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
| 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 De 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 Van de 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, Xiufeng He, Alishir Kurban, Philippe De Maeyer, and Tim Van de Voorde
Hydrol. Earth Syst. Sci., 27, 4551–4562, https://doi.org/10.5194/hess-27-4551-2023, https://doi.org/10.5194/hess-27-4551-2023, 2023
Short summary
Short summary
Using evidence from meteorological stations, this study assessed the climatic, hydrological, and ecological aridity changes in global drylands and their associated mechanisms. A decoupling between atmospheric, hydrological, and vegetation aridity was found. This highlights the added value of using station-scale data to assess dryland change as a complement to results based on coarse-resolution reanalysis data and land surface models.
Haiyang Shi, Geping Luo, Olaf Hellwich, Alishir Kurban, Philippe De Maeyer, and Tim Van de Voorde
Biogeosciences, 20, 2727–2741, https://doi.org/10.5194/bg-20-2727-2023, https://doi.org/10.5194/bg-20-2727-2023, 2023
Short summary
Short summary
In studies on the relationship between ecosystem functions and climate and plant traits, previously used data-driven methods such as multiple regression and random forest may be inadequate for representing causality due to limitations such as covariance between variables. Based on FLUXNET site data, we used a causal graphical model to revisit the control of climate and vegetation traits over ecosystem functions.
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.
Haiyang Shi, Geping Luo, Olaf Hellwich, Xiufeng He, Alishir Kurban, Philippe De Maeyer, and Tim Van de Voorde
Hydrol. Earth Syst. Sci., 27, 4551–4562, https://doi.org/10.5194/hess-27-4551-2023, https://doi.org/10.5194/hess-27-4551-2023, 2023
Short summary
Short summary
Using evidence from meteorological stations, this study assessed the climatic, hydrological, and ecological aridity changes in global drylands and their associated mechanisms. A decoupling between atmospheric, hydrological, and vegetation aridity was found. This highlights the added value of using station-scale data to assess dryland change as a complement to results based on coarse-resolution reanalysis data and land surface models.
Philippe De Maeyer
Abstr. Int. Cartogr. Assoc., 6, 49, https://doi.org/10.5194/ica-abs-6-49-2023, https://doi.org/10.5194/ica-abs-6-49-2023, 2023
Haiyang Shi, Geping Luo, Olaf Hellwich, Alishir Kurban, Philippe De Maeyer, and Tim Van de Voorde
Biogeosciences, 20, 2727–2741, https://doi.org/10.5194/bg-20-2727-2023, https://doi.org/10.5194/bg-20-2727-2023, 2023
Short summary
Short summary
In studies on the relationship between ecosystem functions and climate and plant traits, previously used data-driven methods such as multiple regression and random forest may be inadequate for representing causality due to limitations such as covariance between variables. Based on FLUXNET site data, we used a causal graphical model to revisit the control of climate and vegetation traits over ecosystem functions.
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, 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
Do land models miss key soil hydrological processes controlling soil moisture memory?
Observation-driven model for calculating water-harvesting potential from advective fog in (semi-)arid coastal regions
Review of gridded climate products and their use in hydrological analyses reveals overlaps, gaps, and the need for a more objective approach to selecting model forcing datasets
Downscaling the probability of heavy rainfall over the Nordic countries
Modelling convective cell life cycles with a copula-based approach
Downscaling precipitation over High-mountain Asia using multi-fidelity Gaussian processes: improved estimates from ERA5
Mapping soil moisture across the UK: assimilating cosmic-ray neutron sensors, remotely sensed indices, rainfall radar and catchment water balance data in a Bayesian hierarchical model
Assessing rainfall radar errors with an inverse stochastic modelling framework
Multi-objective calibration and evaluation of the ORCHIDEE land surface model over France at high resolution
Spatiotemporal responses of runoff to climate change in the southern Tibetan Plateau
FROSTBYTE: a reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America
On the combined use of rain gauges and GPM IMERG satellite rainfall products for hydrological modelling: impact assessment of the cellular-automata-based methodology in the Tanaro River basin in Italy
An increase in the spatial extent of European floods over the last 70 years
140-year daily ensemble streamflow reconstructions over 661 catchments in France
The agricultural expansion in South America's Dry Chaco: regional hydroclimate effects
Machine-learning-constrained projection of bivariate hydrological drought magnitudes and socioeconomic risks over China
Improving runoff simulation in the Western United States with Noah-MP and variable infiltration capacity
Distribution, trends and drivers of flash droughts in the United Kingdom
Are dependencies of extreme rainfall on humidity more reliable in convection-permitting climate models?
