Articles | Volume 26, issue 18
https://doi.org/10.5194/hess-26-4603-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-4603-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Sep 2022
Research article |
| 16 Sep 2022
Evaluation of water flux predictive models developed using eddy-covariance observations and machine learning: a meta-analysis
Haiyang Shi
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Department of Geography, Ghent University, Ghent 9000, Belgium
Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium
Geping Luo
CORRESPONDING AUTHOR
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Ürümqi, 830011, China
Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium
Olaf Hellwich
CORRESPONDING AUTHOR
Department of Computer Vision & Remote Sensing, Technische Universität Berlin, 10587 Berlin, Germany
Mingjuan Xie
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Department of Geography, Ghent University, Ghent 9000, Belgium
Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium
Chen Zhang
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Yu Zhang
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Yuangang Wang
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Xiuliang Yuan
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
Xiaofei Ma
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
Wenqiang Zhang
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Department of Geography, Ghent University, Ghent 9000, Belgium
Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium
Alishir Kurban
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Ürümqi, 830011, China
Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium
Philippe Maeyer
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi, Xinjiang, 830011, China
College of Resources and Environment, University of the Chinese Academy of Sciences, 19 (A) Yuquan Road, Beijing, 100049, China
Department of Geography, Ghent University, Ghent 9000, Belgium
Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium
Tim Voorde
Department of Geography, Ghent University, Ghent 9000, Belgium
Sino-Belgian Joint Laboratory for Geo-Information, Ghent 9000, Belgium
Related authors
Haiyang Shi, Geping Luo, Olaf Hellwich, Alishir Kurban, Philippe De Maeyer, and Tim Van de Voorde
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-191, https://doi.org/10.5194/bg-2022-191, 2022
Revised manuscript under review for BG
Short summary
Short summary
In studies of the relationship between ecosystem function and climate and plant traits, previous studies based on data-driven methods such as multiple regression and random forest may be inadequate for the representation of systematic causality due to limitations such as covariance among variables. Based on FLUXNET site data, we used a causal network to revisit the control of climate and vegetation traits on ecosystem function.
Haiyang Shi, Geping Luo, Olaf Hellwich, Mingjuan Xie, Chen Zhang, Yu Zhang, Yuangang Wang, Xiuliang Yuan, Xiaofei Ma, Wenqiang Zhang, Alishir Kurban, Philippe De Maeyer, and Tim Van de Voorde
Biogeosciences, 19, 3739–3756, https://doi.org/10.5194/bg-19-3739-2022, https://doi.org/10.5194/bg-19-3739-2022, 2022
Short summary
Short summary
A number of studies have been conducted by using machine learning approaches to simulate carbon fluxes. We performed a meta-analysis of these net ecosystem exchange (NEE) simulations. Random forests and support vector machines performed better than other algorithms. Models with larger timescales had a lower accuracy. For different plant functional types (PFTs), there were significant differences in the predictors used and their effects on model accuracy.
Haiyang Shi, Geping Luo, Hongwei Zheng, Chunbo Chen, Olaf Hellwich, Jie Bai, Tie Liu, Shuang Liu, Jie Xue, Peng Cai, Huili He, Friday Uchenna Ochege, Tim Van de Voorde, and Philippe de Maeyer
Hydrol. Earth Syst. Sci., 25, 901–925, https://doi.org/10.5194/hess-25-901-2021, https://doi.org/10.5194/hess-25-901-2021, 2021
Short summary
Short summary
Some river basins are considered to be very similar because they have a similar background such as a transboundary, facing threats of human activities. But we still lack understanding of differences under their general similarities. Therefore, we proposed a framework based on a Bayesian network to group watersheds based on similarity levels and compare the causal and systematic differences within the group. We applied it to the Amu and Syr Darya River basin and discussed its universality.
Philippe De Maeyer
Abstr. Int. Cartogr. Assoc., 5, 3, https://doi.org/10.5194/ica-abs-5-3-2022, https://doi.org/10.5194/ica-abs-5-3-2022, 2022
Haiyang Shi, Geping Luo, Olaf Hellwich, Alishir Kurban, Philippe De Maeyer, and Tim Van de Voorde
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-191, https://doi.org/10.5194/bg-2022-191, 2022
Revised manuscript under review for BG
Short summary
Short summary
In studies of the relationship between ecosystem function and climate and plant traits, previous studies based on data-driven methods such as multiple regression and random forest may be inadequate for the representation of systematic causality due to limitations such as covariance among variables. Based on FLUXNET site data, we used a causal network to revisit the control of climate and vegetation traits on ecosystem function.
Haiyang Shi, Geping Luo, Olaf Hellwich, Mingjuan Xie, Chen Zhang, Yu Zhang, Yuangang Wang, Xiuliang Yuan, Xiaofei Ma, Wenqiang Zhang, Alishir Kurban, Philippe De Maeyer, and Tim Van de Voorde
Biogeosciences, 19, 3739–3756, https://doi.org/10.5194/bg-19-3739-2022, https://doi.org/10.5194/bg-19-3739-2022, 2022
Short summary
Short summary
A number of studies have been conducted by using machine learning approaches to simulate carbon fluxes. We performed a meta-analysis of these net ecosystem exchange (NEE) simulations. Random forests and support vector machines performed better than other algorithms. Models with larger timescales had a lower accuracy. For different plant functional types (PFTs), there were significant differences in the predictors used and their effects on model accuracy.
Jana Ameye, Philippe De Maeyer, Mario Hernandez, and Luc Zwartjes
Abstr. Int. Cartogr. Assoc., 3, 5, https://doi.org/10.5194/ica-abs-3-5-2021, https://doi.org/10.5194/ica-abs-3-5-2021, 2021
Sara Top, Lola Kotova, Lesley De Cruz, Svetlana Aniskevich, Leonid Bobylev, Rozemien De Troch, Natalia Gnatiuk, Anne Gobin, Rafiq Hamdi, Arne Kriegsmann, Armelle Reca Remedio, Abdulla Sakalli, Hans Van De Vyver, Bert Van Schaeybroeck, Viesturs Zandersons, Philippe De Maeyer, Piet Termonia, and Steven Caluwaerts
Geosci. Model Dev., 14, 1267–1293, https://doi.org/10.5194/gmd-14-1267-2021, https://doi.org/10.5194/gmd-14-1267-2021, 2021
Short summary
Short summary
Detailed climate data are needed to assess the impact of climate change on human and natural systems. The performance of two high-resolution regional climate models, ALARO-0 and REMO2015, was investigated over central Asia, a vulnerable region where detailed climate information is scarce. In certain subregions the produced climate data are suitable for impact studies, but bias adjustment is required for subregions where significant biases have been identified.
Haiyang Shi, Geping Luo, Hongwei Zheng, Chunbo Chen, Olaf Hellwich, Jie Bai, Tie Liu, Shuang Liu, Jie Xue, Peng Cai, Huili He, Friday Uchenna Ochege, Tim Van de Voorde, and Philippe de Maeyer
Hydrol. Earth Syst. Sci., 25, 901–925, https://doi.org/10.5194/hess-25-901-2021, https://doi.org/10.5194/hess-25-901-2021, 2021
Short summary
Short summary
Some river basins are considered to be very similar because they have a similar background such as a transboundary, facing threats of human activities. But we still lack understanding of differences under their general similarities. Therefore, we proposed a framework based on a Bayesian network to group watersheds based on similarity levels and compare the causal and systematic differences within the group. We applied it to the Amu and Syr Darya River basin and discussed its universality.
