Search

When measuring the energy balance at the earth’s surface using the Eddy covariance technique, the obtained budgets seldom produce a closed energy balance. The measurements often miss some of the energy fluxes. A possible reason is the neglect of non-turbulent surface fluxes of latent heat and sensible heat, i.e. advective fluxes of these quantities. We present estimates of advective latent and sensible heat fluxes for three different sites across Europe based on the ADVEX dataset. The obtained horizontal and vertical advective fluxes were site-specific and characterized by large scatter. In relative terms, the data indicated that the sensible heat budget was less affected by advection than the latent heat budget during nighttime; this is because vertical turbulent latent heat fluxes were very small or close to zero during the night. The results further showed that the additional energy gain by sensible heat advection might have triggered enhanced evaporation for two sites during nighttime. Accounting for advective fluxes improved the energy balance closure for one of the three ADVEX sites. However, the energy balance closure of the other two sites did not improve overall. A comparison with energy balance residuals (energy missed by the measurements without accounting for advection) indicated a large influence of systematic errors. An inspection of the energy balance for the sloped site of the ADVEX dataset underlined the necessity of slope-parallel measurement of radiation.

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.

The algorithm for and results of a newly developed multivariate non-parametric model, the Euclidean distance model (EDM), for the hourly disaggregation of daily climate data are presented here. The EDM is a resampling method based on the assumption that the day to be disaggregated has already occurred once in the past. The Euclidean distance (ED) serves as a measure of similarity to select the most similar day from historical records. EDM is designed to disaggregate daily means/sums of several climate elements at once, here temperature (T), precipitation (P), sunshine duration (SD), relative humidity (rH), and wind speed (WS), while conserving physical consistency over all disaggregated elements. Since weather conditions and hence the diurnal cycles of climate elements depend on the weather pattern, a selection approach including objective weather patterns (OWP) was developed. The OWP serve as an additional criterion to filter the most similar day. For a case study, EDM was applied to the daily climate data of the stations Dresden and Fichtelberg (Saxony, Germany). The EDM results agree well with the observed data, maintaining their statistics. Hourly results fit better for climate elements with homogenous diurnal cycles, e.g., T with very high correlations of up to 0.99. In contrast, the hourly results of the SD and the WS provide correlations up to 0.79. EDM tends to overestimate heavy precipitation rates, e.g., by up to 15% for Dresden and 26% for Fichtelberg, potentially due to, e.g., the smaller data pool for such events, and the equal-weighted impact of P in the ED calculation. The OWPs lead to somewhat improved results for all climate elements in terms of similar climate conditions of the basic stations. Finally, the performance of EDM is compared with the disaggregation tool MELODIST (Förster et al. 2015). Both tools deliver comparable and well corresponding results. All analyses of the generated hourly data show that EDM is a very robust and flexible model that can be applied to any climate station. Since EDM can disaggregate daily data of climate projections, future research should address whether the model is capable to respect and (re)produce future climate trends. Further, possible improvements by including the flow direction and future OWPs should be investigated, also with regard to reduce the overestimation of heavy rainfall rates.

This study investigates the trends of precipitation and temperature extremes for the historical observations (1961–1990) and future period (2061–2090) in the Jhelum River Basin. Future trends are estimated by using ensemble mean of three general circulation models under RCP4.5 and RCP8.5. Therefore, statistical downscaling model has been used to downscale the future precipitation and temperature. A total of 15 precipitation and temperature indices were calculated using the RClimdex package. Man-Kendall and Sen’s slope tests were used to detect the trends in climate extreme indices. Overall, the results of study indicate that there were significant changes in precipitation and temperature patterns as well as in the climate extremes in the basin for both observed as well as projected climate. Generally, more warming and increase in precipitation were observed, which increases from RCP4.5 to RCP8.5. For all the stations, increasing trends were found for both precipitation and temperature for twenty-first century at a 95% significance level. The frequency of warm days (TX90p), warm nights (TN90p), and summer days (SU25) showed significant increasing trends, alternatively the number of cold nights (TN10p) and cold days (TX10p) exhibited opposite behaviors. In addition, an increasing trend of warmest day (TXx) and coldest day (TNn) was observed. Our analysis also reveals that the number of very wet days (R90p) and heavy precipitation days (R10 mm) will likely increase in the future. Meanwhile, the Max 1-day (RX1-day) and 5-day (RX5-day) precipitation indices showed increasing trends at most of the stations of basin. The results of the study is of potential benefit for decision-makers to develop basin wide appropriate mitigation and adaptation measures to combat climate change and its consequences.

Compared to the well-known drivers of spatial variability in gross primary productivity (GPP), the relative importance of climatic variables, soil properties and plant traits to the spatial variability in net ecosystem exchange of CO2 between terrestrial ecosystem and atmosphere (NEE) is poorly understood. We used principal component regression to analyze data from 147 eddy flux sites to disentangle effects of climatic variables, soil properties and plant traits on the spatial variation in annual NEE and its components (GPP and ecosystem respiration (RE)) across global forests and grasslands. Our results showed that the largest unique contribution (proportion of variance only explained by one class of variables) to NEE variance came from climatic variables for forests (24%-30%) and soil properties for grasslands (41%-54%). Specifically, mean annual precipitation and potential evapotranspiration were the most important climatic variables driving forest NEE, whereas available soil water capacity, clay content and cation exchange capacity mainly influenced grassland NEE. Plant traits showed a small unique contribution to NEE in both forests and grasslands. However, leaf phosphorus content strongly interacted with soil total nitrogen density and clay content, and these combined factors represented a major contribution for grassland NEE. For GPP and RE, the majority of spatial variance was attributed to the common contribution of climate, soil and plant traits (50% - 62%, proportion of variance explained by more than one class of variables), rather than their unique contributions. Interestingly, those factors with only minor influences on GPP and RE variability (e.g., soil properties) have significant contributions to the spatial variability in NEE. Such emerging factors and the interactions between climatic variables, soil properties and plant traits are not well represented in current terrestrial biosphere models, which should be considered in future model improvement to accurately predict the spatial pattern of carbon cycling across forests and grasslands globally.

The eddy-covariance (EC) technique is used to determine mass and energy fluxes between the Earth’s surface and the lower atmosphere at high temporal resolution. Despite the frequent and successful use of the EC technique at terrestrial sites, its application over water surfaces is rare. We present one season of EC measurements conducted on the Rappbode Reservoir, Germany’s largest drinking water reservoir. A floating observation platform in the centre of the reservoir is used for observations of fluxes that were unaffected by surrounding land surfaces and therefore representative of the actual water–atmosphere exchange. The temporal patterns of sensible heat flux are inverted compared to land sites, since the maxima and the minima occur at night and day respectively. The latent heat flux and the evaporation are unexpectedly low for a site where evaporation is not limited by the availability of water. The daily totals in summer and autumn are only 50% and 75% of the potential evaporation assessed by the FAO grass-reference evaporation, respectively. Measurement uncertainties and the effects of the energy balance closure are ruled out as potential factors, so that low values appear to be a general feature of large water surfaces. The observed carbon dioxide fluxes are characterized by distinctive diurnal variations in a typical range for lakes and reservoirs. However, the methane fluxes are low compared to other inland waters.

