North Atlantic atmospheric blocking conditions explain part of the winter climate variability in Europe, being associated with anomalous cold winter temperatures. In this study, the generalized extreme value (GEV) distribution is fitted to monthly minima of European winter 6-hourly minimum temperatures from the ECHAM5/MPI-OM global climate model simulations and the ECMWF reanalysis product known as ERA-40, with an indicator for atmospheric blocking conditions being used as covariate. It is demonstrated that relating the location and scale parameter of the GEV distribution to atmospheric blocking improves the fit to extreme minimum temperatures in large areas of Europe. The climate model simulations agree reasonably with ERA-40 in the present climate (1961–2000). Under the influence of atmospheric blocking, a decrease in the 0.95th quantiles of extreme minimum temperatures can be distinguished. This cooling effect of atmospheric blocking is, however, diminished in future climate simulations because of a shift in blocking location, and thus reduces the chances of very cold winters in northeastern parts of Europe.
[1] Reanalysis data and general circulation model outputs typically provide information at a coarse spatial resolution, which cannot directly be used for local impact studies. Downscaling methods have been developed to overcome this problem, and to obtain local-scale information from large-scale atmospheric variables. The deduction of local-scale extremes still is a challenge. Here a probabilistic downscaling approach is presented where the cumulative distribution functions (CDFs) of large-and local-scale extremes are linked by means of a transfer function. In this way, the CDF of the local-scale extremes is obtained for a projection period, and statistical characteristics, like return levels, are inferred. The input series are assumed to be distributed according to an extreme value distribution, the Generalized Pareto distribution (GPD). The GPD parameters are linked to further explanatory variables, hence defining a nonstationary model. The methodology (XCDF-t) results in a parametric CDF, which is as well a GPD. Realizations generated from this CDF provide confidence bands. The approach is applied to downscale National Centers for Environmental Prediction reanalysis precipitation in winter. Daily local precipitation at five stations in southern France is obtained. The calibration period 1951-1985 is used to infer precipitation over the validation period 1986-1999. The applicability of the approach is verified by using observations, quantile-quantile plots, and the continuous ranked probability score. The stationary XCDF-t approach shows good results and outperforms the nonparametric CDF-t approach or quantile mapping for some stations. The inclusion of covariate information improves results only sometimes; therefore, covariates have to be chosen with care.
Abstract. The assessment of trends in climatology and hydrology still is a matter of debate. Capturing typical properties of time series, like trends, is highly relevant for the discussion of potential impacts of global warming or flood occurrences. It provides indicators for the separation of anthropogenic signals and natural forcing factors by distinguishing between deterministic trends and stochastic variability. In this contribution river run-off data from gauges in Southern Germany are analysed regarding their trend behaviour by combining a deterministic trend component and a stochastic model part in a semi-parametric approach. In this way the trade-off between trend and autocorrelation structure can be considered explicitly. A test for a significant trend is introduced via three steps: First, a stochastic fractional ARIMA model, which is able to reproduce short-term as well as longterm correlations, is fitted to the empirical data. In a second step, wavelet analysis is used to separate the variability of small and large time-scales assuming that the trend component is part of the latter. Finally, a comparison of the overall variability to that restricted to small scales results in a test for a trend. The extraction of the large-scale behaviour by wavelet analysis provides a clue concerning the shape of the trend.
Abstract:The aim of this paper is to evaluate the green, blue and grey water footprint (WF) of crops in the Duero river basin. For this purpose CWUModel was developed. CWUModel is able to estimate the green and blue water consumed by crops and the water needed to assimilate the nitrogen leaching resulting from fertilizer application. The total WF of crops in the Spanish Duero river basin was simulated as 9473 Mm 3 /year (59% green, 20% blue and 21% grey). Cultivation of crops in rain-fed lands is responsible for 5548 Mm 3 /year of the WF (86% green and 14% grey), whereas the irrigated WF accounts for 3924 Mm 3 /year (20% green, 47% blue and 33% grey). Barley is the crop with the highest WF, with almost 37% of the total WF for the crops simulated for the basin, followed by wheat (17%). Although maize makes up 16% of the total WF of the basin, the blue and grey components comprise the 36% of the total blue and grey WF in the basin. The relevance of green water goes beyond the rain-fed production, to the extent that in long-cycle irrigated cereals it accounts for over 40% of the total water consumed. Nonetheless, blue water is a key component in agriculture, both for production and economically. The sustainability assessment shows that the current blue water consumption of crops causes a significant or severe water stress level in 2-5 months of the year. The anticipated expansion of irrigation in the coming years could hamper water management, despite the Duero being a relatively humid basin.
[1] Nonirrigated agriculture on the Iberian Peninsula is regularly affected by dry periods that can cause important losses. This paper focuses on the comparison of the classical Standard Precipitation Index (SPI) with a fragility index developed by the multivariate extreme value theory community, which is used to describe monthly precipitation deficits below 30.5 mm (about 1 mm/d) in the Spanish Duero basin. The multivariate extreme value model allows to capture relevant information concerning the dependence structure among extreme precipitation deficits. Maps of those extremal dependence summaries and of loadings of principal components of the SPI provide quantitative information for water management. In addition, jointly analyzing data from several stations improves the inference of uncertainty. Spatial patterns of extremal dependence emerged with respect to orographic features. Most severe dry spells occur in the southeast of the Duero basin. In central plain of the Duero basin, a predominantly agricultural area, a strong fragility index for severity of dry spells is particularly found in eastern regions. Results of the MEVT and SPI analysis point in the same direction. Beyond this, the MEVT assessment gives a quantitative measure of the dependence between stations and regions. Estimates of return periods for extreme dry spell severity are discussed. Deficits below 42.7 mm are also analyzed.Citation: Kallache, M., P. Naveau, and M. Vrac (2013), Spatial assessment of precipitation deficits in the Duero basin (central Spain) with multivariate extreme value statistics, Water Resour. Res., 49,[6716][6717][6718][6719][6720][6721][6722][6723][6724][6725][6726][6727][6728][6729][6730]
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