E. Natsagdorj, T. Renchin, P. De Maeyer, R. Goossens, T. Van de Voorde, and B. Darkhijav
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 149–156, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-149-2020, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-149-2020, 2020
N. Zarrinpanjeh, F. Dadrass Javan, A. Naji, H. Azadi, P. De Maeyer, and F. Witlox
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1285–1291, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1285-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1285-2020, 2020
N. Zarrinpanjeh, F. Dadrass Javan, H. Azadi, P. De Maeyer, and F. Witlox
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2020, 147–154, https://doi.org/10.5194/isprs-annals-V-4-2020-147-2020, https://doi.org/10.5194/isprs-annals-V-4-2020-147-2020, 2020
T. Altanchimeg, T. Renchin, P. De Maeyer, E. Natsagdorj, B. Tseveen, and B. Norov
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5-W3, 7–12, https://doi.org/10.5194/isprs-archives-XLII-5-W3-7-2019, https://doi.org/10.5194/isprs-archives-XLII-5-W3-7-2019, 2019
S. Van Ackere, J. Verbeurgt, L. De Sloover, A. De Wulf, N. Van de Weghe, and P. De Maeyer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W8, 429–436, https://doi.org/10.5194/isprs-archives-XLII-3-W8-429-2019, https://doi.org/10.5194/isprs-archives-XLII-3-W8-429-2019, 2019
Laure De Cock, Kristien Ooms, Nico Van de Weghe, and Philippe De Maeyer
Abstr. Int. Cartogr. Assoc., 1, 55, https://doi.org/10.5194/ica-abs-1-55-2019, https://doi.org/10.5194/ica-abs-1-55-2019, 2019
Philippe De Maeyer
Abstr. Int. Cartogr. Assoc., 1, 56, https://doi.org/10.5194/ica-abs-1-56-2019, https://doi.org/10.5194/ica-abs-1-56-2019, 2019
Merve Keskin, Kristien Ooms, Philippe De Maeyer, and Ahmet Ozgur Dogru
Abstr. Int. Cartogr. Assoc., 1, 171, https://doi.org/10.5194/ica-abs-1-171-2019, https://doi.org/10.5194/ica-abs-1-171-2019, 2019
Lieselot Lapon, Kristien Ooms, and Philippe De Maeyer
Abstr. Int. Cartogr. Assoc., 1, 207, https://doi.org/10.5194/ica-abs-1-207-2019, https://doi.org/10.5194/ica-abs-1-207-2019, 2019
Nina Vanhaeren, Kristien Ooms, and Philippe De Maeyer
Abstr. Int. Cartogr. Assoc., 1, 378, https://doi.org/10.5194/ica-abs-1-378-2019, https://doi.org/10.5194/ica-abs-1-378-2019, 2019
E. Natsagdorj, T. Renchin, P. De Maeyer, B. Tseveen, C. Dari, and E. Dashdondog
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 485–491, https://doi.org/10.5194/isprs-annals-IV-2-W5-485-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-485-2019, 2019
Hanne Glas, Greet Deruyter, Philippe De Maeyer, Arpita Mandal, and Sherene James-Williamson
Nat. Hazards Earth Syst. Sci., 16, 2529–2542, https://doi.org/10.5194/nhess-16-2529-2016, https://doi.org/10.5194/nhess-16-2529-2016, 2016
Short summary
Short summary
Adequate flood damage assessments can help to minimize damage costs in the SIDS. Data availability is, however, a major issue in these areas. In order to determine the minimal data necessary for an adequate result, a sensitivity analysis was performed on the input data. This has shown that population density, in combination with an average number of people per household, is a good parameter to determine building damage. Furthermore, a complete road dataset is visually indispensable.
O. Hellwich, A. Rose, T. Bien, C. Malolepszy, D. Mucha, and T. Krüger
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 829–833, https://doi.org/10.5194/isprs-archives-XLI-B5-829-2016, https://doi.org/10.5194/isprs-archives-XLI-B5-829-2016, 2016
T. Amuti and G. Luo
Solid Earth, 5, 1071–1085, https://doi.org/10.5194/se-5-1071-2014, https://doi.org/10.5194/se-5-1071-2014, 2014
Short summary
Short summary
In this paper, we present research on the spatial-temporal pattern of land cover change in the fragile ecosystem of the arid desert oasis of Hotan in northwest China. Multi-temporal Landsat images were used to analyze the land cover change and to identify change trajectories. The natural and human driving forces of these changes were examined using hydro-climatic data and socio-economic data.
Related subject area
Subject: Hydrometeorology | Techniques and Approaches: Modelling approaches
How well does a convection-permitting regional climate model represent the reverse orographic effect of extreme hourly precipitation?
Regionalisation of rainfall depth–duration–frequency curves with different data types in Germany
The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting
Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models
Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System
Spatial distribution of oceanic moisture contributions to precipitation over the Tibetan Plateau
Ensemble streamflow prediction considering the influence of reservoirs in Narmada River Basin, India
Declining water resources in response to global warming and changes in atmospheric circulation patterns over southern Mediterranean France
Linking the complementary evaporation relationship with the Budyko framework for ungauged areas in Australia
Risks of seasonal extreme rainfall events in Bangladesh under 1.5 and 2.0 °C warmer worlds – how anthropogenic aerosols change the story
Pan evaporation is increased by submerged macrophytes
Characterizing basin-scale precipitation gradients in the Third Pole region using a high-resolution atmospheric simulation-based dataset
Local moisture recycling across the globe
A comparison of hydrological models with different level of complexity in Alpine regions in the context of climate change
Sensitivities of subgrid-scale physics schemes, meteorological forcing, and topographic radiation in atmosphere-through-bedrock integrated process models: A case study in the Upper Colorado River Basin
Modelling evaporation with local, regional and global BROOK90 frameworks: importance of parameterization and forcing
Hydrological concept formation inside long short-term memory (LSTM) networks
A two-step merging strategy for incorporating multi-source precipitation products and gauge observations using machine learning classification and regression over China
Hydrometeorological evaluation of two nowcasting systems for Mediterranean heavy precipitation events with operational considerations
On the links between sub-seasonal clustering of extreme precipitation and high discharge in Switzerland and Europe
Regional, multi-decadal analysis on the Loire River basin reveals that stream temperature increases faster than air temperature
Investigating the response of leaf area index to droughts in southern African vegetation using observations and model simulations
Recent decrease in summer precipitation over the Iberian Peninsula closely links to reduction in local moisture recycling
Exploring the possible role of satellite-based rainfall data in estimating inter- and intra-annual global rainfall erosivity
Critical transitions in the hydrological system: early-warning signals and network analysis
Testing a maximum evaporation theory over saturated land: implications for potential evaporation estimation
The role of morphology in the spatial distribution of short-duration rainfall extremes in Italy
Impact of correcting sub-daily climate model biases for hydrological studies
The Mesoamerican mid-summer drought: the impact of its definition on occurrences and recent changes
Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting
Influence of initial soil moisture in a regional climate model study over West Africa – Part 1: Impact on the climate mean
Influence of initial soil moisture in a regional climate model study over West Africa – Part 2: Impact on the climate extremes
Compound flood impact forecasting: integrating fluvial and flash flood impact assessments into a unified system
Ensemble streamflow forecasting over a cascade reservoir catchment with integrated hydrometeorological modeling and machine learning
Machine-learning methods to assess the effects of a non-linear damage spectrum taking into account soil moisture on winter wheat yields in Germany
Extreme precipitation events in the Mediterranean area: contrasting two different models for moisture source identification
Flexible and consistent quantile estimation for intensity–duration–frequency curves
Evaluation of Asian summer precipitation in different configurations of a high-resolution general circulation model in a range of decision-relevant spatial scales
Rainfall-induced shallow landslides and soil wetness: comparison of physically based and probabilistic predictions
Land use and climate change effects on water yield from East African forested water towers
Easy-to-use spatial random-forest-based downscaling-calibration method for producing precipitation data with high resolution and high accuracy
Improved parameterization of snow albedo in Noah coupled with Weather Research and Forecasting: applicability to snow estimates for the Tibetan Plateau
A 10 km North American precipitation and land-surface reanalysis based on the GEM atmospheric model
Contribution of moisture sources to precipitation changes in the Three Gorges Reservoir Region
Impacts of land use and land cover change and reforestation on summer rainfall in the Yangtze River basin
Mass balance and hydrological modeling of the Hardangerjøkulen ice cap in south-central Norway
Long-term relative decline in evapotranspiration with increasing runoff on fractional land surfaces
Decision tree-based detection of blowing snow events in the European Alps
Changes in the simulation of atmospheric instability over the Iberian Peninsula due to the use of 3DVAR data assimilation
Simulating the evolution of the topography–climate coupled system
Eleonora Dallan, Francesco Marra, Giorgia Fosser, Marco Marani, Giuseppe Formetta, Christoph Schär, and Marco Borga
Hydrol. Earth Syst. Sci., 27, 1133–1149, https://doi.org/10.5194/hess-27-1133-2023, https://doi.org/10.5194/hess-27-1133-2023, 2023
Short summary
Short summary
Convection-permitting climate models could represent future changes in extreme short-duration precipitation, which is critical for risk management. We use a non-asymptotic statistical method to estimate extremes from 10 years of simulations in an orographically complex area. Despite overall good agreement with rain gauges, the observed decrease of hourly extremes with elevation is not fully represented by the model. Climate model adjustment methods should consider the role of orography.
Bora Shehu, Winfried Willems, Henrike Stockel, Luisa-Bianca Thiele, and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 1109–1132, https://doi.org/10.5194/hess-27-1109-2023, https://doi.org/10.5194/hess-27-1109-2023, 2023
Short summary
Short summary
Rainfall volumes at varying duration and frequencies are required for many engineering water works. These design volumes have been provided by KOSTRA-DWD in Germany. However, a revision of the KOSTRA-DWD is required, in order to consider the recent state-of-the-art and additional data. For this purpose, in our study, we investigate different methods and data available to achieve the best procedure that will serve as a basis for the development of the new KOSTRA-DWD product.
Sandra M. Hauswirth, Marc F. P. Bierkens, Vincent Beijk, and Niko Wanders
Hydrol. Earth Syst. Sci., 27, 501–517, https://doi.org/10.5194/hess-27-501-2023, https://doi.org/10.5194/hess-27-501-2023, 2023
Short summary
Short summary
Forecasts on water availability are important for water managers. We test a hybrid framework based on machine learning models and global input data for generating seasonal forecasts. Our evaluation shows that our discharge and surface water level predictions are able to create reliable forecasts up to 2 months ahead. We show that a hybrid framework, developed for local purposes and combined and rerun with global data, can create valuable information similar to large-scale forecasting models.