In East Africa, climate change and variability have shown a strong impact on sectors such as agriculture, energy, and water. To allow mitigation and adaptation of the possible impacts of the projected change in climate, this study applies a Statistical Downscaling Model (SDSM) to generate a high-resolution climate projection, equivalent to future station data, to drive impact assessment models in selected, agricultural intensive, basins of Ethiopia (EthShed), Kenya (KenShed), and Tanzania (TanShed). Observed and large-scale climate variables (predictors) are obtained from the national meteorological agency of Ethiopia and international databases. BROOK90, a physical-based hydrological model, is used to assess the impacts of the projected change in precipitation and maximum and minimum temperature (T-max, and T-min) on the water balance. Based on SDSM, the results show an increase in precipitation, relative to the baseline period (1961-1990), in EthShed (14% - 50%) and KenShed (15% - 86%) and a decrease in TanShed (1.3% - 6.3%) in the 20s (2011-2040), 50s (2041-2070), and 80s (2071-2100) under the three Representative Concentration Pathways (RCPs; RCP2.6, RCP4.5, and RCP8.5). T-max (anomalies up to 3.7 °C) and T-min (anomalies up to 2.76 °C) will be warmer than the baseline period throughout the 21 century in all three basins. In line with the projected change in precipitation and temperature, an increase (decrease) in seasonal and annual streamflow, soil-water, and evaporation in EthShed and KenShed (TanShed) is projected in the 20s, 50s, and 80s. In general, sustainable adaptation measures are required to be developed in a site-specific manner, considering the projected increase in temperature and evaporation in all three basins and a decrease in soil-water and streamflow in TanShed.

In this study, statistical downscaling models were used to project possible future patterns of precipitation and temperature in the Jhelum River basin shared by Pakistan and India. In-situ meteorological data were used to downscale precipitation and temperature using different General Circulation Models (i.e., CanESM2, BCC-CSM1–1, and MICROC5) relative to baseline (1961–1990) under the Representative Concentration Pathway (RCP) scenarios RCP4.5 and RCP8.5. The downscaling models used were the Statistical Downscaling Model (SDSM), which uses multiple linear regression and weather generator methods, and the Long Ashton Research Station Weather Generator (LARS-WG), which uses weather generators. The results showed that the SDSM performance was slightly better than that of LARS-WG during validation and that the representation of the simulated mean monthly precipitation was more correct than that of monthly precipitation. The results also revealed that BCC-CSM1–1 performed better than CanESM2 and MICROC5 in the study region. The future annual mean temperature and precipitation are expected to rise under both RCP scenarios. The changes in the annual mean temperature and precipitation with LARS-WG were relatively higher than those with SDSM. Out of four seasons, winter and autumn are expected to be more diverse with regard to precipitation changes. However, although both models yielded non-identical results, it is certain that the basin will face a hotter climate in the future.

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.).

Convective rainfall can cause dangerous flash floods within less than six hours. Thus, simple approaches are required for issuing quick warnings. The Flash Flood Guidance (FFG) approach pre-calculates rainfall levels (thresholds) potentially causing critical water levels for a specific catchment. Afterwards, only rainfall and soil moisture information is required to issue warn-ings. This study applied the principle of FFG to the Wernersbach Catchment (Germany) with excellent data coverage using the BROOK90 water budget model. The rainfall thresholds were determined for durations of 1 to 24 hours, by running BROOK90 in “inverse” mode, identifying rainfall values for each duration that led to exceedance of critical discharge (fixed value). After calibrating the model based on its runoff, we ran it in hourly mode with four precipitation types and various levels of initial soil moisture for the period 1996 – 2010. The rainfall threshold curves showed a very high probability of detection (POD) of 91% for the 40 extracted flash flood events in the study period, however, the false alarm rate (FAR) of 56% and the critical success index (CSI) of 42% should be improved in further studies. The approach proved potential as an early flood indicator for head-catchments with limited available information.

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-flled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the frst time in this paper. In addition, 206 of these sites are for the frst time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible., European Union (EU), United States Department of Energy (DOE)

Local-scale climate change adaptation is receiving more attention to reduce the adverse effects of climate change. The process of developing adaptation measures at local-scale (e.g., river basins) requires high-quality climate information with higher resolution. Climate projections are available at a coarser spatial resolution from Global Climate Models (GCMs) and require spatial downscaling and bias correction to drive hydrological models. We used the hybrid multiple linear regression and stochastic weather generator model (Statistical Down-Scaling Model, SDSM) to develop a location-based climate projection, equivalent to future station data, from GCMs. Meteorological data from 24 ground stations and the most accurate satellite and reanalysis products identified for the region, such as Climate Hazards Group InfraRed Precipitation with Station Data were used. The Soil Water Assessment Tool (SWAT) was used to assess the impacts of the projected climate on hydrology. Both SDSM and SWAT were calibrated and validated using the observed climate and streamflow data, respectively. Climate projection based on SDSM, in one of the large and agricultural intensive basins in Ethiopia (i.e., Awash), show high variability in precipitation but an increase in maximum (Tmax) and minimum (Tmin) temperature, which agrees with global warming. On average, the projection shows an increase in annual precipitation (>10%), Tmax (>0.4 °C), Tmin (>0.2 °C) and streamflow (>34%) in the 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2100) under RCP2.6-RCP8.5. Although no significant trend in precipitation is found, streamflow during March–May and June–September is projected to increase throughout the 21 century by an average of more than 1.1% and 24%, respectively. However, streamflow is projected to decrease during January–February and October–November by more than 6%. Overall, considering the projected warming and changes in seasonal flow, local-scale adaptation measures to limit the impact on agriculture, water and energy sectors are required.

Highly-resolved data on water balance components (like runoff or storage) are crucial to improve water management, e.g., in drought or flood situations. Because regional observations of these components cannot be acquired adequately, applying water balance models is a feasible solution. 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.

Less is known about evapotranspiration (ET) along elevation gradients of low mountain ranges, especially with regard to different land uses and concerning long-term studies. We investigate ET of four sites of different land-uses along an elevation gradient of a low mountain range over eleven years (2008–2018) based on daily values. Three different ET estimates are inspected, which can give a reasonable range of ET. These estimates are ET based on the energy balance residual (ET_residual), ET corrected for the energy balance closure gap (ET_corr) and ET not corrected for the energy balance closure gap (ET_uncorr). In general, ET_residual showed largest values and ET_uncorr showed lowest values with ET_corr in between. Average annual differences between ET_residual and ET_corr ranged between 111 mm a−1 and 196 mm a−1. Average annual differences between ET_uncorr and ET_corr ranged between 70 mm a−1 and 167 mm a−1. For two site years ET_corr was lower than ET_uncorr. This could be related to gap-filling. Differences between different estimates were site-specific and related to the respective energy balance closure gap. Principal component analysis revealed similar dependency on driving variables for all three estimates and all sites. Given the influence of the energy balance closure gap on ET_uncorr and ET_residual, we recommend using ET_corr, but ET_residual can be still useful especially for sites with low vegetation, which rarely experience water stress. Comparison of two coniferous sites situated at different altitudes showed frequently larger values for the site located at a higher altitude. This might be a result of rainfall interception, which however must be investigated at sub-daily timescale.

