Validation of MODIS snow cover images over Austria

International audienceThis study evaluates the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product over the territory of Austria. The aims are (a) to analyse the spatial and temporal variability of the MODIS snow product classes, (b) to examine the accuracy of the MODIS snow product against in situ snow depth data, and (c) to identify the main factors that may influence the MODIS classification accuracy. We use daily MODIS grid maps (version 4) and daily snow depth measurements at 754 climate stations in the period from February 2000 to December 2005. The results indicate that, on average, clouds obscured 63% of Austria, which may significantly restrict the applicability of the MODIS snow cover images to hydrological modelling. On cloud-free days, however, the classification accuracy is very good with an average of 95%. There is no consistent relationship between the classification errors and dominant land cover type and local topographical variability but there are clear seasonal patterns to the errors. In December and January the errors are around 15% while in summer they are less than 1%. This seasonal pattern is related to the overall percentage of snow cover in Austria, although in spring, when there is a well developed snow pack, errors tend to be smaller than they are in early winter for the same overall percent snow cover. Overestimation and underestimation errors balance during most of the year which indicates little bias. In November and December, however, there appears to exist a tendency for overestimation. Part of the errors may be related to the temporal shift between the in situ snow depth measurements (07:00 a.m.) and the MODIS acquisition time (early afternoon)

A comparison of regionalisation methods for catchment model parameters

International audienceIn this study we examine the relative performance of a range of methods for transposing catchment model parameters to ungauged catchments. We calibrate 11 parameters of a semi-distributed conceptual rainfall-runoff model to daily runoff and snow cover data of 320 Austrian catchments in the period 1987-1997 and verify the model for the period 1976-1986. We evaluate the predictive accuracy of the regionalisation methods by jack-knife cross-validation against daily runoff and snow cover data. The results indicate that two methods perform best. The first is a kriging approach where the model parameters are regionalised independently from each other based on their spatial correlation. The second is a similarity approach where the complete set of model parameters is transposed from a donor catchment that is most similar in terms of its physiographic attributes (mean catchment elevation, stream network density, lake index, areal proportion of porous aquifers, land use, soils and geology). For the calibration period, the median Nash-Sutcliffe model efficiency ME of daily runoff is 0.67 for both methods as compared to ME=0.72 for the at-site simulations. For the verification period, the corresponding efficiencies are 0.62 and 0.66. All regionalisation methods perform similar in terms of simulating snow cover

A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers

The geophysical and hydrological processes governing river flow formation exhibit persistence at several timescales, which may manifest itself with the presence of positive seasonal correlation of streamflow at several different time lags. We investigate here how persistence propagates along subsequent seasons and affects low and high flows. We define the high-flow season (HFS) and the low-flow season (LFS) as the 3-month and the 1-month periods which usually exhibit the higher and lower river flows, respectively. A dataset of 224 rivers from six European countries spanning more than 50 years of daily flow data is exploited. We compute the lagged seasonal correlation between selected river flow signatures, in HFS and LFS, and the average river flow in the antecedent months. Signatures are peak and average river flow for HFS and LFS, respectively. We investigate the links between seasonal streamflow correlation and various physiographic catchment characteristics and hydro-climatic properties. We find persistence to be more intense for LFS signatures than HFS. To exploit the seasonal correlation in the frequency estimation of high and low flows, we fit a bi-variate meta-Gaussian probability distribution to the selected flow signatures and average flow in the antecedent months in order to condition the distribution of high and low flows in the HFS and LFS, respectively, upon river flow observations in the previous months. The benefit of the suggested methodology is demonstrated by updating the frequency distribution of high and low flows one season in advance in a real-world case. Our findings suggest that there is a traceable physical basis for river memory which, in turn, can be statistically assimilated into high- and low-flow frequency estimation to reduce uncertainty and improve predictions for technical purposes

Human signatures derived from nighttime lights along the Eastern Alpine river network in Austria and Italy

Abstract. Understanding how human settlements and economic activities are distributed with reference to the geographical location of streams and rivers is of fundamental relevance for several issues, such as flood risk management, drought management related to increased water demands by human population, fluvial ecosystem services, water pollution and water exploitation. Besides the spatial distribution, the evolution in time of the human presence constitutes an additional key question. This work aims at understanding and analysing the spatial and temporal evolution of human settlements and associated economic activity, derived from nighttime lights, in the Eastern Alpine region. Nightlights, available at a fine spatial resolution and for a 22-year period, constitute an excellent data base, which allows one to explore in details human signatures. In this experiment, nightlights are associated to five distinct distance-from-river classes. Our results clearly point out an overall enhancement of human presence across the considered distance classes during the last 22 years, though presenting some differences among the study regions. In particular, the river network delineation, by considering different groups of river pixels based on the Strahler order, is found to play a central role in the identification of nightlight spatio-temporal trends

Characteristics and process controls of statistical flood moments in Europe - a data based analysis

