Between Clouds, Carpathian Mountains-EUROPHOTOMETEO2012
Climhydex
Climhydex
Aeolian Harp, Dobrogea-EUROPHOTOMETEO2012
Climhydex
Sky Paintings, Cluj Napoca-EUROPHOTOMETEO2012
Climhydex
Peace, Balea Lake-EUROPHOTOMETEO2012
Climhydex
Water Magic, Alba County-EUROPHOTOMETEO2012
Climhydex
Mistic River, Alba County-EUROPHOTOMETEO2012
Climhydex
Summer Storm, Targoviste-EUROPHOTOMETEO2012
Climhydex
The Morning Fog-Ceata de dimineata-Letea, Tulcea-EUROPHOTOMETEO2012
Climhydex
Waiting for the Rain, Brasov-EUROPHOTOMETEO2012
Climhydex

Reports 2012

Scientific report
on the implementation of the project "Changes in climate extremes and associated impact in hydrological events in Romania" (CLIMHYDEX) in 2012
 
During the reported period (May, 11 - Nov., 30 2012), the research activity has been developed according to the approved project schedule. The synthesis of the results is presented below, ordered by work packages (WP) and activities. Several details are included in the extended report, available upon request to the project manager. The results obtained during this period have been presented at several thematic conferences (13 posters and presentations), being also subject to some published or accepted papers (3 research articles in peer-reviewed journals), or doctoral theses (2). The list of presentations and publications will be posted on the project website (http://www.climhydex.meteoromania.ro) under the sections Meetings" and "Publications", respectively.
 
WP2 Data
2.1. Seasonal indexes associated to climatic and hydrological extremes.
As planned, NMA and NIHWM have computed all seasonal indexes associated to climatological / hydrological extreme events, according to the deadline of this activity (D.2.1, completed).
 
Climatic extremes. Starting from daily measurements of minimum and maximum air temperature, precipitation and snow depth, from all available stations with full data records  for the reference period, 1961-2010, the NMA has computed all seasonal indexes associated to climatic extremes, according to the schedule, as follows: the frequency of very warm days/nights (maximum/minimum temperature >= 90th percentile): Frtmax90 (1), Frtmin90 (2); longest period of very warm days/nights: Dtmax90 (3); Dtmin90 (4);  longest period without precipitation: Dmaxpp0 (5); frequency of very wet days (daily precipitation amount above 90th percentile): Frpp90(6); longest very wet period: Dmaxpp90 (7); frequency of snow/rain showers: Frap (8), Fraz (9); meteorological drought index, represented by the standardized precipitation index SPI (10); thermal stress indexes (warm/cold): ITU (11) / IR (12); frequency of days with ITU/IR above/below their critical thresholds: ITU >= 80 (FrITU (13)), IR <= 32 (FrIR (14)); maximum daily precipitation amount: Ppmaxz(15); maximum daily temperature:  Tmax (16); minimum daily temperature: Tmin (17); maximum snow depth: Grmaxz(18); pedological drought index- IP (19), represented by the soil moisture content for soil classes that are meaningful for agricultural areas of Romania, at different soil depths (0-0.2 m, 0-0.5 m, 0-1 m).  Also, the data files resulted from pluviographs (1965-2007) at 45 meteorological stations, containing precipitation amounts within 5 to 1440 minutes, have been updated, in order to create annual data series with maximum cumulated precipitations for each time interval.
 
 Hydrological extremes. The NIHWM filled in the mean monthly discharges data series, for the longest available observation periods. To this purpose, the following were filled-in: data for the Orsova hydrometric station (on the River Danube) for the interval between 1921 and 2010; and for the interval between 1931 and 2010 the data from the stations which monitor the first degree river confluences and the ones from certain representative basins which control certain altitude steps. For this, there were used level observations and multiannual keys for the period between 1931 and 1960, there were used correlations for the levels between correspondent stations, as well as rainfall-runoff analysis methods. Finally, there were generated continuous and homogenous data series for the period between 1931 and 2010 at 45 hydrometric stations. There were calculated the values of the 80% and 20% quantiles used for the sampling of the dry/rainy periods and the definition of periods with scarce/excessive water volume.
2.2. High-resolution meteorological and hydrological datasets.
During the reported period, NMA has developed activities – for the first time in Romania regarding the realization of a gridded (1x1 km resolution) dataset for air temperature at 6-hour time interval (D2.5). The method is based on GIS interpolation techniques of observational data and was developed in a PhD thesis for daily mean temperature (Dumitrescu, 2012). Within the work plan of the CLIMHYDEX project it was proposed the creation of a country-wide database containing the mean daily temperature for 1961-2010, together with a similar database at 6-hour time interval for the selected river basins (Crisul Alb and Barlad). Considering that for robust statistical results, at least 30 meteorological stations are needed for each basin, it has been decided to extend the database of hourly temperature to the entire country. The computational procedure of the data requires the following steps: achieving grid files of multi-annual mean temperature at each time-stamp (01, 07, 13, 19), for every day of the year (1-366) over the interval 1961-2010; achieving  the grid files containing the spatial interpolation of the anomalies related to multi-annual mean of hourly temperatures (4 time stamps), for every day of the 1961-2010 interval; realization of the final dataset by combining the two data sets previously mentioned. At this stage the method has been applied for hourly temperature recorded in 2000. In the future, a similar database will be realized for precipitation. As the NMA database did not contain digital records of 6-hour precipitation, the records from 72 meteorological stations (out of 125) have been manually introduced and validated.
 
