Precipitation downscaling using random cascades: a case study in Italy

Abstract. We present a Stochastic Space Random Cascade (SSRC) approach to downscale precipitation from a Global Climate Model (hereon, GCMs) for an Italian Alpine watershed, the Oglio river (1440 km2). The SSRC model is locally tuned upon Oglio river for spatial downscaling (approx. 2 km) of daily precipitation from the NCAR Parallel Climate Model. We use a 10 years (1990–1999) series of observed daily precipitation data from 25 rain gages. Scale Recursive Estimation coupled with Expectation Maximization algorithm is used for model estimation. Seasonal parameters of the multiplicative cascade are accommodated by statistical distributions conditioned upon climatic forcing, based on regression analysis. The main advantage of the SSRC is to reproduce spatial clustering, intermittency, self-similarity of precipitation fields and their spatial correlation structure, with low computational burden.</p

Review of recent advances in index flood estimation

International audienceIndex flood estimation for regional flood frequency analysis needs to be based on the information available. The most appropriate method depends on the specific application and its choice requires a problem-oriented analysis. This paper presents a simple theoretical framework to deal with index flood estimation for a specific river site. The methodological approaches available for the purpose are reviewed. For each, the information required is specified and the reliability of the estimate, particularly desirable in risk analysis and management, is discussed. Where flood observations are lacking, indirect estimation must be undertaken using scenarios including those commonly met in hydrological practice; generally, these depend on the amount and type of information available. For each scenario, the methodologies are outlined, in order of the expected degree of complexity. After a guided analysis, an investigator can adopt the method providing the best tradeoff between effort in collecting and handling data and the resultant reliability which can be expected. Keywords: direct and indirect methods, index flood estimation, reliability, scenarios

Regional evaluation of three day snow depth for avalanche hazard mapping in Switzerland

The distribution of the maximum annual three day snow fall depth &lt;i&gt;H&lt;sub&gt;72&lt;/sub&gt;&lt;/i&gt;, used for avalanche hazard mapping according to the Swiss procedure (&lt;i&gt;Sp&lt;/i&gt;), is investigated for a network of 124 stations in the Alpine part of Switzerland, using a data set dating back to 1931. Stationarity in time is investigated, showing in practice no significant trend for the considered period. Building on previous studies about climatology of Switzerland and using an iterative approach based on statistical tests for regional homogeneity and scaling of &lt;i&gt;H&lt;sub&gt;72&lt;/sub&gt;&lt;/i&gt; with altitude, seven homogenous regions are identified. A regional approach based on the index value is then developed to estimate the &lt;i&gt;T&lt;/i&gt;-years return period quantiles of &lt;i&gt;H&lt;sub&gt;72&lt;/sub&gt;&lt;/i&gt; at each single site &lt;i&gt;i&lt;/i&gt;, &lt;i&gt;H&lt;sub&gt;72i&lt;/sub&gt;(T)&lt;/i&gt;. The index value is the single site sample average &amp;mu;&lt;sub&gt;&lt;i&gt;H&lt;sub&gt;72i&lt;/sub&gt;&lt;/i&gt;&lt;/sub&gt;. The dimensionless values of &lt;i&gt;H&lt;sup&gt;*&lt;/sup&gt;&lt;sub&gt;72i&lt;/sub&gt;=H&lt;sub&gt;72i&lt;/sub&gt; / &amp;mu;&lt;sub&gt;H&lt;sub&gt;72i&lt;/sub&gt;&lt;/sub&gt;&lt;/i&gt; are grouped in one sample for each region and their frequency of occurrence is accommodated by a General Extreme Value, GEV, probability distribution, including Gumbel. The proposed distributions, valid in each site of the homogeneous regions, can be used to assess the &lt;i&gt;T&lt;/i&gt;-years return period quantiles of &lt;i&gt;H&lt;sup&gt;*&lt;/sup&gt;&lt;sub&gt;72i&lt;/sub&gt;&lt;/i&gt;. It is shown that the value of &lt;i&gt;H&lt;sub&gt;72i&lt;/sub&gt;(T)&lt;/i&gt; estimated with the regional approach is more accurate than that calculated by single site distribution fitting, particularly for high return periods. A sampling strategy based on accuracy is also suggested to estimate the single site index value, i.e. the sample average &amp;mu;&lt;sub&gt;&lt;i&gt;H&lt;sub&gt;72i&lt;/sub&gt;&lt;/i&gt;&lt;/sub&gt;, critical for the evaluation of the distribution of &lt;i&gt;H&lt;sub&gt;72i&lt;/sub&gt;&lt;/i&gt;. The proposed regional approach is valuable because it gives more accurate snow depth input to dynamics models than the present procedure based on single site analysis, so decreasing uncertainty in hazard mapping procedure

