FG Atmosphärische Prozesse
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Understanding the partitioning of downward shortwave radiation into direct and diffuse components is essential for modeling ecosystem energy fluxes. Accurate partitioning functions are critical for land surface models (LSMs) coupled with climate models, yet these functions often depend on regional cloud and aerosol conditions. While data for developing semi-empirical partitioning functions are abundant in mid-latitudes, their performance in tropical regions, particularly in the high Andes, remains poorly understood due to scarce ground-based measurements. This study analyzed a unique dataset of shortwave radiation components from a tropical mountain rainforest (MRF) in southern Ecuador, developing and testing a locally adapted partitioning function using Random Forest Regression. The model achieved high accuracy in predicting the percentage of diffuse radiation (%Dif; R²=0.95, RMSE = 5.33, MAE = 3.74) and absolute diffuse radiation (R²=0.99, RMSE = 5.30, MAE = 14). When applied to simulate upward shortwave radiation, the model outperformed commonly used partitioning functions achieving the lowest RMSE (8.62) and MAE (5.82) while matching the highest R² (0.97). These results underscore the importance of regionally adapted radiation partitioning functions for improving LSM performance, particularly in complex tropical environments. The adapted LSM will be further utilized for studies on heat fluxes and carbon sequestration.
Latent heat flux is a central element of land-atmosphere interactions under climate change. Knowledge is particularly poor in the biodiversity hotspot of the Andes, where heat flux measurements using eddy covariance stations are scarce and land surface models (LSMs) often oversimplify the complexity of the ecosystems. The main objective of this study is to perform latent heat flux simulations for the tropical South Eastern (SE) Ecuadorian Andes using a coupled LSM framework, and to test the performance with heat flux and soil moisture data collected from a tropical high-altitude pasture. Prior to testing, we applied multi-criteria model calibration of sensitive model parameters, focusing on improving simulated soil water conditions and radiation fluxes as a prerequisite for proper heat flux simulations. The most sensitive parameters to improve soil moisture and radiation flux simulations were soil porosity, saturated hydraulic conductivity, leaf area index, soil colour and NIR (Near Infrared) leaf optical properties. The best calibrated model run showed a very good performance for half-hourly latent heat flux simulations with an R² of 0.8 and an RMSE of 34.0 W m⁻², outperforming simulations with uncalibrated and uncoupled LSM simulations in comparable areas. The slight overall overestimation in the simulated latent heat flux can be related to (i) simulation uncertainties in the canopy heat budget, (ii) an imbalance in the observed flux data and (iii) slight overestimations in the simulated soil moisture. Although our study focuses on latent heat fluxes and their relation to simulated radiation fluxes and soil moisture, model outputs of sensible heat fluxes were also discussed. The systematic overestimation of sensible heat flux in the model seems to be mainly a result of overestimated canopy temperatures. The improved simulation for latent heat flux has a high translational potential to support land use strategies in the tropical Andes under climate change.
The determination of the spatial distribution of canopy traits is crucial for understanding the spatio-temporal dynamics of ecosystem functions, such as carbon sequestration, water and energy fluxes in tropical montane forests. Especially in remote areas such as the Andes of south-east Ecuador, remote sensing using satellites has been proven to provide valuable information on canopy traits. However, the performance of the multispectral models to date is limited. Here, we analyse the potential of DESIS (DLR Earth Sensing Imaging Spectrometer) hyperspectral surface reflectance data for the prediction of specific leaf area (SLA), foliar toughness, nitrogen (N) and phosphorus (P) content by calibrating a PLSR model for each foliar trait. Model validation showed a high explanation of variance (R2 : 0.78–0.92) and a low model error (mean-normalized RMSE: 8–13%) for all traits, showing an improved performance compared to models based on multispectral imagery and ancillary data within the same forest type. Hyperspectral models predicting foliar traits from individual tree crowns showed a lower performance for most traits, probably related to the difference in spatial resolution compared to this study. Given the high species richness of the Andean biodiversity hotspot and the complex topography, the high quality of the DESIS models is remarkable, considering that the multispectral models from the region require topographic predictors in addition to reflectances to achieve a good performance. The spatial trait maps generated by our models based on the reflectances only show clear relationships with terrain elevation. Thus, the hyperspectral trait maps from satellite orbit are capable of supporting large-scale ecological analyses between features, diversity and ecosystem functions.
