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Environmental resources and ecological systems are significantly affected by the steady rise of the global temperature. However, the degree of temperature change at the regional and local levels is uncertain. The uncertainty arises from various factors, but mostly due to the short length of ground data and dependency of local studies on the large-scale and spatially coarse output of Global Climate Models (GCMs). Therefore, the output of GCM cannot be directly used in impact assessment studies at a regional and local level. In this study, the Statistical Down-Scaling Model (SDSM) is employed to investigate the magnitude of temperature changes (Minimum and Maximum Temperature) for the future period 2026–2100. The SDSM builds relationships between large-scale predictors and local climate variables, allowing for finer-resolution projections at a regional level. The study utilized the Climate Hazard Infra-Red Temperature with Station (CHIRTS-daily) to complete daily missing records in more than 90 ground stations. Additionally, predictors of the National Center for Environmental Prediction (NCEP) for the historical period (1961–2010) and the Canadian Earth System Model (CanESM2) for the future period (2026–2100) are employed to calibrate SDSM and to build finer-resolution scenarios under two representative concentration pathways; RCP2.6 and RCP8.5. The methodology additionally involved validating the SDSM performance using observed historical data before applying it to future projections. The findings indicate that both minimum and maximum temperatures (T-min and T-max) will increase, with a more pronounced rise in minimum temperature (T-min). Over the future period (2026–2100), the projected average temperature rise is 1.10 °C (T-max) and 1.43 °C (T-min) under RCP2.6. For RCP8.5, the projected average increases are 1.56 °C and 2.3 °C for T-max and T-min, respectively. Overall, the most significant increase is projected to occur in the 2090s (2076–2100) under RCP8.5, particularly in the lowlands and wadis of Al Mahwit and Raymah governorate. In these areas, the minimum temperature (T-min) exhibited an increased absolute value of up to 3.2 °C. This high rise in temperatures is expected to result in increased evapotranspiration, prolonged droughts, and possibly breakouts of some plant diseases and pests. This would require effective adaptation measures such as harvesting rainwater and growing short-time and heat-resistance crops. Engaging in field visits and social discussions added depth to the study by introducing various traditional methods and indigenous practices. Valuable resources for future efforts to mitigate the potential impacts of climate change are offered by these insights.

The study analyses 19 years of hourly quantitative precipitation estimates (RADKLIM-RW) concerning the spatial scaling behaviour of extreme rainfall for Germany as well as general heavy rainfall patterns. Four regions with the same size of 256 km × 256 km were selected with varying characteristics. For each region, spatial rainfall maxima for different aggregation steps with box length of 2, 4, 8, 16, 32, 64, and 128 km were retrieved using sliding and fixed window methods. Rainfall was classified with two parameters into patterns dominated by convection or advection, respectively. Based on the calculated spatial maxima, potential maximum rain rates were extrapolated for sub-pixel scale. Though the power laws of the resulting scaling relationship have a very good fit, no mono-scale power law could be derived from the data due to different rainfall mechanisms on different spatial scales. Typical power laws exist for more convective characteristics up to a scale of 8 km box length and for more advective characteristics with larger box lengths. A regional power law for maxima of convective rainfall could be used to statically refine the radar resolution to 250 m or even 125 m box length. However, when scaled to smaller box lengths or to the size of a Hellmann gauge (200 cm2) the values become increasingly unrealistic. The results indicate that radar derived areal precipitation values are limited to the radar scale (here 1 km2), despite the fact that they are scaled to fit observed values from rain gauges.

Inland waters are an import source of greenhouse gases for the atmosphere. In particular, the emissions of methane from lakes and reservoirs are suspected of almost offsetting the terrestrial carbon sink. However, the estimates found in the literature are subject to large uncertainties due to both missing data and methodological limitations. In particular, there is a gap of observations in the temperate climate zone, and data are especially scarce from eutrophic waters despite the fact that the emissions increase with the degree of eutrophication. We present data from a eutrophic reservoir in the temperate climate zone, measured continuously with a floating eddy-covariance system. Data from two seasons are analyzed, both starting and ending with spring and autumn mixing, respectively, and including the complete period of summer stratification. During the spring and summer months, clear diurnal patterns for the carbon dioxide fluxes were detected in the eddy covariance data reflecting the interplay between photosynthesis and respiration. However, this daylight-driven oscillation weakened with the onset of autumn and disappeared at the end. In contrast to the carbon dioxide fluxes, the methane fluxes did not show any daytime dynamics. Notwithstanding, distinct seasonal patterns with increasing CH4 emissions over summer appeared. The carbon dioxide balance was about −9.8 and −71.0 g C m−2, in the first and second season, respectively. Thus, based on the eddy covariance measurements, the reservoir was a carbon dioxide sink in both study periods. However, the difference between the two seasons indicates a distinct inter-annual variability. The seasonal methane emissions were 24.0 g C m−2 and 23.2 g C m−2, respectively. Accordingly, the seasonal carbon budget resulted in 15.1 g C m−2 and −47.8 g C m−2, respectively, meaning the reservoir was a carbon source in the first and a sink in the second study period. However, considering the significant higher warming potential of methane and transposing these emissions into carbon dioxide equivalents, the reservoir contributed to the greenhouse potential of the atmosphere in both study periods.