Richard Arsenault, Jean-Luc Martel, Frédéric Brunet, François Brissette, and Juliane Mai
Hydrol. Earth Syst. Sci., 27, 139–157, https://doi.org/10.5194/hess-27-139-2023, https://doi.org/10.5194/hess-27-139-2023, 2023
Short summary
Short summary
Predicting flow in rivers where no observation records are available is a daunting task. For decades, hydrological models were set up on these gauges, and their parameters were estimated based on the hydrological response of similar or nearby catchments where records exist. New developments in machine learning have now made it possible to estimate flows at ungauged locations more precisely than with hydrological models. This study confirms the performance superiority of machine learning models.
Shaun Harrigan, Ervin Zsoter, Hannah Cloke, Peter Salamon, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 27, 1–19, https://doi.org/10.5194/hess-27-1-2023, https://doi.org/10.5194/hess-27-1-2023, 2023
Short summary
Short summary
Real-time river discharge forecasts and reforecasts from the Global Flood Awareness System (GloFAS) have been made publicly available, together with an evaluation of forecast skill at the global scale. Results show that GloFAS is skillful in over 93 % of catchments in the short (1–3 d) and medium range (5–15 d) and skillful in over 80 % of catchments in the extended lead time (16–30 d). Skill is summarised in a new layer on the GloFAS Web Map Viewer to aid decision-making.
Ying Li, Chenghao Wang, Ru Huang, Denghua Yan, Hui Peng, and Shangbin Xiao
Hydrol. Earth Syst. Sci., 26, 6413–6426, https://doi.org/10.5194/hess-26-6413-2022, https://doi.org/10.5194/hess-26-6413-2022, 2022
Short summary
Short summary
Spatial quantification of oceanic moisture contribution to the precipitation over the Tibetan Plateau (TP) contributes to the reliable assessments of regional water resources and the interpretation of paleo archives in the region. Based on atmospheric reanalysis datasets and numerical moisture tracking, this work reveals the previously underestimated oceanic moisture contributions brought by the westerlies in winter and the overestimated moisture contributions from the Indian Ocean in summer.
Urmin Vegad and Vimal Mishra
Hydrol. Earth Syst. Sci., 26, 6361–6378, https://doi.org/10.5194/hess-26-6361-2022, https://doi.org/10.5194/hess-26-6361-2022, 2022
Short summary
Short summary
Floods cause enormous damage to infrastructure and agriculture in India. However, the utility of ensemble meteorological forecast for hydrologic prediction has not been examined. Moreover, Indian river basins have a considerable influence of reservoirs that alter the natural flow variability. We developed a hydrologic modelling-based streamflow prediction considering the influence of reservoirs in India.
Camille Labrousse, Wolfgang Ludwig, Sébastien Pinel, Mahrez Sadaoui, Andrea Toreti, and Guillaume Lacquement
Hydrol. Earth Syst. Sci., 26, 6055–6071, https://doi.org/10.5194/hess-26-6055-2022, https://doi.org/10.5194/hess-26-6055-2022, 2022
Short summary
Short summary
The interest of this study is to demonstrate that we identify two zones in our study area whose hydroclimatic behaviours are uneven. By investigating relationships between the hydroclimatic conditions in both clusters for past observations with the overall atmospheric functioning, we show that the inequalities are mainly driven by a different control of the atmospheric teleconnection patterns over the area.
Daeha Kim, Minha Choi, and Jong Ahn Chun
Hydrol. Earth Syst. Sci., 26, 5955–5969, https://doi.org/10.5194/hess-26-5955-2022, https://doi.org/10.5194/hess-26-5955-2022, 2022
Short summary
Short summary
We proposed a practical method that predicts the evaporation rates on land surfaces (ET) where only atmospheric data are available. Using a traditional equation that describes partitioning of precipitation into ET and streamflow, we could approximately identify the key parameter of the predicting formulation based on land–atmosphere interactions. The simple method conditioned by local climates outperformed sophisticated models in reproducing water-balance estimates across Australia.
Ruksana H. Rimi, Karsten Haustein, Emily J. Barbour, Sarah N. Sparrow, Sihan Li, David C. H. Wallom, and Myles R. Allen
Hydrol. Earth Syst. Sci., 26, 5737–5756, https://doi.org/10.5194/hess-26-5737-2022, https://doi.org/10.5194/hess-26-5737-2022, 2022
Short summary
Short summary
Extreme rainfall events are major concerns in Bangladesh. Heavy downpours can cause flash floods and damage nearly harvestable crops in pre-monsoon season. While in monsoon season, the impacts can range from widespread agricultural loss, huge property damage, to loss of lives and livelihoods. This paper assesses the role of anthropogenic climate change drivers in changing risks of extreme rainfall events during pre-monsoon and monsoon seasons at local sub-regional-scale within Bangladesh.
Brigitta Simon-Gáspár, Gábor Soós, and Angela Anda
Hydrol. Earth Syst. Sci., 26, 4741–4756, https://doi.org/10.5194/hess-26-4741-2022, https://doi.org/10.5194/hess-26-4741-2022, 2022
Short summary
Short summary
Due to climate change, it is extremely important to determine evaporation as accurately as possible. In nature, there are sediments and macrophytes in the open waters; thus, one of the aims was to investigate their effect on evaporation. The second aim of this paper was to estimate daily evaporation by using different models, which, according to results, have high priority in the evaporation prediction. Water management can obtain useful information from the results of the current research.
Yaozhi Jiang, Kun Yang, Hua Yang, Hui Lu, Yingying Chen, Xu Zhou, Jing Sun, Yuan Yang, and Yan Wang
Hydrol. Earth Syst. Sci., 26, 4587–4601, https://doi.org/10.5194/hess-26-4587-2022, https://doi.org/10.5194/hess-26-4587-2022, 2022
Short summary
Short summary
Our study quantified the altitudinal precipitation gradients (PGs) over the Third Pole (TP). Most sub-basins in the TP have positive PGs, and negative PGs are found in the Himalayas, the Hengduan Mountains and the western Kunlun. PGs are positively correlated with wind speed but negatively correlated with relative humidity. In addition, PGs tend to be positive at smaller spatial scales compared to those at larger scales. The findings can assist precipitation interpolation in the data-sparse TP.
Jolanda Theeuwen, Arie Staal, Obbe Tuinenburg, Bert Hamelers, and Stefan Dekker
EGUsphere, https://doi.org/10.5194/egusphere-2022-612, https://doi.org/10.5194/egusphere-2022-612, 2022
Short summary
Short summary
Evaporation changes over land affect rainfall over land through moisture recycling. We calculated the local moisture recycling ratio globally, which describes the fraction of evaporated moisture that rains out within approx. 50 km of its source location. This recycling peaks in summer, and over wet and elevated regions. Local moisture recycling gives insight into the local impacts of evaporation changes and can be used to study the influence of regreening on local rainfall.
Francesca Carletti, Adrien Michel, Francesca Casale, Alice Burri, Daniele Bocchiola, Mathias Bavay, and Michael Lehning
Hydrol. Earth Syst. Sci., 26, 3447–3475, https://doi.org/10.5194/hess-26-3447-2022, https://doi.org/10.5194/hess-26-3447-2022, 2022
Short summary
Short summary
High Alpine catchments are dominated by the melting of seasonal snow cover and glaciers, whose amount and seasonality are expected to be modified by climate change. This paper compares the performances of different types of models in reproducing discharge among two catchments under present conditions and climate change. Despite many advantages, the use of simpler models for climate change applications is controversial as they do not fully represent the physics of the involved processes.
Zexuan Xu, Erica R. Siirila-Woodburn, Alan M. Rhoades, and Daniel Feldman
EGUsphere, https://doi.org/10.5194/egusphere-2022-437, https://doi.org/10.5194/egusphere-2022-437, 2022
Short summary
Short summary
The goal of this study is to understand the uncertainties of different modeling configurations for simulating hydroclimate responses in the mountainous watershed. We run a group of climate models with various configurations and evaluate them against various reference datasets. This paper integrates climate model and hydrology model to have a full understanding of the atmospheric-through-bedrock hydrological processes.
Ivan Vorobevskii, Thi Thanh Luong, Rico Kronenberg, Thomas Grünwald, and Christian Bernhofer
Hydrol. Earth Syst. Sci., 26, 3177–3239, https://doi.org/10.5194/hess-26-3177-2022, https://doi.org/10.5194/hess-26-3177-2022, 2022
Short summary
Short summary
In the study we analysed the uncertainties of the meteorological data and model parameterization for evaporation modelling. We have taken a physically based lumped BROOK90 model and applied it in three different frameworks using global, regional and local datasets. Validating the simulations with eddy-covariance data from five stations in Germany, we found that the accuracy model parameterization plays a bigger role than the quality of the meteorological forcing.