Potential of remote sensing and open street data for flood mapping in poorly gauged areas: a case study in Gonaives, Haiti

The Hispaniola Island, in the Caribbean tropical zone, is prone to extreme flood events. Floods are caused by tropical springs and hurricanes and may lead to human losses, economical damages, and spreading of waterborne diseases. Flood studies based upon hydrological and hydraulic modelling are hampered by almost complete lack of hydro-meteorological data. Thenceforth, and given the cost and complexity in the organization of field measurement campaigns, the need for exploitation of remote sensing data, and open source data bases. We present here a feasibility study to explore the potential of (i) high-resolution of digital elevation models (DEMs) from remote imagery and (ii) remotely sensed precipitation data, to feed hydrological flow routing and hydraulic flood modelling, applied to the case study of river La Quinte closed to Gonaives (585 km2), Haiti. We studied one recent flood episode, namely hurricane Ike in 2008, when flood maps from remote sensing were available for validation. The atmospheric input given by hourly rainfall was taken from downscaled Tropical Rainfall Measuring Mission (TRMM) daily estimates, and subsequently fed to a semi-distributed DEM-based hydrological model, providing an hourly flood hydrograph. Then, flood modelling using Hydrologic Engineering Center River Analysis System (HEC-RAS 1D, one-dimensional model for unsteady open channel flow) was carried out under different scenarios of available digital elevation models. The DEMs were generated using optical remote sensing satellite WorldView-1 and Shuttle Radar Topography Mission (SRTM), combined with information from an open source database (OpenStreetMap). Observed flood extent and land use have been extracted using Système Pour l’Observation de la Terre-4 (SPOT-4) imagery. The hydraulic model was tuned for floodplain friction against the observed flooded area. We compared different scenarios of flood simulation and the predictive power given by model tuning. Our study provides acceptable results in depicting flooded areas, especially considering the tremendous lack of ground data, and shows the potential of hydrological modelling approach fed by remote sensing information in Haiti, and in similarly data-scarce areas. Our approach may be useful to provide depiction of flooded areas for the purpose of (i) flood design for urban planning under a frequency-driven approach and (ii) forecasting of flooded areas for warning procedures, pending availability of weather forecast with proper lead time

2008-2011 snow covered area (SCA) variability over 18 watersheds of the central Chile through MODIS data

Snowmelt contributes largely to water budget of several Chilean mountain watersheds. To describe snow covered area (SCA) variability within 18 watersheds in Central Chile during 2008\u20132011 we used MODIS data (i.e. MOD10A2-V5 maximum snow cover extent in eight-day periods). The study area was divided into three different zones (Northern, Central, and Southern), due to its large extent (~205,000 km2), and according to former studies performed by the Direcc\uedon General de Aguas (DGA) of the Chilean Government covering the time window 2000\u20132007. After georeferencing our data to the WGS84 Datum (UTM Projection, zone 19S), the scenes were cropped to fit the study area. We selected and set a threshold for cloud coverage (<30%) in order to discard the images with too cloud cover, so losing only 2% of the sample. Hypsographic and aspect analyses were performed using the SRTM3 elevation model. We found largest values of SCA during 2008\u20132011 in the Central Zone, while the topographic and climatic features (i.e. lower altitudes in the South, and a drier climate in the North) limit snow deposition elsewhere. Similarly, snow line is higher in the Northern zone (due to the presence of the plateau), and lower moving southwards. In the North the minimum SCA is reached sooner than elsewhere, lasting for a longer period (November to March). West side showed the maximum of SCA in all zones throughout the study period. The present work extends in time the dataset of SCA in the Central Chile, adding information for statistic assessment, and trend analysis of snow cover in this area

Modelling hydrological components of the Rio Maipo of Chile, and their prospective evolution under climate change