Abstract Simulating the carbon-water fluxes at more widely distributed meteorological stations based on the sparsely and unevenly distributed eddy covariance flux stations is needed to accurately understand the carbon-water cycle of terrestrial ecosystems. We established a new framework consisting of machine learning, determination coefficient (R2), Euclidean distance, and remote sensing (RS), to simulate the daily net ecosystem carbon dioxide exchange (NEE) and water flux (WF) of the Eurasian meteorological stations using a random forest model or/and RS. The daily NEE and WF datasets with RS-based information (NEE-RS and WF-RS) for 3774 and 4427 meteorological stations during 2002–2020 were produced, respectively. And the daily NEE and WF datasets without RS-based information (NEE-WRS and WF-WRS) for 4667 and 6763 meteorological stations during 1983–2018 were generated, respectively. For each meteorological station, the carbon-water fluxes meet accuracy requirements and have quasi-observational properties. These four carbon-water flux datasets have great potential to improve the assessments of the ecosystem carbon-water dynamics.

Abstract High spatio‐temporal resolution near‐surface projected data is vital for climate change impact studies and adaptation. We derived the highest statistically downscaled resolution multivariate ensemble currently available: daily 1 km until the end of the century. Deep learning models were employed to develop transfer functions for precipitation, water vapor pressure, radiation, wind speed, and, maximum, mean and minimum temperature. Perfect prognosis is the particular statistical downscaling methodology applied, using a subset of the ReKIS data set for Saxony as predictands, the ERA5 reanalysis as during‐training predictors and the CORDEX‐EUR11 ensemble as projected predictors. The performance of the transfer functions was validated with the VALUE framework, yielding highly satisfactory results. Particular attention was given to the three major perfect prognosis assumptions, for which several tests were carried out and thoroughly discussed. From the latter, we corroborated their fulfillment to a high degree, thus, the derived projections are considered adequate and relevant for impact modelers. In total, 18 runs for RCP85, 1 for RCP45, and 4 for RCP26 were downscaled under both stochastic and deterministic approaches. This multivariate ensemble could drive more accurate and diverse impact studies in the region. Generally, the projected climatologies are in agreement with coarser resolution projections. Nevertheless, statistical particularities were observed for some projections, thus, a list of caveats for potential users is given. Due to the scalability of the presented methodology, further possible applications with additional datasets are proposed. Lastly, several potential improvement prospects are discussed toward the ideal subsequent iteration of the perfect prognosis statistical downscaling methodology.

The recently presented Global BROOK90 automatic modeling framework combines a non-calibrated lumped hydrological model with ERA5 reanalysis data as the main driver, as well as with global elevation, land cover and soil datasets. The focus is to simulate the water fluxes within the soil–water–plant system of a single plot or of a small catchment especially in data-scarce regions. The comparison to runoff is an obvious choice for the validation of this approach. Thus, we choose for validation 190 small catchments (with a median size of 64 km2) with discharge observations available within a time period of 1979–2020 and located all over the globe. They represent a wide range of relief, land cover and soil types within all climate zones. The simulation performance was analyzed with standard skill-score criteria: Nash–Sutcliffe Efficiency, Kling–Gupta Efficiency, Kling–Gupta Efficiency Skill Score and Mean Absolute Error. Overall, the framework performed well (better than mean flow prediction) in more than 75% of the cases (KGESS > 0) and significantly better on a monthly rather than on a daily scale. Furthermore, it was found that Global BROOK90 outperforms GloFAS-ERA5 discharge reanalysis. Additionally, cluster analysis revealed that some of the catchment characteristics have a significant influence on the framework performance. HIGHTLIGHTS The study evaluates the runoff component performance of the Global BROOK90 automatic framework for hydrological modeling.; Discharge observations from 190 small catchments located all over the globe were used.; Satisfactory results for more than 75% of the catchments were achieved.; KGE decomposition and influence of catchment characteristics on the framework performance were discussed.;