The ECOsystem Space Thermal Radiometer Experiment on the Space Station (ECOSTRESS) was launched to the International Space Station on June 29, 2018 by the National Aeronautics and Space Administration (NASA). The primary scientific focus of ECOSTRESS is on evapotranspiration (ET), which occurs as Level 3 (L3) latent heat flux (LE) data products. This data is generated from the Level 2 Earth Surface Temperature and Emissivity Product (L2_LSTE), along with the atmospheric and ancillary surface data. Here, we provide the first validation (Stage 1, preliminary) of the global product ECOSTRESS clear - sky ET (L3_ET_PT - JPL, Version 6.0) against LE measurements at 82 eddy covariance sites around the world. Overall, the ECOSTRESS ET product performs well against site measurements (instant clear sky / flyover time: r 2 = 0.88; overall bias = 8%; normalized root mean square error, RMSE = 6%) . ET uncertainty was generally constant across climate zones, biome types, and times of day (ECOSTRESS samples the diurnal cycle), although temperate sites are over-represented. ECOSTRESS 70 m high spatial resolution improved correlations by 85% and RMSE by 62%, relative to 1 km pixels. This document serves as a reference for the accuracy of ECOSTRESS L3 ET and the Stage 1 validation status for subsequent science that continues to use this data. Fil: Fisher, Joshua B. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Lee, Brian. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Purdy, Adam J. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Halverson, Gregory H. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Dohlen, Matthew B. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Cawse - Nicholson, Kerry. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Wang, Audrey. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Anderson, Ray G. U.S. Department of Agriculture; Estados Unidos Fil: Aragon, Bruno. King Abdullah University of Science and Technology. Division of Biological and Environmental Science and Engineering. Hydrology, Agriculture and Land Observation Group (HALO); Arabia Saudita Fil: Arain, M. Alfat. McMaster University. School of Geography and Earth Sciences; Canadá Fil: Baldocchi, Dennis D. University of California, Berkeley. Department of Environmental Science, Policy and Management; Estados Unidos Fil: Baker, John M. U.S. Department of Agriculture; Estados Unidos Fil: Barral, Helene. University Montpellier. HydroSciences Montpellier (HSM); Francia Fil: Bernacchi, Carl J. University of Illinois Urbana‐Champaign. Institute for Sustainability, Energy, and Environment and Carl R. Woese Institute for Genomic Biology. Center for Advanced Bioenergy and Bioproducts Innovation; Estados Unidos. U.S. Department of Agriculture, Global Change and Photosynthesis Research Unit; Estados Unidos Fil: Bernhofer, Christian. Technische Universität Dresden, Institute of Hydrology and Meteorology; Alemania Fil: Biraud, Sebastien C. Lawrence Berkeley National Laboratory; Estados Unidos Fil: Bohrer, Gil. Ohio State University. Department of Civil, Environmental and Geodetic Engineering; Estados Unidos Fil: Brunsell, Nathaniel. University of Kansas. Department of Geography and Atmospheric Science; Estados Unidos Fil: Cappelaere, Bernard. University Montpellier. HydroSciences Montpellier (HSM); Francia Fil: Castro - Contreras, Saulo. University of Alberta. Earth and Atmospheric Sciences Department. Centre for Earth Observation Sciences (CEOS); Canadá Fil: Chum, Junghwa. National Institute of Forest Science; Corea del Sur Fil: Conrad, Bryan. University of Kansas. Department of Geography and Atmospheric Science; Estados Unidos Fil: Cremonese, Edoardo. Environmental Protection Agency of Aosta Valley; Italia Fil: Demarty, Jerome. University Montpellier. HydroSciences Montpellier (HSM); Francia Fil: Desai, Ankur R. University of Wisconsin‐Madison. Department of Atmospheric and Oceanic Sciences; Estados Unidos Fil: De Ligne, Anne. University of Liege. TERRA Teaching and Research Centre. Gembloux Agro‐Bio Tech; Bélgica Fil: Foltynova, Lenka. Czech Academy of Sciences. Global Change Research Institute ; República Checa Fil: Goulden, Michael L. University of California Irvine. Department of Earth System Science; Estados Unidos Fil: Griffis, Timothy J. University of Minnesota. Department of Soil, Water, and Climate; Estados Unidos Fil: Grunwald, Thomas. Technische Universität Dresden, Institute of Hydrology and Meteorology; Alemania Fil: Johnson, Mark S. University of British Columbia. Department of Earth, Ocean, and Atmospheric Sciences; Canadá Fil: Kang, Minseok. National Center for AgroMeteorology; Corea del Sur Fil: Kelbe, Dave. Xerra Earth Observation Institute; Nueva Zelanda Fil: Kowalska, Natalia. Czech Academy of Sciences. Global Change Research Institute ; República Checa Fil: Jong - Hwan, Lim. National Institute of Forest Science; Corea del Sur Fil: Mainassara, Ibrahim. University Montpellier. HydroSciences Montpellier (HSM); Francia Fil: McCabe, Matthew F. King Abdullah University of Science and Technology. Division of Biological and Environmental Science and Engineering. Hydrology, Agriculture and Land Observation Group (HALO); Arabia Saudita Fil: Missik, Justine E.C. Washington State University. Department of Civil and Environmental Engineering. Laboratory for Atmospheric Research; Estados Unidos Fil: Mohanty, Binayak P. Texas A&M University. Texas Water Observatory; Estados Unidos Fil: Moore, Caitlin E. University of Illinois Urbana‐Champaign. Institute for Sustainability, Energy, and Environment and Carl R. Woese Institute for Genomic Biology. Center for Advanced Bioenergy and Bioproducts Innovation; Estados Unidos Fil: Morillas, Laura. University of British Columbia. Department of Earth, Ocean, and Atmospheric Sciences; Canadá Fil: Morrison, Ross. Centre for Ecology and Hydrology; Reino Unido Fil: Munger, J. Willians. Harvard University. School of Engineering and Applied Sciences; Estados Unidos Fil: Posse, Gabriela. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina Fil: Richardson, Andrew D. Northern Arizona University Center for Ecosystem Science and Society; Estados Unidos Fil: Russell, Eric. S. Washington State University. Department of Civil and Environmental Engineering. Laboratory for Atmospheric Research; Estados Unidos Fil: Ryu, Youngryel. Seoul National University . Department of Landscape Architecture and Rural Systems Engineering; Corea del Sur Fil: Sanchez - Azofeifa, Arturo. University of Alberta. Earth and Atmospheric Sciences Department. Centre for Earth Observation Sciences (CEOS); Canadá Fil: Schmidt, Marius. IInstitute of Bio‐ and Geosciences: Agrosphere (IBG‐3) Forschungszentrum Jülich IBG‐3; Alemania Fil: Schwartz, Efrat. Weizmann Institute of Science. Department of Earth and Planetary Sciences, ; Israel Fil: Sharp, Iain. University of Alberta. Earth and Atmospheric Sciences Department. Centre for Earth Observation Sciences (CEOS); Canadá Fil: Sigut, Ladislav. Czech Academy of Sciences. Global Change Research Institute ; República Checa Fil: Tang, Yao. Georgia Institute of Technology. School of Civil and Environmental Engineering; Estados Unidos Fil: Hulley, Glynn. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Anderson, Martha. U.S. Department of Agriculture‐Agricultural Research Service; Estados Unidos Fil: Hain, Christopher. NASA Marshall Space Flight Center; Estados Unidos Fil: French, Andrew. U.S. Department of Agriculture; Estados Unidos Fil: Wood, Eric. Princeton University. Department of Civil and Environmental Engineering; Estados Unidos Fil: Hook, Simón. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos

Drainage from the canopy is one of the sub-processes described in conceptual interception models. In theory, this refers to rainfall water that is temporally stored on the canopy and starts to drip down, when the canopy is saturated. In most Rutter-type models, storage in the canopy is described as a single linear storage (in multilayer models as a storage cascade) from which the drainage occurs after a saturation value is reached. For a precise simulation of the timing and amount of canopy drainage, this approach appears to be insufficient. E.g., with large time-steps and highly filled storage, drainage rates can become larger than the precipitation intensity, leading to a negative storage.In our study, we present a review of different approaches to simulate drainage according to literature. We test those approaches using the interception model CanWat, which allows temporally and spatially resolved simulations of the relevant processes. CanWat relies on a vegetation model derived from terrestrial laser scans for a detailed description of the stand. CanWat is written in R language allowing to process multi-annual time series for a study area up to 1 km² with a spatial resolution of 1 m3 on a PC. For validation purposes, we use rain events that are available from long-term continuous measurements of interception in a spruce stand (mainly Picea abies; a continuous flux site since started within EUROFLUX in 1996) within the Tharandter Wald Southwest of Dresden.We outline benefits and limitations of each approach and evaluate which one best fits the needs for a precise simulation of the timing and amount of canopy drainage. The implementation and refinement of the most suitable drainage simulation approach into CanWat is part of the overall attempt to improve the model description of the interception process being the focus of a German Science Foundation (DFG BE-1721/23-1) interception project.