Spatial variability in the seasonal precipitation lapse rates in complex topographical regions – application in France
Assessing downscaling methods to simulate hydrologically relevant weather scenarios from a global atmospheric reanalysis: case study of the upper Rhône River (1902–2009)
Global total precipitable water variations and trends over the period 1958–2021
Assessing decadal- to centennial-scale nonstationary variability in meteorological drought trends
Identification of compound drought and heatwave events on a daily scale and across four seasons
Leveraging a Disdrometer Network to Develop a Probabilistic Precipitation Phase Model in Eastern Canada
Assessment of seasonal soil moisture forecasts over Central Mediterranean toward groundwater management
Potential for historically unprecedented Australian droughts from natural variability and climate change
Flood risk assessment for Indian sub-continental river basins
Key ingredients in regional climate modelling for improving the representation of typhoon tracks and intensities
Divergent future drought projections in UK river flows and groundwater levels
Predicting extreme sub-hourly precipitation intensification based on temperature shifts
High Resolution Land Surface Modelling over Africa: the role of uncertain soil properties in combination with temporal model resolution
Hydroclimatic processes as the primary drivers of the Early Khvalynian transgression of the Caspian Sea: new developments
Accounting for hydroclimatic properties in flood frequency analysis procedures
Understanding the influence of “hot” models in climate impact studies: a hydrological perspective
A semi-parametric hourly space–time weather generator
A principal-component-based strategy for regionalisation of precipitation intensity–duration–frequency (IDF) statistics
Accounting for precipitation asymmetry in a multiplicative random cascade disaggregation model
Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach
A genetic particle filter scheme for univariate snow cover assimilation into Noah-MP model across snow climates
Investigating the response of land–atmosphere interactions and feedbacks to spatial representation of irrigation in a coupled modeling framework
Validation of precipitation reanalysis products for rainfall-runoff modelling in Slovenia
Statistical post-processing of precipitation forecasts using circulation classifications and spatiotemporal deep neural networks
Sensitivity of the pseudo-global warming method under flood conditions: a case study from the northeastern US
Hybrid forecasting: blending climate predictions with AI models
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
Local moisture recycling across the globe
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
Mohammad A. Farmani, Ali Behrangi, Aniket Gupta, Ahmad Tavakoly, Matthew Geheran, and Guo-Yue Niu
Hydrol. Earth Syst. Sci., 29, 547–566, https://doi.org/10.5194/hess-29-547-2025, https://doi.org/10.5194/hess-29-547-2025, 2025
Short summary
Short summary
Soil moisture memory (SMM) shows how long soil stays moist after rain, impacting climate and ecosystems. Current models often overestimate SMM, causing inaccuracies in evaporation predictions. We enhanced a land model, Noah-MP, to include better water flow and ponding processes, and we tested it against satellite and field data. This improved model reduced overestimations and enhanced short-term predictions, helping create more accurate climate and weather forecasts.
Felipe Lobos-Roco, Jordi Vilà-Guerau de Arellano, and Camilo del Río
Hydrol. Earth Syst. Sci., 29, 109–125, https://doi.org/10.5194/hess-29-109-2025, https://doi.org/10.5194/hess-29-109-2025, 2025
Short summary
Short summary
Water resources are fundamental for the social, economic, and natural development of (semi-)arid regions. Precipitation decreases due to climate change obligate us to find new water resources. Fog harvesting (FH) emerges as a complementary resource in regions where it is abundant but untapped. This research proposes a model to estimate FH potential in coastal (semi-)arid regions. This model could have broader applicability worldwide in regions where FH could be a viable water source.
Kyle R. Mankin, Sushant Mehan, Timothy R. Green, and David M. Barnard
Hydrol. Earth Syst. Sci., 29, 85–108, https://doi.org/10.5194/hess-29-85-2025, https://doi.org/10.5194/hess-29-85-2025, 2025
Short summary
Short summary
We assess 63 gridded ground (G), satellite (S), and reanalysis (R) climate datasets. Higher-density station data and less-hilly terrain improved climate data. In mountainous and humid regions, dataset types performed similarly; however, R outperformed G when underlying data had low station density. G outperformed S or R datasets, although better streamflow modeling did not always follow. Hydrologic analyses need datasets that better represent climate variable dependencies and complex topography.
Rasmus E. Benestad, Kajsa M. Parding, and Andreas Dobler
Hydrol. Earth Syst. Sci., 29, 45–65, https://doi.org/10.5194/hess-29-45-2025, https://doi.org/10.5194/hess-29-45-2025, 2025
Short summary
Short summary
We present a new method to calculate the chance of heavy downpour and the maximum rainfall expected over a 25-year period. It is designed to analyse global climate models' reproduction of past and future climates. For the Nordic countries, it projects a wetter climate in the future with increased intensity but not necessarily more wet days. The analysis also shows that rainfall intensity is sensitive to future greenhouse gas emissions, while the number of wet days appears to be less affected.