Drought is a complex natural phenomenon, which is challenging to define and to describe quantitatively. Canonical drought propagation scheme ‘meteorological → agricultural (or soil related) → hydrological’ does not always reflect the reality in a catchment. Thus it is necessary to include compound or cascading effects of precipitation, soil moisture and discharge interactions on different time scales to get a comprehensive picture on the drought characteristics, as well as on its development and recovery.We studied the linkage between droughts over multiply temporal scales and severity levels using various statistical methods for a case study of a small forested catchment in Germany. It was found that indeed different types of droughts are highly interconnected and their behavior can significantly differ from the classical scheme.A simple empirical approach gives frequencies, seasonality and trends of various combinations of droughts. It showed that among all types the test site is mostly exposed to a light hydro-meteorological one especially in autumn months with an increasing trend. Multivariate distributions can be used to evaluate joint probabilities and return periods of drought components. It was revealed that the well-known European drought in 2018 was also presented as an extreme case of a joint hydro-meteo-soil drought in the examined catchment. By Markov chains one can analyze the transition and persistence between droughts. Well-established propagation pathways between different types and severity levels of droughts with high persistence for longer droughts were found for the study area.

Several studies showed the impact of global climate change in Germany and Saxony including the risk of increasing precipitation extremes. Here, heavy precipitation was analyzed on the basis of daily precipitation sums using the 95th percentile (index R95p). The long term development was studied for selected stations (1917–2013). Transects with high spatial resolution (1×1 km) (1961–2015) complemented the study to gain information about spatial temporal development of the occurrence of precipitation extremes. The non parametric kernel occurrence rate estimation has been applied to reveal changes in the temporal development of daily totals. The most distinct changes have been found for the seasons and the growing seasons and only slight changes for the calendar year and the meteorological half-years. The findings of this study showed a shifting seasonality with decreasing number of heavy precipitation events in the growing season I (April, May, June) and increasing number of events in growing season II (July, August, September). Furthermore, a distinct periodicity has been revealed in all findings for all seasons, particularly striking in the growing seasons, indicating the influence of large scale drivers as potentially the North Atlantic Oscillation on local precipitation extremes. Our data showed, that kernel occurrence rate estimation is a suitable approach to analyze the temporal development of heavy precipitation with a high temporal and spatial resolution.

We developed the “REAL-Fog” (R‑Estimated spatiAL Fog) tool to calculate the ground-level fog by means of liquid water content (lwc) at a temporal resolution of 1 hour. The tool is based on a distributed and mainly physical model and requires a digital elevation model (DEM) and hourly observations of temperature and humidity. The tool spatially interpolates the air temperature and derived vapour pressure to determine whether the air at ground level is supersaturated. From the supersaturation result, the lwc is then calculated. Due to errors in the measured humidity time series, preprocessing is additionally recommended. Different sources of errors, such as sensor ageing, calibration and sensor uncertainty, are discussed, and a correction method is proposed. The “REAL-Fog” method was applied in a case study of Germany from 1949 to 2017. The daily updated results of this analysis can be found at www.nebelzentrale.de. Two validation strategies are applied. First, modelled lwc values are compared with measured lwc values at Mt. Brocken (1141 m a.s.l.), which is one of the longest time series of lwc in the world and the longest in Germany. The event-based (fog, no fog) Heidke skill score (HSS) reached 0.8. Second, the event-based performance was calculated for 306 visibility sites in Germany. HSS was found to be 0.54.

Solar-Induced Chlorophyll Fluorescence (SIF) can provide key information about the state of photosynthesis and offers the prospect of defining remote sensing-based estimation of Gross Primary Production (GPP). There is strong theoretical support for the link between SIF and GPP and this relationship has been empirically demonstrated using ground-based, airborne, and satellite-based SIF observations, as well as modeling. However, most evaluations have been based on monthly and annual scales, yet the GPP:SIF relations can be strongly influenced by both vegetation structure and physiology. At the monthly timescales, the structural response often dominates but short-term physiological variations can strongly impact the GPP:SIF relations. Here, we test how well SIF can predict the inter-daily variation of GPP during the growing season and under stress conditions, while taking into account the local effect of sites and abiotic conditions. We compare the accuracy of GPP predictions from SIF at different timescales (half-hourly, daily, and weekly), while evaluating effect of adding environmental variables to the relationship. We utilize observations for years 2018–2019 at 31 mid-latitudes, forested, eddy covariance (EC) flux sites in North America and Europe and use TROPOMI satellite data for SIF. Our results show that SIF is a good predictor of GPP, when accounting for inter-site variation, probably due to differences in canopy structure. Seasonally averaged leaf area index, fraction of absorbed photosynthetically active radiation (fPAR) and canopy conductance provide a predictor to the site-level effect. We show that fPAR is the main factor driving errors in the linear model at high temporal resolution. Adding water stress indicators, namely canopy conductance, to a multi-linear SIF-based GPP model provides the best improvement in the model precision at the three considered timescales, showing the importance of accounting for water stress in GPP predictions, independent of the SIF signal. SIF is a promising predictor for GPP among other remote sensing variables, but more focus should be placed on including canopy structure, and water stress effects in the relationship, especially when considering intra-seasonal, and inter- and intra-daily resolutions.

