Articles | Volume 17, issue 7
https://doi.org/10.5194/gmd-17-2987-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-2987-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
16 Apr 2024
Model description paper |
| 16 Apr 2024
| 16 Apr 2024
Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation
Mohamad Hakam Shams Eddin
CORRESPONDING AUTHOR
Institute of Computer Science, University of Bonn, Friedrich-Hirzebruch-Allee 5, 53115 Bonn, Germany
Juergen Gall
Institute of Computer Science, University of Bonn, Friedrich-Hirzebruch-Allee 5, 53115 Bonn, Germany
Lamarr Institute for Machine Learning and Artificial Intelligence, 53115 Bonn, Germany
Related subject area
Earth and space science informatics
Tomofast-x 2.0: an open-source parallel code for inversion of potential field data with topography using wavelet compression
Functional analysis of variance (ANOVA) for carbon flux estimates from remote sensing data
The 4D reconstruction of dynamic geological evolution processes for renowned geological features
Accelerating Lagrangian transport simulations on graphics processing units: performance optimizations of MPTRAC v2.6
Machine learning for numerical weather and climate modelling: a review
Overcoming barriers to enable convergence research by integrating ecological and climate sciences: the NCAR–NEON system Version 1
Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale
Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China
A generalized spatial autoregressive neural network method for three-dimensional spatial interpolation
The Common Community Physics Package (CCPP) Framework v6
Causal deep learning models for studying the Earth system
A methodological framework for improving the performance of data-driven models: a case study for daily runoff prediction in the Maumee domain, USA
SHAFTS (v2022.3): a deep-learning-based Python package for simultaneous extraction of building height and footprint from sentinel imagery
Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI
Twenty-five years of the IPCC Data Distribution Centre at the DKRZ and the Reference Data Archive for CMIP data
Effectiveness and computational efficiency of absorbing boundary conditions for full-waveform inversion
LAND-SUITE V1.0: a suite of tools for statistically based landslide susceptibility zonation
Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5
Fast infrared radiative transfer calculations using graphics processing units: JURASSIC-GPU v2.0
CSDMS: a community platform for numerical modeling of Earth surface processes
A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes
Model calibration using ESEm v1.1.0 – an open, scalable Earth system emulator
Turbidity maximum zone index: a novel model for remote extraction of the turbidity maximum zone in different estuaries
dh2loop 1.0: an open-source Python library for automated processing and classification of geological logs
Copula-based synthetic data augmentation for machine-learning emulators
Automated geological map deconstruction for 3D model construction using map2loop 1.0 and map2model 1.0
A spatially explicit approach to simulate urban heat mitigation with InVEST (v3.8.0)
S-SOM v1.0: a structural self-organizing map algorithm for weather typing
Using Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, China
Current status on the need for improved accessibility to climate models code
ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather
A spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and time
A new end-to-end workflow for the Community Earth System Model (version 2.0) for the Coupled Model Intercomparison Project Phase 6 (CMIP6)
HyLands 1.0: a hybrid landscape evolution model to simulate the impact of landslides and landslide-derived sediment on landscape evolution
Comparative analysis of atmospheric radiative transfer models using the Atmospheric Look-up table Generator (ALG) toolbox (version 2.0)
Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model
VISIR-1.b: ocean surface gravity waves and currents for energy-efficient navigation
Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets
Global hydro-climatic biomes identified via multitask learning
A run control framework to streamline profiling, porting, and tuning simulation runs and provenance tracking of geoscientific applications
An improved logistic regression model based on a spatially weighted technique (ILRBSWT v1.0) and its application to mineral prospectivity mapping
High-performance software framework for the calculation of satellite-to-satellite data matchups (MMS version 1.2)
A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1)
Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming
A high-fidelity multiresolution digital elevation model for Earth systems
CPMIP: measurements of real computational performance of Earth system models in CMIP6
Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling
Community Intercomparison Suite (CIS) v1.4.0: a tool for intercomparing models and observations
Asynchronous communication in spectral-element and discontinuous Galerkin methods for atmospheric dynamics – a case study using the High-Order Methods Modeling Environment (HOMME-homme_dg_branch)
GO2OGS 1.0: a versatile workflow to integrate complex geological information with fault data into numerical simulation models
Vitaliy Ogarko, Kim Frankcombe, Taige Liu, Jeremie Giraud, Roland Martin, and Mark Jessell
Geosci. Model Dev., 17, 2325–2345, https://doi.org/10.5194/gmd-17-2325-2024, https://doi.org/10.5194/gmd-17-2325-2024, 2024
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We present a major release of the Tomofast-x open-source gravity and magnetic inversion code that is enhancing its performance and applicability for both industrial and academic studies. We focus on real-world mineral exploration scenarios, while offering flexibility for applications at regional scale or for crustal studies. The optimisation work described in this paper is fundamental to allowing more complete descriptions of the controls on magnetisation, including remanence.
Jonathan Hobbs, Matthias Katzfuss, Hai Nguyen, Vineet Yadav, and Junjie Liu
Geosci. Model Dev., 17, 1133–1151, https://doi.org/10.5194/gmd-17-1133-2024, https://doi.org/10.5194/gmd-17-1133-2024, 2024
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The cycling of carbon among the land, oceans, and atmosphere is a closely monitored process in the global climate system. These exchanges between the atmosphere and the surface can be quantified using a combination of atmospheric carbon dioxide observations and computer models. This study presents a statistical method for investigating the similarities and differences in the estimated surface–atmosphere carbon exchange when different computer model assumptions are invoked.
Jiateng Guo, Zhibin Liu, Xulei Wang, Lixin Wu, Shanjun Liu, and Yunqiang Li
Geosci. Model Dev., 17, 847–864, https://doi.org/10.5194/gmd-17-847-2024, https://doi.org/10.5194/gmd-17-847-2024, 2024
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This study proposes a 3D and temporally dynamic (4D) geological modeling method. Several simulation and actual cases show that the 4D spatial and temporal evolution of regional geological formations can be modeled easily using this method with smooth boundaries. The 4D modeling system can dynamically present the regional geological evolution process under the timeline, which will be helpful to the research and teaching on the formation of typical and complex geological features.
Lars Hoffmann, Kaveh Haghighi Mood, Andreas Herten, Markus Hrywniak, Jiri Kraus, Jan Clemens, and Mingzhao Liu
EGUsphere, https://doi.org/10.5194/egusphere-2023-2547, https://doi.org/10.5194/egusphere-2023-2547, 2024
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Lagrangian particle dispersion models are crucial for studying atmospheric transport, but they can be computationally intensive. To speed up simulations, the MPTRAC model was adapted for GPUs. Performance optimizations of data structures and memory alignment resulted in run-time improvements of up to 75 % on NVIDIA A100 GPUs for ERA5-based simulations with 100 million particles. These optimizations make the MPTRAC model well suited for upcoming HPC systems.
Catherine O. de Burgh-Day and Tennessee Leeuwenburg
Geosci. Model Dev., 16, 6433–6477, https://doi.org/10.5194/gmd-16-6433-2023, https://doi.org/10.5194/gmd-16-6433-2023, 2023
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Machine learning (ML) is an increasingly popular tool in the field of weather and climate modelling. While ML has been used in this space for a long time, it is only recently that ML approaches have become competitive with more traditional methods. In this review, we have summarized the use of ML in weather and climate modelling over time; provided an overview of key ML concepts, methodologies, and terms; and suggested promising avenues for further research.
Danica L. Lombardozzi, William R. Wieder, Negin Sobhani, Gordon B. Bonan, David Durden, Dawn Lenz, Michael SanClements, Samantha Weintraub-Leff, Edward Ayres, Christopher R. Florian, Kyla Dahlin, Sanjiv Kumar, Abigail L. S. Swann, Claire M. Zarakas, Charles Vardeman, and Valerio Pascucci
Geosci. Model Dev., 16, 5979–6000, https://doi.org/10.5194/gmd-16-5979-2023, https://doi.org/10.5194/gmd-16-5979-2023, 2023
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We present a novel cyberinfrastructure system that uses National Ecological Observatory Network measurements to run Community Terrestrial System Model point simulations in a containerized system. The simple interface and tutorials expand access to data and models used in Earth system research by removing technical barriers and facilitating research, educational opportunities, and community engagement. The NCAR–NEON system enables convergence of climate and ecological sciences.
Qianqian Han, Yijian Zeng, Lijie Zhang, Calimanut-Ionut Cira, Egor Prikaziuk, Ting Duan, Chao Wang, Brigitta Szabó, Salvatore Manfreda, Ruodan Zhuang, and Bob Su
Geosci. Model Dev., 16, 5825–5845, https://doi.org/10.5194/gmd-16-5825-2023, https://doi.org/10.5194/gmd-16-5825-2023, 2023
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Using machine learning, we estimated global surface soil moisture (SSM) to aid in understanding water, energy, and carbon exchange. Ensemble models outperformed individual algorithms in predicting SSM under different climates. The best-performing ensemble included K-neighbours Regressor, Random Forest Regressor, and Extreme Gradient Boosting. This is important for hydrological and climatological applications such as water cycle monitoring, irrigation management, and crop yield prediction.
Xiaoyi Shao, Siyuan Ma, and Chong Xu
Geosci. Model Dev., 16, 5113–5129, https://doi.org/10.5194/gmd-16-5113-2023, https://doi.org/10.5194/gmd-16-5113-2023, 2023
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Scientific understandings of the distribution of coseismic landslides, followed by emergency and medium- and long-term risk assessment, can reduce landslide risk. The aim of this study is to propose an improved three-stage spatial prediction strategy and develop corresponding hazard assessment software called Mat.LShazard V1.0, which provides a new application tool for coseismic landslide disaster prevention and mitigation in different stages.
Junda Zhan, Sensen Wu, Jin Qi, Jindi Zeng, Mengjiao Qin, Yuanyuan Wang, and Zhenhong Du
Geosci. Model Dev., 16, 2777–2794, https://doi.org/10.5194/gmd-16-2777-2023, https://doi.org/10.5194/gmd-16-2777-2023, 2023
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We develop a generalized spatial autoregressive neural network model used for three-dimensional spatial interpolation. Taking the different changing trend of geographic elements along various directions into consideration, the model defines spatial distance in a generalized way and integrates it into the process of spatial interpolation with the theories of spatial autoregression and neural network. Compared with traditional methods, the model achieves better performance and is more adaptable.
