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The need to develop and provide integrated observation systems to better understand and manage global and regional environmental change is one of the major challenges facing Earth system science today. In 2008, the German Helmholtz Association took up this challenge and launched the German research infrastructure TERrestrial ENvironmental Observatories (TERENO). The aim of TERENO is the establishment and maintenance of a network of observatories as a basis for an interdisciplinary and long‐term research program to investigate the effects of global environmental change on terrestrial ecosystems and their socio‐economic consequences. State‐of‐the‐art methods from the field of environmental monitoring, geophysics, remote sensing, and modeling are used to record and analyze states and fluxes in different environmental disciplines from groundwater through the vadose zone, surface water, and biosphere, up to the lower atmosphere. Over the past 15 years we have collectively gained experience in operating a long‐term observing network, thereby overcoming unexpected operational and institutional challenges, exceeding expectations, and facilitating new research. Today, the TERENO network is a key pillar for environmental modeling and forecasting in Germany, an information hub for practitioners and policy stakeholders in agriculture, forestry, and water management at regional to national levels, a nucleus for international collaboration, academic training and scientific outreach, an important anchor for large‐scale experiments, and a trigger for methodological innovation and technological progress. This article describes TERENO's key services and functions, presents the main lessons learned from this 15‐year effort, and emphasizes the need to continue long‐term integrated environmental monitoring programmes in the future. Plain Language Summary: This paper discusses the importance of creating comprehensive environmental observation systems to better understand and address global and regional environmental changes. In 2008, a German research infrastructure named Terrestrial Environmental Observatories (TERENO) was established to build and maintain a network of observatories. The goal is to conduct interdisciplinary, long‐term research on the impacts of global environmental changes on terrestrial ecosystems and their socio‐economic effects. The TERENO network employs advanced methods from environmental monitoring, geophysics, remote sensing, and modeling to study various environmental aspects. Over the past 15 years, four observatories have been part of this network, contributing to valuable experience in overcoming challenges and exceeding expectations. Today, TERENO is a crucial component for environmental modeling and forecasting in Germany, serving as an information hub for practitioners and policymakers. It also fosters international collaboration, supports large‐scale experiments, and drives methodological and technological advancements. The article highlights key lessons learned from this 15‐year effort and emphasizes the importance of continuing such integrated environmental monitoring programs in the future. Key Points: Integrated observatories ensure a holistic Earth Systems perspective, offering data for current and future ecological challengesThe scientific and societal value of observatories is invaluable, but their design, construction and operation require considerable effortFor assured long‐term data collection, research infrastructure must have flexible design for adapting to changing research needs [ABSTRACT FROM AUTHOR]

Heat waves are among the most severe climate extreme events. In this study, we address the impact of increased model resolution and tailored model settings on the reproduction of these events by evaluating different regional climate model outputs for Germany and its near surroundings between 1980–2009. Outputs of an ensemble of six EURO-CORDEX models with 12.5 km grid resolution and outputs from a high-resolution (5 km) WRF (Weather Research and Forecasting) model run are employed. The latter was especially tailored for the study region regarding the physics configuration. We analyze the reproduction of the maximum temperature, number of heat wave days, heat wave characteristics (frequency, duration and intensity), the 2003 major event, and trends in the annual number of heat waves. E-OBS is used as the reference, and we utilize the Taylor diagram, the Mann–Kendall trend test and the spatial efficiency metric, while the cumulative heat index is used as a measure of intensity. Averaged over the domain, heat waves occurred about 31 times in the study period, with an average duration of 4 d and an average heat excess of 10 ∘C. The maximum temperature was only reproduced satisfactorily by some models. Despite using the same forcing, the models exhibited a large spread in heat wave reproduction. The domain mean conditions for heat wave frequency and duration were captured reasonably well, but the intensity was reproduced weakly. The spread was particularly pronounced for the 2003 event, indicating how difficult it was for the models to reproduce single major events. All models underestimated the spatial extent of the observed increasing trends. WRF generally did not perform significantly better than the other models. We conclude that increasing the model resolution does not add significant value to heat wave simulation if the base resolution is already relatively high. Tailored model settings seem to play a minor role. The sometimes pronounced differences in performance, however, highlight that the choice of model can be crucial. [ABSTRACT FROM AUTHOR]