Many recent studies have sought to characterize variations of the annual maximum flood discharge series over time and across space in Europe, including some that have elucidated different process controls on different statistical properties of these series. To further support these studies, we conduct a pan-European assessment of process controls on key properties of this series, including the mean annual flood (MAF) and coefficients of variation (CV) and skewness (CS) of flood discharges. These annual maximum flood discharge series consist of instantaneous peaks and daily means observed in 2370 catchments in Europe without strong human modifications covering the period 1960–2010. We explore how the estimated moments MAF, CV and CS vary due to catchment size, climate and other controls across Europe, where their averages are 0.17 m3 s −1 km−2 , 0.52 and 1.28, respectively. The results indicate that MAF is largest along the Atlantic coast, in the high-rainfall areas of the Mediterranean coast and in mountainous regions, while it is smallest in the sheltered parts of the East European Plain. The CV is largest in southern and eastern Europe, while it is smallest in the regions subject to strong Atlantic influence. The pattern of the CS is similar, albeit more erratic, in line with the greater sampling variability of CS. In the Mediterranean, MAF, CV and CS decrease strongly with catchment area, suggesting that floods in small catchments are relatively very large, while in eastern Europe this dependence is much weaker, mainly due to more synchronized timing of snowmelt over large areas. The process controls on the flood moments in five predetermined hydroclimatic regions are identified through correlation and multiple linear regression analyses with a range of covariates, and the interpretation is aided by a seasonality analysis. Precipitation-related covariates are found to be the main controls of the spatial patterns of MAF in most of Europe except for regions in which snowmelt contributes to MAF, where air temperature is more important. The Aridity Index is, by far, the most important control on the spatial pattern of CV in all of Europe. Overall, the findings suggest that, at the continental scale, climate variables dominate over land surface characteristics, such as land use and soil type, in controlling the spatial patterns of flood moments. Finally, to provide a performance baseline for more local studies, we assess the estimation accuracy of regional multiple linear regression models for estimating flood moments in ungauged basin

A three-pillar approach to assessing climate impacts on low flows

The objective of this paper is to present a framework for assessing climate impacts on future low flows that combines different sources of information, termed pillars. To illustrate the framework three pillars are chosen: (a) extrapolation of observed low-flow trends into the future, (b) rainfall–runoff projections based on climate scenarios and (c) extrapolation of changing stochastic rainfall characteristics into the future combined with rainfall–runoff modelling. Alternative pillars could be included in the overall framework. The three pillars are combined by expert judgement based on a synoptic view of data, model outputs and process reasoning. The consistency/inconsistency between the pillars is considered an indicator of the certainty/uncertainty of the projections. The viability of the framework is illustrated for four example catchments from Austria that represent typical climate conditions in central Europe. In the Alpine region where winter low flows dominate, trend projections and climate scenarios yield consistently increasing low flows, although of different magnitudes. In the region north of the Alps, consistently small changes are projected by all methods. In the regions in the south and south-east, more pronounced and mostly decreasing trends are projected but there is disagreement in the magnitudes of the projected changes. The process reasons for the consistencies/inconsistencies are discussed. For an Alpine region such as Austria the key to understanding low flows is whether they are controlled by freezing and snowmelt processes, or by the summer moisture deficit associated with evaporation. It is argued that the three-pillar approach offers a systematic framework of combining different sources of information aimed at more robust projections than that obtained from each pillar alone

Uncertainty contributions to low-flow projections in Austria

The main objective of the paper is to understand the contributions to the uncertainty in low-flow projections resulting from hydrological model uncertainty and climate projection uncertainty. Model uncertainty is quantified by different parameterisations of a conceptual semi-distributed hydrologic model (TUWmodel) using 11 objective functions in three different decades (1976&ndash;1986, 1987&ndash;1997, 1998&ndash;2008), which allows for disentangling the effect of the objective function-related uncertainty and temporal stability of model parameters. Climate projection uncertainty is quantified by four future climate scenarios (ECHAM5-A1B, A2, B1 and HADCM3-A1B) using a delta change approach. The approach is tested for 262 basins in Austria. <br><br> The results indicate that the seasonality of the low-flow regime is an important factor affecting the performance of model calibration in the reference period and the uncertainty of <i>Q</i>₉₅ low-flow projections in the future period. In Austria, the range of simulated <i>Q</i>₉₅ in the reference period is larger in basins with a summer low-flow regime than in basins with a winter low-flow regime. The accuracy of simulated <i>Q</i>₉₅ may result in a range of up to 60 % depending on the decade used for calibration. <br><br> The low-flow projections of Q₉₅ show an increase of low flows in the Alps, typically in the range of 10–30 % and a decrease in the south-eastern part of Austria mostly in the range &minus;5 to &minus;20 % for the climate change projected for the future period 2021&ndash;2050, relative the reference period 1978&ndash;2007. The change in seasonality varies between scenarios, but there is a tendency for earlier low flows in the northern Alps and later low flows in eastern Austria. The total uncertainty of <i>Q</i>₉₅ projections is the largest in basins with a winter low-flow regime and, in some basins the range of <i>Q</i>₉₅ projections exceeds 60 %. In basins with summer low flows, the total uncertainty is mostly less than 20 %. The ANOVA assessment of the relative contribution of the three main variance components (i.e. climate scenario, decade used for model calibration and calibration variant representing different objective function) to the low-flow projection uncertainty shows that in basins with summer low flows climate scenarios contribute more than 75 % to the total projection uncertainty. In basins with a winter low-flow regime, the median contribution of climate scenario, decade and objective function is 29, 13 and 13 %, respectively. The implications of the uncertainties identified in this paper for water resource management are discussed

More green and less blue water in the Alps during warmer summers

Climate change can reduce surface-water supply by enhancing evapotranspiration in forested mountains, especially during heatwaves. We investigate this ‘drought paradox’ for the European Alps using a 1,212-station database and hyper-resolution ecohydrological simulations to quantify blue (runoff) and green (evapotranspiration) water fluxes. During the 2003 heatwave, evapotranspiration in large areas over the Alps was above average despite low precipitation, amplifying the runoff deficit by 32% in the most runoff-productive areas (1,300–3,000 m above sea level). A 3 °C air temperature increase could enhance annual evapotranspiration by up to 100 mm (45 mm on average), which would reduce annual runoff at a rate similar to a 3% precipitation decrease. This suggests that green-water feedbacks—which are often poorly represented in large-scale model simulations—pose an additional threat to water resources, especially in dry summers. Despite uncertainty in the validation of the hyper-resolution ecohydrological modelling with observations, this approach permits more realistic predictions of mountain region water availability