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

Reports 2013

Scientific report

on the implementation of the project "Changes in climate extremes and associated impact in hydrological events in Romania" (CLIMHYDEX) in 2013


During the reported period (December.1.2012 - Nov., 30 2013), the research activity has been developed according to the approved project schedule. The synthesis of the results is presented below, ordered by the main objectives (approved in the working plan of the reported period) and activities. The results obtained during this period have been presented at several national and international thematic conferences (31), being also subject to 12 articles: 2 -published and 6-submitted/under review. The list of presentations and publications are posted on the project website ( under the sections “Meetings" and "Publications", respectively. The list of publications is also attached to this report (Annex 1).


1. Construction of high-resolution meteorological datasets


Two activities have been planned in this objective. They have been carried out by the CO (ANM), started in 2012 and finalized in the reported period. The results are presented in the following.


1.1. Estimation of precipitation (6 minutes temporal resolution) from radar information

Weather radar-based rainfall accumulation is widely used by both meteorologists and hydrologists firstly to detect and monitor severe weather, and when the case to issue severe weather warnings, and in quantitative precipitation estimation (QPE) and flood modeling, respectively. Atmospheric precipitation varies both in space and time; therefore, certain hydro-meteorological applications require high spatiotemporal resolution data to accurately describe the atmospheric conditions associated with the rainfall events. Within this research, QPE improvement in the area of a test river basin, namely Barlad River Basin, was aimed through adjusting the radar-based rainfall estimations using the ground measurements. To achieve this, the mean field bias approach was used on the data provided by WSR-98D weather radar system that is operational in the close proximity of the test basin, at Barnova. The radar data has a spatial resolution of 1x1 km, being sampled at every approximately 6 minutes, while the surface observations (rainfall accumulations) are those made at the weather stations at every 10 minutes. The datasets spans over the 2006–2012 period, the study being focused only on the convective season (April–October interval).


The general analysis involved four main steps: a) conversion of radar reflectivity into rain rate, b) calculation of 6 minutes radar-based accumulations, c) mean field bias calculation, and d) adjusting the radar-based rainfall estimations. Therefore, after the conversion of the reflectivity, the rain rate was integrated in time to estimate the radar accumulations over a given interval. Surface rainfall observations at ground were afterwards used to derive the mean field bias of the radar estimations, bias used to obtain the final grids of radar rainfall field at the level of the Barlad River Basin. The final result of this approach was a 1x1 km and 6 minutes resolutions grid of the spatial rainfall distribution at the basin level over the period 2006-2012.


The assessment of the final results revealed that the radar-based rainfall accumulations slightly underestimate the precipitation in the pilot basin, but after the adjustment was performed the results were significantly improved. The adjusted radar estimations were much closer to the values registered at the ground than in the case of unadjusted radar estimations. Consequently, the method used was proven to be useful, and with great potential to be implemented operationally. The final grids were made available to the Partner no. 2 (INHGA) to be used as input into hydrological models. The results of this research were presented at 3 scientific events, from which one international (EGU-2013) and two national (MeteoRomania scientific annual conference and INHGA scientific conference.


1.2. High resolution spatial interpolation of 6-hour temperature and precipitation data


This activity started in 2012 with the interpolation of the 6-hour temperature data on a high spatial resolution (1kmx1km). The final outcome of this work was a gridded temperature data set over the entire country for the 1961-2010 period. In the reported period, the temperature interpolation on 10 minutes temporal resolution (2007-2012) has been also carried out, even if this activity has not been initially included in the project work plan. However, this data set is necessary to complete the similar precipitation data set (presented in 2.1.1) used in hydr0logical modelling (WP5). Because the observed air temperature data was available for 1-hour time interval, first, the temperature surfaces were interpolated at this time resolution and after that, the final 10-minutes grids were obtained by applying a linear interpolation algorithm in time domain.