The most reliable areal precipitation estimation is usually generated via combinations of different measurements and devices by merging their individual advantages. Path-averaged rain rate can be derived from Commercial Microwave Links (CML), where attenuation of the emitted radiation is strongly related with rainfall rate. CMLs can be combined with data from other rainfall measurements or used individually. They are available almost worldwide and often represent the only opportunity of ground-based measurement in data scarce regions. Deriving rainfall estimates from CML data requires extensive data processing, though. The separation of the attenuation time series in rainy and dry periods (rain event detection) is the most important step in this processing and largely determines the quality of the resulting rainfall estimates. In this study, we investigate the suitability of Meteosat Second Generation Spinning Enhanced Visible and InfraRed Imager (MSG SEVIRI) satellite data as an auxiliary-data-based (ADB) rain event detection method. We compare this method with two time series-based (TSB) rain event detection methods. The investigation uses data from 3901 CMLs in Germany for four months in summer 2021 and is carried out for the two SEVIRI-derived products PC and PC-Ph. We analyse all rain event detection methods for different precipitation intensity, differences between day and night, as well as their influence on the performance of rainfall estimates from individual CMLs. The radar product RADKLIM-YW is used for validation. The results show that both SEVIRI products are promising candidates for ADB rainfall detection methods and led to at least equivalent results as the TSB methods. The main uncertainty of all methods was found for light rain. Slightly better results were obtained during the day than at night, which is caused by dew formation on CML antennas and the reduced availability of SEVIRI channels at night. In general, the ADB methods lead to improvements for CMLs performing comparatively weakly using TSB methods. Based on these results, combinations of ADB and TSB methods were developed by emphasizing their specific advantages. Compared to basic and advanced TSB methods, these combinations were able to improve the Matthews Correlation Coefficient of the rain event detection from 0.53 (0.57 resp.) to 0.62 during the day and from 0.47 (0.55 resp.) to 0.6 during the night. Our results show that utilising MSG SEVIRI data in CML data processing significantly increases the quality of the rain event detection step, in particular for CMLs which are challenging to process with TSB methods. [ABSTRACT FROM AUTHOR]

Climate models face limitations in their ability to accurately represent highly variable atmospheric phenomena. To resolve fine‐scale physical processes, allowing for local impact assessments, downscaling techniques are essential. We propose spateGAN, a novel approach for spatio‐temporal downscaling of precipitation data using conditional generative adversarial networks. Our method is based on a video super‐resolution approach and trained on 10 years of country‐wide radar observations for Germany. It simultaneously increases the spatial and temporal resolution of coarsened precipitation observations from 32 to 2 km and from 1 hr to 10 min. Our experiments indicate that the ensembles of generated temporally consistent rainfall fields are in high agreement with the observational data. Spatial structures with plausible advection were accurately generated. Compared to trilinear interpolation and a classical convolutional neural network, the generative model reconstructs the resolution‐dependent extreme value distribution with high skill. It showed a high fractions skill score of 0.6 (spatio‐temporal scale: 32 km and 1 hr) for rainfall intensities over 15 mm h−1 and a low relative bias of 3.35%. A power spectrum analysis confirmed that the probabilistic downscaling ability of our model further increased its skill. We observed that neural network predictions may be interspersed by recurrent structures not related to rainfall climatology, which should be a known issue for future studies. We were able to mitigate them by using an appropriate model architecture and model selection process. Our findings suggest that spateGAN offers the potential to complement and further advance the development of climate model downscaling techniques, due to its performance and computational efficiency. Plain Language Summary: Natural disasters like floods, hail, or landslides originate from precipitation. Global climate models are an important tool to understand these hazards and derive expected changes in a future climate. However, they operate on spatial and temporal scales that limit the regional ability to reflect their small‐scale characteristics. This has led to the development of dynamical and statistical downscaling methods. Due to their computational efficiency, machine learning algorithms recently got increased attention as a method for improving the spatial resolution of climate data. Here, we describe a new deep learning model that allows to simultaneously increasing both the temporal and spatial resolution of precipitation data. Our presented approach enhances the spatial resolution by a factor of 16 and the temporal resolution by a factor of 6. The generated rain fields are hardly identifiable as artificially generated and exhibit the typical structure, movement, and distribution of observed rain fields. Key Points: High performance simultaneous spatial and temporal precipitation downscaling enabled by 3D convolution approachGeneration of realistic high‐resolution ensembles using probabilistic conditional generative adversarial networksLow computational effort compared to dynamical downscaling approaches [ABSTRACT FROM AUTHOR]

Extreme value analysis (EVA) is well-established to derive hydrometeorological design values for infrastructures that have to withstand extreme events. Since there is concern about increased extremes with higher hazard potential under climate change, alterations of EVA are introduced for which statistical properties are no longer assumed to be constant but vary over time. In this study, both stationary and non-stationary EVA models are used to derive design life levels (DLLs) of daily precipitation in the pre-alpine Oberland region of Southern Germany, an orographically complex region characterized by heavy precipitation events and climate change. As EVA is fraught with uncertainties, it is crucial to quantify its methodological impacts: two theoretical distributions (i.e., Generalized Extreme Value (GEV) and Generalized Pareto (GP) distribution), four different parameter estimation techniques (i.e., Maximum Likelihood Estimation (MLE), L-moments, Generalized Maximum Likelihood Estimation (GMLE), and Bayesian estimation method) are evaluated and compared. The study reveals large methodological uncertainties. Discrepancies due to the parameter estimation methods may reach up to 45% of the mean absolute value, while differences between stationary and non-stationary models are of the same magnitude (differences in DLLs up to 40%). For the end of this century in the Oberland region, there is no robust tendency towards increased extremes found. [ABSTRACT FROM AUTHOR]