Quality of water balance estimations are strongly dependent on the precipitation input. The key limitation here is typically a lack of spatial representation in precipitation data. Quantitative precipitation estimation (QPE) using radar is recognized as capable to significantly enhance the spatial representation of precipitation compared to conventional rain gauge-based methods by calibrating radar pixels with surrounding rain gauges. However, the measured precipitation is often underestimated due to wind drift or funnel evaporation, particularly in mountainous areas. Thus, a post-correction is required before applying radar precipitation in water balance models. Here, we applied the Richter correction for the first time to a radar-based QPE, to model the water balance in ten catchments in Saxony, Germany. The hydrological responses for the period 2001–2017 from the model were validated with discharge observations. The results show that radar data application yielded reliable simulations of water balance (KGE = 0.53 and 0.70 at daily and monthly resolutions, respectively). However, a simple compensation such as the Richter method to conventional precipitation should be used with caution. This study shows that radar-based precipitation has immense potential to advance quality of the precipitation input to distributed hydrologic models not only for flood events but also for climatological analyses.

Abstract Background Considering the lack of research over this region the Statistical Downscaling Model (SDSM) was used as a tool for downscaling meteorological data statistically over four representative regions in the eastern side of Colombia. Data from the two Global Climate Models CanESM2 and IPSL-CM5A-MR, which are part of the CMIP5-project have been used to project future maximum and minimum temperature, precipitation and relative humidity for the periods 2021–2050 and 2071–2100. For both models, the Representative Concentration Pathways RCP2.6 and RCP8.5 were considered, representing two different possible future emission trajectories and radiative forcings. Predictor variables from the National Centre for Environmental Prediction (NCEP-DOE 2) reanalysis dataset, together with analyzed correlation coefficient (R) and root mean square error (RMSE) were used as performance indicators during the calibration and validation process. Results Results indicate that Maximum and minimum temperature is projected to increase for both Global Climate Models and both Representative Concentration Pathways; relative humidity shows a decreasing trend for all scenarios and all regions; and precipitation shows a slight decrease over three regions and an increase over the warmest region. As expected, the results of the simulation for the period 2071–2100 show a more drastic change when compared to the baseline period of observations. Conclusions The SDSM model proves to be efficient in the downscaling of maximum/minimum temperature as well as relative humidity over the studied regions; while showing a lower performance for precipitation, agreeing with the results for other statistical downscaling studies. The results of the projections offer good information for the evaluation of possible future-case scenarios and decision-making management.

Pakistan is among the countries that are highly vulnerable to climate change. The country has experienced severe floods and droughts during recent decades. The agricultural sector in Pakistan is adversely affected by climate change. This systematic review paper set out to analyze the existing literature on adaptation measures at the farm level toward climate change in Pakistan. Adopting a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, a total of 62 articles were identified from the Web of Science and Scopus databases. The review paper indicates that the main adaptation strategies adopted by farmers are as follows: changing cropping practices, changing farm management techniques, advanced land use management practices, and nonagriculture livelihood options. Further, this review shows the factors influencing the farmer’s adaptation measures to climate change. Influencing factors were examined and classified into three groups: demographic, socioeconomic, and resources and institutional. Barriers hindering farmers’ adaptive capacity were identified as lack of access to information and knowledge, lack of access to extension services, lack of access to credit facility, and lack of farm resources.