East Africa is one of the most vulnerable regions of Africa to extreme weather and climate events. Regional and local information on climate extremes is critical for monitoring and managing the impacts and developing sustainable adaptation measures. However, this type of information is not readily available at the necessary spatial resolution. Therefore, here we test trends and variability of temperature (1979–2010) and precipitation (1981–2016) extremes in East Africa, particularly Ethiopia, Kenya, and Tanzania, at a spatial resolution of 0.1 and 0.05°, respectively, using the indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). We use gridded data sets with high accuracy and resolution from the Terrestrial Hydrology Research Group, University of Princeton and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Trends of 19 indices are computed by fitting a linear model and using the nonparametric Mann–Kendall test and the magnitude of change is computed using the Sen's slope method. The results show an increasing trend in monthly maximum and minimum values of daily maximum and minimum temperature in large parts of the region. This is accompanied by significant increasing trends in warm nights (TN90p), warm days (TX90p), warm spell duration index (WSDI), and summer days index (SU). In addition, cold days (TX10p) and cold nights (TN10p) showed a significant decreasing trend. In general, the results show an increasing tendency in temperatures extremes, which is in line with rising global mean temperature. In addition, most of the temperature extremes observed after 2000 are warmer than the long‐term mean (1979–2010). Precipitation indices, on the other hand, showed increasing and decreasing trends in Ethiopia, Kenya, and Tanzania, but no general pattern. The outcomes enable identifying hot spot areas and planning of adaptation and mitigation measures at much finer spatial scale than previously possible.

Managing environmental resources under conditions of climate change and extreme climate events remains among the most challenging research tasks in the field of sustainable development. A particular challenge in many regions such as East Africa is often the lack of sufficiently long-term and spatially representative observed climate data. To overcome this data challenge we used a combination of accessible data sources based on station data, earth observations by remote sensing, and regional climate models. The accuracy of the Africa Rainfall Climatology version 2.0 (ARC2), Climate Hazards Group InfraRed Precipitation (CHIRP), CHIRP with Station data (CHIRPS), Observational-Reanalysis Hybrid (ORH), and regional climate models (RCMs) are evaluated against station data obtained from the respective national weather services and international databases. We did so by performing a comparison in three ways: point to pixel, point to area grid cell average, and stations' average to area grid cell average over 21 regions of East Africa: 17 in Ethiopia, 2 in Kenya, and 2 in Tanzania. We found that the latter method provides better correlation and significantly reduces biases and errors. The correlations were analysed at daily, dekadal (10 days), and monthly resolution for rainfall and maximum and minimum temperature (Tmax and Tmin) covering the period of 1983–2005. At a daily timescale, CHIRPS, followed by ARC2 and CHIRP, is the best performing rainfall product compared to ORH, individual RCMs (I-RCM), and RCMs' mean (RCMs). CHIRPS captures the daily rainfall characteristics well, such as average daily rainfall, amount of wet periods, and total rainfall. Compared to CHIRPS, ARC2 showed higher underestimation of the total (−30 %) and daily (−14 %) rainfall. CHIRP, on the other hand, showed higher underestimation of the average daily rainfall (−53 %) and duration of dry periods (−29 %). Overall, the evaluation revealed that in terms of multiple statistical measures used on daily, dekadal, and monthly timescales, CHIRPS, CHIRP, and ARC2 are the best performing rainfall products, while ORH, I-RCM, and RCMs are the worst performing products.For Tmax and Tmin, ORH was identified as the most suitable product compared to I-RCM and RCMs. Our results indicate that CHIRPS (rainfall) and ORH (Tmax and Tmin), with higher spatial resolution, should be the preferential data sources to be used for climate change and hydrological studies in areas of East Africa where station data are not accessible.

Fog deposition depends strongly on location and orographic setting. Even if the absolute amount can be small at some locations, it could play a decisive role. This is also true for Germany. In the lowlands, the annual amount of fog precipitation is in the range of a few mm per year, in the higher altitudes of the low mountain ranges a few hundred mm per year. In this article, we estimated the fog deposition based on the REAL-Fog model for Germany at daily time steps and one kilometre spatial resolution for a 70-year period for the years 1949 to 2018. These results were compared with another fog deposition modelling approach, station measurements and water balance modelling for different catchments in our study region. Comparing the results gives quite an interesting insight and outlook to the challenges to face in the future. The gridded data of the daily sum of the fog deposition we made available via HydroShare repository.

The seasonal and interannual variability of the terrestrial carbon cycle is regulated by the interactions of climate and ecosystem function. However, the key factors and processes determining the interannual variability of net ecosystem productivity (NEP) in different biomes are far from clear. Here, we quantified yearly anomalies of seasonal and annual NEP, net carbon uptake period (CUP), and the maximum daily NEP (NEPmax) in response to climatic variables in 24 deciduous broadleaf forest (DBF), evergreen forest (EF), and grassland (GRA) ecosystems that include at least eight years of eddy covariance observations. Over the 228 site-years studied, interannual variations in NEP were mostly explained by anomalies of CUP and NEPmax. CUP was determined by spring and autumn net carbon uptake phenology, which were sensitive to annual meteorological variability. Warmer spring temperatures led to an earlier start of net carbon uptake activity and higher spring and annual NEP values in DBF and EF, while warmer autumn temperatures in DBF, higher autumn radiation in EF, and more summer and autumn precipitation in GRA resulted in a later ending date of net carbon uptake and associated higher autumn and annual NEP. Anomalies in NEPmax s were determined by summer precipitation in DBF and GRA, and explained more than 50% of variation in summer NEP anomalies for all the three biomes. Results demonstrate the role of meteorological variability in controlling CUP and NEPmax, which in turn help describe the seasonal and interannual variability of NEP.

The paper presents an integrated approach for the apportionment of water and sediment sources and pathways in ungauged territories. The approach comprises various elements such as a large-scale semi-distributed hydrological and erosion model (using the SWAT tool), a statistical assessment of regular discharge data, the discrimination of runoff sources using end-member mixing analysis (EMMA), the apportionment of sediment sources using fingerprinting technique and sediment concentration and water discharge hysteresis rating curves analyses. The approach has been applied to Western Dvina River Basin (WDRB). The results discussed in the paper indicate main features of the water and sediment regime. The components of the presented framework provide a guide for detailed water and sediment studies making it an inexpensive way to develop a roadmap to understand important hydrological processes at a watershed scale.

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.