Ammonia (NH₃) emissions, which are key precursors of fine particulate matter, pose significant environmental challenges. This study investigated the spatiotemporal variations in NH₃ emissions across the eastern German lowlands from 2013 to 2022 using IASI-B satellite data. Five major Land Cover Classes (LCC) –tree, grassland, cropland, built-up areas, and water bodies– were analyzed. The results showed distinct diurnal variations, with nighttime NH₃ concentrations exceeding 2.0 × 10¹⁶ molecules cm⁻² in the peak months. Seasonal patterns indicated significant emissions in March (1.2 × 10¹⁶ molecules cm⁻²), April (1.1 × 10¹⁶ molecules cm⁻2), and August (9.6 × 10¹⁵ molecules cm⁻²), while the lowest concentrations occurred in September (0.6 × 10¹⁵ molecules cmv²). Persistent hotspots were identified in the northwestern region, where emissions peaked in spring (1.8 × 10¹⁶ molecules cm⁻²) and summer (1.3 × 10¹⁶ molecules cm⁻²), primarily due to agricultural activities. Over the study period, the annual NH₃ concentration peaked in 2015, 2018, and 2022. Using k-means clustering, three distinct emission zones were identified, with Cluster 3 showing the highest NH₃ emission values, particularly in urban centers, and agricultural zones were identified, covering less than 20% of the study area, where cropland predominates (8%). Meteorological factors significantly influenced NH₃ levels, with negative correlations obtained for precipitation, wind speed, and evaporation, while solar radiation, boundary layer height, and instantaneous moisture fluxes showed positive correlations. A case study from March 2022, employing the HYSPLIT trajectory model, confirmed that agricultural practices are the dominant NH₃ source, with emissions reaching 3.2 × 10¹⁶ molecules cm⁻² in hotspot regions.
Evapotranspiration (ET) is a key factor in the water and energy cycle, playing a critical role, especially in agricultural regions. The eastern German lowlands, with their continental climate, are one of the driest regions in Germany, yet agriculture is the dominant land use. This study investigates the spatio-temporal variability of ET and its response to climate and land use/land cover change (LUCC) using MODIS remote sensing products and in-situ measurements over the period 2000-2020. The results show a slight increase in the mean annual ET, with local increases of up to 7.2% in the southern and southeastern parts of Brandenburg. LUCC revealed a 22.2% decrease in grassland to cropland conversion, leading to a decline in ET of 21%, while conversion from grassland to cropland increased by 14.8%, resulting in a 10% increase in ET. The strongest relationship with ET was found for vapor pressure deficit (VPD), temperature (Temp), and relative humidity (RH), which contributed 25.2%, 30.9%, and 23.2%, respectively, to its total variability. In addition to atmospheric factors soil moisture (SM) also contributed 17.7% to ET, but only in grassland. LUCC played a minor role as 22% of the study area was under conversion. Consequently, climate change, represented by the temporal change of the climatic factors, was identified as the dominant driver of ET in the study area, accounting for 97% of its variability. Accordingly, these findings underscore the importance of Temp, RH, and SM in agricultural and water resource management.
Urban planners and engineers rely on historical climate data to plan and design flood protection infrastructure that should withstand extreme flooding events with 1% annual exceedance probability (the 100-year flood). Here, we examine how hourly precipitation extremes are expected to change as temperatures rise and how this will affect urban flooding. The changes to short-duration rainfall extremes, often insufficiently considered in practice, are addressed utilizing a new temperature conditional extreme precipitation scaling approach and a novel regional climate convection-permitting model ensemble for +2 °C and +3 °C global warming scenarios. Based on hydrodynamic modeling, we estimate how future precipitation extremes translate into flood risks in two pre-alpine communes in Germany. Ignoring the impacts of climate change may lead to severe underestimations of flood risks. The +3 °C global warming scenario translates into an increase of 60% of affected buildings by the highest flood risk category (water level of 1 m and above). The increase in flow intensities will be greater in the commune characterized by steeper terrain. The results suggest that recently planned or implemented infrastructure projects may not be adequately equipped to cope with the anticipated effects of climate change in the coming decades.
Europe and the Middle East
(2024)
Recent progress in atmospheric modeling over the Andes – part I: review of atmospheric processes
(2024)
Regular detection of land-use and land-cover (LULC) changes with high accuracy is necessary for natural resources management and sustainable urban planning. The produced LULC maps from Google Earth Engine (GEE) also illustrate the transformation of the LULC for the respective landscape over time. The selected study area, Cottbus City and the Spree-Neisse district in northeastern Germany, has undergone significant development over the past decades due to various factors, including urbanization and industrialization; also, the landscape has been converted in some areas for post-mining activities. Detection of LULC changes that have taken place over the last few decades thus plays a vital role in quantifying the impact of these factors while improving the knowledge of these developments and supporting the city planners or urban management officials before implementing further long-term development initiatives for the future. Therefore, the study aims to (i) detect LULC changes for the time slices 2002 and 2022, testing machine learning (ML) algorithms in supervised and unsupervised classification for Landsat satellite imageries, and (ii) validate the newly produced LULC maps with the available regional database (RDB) from the federal and state statistical offices, Germany, and the Dynamic World (DW) near real-time 10 m global LULC data set powered by artificial intelligence (AI). The results of the Random Forest (RF) and the Smilecart classifiers of supervised classification using Landsat 9 OLI-2/TIRS-2 in 2022 demonstrated a validation accuracy of 88% for both, with Kappa Index (KI) of 83% and 84%, respectively. Moreover, the Training Overall Accuracy (TOA) was 100% for both years. The wekaKMeans cluster of the unsupervised classification also illustrated a similar transformation pattern in the LULC maps. Overall, the produced LULC maps offered an improved representation of the selected region’s various land-cover classes (i.e., vegetation, waterbodies, built areas, and bare ground) in the last two decades (20022 to 2022).