In this article, Gradient Boosting (gb) is introduced as an easily adaptable machine learning method to fill gaps caused by missing or erroneous data in meteorological time series. The gb routine is applied on a large data set of hourly time series of the measurands: air temperature, wind speed and relative humidity for station-based observations in Germany covering the period from 1951 to 2015. Our analysis shows that Gradient Boosting produces small errors by estimating missing values with median RMSE for temperature of 0.73 °C, wind speed of 0.82 m/s, relative humidity of 4.3 %, respectively. The comparison between the results achieved by Gradient Boosting with results from other gap filling techniques like neural networks or multiple linear regression shows considerably better statistics. The comparison clearly shows that the Gradient Boosting approach outperforms the other techniques particularly in calculation time, performance and the handling of missing predictor values.

A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00851-9.

Numerical simulations of rainfall-runoff processes are useful tools for understanding hydrological processes and performing impact assessment studies. The advancements in computer technology and data availability have assisted their rapid development and wide use. This project aims to evaluate the applicability of a physically based, fully distributed rainfall-runoff model TOPKAPI-X for the simulation of flood events in two small watersheds of Saxony, Germany. The results indicate that the model was calibrated well for 4.88 km2 Wernersbach catchment (NSE 0.89), whereas 276 km2 Wesenitz catchment calibration was only satisfactory (NSE 0.7). The addition of the second soil layer improved the model’s performance in comparison to the simulations with only one soil layer for Wernersbach (NSE increase from 0.83 to 0.89). During the validation process, the model showed a variable performance. The best performance was achieved for Wernersbach for the year with the highest runoff (NSE 0.95) in the last decade. The lowest performance for the Wernersbach and Wesenitz catchments was 0.64 for both. The reasons for the model’s low performance in some years are discussed, and include: (i) input data quality and data insufficiency, (ii) methods used within the simulations (interpolation, ETP estimation, etc.), and (iii) assumptions made during the calibration (manual calibration, parameter selection, etc.).

The Penman-Monteith (PM) equation is a state-of-the-art modelling approach to simulate evapotranspiration (ET) at site and local scale. However, its practical application is often restricted by the availability and quality of required parameters. One of these parameters is the canopy conductance. Long term measurements of evapotranspiration by the eddy-covariance method provide an improved data basis to determine this parameter by inverse modelling. Because this approach may also include evaporation from the soil, not only the ‘actual’ canopy conductance but the whole surface conductance (gc$g_{c}$) is addressed. Two full cycles of crop rotation with five different crop types (winter barley, winter rape seed, winter wheat, silage maize, and spring barley) have been continuously monitored for 10 years. These data form the basis for this study. As estimates of gc$g_{c}$ are obtained on basis of measurements, we investigated the impact of measurements uncertainties on obtained values of gc$g_{c }$. Here, two different foci were inspected more in detail. Firstly, the effect of the energy balance closure gap (EBCG) on obtained values of gc$g_{c}$ was analysed. Secondly, the common hydrological practice to use vegetation height (hc$h_{c}$) to determine the period of highest plant activity (i.e., times with maximum gc$g_{c}$ concerning CO2-exchange and transpiration) was critically reviewed. The results showed that hc$h_{c}$ and gc$g_{c}$ do only agree at the beginning of the growing season but increasingly differ during the rest of the growing season. Thus, the utilisation of hc$h_{c}$ as a proxy to assess maximum gc$g_{c}$ (gc,max$g_{c,\text{max}}$) can lead to inaccurate estimates of gc,max$g_{c,\text{max}}$ which in turn can cause serious shortcomings in simulated ET. The light use efficiency (LUE) is superior to hc$h_{c}$ as a proxy to determine periods with maximum gc$g_{c}$. Based on this proxy, crop specific estimates of gc,max$g_{c,\text{max}}$ could be determined for the first (and the second) cycle of crop rotation: winter barley, 19.2 mm s−1 (16.0 mm s−1); winter rape seed, 12.3 mm s−1 (13.1 mm s−1); winter wheat, 16.5 mm s−1 (11.2 mm s−1); silage maize, 7.4 mm s−1 (8.5 mm s−1); and spring barley, 7.0 mm s−1 (6.2 mm s−1).

Highly-resolved data on water balance components (such as runoff or storage) are crucial to improve water management, for example, in drought or flood situations. As regional observations of these components cannot be acquired adequately, a feasible solution is to apply water balance models. We developed an innovative approach using the physically-based lumped-parameter water balance model BROOK90 (R version) integrated into a sensor network platform to derive daily water budget components for catchments in the Free State of Saxony. The model is not calibrated, but rather uses available information on soil, land use, and precipitation only. We applied the hydro response units (HRUs) approach for 6175 small and medium-sized catchments. For the evaluation, model output was cross-evaluated in ten selected head catchments in a low mountain range in Saxony. The mean values of Kling–Gupta efficiency (KGE) for the period 2005–2019 to these catchments are 0.63 and 0.75, for daily and monthly discharge simulations, respectively. The simulated evapotranspiration and soil wetness are in good agreement with the SMAP_L4_GPH product in April 2015–2018. The study can be enhanced by using different data platforms as well as available information on study sites.

