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    Item type:Publication, Access status: with files ,
    Comprehensive inter-comparison of generative AI models for super-resolution precipitation downscaling across hydroclimatic regimes
    (Copernicus Publications, 2026-08-17)
    Singh, Shivam
    ;
    Papalexiou, Simon Michael
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    Abdelmoaty, Hebatallah M.
    ;
    Hartvigsen, Tom
    ;
    Mamalakis, Antonios
    High-resolution precipitation information is essential for hydrologic modeling, flood forecasting, and climate-risk assessment, yet global weather and climate models operate at spatial resolutions too coarse to resolve storm structure, intermittency, and extremes. Deep-learning-based statistical downscaling provides a computationally efficient alternative to dynamical downscaling, but deterministic convolutional neural networks often yield overly smooth predictions and underestimate fine-scale variability and extreme events. Generative deep-learning models, including generative adversarial networks and diffusion models, offer a promising alternative by enabling stochastic downscaling and explicit representation of uncertainty. This study presents a systematic intercomparison of three representative deep-learning architectures for precipitation super-resolution, namely a Convolutional U-Net as baseline, a conditional Wasserstein GAN (WGAN), and a conditional Denoising Diffusion Probabilistic Model (DDPM). Using a perfect-model experimental design based on ERA5-Land precipitation fields over climatologically distinct regions of the United States, models are trained over the Central Plains and Northwest domains and evaluated over an independent Northeast test domain under  8×and 16× downscaling factors, providing a stringent test of cross-regional generalization. Evaluation diagnostics span precipitation distributions, wet–dry occurrence, extremes, spatial autocorrelation, spectral structure, and ensemble-based uncertainty quantification. All three models preserve large-scale precipitation organization, with differences emerging primarily at fine spatial scales and in the representation of extremes and spatial dependence. U-Net provides stable and computationally efficient predictions but consistently smooths fine-scale variability and suppresses extreme precipitation. WGAN improves distributional fidelity and heavy-tail behavior with comparatively modest computational overhead. DDPM yields the most physically coherent spatial structure and natural ensemble diversity for uncertainty quantification, at a substantially higher computational cost. Analysis of seed-based variability further reveals that training uncertainty dominates over stochastic generation variability, underscoring the need for multi-seed evaluation in generative downscaling systems.
    Publicationtype: Journal Article
      12  2
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    Item type:Publication, Access status: without files ,
    CropSuite v1.0 – a comprehensive open-source crop suitability model considering climate variability for climate impact assessment
    (Copernicus Publications, 2025-02-24)
    Zabel, Florian
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    Knüttel, Matthias
    ;
    Poschlod, Benjamin
    Increasing demand for agricultural land resources and changing climate conditions require strategic land-use planning and the development of adaptation strategies. Therefore, information about the suitability of agricultural land is a prerequisite. Current suitability approaches often focus on single crops, can only be applied regionally, and usually neglect the impact of climate variability on crop suitability. Here, we introduce CropSuite, a new, comprehensive, and easy-to-use crop suitability model that allows us to overcome these shortcomings. It provides a graphical user interface (GUI) and a wide range of pre- and postprocessing options, including a tool for data analysis, which allows users to easily apply the model and analyze the results. Further, it includes a spatial downscaling approach to climate data, which enables crop suitability analysis at very high spatial resolution. CropSuite uses a fuzzy-logic approach and is based on the assumption of Liebig’s law of the minimum. An expandable number of environmental and socioeconomic factors that affect crop suitability can be flexibly integrated into CropSuite by determining membership functions. CropSuite allows for the consideration of irrigated and rainfed agricultural systems, vernalization requirements for winter crops, lethal temperature thresholds, photoperiodic sensitivity, and several other limitations for crop growth. The model endogenously calculates and outputs climate, soil, and crop suitability, the optimal sowing and harvest dates, the potential for multiple cropping, the (most-)limiting factor(s), and the recurrence rate of potential crop failure according to the inter-annual climate variability. In this study, we apply CropSuite to 48 crops at a spatial resolution of 30 arcsec (1 km at the Equator) for Africa. Thereby, we consider regionally important staple and cash crops that are usually understudied, such as coffee, cassava, banana, oil palm, cocoa, cowpea, groundnuts, mango, millet, papaya, rubber, sesame, sorghum, sugar cane, tobacco, and yam. We find that the consideration of climate variability when calculating crop suitability makes a significant difference to suitable areas but also affects optimal sowing dates and multiple cropping potentials. The most vulnerable regions for climate variability are identified in Somalia, Kenya, Ethiopia, South Africa, and the Maghreb countries. The results provide valuable crop-specific information that can be further used for climate impact assessments, adaptation, and land-use planning at the global, regional, or local scale. CropSuite is provided as open-source code and could be of interest for model developers, scientists, and a wide range of potential users and stakeholders, such as farmers, companies, governmental organizations (GOs), and non-governmental organizations (NGOs).
