Evapotranspiration simulations in ISIMIP2a-Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets
(2018) In Environmental Research Letters 13(7).- Abstract
Actual land evapotranspiration (ET) is a key component of the global hydrological cycle and an essential variable determining the evolution of hydrological extreme events under different climate change scenarios. However, recently available ET products show persistent uncertainties that are impeding a precise attribution of human-induced climate change. Here, we aim at comparing a range of independent global monthly land ET estimates with historical model simulations from the global water, agriculture, and biomes sectors participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). Among the independent estimates, we use the EartH2Observe Tier-1 dataset (E2O), two commonly used reanalyses, a... (More)
Actual land evapotranspiration (ET) is a key component of the global hydrological cycle and an essential variable determining the evolution of hydrological extreme events under different climate change scenarios. However, recently available ET products show persistent uncertainties that are impeding a precise attribution of human-induced climate change. Here, we aim at comparing a range of independent global monthly land ET estimates with historical model simulations from the global water, agriculture, and biomes sectors participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). Among the independent estimates, we use the EartH2Observe Tier-1 dataset (E2O), two commonly used reanalyses, a pre-compiled ensemble product (LandFlux-EVAL), and an updated collection of recently published datasets that algorithmically derive ET from observations or observations-based estimates (diagnostic datasets). A cluster analysis is applied in order to identify spatio-temporal differences among all datasets and to thus identify factors that dominate overall uncertainties. The clustering is controlled by several factors including the model choice, the meteorological forcing used to drive the assessed models, the data category (models participating in the different sectors of ISIMIP2a, E2O models, diagnostic estimates, reanalysis-based estimates or composite products), the ET scheme, and the number of soil layers in the models. By using these factors to explain spatial and spatio-temporal variabilities in ET, we find that the model choice mostly dominates (24%-40% of variance explained), except for spatio-temporal patterns of total ET, where the forcing explains the largest fraction of the variance (29%). The most dominant clusters of datasets are further compared with individual diagnostic and reanalysis-based estimates to assess their representation of selected heat waves and droughts in the Great Plains, Central Europe and western Russia. Although most of the ET estimates capture these extreme events, the generally large spread among the entire ensemble indicates substantial uncertainties.
(Less)
- author
- publishing date
- 2018-07-06
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Cluster analysis, Evapotranspiration, Hydrological extreme events, ISIMIP2a, Uncertainty
- in
- Environmental Research Letters
- volume
- 13
- issue
- 7
- article number
- aac4bb
- publisher
- IOP Publishing
- external identifiers
-
- scopus:85056414729
- ISSN
- 1748-9318
- DOI
- 10.1088/1748-9326/aac4bb
- language
- English
- LU publication?
- no
- id
- 9d355e21-ab0f-4747-a25f-e447b3ad0809
- date added to LUP
- 2020-11-19 22:20:39
- date last changed
- 2023-02-28 15:44:52
@article{9d355e21-ab0f-4747-a25f-e447b3ad0809,
abstract = {{<p>Actual land evapotranspiration (ET) is a key component of the global hydrological cycle and an essential variable determining the evolution of hydrological extreme events under different climate change scenarios. However, recently available ET products show persistent uncertainties that are impeding a precise attribution of human-induced climate change. Here, we aim at comparing a range of independent global monthly land ET estimates with historical model simulations from the global water, agriculture, and biomes sectors participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). Among the independent estimates, we use the EartH2Observe Tier-1 dataset (E2O), two commonly used reanalyses, a pre-compiled ensemble product (LandFlux-EVAL), and an updated collection of recently published datasets that algorithmically derive ET from observations or observations-based estimates (diagnostic datasets). A cluster analysis is applied in order to identify spatio-temporal differences among all datasets and to thus identify factors that dominate overall uncertainties. The clustering is controlled by several factors including the model choice, the meteorological forcing used to drive the assessed models, the data category (models participating in the different sectors of ISIMIP2a, E2O models, diagnostic estimates, reanalysis-based estimates or composite products), the ET scheme, and the number of soil layers in the models. By using these factors to explain spatial and spatio-temporal variabilities in ET, we find that the model choice mostly dominates (24%-40% of variance explained), except for spatio-temporal patterns of total ET, where the forcing explains the largest fraction of the variance (29%). The most dominant clusters of datasets are further compared with individual diagnostic and reanalysis-based estimates to assess their representation of selected heat waves and droughts in the Great Plains, Central Europe and western Russia. Although most of the ET estimates capture these extreme events, the generally large spread among the entire ensemble indicates substantial uncertainties.</p>}},
author = {{Wartenburger, Richard and Seneviratne, Sonia I. and Hirschi, Martin and Chang, Jinfeng and Ciais, Philippe and Deryng, Delphine and Elliott, Joshua and Folberth, Christian and Gosling, Simon N. and Gudmundsson, Lukas and Henrot, Alexandra Jane and Hickler, Thomas and Ito, Akihiko and Khabarov, Nikolay and Kim, Hyungjun and Leng, Guoyong and Liu, Junguo and Liu, Xingcai and Masaki, Yoshimitsu and Morfopoulos, Catherine and Schmied, Hannes Müller and Nishina, Kazuya and Orth, Rene and Pokhrel, Yadu and Pugh, Thomas A.M. and Satoh, Yusuke and Schaphoff, Sibyll and Schmid, Erwin and Sheffield, Justin and Stacke, Tobias and Steinkamp, Joerg and Tang, Qiuhong and Thiery, Wim and Wada, Yoshihide and Wang, Xuhui and Weedon, Graham P. and Yang, Hong and Zhou, Tian}},
issn = {{1748-9318}},
keywords = {{Cluster analysis; Evapotranspiration; Hydrological extreme events; ISIMIP2a; Uncertainty}},
language = {{eng}},
month = {{07}},
number = {{7}},
publisher = {{IOP Publishing}},
series = {{Environmental Research Letters}},
title = {{Evapotranspiration simulations in ISIMIP2a-Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets}},
url = {{http://dx.doi.org/10.1088/1748-9326/aac4bb}},
doi = {{10.1088/1748-9326/aac4bb}},
volume = {{13}},
year = {{2018}},
}