Quantifying evaporation and its decadal change for Lake Nam Co, central Tibetan Plateau

Abstract

Most lakes in the interior Tibetan Plateau have expanded rapidly since the late 1990s. Because of a lack of observations, lake water balances and their changes are far from well understood. Evaporation is a component of the lake water balance, and this study quantifies its magnitude, decadal change, and its contribution to the water balance changes in Lake Nam Co, one of the largest lakes on the Tibetan Plateau (with an area of approximately 2000 km2 and a mean depth of approximately 40 m). The lake temperature and the evaporation are simulated by the Flake model. The simulation results are validated against observed lake temperature profile from 2013 and Moderate Resolution Imaging Spectroradiometer lake surface temperature data from 2000 to 2014. The simulated latent heat flux and sensible heat flux are validated against Bowen ratio‐derived estimates for 2013. Based on the validated simulation results, the long‐term mean annual evaporation is approximately 832 ± 69 mm, and this value is much less than the potential evaporation estimated using the Penman‐Monteith equation. The annual evaporation from 1980 to 2014 displays a complex decadal oscillation, mainly due to the changes in energy‐related terms (air temperature and radiation). The mean lake evaporation since the late 1990s is greater than previous periods; thus, this change in evaporation has suppressed the recent expansion of Nam Co.

Evaluating and Improving the Performance of Three 1‐D Lake Models in a Large Deep Lake of the Central Tibetan Plateau

“…The data can be freely downloaded from http://en.tpedatabase.cn/portal/. In current study, the three lake models are driven by the ITPCAS data corrected by Lazhu et al (2016) based on the measurements at the weather station with a distance of about 1.5 km from the shoreline in the northeastern Lake Nam Co (Figure 1b). To validate the model results, we used the daily lake water temperature profile data at the depths of 3, 6, 16, 21, 26, 31, and 36 m, which is observed at the station on the southeast part of Lake Nam Co (30°45.74′N, 90°46.83′E) with the water depth of 92 m ( Figure 1b) during 1 January 2012 to 31 December 2013 (Lazhu et al, 2016).…”

“…In current study, the three lake models are driven by the ITPCAS data corrected by Lazhu et al (2016) based on the measurements at the weather station with a distance of about 1.5 km from the shoreline in the northeastern Lake Nam Co (Figure 1b). To validate the model results, we used the daily lake water temperature profile data at the depths of 3, 6, 16, 21, 26, 31, and 36 m, which is observed at the station on the southeast part of Lake Nam Co (30°45.74′N, 90°46.83′E) with the water depth of 92 m ( Figure 1b) during 1 January 2012 to 31 December 2013 (Lazhu et al, 2016). In addition, the observed lake surface temperature (LST) at approximately11:00 and 21:00 local time every day during 2012-2013 with a horizontal resolution of 1 km from the Moderate Resolution Imaging Spectroradiometer (MODIS) product (MOD11A1; Savtchenko et al, 2004;Wan et al, 2004) is also used for the model evaluation.…”

“…Each lake model is driven by the forcing data with the time interval of 10 min derived from the 3-hourly corrected ITPCAS data (Lazhu et al, 2016) using a linear interpolation method. Lake Nam Co is ice-free, and the water is well mixed with almost uniform temperature vertically in early June (Lazhu et al, 2016), so the initial lake water temperature and ice fraction at each model layer can be simply set to 276.65 K and 0 according to the observation, respectively. Considering the effects of the model spin-up time on the model results, we took the model results during 1 January 2012 to 31 December 2013 with the spin up time of three and half years for analysis.…”

“…During the past several years, many studies have conducted a series of validation and intercomparison of the available 1-D lake models' performance in simulating the thermal features of different lakes around the word. These lakes include tropical (Thiery, Martynov, Darchambeau, Descy, Plisnier, Sushama, & van Lipzig, 2014;Thiery, Stepanenko, Fang, Jöhnk, Li, Martynov, Perroud, Subin, Darchambeau, Mironov & van Lipzig, 2014), subtropical (Lazhu et al, 2016), and temperate (Kheyrollah et al, 2012) large deep lakes; subtropical (Deng et al, 2013;Gu et al, 2013;Perroud et al, 2009;Wen et al, 2016) and temperate (Vörös et al, 2010) large shallow lakes; and temperate small shallow lakes Stepanenko et al, 2014;MacKay et al, 2017;Yao et al, 2014). Based on the findings and knowledge obtained from these studies, considerable progresses have been made to improve the 1-D lake models (Bennington et al, 2014;Gu et al, 2015;Subin et al, 2012;Xiao et al, 2016;Xu et al, 2017).…”

Variability of Surface Heat Fluxes and Its Driving Forces at Different Time Scales Over a Large Ephemeral Lake in China

“…The time lag between the R n and LE over lakes is closely related to the WD; deeper lakes exhibit longer time lags. A lag of 5 months exists for Lake Superior in North America (mean depth: 148 m) ; one of 4-5 months exists over Nam Co in Tibet, China (mean depth: 40 m) (Lazhu et al, 2016); one of 2-3 months exists over Qinghai Lake in China (mean depth: 21 m) (Li et al, 2016); and a relatively short lag of up to 1 month exists over Sparkling Lake in the United States (mean depth: 10.9 m; Lenters et al, 2005). Wang et al (2014) reported a lag of 1 month over Lake Taihu in China (mean depth: 1.9 m).…”