Management of water resources under climate change is one of the most challenging tasks in many arid and semiarid regions. A major challenge in countries, such as Yemen, is the lack of sufficient and long-term climate data required to drive hydrological models for better management of water resources. In this study, we evaluated the accuracy of accessible satellite and reanalysis-based precipitation products against observed data from Al Mahwit governorate (highland region, Yemen) during 1998–2007. Here, we evaluated the accuracy of the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data, National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Tropical Rainfall Measuring Mission (TRMM 3B42), Unified Gauge-Based Analysis of Global Daily Precipitation (CPC), and European Atmospheric Reanalysis (ERA-5). The evaluation was performed on daily, monthly, and annual time steps by directly comparing the data from each single station with the data from the nearest grid box for each product. At a daily timescale, CHIRPS captures the daily rainfall characteristics best, such as the number of wet days, with average deviation from wet durations around 11.53%. TRMM 3B42 is the second-best performing product for a daily estimate with an average deviation of around 34.7%. However, CFSR (85.3%) and PERSIANN-CDR (103%) and ERA-5 (−81.13%) show an overestimation and underestimation of wet days and do not reflect rainfall variability of the study area. Moreover, CHIRPS is the most accurate gridded product on a monthly basis with high correlation and lower bias. The average monthly correlation between the observed and CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR is 0.78, 0.56, 0.53, 0.15, 0.20, and 0.51, respectively. The average monthly bias is −2.9, −5.25, 7.35, −25.29, −24.96, and 16.68 mm for CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR, respectively. CHIRPS displays the spatial distribution of annual rainfall pattern well with percent bias (Pbias) of around −8.68% at the five validation points, whereas TRMM 3B42, PERSIANN-CDR, and CFSR show a deviation of greater than 15.30, 22.90, and 66.21%, respectively. CPC and ERA-5 show Pbias of about −88.6% from observed data. Overall, in absence of better data, CHIRPS data can be used for hydrological and climate change studies on the highland region of Yemen where precipitation is often episodical and measurement records are spatially and temporally limited.

It is assumed that the description of the exchange processes between heterogeneous natural surfaces and the atmosphere within turbulence closure models is mainly limited by a realistic three-dimensional (3D) representation of the vegetation architecture. Within this contribution we present a method to record the 3D vegetation structure and to use this information to derive model parameters that are suitable for numerical flow models. A mixed conifer forest stand around a clearing was scanned and represented by a dense 3D point cloud applying a terrestrial laser scanner. Thus, the plant area density (PAD) with a resolution of one cubic meter was provided for analysis and for numerical simulations. Multi-level high-frequency wind velocity measurements were recorded simultaneously by 27 ultrasonic anemometers on 4 towers for a period of one year. The relationship between wind speed, Reynolds stress and PAD was investigated and a parametrization of the drag coefficient CD$C_D$ by the PAD is suggested. The derived 3D vegetation model and a simpler model (based on classical forest assessments of the site) were applied in a boundary layer model (BLM) and in large-eddy simulations (LES). The spatial development of the turbulent flow over the clearing is further demonstrated by the results of a wind tunnel experiment. The project showed, that the simulation results were improved significantly by the usage of realistic vegetation models. 3D simulations are necessary to depict the influence of heterogeneous canopies on the turbulent flow. Whereas we found limits for the mapping of the vegetation structure within the wind tunnel, there is a considerable potential for numerical simulations. The field measurements and the LES gave new insight into the turbulent flow in the vicinity and across the clearing. The results show that the zones of intensive turbulence development can not be restricted to the locations found in previous studies with more idealized canopies.