Unlike actual rainfall, the spatial extent of rainfall maps is often determined by administrative and political boundaries. Similarly, data from commercial microwave links (CMLs) is usually acquired on a national basis and exchange among countries is limited. Up to now, this has prohibited the generation of transboundary CML‐based rainfall maps despite the great extension of networks across the world. We present CML based transboundary rainfall maps for the first time, using independent CML data sets from Germany and the Czech Republic. We show that straightforward algorithms used for quality control strongly reduce anomalies in the results. We find that, after quality control, CML‐based rainfall maps can be generated via joint and consistent processing, and that these maps allow to seamlessly visualize rainfall events traversing the German‐Czech border. This demonstrates that quality control represents a crucial step for large‐scale (e.g., continental) CML‐based rainfall estimation. Plain Language Summary: Rainfall maps are usually based on gauge observations on the ground or radar. They are crucial for predicting or reconstructing flooding events. Commercial microwave links are special kinds of rainfall sensors. Their actual purpose is the signal propagation within a cellular network. However, since the signal is attenuated when it rains, they can also be exploited for rainfall estimation. To estimate rainfall from the observed attenuation requires careful data processing. Algorithms for that are usually adjusted to national data sets with their specific characteristics. In this study, we use, for the first time, two independent data sets of commercial microwave links (from Germany and the Czech Republic) with the aim of generating transboundary rainfall maps. As the data sets vary in many respects, several algorithms need to be adjusted and extended to allow processing them consistently. We show that it is possible to create meaningful rainfall maps of rain events that traverse the border between Germany and the Czech Republic. This can be considered a major step toward seamless rainfall maps on even larger, that is, continental scale. Key Points: Transboundary rainfall maps based on independent networks of commercial microwave links (CMLs) are generated for the first timeGerman and Czech data sets of CMLs differ significantly with respect to network characteristicsQuality control is important for heterogeneous data of CMLs [ABSTRACT FROM AUTHOR]

Heat waves are among the most severe climate extreme events. In this study, we address the impact of increased model resolution and tailored model settings on the reproduction of these events by evaluating different regional climate model outputs for Germany and the near surroundings between 1980-2009. Both, outputs of an ensemble of six EURO-CORDEX models of 12.5 km grid resolution and outputs from a high resolution (5 km) WRF run are employed. The latter was especially tailored for the study region regarding the physics configuration. We analyze the reproduction of maximum temperature, number of heat wave days, heat wave characteristics (frequency, duration and intensity), the 2003 major event and trends in the annual number of heat waves. E-OBS is used as reference and we imply Taylor diagram, Mann-Kendall trend test, spatial efficiency metric and cumulative heat index as a measure for intensity. Averaged over the domain, heat waves occurred about 31 times in the study period with an average duration of 4 days and average heat excess of 10 °C. Maximum temperature was reproduced reasonably well by all models. Despite the same forcing, the models exhibited a large spread in the heat wave reproduction. The domain mean conditions of heat wave frequency and duration were captured reasonably well, but intensity was reproduced weakly. The spread was particularly pronounced for the 2003 event, indicating the difficulty of models to reproduce single major events. All models underestimated the spatial extent of the observed increasing trends. WRF mostly did not perform significantly better than the other models. We conclude that increased model resolution does not add a significant value to heat wave simulation if the base resolution is already relatively high. Tailored model settings seem to play a minor role. The partly distinct differences in performance, however, highlight that the choice of model can be crucial. [ABSTRACT FROM AUTHOR]