Thomas Lees, Steven Reece, Frederik Kratzert, Daniel Klotz, Martin Gauch, Jens De Bruijn, Reetik Kumar Sahu, Peter Greve, Louise Slater, and Simon J. Dadson
Hydrol. Earth Syst. Sci., 26, 3079–3101, https://doi.org/10.5194/hess-26-3079-2022, https://doi.org/10.5194/hess-26-3079-2022, 2022
Short summary
Short summary
Despite the accuracy of deep learning rainfall-runoff models, we are currently uncertain of what these models have learned. In this study we explore the internals of one deep learning architecture and demonstrate that the model learns about intermediate hydrological stores of soil moisture and snow water, despite never having seen data about these processes during training. Therefore, we find evidence that the deep learning approach learns a physically realistic mapping from inputs to outputs.
Huajin Lei, Hongyu Zhao, and Tianqi Ao
Hydrol. Earth Syst. Sci., 26, 2969–2995, https://doi.org/10.5194/hess-26-2969-2022, https://doi.org/10.5194/hess-26-2969-2022, 2022
Short summary
Short summary
How to combine multi-source precipitation data effectively is one of the hot topics in hydrometeorological research. This study presents a two-step merging strategy based on machine learning for multi-source precipitation merging over China. The results demonstrate that the proposed method effectively distinguishes the occurrence of precipitation events and reduces the error in precipitation estimation. This method is robust and may be successfully applied to other areas even with scarce data.
Alexane Lovat, Béatrice Vincendon, and Véronique Ducrocq
Hydrol. Earth Syst. Sci., 26, 2697–2714, https://doi.org/10.5194/hess-26-2697-2022, https://doi.org/10.5194/hess-26-2697-2022, 2022
Short summary
Short summary
The hydrometeorological skills of two new nowcasting systems for forecasting Mediterranean intense rainfall events and floods are investigated. The results reveal that up to 75 or 90 min of forecast the performance of the nowcasting system blending numerical weather prediction and extrapolation of radar estimation is higher than the numerical weather model. For lead times up to 3 h the skills are equivalent in general. Using these nowcasting systems for flash flood forecasting is also promising.
Alexandre Tuel, Bettina Schaefli, Jakob Zscheischler, and Olivia Martius
Hydrol. Earth Syst. Sci., 26, 2649–2669, https://doi.org/10.5194/hess-26-2649-2022, https://doi.org/10.5194/hess-26-2649-2022, 2022
Short summary
Short summary
River discharge is strongly influenced by the temporal structure of precipitation. Here, we show how extreme precipitation events that occur a few days or weeks after a previous event have a larger effect on river discharge than events occurring in isolation. Windows of 2 weeks or less between events have the most impact. Similarly, periods of persistent high discharge tend to be associated with the occurrence of several extreme precipitation events in close succession.
Hanieh Seyedhashemi, Jean-Philippe Vidal, Jacob S. Diamond, Dominique Thiéry, Céline Monteil, Frédéric Hendrickx, Anthony Maire, and Florentina Moatar
Hydrol. Earth Syst. Sci., 26, 2583–2603, https://doi.org/10.5194/hess-26-2583-2022, https://doi.org/10.5194/hess-26-2583-2022, 2022
Short summary
Short summary
Stream temperature appears to be increasing globally, but its rate remains poorly constrained due to a paucity of long-term data. Using a thermal model, this study provides a large-scale understanding of the evolution of stream temperature over a long period (1963–2019). This research highlights that air temperature and streamflow can exert joint influence on stream temperature trends, and riparian shading in small mountainous streams may mitigate warming in stream temperatures.
Shakirudeen Lawal, Stephen Sitch, Danica Lombardozzi, Julia E. M. S. Nabel, Hao-Wei Wey, Pierre Friedlingstein, Hanqin Tian, and Bruce Hewitson
Hydrol. Earth Syst. Sci., 26, 2045–2071, https://doi.org/10.5194/hess-26-2045-2022, https://doi.org/10.5194/hess-26-2045-2022, 2022
Short summary
Short summary
To investigate the impacts of drought on vegetation, which few studies have done due to various limitations, we used the leaf area index as proxy and dynamic global vegetation models (DGVMs) to simulate drought impacts because the models use observationally derived climate. We found that the semi-desert biome responds strongly to drought in the summer season, while the tropical forest biome shows a weak response. This study could help target areas to improve drought monitoring and simulation.
Yubo Liu, Monica Garcia, Chi Zhang, and Qiuhong Tang
Hydrol. Earth Syst. Sci., 26, 1925–1936, https://doi.org/10.5194/hess-26-1925-2022, https://doi.org/10.5194/hess-26-1925-2022, 2022
Short summary
Short summary
Our findings indicate that the reduction in contribution to the Iberian Peninsula (IP) summer precipitation is mainly concentrated in the IP and its neighboring grids. Compared with 1980–1997, both local recycling and external moisture were reduced during 1998–2019. The reduction in local recycling in the IP closely links to the disappearance of the wet years and the decreasing contribution in the dry years.
Nejc Bezak, Pasquale Borrelli, and Panos Panagos
Hydrol. Earth Syst. Sci., 26, 1907–1924, https://doi.org/10.5194/hess-26-1907-2022, https://doi.org/10.5194/hess-26-1907-2022, 2022
Short summary
Short summary
Rainfall erosivity is one of the main factors in soil erosion. A satellite-based global map of rainfall erosivity was constructed using data with a 30 min time interval. It was shown that the satellite-based precipitation products are an interesting option for estimating rainfall erosivity, especially in regions with limited ground data. However, ground-based high-frequency precipitation measurements are (still) essential for accurate estimates of rainfall erosivity.
Xueli Yang, Zhi-Hua Wang, and Chenghao Wang
Hydrol. Earth Syst. Sci., 26, 1845–1856, https://doi.org/10.5194/hess-26-1845-2022, https://doi.org/10.5194/hess-26-1845-2022, 2022
Short summary
Short summary
In this study, we investigated potentially catastrophic transitions in hydrological processes by identifying the early-warning signals which manifest as a
critical slowing downin complex dynamic systems. We then analyzed the precipitation network of cities in the contiguous United States and found that key network parameters, such as the nodal density and the clustering coefficient, exhibit similar dynamic behaviour, which can serve as novel early-warning signals for the hydrological system.
Zhuoyi Tu, Yuting Yang, and Michael L. Roderick
Hydrol. Earth Syst. Sci., 26, 1745–1754, https://doi.org/10.5194/hess-26-1745-2022, https://doi.org/10.5194/hess-26-1745-2022, 2022
Short summary
Short summary
Here we test a maximum evaporation theory that acknowledges the interdependence between radiation, surface temperature, and evaporation over saturated land. We show that the maximum evaporation approach recovers observed evaporation and surface temperature under non-water-limited conditions across a broad range of bio-climates. The implication is that the maximum evaporation concept can be used to predict potential evaporation that has long been a major difficulty for the hydrological community.
Paola Mazzoglio, Ilaria Butera, Massimiliano Alvioli, and Pierluigi Claps
Hydrol. Earth Syst. Sci., 26, 1659–1672, https://doi.org/10.5194/hess-26-1659-2022, https://doi.org/10.5194/hess-26-1659-2022, 2022
Short summary
Short summary
We have analyzed the spatial dependence of rainfall extremes upon elevation and morphology in Italy. Regression analyses show that previous rainfall–elevation relations at national scale can be substantially improved with new data, both using topography attributes and constraining the analysis within areas stemming from geomorphological zonation. Short-duration mean rainfall depths can then be estimated, all over Italy, using different parameters in each area of the geomorphological subdivision.
Mina Faghih, François Brissette, and Parham Sabeti
Hydrol. Earth Syst. Sci., 26, 1545–1563, https://doi.org/10.5194/hess-26-1545-2022, https://doi.org/10.5194/hess-26-1545-2022, 2022
Short summary
Short summary
The diurnal cycles of precipitation and temperature generated by climate models are biased. This work investigates whether or not impact modellers should correct the diurnal cycle biases prior to conducting hydrological impact studies at the sub-daily scale. The results show that more accurate streamflows are obtained when the diurnal cycles biases are corrected. This is noticeable for smaller catchments, which have a quicker reaction time to changes in precipitation and temperature.
Edwin P. Maurer, Iris T. Stewart, Kenneth Joseph, and Hugo G. Hidalgo
Hydrol. Earth Syst. Sci., 26, 1425–1437, https://doi.org/10.5194/hess-26-1425-2022, https://doi.org/10.5194/hess-26-1425-2022, 2022
Short summary
Short summary
The mid-summer drought (MSD) is common in Mesoamerica. It is a short (weeks-long) period of reduced rainfall near the middle of the rainy season. When it occurs, how long it lasts, and how dry it is all have important implications for smallholder farmers. Studies of changes in MSD characteristics rely on defining characteristics of an MSD. Different definitions affect whether an area would be considered to experience an MSD as well as the changes that have happened in the last 40 years.
