New high-resolution gridded dataset of daily mean, minimum, and maximum temperature and relative humidity for Central Europe (HYRAS)

Razafimaharo, Christène; Krähenmann, Stefan; Höpp, Simona; Rauthe, M.; Deutschländer, Thomas

doi:10.1007/s00704-020-03388-w

Cited by 35 publications

(14 citation statements)

References 42 publications

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“…While the box plots and the density functions demonstrate a clear shift of the relative humidity to lower values (significant except SON), no significant differences appeared in any of the skewness or kurtosis coefficients. Decreasing trends in the relative humidity of both 30-year normal periods appearing in the CR are in solid agreement with similar prevailing trends reported in many European studies (e.g., [52][53][54][55][56][57]) and are also reflected in projected future changes [58].…”

Section: Discussionsupporting

confidence: 90%

Circulation and Climate Variability in the Czech Republic between 1961 and 2020: A Comparison of Changes for Two “Normal” Periods

et al. 2022

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Thirty-year periods are treated in climatology as spans with relatively representative and stable climatic patterns, which can be used for calculating climate normals. Annual and seasonal series of circulation types were used to compare two 30-year sub-periods, 1961–1990 and 1991–2020, the second one being strongly influenced by recent global warming. This analysis was conducted according to the objective classification of circulation types and the climatic characteristics of sunshine duration, temperature, humidity, precipitation, and wind speed as calculated for the territory of the Czech Republic during the 1961–2020 period. For both sub-periods, their statistical characteristics were calculated, and the statistical significance of differences between them was evaluated. There was a statistically significant increase in the annual frequencies of anticyclonic circulation types and a significant decrease in cyclonic circulation types during 1991–2020 compared with 1961–1990. Generally, in both 30-year periods, significant differences in means, variability, characteristics of distribution, density functions, and linear trends appear for all climatic variables analysed except precipitation. This indicates that the recent 30-year “normal” period of 1991–2020, known to be influenced more by recent climate change, is by its climatic characteristics unrepresentative of the stable climatic patterns of previous 30-year periods.

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Section: Discussionsupporting

confidence: 90%

Circulation and Climate Variability in the Czech Republic between 1961 and 2020: A Comparison of Changes for Two “Normal” Periods

et al. 2022

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“…Due to the lack of physical restraints, statistical modelling approaches are often suspected of failing when extrapolating outside their training data range (Benyahya et al, 2007). However, machine-learning methods are more powerful and flexible than previous modelling approaches and are able to simultaneously use spatial and temporal information at different scales (Reichstein et al, 2019). This is especially important for climate change studies, where increasing air temperature might change the statistical relationships between meteorological drivers and stream water temperature.…”

Section: Discussionmentioning

confidence: 99%

Machine-learning methods for stream water temperature prediction

Feigl

Lebiedzinski

Herrnegger

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. Water temperature in rivers is a crucial environmental factor with the ability to alter hydro-ecological as well as socio-economic conditions within a catchment. The development of modelling concepts for predicting river water temperature is and will be essential for effective integrated water management and the development of adaptation strategies to future global changes (e.g. climate change). This study tests the performance of six different machine-learning models: step-wise linear regression, random forest, eXtreme Gradient Boosting (XGBoost), feed-forward neural networks (FNNs), and two types of recurrent neural networks (RNNs). All models are applied using different data inputs for daily water temperature prediction in 10 Austrian catchments ranging from 200 to 96 000 km2 and exhibiting a wide range of physiographic characteristics. The evaluated input data sets include combinations of daily means of air temperature, runoff, precipitation and global radiation. Bayesian optimization is applied to optimize the hyperparameters of all applied machine-learning models. To make the results comparable to previous studies, two widely used benchmark models are applied additionally: linear regression and air2stream. With a mean root mean squared error (RMSE) of 0.55 ∘C, the tested models could significantly improve water temperature prediction compared to linear regression (1.55 ∘C) and air2stream (0.98 ∘C). In general, the results show a very similar performance of the tested machine-learning models, with a median RMSE difference of 0.08 ∘C between the models. From the six tested machine-learning models both FNNs and XGBoost performed best in 4 of the 10 catchments. RNNs are the best-performing models in the largest catchment, indicating that RNNs mainly perform well when processes with long-term dependencies are important. Furthermore, a wide range of performance was observed for different hyperparameter sets for the tested models, showing the importance of hyperparameter optimization. Especially the FNN model results showed an extremely large RMSE standard deviation of 1.60 ∘C due to the chosen hyperparameters. This study evaluates different sets of input variables, machine-learning models and training characteristics for daily stream water temperature prediction, acting as a basis for future development of regional multi-catchment water temperature prediction models. All preprocessing steps and models are implemented in the open-source R package wateRtemp to provide easy access to these modelling approaches and facilitate further research.

