The effect of year-to-year variability of leaf area index on Variable Infiltration Capacity model performance and simulation of runoff

Abstract

Please cite this article as: Z.K. Tesemma , Y. Wei , M.C. Peel , A.W. Western , The effect of yearto-year variability of leaf area index on Variable Infiltration Capacity model performance and simulation of runoff, Advances in Water Resources (2015),1 Highlights  The study assesses the impact of using year-to-year variable monthly LAI to calibrate VIC model and its performance.  VIC model efficiency can be improved when the year-to-year variable monthly LAI is used to calibrate the model.  Leaf area index elasticity of runoff is strongly related with catchment characteristics. Uses of long-term mean monthly LAI in VIC model tend to underestimate simulate runoff in dry period and overestimate in wet period. AbstractThis study assessed the effect of using observed monthly leaf area index (LAI) on hydrological model performance and the simulation of runoff using the Variable Infiltration Capacity (VIC) hydrological model in the Goulburn-Broken catchment of Australia, which has heterogeneous vegetation, soil and climate zones. VIC was calibrated with both observed monthly LAI and long-term mean monthly LAI, which were derived from the Global Land Surface Satellite (GLASS) leaf area index dataset covering the period from 1982 to 2012.The model performance under wet and dry climates for the two different LAI inputs was assessed using three criteria, the classical Nash-Sutcliffe efficiency, the logarithm transformed flow Nash-Sutcliffe efficiency and the percentage bias. Finally, the deviation of the simulated monthly runoff using the observed monthly LAI from simulated runoff using long-term mean monthly LAI was computed. The VIC model predicted monthly runoff in the selected sub-catchments with model efficiencies ranging from 61.5% to 95.9% during calibration (1982-1997) and 59% to 92.4% during validation (1998-2012). Our results suggest systematic improvements, from 4% to 25% in Nash-Sutcliffe efficiency, in sparsely forested sub-catchments when the VIC model was calibrated with observed monthly LAI instead of long-term mean monthly LAI. There was limited systematic improvement in tree dominated sub-catchments. The results also suggest that the model overestimation or underestimation of runoff during wet and dry periods can be reduced to 25 mm and 35 mm respectively by including the year-to-year variability of LAI in the model, thus reflecting the responses of vegetation to fluctuations in climate and other factors. Hence, the year-to-year ACCEPTED MANUSCRIPT A C C E P T E D M A N U S C R I P T 3 variability in LAI should not be neglected; rather it should be included in model calibration as well as simulation of monthly water balance.

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

“…The most frequently used ancillary information is meteorological data, such as the growing degree days and radiation (Barr et al, ; R. Xu et al, ), air temperature (Koetz et al, ; L. Y. Sun & Schulz, ), thermal time (Duveiller et al, ; Lucas et al, ), and precipitation and potential evapotranspiration (ET; Tesemma et al, , ). Indeed, multiple climatic variables can be jointly used to predict LAI (Iio et al, ; Pfeifer et al, ; Savoy & Mackay, ; L. Y.…”

“…Some LSMs parameterize vegetation using a simple seasonally invariant LAI (G. B. Bonan, Levis, et al, ; Ford & Quiring, ; Sellers et al, ). However, the static LAI parameter tends to overestimate LAI and soil moisture during anomalously dry seasons (Ford & Quiring, ; Tesemma et al, ). Simulations with seasonally varying LAI represent a more realistic climatology and are recommended for LSM simulations (S. Boussetta et al, ; Ford & Quiring, ; A. Loew et al, ).…”

V2Karst V1.1: a parsimonious large-scale integrated vegetation–recharge model to simulate the impact of climate and land cover change in karst regions

“…PCR-GLOBWB PM Empirical reference crop scheme (Allen et al, 1998) (Jarvis, 1976;Stewart, 1988) 12 Function of soil moisture which multiplies r s for transpiration and PET for soil evaporation. a T: Thornthwaite (1948); HS: Hargreaves and Samani (1985); PT: Priestley-Taylor (1972); PM: Penman-Monteith (Monteith, 1965); SW: Shuttleworth and Wallace (1985). b This approach consists of calculating a value of PET for a reference grass surface with known properties and to adjust this potential rate using land cover specific empirical crop factors.…”

Effectiveness of introducing crop coefficient and leaf area index to enhance evapotranspiration simulations in hydrologic models

“…Verstraeten et al (2005) investigated the impact of forest and cropland on the hydrologic cycle using LAI measurements and considering the crop coefficient (K c ) as a model parameter. Some studies used the LAI obtained from remote sensing images (Gigante, Iacobellis, Manfreda, Milella, & Portoghese, 2009;Mendiguren, Koch, & Stisen, 2017;Tesemma, Wei, Peel, & Western, 2015). Gassmann, Gardiol, and Serio (2011) evaluated the performance the K c and LAI in a maize crop field study using the soil water content model considering 2 years of summer season data.…”