ALBERT

Beschreibung: Soil moisture is an essential climate variable (ECV) of major importance for land–atmosphere interactions and global hydrology. An appropriate representation of soil moisture dynamics in global climate models is therefore important. Recently, a first multidecadal, observation-based soil moisture dataset has become available that provides information on soil moisture dynamics from satellite observations (ECVSM, essential climate variable soil moisture). The present study investigates the potential and limitations of this new dataset for several applications in climate model evaluation. We compare soil moisture data from satellite observations, reanalysis and simulations from a state-of-the-art land surface model and analyze relationships between soil moisture and precipitation anomalies in the different dataset. Other potential applications like model parameter optimization or model initialization are not investigated in the present study. In a detailed regional study, we show that ECVSM is capable to capture well the interannual and intraannual soil moisture and precipitation dynamics in the Sahelian region. Current deficits of the new dataset are critically discussed and summarized at the end of the paper to provide guidance for an appropriate usage of the ECVSM dataset for climate studies.

Beschreibung: Due to biases in the output of climate models, a bias correction is often needed to make the output suitable for use in hydrological simulations. In most cases only the temperature and precipitation values are bias corrected. However, often there are also biases in other variables such as radiation, humidity and wind speed. In this study we tested to what extent it is also needed to bias correct these variables. Responses to radiation, humidity and wind estimates from two climate models for four large-scale hydrological models are analysed. For the period 1971–2000 these hydrological simulations are compared to simulations using meteorological data based on observations and reanalysis; i.e. the baseline simulation. In both forcing datasets originating from climate models precipitation and temperature are bias corrected to the baseline forcing dataset. Hence, it is only effects of radiation, humidity and wind estimates that are tested here. The direct use of climate model outputs result in substantial different evapotranspiration and runoff estimates, when compared to the baseline simulations. A simple bias correction method is implemented and tested by rerunning the hydrological models using bias corrected radiation, humidity and wind values. The results indicate that bias correction can successfully be used to match the baseline simulations. Finally, historical (1971–2000) and future (2071–2100) model simulations resulting from using bias corrected forcings are compared to the results using non-bias corrected forcings. The relative changes in simulated evapotranspiration and runoff are relatively similar for the bias corrected and non bias corrected hydrological projections, although the absolute evapotranspiration and runoff numbers are often very different. The simulated relative and absolute differences when using bias corrected and non bias corrected climate model radiation, humidity and wind values are, however, smaller than literature reported differences resulting from using bias corrected and non bias corrected climate model precipitation and temperature values.

Beschreibung: It is well known that output from climate models cannot be used to force hydrological simulations without some form of preprocessing to remove the existing biases. In principle, statistical bias correction methodologies act on model output so the statistical properties of the corrected data match those of the observations. However, the improvements to the statistical properties of the data are limited to the specific timescale of the fluctuations that are considered. For example, a statistical bias correction methodology for mean daily temperature values might be detrimental to monthly statistics. Also, in applying bias corrections derived from present day to scenario simulations, an assumption is made on the stationarity of the bias over the largest timescales. First, we point out several conditions that have to be fulfilled by model data to make the application of a statistical bias correction meaningful. We then examine the effects of mixing fluctuations on different timescales and suggest an alternative statistical methodology, referred to here as a cascade bias correction method, that eliminates, or greatly reduces, the negative effects.

Beschreibung: Many studies have investigated potential climate change impacts on regional hydrology; less attention has been given to the components of uncertainty that affect these scenarios. This study quantifies uncertainties resulting from (i) General Circulation Models (GCMs), (ii) Regional Climate Models (RCMs), (iii) bias-correction of RCMs, and (iv) hydrological model parameterization using a multi-model framework. This consists of three GCMs, three RCMs, three bias-correction techniques, and sets of hydrological model parameters. The study is performed for the Lech watershed (~ 1000 km2), located in the Northern Limestone Alps, Austria. Bias-corrected climate data are used to drive the hydrological model HQsim to simulate runoff under present (1971–2000) and future (2070–2099) climate conditions. Hydrological model parameter uncertainty is assessed by Monte Carlo sampling. The model chain is found to perform well under present climate conditions. However, hydrological projections are associated with high uncertainty, mainly due to the choice of GCM and RCM. Uncertainty due to bias-correction is found to have greatest influence on projections of extreme river flows, and the choice of method(s) is an important consideration in snowmelt systems. Overall, hydrological model parameterization is least important. The study also demonstrates how an improved understanding of the physical processes governing future river flows can help focus attention on the scientifically tractable elements of the uncertainty.