Dominikus Heinzeller, Ligia Bernardet, Grant Firl, Man Zhang, Xia Sun, and Michael Ek
Geosci. Model Dev., 16, 2235–2259, https://doi.org/10.5194/gmd-16-2235-2023, https://doi.org/10.5194/gmd-16-2235-2023, 2023
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The Common Community Physics Package is a collection of physical atmospheric parameterizations for use in Earth system models and a framework that couples the physics to a host model’s dynamical core. A primary goal for this effort is to facilitate research and development of physical parameterizations and physics–dynamics coupling methods while offering capabilities for numerical weather prediction operations, for example in the upcoming implementation of the Global Forecast System (GFS) v17.
Tobias Tesch, Stefan Kollet, and Jochen Garcke
Geosci. Model Dev., 16, 2149–2166, https://doi.org/10.5194/gmd-16-2149-2023, https://doi.org/10.5194/gmd-16-2149-2023, 2023
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A recent statistical approach for studying relations in the Earth system is to train deep learning (DL) models to predict Earth system variables given one or several others and use interpretable DL to analyze the relations learned by the models. Here, we propose to combine the approach with a theorem from causality research to ensure that the deep learning model learns causal rather than spurious relations. As an example, we apply the method to study soil-moisture–precipitation coupling.
Yao Hu, Chirantan Ghosh, and Siamak Malakpour-Estalaki
Geosci. Model Dev., 16, 1925–1936, https://doi.org/10.5194/gmd-16-1925-2023, https://doi.org/10.5194/gmd-16-1925-2023, 2023
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Data-driven models (DDMs) gain popularity in earth and environmental systems, thanks in large part to advancements in data collection techniques and artificial intelligence (AI). The performance of these models is determined by the underlying machine learning (ML) algorithms. In this study, we develop a framework to improve the model performance by optimizing ML algorithms and demonstrate the effectiveness of the framework using a DDM to predict edge-of-field runoff in the Maumee domain, USA.
Ruidong Li, Ting Sun, Fuqiang Tian, and Guang-Heng Ni
Geosci. Model Dev., 16, 751–778, https://doi.org/10.5194/gmd-16-751-2023, https://doi.org/10.5194/gmd-16-751-2023, 2023
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We developed SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery), a multi-task deep-learning-based Python package, to estimate average building height and footprint from Sentinel imagery. Evaluation in 46 cities worldwide shows that SHAFTS achieves significant improvement over existing machine-learning-based methods.
Feng Yin, Philip E. Lewis, and Jose L. Gómez-Dans
Geosci. Model Dev., 15, 7933–7976, https://doi.org/10.5194/gmd-15-7933-2022, https://doi.org/10.5194/gmd-15-7933-2022, 2022
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The proposed SIAC atmospheric correction method provides consistent surface reflectance estimations from medium spatial-resolution satellites (Sentinel 2 and Landsat 8) with per-pixel uncertainty information. The outputs from SIAC have been validated against a wide range of ground measurements, and it shows that SIAC can provide accurate estimations of both surface reflectance and atmospheric parameters, with meaningful uncertainty information.
Martina Stockhause and Michael Lautenschlager
Geosci. Model Dev., 15, 6047–6058, https://doi.org/10.5194/gmd-15-6047-2022, https://doi.org/10.5194/gmd-15-6047-2022, 2022
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The Data Distribution Centre (DDC) of the Intergovernmental Panel on Climate Change (IPCC) celebrates its 25th anniversary in 2022. DDC Partner DKRZ has supported the IPCC Assessments and preserved the quality-assured, citable climate model data underpinning the Assessment Reports over these years over the long term. With the introduction of the IPCC FAIR Guidelines into the current AR6, the value of DDC services has been recognized. However, DDC sustainability remains unresolved.
Daiane Iglesia Dolci, Felipe A. G. Silva, Pedro S. Peixoto, and Ernani V. Volpe
Geosci. Model Dev., 15, 5857–5881, https://doi.org/10.5194/gmd-15-5857-2022, https://doi.org/10.5194/gmd-15-5857-2022, 2022
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We investigate and compare the theoretical and computational characteristics of several absorbing boundary conditions (ABCs) for the full-waveform inversion (FWI) problem. The different ABCs are implemented in an optimized computational framework called Devito. The computational efficiency and memory requirements of the ABC methods are evaluated in the forward and adjoint wave propagators, from simple to realistic velocity models.
Mauro Rossi, Txomin Bornaetxea, and Paola Reichenbach
Geosci. Model Dev., 15, 5651–5666, https://doi.org/10.5194/gmd-15-5651-2022, https://doi.org/10.5194/gmd-15-5651-2022, 2022
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LAND-SUITE is a software package designed to support landslide susceptibility zonation. The software integrates, extends, and completes LAND-SE (Rossi et al., 2010; Rossi and Reichenbach, 2016). The software is implemented in R, a free software environment for statistical computing and graphics, and gives expert users the possibility to perform easier, more flexible, and more informed statistically based landslide susceptibility applications and zonations.
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, and Karthik Kashinath
Geosci. Model Dev., 15, 2221–2237, https://doi.org/10.5194/gmd-15-2221-2022, https://doi.org/10.5194/gmd-15-2221-2022, 2022
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There is growing interest in data-driven weather forecasting, i.e., to predict the weather by using a deep neural network that learns from the evolution of past atmospheric patterns. Here, we propose three components to add to the current data-driven weather forecast models to improve their performance. These components involve a feature that incorporates physics into the neural network, a method to add data assimilation, and an algorithm to use several different time intervals in the forecast.
Paul F. Baumeister and Lars Hoffmann
Geosci. Model Dev., 15, 1855–1874, https://doi.org/10.5194/gmd-15-1855-2022, https://doi.org/10.5194/gmd-15-1855-2022, 2022
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The efficiency of the numerical simulation of radiative transport is shown on modern server-class graphics cards (GPUs). The low-cost prefactor on GPUs compared to general-purpose processors (CPUs) enables future large retrieval campaigns for multi-channel data from infrared sounders aboard low-orbit satellites. The validated research software JURASSIC is available in the public domain.
Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Benjamin Campforts, Tian Gan, Katherine R. Barnhart, Albert J. Kettner, Irina Overeem, Scott D. Peckham, Lynn McCready, and Jaia Syvitski
Geosci. Model Dev., 15, 1413–1439, https://doi.org/10.5194/gmd-15-1413-2022, https://doi.org/10.5194/gmd-15-1413-2022, 2022
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Scientists use computer simulation models to understand how Earth surface processes work, including floods, landslides, soil erosion, river channel migration, ocean sedimentation, and coastal change. Research benefits when the software for simulation modeling is open, shared, and coordinated. The Community Surface Dynamics Modeling System (CSDMS) is a US-based facility that supports research by providing community support, computing tools and guidelines, and educational resources.
Danilo César de Mello, Gustavo Vieira Veloso, Marcos Guedes de Lana, Fellipe Alcantara de Oliveira Mello, Raul Roberto Poppiel, Diego Ribeiro Oquendo Cabrero, Luis Augusto Di Loreto Di Raimo, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes Filho, Emilson Pereira Leite, and José Alexandre Melo Demattê
Geosci. Model Dev., 15, 1219–1246, https://doi.org/10.5194/gmd-15-1219-2022, https://doi.org/10.5194/gmd-15-1219-2022, 2022
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We used soil parent material, terrain attributes, and geophysical data from the soil surface to test and compare different and unprecedented geophysical sensor combination, as well as different machine learning algorithms to model and predict several soil attributes. Also, we analyzed the importance of pedoenvironmental variables. The soil attributes were modeled throughout different machine learning algorithms and related to different geophysical sensor combinations.
Duncan Watson-Parris, Andrew Williams, Lucia Deaconu, and Philip Stier
Geosci. Model Dev., 14, 7659–7672, https://doi.org/10.5194/gmd-14-7659-2021, https://doi.org/10.5194/gmd-14-7659-2021, 2021
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The Earth System Emulator (ESEm) provides a fast and flexible framework for emulating a wide variety of Earth science datasets and tools for constraining (or tuning) models of any complexity. Three distinct use cases are presented that demonstrate the utility of ESEm and provide some insight into the use of machine learning for emulation in these different settings. The open-source Python package is freely available so that it might become a valuable tool for the community.
Chongyang Wang, Li Wang, Danni Wang, Dan Li, Chenghu Zhou, Hao Jiang, Qiong Zheng, Shuisen Chen, Kai Jia, Yangxiaoyue Liu, Ji Yang, Xia Zhou, and Yong Li
Geosci. Model Dev., 14, 6833–6846, https://doi.org/10.5194/gmd-14-6833-2021, https://doi.org/10.5194/gmd-14-6833-2021, 2021
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The turbidity maximum zone (TMZ) is a special phenomenon in estuaries worldwide. However, the extraction methods and criteria used to describe the TMZ vary significantly both spatially and temporally. This study proposes an new index, the turbidity maximum zone index, based on the corresponding relationship of total suspended solid concentration and Chl a concentration, which could better extract TMZs in different estuaries and on different dates.
Ranee Joshi, Kavitha Madaiah, Mark Jessell, Mark Lindsay, and Guillaume Pirot
Geosci. Model Dev., 14, 6711–6740, https://doi.org/10.5194/gmd-14-6711-2021, https://doi.org/10.5194/gmd-14-6711-2021, 2021
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We have developed a software that allows the user to extract and standardize drill hole information from legacy datasets and/or different drilling campaigns. It also provides functionality to upscale the lithological information. These functionalities were possible by developing thesauri to identify and group geological terminologies together.
David Meyer, Thomas Nagler, and Robin J. Hogan
Geosci. Model Dev., 14, 5205–5215, https://doi.org/10.5194/gmd-14-5205-2021, https://doi.org/10.5194/gmd-14-5205-2021, 2021
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A major limitation in training machine-learning emulators is often caused by the lack of data. This paper presents a cheap way to increase the size of training datasets using statistical techniques and thereby improve the performance of machine-learning emulators.