Chien-Yu Tseng, Li-Pen Wang, and Christian Onof
Hydrol. Earth Syst. Sci., 29, 1–25, https://doi.org/10.5194/hess-29-1-2025, https://doi.org/10.5194/hess-29-1-2025, 2025
Short summary
Short summary
This study presents a new algorithm to model convective storms. We used advanced tracking methods to analyse 165 storm events in Birmingham (UK) and reconstruct storm cell life cycles. We found that cell properties like intensity and size are interrelated and vary over time. The new algorithm, based on vine copulas, accurately simulates these properties and their evolution. It also integrates an exponential shape function for realistic rainfall patterns, enhancing its hydrological applicability.
Kenza Tazi, Andrew Orr, Javier Hernandez-González, Scott Hosking, and Richard E. Turner
Hydrol. Earth Syst. Sci., 28, 4903–4925, https://doi.org/10.5194/hess-28-4903-2024, https://doi.org/10.5194/hess-28-4903-2024, 2024
Short summary
Short summary
This work aims to improve the understanding of precipitation patterns in High-mountain Asia, a crucial water source for around 1.9 billion people. Through a novel machine learning method, we generate high-resolution precipitation predictions, including the likelihoods of floods and droughts. Compared to state-of-the-art methods, our method is simpler to implement and more suitable for small datasets. The method also shows accuracy comparable to or better than existing benchmark datasets.
Peter E. Levy and the COSMOS-UK team
Hydrol. Earth Syst. Sci., 28, 4819–4836, https://doi.org/10.5194/hess-28-4819-2024, https://doi.org/10.5194/hess-28-4819-2024, 2024
Short summary
Short summary
Having accurate up-to-date maps of soil moisture is important for many purposes. However, current modelled and remotely sensed maps are rather coarse and not very accurate. Here, we demonstrate a simple but accurate approach that is closely linked to direct measurements of soil moisture at a network sites across the UK, to the water balance (precipitation minus drainage and evaporation) measured at a large number of catchments (1212) and to remotely sensed satellite estimates.
Amy C. Green, Chris Kilsby, and András Bárdossy
Hydrol. Earth Syst. Sci., 28, 4539–4558, https://doi.org/10.5194/hess-28-4539-2024, https://doi.org/10.5194/hess-28-4539-2024, 2024
Short summary
Short summary
Weather radar is a crucial tool in rainfall estimation, but radar rainfall estimates are subject to many error sources, with the true rainfall field unknown. A flexible model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard processing methods. This flexible and efficient model performs well in generating realistic weather radar images visually for a large range of event types.
Peng Huang, Agnès Ducharne, Lucia Rinchiuso, Jan Polcher, Laure Baratgin, Vladislav Bastrikov, and Eric Sauquet
Hydrol. Earth Syst. Sci., 28, 4455–4476, https://doi.org/10.5194/hess-28-4455-2024, https://doi.org/10.5194/hess-28-4455-2024, 2024
Short summary
Short summary
We conducted a high-resolution hydrological simulation from 1959 to 2020 across France. We used a simple trial-and-error calibration to reduce the biases of the simulated water budget compared to observations. The selected simulation satisfactorily reproduces water fluxes, including their spatial contrasts and temporal trends. This work offers a reliable historical overview of water resources and a robust configuration for climate change impact analysis at the nationwide scale of France.
He Sun, Tandong Yao, Fengge Su, Wei Yang, and Deliang Chen
Hydrol. Earth Syst. Sci., 28, 4361–4381, https://doi.org/10.5194/hess-28-4361-2024, https://doi.org/10.5194/hess-28-4361-2024, 2024
Short summary
Short summary
Our findings show that runoff in the Yarlung Zangbo (YZ) basin is primarily driven by rainfall, with the largest glacier runoff contribution in the downstream sub-basin. Annual runoff increased in the upper stream but decreased downstream due to varying precipitation patterns. It is expected to rise throughout the 21st century, mainly driven by increased rainfall.
Louise Arnal, Martyn P. Clark, Alain Pietroniro, Vincent Vionnet, David R. Casson, Paul H. Whitfield, Vincent Fortin, Andrew W. Wood, Wouter J. M. Knoben, Brandi W. Newton, and Colleen Walford
Hydrol. Earth Syst. Sci., 28, 4127–4155, https://doi.org/10.5194/hess-28-4127-2024, https://doi.org/10.5194/hess-28-4127-2024, 2024
Short summary
Short summary
Forecasting river flow months in advance is crucial for water sectors and society. In North America, snowmelt is a key driver of flow. This study presents a statistical workflow using snow data to forecast flow months ahead in North American snow-fed rivers. Variations in the river flow predictability across the continent are evident, raising concerns about future predictability in a changing (snow) climate. The reproducible workflow hosted on GitHub supports collaborative and open science.
