molly E. Brown,et al.  Evaluation of the consistency of long-term NDVI time series derived from AVHRR,SPOT-vegetation, SeaWiFS, MODIS, and Landsat ETM+ sensors , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[18]  J. Albertson,et al.  Dynamical effects of the statistical structure of annual rainfall on dryland vegetation , 2006 .

[19]  J. Kutzbach,et al.  Impact of climate variability on present and Holocene vegetation: A model-based study , 2006 .

[20]  D. Goodrich,et al.  Ecohydrological impacts of woody‐plant encroachment: seasonal patterns of water and carbon dioxide exchange within a semiarid riparian environment , 2006 .

[21]  Kelly K. Caylor,et al.  Determinants of woody cover in African savannas , 2005, Nature.

[22]  John L. Dwyer,et al.  Multi-platform comparisons of MODIS and AVHRR normalized difference vegetation index data , 2005 .

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

[24]  Edwin W. Pak,et al.  An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data , 2005 .

[25]  Kelly K. Caylor,et al.  Dynamic response of grass cover to rainfall variability: implications for the function and persistence of savanna ecosystems , 2005 .

[26]  K. Eckhardt How to construct recursive digital filters for baseflow separation , 2005 .

[27]  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 .

[28]  J. Zak,et al.  Convergence across biomes to a common rain-use efficiency , 2004, Nature.

[29]  John L. Dwyer,et al.  Comparison of MODIS and AVHRR 16‐day normalized difference vegetation index composite data , 2004 .

[30]  Salvatore Manfreda,et al.  On the coupled geomorphological and ecohydrological organization of river basins , 2003 .

[31]  J. Jacobs Ecohydrology: Darwinian Expression of Vegetation Form and Function , 2003 .

[32]  C. Tucker,et al.  Climate-Driven Increases in Global Terrestrial Net Primary Production from 1982 to 1999 , 2003, Science.

[33]  D. Moorhead,et al.  Increasing risk of great floods in a changing climate , 2002, Nature.

[34]  A. Sankarasubramanian,et al.  Climate elasticity of streamflow in the United States , 2001 .

[35]  A. Knapp,et al.  Variation among biomes in temporal dynamics of aboveground primary production. , 2001, Science.

[36]  H. Grau,et al.  Rainfall variability, fire and vegetation dynamics in neotropical montane ecosystems in north‐western Argentina , 2000 .

[37]  Michael Bruen,et al.  A simple model for estimating the sensitivity of runoff to long-term changes in precipitation without a change in vegetation , 1999 .

[38]  A. Sankarasubramanian,et al.  Comparisons of Climate Elasticity of Streamflow in the United States , 1999 .

[39]  Richard M. Vogel,et al.  REGIONAL REGRESSION MODELS OF ANNUAL STREAMFLOW FOR THE UNITED STATES , 1999 .

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

[41]  Steven W. Running,et al.  Comparing global models of terrestrial net primary productivity (NPP): the importance of water availability , 1999 .

[42]  Steven W. Leavit Biogeochemistry, An Analysis of Global Change , 1998 .

[43]  A. Shetty,et al.  A conceptual model of catchment water balance: 1. Formulation and calibration , 1995 .

[44]  A. Shetty,et al.  A conceptual model of catchment water balance: 2. Application to runoff and baseflow modeling , 1995 .

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

[46]  James C.I. Dooge,et al.  Sensitivity of Runoff to Climate Change: A Hortonian Approach , 1992 .

[47]  W. Schlesinger Biogeochemistry: An Analysis of Global Change , 1991 .

[48]  N. Stephenson Climatic Control of Vegetation Distribution: The Role of the Water Balance , 1990, The American Naturalist.

[49]  Peter S. Eagleson,et al.  Climate, soil, and vegetation: 1. Introduction to water balance dynamics , 1978 .

[50]  M. Budyko,et al.  Climate and life , 1975 .

[51]  M. Rosenzweig Net Primary Productivity of Terrestrial Communities: Prediction from Climatological Data , 1968, The American Naturalist.

[52]  R. Horton The Rôle of infiltration in the hydrologic cycle , 1933 .

[53]  L. Kahn,et al.  Planning as a tool to improve production and function of grasslands in the mid-north of South Australia , 2005 .

[54]  Murray C. Peel,et al.  National Land and Water Resources Audit Theme 1-Water Availability Extension of Unimpaired Monthly Streamflow Data and Regionalisation of Parameter Values to Estimate Streamflow in Ungauged Catchments , 2000 .

[55]  N. Arnell,et al.  Global warming, river flows and water resources , 1996 .

[56]  J. Ramirez,et al.  Hydrology and Earth System Sciences Modeling the Monthly Mean Soil-water Balance with a Statistical-dynamical Ecohydrology Model as Coupled to a Two-component Canopy Model , 2022 .