Réf. Allamano & al 2009

Référence bibliographique complète

ALLAMANO, P., CLAPS, P., LAIO, F. 2009. Global warming increases flood risk in mountainous areas, Geophysical Research Letters, 36, L24404, doi:10.1029/2009GL041395.

Abstract: The paper aims at assessing the impact of global warming on flood risk in mountainous regions, providing measurable evidence of possible hydrologic changes due to temperature increase. It shows that large floods in mountain basins are now more frequent than in the past and that they may become even more frequent under global warming. The morpho-climatic model used for prediction is very simple and does not require calibration, which makes it suitable for application in scarcely gauged mountainous areas of the world.

[AGU Editors' Highlight]: The world's mountainous regions are home to about 800 million people and the source of some of the world's major rivers. In these regions, runoff is strongly affected by temperature. This suggests that flooding could be quite sensitive to global warming, but there has been some lack of scientific consensus on the effects of temperature variations on floods. Allamano et al. show that global warming does increase flood risk significantly. The authors analyze runoff data recorded by 27 stations in the Swiss Alps and use a simple probabilistic model to study how flood risk varies with temperature, precipitation, and elevation in mountainous regions. The researchers find that large floods have occurred more frequently in recent years than in the past, and they predict that global warming will result in such floods occurring even more often in the future. In particular, they find that if global temperatures increase by 2 degrees Celsius (3.6 degrees Fahrenheit), then large floods that occurred about once every 100 years could occur up to 5 times more often.

Mots-clés
 

Organismes / Contact

Dipartimento di Idraulica, Trasporti ed Infrastrutture Civili, Politecnico di Torino, Turin, Italy. (paola.allamano@polito.it)


(1) - Paramètre(s) atmosphérique(s) modifié(s)
(2) - Elément(s) du milieu impacté(s)
(3) - Type(s) d'aléa impacté(s)
(3) - Sous-type(s) d'aléa
Temperature, Precipitation (snow / rain)   Floods  

Pays / Zone
Massif / Secteur
Site(s) d'étude
Exposition
Altitude
Période(s) d'observation
World mountains / Switzerland 27 Swiss watersheds / Swiss river network        

(1) - Modifications des paramètres atmosphériques
Reconstitutions
 
Observations
 
Modélisations
 
Hypothèses
 

Informations complémentaires (données utilisées, méthode, scénarios, etc.)
 

(2) - Effets du changement climatique sur le milieu naturel
Reconstitutions
 
Observations
 
Modélisations
 
Hypothèses
 

Sensibilité du milieu à des paramètres climatiques
Informations complémentaires (données utilisées, méthode, scénarios, etc.)
 

 


(3) - Effets du changement climatique sur l'aléa
Reconstitutions
 
Observations

Analyzing peak discharge time-series recorded in 27 gauging stations in the Swiss Alps [the authors] find a significant increase of flood peaks during the last century. [They] interpret this increase through a simple model as the effect of recorded increases of temperature and precipitation in the same period:

Fluctuations of peak discharge collected at 27 Swiss discharge gauging stations:
Annual maxima of specific discharge for 27 Swiss watersheds versus their year of occurrence together with the outcomes of a linear quantile regression analysis show an apparent increase in the number of large floods in recent years, in particular as regards the 0.95 sample quantile (the data are assumed to be independent when analyzing the significance of the relation). Determining whether these variations are symptoms of increasing trends or just long-term cycles due to the presence of long-term persistence [Koutsoyiannis and Montanari, 2007] is out of the scope of this paper. However, [the authors] computed the lag-1 autocorrelation coefficient on all series, obtaining a very low average value (<0.1), which goes in the direction of confuting the hypothesis of long-term persistence.

In addition, one may argue that the trend in flood values could be influenced by local situations as, for example, the progressive activation/dismissal of gauging stations for basins with anomalous morpho-climatic features (with respect to the sample average). [The authors] account for these possible distortions by performing multiple linear quantile regressions between specific peak discharge and a number of morpho-climatic descriptors, such as watershed mean elevation, catchment area, the growth-rate and exponent of the areal depth-duration-frequency curves, obtaining significant relations in all the coefficients at a 5% level. After removal of the above dependencies, however, [they] still find a significant relation between specific discharge and time. In this case, trend coefficients are smaller than those [obtained by linear quantile regressions], but remain positive and statistically significant for all quantiles.