Qichun Yang, Quan J. Wang, Andrew W. Western, Wenyan Wu, Yawen Shao, and Kirsti Hakala
Hydrol. Earth Syst. Sci., 26, 941–954, https://doi.org/10.5194/hess-26-941-2022, https://doi.org/10.5194/hess-26-941-2022, 2022
Short summary
Short summary
Forecasts of evaporative water loss in the future are highly valuable for water resource management. These forecasts are often produced using the outputs of climate models. We developed an innovative method to correct errors in these forecasts, particularly the errors caused by deficiencies of climate models in modeling the changing climate. We apply this method to seasonal forecasts of evaporative water loss across Australia and achieve significant improvements in the forecast quality.
Brahima Koné, Arona Diedhiou, Adama Diawara, Sandrine Anquetin, N'datchoh Evelyne Touré, Adama Bamba, and Arsene Toka Kobea
Hydrol. Earth Syst. Sci., 26, 711–730, https://doi.org/10.5194/hess-26-711-2022, https://doi.org/10.5194/hess-26-711-2022, 2022
Short summary
Short summary
The impact of initial soil moisture anomalies can persist for up to 3–4 months and is greater on temperature than on precipitation over West Africa. The strongest homogeneous impact on temperature is located over the Central Sahel, with a peak change of −1.5 and 0.5 °C in the wet and dry experiments, respectively. The strongest impact on precipitation in the wet and dry experiments is found over the West and Central Sahel, with a peak change of about 40 % and −8 %, respectively.
Brahima Koné, Arona Diedhiou, Adama Diawara, Sandrine Anquetin, N'datchoh Evelyne Touré, Adama Bamba, and Arsene Toka Kobea
Hydrol. Earth Syst. Sci., 26, 731–754, https://doi.org/10.5194/hess-26-731-2022, https://doi.org/10.5194/hess-26-731-2022, 2022
Short summary
Short summary
The impact of initial soil moisture is more significant on temperature extremes than on precipitation extremes. A stronger impact is found on maximum temperature than on minimum temperature. The impact on extreme precipitation indices is homogeneous, especially over the Central Sahel, and dry (wet) experiments tend to decrease (increase) the number of precipitation extreme events but not their intensity.
Josias Láng-Ritter, Marc Berenguer, Francesco Dottori, Milan Kalas, and Daniel Sempere-Torres
Hydrol. Earth Syst. Sci., 26, 689–709, https://doi.org/10.5194/hess-26-689-2022, https://doi.org/10.5194/hess-26-689-2022, 2022
Short summary
Short summary
During flood events, emergency managers such as civil protection authorities rely on flood forecasts to make informed decisions. In the current practice, they monitor several separate forecasts, each one of them covering a different type of flooding. This can be time-consuming and confusing, ultimately compromising the effectiveness of the emergency response. This work illustrates how the automatic combination of flood type-specific impact forecasts can improve decision support systems.
Junjiang Liu, Xing Yuan, Junhan Zeng, Yang Jiao, Yong Li, Lihua Zhong, and Ling Yao
Hydrol. Earth Syst. Sci., 26, 265–278, https://doi.org/10.5194/hess-26-265-2022, https://doi.org/10.5194/hess-26-265-2022, 2022
Short summary
Short summary
Hourly streamflow ensemble forecasts with the CSSPv2 land surface model and ECMWF meteorological forecasts reduce both the probabilistic and deterministic forecast error compared with the ensemble streamflow prediction approach during the first week. The deterministic forecast error can be further reduced in the first 72 h when combined with the long short-term memory (LSTM) deep learning method. The forecast skill for LSTM using only historical observations drops sharply after the first 24 h.
Michael Peichl, Stephan Thober, Luis Samaniego, Bernd Hansjürgens, and Andreas Marx
Hydrol. Earth Syst. Sci., 25, 6523–6545, https://doi.org/10.5194/hess-25-6523-2021, https://doi.org/10.5194/hess-25-6523-2021, 2021
Short summary
Short summary
Using a statistical model that can also take complex systems into account, the most important factors affecting wheat yield in Germany are determined. Different spatial damage potentials are taken into account. In many parts of Germany, yield losses are caused by too much soil water in spring. Negative heat effects as well as damaging soil drought are identified especially for north-eastern Germany. The model is able to explain years with exceptionally high yields (2014) and losses (2003, 2018).
Sara Cloux, Daniel Garaboa-Paz, Damián Insua-Costa, Gonzalo Miguez-Macho, and Vicente Pérez-Muñuzuri
Hydrol. Earth Syst. Sci., 25, 6465–6477, https://doi.org/10.5194/hess-25-6465-2021, https://doi.org/10.5194/hess-25-6465-2021, 2021
Short summary
Short summary
We examine the performance of a widely used Lagrangian method for moisture tracking by comparing it with a highly accurate Eulerian tool, both operating on the same WRF atmospheric model fields. Although the Lagrangian approach is very useful for a qualitative analysis of moisture sources, it has important limitations in quantifying the contribution of individual sources to precipitation. These drawbacks should be considered by other authors in the future so as to not draw erroneous conclusions.
Felix S. Fauer, Jana Ulrich, Oscar E. Jurado, and Henning W. Rust
Hydrol. Earth Syst. Sci., 25, 6479–6494, https://doi.org/10.5194/hess-25-6479-2021, https://doi.org/10.5194/hess-25-6479-2021, 2021
Short summary
Short summary
Extreme rainfall events are modeled in this study for different timescales. A new parameterization of the dependence between extreme values and their timescale enables our model to estimate extremes on very short (1 min) and long (5 d) timescales simultaneously. We compare different approaches of modeling this dependence and find that our new model improves performance for timescales between 2 h and 2 d without affecting model performance on other timescales.
Mark R. Muetzelfeldt, Reinhard Schiemann, Andrew G. Turner, Nicholas P. Klingaman, Pier Luigi Vidale, and Malcolm J. Roberts
Hydrol. Earth Syst. Sci., 25, 6381–6405, https://doi.org/10.5194/hess-25-6381-2021, https://doi.org/10.5194/hess-25-6381-2021, 2021
Short summary
Short summary
Simulating East Asian Summer Monsoon (EASM) rainfall poses many challenges because of its multi-scale nature. We evaluate three setups of a 14 km global climate model against observations to see if they improve simulated rainfall. We do this over catchment basins of different sizes to estimate how model performance depends on spatial scale. Using explicit convection improves rainfall diurnal cycle, yet more model tuning is needed to improve mean and intensity biases in simulated summer rainfall.
Elena Leonarduzzi, Brian W. McArdell, and Peter Molnar
Hydrol. Earth Syst. Sci., 25, 5937–5950, https://doi.org/10.5194/hess-25-5937-2021, https://doi.org/10.5194/hess-25-5937-2021, 2021
Short summary
Short summary
Landslides are a dangerous natural hazard affecting alpine regions, calling for effective warning systems. Here we consider different approaches for the prediction of rainfall-induced shallow landslides at the regional scale, based on open-access datasets and operational hydrological forecasting systems. We find antecedent wetness useful to improve upon the classical rainfall thresholds and the resolution of the hydrological model used for its estimate to be a critical aspect.
Charles Nduhiu Wamucii, Pieter R. van Oel, Arend Ligtenberg, John Mwangi Gathenya, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 25, 5641–5665, https://doi.org/10.5194/hess-25-5641-2021, https://doi.org/10.5194/hess-25-5641-2021, 2021
Short summary
Short summary
East African water towers (WTs) are under pressure from human influences within and without, but the water yield (WY) is more sensitive to climate changes from within. Land use changes have greater impacts on WY in the surrounding lowlands. The WTs have seen a strong shift towards wetter conditions while, at the same time, the potential evapotranspiration is gradually increasing. The WTs were identified as non-resilient, and future WY may experience more extreme variations.
Chuanfa Chen, Baojian Hu, and Yanyan Li
Hydrol. Earth Syst. Sci., 25, 5667–5682, https://doi.org/10.5194/hess-25-5667-2021, https://doi.org/10.5194/hess-25-5667-2021, 2021
Short summary
Short summary
This study proposes an easy-to-use downscaling-calibration method based on a spatial random forest with the incorporation of high-resolution variables. The proposed method is general, robust, accurate and easy to use as it shows more accurate results than the classical methods in the study area with heterogeneous terrain morphology and precipitation. It can be easily applied to other regions where precipitation data with high resolution and high accuracy are urgently required.
Lian Liu, Yaoming Ma, Massimo Menenti, Rongmingzhu Su, Nan Yao, and Weiqiang Ma
Hydrol. Earth Syst. Sci., 25, 4967–4981, https://doi.org/10.5194/hess-25-4967-2021, https://doi.org/10.5194/hess-25-4967-2021, 2021
Short summary
Short summary
Albedo is a key factor in land surface energy balance, which is difficult to successfully reproduce by models. Here, we select eight snow events on the Tibetan Plateau to evaluate the universal improvements of our improved albedo scheme. The RMSE relative reductions for temperature, albedo, sensible heat flux and snow depth reach 27%, 32%, 13% and 21%, respectively, with remarkable increases in the correlation coefficients. This presents a strong potential of our scheme for modeling snow events.