We used the Poly-Hydro model to assess the main hydrological components of the snow-ice melt driven Maipo River in Chile, and glaciers' retreat under climate change therein until 2100. We used field data of ice ablation, ice thickness, weather and hydrological data, and precipitation from TRMM. Snow cover and temperature were taken from MODIS. We forced the model using weather projections until 2100 from three GCMs from the IPCC AR5, under three different radiative concentration pathways (RCPs 2.6, 4.5, 8.5). We investigated trends of precipitation, temperature, and hydrology until 2100 in the projection period (PR, 2014-2100) and the whole period (CM 1980-2100, composite), against historical trends in control period (CP, 1980-2013). We found potentially increasing temperature until 2100, except for Spring (OND). In the PR period, yearly flow decreases significantly under RCP85, on average -0.25 m 3 \ub7s -1 \ub7year -1 , and down to -0.48 m 3 \ub7s -1 \ub7year -1 , i.e., -0.4% year -1 against CP yearly average (120 m 3 s -1 ). In the long run (CM) significant flow decrease would, occur under almost all scenarios, confirming persistence of a historical decrease, down to -0.39 m 3 \ub7s -1 \ub7year -1 during CM. Large flow decreases are expected under all scenarios in Summer (JFM) during PR, down to -1.6 m 3 \ub7s -1 \ub7year -1 , or -1% year -1 against CP for RCP8.5, due to increase of evapotranspiration in response to higher temperatures. Fall (AMJ) flows would be mostly unchanged, whileWinter (JAS) flows would be projected to increase significantly, up to 0.7 m 3 \ub7s -1 \ub7year -1 during 2014-2100, i.e., +0.9% year -1 vs. CP under RCP8.5, due to large melting therein. Spring (OND) flows would decrease largely under RCP8.5, down to -0.67 m 3 s -1 \ub7year -1 , or -0.4% year -1 vs. CP, again due to evapotranspiration. Glacier down wasting is projected to speed up, and increasingly so with RCPs. Until 2100 ice loss would range from -13% to -49% (-9%, and -39% at 2050) of the estimated volume at 2012, which changed by -24% to -56% (-21%, and -39% at 2050) vs. ice volume in 1982, thus with rapider depletion in the first half of the century. Policy makers will have to cope with modified hydrological cycle in the Maipo River, and greatly decreasing ice cover in the area

Prediction of future hydrological regimes in poorly gauged high altitude basins: the case study of the upper Indus, Pakistan

In the mountain regions of the Hindu Kush, Karakoram and Himalaya (HKH) the "third polar ice cap" of our planet, glaciers play the role of "water towers" by providing significant amount of melt water, especially in the dry season, essential for agriculture, drinking purposes, and hydropower production. Recently, most glaciers in the HKH have been retreating and losing mass, mainly due to significant regional warming, thus calling for assessment of future water resources availability for populations down slope. However, hydrology of these high altitude catchments is poorly studied and little understood. Most such catchments are poorly gauged, thus posing major issues in flow prediction therein, and representing in fact typical grounds of application of PUB concepts, where simple and portable hydrological modeling based upon scarce data amount is necessary for water budget estimation, and prediction under climate change conditions. In this preliminarily study, future (2060) hydrological flows in a particular watershed (Shigar river at Shigar, ca. 7000 km&lt;sup&gt;2&lt;/sup&gt;), nested within the upper Indus basin and fed by seasonal melt from major glaciers, are investigated. &lt;br&gt;&lt;br&gt; The study is carried out under the umbrella of the SHARE-Paprika project, aiming at evaluating the impact of climate change upon hydrology of the upper Indus river. We set up a minimal hydrological model, tuned against a short series of observed ground climatic data from a number of stations in the area, in situ measured ice ablation data, and remotely sensed snow cover data. The future, locally adjusted, precipitation and temperature fields for the reference decade 2050–2059 from &lt;i&gt;CCSM3&lt;/i&gt; model, available within the IPCC's panel, are then fed to the hydrological model. We adopt four different glaciers' cover scenarios, to test sensitivity to decreased glacierized areas. The projected flow duration curves, and some selected flow descriptors are evaluated. The uncertainty of the results is then addressed, and use of the model for nearby catchments discussed. The proposed approach is valuable as a tool to investigate the hydrology of poorly gauged high altitude areas, and to project forward their hydrological behavior pending climate change