    Publicationtype: Journal Article
      9
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    GPEP v1.0: the geospatial probabilistic estimation package to support earth science applications
    (Copernicus Publications, 2024-02-12)
    Tang, Guoqiang
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    Wood, Andrew W.
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    Newman Andrew J.
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    Clark, Martyn P.
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    Papalexiou, Simon Michael
    Ensemble geophysical datasets are foundational for research to understand the Earth system in an uncertainty-aware context and to drive applications that require quantification of uncertainties, such as probabilistic hydro-meteorological estimation or prediction. Yet ensemble estimation is more challenging than single-value spatial interpolation, and open-access routines and tools are limited in this area, hindering the generation and application of ensemble geophysical datasets. A notable exception in the last decade has been the Gridded Meteorological Ensemble Tool (GMET), which is implemented in FORTRAN and has typically been configured for ensemble estimation of precipitation, mean air temperature, and daily temperature range, based on station observations. GMET has been used to generate a variety of local, regional, national, and global meteorological datasets, which in turn have driven multiple retrospective and real-time hydrological applications. Motivated by an interest in expanding GMET flexibility, application scope, and range of methods, we have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP) that offers GMET functionality along with additional methodological and usability improvements, including variable independence and flexibility, an efficient alternative cross-validation strategy, internal parallelization, and the availability of the scikit-learn machine learning library for both local and global regression. This paper describes GPEP and illustrates some of its capabilities using several demonstration experiments, including the estimation of precipitation, temperature, and snow water equivalent ensemble analyses on various scales.
    Publicationtype: Journal Article
      4
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    The Teddy tool v1.1: temporal disaggregation of daily climate model data for climate impact analysis
    (Copernicus Publications, 2023-09-21)
    Zabel, Florian
    ;
    Poschlod, Benjamin
    Climate models provide the required input data for global or regional climate impact analysis in temporally aggregated form, often in daily resolution to save space on data servers. Today, many impact models work with daily data; however, sub-daily climate information is becoming increasingly important for more and more models from different sectors, such as the agricultural, water, and energy sectors. Therefore, the open-source Teddy tool (temporal disaggregation of daily climate model data) has been developed to disaggregate (temporally downscale) daily climate data to sub-daily hourly values. Here, we describe and validate the temporal disaggregation, which is based on the choice of daily climate analogues. In this study, we apply the Teddy tool to disaggregate bias-corrected climate model data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We choose to disaggregate temperature, precipitation, humidity, longwave radiation, shortwave radiation, surface pressure, and wind speed. As a reference, globally available bias-corrected hourly reanalysis WFDE5 (WATCH Forcing Data methodology applied to ERA5) data from 1980-2019 are used to take specific local and seasonal features of the empirical diurnal profiles into account. For a given location and day within the climate model data, the Teddy tool screens the reference data set to find the most similar meteorological day based on rank statistics. The diurnal profile of the reference data is then applied on the climate model. The physical dependency between variables is preserved, since the diurnal profile of all variables is taken from the same, most similar meteorological day of the historical reanalysis dataset. Mass and energy are strictly preserved by the Teddy tool to exactly reproduce the daily values from the climate models. For evaluation, we aggregate the hourly WFDE5 data to daily values and apply the Teddy tool for disaggregation. Thereby, we compare the original hourly data with the data disaggregated by Teddy. We perform a sensitivity analysis of different time window sizes used for finding the most similar meteorological day in the past. In addition, we perform a cross-validation and autocorrelation analysis for 30 globally distributed samples around the world that represent different climate zones. The validation shows that Teddy is able to reproduce historical diurnal courses with high correlations >0.9 for all variables, except for wind speed (>0.75) and precipitation (>0.5). We discuss the limitations of the method regarding the reproduction of precipitation extremes, interday connectivity, and disaggregation of end-of-century projections with strong warming. Depending on the use case, sub-daily data provided by the Teddy tool could make climate impact assessments more robust and reliable.
