Models and measurements of seven years of evapotranspiration on a high elevation site on the Central Tibetan Plateau

Wang, Lihui; He, Xiaobo; Steiner, Jakob; Zhang, Dongwei; Wu, J.; Wang, Shaoyong; Ding, Yongjian

doi:10.1007/s11629-020-6051-1

Cited by 6 publications

(1 citation statement)

References 66 publications

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“…750 km north, between 3,500 and 5,000 m.a.s.l.) find rates between 1 and 3 mm d −1 , during the summer months (Chang et al., 2017; Wang et al., 2020). For the 7,000‐m elevation band in the Langtang catchment, we simulated October to November sublimation rates of 0.8 and 1.0 mm d −1 for the years 2017 and 2018, respectively, which is similar to the results of Sherpa et al.…”

Section: Discussionmentioning

confidence: 99%

Land Surface Modeling in the Himalayas: On the Importance of Evaporative Fluxes for the Water Balance of a High‐Elevation Catchment

Buri,

Fatichi,

Shaw

et al. 2023

Water Resources Research

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High Mountain Asia (HMA) is among the most vulnerable water towers globally and yet future projections of water availability in and from its high‐mountain catchments remain uncertain, as their hydrologic response to ongoing environmental changes is complex. Mechanistic modeling approaches incorporating cryospheric, hydrological, and vegetation processes in high spatial, temporal, and physical detail have never been applied for high‐elevation catchments of HMA. We use a land surface model at high spatial and temporal resolution (100 m and hourly) to simulate the coupled dynamics of energy, water, and vegetation for the 350 km2 Langtang catchment (Nepal). We compare our model outputs for one hydrological year against a large set of observations to gain insight into the partitioning of the water balance at the subseasonal scale and across elevation bands. During the simulated hydrological year, we find that evapotranspiration is a key component of the total water balance, as it causes about the equivalent of 20% of all the available precipitation or 154% of the water production from glacier melt in the basin to return directly to the atmosphere. The depletion of the cryospheric water budget is dominated by snow melt, but at high elevations is primarily dictated by snow and ice sublimation. Snow sublimation is the dominant vapor flux (49%) at the catchment scale, accounting for the equivalent of 11% of snowfall, 17% of snowmelt, and 75% of ice melt, respectively. We conclude that simulations should consider sublimation and other evaporative fluxes explicitly, as otherwise water balance estimates can be ill‐quantified.

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Section: Discussionmentioning

confidence: 99%

Land Surface Modeling in the Himalayas: On the Importance of Evaporative Fluxes for the Water Balance of a High‐Elevation Catchment

Buri,

Fatichi,

Shaw

et al. 2023

Water Resources Research

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Selecting essential factors for predicting reference crop evapotranspiration through tree-based machine learning and Bayesian optimization

Zhao,

Wang,

Shi

et al. 2023

Theor Appl Climatol

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Reference crop evapotranspiration (ETO) is a basic component of the hydrological cycle ,and its estimation is critical for agricultural water resource management and scheduling. In this study, three tree-based machine learning algorithms (random forest [RF], gradient boosting decision tree [GBDT], and extreme gradient boosting [XGBoost]) were adopted to determine the essential factors for ETO prediction. The tree-based models were optimised using the Bayesian optimisation (BO) algorithm, and they were compared with three standalone models in terms of daily ETO and monthly mean ETO estimation in North China, with different input combinations of essential variables. The results indicated that solar radiation (Rs) and air temperature (Ts), including the maximum, minimum, and average temperature, in daily ETO were the key parameters affecting model prediction accuracy. Rs was the most influential factor in the monthly average ETO model, followed by Ts. Both relative humidity (RH) and wind speed at 2 m (U2) had little impact on ETO prediction at different scales, although their importance differed. Compared with the GBDT and RF models, the XGBoost model exhibited the highest performance for daily ETO and monthly mean ETO estimation. The hybrid tree-based models with the BO algorithm outperformed the standalone tree-based models. Overall, compared with other inputs, the model with three inputs (Rs, Ts, and RH/U2) had the highest accuracy. The BO-XGBoost model exhibited superior performance in terms of the global performance index (GPI) for daily ETO and monthly mean ETO 2 prediction and it is recommended as a more accurate model predicting daily ETO and monthly mean ETO in North China or areas with a similar climate.

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Comparison of different empirical methods and data-driven models for estimating reference evapotranspiration in semi-arid Central Anatolian Region of Turkey

Yurtseven

Serengil

2021

Arab J Geosci

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Models and measurements of seven years of evapotranspiration on a high elevation site on the Central Tibetan Plateau

Cited by 6 publications

References 66 publications

Land Surface Modeling in the Himalayas: On the Importance of Evaporative Fluxes for the Water Balance of a High‐Elevation Catchment

Land Surface Modeling in the Himalayas: On the Importance of Evaporative Fluxes for the Water Balance of a High‐Elevation Catchment

Selecting essential factors for predicting reference crop evapotranspiration through tree-based machine learning and Bayesian optimization

Comparison of different empirical methods and data-driven models for estimating reference evapotranspiration in semi-arid Central Anatolian Region of Turkey

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