Nicolas Gasset, Vincent Fortin, Milena Dimitrijevic, Marco Carrera, Bernard Bilodeau, Ryan Muncaster, Étienne Gaborit, Guy Roy, Nedka Pentcheva, Maxim Bulat, Xihong Wang, Radenko Pavlovic, Franck Lespinas, Dikra Khedhaouiria, and Juliane Mai
Hydrol. Earth Syst. Sci., 25, 4917–4945, https://doi.org/10.5194/hess-25-4917-2021, https://doi.org/10.5194/hess-25-4917-2021, 2021
Short summary
Short summary
In this paper, we highlight the importance of including land-data assimilation as well as offline precipitation analysis components in a regional reanalysis system. We also document the performance of the first multidecadal 10 km reanalysis performed with the GEM atmospheric model that can be used for seamless land-surface and hydrological modelling in North America. It is of particular interest for transboundary basins, as existing datasets often show discontinuities at the border.
Ying Li, Chenghao Wang, Hui Peng, Shangbin Xiao, and Denghua Yan
Hydrol. Earth Syst. Sci., 25, 4759–4772, https://doi.org/10.5194/hess-25-4759-2021, https://doi.org/10.5194/hess-25-4759-2021, 2021
Short summary
Short summary
Precipitation change in the Three Gorges Reservoir Region (TGRR) plays a critical role in the operation and regulation of the Three Gorges Dam and the protection of residents and properties. We investigated the long-term contribution of moisture sources to precipitation changes in this region with an atmospheric moisture tracking model. We found that southwestern source regions (especially the southeastern tip of the Tibetan Plateau) are the key regions that control TGRR precipitation changes.
Wei Li, Lu Li, Jie Chen, Qian Lin, and Hua Chen
Hydrol. Earth Syst. Sci., 25, 4531–4548, https://doi.org/10.5194/hess-25-4531-2021, https://doi.org/10.5194/hess-25-4531-2021, 2021
Short summary
Short summary
Reforestation can influence climate, but the sensitivity of summer rainfall to reforestation is rarely investigated. We take two reforestation scenarios to assess the impacts of reforestation on summer rainfall under different reforestation proportions and explore the potential mechanisms. This study concludes that reforestation increases summer rainfall amount and extremes through thermodynamics processes, and the effects are more pronounced in populated areas than over the whole basin.
Trude Eidhammer, Adam Booth, Sven Decker, Lu Li, Michael Barlage, David Gochis, Roy Rasmussen, Kjetil Melvold, Atle Nesje, and Stefan Sobolowski
Hydrol. Earth Syst. Sci., 25, 4275–4297, https://doi.org/10.5194/hess-25-4275-2021, https://doi.org/10.5194/hess-25-4275-2021, 2021
Short summary
Short summary
We coupled a detailed snow–ice model (Crocus) to represent glaciers in the Weather Research and Forecasting (WRF)-Hydro model and tested it on a well-studied glacier. Several observational systems were used to evaluate the system, i.e., satellites, ground-penetrating radar (used over the glacier for snow depth) and stake observations for glacier mass balance and discharge measurements in rivers from the glacier. Results showed improvements in the streamflow projections when including the model.
Ren Wang, Pierre Gentine, Jiabo Yin, Lijuan Chen, Jianyao Chen, and Longhui Li
Hydrol. Earth Syst. Sci., 25, 3805–3818, https://doi.org/10.5194/hess-25-3805-2021, https://doi.org/10.5194/hess-25-3805-2021, 2021
Short summary
Short summary
Assessment of changes in the global water cycle has been a challenge. This study estimated long-term global latent heat and sensible heat fluxes for recent decades using machine learning and ground observations. The results found that the decline in evaporative fraction was typically accompanied by an increase in long-term runoff in over 27.06 % of the global land areas. The observation-driven findings emphasized that surface vegetation has great impacts in regulating water and energy cycles.
Zhipeng Xie, Weiqiang Ma, Yaoming Ma, Zeyong Hu, Genhou Sun, Yizhe Han, Wei Hu, Rongmingzhu Su, and Yixi Fan
Hydrol. Earth Syst. Sci., 25, 3783–3804, https://doi.org/10.5194/hess-25-3783-2021, https://doi.org/10.5194/hess-25-3783-2021, 2021
Short summary
Short summary
Ground information on the occurrence of blowing snow has been sorely lacking because direct observations of blowing snow are sparse in time and space. In this paper, we investigated the potential capability of the decision tree model to detect blowing snow events in the European Alps. Trained with routine meteorological observations, the decision tree model can be used as an efficient tool to detect blowing snow occurrences across different regions requiring limited meteorological variables.
Santos J. González-Rojí, Sheila Carreno-Madinabeitia, Jon Sáenz, and Gabriel Ibarra-Berastegi
Hydrol. Earth Syst. Sci., 25, 3471–3492, https://doi.org/10.5194/hess-25-3471-2021, https://doi.org/10.5194/hess-25-3471-2021, 2021
Short summary
Short summary
The simulation of precipitation extreme events is a known problem in modelling. That is why the atmospheric conditions favourable for its development as simulated by two WRF experiments are evaluated in this paper. The experiment including 3DVAR data assimilation outperforms the one without in simulating the TT index, CAPE, and CIN over the Iberian Peninsula. The ingredients for convective precipitation in winter are found at the Atlantic coast, but in summer they are at the Mediterranean coast.
Kyungrock Paik and Won Kim
Hydrol. Earth Syst. Sci., 25, 2459–2474, https://doi.org/10.5194/hess-25-2459-2021, https://doi.org/10.5194/hess-25-2459-2021, 2021
Short summary
Short summary
Climate, topography, and tectonics evolve together. To simulate their co-evolution, a fully coupled computer simulation model between local climate and topography is developed in this study. We simulated how the mountain development enhances local rainfall and its feedback on topography through stronger erosion. We found that the evolution of the coupled system can be more complicated than previously thought. The channel concavity on the windward side becomes lower as the wind grows.
Cited articles
Adams, D. C., Gurevitch, J., and Rosenberg, M. S.: Resampling tests for
meta of ecological data, Ecology, 78, 1277–1283, 1997.
Allen, R. G., Pereira, L. S., Howell, T. A., and Jensen, M. E.:
Evapotranspiration information reporting: I. Factors governing measurement
accuracy, Agr. Water Manage., 98, 899–920,
https://doi.org/10.1016/j.agwat.2010.12.015, 2011.
Anderson, M. C., Allen, R. G., Morse, A., and Kustas, W. P.: Use of Landsat
thermal imagery in monitoring evapotranspiration and managing water
resources, Remote Sens. Environ., 122, 50–65,
https://doi.org/10.1016/j.rse.2011.08.025, 2012.
Barman, R., Jain, A. K., and Liang, M.: Climate-driven uncertainties in
modeling terrestrial energy and water fluxes: a site-level to global-scale
analysis, Global Change Biol., 20, 1885–1900,
https://doi.org/10.1111/gcb.12473, 2014.
Borenstein, M., Hedges, L. V., Higgins, J. P., and Rothstein, H. R.: Introduction to meta-analysis, John Wiley & Sons, https://doi.org/10.1002/9780470743386, 2011.
Charuchittipan, D., Babel, W., Mauder, M., Leps, J.-P., and Foken, T.:
Extension of the Averaging Time in Eddy-Covariance Measurements and Its
Effect on the Energy Balance Closure, Bound.-Lay. Meteorol., 152,
303–327, https://doi.org/10.1007/s10546-014-9922-6, 2014.
Chen, Y., Xia, J., Liang, S., Feng, J., Fisher, J. B., Li, X., Li, X., Liu,
S., Ma, Z., Miyata, A., Mu, Q., Sun, L., Tang, J., Wang, K., Wen, J., Xue,
Y., Yu, G., Zha, T., Zhang, L., Zhang, Q., Zhao, T., Zhao, L., and Yuan, W.:
Comparison of satellite-based evapotranspiration models over terrestrial
ecosystems in China, Remote Sens. Environ., 140, 279–293,
https://doi.org/10.1016/j.rse.2013.08.045, 2014.
Chen, Y., Wang, S., Ren, Z., Huang, J., Wang, X., Liu, S., Deng, H., and
Lin, W.: Increased evapotranspiration from land cover changes intensified
water crisis in an arid river basin in northwest China, J. Hydrol., 574, 383–397, https://doi.org/10.1016/j.jhydrol.2019.04.045,
2019.
Don, A., Schumacher, J., and Freibauer, A.: Impact of tropical land-use
change on soil organic carbon stocks – a meta-analysis, Global Change Biol., 17, 1658–1670, https://doi.org/10.1111/j.1365-2486.2010.02336.x,
2011.
Eichelmann, E., Mantoani, M. C., Chamberlain, S. D., Hemes, K. S., Oikawa,
P. Y., Szutu, D., Valach, A., Verfaillie, J., and Baldocchi, D. D.: A novel
approach to partitioning evapotranspiration into evaporation and
transpiration in flooded ecosystems, Global Change Biol., 28, 990–1007,
https://doi.org/10.1111/gcb.15974, 2022.