Accurate predictions of net ecosystem productivity (NEPc) of forest ecosystems are essential for climate change decisions and requirements in the context of national forest growth and greenhouse gas inventories. However, drivers and underlying mechanisms determining NEPc (e.g., climate and nutrients) are not entirely understood yet, particularly when considering the influence of past periods. Here we explored the explanatory power of the compensation day (cDOY)-defined as the day of year when winter net carbon losses are compensated by spring assimilation-for NEPc in 26 forests in Europe, North America, and Australia, using different NEPc integration methods. We found cDOY to be a particularly powerful predictor for NEPc of temperate evergreen needleleaf forests (R2=0.58) and deciduous broadleaf forests (R2=0.68). In general, the latest cDOY correlated with the lowest NEPc. The explanatory power of cDOY depended on the integration method for NEPc, forest type, and whether the site had a distinct winter net respiratory carbon loss or not. The integration methods starting in autumn led to better predictions of NEPc from cDOY then the classical calendar method starting 1 January. Limited explanatory power of cDOY for NEPc was found for warmer sites with no distinct winter respiratory loss period. Our findings highlight the importance of the influence of winter processes and the delayed responses of previous seasons' climatic conditions on current year's NEPc. Such carry-over effects may contain information from climatic conditions, carbon storage levels, and hydraulic traits of several years back in time. (Less)

Forest ecosystems play an important role in the interaction between the land surface and the atmosphere. Measurements and modelling efforts have revealed significant uncertainties in state-of-the-art flux assessments due to spatial inhomogeneities in the airflow and land surface. Here, a field experiment is used to describe the turbulent flow across a typical Central European forest clearing. A three-dimensional model of the inhomogeneous forest stand was developed using an innovative approach based on terrestrial laser-scanner technology. The comparison of the wind statistics of two measurement campaigns (5 and 12 months long) showed the spatial and temporal representativeness of the ultrasonic anemometer measurements within the canopy. An improved method for the correction of the vertical velocity enables the distinction between the instrumental offsets and the vertical winds due to the inclination of the instrument. Despite a 13 % fraction of deciduous plants within the otherwise evergreen canopy, the effects of phenological seasons on the velocity profiles were small. The data classified according to the wind speed revealed the intermittent nature of recirculating air in the clearing. Furthermore, the development of sub-canopy wind-speed maxima is explained by considering the velocity moments and the momentum equation (including measurements of the local pressure gradient). Clearings deflect the flow downward and feed the sub-canopy flow, i.e., advective fluxes, according to wind speed and, likely, clearing size, whereas local pressure gradients play an important role in the development of sub-canopy flow. The presented dataset is freely available at the project homepage.

As a contribution to an Integrated Water Resources Management (IWRM) project in Distrito Federal, Brazil, we address several aspects for a credible downscaling of near-surface air temperature and precipitation using the Statistical DownScaling Model (SDSM4.2). For instance, we apply a detailed screening of predictors, consider the end user needs in the validation procedure, assess the added value of the downscaling model and include several sources of uncertainties until the downscaling step. Results suggest that the interpolation of large-scale predictors to the target site is a reasonable alternative to predictors derived from grid-boxes. The validation metrics, measures (i.e. bias, root-mean-square error, and Pearson's correlation coefficient) and quantile–quantile plots reveal that model tends to underestimate near-surface temperature and precipitation; whereas extreme values are subject of considerable uncertainties. Single-site projections at daily scale are derived from 27 climate models from the Coupled Model Intercomparison Project phase 5 (CMIP5) forced by Representative Concentration Pathways (i.e. RCP2.6, RCP4.5, RCP6.0 and RCP8.5) scenarios. The downscaling model adds substantial value in terms of amplitude of variability when compared to the host coarse-resolution projections. Its performance is higher than a quantile-mapping bias correction technique, particularly in reproducing observed trends. In spite of the elevated level of uncertainties in the magnitude of change, most of the downscaled projections agree on positive changes in near-surface temperature and precipitation for the period of 2036–2055 when compared to the reference period (i.e. 1986–2005). The massive amount of downscaled projections is of limited application in hydrological studies and, therefore, we suggest a summarized group of projections which are representative to the central tendency and spread of the ensemble.

The factors influencing the interception process such as the vegetation structure and the meteorological conditions, especially rainfall characteristics, are diverse and highly variable. Therefore, derivation of canopy parameters for interception modelling requires data from field experiments over a long period. In this study, we analyse long-term changes in canopy parameters and rainfall interception for an old-growth spruce stand (composed mainly of Picea abies at a continuous flux site started within EUROFLUX in 1996) within the Tharandter Wald, Southwest of Dresden, Germany. The 10-minute data from the continuous measurements of gross precipitation (P) and throughfall (TF) from 1997 to 2018 were used as follows: Rainfall events were separated by a minimum time of three hours without rainfall and divided into rainfall classes according to their total amount and intensity. Due to vegetation changes in the measuring period caused by two thinning operations, the complete study period was divided into three periods. Canopy parameters S (storage capacity), p (free throughfall coefficient) and Ps (canopy saturation point) were then generated applying a regression-based method using the relationship between P and TF. We generated parameter sets for the complete period, for the three periods reflecting the vegetation changes and for single years. These parameter sets were further tested using a regression model. The statistical analysis of the long-term data set showed that stand parameters and interception are largely influenced by vegetation changes. S and Ps decreased with each thinning. Interestingly, interception and rainfall only changed after the first thinning (increase in p, decrease in interception), indicating counter-acting factors after the second thinning, such as changes in meteorological conditions, rainfall patterns or thinning induced changes in the microclimate of the canopy. Generally, the reliability of the derived canopy parameters depends on the length of the dataset used for the regression analysis. However, the parameter sets that considered three periods produced the best model fit compared to parameter sets for the complete period or for single years. This clearly shows that model performance of statistical interception models depend on a reliable parameterisation that can be improved when taking into account changes in stand characteristics.

Drought and heat events, such as the 2018 European drought, interact with the exchange of energy between the land surface and the atmosphere, potentially affecting albedo, sensible and latent heat fluxes, as well as CO 2 exchange. Each of these quantities may aggravate or mitigate the drought, heat, their side effects on productivity, water scarcity and global warming. We used measurements of 56 eddy covariance sites across Europe to examine the response of fluxes to extreme drought prevailing most of the year 2018 and how the response differed across various ecosystem types (forests, grasslands, croplands and peatlands). Each component of the surface radiation and energy balance observed in 2018 was compared to available data per site during a reference period 2004–2017. Based on anomalies in precipitation and reference evapotranspiration, we classified 46 sites as drought affected. These received on average 9% more solar radiation and released 32% more sensible heat to the atmosphere compared to the mean of the reference period. In general, drought decreased net CO 2 uptake by 17.8%, but did not significantly change net evapotranspiration. The response of these fluxes differed characteristically between ecosystems; in particular, the general increase in the evaporative index was strongest in peatlands and weakest in croplands. This article is part of the theme issue ‘Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale’.

Analysis of climate variability and change as a basis for adaptation and mitigation strategies requires long-term observations. However, the limited availability of ground station data constrains studies focusing on detecting variability and changes in climate and drought monitoring, particularly in developing countries of East Africa. Here, we use high-resolution precipitation (1981–2016) and maximum and minimum temperature (T-max and T-min) (1979–2012) datasets from international databases like the Climate Hazard Group (CHG), representing the most accurate data sources for the region. We assessed seasonal, annual, and decadal variability in rainfall, T-max and T-min and drought conditions using the Standardized Precipitation Index (SPI). The impact of changes in Sea Surface Temperature on rainfall variability and droughts is assessed using the Nino3.4 and Indian Ocean Dipole (IOD) indices. The results show maximum variability in rainfall during October–December (OND, short rainy season) followed by March–May (MAM, long rainy season). Rainfall variability during OND showed a significant correlation with IOD in Ethiopia (69%), Kenya (80%), and Tanzania (63%). In Ethiopia, the period June–September (JJAS) showed a significant negative correlation (−56%) with the Nino3.4. Based on the 12-month SPI, the eastern and western parts of the region are getting drier and wetter, respectively with an average of mild, moderate, and severe droughts of more than 37%, 6%, and 2% of the study period, respectively. The observed severe droughts (e.g., 1999/2000) and extreme floods (e.g., 1997/1998) were found to be linked to respective negative and positive anomalies of the Nino3.4. In general, climate data products with high spatial resolution and accuracy help detect changes and variability in climate at local scale where adaptation is required.

