A water availability and low-flow analysis of the Tagliamento River discharge in Italy under changing climate conditions

Abstract

Abstract. This study estimated the effects of projected variations in precipitation and temperature on snowfall-snowmelt processes and subsequent river discharge variations in the Tagliamento River in Italy. A lumped-parameter, non-linear, rainfall-runoff model with 10 general circulation model (GCM) scenarios was used. Spatial and temporal changes in snow cover were assessed using 15 high-quality Landsat images. The 7Q10 low-flow probability distribution approximated by the Log-Pearson type III distribution function was used to examine river discharge variations with respect to climate extremes in the future. On average, the results obtained for 10 scenarios indicate a consistent warming rate for all time periods, which may increase the maximum and minimum temperatures by 2.3 • C (0.6-3.7 • C) and 2.7 • C (1.0-4.0 • C), respectively, by the end of the 21st century compared to the present climate. Consequently, the exponential rate of frost day decrease for 1 • C winter warming in lowerelevation areas is approximately three-fold (262 %) higher than that in higher-elevation areas, revealing that snowfall in lower-elevation areas will be more vulnerable under a changing climate. In spite of the relatively minor changes in annual precipitation (−17.4 ∼ 1.7 % compared to the average of the baseline (1991-2010) period), snowfall will likely decrease by 48-67% during the 2080-2099 time period. The mean river discharges are projected to decrease in all seasons, except winter. The low-flow analysis indicated that while the magnitude of the minimum river discharge will increase (e.g. a 25 % increase in the 7Q10 estimations for the winter season in the 2080-2099 time period), the number of annual average low-flow events will also increase (e.g. 16 and 15 more days during the spring and summer seasons, respectively, in the 2080-2099 time period compared to the average during the baseline period), leading to a future with a highly variable river discharge. Moreover, a consistent shift in river discharge timing would eventually cause snowmeltgenerated river discharge to occur approximately 12 days earlier during the 2080-2099 time period compared to the baseline climate. These results are expected to raise the concern of policy makers, leading to the development of new water management strategies in the Tagliamento River basin to cope with changing climate conditions.

Observed shift towards earlier spring discharge in the main Alpine rivers

“…Similar results are found for the Rhone (Beniston et al, 2011;Beniston, 2012), and in general for most Alpine rivers (Jasper et al, 2004;Zierl and Bugmann, 2005;Horton et al, 2006;Gunawardhana and Kazama, 2012).…”

“…the Danube, the Rhine, the Po and the Rhone. In this paper we focus on the timing of the spring discharge, which can affect water quality and management (Hänggi and Weingartner, 2011;Gunawardhana and Kazama, 2012;Vanham, 2012), flood risk (Eckhardt and Ulbrich, 2003;Wetter et al, 2011;Bard et al, 2012;Dobler et al, 2012), river navigation and water availability (Middelkoop et al, 2001), tourism (Elsasser et Burki, 2002;Beniston et al, 2011), energy production (Hänggi, 2012), insurance (Beniston, 2012) and natural ecosystems (Keller et al, 2005).…”

Temporal glacier area changes correlated with the El Ni^|^ntilde;o/La Ni^|^ntilde;a Southern Oscillation using satellite imagery

“…In this study, we used the Normalized Difference Snow Index (NDSI), calculated from the midinfrared (1.55-1.75 μm) and green (0.52-0.60 μm) bands. This index is a simple but accurate way to distinguish glacial areas (e.g., Gunawardhana and Kazama, 2012;Paul et al, 2004).…”

Stochastic approach to analyzing the uncertainties and possible changes in the availability of water in the future based on scenarios of climate change

“…The choice of a three-layer architecture was based on the Kolmogorov mapping neural network existence theorem (Hecht-Nielsen, 1987), which stated that any continuous function with n inputs can be implemented exactly by a three-layer feed-forward neural network with 2 n + 1 processing elements in the intermediate layer.…”

“…Currently, in the scientific literature, there are several studies that analyze the effects of climate changes on water availability (e.g., Kleinn et al, 2005;Hughes et al, 2011;Gunawardhana and Kazama, 2012). On a continental or global scale, normally, the outputs of the GCMs are used in combination with the empirical macroscale hydrological models that perform the water balance (for instance, Arnell, 1999Arnell, , 2004Nijssen et al, 2001;Milly et al, 2005;Nohara et al, 2006).…”