Mark Jessell, Vitaliy Ogarko, Yohan de Rose, Mark Lindsay, Ranee Joshi, Agnieszka Piechocka, Lachlan Grose, Miguel de la Varga, Laurent Ailleres, and Guillaume Pirot
Geosci. Model Dev., 14, 5063–5092, https://doi.org/10.5194/gmd-14-5063-2021, https://doi.org/10.5194/gmd-14-5063-2021, 2021
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We have developed software that allows the user to extract sufficient information from unmodified digital maps and associated datasets that we are able to use to automatically build 3D geological models. By automating the process we are able to remove human bias from the procedure, which makes the workflow reproducible.
Martí Bosch, Maxence Locatelli, Perrine Hamel, Roy P. Remme, Jérôme Chenal, and Stéphane Joost
Geosci. Model Dev., 14, 3521–3537, https://doi.org/10.5194/gmd-14-3521-2021, https://doi.org/10.5194/gmd-14-3521-2021, 2021
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The article presents a novel approach to simulate urban heat mitigation from land use/land cover data based on three biophysical mechanisms: tree shade, evapotranspiration and albedo. An automated procedure is proposed to calibrate the model parameters to best fit temperature observations from monitoring stations. A case study in Lausanne, Switzerland, shows that the approach outperforms regressions based on satellite data and provides valuable insights into design heat mitigation policies.
Quang-Van Doan, Hiroyuki Kusaka, Takuto Sato, and Fei Chen
Geosci. Model Dev., 14, 2097–2111, https://doi.org/10.5194/gmd-14-2097-2021, https://doi.org/10.5194/gmd-14-2097-2021, 2021
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This study proposes a novel structural self-organizing map (S-SOM) algorithm. The superiority of S-SOM is that it can better recognize the difference (or similarity) among spatial (or temporal) data used for training and thus improve the clustering quality compared to traditional SOM algorithms.
Batunacun, Ralf Wieland, Tobia Lakes, and Claas Nendel
Geosci. Model Dev., 14, 1493–1510, https://doi.org/10.5194/gmd-14-1493-2021, https://doi.org/10.5194/gmd-14-1493-2021, 2021
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Extreme gradient boosting (XGBoost) can provide alternative insights that conventional land-use models are unable to generate. Shapley additive explanations (SHAP) can interpret the results of the purely data-driven approach. XGBoost achieved similar and robust simulation results. SHAP values were useful for analysing the complex relationship between the different drivers of grassland degradation.
Juan A. Añel, Michael García-Rodríguez, and Javier Rodeiro
Geosci. Model Dev., 14, 923–934, https://doi.org/10.5194/gmd-14-923-2021, https://doi.org/10.5194/gmd-14-923-2021, 2021
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This work shows that it continues to be hard, if not impossible, to obtain some of the most used climate models worldwide. We reach this conclusion through a systematic study and encourage all development teams and research centres to make public the models they use to produce scientific results.
Prabhat, Karthik Kashinath, Mayur Mudigonda, Sol Kim, Lukas Kapp-Schwoerer, Andre Graubner, Ege Karaismailoglu, Leo von Kleist, Thorsten Kurth, Annette Greiner, Ankur Mahesh, Kevin Yang, Colby Lewis, Jiayi Chen, Andrew Lou, Sathyavat Chandran, Ben Toms, Will Chapman, Katherine Dagon, Christine A. Shields, Travis O'Brien, Michael Wehner, and William Collins
Geosci. Model Dev., 14, 107–124, https://doi.org/10.5194/gmd-14-107-2021, https://doi.org/10.5194/gmd-14-107-2021, 2021
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Detecting extreme weather events is a crucial step in understanding how they change due to climate change. Deep learning (DL) is remarkable at pattern recognition; however, it works best only when labeled datasets are available. We create
ClimateNet– an expert-labeled curated dataset – to train a DL model for detecting weather events and predicting changes in extreme precipitation. This work paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data.
Xiang Que, Xiaogang Ma, Chao Ma, and Qiyu Chen
Geosci. Model Dev., 13, 6149–6164, https://doi.org/10.5194/gmd-13-6149-2020, https://doi.org/10.5194/gmd-13-6149-2020, 2020
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This paper presents a spatiotemporal weighted regression (STWR) model for exploring nonstationary spatiotemporal processes in nature and socioeconomics. A value change rate is introduced in the temporal kernel, which presents significant model fitting and accuracy in both simulated and real-world data. STWR fully incorporates observed data in the past and outperforms geographic temporal weighted regression (GTWR) and geographic weighted regression (GWR) models in several experiments.
Sheri Mickelson, Alice Bertini, Gary Strand, Kevin Paul, Eric Nienhouse, John Dennis, and Mariana Vertenstein
Geosci. Model Dev., 13, 5567–5581, https://doi.org/10.5194/gmd-13-5567-2020, https://doi.org/10.5194/gmd-13-5567-2020, 2020
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Every generation of MIP exercises introduces new layers of complexity and an exponential growth in the amount of data requested. CMIP6 required us to develop a new tool chain and forced us to change our methodologies. The new methods discussed in this paper provided us with an 18 times faster speedup over our existing methods. This allowed us to meet our deadlines and we were able to publish more than half a million data sets on the Earth System Grid Federation (ESGF) for the CMIP6 project.
Benjamin Campforts, Charles M. Shobe, Philippe Steer, Matthias Vanmaercke, Dimitri Lague, and Jean Braun
Geosci. Model Dev., 13, 3863–3886, https://doi.org/10.5194/gmd-13-3863-2020, https://doi.org/10.5194/gmd-13-3863-2020, 2020
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Landslides shape the Earth’s surface and are a dominant source of terrestrial sediment. Rivers, then, act as conveyor belts evacuating landslide-produced sediment. Understanding the interaction among rivers and landslides is important to predict the Earth’s surface response to past and future environmental changes and for mitigating natural hazards. We develop HyLands, a new numerical model that provides a toolbox to explore how landslides and rivers interact over several timescales.
Jorge Vicent, Jochem Verrelst, Neus Sabater, Luis Alonso, Juan Pablo Rivera-Caicedo, Luca Martino, Jordi Muñoz-Marí, and José Moreno
Geosci. Model Dev., 13, 1945–1957, https://doi.org/10.5194/gmd-13-1945-2020, https://doi.org/10.5194/gmd-13-1945-2020, 2020
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The modeling of light propagation through the atmosphere is key to process satellite images and to understand atmospheric processes. However, existing atmospheric models can be complex to use in practical applications. Here we aim at providing a new software tool to facilitate using advanced models and to generate large databases of simulated data. As a test case, we use this tool to analyze differences between several atmospheric models, showing the capabilities of this open-source tool.
Jiali Wang, Prasanna Balaprakash, and Rao Kotamarthi
Geosci. Model Dev., 12, 4261–4274, https://doi.org/10.5194/gmd-12-4261-2019, https://doi.org/10.5194/gmd-12-4261-2019, 2019
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Parameterizations are frequently used in models representing physical phenomena and are often the computationally expensive portions of the code. Using model output from simulations performed using a weather model, we train deep neural networks to provide an accurate alternative to a physics-based parameterization. We demonstrate that a domain-aware deep neural network can successfully simulate the entire diurnal cycle of the boundary layer physics and the results are transferable.
Gianandrea Mannarini and Lorenzo Carelli
Geosci. Model Dev., 12, 3449–3480, https://doi.org/10.5194/gmd-12-3449-2019, https://doi.org/10.5194/gmd-12-3449-2019, 2019
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The VISIR ship-routing model is updated in order to deal with ocean currents.
The optimal tracks we computed through VISIR in the Atlantic ocean show great seasonal and regional variability, following a variable influence of surface gravity waves and currents. We assess how these tracks contribute to voyage energy-efficiency gains through a standard indicator (EEOI) of the International Maritime Organization. Also, the new model features are validated against an exact analytical benchmark.
Grzegorz Muszynski, Karthik Kashinath, Vitaliy Kurlin, Michael Wehner, and Prabhat
Geosci. Model Dev., 12, 613–628, https://doi.org/10.5194/gmd-12-613-2019, https://doi.org/10.5194/gmd-12-613-2019, 2019
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We present the automated method for recognizing atmospheric rivers in climate data, i.e., climate model output and reanalysis product. The method is based on topological data analysis and machine learning, both of which are powerful tools that the climate science community often does not use. An advantage of the proposed method is that it is free of selection of subjective threshold conditions on a physical variable. This method is also suitable for rapidly analyzing large amounts of data.
Christina Papagiannopoulou, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, and Willem Waegeman
Geosci. Model Dev., 11, 4139–4153, https://doi.org/10.5194/gmd-11-4139-2018, https://doi.org/10.5194/gmd-11-4139-2018, 2018
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Common global land cover and climate classifications are based on vegetation–climatic characteristics derived from observational data, ignoring the interaction between the local climate and biome. Here, we model the interplay between vegetation and local climate by discovering spatial relationships among different locations. The resulting global
hydro-climatic biomescorrespond to regions of coherent climate–vegetation interactions that agree well with traditional global land cover maps.
Wendy Sharples, Ilya Zhukov, Markus Geimer, Klaus Goergen, Sebastian Luehrs, Thomas Breuer, Bibi Naz, Ketan Kulkarni, Slavko Brdar, and Stefan Kollet
Geosci. Model Dev., 11, 2875–2895, https://doi.org/10.5194/gmd-11-2875-2018, https://doi.org/10.5194/gmd-11-2875-2018, 2018
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Next-generation geoscientific models are based on complex model implementations and workflows. Next-generation HPC systems require new programming paradigms and code optimization. In order to meet the challenge of running complex simulations on new massively parallel HPC systems, we developed a run control framework that facilitates code portability, code profiling, and provenance tracking to reduce both the duration and the cost of code migration and development, while ensuring reproducibility.
Daojun Zhang, Na Ren, and Xianhui Hou
Geosci. Model Dev., 11, 2525–2539, https://doi.org/10.5194/gmd-11-2525-2018, https://doi.org/10.5194/gmd-11-2525-2018, 2018
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Geographically weighted regression is a widely used method to deal with spatial heterogeneity, which is common in geostatistics. However, most existing software does not support logistic regression and cannot deal with missing data, which exist extensively in mineral prospectivity mapping. This work generalized logistic regression to spatial statistics based on a spatially weighted technique. The new model also supports an anisotropic local window, which is another innovative point.