The prediction of climate effects on terrestrial ecosystems and water resources is one of the major research questions in hydrology. Conceptual water-energy balance models can be used to gain a first order estimate of how long-term average streamflow is changing with a change in water and energy supply. A common framework for investigation of this question is based on the Budyko hypothesis, which links hydrological response to aridity. Recently, Renner et al. (2012) introduced the climate change impact hypothesis (CCUW), which is based on the assumption that the total efficiency of the catchment ecosystem to use the available water and energy for actual evapotranspiration remains constant even under climate changes. Here, we confront the climate sensitivity approaches (the Budyko approach of Roderick and Farquhar, 2011, and the CCUW) with data of more than 400 basins distributed over the continental United States. We first estimate the sensitivity of streamflow to changes in precipitation using long-term average data of the period 1949 to 2003. This provides a hydro-climatic status of the respective basins as well as their expected proportional effect to changes in climate. Next, we test the ability of both approaches to predict climate impacts on streamflow by splitting the data into two periods. We (i) analyse the long-term average changes in hydro-climatology and (ii) derive a statistical classification of potential climate and basin change impacts based on the significance of observed changes in runoff, precipitation and potential evapotranspiration. Then we (iii) use the different climate sensitivity methods to predict the change in streamflow given the observed changes in water and energy supply and (iv) evaluate the predictions by (v) using the statistical classification scheme and (vi) a conceptual approach to separate the impacts of changes in climate from basin characteristics change on streamflow. This allows us to evaluate the observed changes in streamflow as well as to assess the impact of basin changes on the validity of climate sensitivity approaches. The apparent increase of streamflow of the majority of basins in the US is dominated by an increase in precipitation. It is further evident that impacts of changes in basin characteristics appear simultaneously with climate changes. There are coherent spatial patterns with catchments where basin changes compensate for climatic changes being dominant in the western and central parts of the US. A hot spot of basin changes leading to excessive runoff is found within the US Midwest. The impact of basin changes on the prediction is large and can be twice as much as the observed change signal. Although the CCUW and the Budyko approach yield similar predictions for most basins, the data of water-limited basins support the Budyko framework rather than the CCUW approach, which is known to be invalid under limiting climatic conditions.

The timing of the seasons strongly effects ecosystems and human activities. Recently, there is increasing evidence of changes in the timing of the seasons, such as earlier spring seasons detected in phenological records, advanced seasonal timing of surface temperature, earlier snow melt or streamflow timing. For water resources management there is a need to quantitatively describe the variability in the timing of hydrological regimes and to understand how climatic changes control the seasonal water budget of river basins on the regional scale. In this study, changes of the annual cycle of hydrological variables are analysed for 27 river basins in Saxony/Germany. Thereby monthly series of basin runoff ratios, the ratio of runoff and basin precipitation are investigated for changes and variability of their annual periodicity over the period 1930–2009. Approximating the annual cycle by the means of harmonic functions gave acceptable results, while only two parameters, phase and amplitude, are required. It has been found that the annual phase of runoff ratio, representing the timing of the hydrological regime, is subject to considerable year-to-year variability, being concurrent with basins in similar hydro-climatic conditions. Two distinct basin classes have been identified, whereby basin elevation has been found to be the delimiting factor. An increasing importance of snow on the basin water balance with elevation is apparent and mainly governs the temporal variability of the annual timing of hydrological regimes. Further there is evidence of coincident changes in trend direction (change points in 1971 and 1988) in snow melt influenced basins. In these basins the timing of the runoff ratio is significantly correlated with the timing of temperature, and effects on runoff by temperature phase changes are even amplified. Interestingly, temperature effects may explain the low frequent variability of the second change point until today. However, the first change point can not be explained by temperature alone and other causes have to be considered.

Long term average change in streamflow is a major concern in hydrology and water resources management. Some simple analytical methods exist for the assessment of the sensitivity of streamflow to climatic variations. These are based on the Budyko hypothesis, which assumes that long term average streamflow can be predicted by climate conditions, namely by annual average precipitation and evaporative demand. Recently, Tomer and Schilling (2009) presented an ecohydrological concept to distinguish between effects of climate change and basin characteristics change on streamflow. We relate the concept to a coupled consideration of the water and energy balance. We show that the concept is equivalent to the assumption that the sum of the ratio of annual actual evapotranspiration to precipitation and the ratio of actual to potential evapotranspiration is constant, even when climate conditions are changing. Here, we use this assumption to derive analytical solutions to the problem of streamflow sensitivity to climate. We show how, according to this assumption, climate sensitivity would be influenced by different climatic conditions and the actual hydrological response of a basin. Finally, the properties and implications of the method are compared with established Budyko sensitivity methods and illustrated by three case studies. It appears that the largest differences between both approaches occur under limiting conditions. Specifically, the sensitivity framework based on the ecohydrological concept does not adhere to the water and energy limits, while the Budyko approach accounts for limiting conditions by increasing the sensitivity of streamflow to a catchment parameter encoding basin characteristics. Our findings do not support any application of the ecohydrological concept under conditions close to the water or energy limits, instead we suggest a correction based on the Budyko framework.

The eddy covariance technique was used to measure the CO2 flux over four differently grazed Leymus chinensis steppe ecosystems (ungrazed since 1979 (UG79), winter grazed (WG), continuously grazed (CG), and heavily grazed (HG) sites) during four growing seasons (May to September) from 2005 to 2008, to investigate the response of the net ecosystem exchange (NEE) over grassland ecosystems to meteorological factors and grazing intensity. At UG79, the optimal air temperature for the half-hourly NEE occurred between 17 and 20 °C, which was relatively low for semi-arid grasslands. The saturated NEE (NEEsat) and temperature sensitivity coefficient (Q 10) of ecosystem respiration (RE) exhibited clear seasonal and interannual variations, which increased with canopy development and the soil water content (SWC, at 5 cm). The total NEE values for the growing seasons from 2005 to 2008 were −32.0, −41.5, −66.1, and −89.8 g C m−2, respectively. Both the amounts and distribution of precipitation during the growing season affected the NEE. The effects of grazing on the CO2 flux increased with the grazing intensity. During the peak growth stage, heavy grazing and winter grazing decreased NEEsat and gross primary production (45 % for HG and 34 % for WG) due to leaf area removal. Both RE and Q 10 were clearly reduced by heavy grazing. Heavy grazing changed the ecosystem from a CO2 sink into a CO2 source, and winter grazing reduced the total CO2 uptake by 79 %. In the early growing season, there was no difference in the NEE between CG and UG79. In addition to the grazing intensity, the effects of grazing on the CO2 flux also varied with the vegetation growth stages and SWC.

Grazing intensity effects on the evapotranspiration (ET) partitioning into evaporation (E) and transpiration (T) were investigated using eddy covariance measurement data during the growing season (May to September) from 2005 to 2008 over typical steppes in Inner Mongolia, China. Four grazing intensities were examined (UG79, ungrazed since 1979; WG, winter grazed; CG, continuously grazed; HG, heavily grazed). Soil water content (SWC) significantly influenced daily T/ET by affecting T and E in different ways. On a monthly scale, T/ET at UG79 (0.46–0.77) increased with vegetation growth regardless of the uneven distribution of precipitation. The mean T/ET at UG79 for the growing season ranged from 0.50 to 0.74 for 2005 to 2008. In the normal precipitation year (2006), WG and HG reduced T/ET because they significantly reduced T through the removal of leaf area. In the dry year (2005), this negative effect on T was depressed by summer droughts, while WG and HG decreased E primarily due to the decreased interception of rainfall by the canopy. The grazing effect on T/ET was opposite to that in 2006. These results indicate that grazing effects on ET partitioning could vary with soil water conditions in the semiarid typical steppe ecosystems.