Modélisations

The model predicts, under the hypothesis of a 2°C temperature increase and of 10% increase in the precipitation intensity [Klein Tank and Können, 2003; Schmidli and Frei, 2005; Bates et al., 2008], that the 100-year flood discharge will reduce its return period to about 20 years (becoming five times more frequent), with possible relevant consequences on high elevation ecosystems and anthroposystems:

Return period ratio sensitivity to different increases of temperature and rainfall intensity:
Trend coefficients for flood quantiles are then reproduced by using the morpho-climatic model. Flood quantiles variations over time are obtained by imposing a temporal trend to temperature T(t) and to the precipitation intensity a. Different scenarios, inspired by literature studies referred to the Swiss territory [Beniston et al., 1997; Klein Tank and Können, 2003; Schmidli and Frei, 2005], have been considered. Model performances are presented for two of these scenarios [see details] [and] the resulting trend coefficients [for RP = 100 years and zmin = 2000 m] are compared with those obtained from regressions.

When temperature and precipitation increase are combined together (DT +Da scenario), the increase of flood quantiles obtained from the regressions and from the model appear rather similar [...]. In fact, the increase in flood quantiles obtained with the model (with the exception of the 0.25 quantile) is found to fall within one standard error distance from the regression-based trend coefficient. When the temperature change alone is applied (DT scenario), the time-discharge dependence is captured only partially. Therefore, both precipitation and temperature seem to be responsible for the discharge temporal trend, even though none of the two, considered separately, is able to explain the behavior completely. This example proves that the model can be useful in investigating the mechanisms behind changes in the flood frequency at high elevation basins. In this sense, the agreement between regression and model based flood temporal changes represents a validation of the hypotheses on which the model is based, which is especially striking because results are obtained without any calibration. Model parameters, in fact, are fixed to literature values and kept constant all over the region.

Graph relateing the return period ratio to the undisturbed return period RP (x-axis) and to the elevation of the basin outlet (y-axis), (under the hypothesis of DT = 2°C and Da = 10%):
An application of the model to a wide range of basin morphologies (i.e., with different elevation characteristics) is performed by applying the model to hypothetical basins having different elevation features but subjected to the same meteorological forcings and trends. The results show the combined effect of temperature and precipitation variation (according to the DT + Da scenario) in terms of changes in the return period. [...]. As anticipated, it is found that extreme floods under global warming will tend to become more frequent in time. In fact, the return period ratio is always greater than 1. As expected, the ratio is found to increase for increasing undisturbed return periods and for increasing elevations, at least until an upper bound is reached (in correspondence of basins with outlets between 2500 and 3000 m). For example, the 100-year flood discharge estimated today in a watershed having the outlet at 2000 m will have, under a 2°C temperature and a +10% precipitation increase, a return period ratio equal to 4.6, that means that the same discharge value under altered conditions will be, on average, a 20-year flood (i.e., will become five time more frequent). For very high elevations, the return period ratio increases more slowly. This is due to the lesser influence of the temperature increase on very high watersheds, which are almost exempt from the effects of small shifts of the temperature regime (i.e., in the order of magnitude of the one considered in this study), because they remain almost always above the freezing level. [...]

Variability of the return period ratio (under the hypothesis of DT = 2°C and Da = 10%) along the Swiss river network:
A spatial application to the Swiss territory show the return period ratio return period ratio computed for real basins (with their own hypsometry, and average climatic parameters) and mapped along the river network, [under the hypothesis of DT = 2°C and Da = 10%], in correspondence of an undisturbed RP of 100 years. [...] It can be observed that the basins that are most exposed to the flood frequency increase are in the Southern part of Switzerland [with return period ratio close to five], where the elevations are typically higher. Moving northwards, the return period ratios are found to drop to values between 2 and 3. Another interesting outcome of the analysis is represented by the variability of the return period ratios in the Northern part of the country, for example along the Aare river or, analogously, along the Reuss river. In both cases, in fact, moving downstream the return period ratios are found to assume higher values along the main river than along the tributary network. This happens because the mean elevations of the catchments drained by the tributary streams (of the Aare or Reuss rivers) are lower than the mean elevation of the main basin, making the latter more exposed to flood risk increase than its tributaries.