    Publicationtype: Journal Article
      13
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    Item type:Publication, Access status: with files ,
    A Python library for computing individual and merged non-CO2 algorithmic climate change functions: CLIMaCCF V1.0
    (Copernicus Publications, 2023-08-02)
    Dietmüller, Simone
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    Matthes, Sigrun
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    Dahlmann, Katrin
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    Yamashita, Hiroshi
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    Simorgh, Abolfazl
    Aviation aims to reduce its climate effect by adopting trajectories that avoid regions of the atmosphere where aviation emissions have a large impact. To that end, prototype algorithmic climate change functions (aCCFs) can be used, which provide spatially and temporally resolved information on aviation's climate effect in terms of future near-surface temperature change. These aCCFs can be calculated with meteorological input data obtained from, e.g., numerical weather prediction models. We present here the open-source Python library called CLIMaCCF, an easy-to-use and flexible tool which efficiently calculates both the individual aCCFs (i.e., aCCF of water vapor, nitrogen oxide (NOx)-induced ozone production and methane depletion, and contrail cirrus) and the merged non-CO2 aCCFs that combine all these individual contributions. To construct merged aCCFs all individual aCCFs are converted to the same physical unit. This unit conversion needs the technical specification of aircraft and engine parameters, i.e., NOx emission indices and flown distance per kilogram of burned fuel. These aircraft- and engine-specific values are provided within CLIMaCCF version V1.0 for a set of aggregated aircraft and engine classes (i.e., regional, single-aisle, wide-body). Moreover, CLIMaCCF allows the user to choose from a range of physical climate metrics (i.e., average temperature response for pulse or future scenario emissions over the time horizons of 20, 50, or 100 years). Finally, we demonstrate the abilities of CLIMaCCF through a series of example applications.
    Publicationtype: Journal Article
    Scopus© Citations 7  31  67
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    Item type:Publication, Access status: with files ,
    Robust 4D climate-optimal flight planning in structured airspace using parallelized simulation on GPUs: ROOST V1.0
    (2023-07-06)
    Simorgh, Abolfazl
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    Soler, Manuel
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    González-Arribas, Daniel
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    Linke, Florian
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    Lührs, Benjamin
    The climate impact of non-CO2 emissions, which are responsible for two-thirds of aviation radiative forcing, highly depends on the atmospheric chemistry and weather conditions. Hence, by planning aircraft trajectories to reroute areas where the non-CO2 climate impacts are strongly enhanced, called climate-sensitive regions, there is a potential to reduce aviation-induced non-CO2 climate effects. Weather forecast is inevitably uncertain, which can lead to unreliable determination of climate-sensitive regions and aircraft dynamical behavior and, consequently, inefficient trajectories. In this study, we propose robust climate-optimal aircraft trajectory planning within the currently structured airspace considering uncertainties in standard weather forecasts. The ensemble prediction system is employed to characterize uncertainty in the weather forecast, and climate-sensitive regions are quantified using the prototype algorithmic climate change functions. As the optimization problem is constrained by the structure of airspace, it is associated with hybrid decision spaces. To account for discrete and continuous decision variables in an integrated and more efficient manner, the optimization is conducted on the space of probability distributions defined over flight plans instead of directly searching for the optimal profile. A heuristic algorithm based on the augmented random search is employed and implemented on graphics processing units to solve the proposed stochastic optimization computationally fast. An open-source Python library called ROOST (V1.0) is developed based on the aircraft trajectory optimization technique. The effectiveness of our proposed strategy to plan robust climate-optimal trajectories within the structured airspace is analyzed through two scenarios: a scenario with a large contrail climate impact and a scenario with no formation of persistent contrails. It is shown that, for a nighttime flight from Frankfurt to Kyiv, a 55% reduction in climate impact can be achieved at the expense of a 4% increase in the operating cost.