Annalina Lombardi, Barbara Tomassetti, Valentina Colaiuda, Ludovico Di Antonio, Paolo Tuccella, Mario Montopoli, Giovanni Ravazzani, Frank Silvio Marzano, Raffaele Lidori, and Giulia Panegrossi
Hydrol. Earth Syst. Sci., 28, 3777–3797, https://doi.org/10.5194/hess-28-3777-2024, https://doi.org/10.5194/hess-28-3777-2024, 2024
Short summary
Short summary
The accurate estimation of precipitation and its spatial variability within a watershed is crucial for reliable discharge simulations. The study is the first detailed analysis of the potential usage of the cellular automata technique to merge different rainfall data inputs to hydrological models. This work shows an improvement in the performance of hydrological simulations when satellite and rain gauge data are merged.
Beijing Fang, Emanuele Bevacqua, Oldrich Rakovec, and Jakob Zscheischler
Hydrol. Earth Syst. Sci., 28, 3755–3775, https://doi.org/10.5194/hess-28-3755-2024, https://doi.org/10.5194/hess-28-3755-2024, 2024
Short summary
Short summary
We use grid-based runoff from a hydrological model to identify large spatiotemporally connected flood events in Europe, assess extent trends over the last 70 years, and attribute the trends to different drivers. Our findings reveal a general increase in flood extent, with regional variations driven by diverse factors. The study not only enables a thorough examination of flood events across multiple basins but also highlights the potential challenges arising from changing flood extents.
Alexandre Devers, Jean-Philippe Vidal, Claire Lauvernet, Olivier Vannier, and Laurie Caillouet
Hydrol. Earth Syst. Sci., 28, 3457–3474, https://doi.org/10.5194/hess-28-3457-2024, https://doi.org/10.5194/hess-28-3457-2024, 2024
Short summary
Short summary
Daily streamflow series for 661 near-natural French catchments are reconstructed over 1871–2012 using two ensemble datasets: HydRE and HydREM. They include uncertainties coming from climate forcings, streamflow measurement, and hydrological model error (for HydrREM). Comparisons with other hydrological reconstructions and independent/dependent observations show the added value of the two reconstructions in terms of quality, uncertainty estimation, and representation of extremes.
María Agostina Bracalenti, Omar V. Müller, Miguel A. Lovino, and Ernesto Hugo Berbery
Hydrol. Earth Syst. Sci., 28, 3281–3303, https://doi.org/10.5194/hess-28-3281-2024, https://doi.org/10.5194/hess-28-3281-2024, 2024
Short summary
Short summary
The Gran Chaco is a large, dry forest in South America that has been heavily deforested, particularly in the dry Chaco subregion. This deforestation, mainly driven by the expansion of the agricultural frontier, has changed the land's characteristics, affecting the local and regional climate. The study reveals that deforestation has resulted in reduced precipitation, soil moisture, and runoff, and if intensive agriculture continues, it could make summers in this arid region even drier and hotter.
Rutong Liu, Jiabo Yin, Louise Slater, Shengyu Kang, Yuanhang Yang, Pan Liu, Jiali Guo, Xihui Gu, Xiang Zhang, and Aliaksandr Volchak
Hydrol. Earth Syst. Sci., 28, 3305–3326, https://doi.org/10.5194/hess-28-3305-2024, https://doi.org/10.5194/hess-28-3305-2024, 2024
Short summary
Short summary
Climate change accelerates the water cycle and alters the spatiotemporal distribution of hydrological variables, thus complicating the projection of future streamflow and hydrological droughts. We develop a cascade modeling chain to project future bivariate hydrological drought characteristics over China, using five bias-corrected global climate model outputs under three shared socioeconomic pathways, five hydrological models, and a deep-learning model.
Lu Su, Dennis P. Lettenmaier, Ming Pan, and Benjamin Bass
Hydrol. Earth Syst. Sci., 28, 3079–3097, https://doi.org/10.5194/hess-28-3079-2024, https://doi.org/10.5194/hess-28-3079-2024, 2024
Short summary
Short summary
We fine-tuned the variable infiltration capacity (VIC) and Noah-MP models across 263 river basins in the Western US. We developed transfer relationships to similar basins and extended the fine-tuned parameters to ungauged basins. Both models performed best in humid areas, and the skills improved post-calibration. VIC outperforms Noah-MP in all but interior dry basins following regionalization. VIC simulates annual mean streamflow and high flow well, while Noah-MP performs better for low flows.