Remote sensing data (D2.3, completed) has been commonly agreed on with NIHWM, and prepared by NMA, as follows: snow cover extent, at 8-day synthesis, derived from MODIS at 500 m spatial resolution, for Barlad, Crisul Alb, and Tinoasa-Ciurea river basins, for the interval January-April of the years 2005, 2006, and 2012, respectively; land cover, extracted from Corine Land Cover (CLC1990, CLC2000, CLC2006) for the selected river basins, at a spatial resolution of < 50 m for CLC1990, and <25 m for CLC2000 and CLC 2006; MODIS products of reflectance, 8-day synthesis, 1 km spatial resolution, used for computing datasets of vegetation indexes (years 2003, 2005, 2007 and 2008), namely: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Infrared Index (NDII).
 
In order to compute the precipitation amount at fine spatial resolution (1 km) and time interval (6 minutes) for the selected river basins (D2.2), by NMA, the following aspects concerning the radar-based precipitation estimation for short time interval have been considered: the areas of the selected river basins proposed for testing the methodology were identified; the radar reflectivity measurements covering the test areas were extracted; the instantaneous precipitation intensity was computed from the radar data; the 6-minute cumulated precipitation amount was obtained, using the instantaneous intensity. Details regarding the methodology are presented in the extended report.
 
Fine resolution spatial data, used as inputs for hydrological models (D2.6, finalized), were calculated by the NIHWM for two medium size river basins (Crişul Alb and Bârlad) and two representative river basins (Moneasa and Tinoasa-Ciurea) and refers to the performance of certain thematic layers (hydrographic network, reservoirs, hydrometric stations, etc.), production of Digital Terrain Models, generation of morphometric parameters of the hydrometric station catchments, as well as of the following parameters: field capacity, runoff coefficient, evaporation capacity, using the CN (Curve Number) index. The map with the maximum retention capacity was achieved. Soil moisture in the unsaturated area was determined based on the relations described in the Vidra hydrological model.
 
2.3. Outputs from regional (RCMs) and global (GCMs) climate models (D2.4)
 Within the NMA, daily data of maximum / minimum air temperature and precipitation amount resulted from the simulations of 7 RCMs (two more than the planned minimum of 5) elaborated within the FP6 ENSEMBLES project (http://ensemblesrt3.dmi.dk) for the period 1951-2100, were downloaded. These data will be used in the next stages of the project in order to compute the climatic extremes indices mentioned at section 2.1 for the actual period, and changes in their regime in the future for 2020-2050 and 2070-2100 time intervals, under the A1B emission scenario.
 
WP3 Mechanisms controlling the variability of climatic extremes
 
3.1. Characteristics of spatio-temporal variability of seasonal climatic / hydrological extremes (D3.1a)
Related to this activity, there have been obtained preliminary results concerning the significance of the general trends and shifts in the mean (using Mann-Kendall and Pettitt tests), as well as the assessment of spatial variability (by means of empirical orthogonal functions – EOF) for the hydroclimatic extremes presented at section 2.1. Within the NMA, the linear trends and shifts in the mean for the period 1962-2010 have been computed for 12 seasonal indices (out of 19 mentioned at WP2). The results show a significant increasing trend, in all seasons (except for autumn), for all indices associated to the 4 thermal extremes (Frtmax90, Frtmin90, Dtmax90, Dtmin90), the increase rate being more pronounced in summer, when it is significant at 5% level for the entire country, and less pronounced in spring. Thus, the frequency of very warm days/nights show a country-wide increase during 1962-2010 of ~4 days (in spring) and 15 days during summer. The increase was more pronounced for the frequency of very warm nights during spring, and for the frequency of very warm days during winter. Similar characteristics have been identified for the maximum duration of consecutive very warm days/nights, except for spring, when the signal is not significant for the duration of very warm days. In order to enforce these findings, the same data series computed for 110 years at the Bucuresti-Filaret station have been analyzed. The results were confirmed only for Frtmax90, in all seasons. Regarding precipitation, there were found significant increasing trends over large areas in the frequency of very wet days, during autumn, and in the maximum interval without precipitation, during summer. All other trends are not significant, except for few isolated locations. The maximum snow depth (Grmaxz) presents a significant decreasing trend at ~30% of the stations, while maximum daily precipitation (Ppmaxz) shows increasing trends at few stations in the central and northwestern part of the country, in all seasons (Birsan and Dumitrescu, 2012). SPI has a significant increasing trend in autumn. There are, however, pronounced spatial differences in the intensity of the general trend variation, which are presented in detail in the extended scientific report. The analyses realized by NIHWM on monthly and seasonal streamflow data series over Romania show decreasing trends in summer in the southern and eastern regions of the country, and increasing trends during winter.
 
