- H. Biemans (1)
- M.F.P. Bierkens (1)
- D. Clark (1)
- J. Eberle (1)
- T. Eikelboom (1)
- S. Folwell (1)
- M. Forkel (1)
- W.H.P. Franssen (1)
- D. Gerten (2)
- S. Gomes (1)
- S. Gosling (1)
- B. Graler (1)
- I. Haddeland (1)
- S. Hagemann (1)
- N. Hanasaki (1)
- R. Harding (1)
- J. Heinke (1)
- T. Hengl (1)
- M. Herold (1)
- G.B.M. Heuvelink (1)
- R.W.A. Hutjes (1)
- C. Huttich (1)
- P. Kabat (2)
- M. Kilibarda (1)
- S. Koirala (1)
- F. Ludwig (1)
- T. Oki (1)
- E. Pebesma (1)
- J. Polcher (1)
- S. Rost (1)
- C. Schmullius (1)
- T. Stacke (1)
- M.P. Tadic (1)
- M. Urban (1)
- P. Viterbo (1)
- M.T.H. Vliet van (1)
- F. Voss (1)
- G.P. Weedon (1)
- P. Yeh (1)
Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution
Kilibarda, M. ; Hengl, T. ; Heuvelink, G.B.M. ; Graler, B. ; Pebesma, E. ; Tadic, M.P. ; Bajat, B. - \ 2014
Journal of Geophysical Research: Atmospheres 119 (2014)5. - ISSN 2169-897X - p. 2294 - 2313.
daily climate extremes - space-time climate - data set - spatial interpolation - surface temperature - daily precipitation - air-temperature - part ii - variability - geostatistics
Combined Global Surface Summary of Day and European Climate Assessment and Dataset daily meteorological data sets (around 9000 stations) were used to build spatio-temporal geostatistical models and predict daily air temperature at ground resolution of 1km for the global land mass. Predictions in space and time were made for the mean, maximum, and minimum temperatures using spatio-temporal regression-kriging with a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) 8 day images, topographic layers (digital elevation model and topographic wetness index), and a geometric temperature trend as covariates. The accuracy of predicting daily temperatures was assessed using leave-one-out cross validation. To account for geographical point clustering of station data and get a more representative cross-validation accuracy, predicted values were aggregated to blocks of land of size 500x500km. Results show that the average accuracy for predicting mean, maximum, and minimum daily temperatures is root-mean-square error (RMSE) =2 degrees C for areas densely covered with stations and between 2 degrees C and 4 degrees C for areas with lower station density. The lowest prediction accuracy was observed at high altitudes (>1000m) and in Antarctica with an RMSE around 6 degrees C. The model and predictions were built for the year 2011 only, but the same methodology could be extended for the whole range of the MODIS land surface temperature images (2001 to today), i.e., to produce global archives of daily temperatures (a next-generation repository) and to feed various global environmental models. Key Points Global spatio-temporal regression-kriging daily temperature interpolation Fitting of global spatio-temporal models for the mean, maximum, and minimum temperatures Time series of MODIS 8 day images as explanatory variables in regression part
Pan-arctic climate and land cover trends derived from multi-variate and multi-scale analyses (1981-2012)
Urban, M. ; Forkel, M. ; Eberle, J. ; Huttich, C. ; Schmullius, C. ; Herold, M. - \ 2014
Remote Sensing 6 (2014)3. - ISSN 2072-4292 - p. 2296 - 2316.
snow water equivalent - space-time climate - photosynthetic trends - surface temperature - northern siberia - shrub expansion - radiometer data - orbital drift - vegetation - tundra
Arctic ecosystems have been afflicted by vast changes in recent decades. Changes in temperature, as well as precipitation, are having an impact on snow cover, vegetation productivity and coverage, vegetation seasonality, surface albedo, and permafrost dynamics. The coupled climate-vegetation change in the arctic is thought to be a positive feedback in the Earth system, which can potentially further accelerate global warming. This study focuses on the co-occurrence of temperature, precipitation, snow cover, and vegetation greenness trends between 1981 and 2012 in the pan-arctic region based on coarse resolution climate and remote sensing data, as well as ground stations. Precipitation significantly increased during summer and fall. Temperature had the strongest increase during the winter months (twice than during the summer months). The snow water equivalent had the highest trends during the transition seasons of the year. Vegetation greenness trends are characterized by a constant increase during the vegetation-growing period. High spatial resolution remote sensing data were utilized to map structural vegetation changes between 1973 and 2012 for a selected test region in Northern Siberia. An intensification of woody vegetation cover at the taiga-tundra transition area was found. The observed co-occurrence of climatic and ecosystem changes is an example of the multi-scale feedbacks in the arctic ecosystems.