Downstream areas are often densely populated. On these areas the increase in flood frequency can have dramatic effects in terms of expected damage to civil infrastructures and loss of human lives. Results analogous to those shown could be easily obtained also in regions with scarce data availability, thanks to the ease of use and parsimony of the model, which is suitable to produce these scenarios using only a digital elevation model and basic precipitation data. Still, the model-based conclusions are inevitably conditioned by the model assumptions.

In this specific case, assumptions are introduced about the rainfall model; it is assumed that the flood runoff is directly proportional to the liquid precipitation, and that the related proportionality coefficient is not changing in a changing climate; relevant changes in time of the climatic forcing (temperature and precipitation) are finally hypothesized. Also in this respect, [the authors] believe the adoption of a parsimonious model represents a relevant added-value, with respect to the existing methods, to analyse the vulnerability of mountain areas to climate change.

Hypothèses
 

Paramètre de l'aléa
Sensibilité des paramètres de l'aléa à des paramètres climatiques
Informations complémentaires (données utilisées, méthode, scénarios, etc.)
 

The seasonality of streamflow in mountainous basins has been found to be extremely sensitive to global warming [Diaz et al., 2003; Barnett et al., 2005; Bates et al., 2008; Marty, 2008]. While the concern about the increase of flood risk in these areas is rapidly raising [Olsen et al., 1998; Palmer and Räisänen, 2002], in the scientific literature there is still a lack of consensus about the effects of temperature variations on floods [Mudelsee et al., 2003; Birsan et al., 2005; Koutsoyiannis et al., 2009].

[The authors] propose to reproduce the essential mechanisms that connect climatic variability to flood frequency by means of a simple morpho-climatic probabilistic model that aims to generalize the classical site-specific flood frequency approach. [They] are not interested in investigating the actual magnitude of the floods corresponding to a given return period, but rather the rate of change of the flood itself in the presence of climate change. The model is conceived to describe the dominant components of flood runoff in basins subjected to a seasonal transition of the freezing elevation ZT(t). In the model, the freezing elevation is related to the temperature regime T(t) (i.e., the curve of seasonal variations of average temperature) according to a standard lapse rate of temperature. For a given watershed, the areas above ZT(t) are determined according to the basin hypsometric curve. The extension of the area above the freezing elevation becomes a basin-characteristic factor of mitigation of peak flow, because of the occurrence of precipitation as snow above ZT(t). Given the structure of the precipitation process, flow mitigation hence depends on the seasonality of temperature and on the distribution of elevation within the basin, that concur in determining the portion of the watershed that receives liquid precipitation, called contributing area, Ac.

Considering flood runoff directly proportional to the amount of liquid precipitation, the probability distribution of specific annual discharge extremes is obtained in close analytical form, assuming that precipitation follows a Poisson distribution of storm arrivals in time, and that the depth of each storm follows an exponential distribution with mean a. The parameters of the resulting probabilistic model are the freezing-curve boundaries, ZTmax and ZTmin, and the parameters of the precipitation model. Details on the analytical derivation of the flood frequency curves are provided by Allamano et al. [2009]. Time-dependent parameter values can be used when flood response to long-term fluctuations of temperature and precipitation are investigated.

Fluctuations of peak discharge collected at 27 Swiss discharge gauging stations:
The authors exemplify their approach on a sample of annual maxima of specific discharge for 27 Swiss discharge gauging stations. Specific discharge values (i.e., divided by catchment area) are shown versus their year of occurrence (i.e., divided by catchment area), together with the outcomes of a linear quantile regression analysis [Koenker and Bassett, 1978] [the 0.25, 0.5, 0.75 and 0.95 quantiles are reported].