The spatial characteristics of the seasonal variability of 8 indices (out of the aforementioned 12) have been analysed by NMA using the EOF method. The results point out that the main variability mode (EOF1), which presents the same signal throughout the country, explains an important fraction of the observed variance for indices related to thermal extremes, especially for Frtmax90 (over 80%) and less for Dtmin (~50%). These results show that the simultaneous, country-wide variability of these indices is significantly driven by the same large-scale mechanism (analysed in various ways within the activities 3.2 and 3.3), and on the other hand, that the spatial variability of pluviometric extremes is higher, compared to the thermal ones. The time series associated to EOF1 synthesizes the characteristics of temporal variability for the whole country. Analyzing the trend and shift in the mean for this time series confirms the station-based analysis. A significant increasing shift in the seasonal means of thermal extremes (except for autumn) was found, around 1987.
 
3.2. The connection between the variability of climatic extremes in Romania and the simultaneous variability of the large-scale climatic anomalies configurations - the CCA method (D3.1c)
During this period, the NMA has applied the Canonical Correlation Analysis (CCA) between the spatial vector of the anomalies of various seasonal indices (predictants) and the large-scale anomalies of the sea-level pressure (SLP, area 5-45E, 30-55N) and temperature at 850 hPa (T850, area 10-40E, 30-55N), predictors, taken from the NCEP/NCAR. The CCA method has been applied both separately, between each predictor and predictant, and between different combinations of predictors and predictants, this kind of analysis being realized for the first time in Romania. During the reported period, there have been realized tests for summer and winter, taking into account the anomalies of Frtmax90, Frpp90 and Dmaxpp0, as predictors (separately or combined). The analysis has pointed out coherent relationships between spatial configurations of the simultaneous variability of these predictors and the spatial configurations of the simultaneous variability of SLP and T850.
 
3.3. The connection between the variability of climatic and hydrological extremes in Romania and blocking / circulation indices (D3.1d)
NMA has analyzed the heavy precipitation events, represented by daily cumulated precipitation >= 50 mm / 24 h (P50), and >= 100 mm / 24 h (P100). The analysis was carried out for the 1980-2009 interval (2539 events), being the subject to a chapter of a PhD thesis (Stefanescu, 2012). It is the first time in Romania when an analysis of heavy precipitation was done country-wide. Compared to previous studies, the events were counted regionally, i.e., for a given day, an event is recorded at least at one meteorological station. This approach overpass the difficulty that occurs when analyzing extremely intense events (those data series contain many zero values, leading to difficulties in statistical analysis). For the analyzed period, statistically significant increasing trends in the annual frequency of P50 and P100 were found (Stefanescu et al., 2012).  The analysis will be extended for 1961-2010, in next stages of the project, and the results will be compared to those obtained at the activity 3.1, for the same types of events.
 
The main objectives of UB-FF(P2) for the reporting period were: to create a data-base to be used for analysis of the atmospheric blocking variability for the 1961-2010; to study different catalogues of both sea and upper level atmospheric circulation types; to realize and test computer codes for blocking indices calculation; to  investigate mechanisms of different extreme phenomena and their variability at different time scales;  analysis of the correlation between some seasonal indices associated to extreme climate phenomena (calculated by ANM) and blocking indices. The data base realized in this stage of the project contains global daily 500 hPa field extracted from NCEP/NCAR reanalysis project for the period 1948-2011 as well as from "Daily NOAA-CIRES 20th Century Reanalysis" project for 1871-2010. To find an appropriate method to relate atmospheric circulation types and extreme climate phenomena, two circulation catalogues from COST 733 programme, GWT (GrossWetter-Typen) and WLKC733 (Objektive Wetter Lagen Klassifikation), were used simultaneously. Both catalogues, which are used extensively for study the Central Europe weather and climate, are based on objective methods. In GWT and WLKC733 catalogues, the circulation of each day is assimilated with a specific circulation type both at sea level and at upper levels. The region for analysis is 5E-45E; 35N-55N. The same region was used for CCA (activity 3.1) so that the comparison between the results of the two methods is not difficult. Algorithms used for blocking frequency calculations were tested for the period 1948-2010 using the data-base created within this project. Mean frequencies of both one-dimensional and two-dimensional blocking indicators are similar with the corresponding blocking distributions published in the literature. This proves that blocking algorithms were corrected implemented. The relationship between seasonal indices of the frequency of days characterized by extreme positive temperatures (delivered by ANM) and atmospheric blocking indices was also investigated. Preliminary results show that there is a strong relationship between the frequency of extreme high temperatures in Romania and the frequency of atmospheric blocking in all seasons. A part of the results achieved by the UB-FF in the reported period were published by Ionita-Scholz et al. (2012).
3.4. Mechanisms generating heavy precipitations in very short time, using radar data (D3.2)
 NMA has prepared the database containing 12-hour records of precipitation at four stations located in the Barlad river basin, from January 2003 to October 2012, this being the period that also has radar data available. The days with precipitation amounts exceeding 20 mm for at least one station have been selected, and the related radar data has been extracted. The data were organized on a seasonal basis (cold/warm).
 