A physically based model of global freshwater surface temperature
Beek, L.P.H. van; Eikelboom, T. ; Vliet, M.T.H. van; Bierkens, M.F.P. - \ 2012
Water Resources Research 48 (2012). - ISSN 0043-1397
space-time climate - stream temperature - river temperature - lake - variability - dynamics - basins - waves
Temperature determines a range of physical properties of water and exerts a strong control on surface water biogeochemistry. Thus, in freshwater ecosystems the thermal regime directly affects the geographical distribution of aquatic species through their growth and metabolism and indirectly through their tolerance to parasites and diseases. Models used to predict surface water temperature range between physically based deterministic models and statistical approaches. Here we present the initial results of a physically based deterministic model of global freshwater surface temperature. The model adds a surface water energy balance to river discharge modeled by the global hydrological model PCR-GLOBWB. In addition to advection of energy from direct precipitation, runoff, and lateral exchange along the drainage network, energy is exchanged between the water body and the atmosphere by shortwave and longwave radiation and sensible and latent heat fluxes. Also included are ice formation and its effect on heat storage and river hydraulics. We use the coupled surface water and energy balance model to simulate global freshwater surface temperature at daily time steps with a spatial resolution of 0.5 degrees on a regular grid for the period 1976-2000. We opt to parameterize the model with globally available data and apply it without calibration in order to preserve its physical basis with the outlook of evaluating the effects of atmospheric warming on freshwater surface temperature. We validate our simulation results with daily temperature data from rivers and lakes (U.S. Geological Survey (USGS), limited to the USA) and compare mean monthly temperatures with those recorded in the Global Environment Monitoring System (GEMS) data set. Results show that the model is able to capture the mean monthly surface temperature for the majority of the GEMS stations, while the interannual variability as derived from the USGS and NOAA data was captured reasonably well. Results are poorest for the Arctic rivers because the timing of ice breakup is predicted too late in the year due to the lack of including a mechanical breakup mechanism. Moreover, surface water temperatures for tropical rivers were overestimated, most likely due to an overestimation of rainfall temperature and incoming shortwave radiation. The spatiotemporal variation of water temperature reveals large temperature differences between water and atmosphere for the higher latitudes, while considerable lateral transport of heat can be observed for rivers crossing hydroclimatic zones, such as the Nile, the Mississippi, and the large rivers flowing to the Arctic. Overall, our model results show promise for future projection of global surface freshwater temperature under global change.
Multimodel estimate of the global terrestrial water balance: Setup and first results
Haddeland, I. ; Clark, D. ; Franssen, W.H.P. ; Ludwig, F. ; Voss, F. ; Arnell, N.W. ; Bertrand, N. ; Best, M. ; Folwell, S. ; Gerten, D. ; Gomes, S. ; Gosling, S. ; Hagemann, S. ; Hanasaki, N. ; Harding, R. ; Heinke, J. ; Kabat, P. ; Koirala, S. ; Oki, T. ; Polcher, J. ; Stacke, T. ; Viterbo, P. ; Weedon, G.P. ; Yeh, P. - \ 2011
Journal of Hydrometeorology 12 (2011)5. - ISSN 1525-755X - p. 869 - 884.
land-surface scheme - space-time climate - parameterization schemes - integrated model - project - simulation - resources - runoff - gcm - precipitation
Six land surface models and five global hydrological models participate in a model intercomparison project [Water Model Intercomparison Project (WaterMIP)], which for the first time compares simulation results of these different classes of models in a consistent way. In this paper, the simulation setup is described and aspects of the multimodel global terrestrial water balance are presented. All models were run at 0.5° spatial resolution for the global land areas for a 15-yr period (1985–99) using a newly developed global meteorological dataset. Simulated global terrestrial evapotranspiration, excluding Greenland and Antarctica, ranges from 415 to 586 mm yr-1 (from 60 000 to 85 000 km3 yr-1), and simulated runoff ranges from 290 to 457 mm yr-1 (from 42 000 to 66 000 km3 yr-1). Both the mean and median runoff fractions for the land surface models are lower than those of the global hydrological models, although the range is wider. Significant simulation differences between land surface and global hydrological models are found to be caused by the snow scheme employed. The physically based energy balance approach used by land surface models generally results in lower snow water equivalent values than the conceptual degree-day approach used by global hydrological models. Some differences in simulated runoff and evapotranspiration are explained by model parameterizations, although the processes included and parameterizations used are not distinct to either land surface models or global hydrological models. The results show that differences between models are a major source of uncertainty. Climate change impact studies thus need to use not only multiple climate models but also some other measure of uncertainty (e.g., multiple impact models).
Effects of precipitation uncertainty on discharge calculations for main river basins
Biemans, H. ; Hutjes, R.W.A. ; Kabat, P. ; Gerten, D. ; Rost, S. - \ 2009
Journal of Hydrometeorology 10 (2009)4. - ISSN 1525-755X - p. 1011 - 1025.
global water-resources - space-time climate - vegetation model - runoff - availability - balance - variability - impacts - validation - streamflow
This study quantifies the uncertainty in discharge calculations caused by uncertainty in precipitation input for 294 river basins worldwide. Seven global gridded precipitation datasets are compared at river basin scale in terms of mean annual and seasonal precipitation. The representation of seasonality is similar in all datasets, but the uncertainty in mean annual precipitation is large, especially in mountainous, arctic, and small basins. The average precipitation uncertainty in a basin is 30%, but there are strong differences between basins. The effect of this precipitation uncertainty on mean annual and seasonal discharge was assessed using the uncalibrated dynamic global vegetation and hydrology model Lund-Potsdam-Jena managed land (LPJmL), yielding even larger uncertainties in discharge (average 90%). For 95 basins (out of 213 basins for which measurements were available) calibration of model parameters is problematic because the observed discharge falls within the uncertainty of the simulated discharge. A method is presented to account for precipitation uncertainty in discharge simulations.