Thomas Block, Sabine Embacher, Christopher J. Merchant, and Craig Donlon
Geosci. Model Dev., 11, 2419–2427, https://doi.org/10.5194/gmd-11-2419-2018, https://doi.org/10.5194/gmd-11-2419-2018, 2018
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For calibration and validation purposes it is necessary to detect simultaneous data acquisitions from different spaceborne platforms. We present an algorithm and a software system which implements a general approach to resolve this problem. The multisensor matchup system (MMS) can detect simultaneous acquisitions in a large dataset (> 100 TB) and extract data for matching locations for further analysis. The MMS implements a flexible software infrastructure and allows for high parallelization.
David Hassell, Jonathan Gregory, Jon Blower, Bryan N. Lawrence, and Karl E. Taylor
Geosci. Model Dev., 10, 4619–4646, https://doi.org/10.5194/gmd-10-4619-2017, https://doi.org/10.5194/gmd-10-4619-2017, 2017
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We present a formal data model for version 1.6 of the CF (Climate and Forecast) metadata conventions that provide a description of the physical meaning of geoscientific data and their spatial and temporal properties. We describe the CF conventions and how they lead to our CF data model, and compare it other data models for storing data and metadata. We present cf-python version 2.1: a software implementation of the CF data model capable of manipulating any CF-compliant dataset.
Iulia Ilie, Peter Dittrich, Nuno Carvalhais, Martin Jung, Andreas Heinemeyer, Mirco Migliavacca, James I. L. Morison, Sebastian Sippel, Jens-Arne Subke, Matthew Wilkinson, and Miguel D. Mahecha
Geosci. Model Dev., 10, 3519–3545, https://doi.org/10.5194/gmd-10-3519-2017, https://doi.org/10.5194/gmd-10-3519-2017, 2017
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Accurate representation of land-atmosphere carbon fluxes is essential for future climate projections, although some of the responses of CO2 fluxes to climate often remain uncertain. The increase in available data allows for new approaches in their modelling. We automatically developed models for ecosystem and soil carbon respiration using a machine learning approach. When compared with established respiration models, we found that they are better in prediction as well as offering new insights.
Xinqiao Duan, Lin Li, Haihong Zhu, and Shen Ying
Geosci. Model Dev., 10, 239–253, https://doi.org/10.5194/gmd-10-239-2017, https://doi.org/10.5194/gmd-10-239-2017, 2017
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This article proposes an optimized transformation for topographic datasets. The resulting topographic grid exhibits good surface approximation and quasi-uniform high-quality. Both features of the processed topography build a concrete base from which improved endogenous or exogenous parameters can be derived, and makes it suitable for Earth and environmental simulations.
Venkatramani Balaji, Eric Maisonnave, Niki Zadeh, Bryan N. Lawrence, Joachim Biercamp, Uwe Fladrich, Giovanni Aloisio, Rusty Benson, Arnaud Caubel, Jeffrey Durachta, Marie-Alice Foujols, Grenville Lister, Silvia Mocavero, Seth Underwood, and Garrett Wright
Geosci. Model Dev., 10, 19–34, https://doi.org/10.5194/gmd-10-19-2017, https://doi.org/10.5194/gmd-10-19-2017, 2017
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Climate models are among the most computationally expensive scientific applications in the world. We present a set of measures of computational performance that can be used to compare models that are independent of underlying hardware and the model formulation. They are easy to collect and reflect performance actually achieved in practice. We are preparing a systematic effort to collect these metrics for the world's climate models during CMIP6, the next Climate Model Intercomparison Project.
Massimiliano Alvioli, Ivan Marchesini, Paola Reichenbach, Mauro Rossi, Francesca Ardizzone, Federica Fiorucci, and Fausto Guzzetti
Geosci. Model Dev., 9, 3975–3991, https://doi.org/10.5194/gmd-9-3975-2016, https://doi.org/10.5194/gmd-9-3975-2016, 2016
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Slope units are morphological mapping units bounded by drainage and divide lines that maximize within-unit homogeneity and between-unit heterogeneity. We use r.slopeunits, a software for the automatic delination of slope units. We outline an objective procedure to optimize the software input parameters for landslide susceptibility (LS) zonation. Optimization is achieved by maximizing an objective function that simultaneously evaluates terrain aspect segmentation quality and LS model performance.
Duncan Watson-Parris, Nick Schutgens, Nicholas Cook, Zak Kipling, Philip Kershaw, Edward Gryspeerdt, Bryan Lawrence, and Philip Stier
Geosci. Model Dev., 9, 3093–3110, https://doi.org/10.5194/gmd-9-3093-2016, https://doi.org/10.5194/gmd-9-3093-2016, 2016
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In this paper we describe CIS, a new command line tool for the easy visualization, analysis and comparison of a wide variety of gridded and ungridded data sets used in Earth sciences. Users can now use a single tool to not only view plots of satellite, aircraft, station or model data, but also bring them onto the same spatio-temporal sampling. This allows robust, quantitative comparisons to be made easily. CIS is an open-source project and welcomes input from the community.
Benjamin F. Jamroz and Robert Klöfkorn
Geosci. Model Dev., 9, 2881–2892, https://doi.org/10.5194/gmd-9-2881-2016, https://doi.org/10.5194/gmd-9-2881-2016, 2016
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The scalability of computational applications on current and next-generation supercomputers is increasingly limited by the cost of inter-process communication. We implement communication hiding data exchange in the High-Order Methods Modeling Environment (HOMME) for the time integration of the hydrostatic fluid equations using both the spectral-element and discontinuous Galerkin methods. The presented approach produces significant performance and scalability gains in large-scale simulations.
T. Fischer, D. Naumov, S. Sattler, O. Kolditz, and M. Walther
Geosci. Model Dev., 8, 3681–3694, https://doi.org/10.5194/gmd-8-3681-2015, https://doi.org/10.5194/gmd-8-3681-2015, 2015
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We present a workflow to convert geological models into the open-source VTU format for usage in numerical simulation models. Tackling relevant scientific questions or engineering tasks often involves multidisciplinary approaches. Conversion workflows are needed between the diverse tools of the various disciplines. Our approach offers an open-source, platform-independent, robust, and comprehensible method that is potentially useful for a multitude of similar environmental studies.
Cited articles
Adede, C., Oboko, R., Wagacha, P. W., and Atzberger, C.: A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya’s Operational Drought Monitoring, Remote Sens., 11, 1099, https://doi.org/10.3390/rs11091099, 2019. a
Aleissaee, A. A., Kumar, A., Anwer, R. M., Khan, S., Cholakkal, H., Xia, G.-S., and Khan, F. S.: Transformers in Remote Sensing: A Survey, Remote Sens., 15, 1860, https://doi.org/10.3390/rs15071860, 2023. a
Ba, J. L., Kiros, J. R., and Hinton, G. E.: Layer normalization, arXiv [preprint], https://doi.org/10.48550/arXiv.1607.06450, 2016. a
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities, Mon. Weather Rev., 139, 3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. a
Baur, F., Scheck, L., Stumpf, C., Köpken-Watts, C., and Potthast, R.: A neural-network-based method for generating synthetic 1.6 µm near-infrared satellite images, Atmos. Meas. Tech., 16, 5305–5326, https://doi.org/10.5194/amt-16-5305-2023, 2023. a
Ben-Bouallegue, Z., Clare, M. C. A., Magnusson, L., Gascon, E., Maier-Gerber, M., Janousek, M., Rodwell, M., Pinault, F., Dramsch, J. S., Lang, S. T. K., Raoult, B., Rabier, F., Chevallier, M., Sandu, I., Dueben, P., Chantry, M., and Pappenberger, F.: The rise of data-driven weather forecasting, arXiv [preprint], https://doi.org/10.48550/arXiv.2307.10128, 2023. a
Benson, V., Requena-Mesa, C., Robin, C., Alonso, L., Cortés, J., Gao, Z., Linscheid, N., Weynants, M., and Reichstein, M.: Forecasting localized weather impacts on vegetation as seen from space with meteo-guided video prediction, arXiv [preprint], https://doi.org/10.48550/arXiv.2303.16198 2023. a
Bergen, K. J., Johnson, P. A., de Hoop, M. V., and Beroza, G. C.: Machine learning for data-driven discovery in solid Earth geoscience, Science, 363, eaau0323, https://doi.org/10.1126/science.aau0323, 2019. a
Betancourt, C., Stomberg, T. T., Edrich, A.-K., Patnala, A., Schultz, M. G., Roscher, R., Kowalski, J., and Stadtler, S.: Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties, Geosci. Model Dev., 15, 4331–4354, https://doi.org/10.5194/gmd-15-4331-2022, 2022. a, b
Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., and Tian, Q.: Accurate medium-range global weather forecasting with 3D neural networks, Nature, 619, 533–538, https://doi.org/10.1038/s41586-023-06185-3, 2023. a
Blanchard, A., Parashar, N., Dodov, B., Lessig, C., and Sapsis, T.: A Multi-Scale Deep Learning Framework for Projecting Weather Extremes, in: NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning, https://www.climatechange.ai/papers/neurips2022/65 (last access: 4 April 2024), 2022. a
Chen, K., Han, T., Gong, J., Bai, L., Ling, F., Luo, J.-J., Chen, X., Ma, L., Zhang, T., Su, R., Ci, Y., Li, B., Yang, X., and Ouyang, W.: FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead, arXiv [preprint], https://doi.org/10.48550/arXiv.2304.02948, 2023. a
Chen, Z., Liu, H., Xu, C., Wu, X., Liang, B., Cao, J., and Chen, D.: Modeling vegetation greenness and its climate sensitivity with deep-learning technology, Ecol. Evol., 11, 7335–7345, https://doi.org/10.1002/ece3.7564, 2021. a, b
Christian, J. I., Basara, J. B., Hunt, E. D., Otkin, J. A., Furtado, J. C., Mishra, V., Xiao, X., and Randall, R. M.: Global distribution, trends, and drivers of flash drought occurrence, Nat. Commun., 12, 6330, https://doi.org/10.1038/s41467-021-26692-z, 2021. a
Christian, J. I., Martin, E. R., Basara, J. B., Furtado, J. C., Otkin, J. A., Lowman, L. E., Hunt, E. D., Mishra, V., and Xiao, X.: Global projections of flash drought show increased risk in a warming climate, Commun. Earth Environ., 4, 165, https://doi.org/10.1038/s43247-023-00826-1, 2023. a
Das, M. and Ghosh, S. K.: Deep-STEP: A Deep Learning Approach for Spatiotemporal Prediction of Remote Sensing Data, IEEE Geosci. Remote Sens. Lett., 13, 1984–1988, https://doi.org/10.1109/LGRS.2016.2619984, 2016. a
de Burgh-Day, C. O. and Leeuwenburg, T.: Machine learning for numerical weather and climate modelling: a review, Geosci. Model Dev., 16, 6433–6477, https://doi.org/10.5194/gmd-16-6433-2023, 2023. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L.: ImageNet: A large-scale hierarchical image database, in: 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 248–255, https://doi.org/10.1109/CVPR.2009.5206848, 2009. a, b
Diaconu, C.-A., Saha, S., Günnemann, S., and Xiang Zhu, X.: Understanding the Role of Weather Data for Earth Surface Forecasting using a ConvLSTM-based Model, in: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans, LA, USA, 1361–1370, https://doi.org/10.1109/CVPRW56347.2022.00142, 2022. a
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N.: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, in: International Conference on Learning Representations, https://openreview.net/forum?id=YicbFdNTTy (last access: 4 April 2024), 2021. a
Düben, P., Modigliani, U., Geer, A., Siemen, S., Pappenberger, F., Bauer, P., Brown, A., Palkovic, M., Raoult, B., Wedi, N., and Baousis, V.: Machine learning at ECMWF: A roadmap for the next 10 years, ECMWF Technical Memoranda, https://doi.org/10.21957/ge7ckgm, 2021. a
Eisfelder, C., Asam, S., Hirner, A., Reiners, P., Holzwarth, S., Bachmann, M., Gessner, U., Dietz, A., Huth, J., Bachofer, F., and Kuenzer, C.: Seasonal Vegetation Trends for Europe over 30 Years from a Novel Normalised Difference Vegetation Index (NDVI) Time-Series-The TIMELINE NDVI Product, Remote Sens. 15, 3616, https://doi.org/10.3390/rs15143616, 2023. a
Essa, Y. H., Hirschi, M., Thiery, W., El-Kenawy, A. M., and Yang, C.: Drought characteristics in Mediterranean under future climate change, npj Clim. Atmos. Sci., 6, 133, https://doi.org/10.1038/s41612-023-00458-4, 2023. a
Fazry, L., Ramadhan, M. M. L., and Jatmiko, W.: Change Detection of High-Resolution Remote Sensing Images Through Adaptive Focal Modulation on Hierarchical Feature Maps, IEEE Access, 11, 69072–69090, https://doi.org/10.1109/ACCESS.2023.3292531, 2023. a
Ferchichi, A., Abbes, A. B., Barra, V., and Farah, I. R.: Forecasting vegetation indices from spatio-temporal remotely sensed data using deep learning-based approaches: A systematic literature review, Ecol. Inf., 68, 101552, https://doi.org/10.1016/j.ecoinf.2022.101552, 2022. a
Furusho-Percot, C., Goergen, K., Hartick, C., Kulkarni, K., Keune, J., and Kollet, S.: Pan-European groundwater to atmosphere terrestrial systems climatology from a physically consistent simulation, Sci. Data, 6, 320, https://doi.org/10.1038/s41597-019-0328-7, 2019a. a, b, c, d
Furusho-Percot, C., Goergen, K., Keune, J., Kulkarni, K., and Kollet, S.: Pan-european, physically consistent simulations from groundwater to the atmosphere with the Terrestrial Systems Modeling Platform, TerrSysMP (1989–2018 daily time-series), Data Publication Server Forschungszentrum Jülich [data set], https://doi.org/10.17616/R31NJMGR, 2019b. a
Furusho-Percot, C., Goergen, K., Keune, J., Kulkarni, K., and Kollet, S.: Pan-european, physically consistent simulations from groundwater to the atmosphere with the Terrestrial Systems Modeling Platform, TerrSysMP (1989–2018 daily time-series), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.901823, 2019c. a
Furusho-Percot, C., Goergen, K., Hartick, C., Poshyvailo-Strube, L., and Kollet, S.: Groundwater Model Impacts Multiannual Simulations of Heat Waves, Geophys. Res. Lett., 49, e2021GL096781, https://doi.org/10.1029/2021GL096781, 2022. a
Gao, L., Liu, H., Yang, M., Chen, L., Wan, Y., Xiao, Z., and Qian, Y.: STransFuse: Fusing Swin Transformer and Convolutional Neural Network for Remote Sensing Image Semantic Segmentation, IEEE J. Sel. Top. Appl. Earth Obs., 14, 10990–11003, https://doi.org/10.1109/JSTARS.2021.3119654, 2021. a
Gao, P., Du, W., Lei, Q., Li, J., Zhang, S., and Li, N.: NDVI Forecasting Model Based on the Combination of Time Series Decomposition and CNN – LSTM, Water Resour. Manage., 37, 1481–1497, https://doi.org/10.1007/s11269-022-03419-3, 2023. a
Gasper, F., Goergen, K., Shrestha, P., Sulis, M., Rihani, J., Geimer, M., and Kollet, S.: Implementation and scaling of the fully coupled Terrestrial Systems Modeling Platform (TerrSysMP v1.0) in a massively parallel supercomputing environment – a case study on JUQUEEN (IBM Blue Gene/Q), Geosci. Model Dev., 7, 2531–2543, https://doi.org/10.5194/gmd-7-2531-2014, 2014. a, b
Geiss, S., Scheck, L., de Lozar, A., and Weissmann, M.: Understanding the model representation of clouds based on visible and infrared satellite observations, Atmos. Chem. Phys., 21, 12273–12290, https://doi.org/10.5194/acp-21-12273-2021, 2021. a, b
Giorgi, F., Jones, C., and Asrar, G. R.: Addressing climate information needs at the regional level: the CORDEX framework., Bulletin – World Meteorological Organization, 58, 175–183, 2009. a
Gutowski Jr., W. J., Giorgi, F., Timbal, B., Frigon, A., Jacob, D., Kang, H.-S., Raghavan, K., Lee, B., Lennard, C., Nikulin, G., O'Rourke, E., Rixen, M., Solman, S., Stephenson, T., and Tangang, F.: WCRP COordinated Regional Downscaling EXperiment (CORDEX): a diagnostic MIP for CMIP6, Geosci. Model Dev., 9, 4087–4095, https://doi.org/10.5194/gmd-9-4087-2016, 2016. a
Hammad, A. T. and Falchetta, G.: Probabilistic forecasting of remotely sensed cropland vegetation health and its relevance for food security, Sci. Total Environ., 838, 156157, https://doi.org/10.1016/j.scitotenv.2022.156157, 2022. a
Hendrycks, D. and Gimpel, K.: Gaussian error linear units (gelus), arXiv [preprint], https://doi.org/10.48550/arXiv.1606.08415, 2016. a
Horn, B.: Hill shading and the reflectance map, P. IEEE, 69, 14–47, https://doi.org/10.1109/PROC.1981.11918, 1981. a
Hu, J., Shen, L., and Sun, G.: Squeeze-and-Excitation Networks, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 7132–7141, https://doi.org/10.1109/CVPR.2018.00745, 2018. a
Jacob, D., Teichmann, C., Sobolowski, S., Katragkou, E., Anders, I., Belda, M., Benestad, R., Boberg, F., Buonomo, E., Cardoso, R. M., Casanueva, A., Christensen, O. B., Christensen, J. H., Coppola, E., De Cruz, L., Davin, E. L., Dobler, A., Domínguez, M., Fealy, R., Fernandez, J., Gaertner, M. A., García-Díez, M., Giorgi, F., Gobiet, A., Goergen, K., Gómez-Navarro, J. J., Alemán, J. J. G., Gutiérrez, C., Gutiérrez, J. M., Güttler, I., Haensler, A., Halenka, T., Jerez, S., Jiménez-Guerrero, P., Jones, R. G., Keuler, K., Kjellström, E., Knist, S., Kotlarski, S., Maraun, D., van Meijgaard, E., Mercogliano, P., Montávez, J. P., Navarra, A., Nikulin, G., de Noblet-Ducoudré, N., Panitz, H.-J., Pfeifer, S., Piazza, M., Pichelli, E., Pietikäinen, J.-P., Prein, A. F., Preuschmann, S., Rechid, D., Rockel, B., Romera, R., Sánchez, E., Sieck, K., Soares, P. M. M., Somot, S., Srnec, L., Sørland, S. L., Termonia, P., Truhetz, H., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.: Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community, Reg. Environ. Change, 20, 51, https://doi.org/10.1007/s10113-020-01606-9, 2020. a
Jefferson, J. L. and Maxwell, R. M.: Evaluation of simple to complex parameterizations of bare ground evaporation, J. Adv. Model. Earth Sy., 7, 1075–1092, https://doi.org/10.1002/2014MS000398, 2015. a