Iván Noguera, Jamie Hannaford, and Maliko Tanguy
EGUsphere, https://doi.org/10.5194/egusphere-2024-1969, https://doi.org/10.5194/egusphere-2024-1969, 2024
Short summary
Short summary
In this study, we present a detailed characterisation of flash drought in United Kingdom over the period 1969–2021.The spatiotemporal distribution of flash droughts is highly variable, with important regional and seasonal contrasts. In the UK, flash drought occurrence responds primarily to precipitation variability, although the atmospheric evaporative demand (AED) is important as a secondary driver. The atmospheric and oceanic conditions during flash droughts development were also analyzed.
Geert Lenderink, Nikolina Ban, Erwan Brisson, Ségolène Berthou, Virginia Edith Cortés-Hernández, Elizabeth Kendon, Hayley Fowler, and Hylke de Vries
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-132, https://doi.org/10.5194/hess-2024-132, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Future extreme rainfall events are influenced by changes in both absolute and relative humidity. The impact of increasing absolute humidity is reasonably well understood, but the role of relative humidity decreases over land remains largely unknown. Using hourly observations from France and The Netherlands, we find that lower relative humidity generally leads to more intense rainfall extremes. This relation is only captured well in recently developed convection-permitting climate models.
Valentin Dura, Guillaume Evin, Anne-Catherine Favre, and David Penot
Hydrol. Earth Syst. Sci., 28, 2579–2601, https://doi.org/10.5194/hess-28-2579-2024, https://doi.org/10.5194/hess-28-2579-2024, 2024
Short summary
Short summary
The increase in precipitation as a function of elevation is poorly understood in areas with complex topography. In this article, the reproduction of these orographic gradients is assessed with several precipitation products. The best product is a simulation from a convection-permitting regional climate model. The corresponding seasonal gradients vary significantly in space, with higher values for the first topographical barriers exposed to the dominant air mass circulations.
Caroline Legrand, Benoît Hingray, Bruno Wilhelm, and Martin Ménégoz
Hydrol. Earth Syst. Sci., 28, 2139–2166, https://doi.org/10.5194/hess-28-2139-2024, https://doi.org/10.5194/hess-28-2139-2024, 2024
Short summary
Short summary
Climate change is expected to increase flood hazard worldwide. The evolution is typically estimated from multi-model chains, where regional hydrological scenarios are simulated from weather scenarios derived from coarse-resolution atmospheric outputs of climate models. We show that two such chains are able to reproduce, from an atmospheric reanalysis, the 1902–2009 discharge variations and floods of the upper Rhône alpine river, provided that the weather scenarios are bias-corrected.
Nenghan Wan, Xiaomao Lin, Roger A. Pielke Sr., Xubin Zeng, and Amanda M. Nelson
Hydrol. Earth Syst. Sci., 28, 2123–2137, https://doi.org/10.5194/hess-28-2123-2024, https://doi.org/10.5194/hess-28-2123-2024, 2024
Short summary
Short summary
Global warming occurs at a rate of 0.21 K per decade, resulting in about 9.5 % K−1 of water vapor response to temperature from 1993 to 2021. Terrestrial areas experienced greater warming than the ocean, with a ratio of 2 : 1. The total precipitable water change in response to surface temperature changes showed a variation around 6 % K−1–8 % K−1 in the 15–55° N latitude band. Further studies are needed to identify the mechanisms leading to different water vapor responses.
Kyungmin Sung, Max C. A. Torbenson, and James H. Stagge
Hydrol. Earth Syst. Sci., 28, 2047–2063, https://doi.org/10.5194/hess-28-2047-2024, https://doi.org/10.5194/hess-28-2047-2024, 2024
Short summary
Short summary
This study examines centuries of nonstationary trends in meteorological drought and pluvial climatology. A novel approach merges tree-ring proxy data (North American Seasonal Precipitation Atlas – NASPA) with instrumental precipitation datasets by temporally downscaling proxy data, correcting biases, and analyzing shared trends in normal and extreme precipitation anomalies. We identify regions experiencing recent unprecedented shifts towards drier or wetter conditions and shifts in seasonality.
Baoying Shan, Niko E. C. Verhoest, and Bernard De Baets
Hydrol. Earth Syst. Sci., 28, 2065–2080, https://doi.org/10.5194/hess-28-2065-2024, https://doi.org/10.5194/hess-28-2065-2024, 2024
Short summary
Short summary
This study developed a convenient and new method to identify the occurrence of droughts, heatwaves, and co-occurring droughts and heatwaves (CDHW) across four seasons. Using this method, we could establish the start and/or end dates of drought (or heatwave) events. We found an increase in the frequency of heatwaves and CDHW events in Belgium caused by climate change. We also found that different months have different chances of CDHW events.