This study investigates the effects of different urban planning scenarios on the thermal comfort of pedestrians in two districts of the city of Dresden. A total of three alternatives to reconstruct these districts were created by considering realistic approaches for urban planning projections in the next ten years. These alternatives include the development and densification of existing buildings as well as the greening of undeveloped areas or the reduction of existing green areas. Based on these planning alternatives, micro-meteorological conditions as well as bioclimatic indices within the chosen city districts of Dresden for a hot summer day were simulated with a combination of the 3D microclimatic model ENVI-met and the bioclimatic model Rayman. Using both, potential air temperature (T) and the bioclimatic index UTCI (Universal Thermal Comfort Index), spatial distribution at different times of the day, the cumulative thermal stress conditions at both exposed "receptor" points and on a virtual course of pedestrians through the inner city of Dresden, were analysed. Simulation results clearly show the local reduction of T and UTCI due to the shading effect of denser vegetation or building structures during the daytime. For example, a greening of an industrial wasteland locally reduces T and UTCI in the early afternoon of a hot and sunny summer day by up to 2 and 14 K, respectively. The same increase in T and UTCI can be found in non-shaded partitions of this area if it is sparsely rebuilt. At night, areas with widespread building placement show a higher T and UTCI (up to 1 and 4 K, respectively), and areas with dense vegetation are cooler (0.1 to 0.4 K) than open areas. Due to the small extension of modified urban structures, the mean effect over the complete area amounts to only a fraction of a degree at most. The simulation results demonstrate that a greening of urban wastelands has primarily positive effects (that means cooling) on thermal extremes during hot summer periods. A dense rebuilding causes similar positive effects during the daytime due to the generation of new shading zones as well as negative effects (warming) by night. For citizens occupying these city areas, the local effect of a modified urban structure could be more relevant than the mean effect over the whole city district. Based on these findings, modern approaches of city development should optimally combine green areas, shadow zones of large buildings and the maintenance of "street canyon ventilation".

We present a radar-based climatology of precipitation fields summarised into characteristic daily precipitation patterns. These patterns were derived by temporal classification, applying a neural network and data from Saxony during the period from 2004 to 2010. The properties of the dataset (RADOLAN rw-product) are discussed in detail and reviewed with respect to their adequacy for the intended application. The analysis showed a systematic dependence of the precipitation error on the altitude and aggregation period. Accordingly, for future applications of the considered radar product, we recommend the use of a maximal aggregation time step of 24 hours. The classification reveals significant precipitation patterns. Comparison of the qualitative features exhibited by the precipitation patterns, such as the synoptic scale flow direction, pressure distribution and atmospheric humidity, showed general trends as well as distinct spatial and atmospheric properties in dependence of the incidence rate. The lowest statistical qualities were shown by the patterns with the most distinct spatial characteristics due to a low incidence rate and high standard deviations. Nevertheless, the applied method led to a robust classification and the derived patterns appropriately summarized the mean daily precipitation behaviour in Saxony.

In order to overcome limitations of climate projections from Global Climate Models (GCMs), such as coarse spatial resolution and biases, in this study, the Statistical Down-Scaling Model (SDSM) is used to downscale daily precipitation and maximum and minimum temperature (T-max and T-min) required by impact assessment models. We focus on East Africa, a region known to be highly vulnerable to climate change and at the same time facing challenges concerning availability and accessibility of climate data. SDSM is first calibrated and validated using observed daily precipitation, (T-max, and T-min) from 214 stations and predictors derived from the reanalysis data of the National Centers for Environmental Prediction. For projection (2006–2100), the same predictors derived from the second generation Canadian Earth System Model (CanESM2) are used. SDSM projections show an increase in precipitation during the short-rain season (October–December) in large parts of the region in the 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2100). During the long-rain season (March–May (MAM)) precipitation is expected to increase (up to 680 mm) in Ethiopia, mainly in the western part, and Kenya and decrease (up to −500 mm) in Tanzania in the 2020s, 2050s, and 2080s. However, the western part of Ethiopia will be much drier than the baseline period (1961–1990) during June–September (JJAS) in the 2020s, 2050s, and 2080s, which indicates a shift in precipitation from JJAS to MAM. Annually, precipitation, T-max, and T-min will be higher than during the baseline period throughout the 21 century in large parts of the region. The projection based on SDSM is in line with the direction of CMIP5 GCMs but differs in magnitude, particularly for T-max and T-min. Overall, we conclude that the downscaled data allow for much more fine-scaled adaptation plans and ultimately better management of the impacts of projected climate in basins of Ethiopia, Kenya, and Tanzania.

The deposition of nitric oxides into a spruce stand at the Tharandt Forest, near Dresden, Saxony, was analysed. Eddy Covariance measurements, standard meteorological measurements and concentrations of nitric oxides of the anchor station nearby were available as model input. We used a resistance analogy model with a location-specific parametrisation for the resistances of transport to determine the deposition rates of NO and NO2 for 1998 and 1999. The annual sums of deposition of NO2-N average between 0.96 and 1.16 kg/(ha*a) for 1998 and between 1.12 and 1.35 kg/(ha*a) for 1999. These results match well results of other studies from sites with similar characteristics. As expected, the comparison of the results of a diurnal-based model (DM) with a 30 min model (HHM) shows a better adaptation of the HHM. However, due to the better availability of daily data it is necessary to use the daily model for the estimation of long term entries beyond the period of parametrisation.