Eshonkulov, R., Poyda, A., Ingwersen, J., Wizemann, H.-D., Weber, T. K. D., Kremer, P., Högy, P., Pulatov, A., and Streck, T.: Evaluating multi-year, multi-site data on the energy balance closure of eddy-covariance flux measurements at cropland sites in southwestern Germany, Biogeosciences, 16, 521–540, https://doi.org/10.5194/bg-16-521-2019, 2019.
Fang, B., Lei, H., Zhang, Y., Quan, Q., and Yang, D.: Spatio-temporal
patterns of evapotranspiration based on upscaling eddy covariance
measurements in the dryland of the North China Plain, Agr. Forest Meteorol., 281, 107844, https://doi.org/10.1016/j.agrformet.2019.107844,
2020.
Field, A. P. and Gillett, R.: How to do a meta, British J.
Math. Stat. Psychol., 63, 665–694, 2010.
Fisher, J. B., Melton, F., Middleton, E., Hain, C., Anderson, M., Allen, R.,
McCabe, M. F., Hook, S., Baldocchi, D., Townsend, P. A., Kilic, A., Tu, K.,
Miralles, D. D., Perret, J., Lagouarde, J.-P., Waliser, D., Purdy, A. J.,
French, A., Schimel, D., Famiglietti, J. S., Stephens, G., and Wood, E. F.:
The future of evapotranspiration: Global requirements for ecosystem
functioning, carbon and climate feedbacks, agricultural management, and
water resources, Water Resour. Res., 53, 2618–2626,
https://doi.org/10.1002/2016WR020175, 2017.
Foken, T.: The energy balance closure problem: An overview, Ecol. Appl., 18, 1351–1367, 2008.
Gaston, K. J.: Global patterns in biodiversity, Nature, 405, 220–227,
https://doi.org/10.1038/35012228, 2000.
Hui, D., Wan, S., Su, B., Katul, G., Monson, R., and Luo, Y.: Gap-filling
missing data in eddy covariance measurements using multiple imputation (MI)
for annual estimations, Agr. Forest Meteorol., 121, 93–111,
https://doi.org/10.1016/S0168-1923(03)00158-8, 2004.
Jacobs, A. F. G., Heusinkveld, B. G., and Holtslag, A. A. M.: Towards
Closing the Surface Energy Budget of a Mid-latitude Grassland,
Bound.-Lay. Meteorol., 126, 125–136,
https://doi.org/10.1007/s10546-007-9209-2, 2008.
Jung, M., Reichstein, M., and Bondeau, A.: Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model, Biogeosciences, 6, 2001–2013, https://doi.org/10.5194/bg-6-2001-2009, 2009.
Jung, M., Reichstein, M., Margolis, H. A., Cescatti, A., Richardson, A. D.,
Arain, M. A., Arneth, A., Bernhofer, C., Bonal, D., Chen, J., Gianelle, D.,
Gobron, N., Kiely, G., Kutsch, W., Lasslop, G., Law, B. E., Lindroth, A.,
Merbold, L., Montagnani, L., Moors, E. J., Papale, D., Sottocornola, M.,
Vaccari, F., and Williams, C.: Global patterns of land-atmosphere fluxes of
carbon dioxide, latent heat, and sensible heat derived from eddy covariance,
satellite, and meteorological observations, J. Geophys. Res.-Biogeo., 116, G00J07, https://doi.org/10.1029/2010JG001566, 2011.
Jung, M., Koirala, S., Weber, U., Ichii, K., Gans, F., Camps-Valls, G.,
Papale, D., Schwalm, C., Tramontana, G., and Reichstein, M.: The FLUXCOM
ensemble of global land-atmosphere energy fluxes, Sci. Data, 6, 74,
https://doi.org/10.1038/s41597-019-0076-8, 2019.
Kaur, H., Pannu, H. S., and Malhi, A. K.: A Systematic Review on Imbalanced
Data Challenges in Machine Learning: Applications and Solutions, ACM Comput.
Surv., 52, 1–36, https://doi.org/10.1145/3343440, 2019.
Li, X., He, Y., Zeng, Z., Lian, X., Wang, X., Du, M., Jia, G., Li, Y., Ma,
Y., Tang, Y., Wang, W., Wu, Z., Yan, J., Yao, Y., Ciais, P., Zhang, X.,
Zhang, Y., Zhang, Y., Zhou, G., and Piao, S.: Spatiotemporal pattern of
terrestrial evapotranspiration in China during the past thirty years,
Agr. Forest Meteorol., 259, 131–140,
https://doi.org/10.1016/j.agrformet.2018.04.020, 2018.
Li, X., Kang, S., Niu, J., Huo, Z., and Liu, J.: Improving the
representation of stomatal responses to CO2 within the Penman–Monteith
model to better estimate evapotranspiration responses to climate change,
J. Hydrol., 572, 692–705,
https://doi.org/10.1016/j.jhydrol.2019.03.029, 2019.
Liu, Q., Zhang, Y., Liu, B., Amonette, J. E., Lin, Z., Liu, G., Ambus, P.,
and Xie, Z.: How does biochar influence soil N cycle?, A meta-analysis, Plant
Soil, 426, 211–225, 2018a.
Liu, Y., Xiao, J., Ju, W., Zhu, G., Wu, X., Fan, W., Li, D., and Zhou, Y.:
Satellite-derived LAI products exhibit large discrepancies and can lead to
substantial uncertainty in simulated carbon and water fluxes, Remote Sens. Environ., 206, 174–188, https://doi.org/10.1016/j.rse.2017.12.024,
2018b.
Lu, X. and Zhuang, Q.: Evaluating evapotranspiration and water-use
efficiency of terrestrial ecosystems in the conterminous United States using
MODIS and AmeriFlux data, Remote Sens. Environ., 114, 1924–1939,
https://doi.org/10.1016/j.rse.2010.04.001, 2010.
Mauder, M., Cuntz, M., Drüe, C., Graf, A., Rebmann, C., Schmid, H. P.,
Schmidt, M., and Steinbrecher, R.: A strategy for quality and uncertainty
assessment of long-term eddy-covariance measurements, Agr. Forest Meteorol., 169, 122–135,
https://doi.org/10.1016/j.agrformet.2012.09.006, 2013.
Mauder, M., Genzel, S., Fu, J., Kiese, R., Soltani, M., Steinbrecher, R.,
Zeeman, M., Banerjee, T., De Roo, F., and Kunstmann, H.: Evaluation of
energy balance closure adjustment methods by independent evapotranspiration
estimates from lysimeters and hydrological simulations, Hydrol. Proc., 32, 39–50, https://doi.org/10.1002/hyp.11397, 2018.
McColl, K. A.: Practical and Theoretical Benefits of an Alternative to the
Penman-Monteith Evapotranspiration Equation, Water Resour. Res., 56,
e2020WR027106, https://doi.org/10.1029/2020WR027106, 2020.
Minacapilli, M., Agnese, C., Blanda, F., Cammalleri, C., Ciraolo, G., D'Urso, G., Iovino, M., Pumo, D., Provenzano, G., and Rallo, G.: Estimation of actual evapotranspiration of Mediterranean perennial crops by means of remote-sensing based surface energy balance models, Hydrol. Earth Syst. Sci., 13, 1061–1074, https://doi.org/10.5194/hess-13-1061-2009, 2009.
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011.
Miralles, D. G., Teuling, A. J., van Heerwaarden, C. C., and Vilà-Guerau
de Arellano, J.: Mega-heatwave temperatures due to combined soil desiccation
and atmospheric heat accumulation, Nat. Geosci, 7, 345–349,
https://doi.org/10.1038/ngeo2141, 2014.
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., and Prisma Group: Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement, PLoS medicine, 6, e1000097, https://doi.org/10.1136/bmj.b2535, 2009.
Mu, Q., Zhao, M., and Running, S. W.: Improvements to a MODIS global
terrestrial evapotranspiration algorithm, Remote Sens. Environ.,
115, 1781–1800, https://doi.org/10.1016/j.rse.2011.02.019, 2011.
Nemani, R. R., Keeling, C. D., Hashimoto, H., Jolly, W. M., Piper, S. C.,
Tucker, C. J., Myneni, R. B., and Running, S. W.: Climate-Driven Increases
in Global Terrestrial Net Primary Production from 1982 to 1999, Science,
300, 1560–1563, https://doi.org/10.1126/science.1082750, 2003.
Pan, S., Tian, H., Dangal, S. R. S., Yang, Q., Yang, J., Lu, C., Tao, B.,
Ren, W., and Ouyang, Z.: Responses of global terrestrial evapotranspiration
to climate change and increasing atmospheric CO2 in the 21st century,
Earth's Future, 3, 15–35, https://doi.org/10.1002/2014EF000263, 2015.
Pan, S., Pan, N., Tian, H., Friedlingstein, P., Sitch, S., Shi, H., Arora, V. K., Haverd, V., Jain, A. K., Kato, E., Lienert, S., Lombardozzi, D., Nabel, J. E. M. S., Ottlé, C., Poulter, B., Zaehle, S., and Running, S. W.: Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling, Hydrol. Earth Syst. Sci., 24, 1485–1509, https://doi.org/10.5194/hess-24-1485-2020, 2020.