Return period ratio sensitivity to different increases of temperature and rainfall intensity:
Trend coefficients for flood quantiles are then reproduced by using the morpho-climatic model. Flood quantiles variations over time are obtained by imposing a temporal trend to temperature T(t) and to the precipitation intensity a. Different scenarios, inspired by literature studies referred to the Swiss territory [Beniston et al., 1997; Klein Tank and Können, 2003; Schmidli and Frei, 2005], have been considered. Model performances are presented hereafter for two of these scenarios: (1) a temperature increase of 2°C in 100 years, resulting in a rigid upward shift of the temperature seasonal curve T(t) (called the DT scenario) and (2) a 10% increase of the precipitation parameter a, simultaneous with the temperature increase (identified as the DT +Da scenario). Model results are obtained taking as model parameters the average characteristics of the sample of basins (i.e., minimum elevation zmin = 650 m a.s.l, maximum elevation zmax = 3200 m a.s.l., mean elevation zmean = 1900 m a.s.l., a = 25 mm/d; l = 20 yr-1; lapse rate = 6.5°C/km). The resulting trend coefficients [for RP = 100 years and zmin = 2000 m] are compared with those obtained from regressions.

Graph relateing the return period ratio to the undisturbed return period RP and to the elevation of the basin outlet (under the hypothesis of DT = 2°C and Da = 10%):
An application of the model to a wide range of basin morphologies (i.e., with different elevation characteristics) is is performed by applying the model to hypothetical basins having different elevation features but subjected to the same meteorological forcings and trends. The results show the combined effect of temperature and precipitation variation (according to the DT + Da scenario) in terms of changes in the return period. For a given flood event with peak discharge Q, the graph reports on the x-axis the ‘‘undisturbed’’ return period RP [being the inverse of the probability of exceedance of Q under undisturbed temperature and precipitation conditions, see formula in the study]. The elevation of the basin outlet is on the y-axis. In each point of the plane the intensity of the grey-shade is proportional to the ‘‘return period ratio’’, defined as the ratio of RP to the ‘‘altered’’ return period RP' [being the inverse of the exceedance probability of Q under modified T + DT and a + Da conditions]. A complete analysis of the return period ratio sensitivity to different hypotheses of increase of temperature and rainfall intensity is presented [see the study].

Variability of the return period ratio (under the hypothesis of DT = 2°C and Da = 10%) along the Swiss river network:
Diagrams like those shown entail the possibility to use the model to represent also in space the increase of flood risk under varied climatic conditions. A spatial application to the Swiss territory show the return period ratio [referred to the 100-years undisturbed flood, under the hypothesis of DT = 2°C and Da = 10%] computed for real basins (with their own hypsometry, and average climatic parameters) and mapped along the river network. [...] In the map, each section of the river network is colored according to the return period ratio computed for the basin having the outlet in that point.


(4) - Remarques générales

The Swiss alpine region is one of the most intensely monitored mountainous areas in the world. Temporal variations in temperature and precipitation regimes in this region are widely documented in the literature [Klein Tank and Können, 2003; Birsan et al., 2005; Schmidli and Frei, 2005]. [The aim of this paper] is to understand if small variations in time of temperature and precipitation may have had an effect on the flood frequency distribution in mountain basins that is compatible with the observed variations.

The links between climatic fluctuations and temporal variation in flood frequency are the object of investigation by numerous authors [Panagoulia and Dimou, 1997; Olsen et al., 1998; Milly et al., 2002]. The majority of the literature studies, however, addresses the problem from a site-specific point of view, whereas the literature appears to be scarce of studies that examine the problem at the regional scale. A different type of approach is one that evaluates the effects of climatic change on the evolution of flood frequency by coupling high-resolution regional climate models with hydrologic models [Prudhomme et al., 2002; Bronstert, 2003]. The use of this approach, however, becomes rather questionable in mountainous regions, where topography is very poorly resolved even by regional climate models, and the scarcity of data may seriously compromise the representativeness of hydrological models [Hostetler, 1994; Xu, 1999].