Alexis Bédard-Therrien, François Anctil, Julie M. Thériault, Olivier Chalifour, Fanny Payette, Alexandre Vidal, and Daniel F. Nadeau
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-78, https://doi.org/10.5194/hess-2024-78, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Observations from a study site network in eastern Canada showed a temperature interval the overlapping probabilities for rain, snow or a mix of both. Models using random forest algorithms were developed to classify the precipitation phase using meteorological data to evaluate operational applications. They showed significantly improved phase classification compared to benchmarks, but misclassification led to costlier errors. However, accurate prediction of mixed phase remains a challenge.
Lorenzo Silvestri, Miriam Saraceni, Giulia Passadore, and Paolina Bongioannini Cerlini
EGUsphere, https://doi.org/10.5194/egusphere-2024-889, https://doi.org/10.5194/egusphere-2024-889, 2024
Short summary
Short summary
This work demonstrates that seasonal forecasts of soil moisture are a valuable resource for groundwater management in certain areas of the Central Mediterranean. In particular, they show significant correlation coefficients and forecast skill for the deepest soil moisture at 289 cm depth. Wet and dry events can be predicted 6 months in advance and, in general, dry events are better captured than wet events.
Georgina M. Falster, Nicky M. Wright, Nerilie J. Abram, Anna M. Ukkola, and Benjamin J. Henley
Hydrol. Earth Syst. Sci., 28, 1383–1401, https://doi.org/10.5194/hess-28-1383-2024, https://doi.org/10.5194/hess-28-1383-2024, 2024
Short summary
Short summary
Multi-year droughts have severe environmental and economic impacts, but the instrumental record is too short to characterise multi-year drought variability. We assessed the nature of Australian multi-year droughts using simulations of the past millennium from 11 climate models. We show that multi-decadal
megadroughtsare a natural feature of the Australian hydroclimate. Human-caused climate change is also driving a tendency towards longer droughts in eastern and southwestern Australia.
Urmin Vegad, Yadu Pokhrel, and Vimal Mishra
Hydrol. Earth Syst. Sci., 28, 1107–1126, https://doi.org/10.5194/hess-28-1107-2024, https://doi.org/10.5194/hess-28-1107-2024, 2024
Short summary
Short summary
A large population is affected by floods, which leave their footprints through human mortality, migration, and damage to agriculture and infrastructure, during almost every summer monsoon season in India. Despite the massive damage of floods, sub-basin level flood risk assessment is still in its infancy and needs to be improved. Using hydrological and hydrodynamic models, we reconstructed sub-basin level observed floods for the 1901–2020 period.
Qi Sun, Patrick Olschewski, Jianhui Wei, Zhan Tian, Laixiang Sun, Harald Kunstmann, and Patrick Laux
Hydrol. Earth Syst. Sci., 28, 761–780, https://doi.org/10.5194/hess-28-761-2024, https://doi.org/10.5194/hess-28-761-2024, 2024
Short summary
Short summary
Tropical cyclones (TCs) often cause high economic loss due to heavy winds and rainfall, particularly in densely populated regions such as the Pearl River Delta (China). This study provides a reference to set up regional climate models for TC simulations. They contribute to a better TC process understanding and assess the potential changes and risks of TCs in the future. This lays the foundation for hydrodynamical modelling, from which the cities' disaster management and defence could benefit.
Simon Parry, Jonathan D. Mackay, Thomas Chitson, Jamie Hannaford, Eugene Magee, Maliko Tanguy, Victoria A. Bell, Katie Facer-Childs, Alison Kay, Rosanna Lane, Robert J. Moore, Stephen Turner, and John Wallbank
Hydrol. Earth Syst. Sci., 28, 417–440, https://doi.org/10.5194/hess-28-417-2024, https://doi.org/10.5194/hess-28-417-2024, 2024
Short summary
Short summary
We studied drought in a dataset of possible future river flows and groundwater levels in the UK and found different outcomes for these two sources of water. Throughout the UK, river flows are likely to be lower in future, with droughts more prolonged and severe. However, whilst these changes are also found in some boreholes, in others, higher levels and less severe drought are indicated for the future. This has implications for the future balance between surface water and groundwater below.
Francesco Marra, Marika Koukoula, Antonio Canale, and Nadav Peleg
Hydrol. Earth Syst. Sci., 28, 375–389, https://doi.org/10.5194/hess-28-375-2024, https://doi.org/10.5194/hess-28-375-2024, 2024
Short summary
Short summary
We present a new physical-based method for estimating extreme sub-hourly precipitation return levels (i.e., intensity–duration–frequency, IDF, curves), which are critical for the estimation of future floods. The proposed model, named TENAX, incorporates temperature as a covariate in a physically consistent manner. It has only a few parameters and can be easily set for any climate station given sub-hourly precipitation and temperature data are available.