Papale, D., Black, T. A., Carvalhais, N., Cescatti, A., Chen, J., Jung, M.,
Kiely, G., Lasslop, G., Mahecha, M. D., Margolis, H., Merbold, L.,
Montagnani, L., Moors, E., Olesen, Jø. E., Reichstein, M., Tramontana,
G., Van Gorsel, E., Wohlfahrt, G., and Ráduly, B.: Effect of spatial
sampling from European flux towers for estimating carbon and water fluxes
with artificial neural networks, J. Geophys. Res.-Biogeo., 120, 1941–1957, https://doi.org/10.1002/2015JG002997, 2015.
Paul-Limoges, E., Wolf, S., Schneider, F. D., Longo, M., Moorcroft, P.,
Gharun, M., and Damm, A.: Partitioning evapotranspiration with concurrent
eddy covariance measurements in a mixed forest, Agr. Forest Meteorol., 280, 107786, https://doi.org/10.1016/j.agrformet.2019.107786,
2020.
Peel, M. C., Finlayson, B. L., and McMahon, T. A.: Updated world map of the Köppen-Geiger climate classification, Hydrol. Earth Syst. Sci., 11, 1633–1644, https://doi.org/10.5194/hess-11-1633-2007, 2007.
Rigden, A. J. and Salvucci, G. D.: Evapotranspiration based on equilibrated
relative humidity (ETRHEQ): Evaluation over the continental U.S., Water
Resour. Res., 51, 2951–2973, https://doi.org/10.1002/2014WR016072,
2015.
Sahoo, A. K., Pan, M., Troy, T. J., Vinukollu, R. K., Sheffield, J., and
Wood, E. F.: Reconciling the global terrestrial water budget using satellite
remote sensing, Remote Sens. Environ., 115, 1850–1865,
https://doi.org/10.1016/j.rse.2011.03.009, 2011.
Sándor, R., Barcza, Z., Hidy, D., Lellei-Kovács, E., Ma, S., and
Bellocchi, G.: Modelling of grassland fluxes in Europe: Evaluation of two
biogeochemical models, Agr. Ecosyst. Environ., 215, 1–19,
https://doi.org/10.1016/j.agee.2015.09.001, 2016.
Shi, H., Hellwich, O., Luo, G., Chen, C., He, H., Ochege, F. U., Van de Voorde, T., Kurban, A., and de Maeyer, P.: A global meta-analysis of soil salinity prediction integrating satellite remote sensing, soil sampling, and machine learning, IEEE T. Geosci. Remote, 60, 1–15, https://doi.org/10.1109/TGRS.2021.3109819, 2021.
Shi, H., Luo, G., Hellwich, O., Xie, M., Zhang, C., Zhang, Y., Wang, Y., Yuan, X., Ma, X., Zhang, W., Kurban, A., De Maeyer, P., and Van de Voorde, T.: Variability and uncertainty in flux-site-scale net ecosystem exchange simulations based on machine learning and remote sensing: a systematic evaluation, Biogeosciences, 19, 3739–3756, https://doi.org/10.5194/bg-19-3739-2022, 2022.
Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps-Valls, G., Ráduly, B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., Merbold, L., Serrano-Ortiz, P., Sickert, S., Wolf, S., and Papale, D.: Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms, Biogeosciences, 13, 4291–4313, https://doi.org/10.5194/bg-13-4291-2016, 2016.
Van Hulse, J., Khoshgoftaar, T. M., and Napolitano, A.: Experimental
perspectives on learning from imbalanced data, in: Proceedings of the 24th
international conference on Machine learning, New York, NY, USA, 935–942,
https://doi.org/10.1145/1273496.1273614, 2007.
Van Wijk, M. T. and Bouten, W.: Water and carbon fluxes above European
coniferous forests modelled with artificial neural networks, Ecol. Modell., 120, 181–197, https://doi.org/10.1016/S0304-3800(99)00101-5, 1999.
Virkkala, A.-M., Aalto, J., Rogers, B. M., Tagesson, T., Treat, C. C.,
Natali, S. M., Watts, J. D., Potter, S., Lehtonen, A., Mauritz, M., Schuur,
E. A. G., Kochendorfer, J., Zona, D., Oechel, W., Kobayashi, H., Humphreys,
E., Goeckede, M., Iwata, H., Lafleur, P. M., Euskirchen, E. S., Bokhorst,
S., Marushchak, M., Martikainen, P. J., Elberling, B., Voigt, C., Biasi, C.,
Sonnentag, O., Parmentier, F.-J. W., Ueyama, M., Celis, G., St.Louis, V. L.,
Emmerton, C. A., Peichl, M., Chi, J., Järveoja, J., Nilsson, M. B.,
Oberbauer, S. F., Torn, M. S., Park, S.-J., Dolman, H., Mammarella, I.,
Chae, N., Poyatos, R., López-Blanco, E., Christensen, T. R., Kwon, M.
J., Sachs, T., Holl, D., and Luoto, M.: Statistical upscaling of ecosystem
CO2 fluxes across the terrestrial tundra and boreal domain: Regional
patterns and uncertainties, Global Change Biol., 27, 4040–4059,
https://doi.org/10.1111/gcb.15659, 2021.
Wagle, P., Bhattarai, N., Gowda, P. H., and Kakani, V. G.: Performance of
five surface energy balance models for estimating daily evapotranspiration
in high biomass sorghum, ISPRS J. Photogr. Remote Sens.,
128, 192–203, https://doi.org/10.1016/j.isprsjprs.2017.03.022, 2017.
Xie, M., Luo, G., Hellwich, O., Frankl, A., Zhang, W., Chen, C., Zhang, C.,
and De Maeyer, P.: Simulation of site-scale water fluxes in desert and
natural oasis ecosystems of the arid region in Northwest China, Hydrol. Proc., 35, e14444, https://doi.org/10.1002/hyp.14444, 2021.
Xu, T., Guo, Z., Liu, S., He, X., Meng, Y., Xu, Z., Xia, Y., Xiao, J.,
Zhang, Y., Ma, Y., and Song, L.: Evaluating Different Machine Learning
Methods for Upscaling Evapotranspiration from Flux Towers to the Regional
Scale, J. Geophys. Res.-Atmos., 123, 8674–8690,
https://doi.org/10.1029/2018JD028447, 2018.
Yang, F., White, M. A., Michaelis, A. R., Ichii, K., Hashimoto, H., Votava,
P., Zhu, A.-X., and Nemani, R. R.: Prediction of Continental-Scale
Evapotranspiration by Combining MODIS and AmeriFlux Data Through Support
Vector Machine, IEEE T. Geosci. Remote Sens., 44,
3452–3461, https://doi.org/10.1109/TGRS.2006.876297, 2006.
Zeng, J., Matsunaga, T., Tan, Z.-H., Saigusa, N., Shirai, T., Tang, Y., Peng, S., and Fukuda, Y.: Global terrestrial carbon fluxes of 1999–2019 estimated by upscaling eddy covariance data with a random forest, Sci. Data, 7, 313, https://doi.org/10.1038/s41597-020-00653-5, 2020.
Zeng, Y., Hao, D., Huete, A., Dechant, B., Berry, J., Chen, J. M., Joiner,
J., Frankenberg, C., Bond-Lamberty, B., Ryu, Y., Xiao, J., Asrar, G. R., and
Chen, M.: Optical vegetation indices for monitoring terrestrial ecosystems
globally, Nat. Rev. Earth Environ., 3, 477–493
https://doi.org/10.1038/s43017-022-00298-5, 2022.
Zhang, C., Luo, G., Hellwich, O., Chen, C., Zhang, W., Xie, M., He, H., Shi,
H., and Wang, Y.: A framework for estimating actual evapotranspiration at
weather stations without flux observations by combining data from MODIS and
flux towers through a machine learning approach, J. Hydrol., 603,
127047, https://doi.org/10.1016/j.jhydrol.2021.127047, 2021.
Zhang, K., Kimball, J. S., Nemani, R. R., and Running, S. W.: A continuous
satellite-derived global record of land surface evapotranspiration from 1983
to 2006, Water Resour. Res., 46, W09522, https://doi.org/10.1029/2009WR008800,
2010.
Zhao, W. L., Gentine, P., Reichstein, M., Zhang, Y., Zhou, S., Wen, Y., Lin,
C., Li, X., and Qiu, G. Y.: Physics-Constrained Machine Learning of
Evapotranspiration, Geophys. Res. Lett., 46, 14496–14507,
https://doi.org/10.1029/2019GL085291, 2019.
Short summary
There have been many machine learning simulation studies based on eddy-covariance observations for water flux and evapotranspiration. We performed a meta-analysis of such studies to clarify the impact of different algorithms and predictors, etc., on the reported prediction accuracy. It can, to some extent, guide future global water flux modeling studies and help us better understand the terrestrial ecosystem water cycle.
There have been many machine learning simulation studies based on eddy-covariance observations...