Droughts are among the most relevant natural disasters related to climate change. We evaluated different regional climate model outputs and their ability to reproduce observed drought indices in Germany and its near surroundings between 1980–2009. Both outputs of an ensemble of six EURO-CORDEX models of 12.5 km grid resolution and outputs from a high-resolution (5 km) Weather Research and Forecasting (WRF) run were employed. The latter model was especially tailored for the study region regarding the physics configuration. We investigated drought-related variables and derived the 3-month standardized precipitation evapotranspiration index (SPEI-3) to account for meteorological droughts. Based on that, we analyzed correlations, the 2003 event, trends and drought characteristics (frequency, duration and severity) and compared the results to E-OBS. Methods used include Taylor diagrams, the Mann–Kendall trend test and the spatial efficiency (SPAEF) metric to account for spatial agreement of patterns. Averaged over the domain, meteorological droughts were found to occur approximately 16 times in the study period with an average duration of 3.1 months and average severity of 1.47 SPEI units. WRF's resolution and setup were shown to be less important for the reproduction of the single drought event and overall drought characteristics. Depending on the specific goals of drought analyses, computation resources could therefore be saved, since a coarser resolution can provide similar results. Benefits of WRF were found in the correlation analysis. The greatest benefits were identified in the trend analysis: only WRF was able to reproduce the observed negative SPEI trends to a fairly high spatial accuracy, while the other regional climate models (RCMs) completely failed in this regard. This was mainly due to the WRF model settings, highlighting the importance of appropriate model configuration tailored to the target region. Our findings are especially relevant in the context of climate change studies, where the appropriate reproduction of trends is of high importance. [ABSTRACT FROM AUTHOR]

Accurate spatiotemporal precipitation quantification is a crucial prerequisite for hydrological analyses. The optimal reconstruction of the spatial distribution, that is, the rainfall patterns, is particularly challenging. In this study, we reconstructed spatial rainfall on a countrywide scale for Germany by combining commercial microwave link and rain gauge observations for a better representation of the variability and spatial structure of rainfall. We further developed and applied the Random‐Mixing‐Whittaker‐Shannon method, enabling the stochastic reconstruction of ensembles of spatial fields via linear combinations of unconditional random fields. The pattern of rainfall objects is evaluated by three performance characteristics, that is, ensemble Structure‐, Amplitude‐, and Location‐error. Precipitation estimates obtained are in good agreement with the gauge‐adjusted weather radar product RADOLAN‐RW of the German Weather Service (DWD) which was used as a reference. Compared to reconstructions by Ordinary Kriging, Random Mixing showed clear advantages in the pattern representation via a five times smaller median structure error. Plain Language Summary: Rainfall is commonly measured by dedicated sensors such as rain gauges or weather radars. Commercial microwave links (CMLs), which have the primary purpose of signal forwarding within cellular networks, can be used for rainfall measurements too. The signal, which is transmitted from one antenna to another, is being attenuated if it rains along the path. From the amount of attenuation an average rain rate can be retrieved. For many hydrological applications, it is of major interest to estimate area‐wide rainfall (i.e., rainfall maps) while observations provide only scattered information. In this study, we used the local information from almost 1,000 rain gauges and the information along the paths of 3,900 CMLs distributed over Germany to reconstruct rainfall maps. We did this by applying a method of stochastic simulation (called Random Mixing) which we compared to a more common method of estimation (Ordinary Kriging). To evaluate the quality of the obtained maps, we compared them to rainfall information from weather radars. We found that the general agreement is high, and that maps reconstructed by Random Mixing have particular advantages in representing the spatial structure, that is, the shape of rainfall cells. Key Points: Geostatistical Random Mixing simulation now capable of countrywide spatial rainfall interpolationVariability assessment via commercial microwave link path consideration and ensemble estimationRealistic rainfall pattern representation quantified by ensemble Structure‐, Amplitude‐, and Location‐error metrics [ABSTRACT FROM AUTHOR]

Observational evidence suggests that forests in the Northern Alps are changing at an increasing rate as a consequence of climate change. Yet, it remains unclear whether the acceleration of forest change will continue in the future, or whether downregulating feedbacks will eventually decouple forest dynamics from climate change. Here we studied future forest dynamics at Berchtesgaden National Park, Germany by means of a process‐based forest landscape model, simulating an ensemble of 22 climate projections until the end of the 21st century. Our objectives were (i) to assess whether the observed acceleration of forest dynamics will continue in the future, (ii) to analyze how uncertainty in future climate translates to variation in future forest disturbance, structure, and composition, and (iii) to determine the main drivers of future forest dynamics. We found that forest dynamics continue to accelerate in the coming decades, with a trend towards denser, structurally more complex and more species rich forests. However, changes in forest structure leveled off in the second half of the 21st century regardless of climate scenario. In contrast, climate scenarios caused trajectories of tree species change to diverge in the second half of the 21st century, with stabilization under RCP 2.6 and RCP 4.5 scenarios and accelerated loss of conifers under RCP 8.5. Disturbance projections were 3 to 20 times more variable than future climate, whereas projected future forest structure and composition varied considerably less than climate. Indirect effects of climate change via alterations of the disturbance regime had a stronger impact on future forest dynamics than direct effects. Our findings suggest that dampening feedbacks within forest dynamics will decelerate forest change in the second half of the 21st century. However, warming beyond the levels projected under RCP 4.5 might profoundly alter future forest disturbance and composition, challenging conservation efforts and ecosystem service supply. [ABSTRACT FROM AUTHOR]