Jin, H., Vicente-Serrano, S. M., Tian, F., Cai, Z., Conradt, T., Boincean, B., Murphy, C., Farizo, B. A., Grainger, S., López-Moreno, J. I., and Eklundh, L.: Higher vegetation sensitivity to meteorological drought in autumn than spring across European biomes, Commun. Earth Environ., 4, 299, https://doi.org/10.1038/s43247-023-00960-w, 2023. a
Johnson, J., Alahi, A., and Fei-Fei, L.: Perceptual Losses for Real-Time Style Transfer and Super-Resolution, in: Computer Vision – ECCV 2016, edited by: Leibe, B., Matas, J., Sebe, N., and Welling, M., Springer International Publishing, Cham, 694–711, https://doi.org/10.1007/978-3-319-46475-6_43, 2016. a
Jones, J. E. and Woodward, C. S.: Newton–Krylov-multigrid solvers for large-scale, highly heterogeneous, variably saturated flow problems, Adv. Water Resour., 24, 763–774, https://doi.org/10.1016/S0309-1708(00)00075-0, 2001. a
Jones, P. W.: First- and Second-Order Conservative Remapping Schemes for Grids in Spherical Coordinates, Mon. Weather Rev., 127, 2204–2210, https://doi.org/10.1175/1520-0493(1999)127<2204:FASOCR>2.0.CO;2, 1999. a
Kew, S. F., Philip, S. Y., Hauser, M., Hobbins, M., Wanders, N., van Oldenborgh, G. J., van der Wiel, K., Veldkamp, T. I. E., Kimutai, J., Funk, C., and Otto, F. E. L.: Impact of precipitation and increasing temperatures on drought trends in eastern Africa, Earth Syst. Dynam., 12, 17–35, https://doi.org/10.5194/esd-12-17-2021, 2021. a
Kladny, K.-R., Milanta, M., Mraz, O., Hufkens, K., and Stocker, B. D.: Deep learning for satellite image forecasting of vegetation greenness, bioRxiv [preprint], https://doi.org/10.1101/2022.08.16.504173, 2022. a
Klimavičius, L., Rimkus, E., Stonevičius, E., and Mačiulytė, V.: Seasonality and long-term trends of NDVI values in different land use types in the eastern part of the Baltic Sea basin, Oceanologia, 65, 171–181, https://doi.org/10.1016/j.oceano.2022.02.007, 2023. a
Kogan, F., Guo, W., and Jelenak, A.: Global Vegetation Health: Long-Term Data Records, in: Use of Satellite and In-Situ Data to Improve Sustainability, edited by: Kogan, F., Powell, A., and Fedorov, O., Springer Netherlands, Dordrecht, 247–255, https://doi.org/10.1007/978-90-481-9618-0_28, 2011. a
Kogan, F., Goldberg, M., Schott, T., and Guo, W.: Suomi NPP/VIIRS: improving drought watch, crop loss prediction, and food security, International J. Remote Sens., 36, 5373–5383, https://doi.org/10.1080/01431161.2015.1095370, 2015. a, b
Kogan, F., Guo, W., and Yang, W.: Near 40-year drought trend during 1981-2019 earth warming and food security, Geomatics, Nat. Hazards Risk, 11, 469–490, https://doi.org/10.1080/19475705.2020.1730452, 2020. a, b
Kogan, F. N.: Remote sensing of weather impacts on vegetation in non-homogeneous areas, Int. J. Remote Sens., 11, 1405–1419, https://doi.org/10.1080/01431169008955102, 1990. a
Kogan, F. N.: Application of vegetation index and brightness temperature for drought detection, Adv. Space Res., 15, 91–100, https://doi.org/10.1016/0273-1177(95)00079-T, 1995a. a, b
Kogan, F. N.: Droughts of the Late 1980s in the United States as Derived from NOAA Polar-Orbiting Satellite Data, B. Am. Meteorol. Soc., 76, 655–668, https://doi.org/10.1175/1520-0477(1995)076<0655:DOTLIT>2.0.CO;2, 1995b. a, b
Kollet, S. J. and Maxwell, R. M.: Integrated surface–groundwater flow modeling: A free-surface overland flow boundary condition in a parallel groundwater flow model, Adv. Water Resour., 29, 945–958, https://doi.org/10.1016/j.advwatres.2005.08.006, 2006. a
Kraft, B., Jung, M., Körner, M., Requena Mesa, C., Cortés, J., and Reichstein, M.: Identifying Dynamic Memory Effects on Vegetation State Using Recurrent Neural Networks, Front. Big Data, 2, 31, https://doi.org/10.3389/fdata.2019.00031, 2019. a
Kuffour, B. N. O., Engdahl, N. B., Woodward, C. S., Condon, L. E., Kollet, S., and Maxwell, R. M.: Simulating coupled surface–subsurface flows with ParFlow v3.5.0: capabilities, applications, and ongoing development of an open-source, massively parallel, integrated hydrologic model, Geosci. Model Dev., 13, 1373–1397, https://doi.org/10.5194/gmd-13-1373-2020, 2020. a
Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Pritzel, A., Ravuri, S., Ewalds, T., Alet, F., Eaton-Rosen, Z., Hu, W., Merose, A., Hoyer, S., Holland, G., Stott, J., Vinyals, O., Mohamed, S., and Battaglia, P.: GraphCast: Learning skillful medium-range global weather forecasting, arXiv [preprint], https://doi.org/10.48550/arXiv.2212.12794, 2022. a
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., Collier, N., Ghimire, B., van Kampenhout, L., Kennedy, D., Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J., Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A., Badger, A. M., Bisht, G., van den Broeke, M., Brunke, M. A., Burns, S. P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Val Martin, M., and Zeng, X.: The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty, J. Adv. Model. Earth Sy., 11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019. a
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., and Shi, W.: Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 105–114, https://doi.org/10.1109/CVPR.2017.19, 2017. a
Lees, T., Tseng, G., Atzberger, C., Reece, S., and Dadson, S.: Deep Learning for Vegetation Health Forecasting: A Case Study in Kenya, Remote Sens., 14, 698, https://doi.org/10.3390/rs14030698, 2022. a, b
Lessig, C., Luise, I., Gong, B., Langguth, M., Stadler, S., and Schultz, M.: AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning, arXiv [preprint], https://doi.org/10.48550/arXiv.2308.13280, 2023. a
Li, J., Geer, A. J., Okamoto, K., Otkin, J. A., Liu, Z., Han, W., and Wang, P.: Satellite all-sky infrared radiance assimilation: Recent progress and future perspectives, Adv. Atmos. Sci., 39, 9–21, https://doi.org/10.1007/s00376-021-1088-9, 2022. a
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B.: Swin Transformer: Hierarchical Vision Transformer using Shifted Windows, in: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 9992–10002, https://doi.org/10.1109/ICCV48922.2021.00986, 2021. a
Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., Dong, L., Wei, F., and Guo, B.: Swin Transformer V2: Scaling Up Capacity and Resolution, in: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Montreal, QC, Canada, 11999–12009, https://doi.org/10.1109/CVPR52688.2022.01170, 2022. a
Loshchilov, I. and Hutter, F.: Decoupled Weight Decay Regularization, in: International Conference on Learning Representations, https://openreview.net/forum?id=Bkg6RiCqY7 (last access: 4 April 2024), 2019. a
Ma, Y., Montzka, C., Bayat, B., and Kollet, S.: An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis, Front. Water, 3, 723548, https://doi.org/10.3389/frwa.2021.723548, 2021. a, b
Marj, A. F. and Meijerink, A. M. J.: Agricultural drought forecasting using satellite images, climate indices and artificial neural network, Int. J. Remote Sens., 32, 9707–9719, https://doi.org/10.1080/01431161.2011.575896, 2011. a, b
Martínez-Fernández, J., González-Zamora, A., Sánchez, N., and Gumuzzio, A.: A soil water based index as a suitable agricultural drought indicator, J. Hydrol., 522, 265–273, https://doi.org/10.1016/j.jhydrol.2014.12.051, 2015. a
Maxwell, R. M., Condon, L. E., and Kollet, S. J.: A high-resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3, Geosci. Model Dev., 8, 923–937, https://doi.org/10.5194/gmd-8-923-2015, 2015. a
McKee, T. B.: Drought monitoring with multiple time scales, in: Proceedings of the 9th Conference on Applied Climatology, 15–20 January 1995, Dallas, TX, American Meteorological Society, 233–236, 1995. a
Meza, I., Siebert, S., Döll, P., Kusche, J., Herbert, C., Eyshi Rezaei, E., Nouri, H., Gerdener, H., Popat, E., Frischen, J., Naumann, G., Vogt, J. V., Walz, Y., Sebesvari, Z., and Hagenlocher, M.: Global-scale drought risk assessment for agricultural systems, Nat. Hazards Earth Syst. Sci., 20, 695–712, https://doi.org/10.5194/nhess-20-695-2020, 2020. a
Miao, L., Ye, P., He, B., Chen, L., and Cui, X.: Future Climate Impact on the Desertification in the Dry Land Asia Using AVHRR GIMMS NDVI3g Data, Remote Sens., 7, 3863–3877, https://doi.org/10.3390/rs70403863, 2015. a, b
Miralles, D. G., Gentine, P., Seneviratne, S. I., and Teuling, A. J.: Land–atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges, Ann. NY Acad. Sci., 1436, 19–35, https://doi.org/10.1111/nyas.13912, 2019. a, b
Moravec, D., Komárek, J., López-Cuervo Medina, S., and Molina, I.: Effect of Atmospheric Corrections on NDVI: Intercomparability of Landsat 8, Sentinel-2, and UAV Sensors, Remote Sens., 13, 3550, https://doi.org/10.3390/rs13183550, 2021. a
Naderi, M., Givkashi, M., Piri, F., Karimi, N., and Samavi, S.: Focal-UNet: UNet-like Focal Modulation for Medical Image Segmentation, arXiv [preprint], https://doi.org/10.48550/arXiv.2212.09263, 2022. a
Nagol, J. R., Vermote, E. F., and Prince, S. D.: Effects of atmospheric variation on AVHRR NDVI data, Remote Sens. Environ., 113, 392–397, https://doi.org/10.1016/j.rse.2008.10.007, 2009. a, b
Nay, J., Burchfield, E., and Gilligan, J.: A machine-learning approach to forecasting remotely sensed vegetation health, Int. J. Remote Sens., 39, 1800–1816, https://doi.org/10.1080/01431161.2017.1410296, 2018. a