WP5: Impact of climate change on Hydrology (NIHWM)
5.1. Identification and assessment of spatial and temporal characteristics of flood and drought phenomena in selected basins.
This work was done mainly by analyzing hydrometeorological data collected from representative basins and the two analyzed river basins (Crisul Alb and Barlad) for the expansion of the types of parameters measured, in view of identifying the variability of extremes (D5.1). The analysis showed the need to improve monitoring activities by: installing level sensors or limnigraphs to all hydrometric sections, equipping with sensors for the measurement of evapotranspiration, etc. For the development of data collection network, automatic monitoring equipment were installed, taking into account the appropriateness of measurements in order to make them compatible with the conventional measurements. Regarding the input and output sediment discharge from the Tungujei reservoir (D5.2), an analysis specific to periods with hydrological extremes was made. Also, in order to capitalize the previous research on the mentioned sediment transport, documentation and a review of existing studies on Tungujei reservoir was made, studies elaborated since 1988 until present day. The analysis of groundwater resources and the exchange between surface water and ground water (D5.4) performed in this phase included documenting and field activities. The documentation work was completed by making maps regarding the distribution of wells from the National Hydrogeological Network which open the phreatic aquifer from the Crisul Alb area, between Varsand (to the west) and Gurahont (to the east).
5.2. Development of hydrological models at different spatial and temporal scales in selected river basins
In terms of deterministic modeling, during the reporting period, we proceeded to calibrate the model with NOAH distributed parameters in selected representative basins (D5.6) by making specific configurations for the Moneasa representative river basin within the Crisul Alb river basin. This was done by analyzing the physical-geographical characteristics of the Moneasa representative river basin and by establishing initial values of the parameters for the NOAH distributed parameters hydrological models; analysis, validation and storage of hydrometeorological data necessary for the calibration of the NOAH model for the Moneasa representative basin. The mentioned activities are ongoing and will be completed in the next stage.
5.3 Estimating the impact of climate change on extreme flow regime through advanced statistical methods (D5.8).
Research within this activity targeted two main directions: the analysis of climatic variability of hydrometeorological fields obtained from observation data and the estimation of the capability of climate change models to reproduce the precipitation field. The following preliminary results were obtained: analysis by EVT (Extreme Value Theory) of discharges from the Gurahont hydrometric station emphasized that discharges between 130 and 470 m3/s have return periods between 5 and 100 years for winter and spring months. For summer and autumn months, discharges reaching even 1300 m3/ s have return periods between 5 and 1000 years (Chelcea et. al., 2012). A first evaluation of the Pardé coefficients show certain homogenous areas of  discharges for spring months; a signifocant correlation between the NAO index (EOF1-SLP) and the discharges from the Crisul Alb basin in the winter months were identified.
Publications 2012
1) PhD thesis
Dumitrescu Alexandru: Spatializarea parametrilor meteorologici si climatici prin tehnici SIG. Sustinuta public in data de 19.09.2012, Facultatea de Geografie, Universitatea Bucuresti.
Victor Stefanescu:  Elaborarea unui sistem informational pentru reducerea impactului fenomenelor meteorologice severe. Susținuta public in data de 20.09.2012, Facultatea de Fizica, Universitatea Bucuresti.
2) Peer-reviewed (ISI) publications
Birsan MV, Dumitrescu A., 2012: Snow variability in Romania in connection to large-scale atmospheric circulation. Int J Climatol (acepted).
Ionita-Scholz Monica, Norel Râmbu, Silvia Chelcea, Simona Pătruţ, 2012: Multidecadal variability of summer temperature over Romania and its relation with Atlantic Multidecadal Oscillation, Theor Appl Climatol DOI 10.1007/s00704-012-0786-8.
Stefanescu, V., Stefan, S., Georgescu, F., 2012: Spatial distribution of heavy precipitation in Romania between 1980 and 2009. Meteorological Application (under second revision)
 
Project Director,
Dr. Aristita BUSUIOC