(5) - Syntèses et préconisations
 

Références citées :

Allamano, P., P. Claps, and F. Laio (2009), An analytical model of the effects of catchment elevation on the flood frequency distribution, Water Resour. Res., 45, W01402, doi:10.1029/2007WR006658.

Barnett, T., J. Adam, and D. Lettenmaier (2005), Potential impacts of a warming climate on water availability in snow-dominated regions, Nature, 438, 303– 309, doi:10.1038/nature04141. [Fiche Biblio]

Bates, B., Z. Kundzewicz, S. Wu, and J. Palutikof (Eds.) (2008), Observed and projected changes in climate as they relate to water, in Climate Change and Water, IPCC Tech. Pap. VI, pp. 13–31, Intergov. Panel on Clim. Change Secr., Geneva, Switzerland.

Beniston, M., H. Diaz, and R. Bradley (1997), Climatic change at high elevation sites: An overview, Clim. Change, 36, 233– 251.

Birsan, M., P. Molnar, P. Burlando, and M. Pfaundler (2005), Streamflow trends in Switzerland, J. Hydrol., 314, 312– 329. [Fiche Biblio]

Bronstert, A. (2003), Floods and climate change: Interactions and impacts, Risk Anal., 23, 545–557.

Diaz, H., J. Eischeid, C. Duncan, and R. Bradley (2003), Variability of freezing levels, melting season indicators, and snow cover for selected high-elevation and continental regions in the last 50 years, Clim. Change, 59, 33– 52.

Hostetler, S. (1994), Hydrologic and atmpospheric models: The (continuing) problem of discordant scales, Clim. Change, 27, 345– 350.

Klein Tank, A., and G. Können (2003), Trends in indices of daily temperature and precipitation extremes in Europe, 1946–99, J. Clim., 16, 3665– 3680.

Koenker, R., and G. Bassett (1978), Regression quantiles, Econometrica, 46(1), 33–50.

Koutsoyiannis, D., and A. Montanari (2007), Statistical analysis of hydroclimatic time series: Uncertainty and insights, Water Resour. Res., 43, W05429, doi:10.1029/2006WR005592.

Koutsoyiannis, D., A. Montanari, H. Lins, and T. Cohn (2009), Discussion of: The implications of projected climate change for freshwater resources and their management, Hydrol. Sci. J. Sci. Hydrol., 54, 394– 405.

Marty, C. (2008), Regime shift of snow days in Switzerland, Geophys. Res. Lett., 35, L12501, doi:10.1029/2008GL033998. [Fiche Biblio]

Milly, P., R. Wetherald, K. Dunne, and T. Delworth (2002), Increasing risk of great floods in a changing climate, Nature, 415, 514–517. [Fiche Biblio]

Mudelsee, M., M. Börngen, G. Tetzlaff, and U. Grünewald (2003), No upward trends in the occurrence of extreme floods in central Europe, Nature, 425, 166– 169.

Olsen, R., J. Lambert, and Y. Haimes (1998), Risk of extreme events under nonstationary conditions, Risk Anal., 18, 497– 510.

Palmer, T., and J. Räisänen (2002), Quantifying the risk of extreme seasonal precipitation events in a changing climate, Nature, 415, 512– 514. [Fiche Biblio]

Panagoulia, D., and G. Dimou (1997), Sensitivity of flood events to global climate change, J. Hydrol., 191, 208–222.

Prudhomme, C., N. Reynard, and S. Crooks (2002), Downscaling of global climate models for flood frequency analysis: Where are we now?, Hydrol. Processes, 16, 1137– 1150.

Schmidli, J., and C. Frei (2005), Trends of heavy precipitation and wet and dry spells in Switzerland during the 20th century, J. Climatol., 25, 753– 771.

Xu, C. (1999), Climate change and hydrologic models: A review of existing gaps and recent research developments, Water Resour. Manage., 13, 369– 382.