Naz, B. S., Sharples, W., Ma, Y., Goergen, K., and Kollet, S.: Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe, Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023, 2023. a, b
Nguyen, T., Brandstetter, J., Kapoor, A., Gupta, J. K., and Grover, A.: ClimaX: A foundation model for weather and climate, arXiv [preprint], https://doi.org/10.48550/arXiv.2301.10343, 2023. a
Oleson, K., Yongjiu, D., Bosilovich, M., Dickinson, R., Dirmeyer, P., Hoffman, F., Houser, P., Levis, S., Niu, G.-Y., Thornton, P., Vertenstein, M., Yang, Z.-L., and Xubin, Z.: Technical Description of the Community Land Model (CLM), (No. NCAR/TN-461+STR), University Corporation for Atmospheric Research, https://doi.org/10.5065/D6N877R0, 2004. a
Oleson, K. W., Niu, G.-Y., Yang, Z.-L., Lawrence, D. M., Thornton, P. E., Lawrence, P. J., Stöckli, R., Dickinson, R. E., Bonan, G. B., Levis, S., Dai, A., and Qian, T.: Improvements to the Community Land Model and their impact on the hydrological cycle, J. Geophys. Res.-Biogeo., 113, G01021, https://doi.org/10.1029/2007JG000563, 2008. a
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S.: PyTorch: An Imperative Style, High-Performance Deep Learning Library, in: Advances in Neural Information Processing Systems, Curran Associates, Inc., 32, 8024–8035, http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (last access: 31 July 2023), 2019. a
Patacca, M., Lindner, M., Lucas-Borja, M. E., Cordonnier, T., Fidej, G., Gardiner, B., Hauf, Y., Jasinevičius, G., Labonne, S., Linkevičius, E., Mahnken, M., Milanovic, S., Nabuurs, G.-J., Nagel, T. A., Nikinmaa, L., Panyatov, M., Bercak, R., Seidl, R., Ostrogović Sever, M. Z., Socha, J., Thom, D., Vuletic, D., Zudin, S., and Schelhaas, M.-J.: Significant increase in natural disturbance impacts on European forests since 1950, Glob. Change Biol., 29, 1359–1376, https://doi.org/10.1111/gcb.16531, 2023. a
Patakchi Yousefi, K. and Kollet, S.: Deep learning of model- and reanalysis-based precipitation and pressure mismatches over Europe, Front. Water, 5, 1178114, https://doi.org/10.3389/frwa.2023.1178114, 2023. a
Patil, S. D., Gu, Y., Dias, F. S. A., Stieglitz, M., and Turk, G.: Predicting the spectral information of future land cover using machine learning, Int. J. Remote Sens., 38, 5592–5607, https://doi.org/10.1080/01431161.2017.1343512, 2017. a
Pirret, J., Fung, F., Lowe, J., McInnes, R., Mitchell, J., and Murphy, J.: UKCP Factsheet: Soil Moisture, https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/data/factsheets (last access: 4 April 2024), 2020. a
Pokhrel, Y., Felfelani, F., Satoh, Y., Boulange, J. E. S., Burek, P., Gädeke, A., Gerten, D., Gosling, S. N., Grillakis, M. G., Gudmundsson, L., Hanasaki, N., Kim, H., Koutroulis, A. G., Liu, J., Papadimitriou, L., Schewe, J., Schmied, H. M., Stacke, T. I., Telteu, C.-E., Thiery, W., Veldkamp, T. I. E., Zhao, F., and Wada, Y.: Global terrestrial water storage and drought severity under climate change, Nat. Clim. Change, 11, 226–233, https://doi.org/10.1038/s41558-020-00972-w, 2021. a, b
Poshyvailo-Strube, L., Wagner, N., Goergen, K., Furusho-Percot, C., Hartick, C., and Kollet, S.: Groundwater in terrestrial systems modelling: a new climatology of extreme heat events in Europe, Earth Syst. Dynam. Discuss. [preprint], https://doi.org/10.5194/esd-2022-53, in review, 2022. a
Prodhan, F. A., Zhang, J., Yao, F., Shi, L., Pangali Sharma, T. P., Zhang, D., Cao, D., Zheng, M., Ahmed, N., and Mohana, H. P.: Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data, Remote Sens., 13, 1715, https://doi.org/10.3390/rs13091715, 2021. a
Qin, Q., Wu, Z., Zhang, T., Sagan, V., Zhang, Z., Zhang, Y., Zhang, C., Ren, H., Sun, Y., Xu, W., and Zhao, C.: Optical and Thermal Remote Sensing for Monitoring Agricultural Drought, Remote Sens., 13, 5092, https://doi.org/10.3390/rs13245092, 2021. a
Rasoulian, A., Salari, S., and Xiao, Y.: Weakly supervised segmentation of intracranial aneurysms using a 3D focal modulation UNet, arXiv [preprint], https://doi.org/10.48550/arXiv.2308.03001, 2023. a
Requena-Mesa, C., Benson, V., Reichstein, M., Runge, J., and Denzler, J.: EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task, in: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Nashville, TN, USA, 1132–1142, https://doi.org/10.1109/CVPRW53098.2021.00124, 2021. a
Reyniers, N., Osborn, T. J., Addor, N., and Darch, G.: Projected changes in droughts and extreme droughts in Great Britain strongly influenced by the choice of drought index, Hydrol. Earth Syst. Sci., 27, 1151–1171, https://doi.org/10.5194/hess-27-1151-2023, 2023. a, b
Robin, C., Requena-Mesa, C., Benson, V., Poehls, J., Alonzo, L., Carvalhais, N., and Reichstein, M.: Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs, in: NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning, https://www.climatechange.ai/papers/neurips2022/88 (last access: 4 April 2024), 2022. a
Ronneberger, O., Fischer, P., and Brox, T.: U-net: Convolutional networks for biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, 5–9 October 2015, Proceedings, Part III 18, Springer, 234–241, https://doi.org/10.1007/978-3-319-24574-4_28, 2015. a, b
Salakpi, E. E., Hurley, P. D., Muthoka, J. M., Barrett, A. B., Bowell, A., Oliver, S., and Rowhani, P.: Forecasting vegetation condition with a Bayesian auto-regressive distributed lags (BARDL) model, Nat. Hazards Earth Syst. Sci., 22, 2703–2723, https://doi.org/10.5194/nhess-22-2703-2022, 2022a. a
Salakpi, E. E., Hurley, P. D., Muthoka, J. M., Bowell, A., Oliver, S., and Rowhani, P.: A dynamic hierarchical Bayesian approach for forecasting vegetation condition, Nat. Hazards Earth Syst. Sci., 22, 2725–2749, https://doi.org/10.5194/nhess-22-2725-2022, 2022b. a
Satoh, Y., Shiogama, H., Hanasaki, N., Pokhrel, Y., Boulange, J. E. S., Burek, P., Gosling, S. N., Grillakis, M., Koutroulis, A., Schmied, H. M., Thiery, W., and Yokohata, T.: A quantitative evaluation of the issue of drought definition: a source of disagreement in future drought assessments, Environ. Res. Lett., 16, 104001, https://doi.org/10.1088/1748-9326/ac2348, 2021. a
Scheck, L., Frèrebeau, P., Buras-Schnell, R., and Mayer, B.: A fast radiative transfer method for the simulation of visible satellite imagery, J. Quant. Spectrosc. Ra., 175, 54–67, https://doi.org/10.1016/j.jqsrt.2016.02.008, 2016. a
Schultz, M. G., Betancourt, C., Gong, B., Kleinert, F., Langguth, M., Leufen, L. H., Mozaffari, A., and Stadtler, S.: Can deep learning beat numerical weather prediction?, Philos. T. Roy. Soc. A, 379, 20200097, https://doi.org/10.1098/rsta.2020.0097, 2021. a
Seneviratne, S., Zhang, X., Adnan, M., Badi, W., Dereczynski, C., Di Luca, A., Ghosh, S., Iskandar, I., Kossin, J., Lewis, S., Otto, F., Pinto, I., Satoh, M., Vicente-Serrano, S., Wehner, M., and Zhou, B.: Weather and Climate Extreme Events in a Changing Climate, Cambridge University Press, 1513–1766, https://doi.org/10.1017/9781009157896.013, 2021. a
Shams Eddin, M. H. and Gall, J.: Focal-TSMP: Deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation, Zenodo [code], https://doi.org/10.5281/zenodo.10015048, 2023a. a
Shams Eddin, M. H. and Gall, J.: Focal-TSMP: Deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation, Zenodo [data set], https://doi.org/10.5281/zenodo.10008814, 2023b. a
Sheffield, J., Wood, E. F., and Roderick, M. L.: Little change in global drought over the past 60 years, Nature, 491, 435–438, 2012. a
Shi, X., Li, Y., Liu, J., Xiang, X., and Liu, L.: Simulation of FY-2D infrared brightness temperature and sensitivity analysis to the errors of WRF simulated cloud variables, Sci. China Earth Sci., 61, 1–16, https://doi.org/10.1007/s11430-017-9150-0, 2018. a
Shrestha, P., Sulis, M., Masbou, M., Kollet, S., and Simmer, C.: A Scale-Consistent Terrestrial Systems Modeling Platform Based on COSMO, CLM, and ParFlow, Mon. Weather Rev., 142, 3466–3483, https://doi.org/10.1175/MWR-D-14-00029.1, 2014. a, b
Simonyan, K. and Zisserman, A.: Very deep convolutional networks for large-scale image recognition, arXiv [preprint], https://doi.org/10.48550/arXiv.1409.1556, 2014. a
Tang, Y., Han, K., Guo, J., Xu, C., Li, Y., Xu, C., and Wang, Y.: An Image Patch is a Wave: Phase-Aware Vision MLP, in: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 10925–10934, https://doi.org/10.1109/CVPR52688.2022.01066, 2022. a
Tucker, C. J.: Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ. 8, 127–150, https://doi.org/10.1016/0034-4257(79)90013-0, 1979. a
Tuia, D., Schindler, K., Demir, B., Camps-Valls, G., Zhu, X. X., Kochupillai, M., Džeroski, S., van Rijn, J. N., Hoos, H. H., Del Frate, F., Datcu, M., Quiané-Ruiz, J.-A., Markl, V., Saux, B. L., and Schneider, R.: Artificial intelligence to advance Earth observation: a perspective, arXiv [preprint], https://doi.org/10.48550/arXiv.2305.08413, 2023. a
Valcke, S.: The OASIS3 coupler: a European climate modelling community software, Geosci. Model Dev., 6, 373–388, https://doi.org/10.5194/gmd-6-373-2013, 2013. a