Bamidele Joseph Oloruntoba, Stefan Kollet, Carsten Montzka, Harry Vereecken, and Harrie-Jan Hendricks Franssen
EGUsphere, https://doi.org/10.5194/egusphere-2023-3132, https://doi.org/10.5194/egusphere-2023-3132, 2024
Short summary
Short summary
This study uses simulations to understand how the soil information across Africa affects the water balance, using 4 soil databases and 3 different rainfall datasets. Results show that the soil information impacts water balance estimates, especially with a higher rate of rainfall.
Alexander Gelfan, Andrey Panin, Andrey Kalugin, Polina Morozova, Vladimir Semenov, Alexey Sidorchuk, Vadim Ukraintsev, and Konstantin Ushakov
Hydrol. Earth Syst. Sci., 28, 241–259, https://doi.org/10.5194/hess-28-241-2024, https://doi.org/10.5194/hess-28-241-2024, 2024
Short summary
Short summary
Paleogeographical data show that 17–13 ka BP, the Caspian Sea level was 80 m above the current level. There are large disagreements on the genesis of this “Great” Khvalynian transgression of the sea, and we tried to shed light on this issue. Using climate and hydrological models as well as the paleo-reconstructions, we proved that the transgression could be initiated solely by hydroclimatic factors within the deglaciation period in the absence of the glacial meltwater effect.
Joeri B. Reinders and Samuel E. Munoz
Hydrol. Earth Syst. Sci., 28, 217–227, https://doi.org/10.5194/hess-28-217-2024, https://doi.org/10.5194/hess-28-217-2024, 2024
Short summary
Short summary
Flooding presents a major hazard for people and infrastructure along waterways; however, it is challenging to study the likelihood of a flood magnitude occurring regionally due to a lack of long discharge records. We show that hydroclimatic variables like Köppen climate regions and precipitation intensity explain part of the variance in flood frequency distributions and thus reduce the uncertainty of flood probability estimates. This gives water managers a tool to locally improve flood analysis.
Mehrad Rahimpour Asenjan, Francois Brissette, Jean-Luc Martel, and Richard Arsenault
Hydrol. Earth Syst. Sci., 27, 4355–4367, https://doi.org/10.5194/hess-27-4355-2023, https://doi.org/10.5194/hess-27-4355-2023, 2023
Short summary
Short summary
Climate models are central to climate change impact studies. Some models project a future deemed too hot by many. We looked at how including hot models may skew the result of impact studies. Applied to hydrology, this study shows that hot models do not systematically produce hydrological outliers.
Ross Pidoto and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 3957–3975, https://doi.org/10.5194/hess-27-3957-2023, https://doi.org/10.5194/hess-27-3957-2023, 2023
Short summary
Short summary
Long continuous time series of meteorological variables (i.e. rainfall, temperature) are required for the modelling of floods. Observed time series are generally too short or not available. Weather generators are models that reproduce observed weather time series. This study extends an existing station-based rainfall model into space by enforcing observed spatial rainfall characteristics. To model other variables (i.e. temperature) the model is then coupled to a simple resampling approach.
Kajsa Maria Parding, Rasmus Emil Benestad, Anita Verpe Dyrrdal, and Julia Lutz
Hydrol. Earth Syst. Sci., 27, 3719–3732, https://doi.org/10.5194/hess-27-3719-2023, https://doi.org/10.5194/hess-27-3719-2023, 2023
Short summary
Short summary
Intensity–duration–frequency (IDF) curves describe the likelihood of extreme rainfall and are used in hydrology and engineering, for example, for flood forecasting and water management. We develop a model to estimate IDF curves from daily meteorological observations, which are more widely available than the observations on finer timescales (minutes to hours) that are needed for IDF calculations. The method is applied to all data at once, making it efficient and robust to individual errors.
Kaltrina Maloku, Benoit Hingray, and Guillaume Evin
Hydrol. Earth Syst. Sci., 27, 3643–3661, https://doi.org/10.5194/hess-27-3643-2023, https://doi.org/10.5194/hess-27-3643-2023, 2023
Short summary
Short summary
High-resolution precipitation data, needed for many applications in hydrology, are typically rare. Such data can be simulated from daily precipitation with stochastic disaggregation. In this work, multiplicative random cascades are used to disaggregate time series of 40 min precipitation from daily precipitation for 81 Swiss stations. We show that very relevant statistics of precipitation are obtained when precipitation asymmetry is accounted for in a continuous way in the cascade generator.
Theresa Boas, Heye Reemt Bogena, Dongryeol Ryu, Harry Vereecken, Andrew Western, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 27, 3143–3167, https://doi.org/10.5194/hess-27-3143-2023, https://doi.org/10.5194/hess-27-3143-2023, 2023
Short summary
Short summary
In our study, we tested the utility and skill of a state-of-the-art forecasting product for the prediction of regional crop productivity using a land surface model. Our results illustrate the potential value and skill of combining seasonal forecasts with modelling applications to generate variables of interest for stakeholders, such as annual crop yield for specific cash crops and regions. In addition, this study provides useful insights for future technical model evaluations and improvements.