International audience; The response of crops to changing climate and atmospheric CO 2 concentration ([CO 2 ]) could have large effects on food production, and impact carbon, water, and energy fluxes, causing feedbacks to the climate. To simulate the response of temperate crops to changing climate and [CO 2 ], which accounts for the specific phenology of crops mediated by management practice, we describe here the development of a process-oriented terrestrial biogeochemical model named ORCHIDEE-CROP (v0), which integrates a generic crop phenology and harvest module, and a very simple pa-rameterization of nitrogen fertilization, into the land surface model (LSM) ORCHIDEEv196, in order to simulate bio-physical and biochemical interactions in croplands, as well as plant productivity and harvested yield. The model is applicable for a range of temperate crops, but is tested here using maize and winter wheat, with the phenological parameteriza-tions of two European varieties originating from the STICS agronomical model. We evaluate the ORCHIDEE-CROP (v0) model against eddy covariance and biometric measurements at seven winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are CO 2 fluxes (net ecosystem exchange, NEE), latent heat, and sensible heat fluxes. Additional measurements of leaf area index (LAI) and aboveground biomass and yield are used as well. Evaluation results revealed that ORCHIDEE-CROP (v0) reproduced the observed timing of crop development stages and the amplitude of the LAI changes. This is in contrast to ORCHIDEEv196 where, by default, crops have the same phenology as grass. A halving of the root mean square error for LAI from 2.38 ± 0.77 to 1.08 ± 0.34 m 2 m −2 was obtained when ORCHIDEEv196 and ORCHIDEE-CROP (v0) were compared across the seven study sites. Improved crop Published by Copernicus Publications on behalf of the European Geosciences Union. 858 X. Wu et al.: ORCHIDEE-CROP (v0), a new process-based agro-land surface model phenology and carbon allocation led to a good match between modeled and observed aboveground biomass (with a normalized root mean squared error (NRMSE) of 11.0-54.2 %), crop yield, daily carbon and energy fluxes (with a NRMSE of ∼ 9.0-20.1 and ∼ 9.4-22.3 % for NEE), and sensible and latent heat fluxes. The simulated yields for winter wheat and maize from ORCHIDEE-CROP (v0) showed a good match with the simulated results from STICS for three sites with available crop yield observations, where the average NRMSE was ∼ 8.8 %. The model data misfit for energy fluxes were within the uncertainties of the measurements , which themselves showed an incomplete energy balance closure within the range 80.6-86.3 %. The remaining discrepancies between the modeled and observed LAI and other variables at specific sites were partly attributable to un-realistic representations of management events by the model. ORCHIDEE-CROP (v0) has the ability to capture the spatial gradients of carbon and energy-related variables, such as gross primary productivity, NEE, and sensible and latent heat fluxes across the sites in Europe, which is an important requirement for future spatially explicit simulations. Further improvement of the model, with an explicit parameterization of nutritional dynamics and management, is expected to improve its predictive ability to simulate croplands in an Earth system model.

Accurate estimation of the terrestrial latent heat flux (LE) for each plant functional type (PFT) at high spatial and temporal scales remains a major challenge. We developed a satellite-based hybrid algorithm to determine the Priestley–Taylor (PT) parameter for estimating global terrestrial LE across multiple biomes. The hybrid algorithm combines a simple empirical equation with physically based ecophysiological constraints to obtain the sum of the weighted ecophysiological constraints (f(e)) from satellite-based normalized difference vegetation index (NDVI) and ground-measured air temperature (Ta), relative humidity (RH), vapor pressure deficit (VPD) and LE for 2000 to 2009 provided by 240 globally distributed FLUXNET eddy covariance (ECOR) tower sites. Cross-validation analysis indicated that the optimization at a PFT level performed well with a RMSE of less than 0.15 and a R2 between 0.61 and 0.88 for estimated monthly f(e). Cross-validation analysis also revealed good performance of the hybrid-based PT method in estimating seasonal variability with a RMSE of the monthly LE varying from 4.3 W/m2 (for 6 deciduous needleleaf forest sites) to 18.1 W/m2 (for 34 crop sites) and with a R2 of more than 0.67. The algorithm's performance was also good for predicting among-site and inter-annual variability with a R2 of more than 0.78 and 0.70, respectively. We implemented the global terrestrial LE estimation from 2003 to 2005 for a spatial resolution of 0.05°by recalibrating the coefficients of the hybrid algorithm using Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorological data, Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI product and ground-measured LE. This simple but accurate hybrid algorithm provides an alternative method for mapping global terrestrial LE, with a performance generally improved as compared to other satellite algorithms that are not calibrated with tower. The calibrated f(e) differs for different PFTs, and all driving forces of the algorithm can be acquired from satellite and meteorological observations.

For a sustainable forest management, a site-specific knowledge on the water balance is a prerequisite. A simple and popular field method for assessing the water balance of forest sites is based on overlaying relief and soil information. Furthermore, climatic influence on the water balance is often restricted to longtime average values of precipitation and air temperature (whole year and/or growing season). However, the impacts of climate change and climatic extremes, as well as silvicultural changes, are inadequately considered. To overcome these short-comings, we integrated the 1D-SVAT model BROOK90 and a radiation model in a GIS to simulate the spatially distributed components of water balance of forest sites. In this paper, we present the model concept and show an approach to describe the influence of a complex terrain on parameters controlling the spatial distribution of energy and water fluxes.

Available climatological information of Distrito Federal does not satisfy the requirements for detailed climate diagnosis, as they do not provide the necessary spatial resolution for water resources management purposes. Annual and seasonal climatology (1971–2000) of precipitation from 6 meteorological stations and 54 rain gauges from Central Brazil were used to test eight different spatial interpolation methods. Geographical factors (i.e., altitude, longitude and latitude) explain a large portion of precipitation in the region, and therefore, multivariate models were included. The performance of estimations was assessed through independent validation using mean square error, correlation coefficient and Nash–Sutcliffe efficiency criterion. Inverse distance weighting (IDW), ordinary kriging (OK) and the multivariate regression with interpolation of residuals by IDW (MRegIDW) and OK (MRegOK) have performed the lowest errors and the highest correlation and Nash–Sutcliffe efficiency criterion. In general, interpolation methods provide similar spatial distributions of rainfall wherever observation network is dense. However, the inclusion of geographical variables to the interpolation method should improve estimates in areas where the observation network density is low. Nevertheless, the assessment of uncertainties using a geostatistical method provides supplementary and qualitative information which should be considered when interpreting the spatial distribution of rainfall.

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.

Stability indices provide simple metrics to characterize tropospheric states favouring convection. Not only for regions where precipitation is mainly related to convective processes, important aspects of the climate comprising information from different levels and variables can therefore be summarized in scalar metrics. Although the linkage between tropospheric stability and convective precipitation is blurred by additional prerequisites often not resolved in atmospheric models, analysis of stability indices from global climate models (GCMs) provides a more process-orientated assessment than the separate analysis of the individual climate variables. This paper presents an assessment of GCM capabilities to simulate tropospheric stability on the Arabian Peninsula. Therefore, six stability indices (K-Index, Total Totals Index, Vertical Totals Index, Showalter Index, SWEAT and Cross Totals Index) were calculated for several GCMs from the Climate Model Intercomparison Project 3 archive for three locations. GCM indices were compared with reanalysis data from the National Centers for Environmental Prediction in collaboration with the National Center for Atmospheric Research as well as radiosoundings from the Integrated Global Radiosounding Archive. Comparison was done by means of a classification approach based on quantile values of the index distribution. Input parameters of the indices were also analysed and biases for indices as well as input parameters were identified. There are biases found for the different indices which can be attributed to some extent to biases of the input parameters. There is no input parameter which is biased in the same direction for all indices and locations. Due to the variability of the results, rankings of the models are characterized by large rank differences so that there is no overall best performing model. Nevertheless, this study provides some insight in the performance of the GCMs to simulate tropospheric stability at individual locations and so presents an alternative method of model performance assessment.