Valmassoi, A., Keller, J. D., Kleist, D. T., English, S., Ahrens, B., Ďurán, I. B., Bauernschubert, E., Bosilovich, M. G., Fujiwara, M., Hersbach, H., Lei, L., Löhnert, U., Mamnun, N., Martin, C. R., Moore, A., Niermann, D., Ruiz, J. J., and Scheck, L.: Current challenges and future directions in data assimilation and reanalysis, B. Am. Meteorol. Soc., https://doi.org/10.1175/BAMS-D-21-0331.1, 2022. a
Van Loon, A. F., Stahl, K., Di Baldassarre, G., Clark, J., Rangecroft, S., Wanders, N., Gleeson, T., Van Dijk, A. I. J. M., Tallaksen, L. M., Hannaford, J., Uijlenhoet, R., Teuling, A. J., Hannah, D. M., Sheffield, J., Svoboda, M., Verbeiren, B., Wagener, T., and Van Lanen, H. A. J.: Drought in a human-modified world: reframing drought definitions, understanding, and analysis approaches, Hydrol. Earth Syst. Sci., 20, 3631–3650, https://doi.org/10.5194/hess-20-3631-2016, 2016. a
Van Oldenborgh, G. J., van Der Wiel, K., Kew, S., Philip, S., Otto, F., Vautard, R., King, A., Lott, F., Arrighi, J., Singh, R., and van Aalst, M.: Pathways and pitfalls in extreme event attribution, Climatic Change, 166, 13, https://doi.org/10.1007/s10584-021-03071-7, 2021. a
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I.: Attention is all you need, 31st Conference on Neural Information Processing Systems, Long Beach, CA, USA, 2017. a
Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index, J. Climate, 23, 1696–1718, https://doi.org/10.1175/2009JCLI2909.1, 2010. a
Vicente-Serrano, S. M., Peña-Angulo, D., Beguería, S., Domínguez-Castro, F., Tomás-Burguera, M., Noguera, I., Gimeno-Sotelo, L., and El Kenawy, A.: Global drought trends and future projections, Philos. T. Roy. Soc. A, 380, 20210285, https://doi.org/10.1098/rsta.2021.0285, 2022. a
Vo, T. Q., Kim, S.-H., Nguyen, D. H., and hyo Bae, D.: LSTM-CM: a hybrid approach for natural drought prediction based on deep learning and climate models, Stoch. Env. Res. Risk A., 37, 2035–2051, https://doi.org/10.1007/s00477-022-02378-w, 2023. a
Vreugdenhil, M., Greimeister-Pfeil, I., Preimesberger, W., Camici, S., Dorigo, W., Enenkel, M., van der Schalie, R., Steele-Dunne, S., and Wagner, W.: Microwave remote sensing for agricultural drought monitoring: Recent developments and challenges, Front. Water, 4, 1045451, https://doi.org/10.3389/frwa.2022.1045451, 2022. a
Wang, L., Li, R., Duan, C., Zhang, C., Meng, X., and Fang, S.: A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images, IEEE Geoscie. Remote Sens. Lett., 19, 1–5, https://doi.org/10.1109/LGRS.2022.3143368, 2022a. a
Wang, L., Li, R., Zhang, C., Fang, S., Duan, C., Meng, X., and Atkinson, P. M.: UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery, ISPRS J. Photogramm., 190, 196–214, https://doi.org/10.1016/j.isprsjprs.2022.06.008, 2022b. a
Wasim, S. T., Khattak, M. U., Naseer, M., Khan, S., Shah, M., and Khan, F. S.: Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition, in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 13778–13789, https://openaccess.thecvf.com/content/ICCV2023/html/Wasim_Video-FocalNets_Spatio-Temporal_Focal_Modulation_for_Video_Action_Recognition_ICCV_2023_paper.html (last access: 4 April 2024), 2023. a
Wei, J., Liu, X., and Zhou, B.: Sensitivity of Vegetation to Climate in Mid-to-High Latitudes of Asia and Future Vegetation Projections, Remote Sens., 15, 2648, https://doi.org/10.3390/rs15102648, 2023. a
Wilson, M. F. J., O’Connell, B., Brown, C., Guinan, J. C., and Grehan, A. J.: Multiscale Terrain Analysis of Multibeam Bathymetry Data for Habitat Mapping on the Continental Slope, Mar. Geod., 30, 3–35, https://doi.org/10.1080/01490410701295962, 2007. a
Woo, S., Park, J., Lee, J.-Y., and Kweon, I. S.: Cbam: Convolutional block attention module, in: Proceedings of the European conference on computer vision (ECCV), Lecture Notes in Computer Science, vol 11211, Springer, Cham, 3–19, https://doi.org/10.1007/978-3-030-01234-2_1, 2018. a
Wu, T., Feng, F., Lin, Q., and Bai, H.: A spatio-temporal prediction of NDVI based on precipitation: an application for grazing management in the arid and semi-arid grasslands, Int. J. Remote Sens., 41, 2359–2373, https://doi.org/10.1080/01431161.2019.1688418, 2020. a, b
Wu, Y. and He, K.: Group Normalization, in: Computer Vision – ECCV 2018, Lecture Notes in Computer Science, edited by: Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y., 3–19, Springer International Publishing, Cham, https://doi.org/10.1007/978-3-030-01261-8_1, 2018. a
Xoplaki, E., Ellsäßer, F., Grieger, J., Nissen, K. M., Pinto, J., Augenstein, M., Chen, T.-C., Feldmann, H., Friederichs, P., Gliksman, D., Goulier, L., Haustein, K., Heinke, J., Jach, L., Knutzen, F., Kollet, S., Luterbacher, J., Luther, N., Mohr, S., Mudersbach, C., Müller, C., Rousi, E., Simon, F., Suarez-Gutierrez, L., Szemkus, S., Vallejo-Bernal, S. M., Vlachopoulos, O., and Wolf, F.: Compound events in Germany in 2018: drivers and case studies, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1460, 2023. a
Yang, J., Li, C., Zhang, P., Dai, X., Xiao, B., Yuan, L., and Gao, J.: Focal Attention for Long-Range Interactions in Vision Transformers, in: Advances in Neural Information Processing Systems, edited by: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., and Vaughan, J. W., Curran Associates Inc., vol. 34, 30008–30022, https://proceedings.neurips.cc/paper_files/paper/2021/file/fc1a36821b02abbd2503fd949bfc9131-Paper.pdf (last access: 31 July 2023), 2021a. a
Yang, W., Guo, W., and Kogan, F.: VIIRS-based high resolution spectral vegetation indices for quantitative assessment of vegetation health: second version, Int. J. Remote Sens., 39, 7417–7436, https://doi.org/10.1080/01431161.2018.1470701, 2018. a, b
Yang, W., Kogan, F., Guo, W., and Chen, Y.: A novel re-compositing approach to create continuous and consistent cross-sensor/cross-production global NDVI datasets, Int. J. Remote Sens., 42, 6023–6047, https://doi.org/10.1080/01431161.2021.1934597, 2021b. a, b, c, d
Yu, S., Hannah, W. M., Peng, L., Bhouri, M. A., Gupta, R., Lin, J., Lütjens, B., Will, J. C., Beucler, T., Harrop, B. E., R. Hillman, B., Jenney, A., Ferretti, S., Liu, N., Anandkumar, A., Brenowitz, N. D., Eyring, V., Geneva, N., Gentine, P., Mandt, S., Pathak, J., Subramaniam, A., Vondrick, C., Yu, R., Zanna, L., Zheng, T., Abernathey, R., Ahmed, F., Bader, D. C., Baldi, P., Barnes, E., Bretherton, C., Caldwell, P., Chuang, W., Han, Y., Huang, Y., Iglesias-Suarez, F., Jantre, S., Kashinath, K., Khairoutdinov, M., Kurth, T., Lutsko, N., Ma, P.-L., Mooers, G., Neelin, J. D., Randall, D., Shamekh, S., Taylor, M. A., Urban, N., Yuval, J., Zhang, G., and Pritchard, M.: ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators, arXiv [preprint], https://doi.org/10.48550/arXiv.2306.08754, 2023. a
Yu, W., Li, J., Liu, Q., Zhao, J., Dong, Y., Wang, C., Lin, S., Zhu, X., and Zhang, H.: Spatial–Temporal Prediction of Vegetation Index With Deep Recurrent Neural Networks, IEEE Geosci. Remote Sens. Lett., 19, 1–5, https://doi.org/10.1109/LGRS.2021.3064814, 2022. a
Yuan, X., Wang, Y., Ji, P., Wu, P., Sheffield, J., and Otkin, J. A.: A global transition to flash droughts under climate change, Science, 380, 187–191, https://doi.org/10.1126/science.abn6301, 2023. a
Zeng, J., Zhang, R., Qu, Y., Bento, V. A., Zhou, T., Lin, Y., Wu, X., Qi, J., Shui, W., and Wang, Q.: Improving the drought monitoring capability of VHI at the global scale via ensemble indices for various vegetation types from 2001 to 2018, Weather and Climate Extremes, 35, 100412, https://doi.org/10.1016/j.wace.2022.100412, 2022. a
Zeng, J., Zhou, T., Qu, Y., Bento, V., Qi, J., Xu, Y., Li, Y., and Wang, Q.: An improved global vegetation health index dataset in detecting vegetation drought, Sci. Data, 10, 338, https://doi.org/10.1038/s41597-023-02255-3, 2023. a, b
Zhu, Z., Piao, S., Myneni, R. B., Huang, M., Zeng, Z., Canadell, J. G., Ciais, P., Sitch, S., Friedlingstein, P., Arneth, A., Cao, C., Cheng, L., Kato, E., Koven, C., Li, Y., Lian, X., Liu, Y., Liu, R., Mao, J., Pan, Y., Peng, S., Peñuelas, J., Poulter, B., Pugh, T. A. M., Stocker, B. D., Viovy, N., Wang, X., Wang, Y., Xiao, Z., Yang, H., Zaehle, S., and Zeng, N.: Greening of the Earth and its drivers, Nat. Clim. Change, 6, 791–795, https://doi.org/10.1038/nclimate3004, 2016. a
Zhuang, J., raphael dussin, Jüling, A., and Rasp, S.: JiaweiZhuang/xESMF: v0.3.0 Adding ESMF.LocStream capabilities, Zenodo [code], https://doi.org/10.5281/zenodo.3700105, 2020. a
Short summary
In this study, we use deep learning and a climate simulation to predict the vegetation health as it would be observed from satellites. We found that the developed model can help to identify regions with a high risk of agricultural drought. The main applications of this study are to estimate vegetation products for periods where no satellite data are available and to forecast the future vegetation response to climate change based on climate scenarios.
In this study, we use deep learning and a climate simulation to predict the vegetation health as...