Yuanhong You, Chunlin Huang, Zuo Wang, Jinliang Hou, Ying Zhang, and Peipei Xu
Hydrol. Earth Syst. Sci., 27, 2919–2933, https://doi.org/10.5194/hess-27-2919-2023, https://doi.org/10.5194/hess-27-2919-2023, 2023
Short summary
Short summary
This study aims to investigate the performance of a genetic particle filter which was used as a snow data assimilation scheme across different snow climates. The results demonstrated that the genetic algorithm can effectively solve the problem of particle degeneration and impoverishment in a particle filter algorithm. The system has revealed a low sensitivity to the particle number in point-scale application of the ground snow depth measurement.
Patricia Lawston-Parker, Joseph A. Santanello Jr., and Nathaniel W. Chaney
Hydrol. Earth Syst. Sci., 27, 2787–2805, https://doi.org/10.5194/hess-27-2787-2023, https://doi.org/10.5194/hess-27-2787-2023, 2023
Short summary
Short summary
Irrigation has been shown to impact weather and climate, but it has only recently been considered in prediction models. Prescribing where (globally) irrigation takes place is important to accurately simulate its impacts on temperature, humidity, and precipitation. Here, we evaluated three different irrigation maps in a weather model and found that the extent and intensity of irrigated areas and their boundaries are important drivers of weather impacts resulting from human practices.
Marcos Julien Alexopoulos, Hannes Müller-Thomy, Patrick Nistahl, Mojca Šraj, and Nejc Bezak
Hydrol. Earth Syst. Sci., 27, 2559–2578, https://doi.org/10.5194/hess-27-2559-2023, https://doi.org/10.5194/hess-27-2559-2023, 2023
Short summary
Short summary
For rainfall-runoff simulation of a certain area, hydrological models are used, which requires precipitation data and temperature data as input. Since these are often not available as observations, we have tested simulation results from atmospheric models. ERA5-Land and COSMO-REA6 were tested for Slovenian catchments. Both lead to good simulations results. Their usage enables the use of rainfall-runoff simulation in unobserved catchments as a requisite for, e.g., flood protection measures.
Tuantuan Zhang, Zhongmin Liang, Wentao Li, Jun Wang, Yiming Hu, and Binquan Li
Hydrol. Earth Syst. Sci., 27, 1945–1960, https://doi.org/10.5194/hess-27-1945-2023, https://doi.org/10.5194/hess-27-1945-2023, 2023
Short summary
Short summary
We use circulation classifications and spatiotemporal deep neural networks to correct raw daily forecast precipitation by combining large-scale circulation patterns with local spatiotemporal information. We find that the method not only captures the westward and northward movement of the western Pacific subtropical high but also shows substantially higher bias-correction capabilities than existing standard methods in terms of spatial scale, timescale, and intensity.
Zeyu Xue, Paul Ullrich, and Lai-Yung Ruby Leung
Hydrol. Earth Syst. Sci., 27, 1909–1927, https://doi.org/10.5194/hess-27-1909-2023, https://doi.org/10.5194/hess-27-1909-2023, 2023
Short summary
Short summary
We examine the sensitivity and robustness of conclusions drawn from the PGW method over the NEUS by conducting multiple PGW experiments and varying the perturbation spatial scales and choice of perturbed meteorological variables to provide a guideline for this increasingly popular regional modeling method. Overall, we recommend PGW experiments be performed with perturbations to temperature or the combination of temperature and wind at the gridpoint scale, depending on the research question.
Louise J. Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, https://doi.org/10.5194/hess-27-1865-2023, 2023
Short summary
Short summary
Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
Zexuan Xu, Erica R. Siirila-Woodburn, Alan M. Rhoades, and Daniel Feldman
Hydrol. Earth Syst. Sci., 27, 1771–1789, https://doi.org/10.5194/hess-27-1771-2023, https://doi.org/10.5194/hess-27-1771-2023, 2023
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 a climate model and a hydrology model to have a full understanding of the atmospheric-through-bedrock hydrological processes.
Jolanda J. E. Theeuwen, Arie Staal, Obbe A. Tuinenburg, Bert V. M. Hamelers, and Stefan C. Dekker
Hydrol. Earth Syst. Sci., 27, 1457–1476, https://doi.org/10.5194/hess-27-1457-2023, https://doi.org/10.5194/hess-27-1457-2023, 2023
Short summary
Short summary
Evaporation changes over land affect rainfall over land via 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 as well as over wet and elevated regions. Local moisture recycling provides insight into the local impacts of evaporation changes and can be used to study the influence of regreening on local rainfall.
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.
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...
