Hydrological model uncertainty due to spatial evapotranspiration estimation methods

Abstract

a b s t r a c tEvapotranspiration (ET) continues to be a difficult process to estimate in seasonal and long-term water balances in catchment models. Approaches to estimate ET typically use vegetation parameters (e.g., leaf area index [LAI], interception capacity) obtained from field observation, remote sensing data, national or global land cover products, and/or simulated by ecosystem models. In this study we attempt to quantify the uncertainty that spatial evapotranspiration estimation introduces into hydrological simulations when the age of the forest is not precisely known. The Penn State Integrated Hydrologic Model (PIHM) was implemented for the Lysina headwater catchment, located 50°03′N, 12°40′E in the western part of the Czech Republic. The spatial forest patterns were digitized from forest age maps made available by the Czech Forest Administration. Two ET methods were implemented in the catchment model: the Biome-BGC forest growth sub-model (1-way coupled to PIHM) and with the fixed-seasonal LAI method. From these two approaches simulation scenarios were developed. We combined the estimated spatial forest age maps and two ET estimation methods to drive PIHM. A set of spatial hydrologic regime and streamflow regime indices were calculated from the modeling results for each method. Intercomparison of the hydrological responses to the spatial vegetation patterns suggested considerable variation in soil moisture and recharge and a small uncertainty in the groundwater table elevation and streamflow. The hydrologic modeling with ET estimated by Biome-BGC generated less uncertainty due to the plant physiology-based method. The implication of this research is that overall hydrologic variability induced by uncertain management practices was reduced by implementing vegetation models in the catchment models.

An improved SWAT vegetation growth module and its evaluation for four tropical ecosystems

“…The leaf area index (LAI) -the area of green leaves per unit area of land -is a vegetation attribute commonly used in ecohydrological modelling, as it strongly correlates with the vegetation phe-nological development. Thus, an enhanced representation of the LAI dynamics can improve the predictive capability of hydrologic models, as already noted in several studies (Andersen et al, 2002;Yu et al, 2016;Zhang et al, 2009). Arnold et al (2012) underscored the need for a realistic representation of the local and regional plant growth processes to reliably simulate the water balance, the erosion and the nutrient yields using SWAT.…”

“…However, such an approach requires a calibration of the SWAT parameters that govern the soil water balance dynamics. The latter is not obvious when only observed streamflow data are used for the calibration (Yu et al, 2016).…”

“…It is worthwhile to note that phenological changes in vegetation affect the biophysical and hydrological processes in the basin and thus play a key role in integrated hydrologic and ecosystem modelling (Jolly and Running, 2004;Kiniry and MacDonald, 2008;Shen et al, 2013;Strauch and Volk, 2013;Yang and Zhang, 2016;Yu et al, 2016). The leaf area index (LAI) -the area of green leaves per unit area of land -is a vegetation attribute commonly used in ecohydrological modelling, as it strongly correlates with the vegetation phe-nological development.…”

The Uncertainty of Penman‐Monteith Method and the Energy Balance Closure Problem

“…Evapotranspiration is a main component of the water cycle and plays an important role in surface energy balance (Y. Zhang et al, ). In general, evapotranspiration can be estimated by vegetation parameters that obtained from field observation, remote sensing data, national or global land cover products (Z. L Li et al, ; Michel et al, ), and/or simulated by ecosystem models (Y. Liu et al, ; Yu et al, ). However, due to the difference of theoretical foundations and research scales, such estimation methods often have great uncertainties.…”

“…L Li et al, 2009;Michel et al, 2016), and/or simulated by ecosystem models (Y. Liu et al, 2015;Yu et al, 2016). However, due to the difference of theoretical foundations and research scales, such estimation methods often have great uncertainties.…”