Abstract
This 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 variability in LAI should not be neglected; rather it should be included in model calibration as well as simulation of monthly water balance.
Highlights
The challenge of making accurate runoff predictions using hydrological models under changing or ‘non-stationary’ conditions, due to either changing climate and/or human intervention, is a significant issue in hydrology [5,26,33]
Some sub-catchments only respond to precipitation events after the catchment becomes sufficiently wet and saturated areas develop [18] and become connected to the stream network [46,47]. The former can be addressed by modification of the relationship between soil moisture and runoff with addition of one parameter as suggested by [18]; the issue of connectivity is related to dynamic changes in soil moisture patterns, which implies that the soil moisture–runoff relationship changes over time and this would be harder to incorporate into Variable Infiltration Capacity (VIC) [47]
Zhang et al [49] showed an improvement in model performance when a rainfall–runoff model was coupled with actual evapotranspiration estimates from the Penman–Monteith equation and remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI)
Summary
The challenge of making accurate runoff predictions using hydrological models under changing or ‘non-stationary’ conditions, due to either changing climate and/or human intervention, is a significant issue in hydrology [5,26,33]. Rainfall–runoff models that lack representations of biophysical processes, such as vegetation dynamics, have been found to perform poorly when calibrated in a wet climate period and validated in dry climate period [6,25,43]. To address this problem different studies have suggested approaches including calibrating model parameters on a portion of the record with conditions similar to those of the future period to simulate [43], using temporal clusters [9] and adjusting the parameters according to the aridity of the catchment [38]. Lack of representation of the year to year variability of the monthly LAI in hydrological models may lead to lower monthly model performance due to underestimation of flow in dry periods and overestimation of flow in wet periods
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