    Publicationtype: Journal Article
    Scopus© Citations 5  29  48
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    Item type:Publication, Access status: with files ,
    Computation of backwater effects in surface waters of lowland catchments including control structures – an efficient and re-usable method implemented in the hydrological open-source model Kalypso-NA (4.0)
    (Copernicus Publications, 2022-02-04)
    Hellmers, Sandra
    ;
    Fröhle, Peter
    Backwater effects in surface water streams and on adjacent lowland areas caused by mostly complex drainage and flow control structures are not directly computed with hydrological approaches yet. A solution to this weakness in hydrological modelling is presented in this article. The developed method enables transfer of discharges into water levels and calculation of backwater volume routing along streams and adjacent lowland areas by balancing water level slopes. The implemented and evaluated method extends the application of hydrological models for rainfall–runoff simulations of backwater-affected catchments with the advantages of (1) modelling complex flow control systems in tidal backwater-affected lowlands, (2) less effort to parameterise river streams, (3) directly defined input factors of driving forces (climate change and urbanisation) and (4) runtime reduction of 1 to 2 orders of magnitude in comparison to coupled hydrodynamic models. The developed method is implemented in the open-source rainfall–runoff model Kalypso-NA (4.0). Evaluation results show the applicability of the model for simulating rainfall–runoff regimes and backwater effects in an exemplary lowland catchment (Hamburg, Germany) with a complex flow control system and where the drainage is influenced by a tidal range of about 4 m. The proposed method is applicable to answer a wide scope of hydrological and water management questions, e.g. water balances, flood forecasts and effectiveness of flood mitigation measures. It is re-usable to other hydrological numerical models, which apply conceptual hydrological flood-routing approaches (e.g. Muskingum–Cunge or Kalinin–Miljukov).
    Publicationtype: Journal Article
    Scopus© Citations 2  87  179
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    Item type:Publication, Access status: without files ,
    GlmGUI v1.0: an R-based graphical user interface and toolbox for GLM (General Lake Model) simulations
    (Copernicus Publications, 2020-02-13)
    Bueche, Thomas
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    Wenk, Marko
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    Poschlod, Benjamin
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    Giadrossich, Filippo
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    Pirastru, Mario
    Numerical modeling provides an opportunity to quantify the reaction of lakes to alterations in their environment, such as changes in climate or hydrological conditions. The one-dimensional hydrodynamic General Lake Model (GLM) is an open-source software and widely used within the limnological research community. Nevertheless, no interface to process the input data and run the model and no tools for an automatic parameter calibration yet exist. Hence, we developed glmGUI, a graphical user interface (GUI) including a toolbox for an autocalibration, parameter sensitivity analysis, and several plot options. The tool is provided as a package for the freely available scientific code language R. The model parameters can be analyzed and calibrated for the simulation output variables water temperature and lake level. The glmGUI package is tested for two sites (lake Ammersee, Germany, and lake Baratz, Italy), distinguishing size, mixing regime, hydrology of the catchment area (i.e., the number of inflows and their runoff seasonality), and climatic conditions. A robust simulation of water temperature for both lakes (Ammersee: RMSE D 1:17 °C; Baratz: RMSE D 1:30 °C) is achieved by a quick automatic calibration. The quality of a water temperature simulation can be assessed immediately by means of a difference plot provided by glmGUI, which displays the distribution of the spatial (vertical) and temporal deviations. The calibration of the lake-level simulations of lake Ammersee for multiple hydrological inputs including also unknown inflows yielded a satisfactory model fit (RMSE D 0:20 m). This shows that GLM can also be used to estimate the water balance of lakes correctly. The tools provided by glmGUI enable a less time-consuming and simplified parameter optimization within the calibration process. Due to this, i.e., the free availability and the implementation in a GUI, the presented R package expands the application of GLM to a broader field of lake modeling research and even beyond limnological experts.
    Publicationtype: Journal Article
      9