The IWAS project Ukraine deals among others with the water balance in the headwaters of the river Western Bug. During investigations in the sub-catchment Sasiv, a gap in the water balance was detected by Pluntke et al. (Environ Earth Sci, this issue, 2014). The runoff of this sub-catchment is significantly higher than that of any other catchment in the region. Unexpectedly, in 2011 the runoff was observed to be higher than precipitation. We hypothesize additional subsurface flow in the Sasiv catchment from adjacent aquifers. The hydrological model BROOK90 was used to model water flows. In addition to publicly available meteorological input data from the NOAA and ECA&C databases, own precipitation measurements in Sasiv were used. The mean seasonal leaf area index (LAI)—cycle of forest, grass, and agricultural land was determined using satellite data. To improve the output quality, the model was adapted for the superior catchment Kamianka-Buzka/Western Bug (2,500 km2). It was calibrated for the period 2001–2006 and validated for 2007–2011. Infiltration and drainage parameters were unknown, and were therefore calibrated applying Monte Carlo simulations. It was not possible to calibrate the sub-catchment Sasiv (107 km2) due to high deviations of the natural water balance. Thus, the model was calibrated for the superior catchment and afterwards applied to the Sasiv catchment. The monthly estimated inflow was calculated based on, first, a constant term, which is a fixed monthly inflow with its minimum in October (22 mm) and its maximum in May (41 mm). Secondly, a dynamic term was added. Here, different dependencies were tested, e.g., precipitation, soil moisture, groundwater level, and modelled runoff. The best coefficient of determination was achieved using a monthly modelled runoff from the 4 months before the current time. The model showed a good performance in the validation period (R2 = 0.88). On average, the runoff observed in Sasiv consists of 60 percent groundwater inflow. At the gauge Kamianka-Buzka, this fraction is still about 5 %. The determined LAI cycles and the usage of an additional precipitation station near Sasiv significantly improved the quality of the water balance modelling in the Sasiv sub-catchment. The presented hydrological model approach was extended by hydrogeological simulations to enhance the understanding of processes in groundwater and surface water interactions. Therefore, it was a special challenge to model with scarce data the heterogeneous hydrogeological character of the catchment. The multi-level calibration enables a comprehensible estimation of annual groundwater recharge rates (30–700 mm) of the shallow quaternary aquifer, which is drained by the river Bug system.

A key challenge for climate projection science is to serve the growing needs of impact assessments in an environment with substantial differences in the projections of climate models and an increasing number of relevant climate model results. In order to assist the assessment of water resources impacts under future climate change, this work provides a synthesis of the simulations of General Circulation Models (GCMs) for the region of Distrito Federal, Brazil. The work analyzes projections of mean surface air temperature and precipitation of 22 GCMs, as well as seven extreme indices of 10 GCMs. Trends of the multi-model ensemble median, as well as their significance, were calculated. The consistency in the sign of change was assessed through the percentage of agreement of simulations with the median. Finally, the probability density function of the multi-model ensemble provides valuable information about the uncertainties of projections. Investigations were performed for annual and seasonal temporal scales for the period 2011–2050. The main results here identified are: (a) a consensus of the multi-model ensemble and median to increasing temperature; (b) a slightly, but less consistent, decrease of precipitation in the dry season; and (c) increase of heat waves and droughts events, although changes in precipitation extremes are much less coherent than for temperature. The approach used gives a comprehensive assessment of the possible future climate until the middle of the twenty-first century, as well as the uncertainties involved in the multi-model ensemble projections.

Validation of Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface reflectance products is important to effective utilization of such products for earth systems science. Ground-based measurements are normally utilized for such validation. However, the major scale mismatch between the ground ‘point’ measurement and MODIS resolution (500 m and 1 km) makes direct comparison infeasible over many land surface types. In this paper, an indirect comparison between ground ‘point’ measurements and MODIS land surface products via high-resolution remotely sensed imagery (Landsat Thematic Mapper/TM) was utilized in semi-arid grassland of Inner Mongolia in summer 2005, where ground measurements are relatively sparse in comparison with other locations around the world. Within the validation, the TM reflectance imagery was first calibrated by the ground ‘point’ measurements, and then aggregated to MODIS data resolution for determination of their accuracy. Besides common direct spectral band comparison of re...

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".

In the framework of the IWAS/Agua-DF project, this study focuses on changes in mean surface air temperature and accumulated precipitation in Central Brazil over the past 40 years. It has two main objectives: (1) comparison between two climatological periods (2001–2010 and 1971–2000) and (2) trend analysis of climate variables. Time series of meteorological and rain gauge stations from Central Brazil have been organized in a databank, which contains tools for homogeneity tests. From that, 4 temperature and 55 precipitation time series were sufficient homogeneous, while 1 temperature and 5 precipitation time series were identified as inhomogeneous. Reliable spatial distribution was produced using proper interpolation method. Trends and significance levels were calculated by Rapp’s estimator of slope and Mann–Kendall test, respectively. The most important results of the comparisons and trend analysis in the last four decades are: (1) marked increase in annual and seasonal mean surface air temperature, (2) evident decreases of accumulated rainfall in winter and autumn, and (3) apparent increase of precipitation amounts in the rainy season.

Summary Numerical water balance models are widely used in ecological and hydro sciences. However, their application is related to specific problems and uncertainties. The reliability of model prediction depends on (i) model concept, (ii) parameters, (iii) uncertainty of input data, and (iv) uncertainty of reference data. How model concept (i) and parameters (ii) effect the model’s performance is an often treated problem. However, the effects of (iii) and (iv) are typically ignored or only barely treated in context of regionalisation and generalisation. In this study, the actual measurement uncertainties of input and reference data are the main focus. Furthermore, the evaluation of model results is analysed with regard to uncertainties of reference data. A special feature is the use of evapotranspiration (measured via the eddy covariance) instead of runoff for evaluation of simulation results. It is shown that seemingly small uncertainties of measurements can create significant uncertainties in simulation results depending on the temporal scale of investigation. As an example, the uncertainty of measurements of daily global radiation sum up to an uncertainty of 250 MJ (equivalent to 100 mm) on an annual scale, which causes an uncertainty of 40 mm in simulated grass-reverence evapotranspiration. Summarised and generalised, the measurement uncertainties of all input data create an uncertainty on average of around 5% in the simulated annual evapotranspiration and of around 10% in the simulated annual seepage. However, the effects can be significantly higher in years with extreme events and can reach up to 15%. It is demonstrated that uncertainties of individual variables are not simply superposed but interact in a complex way. Thereby, it has become apparent that the effects of measurement uncertainties on model results are similar for complex and for simple models.

[1] Drought is typically associated with a lack of precipitation, whereas the contribution of evapotranspiration and runoff to drought evolution is not well understood. Here we use unique long-term observations made in four headwater catchments in central and western Europe to reconstruct storage anomalies and study the drivers of storage anomaly evolution during drought. We provide observational evidence for the “drought-paradox” in that region: a consistent and significant increase in evapotranspiration during drought episodes, which acts to amplify the storage anomalies. In contrast, decreases in runoff act to limit storage anomalies. Our findings stress the need for the correct representation of evapotranspiration and runoff processes in drought indices.