Beschreibung: Many studies have investigated potential climate change impacts on regional hydrology; less attention has been given to the components of uncertainty that affect these scenarios. This study quantifies uncertainties resulting from (i) General Circulation Models (GCMs), (ii) Regional Climate Models (RCMs), (iii) bias-correction of RCMs, and (iv) hydrological model parameterization using a multi model framework. This consists of three GCMs, three RCMs, three bias-correction techniques, and sets of hydrological model parameters. The study is performed for the Lech watershed (~1000 km2), located in the Northern Limestone Alps, Austria. Bias-corrected climate data are used to drive the hydrological model HQsim to simulate runoff under present (1971–2000) and future (2070–2099) climate conditions. Hydrological model parameter uncertainty is assessed by Monte Carlo sampling. The model chain is found to perform well under present climate conditions. However, hydrological projections are associated with large uncertainty, mainly due to the choice of GCM and RCM. Uncertainty due to bias-correction is found to have greatest influence on projections of extreme river flows and the choice of method(s) is an important consideration in snowmelt systems. Overall, hydrological model parameterization is least important. The study also demonstrates how an improved understanding of the physical processes governing future river flows can help focus attention on the scientifically tractable elements of the uncertainty.

Beschreibung: Due to biases in the output of climate models, a bias correction is often needed to make the output suitable for use in hydrological simulations. In most cases only the temperature and precipitation values are bias corrected. However, often there are also biases in other variables such as radiation, humidity and wind speed. In this study we tested to what extent it is also needed to bias correct these variables. Responses to radiation, humidity and wind estimates from two climate models for four large-scale hydrological models are analysed. For the period 1971–2000 these hydrological simulations are compared to simulations using meteorological data based on observations and reanalysis; i.e. the baseline simulation. In both forcing datasets originating from climate models precipitation and temperature are bias corrected to the baseline forcing dataset. Hence, it is only effects of radiation, humidity and wind estimates that are tested here. The direct use of climate model outputs result in substantial different evapotranspiration and runoff estimates, when compared to the baseline simulations. A simple bias correction method is implemented and tested by rerunning the hydrological models using bias corrected radiation, humidity and wind values. The results indicate that bias correction can successfully be used to match the baseline simulations. Finally, historical (1971–2000) and future (2071–2100) model simulations resulting from using bias corrected forcings are compared to the results using non-bias corrected forcings. The relative changes in simulated evapotranspiration and runoff are relatively similar for the bias corrected and non bias corrected hydrological projections, although the absolute evapotranspiration and runoff numbers are often very different. The simulated relative and absolute differences when using bias corrected and non bias corrected climate model radiation, humidity and wind values are, however, smaller than literature reported differences resulting from using bias corrected and non bias corrected climate model precipitation and temperature values.

Beschreibung: Droughts can impact on forest functioning and production, and even lead to tree mortality. However, drought is an elusive phenomenon that is difficult to quantify and define universally. In this study, we assessed the performance of a set of indicators that have been used to describe drought conditions in the summer months (June, July, August) over a 30 year period (1981–2010) in Finland. Those indicators include the Standardized Precipitation Index (SPI), the Standardized Precipitation–Evapotranspiration Index (SPEI), the Soil Moisture Index (SMI) and the Soil Moisture Anomaly (SMA). Herein, regional soil moisture was produced by the land surface model JSBACH. While SPI, SPEI, and SMA show a degree of anomalies from the statistical means over a period, SMI is directly connected to plant available water and closely dependent on soil properties. Moreover, the buffering effect of soil moisture and the associated soil moisture memory can impact on the onset and duration of drought as indicated by the SMI and SMA, whereas SPI and SPEI are directly controlled by meteorological conditions. In particular, we investigated whether the SMI, SMA and SPEI are able to indicate the Extreme Drought affecting Forest health (EDF) in Finland. EDF thresholds for these indicators are suggested, based on the spatially representative statistics of forest health observations in the exceptional dry year 2006. Our results showed that SMI was the best indicator in capturing the spatial extent of forest damage induced by the extreme drought in 2006. In addition, the derived thresholds were applied to those indicators to capture EDF events over the summer months of the 30 year study period. The SPEI and SMA showed more frequent EDF events over the 30 year period, and typically described a higher fraction of influenced area than SMI. In general, the suggested EDF thresholds for those indicators may be used for the indication of EDF events in Finland or other boreal forests areas in the context of future climate scenarios. However, the results have to be interpreted carefully, with due consideration of their different properties and the complexity of drought. Our results would suggest that in order to take appropriate precautions to mitigate against possible forest losses, an integrated analysis of projected drought with drought indicators is recommended.

Beschreibung: Soil moisture is an essential climate variable of major importance for land-atmosphere interactions and global hydrology. An appropriate representation of soil moisture dynamics in global climate models is therefore important. Recently, a first multidecadal, observational based soil moisture data set has become available that provides information on soil moisture dynamics from satellite observations (ECVSM). The present study investigates the potential and limitations of this new dataset for several applications for climate model evaluation. We compare soil moisture data from satellite observations, reanalysis data and simulation results from a state-of-the-art climate model and analyze relationships between soil moisture and precipitation anomalies in the different datasets. In a detailed regional study, we show that ECVSM is capable to capture well interannual and intraannual soil moisture and precipitation dynamics in the Sahelian region. Current deficits of the new dataset are critically discussed and summarized at the end of the paper to provide guidance for an appropriate usage of the ECVSM dataset for climate studies.