Within climate change research vegetation plays an important role in both indicating climate change by plant phenology and mitigating climate change by carbon sequestration. A 41-year study period of phenological observations at Tharandt International Phenological Garden and an 8-year study period of continuous flux measurements of carbon dioxide above Norway spruce (Picea abies L. [Karst.]) in the Tharandt forest were used to investigate long-term trends of phenological observations concurrent with climatic trends and to assess the importance of phenological changes for annual carbon gain of the forest. It could be shown that length of growing season determined from phenology was less variable than determined from temperature levels and significantly correlated with mean annual temperature. The slight increase in the length of growing season (LGS) (0.14 d/a) resulted from an earlier onset of spring (−0.32 d/a) and of autumn phases (−0.18 d/a). Obviously, this reflected regional climatic trends with significant temperature increase in spring and slight decrease in autumn. The pronounced effect in spring is also reflected by strongest correlation of annual carbon gain with the emergence of May shoot while the correlation with LGS was less significant. It was therefore concluded that earlier emergence of May shoot was more important for annual carbon gain (22.4 gC/m2/d gross uptake) than the increase in total LGS (17.1 gC/m2/d gross uptake). However, positive effects of premature phenological stages on carbon gain may be reduced by drought effects during summer as observed for the study year 2003.

The coupled atmospheric boundary layer model HIRVAC (High Resolution Vegetation Atmosphere Coupler) combines a 1.5 dimensional planetary boundary layer model (developed at the Humboldt University Berlin) and the mechanistic photosynthesis module PSN6 (University of Bayreuth) as well as a soil water module. This multi layer model includes a vertically structured vegetation and covers a range from 0-2 km height. The model layers are coupled via the turbulent exchange using a K-approach. Recent work aimed to include several new modules to enable model calculations for realistic conditions. Therefore rainfall and a multi layer interception and a soil water module were included. Further the light extinction through the canopy was upgraded by including sunfleck dynamics for calculating a more realistic radiation distribution within and below canopy. The upgraded model version was run for the Anchor Station in Tharandt where various measurements for comparison with model results are available. The obtained results demonstrate that periods of several days can be modelled successfully. A good model performance was found for both, soil water and interception. The modelled energy fluxes finally confirm the reliability of the overall model performance.

From April to July 1996, ground-based measurements of radiation budget components with half-hourly resolution at the Anchorstation Tharandter Wald (near Dresden/Germany) over a homogeneous spruce forest were used to study the effect of clouds on the emitted infrared radiation. The main task of this study was the determination of empirical relations between the cloud optical depth and the reduction of the emitted infrared radiation. Thus, the cloud optical depth was calculated through an inverse technique based on a two-stream approximation for radiative transfer. The reductions in the emitted infrared radiation were estimated by comparing cloudy and clear-sky conditions (reference days). Three different cloud/weather classes with adequate conditions could be investigated: Stratus, Altostratus and Cumulus. The average decrease in emitted infrared radiation if clouds occur, measured over a spruce forest, for Stratus was -59 Wm-2, for Altostratus -39 Wm-2, and for Cumulus -22 Wm-2. The maximum influences, which could be observed, were for Stratus -69 Wm-2, for Altostratus -64 Wm-2, and for Cumulus -33 Wm-2. These empirical relations for cloud optical depth were applied to Meteosat and NOAA-AVHRR data, where a significant improvement in the satellite analysis could be achieved.

Several lines of evidence suggest that the warming climate plays a vital role in driving certain types of extreme weather. The impact of warming and of extreme weather on forest carbon assimilation capacity is poorly known. Filling this knowledge gap is critical towards understanding the amount of carbon that forests can hold. Here, we used a perfect-deficit approach to identify forest canopy photosynthetic capacity (CPC) deficits and analyze how they correlate to climate extremes, based on observational data measured by the eddy covariance method at 27 forest sites over 146 site-years. We found that droughts severely affect the carbon assimilation capacities of evergreen broadleaf forest (EBF) and deciduous broadleaf forest. The carbon assimilation capacities of Mediterranean forests were highly sensitive to climate extremes, while marine forest climates tended to be insensitive to climate extremes. Our estimates suggest an average global reduction of forest CPC due to unfavorable climate extremes of 6.3 Pg C (∼5.2% of global gross primary production) per growing season over 2001–2010, with EBFs contributing 52% of the total reduction.