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“…The nocturnal warm bias can be systematically reduced by accounting for skin temperature as demonstrated by Schulz et al (2020). However, the small differences between model results and observations are well within the uncertainty of the observations (see Razafimaharo et al, 2020). Furthermore, differences in the annual cycle of temperature result from different land cover maps and mostly appear during the vegetation period (April to September), but the impact is less strong than the bias to the observations.…”

Section: Discussionmentioning

confidence: 79%

“…Prior to this analysis the simulation experiments are compared with observations. The HYRAS high-resolution observational dataset from the German Weather Service (Razafimaharo et al, 2020) is used to evaluate the simulation results. These data are gridded daily at 5-km horizontal resolution based on station data, which are available over all of Germany for the period of 1951-2015.…”

Section: Simulation Experiments and Data Analysismentioning

confidence: 99%

Sensitivity of Convection-Permitting Regional Climate Simulations to Changes in Land Cover Input Data: Role of Land Surface Characteristics for Temperature and Climate Extremes

Tölle

Churiulin

2021

Front. Earth Sci.

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Characterization of climate uncertainties due to different land cover maps in regional climate models is essential for adaptation strategies. The spatiotemporal heterogeneity in surface characteristics is considered to play a key role in terrestrial surface processes. Here, we quantified the sensitivity of model results to changes in land cover input data (GlobCover 2009, GLC 2000, CCI, and ECOCLIMAP) in the regional climate model (RCM) COSMO-CLM (v5.0_clm16). We investigated land cover changes due to the retrieval year, number, fraction and spatial distribution of land cover classes by performing convection-permitting simulations driven by ERA5 reanalysis data over Germany from 2002 to 2011. The role of the surface parameters on the surface turbulent fluxes and temperature is examined, which is related to the land cover classes. The bias of the annual temperature cycle of all the simulations compared with observations is larger than the differences between simulations. The latter is well within the uncertainty of the observations. The land cover class fractional differences are small among the land cover maps. However, some land cover types, such as croplands and urban areas, have greatly changed over the years. These distribution changes can be seen in the temperature differences. Simulations based on the CCI retrieved in 2000 and 2015 revealed no accreditable difference in the climate variables as the land cover changes that occurred between these years are marginal, and thus, the influence is small over Germany. Increasing the land cover types as in ECOCLIMAP leads to higher temperature variability. The largest differences among the simulations occur in maximum temperature and from spring to autumn, which is the main vegetation period. The temperature differences seen among the simulations relate to changes in the leaf area index, plant coverage, roughness length, latent and sensible heat fluxes due to differences in land cover types. The vegetation fraction was the main parameter affecting the seasonal evolution of the latent heat fluxes based on linear regression analysis, followed by roughness length and leaf area index. If the same natural vegetation (e.g. forest) or pasture grid cells changed into urban types in another land cover map, daily maximum temperatures increased accordingly. Similarly, differences in climate extreme indices are strongest for any land cover type change to urban areas. The uncertainties in regional temperature due to different land cover datasets were overall lower than the uncertainties associated with climate projections. Although the impact and their implications are different on different spatial and temporal scales as shown for urban area differences in the land cover maps. For future development, more attention should be given to land cover classification in complex areas, including more land cover types or single vegetation species and regional representative classification sample selection. Including more sophisticated urban and vegetation modules with synchronized input data in RCMs would improve the underestimation of the urban and vegetation effect on local climate.

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New high-resolution gridded dataset of daily mean, minimum, and maximum temperature and relative humidity for Central Europe (HYRAS)

Cited by 35 publications

References 42 publications

Circulation and Climate Variability in the Czech Republic between 1961 and 2020: A Comparison of Changes for Two “Normal” Periods

Circulation and Climate Variability in the Czech Republic between 1961 and 2020: A Comparison of Changes for Two “Normal” Periods

Machine-learning methods for stream water temperature prediction

Sensitivity of Convection-Permitting Regional Climate Simulations to Changes in Land Cover Input Data: Role of Land Surface Characteristics for Temperature and Climate Extremes

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