In addition to the gridded temperature data set presented above, a similar gridded 6-hour precipitation time series on same spatial resolution (1kmx1km), period 1975-2010), have been constructed in the reported period. A similar technique, as presented for temperature, has been used. The computational procedure of the data requires the following steps: to carry out the grid files of multi-annual mean precipitation at each time-stamp (01, 07, 13, 19), for every day of the year (1-366) over the interval 1975-2010;  to construct the grid files containing the spatial interpolation of the anomalies related to multi-annual mean of hourly  (4 time stamps), for every day of the 1975-2010 interval;  the final 6-hour gridded precipitation dataset was obtained by combining the two data sets previously mentioned. The Residual Kriging method was used with potential predictors derived from the SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model) and CORINE Land Cover product. For interpolating the residuals of the regression model, three gridding methods were tested: Multiquadratic (MQ), Ordinary Kriging (OK) and 3D Kriging (using time as a third dimension). It was found that the MQ method is the most appropriate. The cross validation was used to establish the model skill.

The results obtained for precipitation have been used in a paper submitted for publication in an international peer-reviewed journal (Dumitrescu, 2013, under review).


2. Understanding the large-scale mechanisms controlling the variability of climate extremes in Romania


The four activities initially planned to be carried out to reach this objective, have mainly been finalised in the reported period, with an exception (due to the budget changes), as presented in the following.


2.1-2.3. Characteristics of spatial-temporal variability of climate/hydrological extremes and link with large-scale climate anomalies


Two activities (2.1 and 2.3) are analysed together in this section due to the fact that they are strong connected and four articles have been prepared (three already submitted for publication in international peer-reviewed journals, see attached Annex 1). The activity 2.1 is related to the identification of linear trends (using the Mann-Kendall test), shift in the mean (using the Pettitt test) and main modes of spatial variability (using EOF-empirical orthogonal functions) of climate extremes (Co activity) and Hydrological extremes (P1 activity). In the reported period, ANM analysed 8 extreme indices computes at all stations with complete data set over the period 1961-2010 as follows: frequency of rain and snow showers-Frploi, Frzap (81 stations; hot index-ITU (87 stations), cold index -IR; frequency of days with dangerous ITU/IR values-FrITU/FrIR; maximum precipitation amount over various intervals (41 stations) and pedological drought for corn crop and winter wheat crop (12 agrometeorological stations). The activity 2.3 is related to analyse the link between the variability of several climate extremes in Romania and large-scale climate anomalies to find an objective justification (e.g. physical associated mechanisms) of behaviours found in 2.1. The results are presented in 2.1a and 2.1b.


2.1. a. General trends and variability modes of climate extremes in Romania

- There is a significant upward trend over the entire country of both stress indices (ITU, IR), with a significant upward shift around the year 1985. This signal is in agreement with the signal related to mean temperature and other temperature extremes analysed in previous studies. The shift in the mean of the two thermal stress indices induced changes in the relative frequency of their extremes (dangerous values for human health): increase in FrITU and decrease in FrIR.


- The main variability mode of both indices exhibits the same sign over the entire country, with some spatial differences, suggesting the existence of a large- scale mechanism responsible for this behaviour. The second mode, exhibits a dipole structure and suggests the influence of the regional

factors (e.g., the Carpathians), more pronounced for the IR index.


- The frequency of rain showers in Romania exhibits a significant increasing trend in spring and summer over almost the entire country that is in agreement with the increase in the frequency of Cumulonimbus clouds, showing an increase of the atmospheric instability. However, this result is not in agreement with the trend of frequency of other extreme precipitation indices (e.g. frequency of very wet days) presented in the previous report (2012) and in a paper under review (Busuioc et al., 2013), showing no significant linear trend. This suggests a change in precipitation characteristics, mainly to more frequent heavy precipitation recorded in short time. This conclusion is also proved by the trend in maximum precipitation amount recorded in 5 minutes, calculated in the reported period from time series derived from the pluviograms (41 stations). These results are also in agreement with other regional studies explaining that warmer climates, due to increased water vapour, lead to more intense precipitation events, even when the total annual precipitation is slightly reduced. The amount of moisture in the atmosphere, which is governed by the Clausius– Clapeyron (CC) equation, is expected to rise much faster than the total precipitation amount, which is governed by the surface heat budget through evaporation. For Romania, this aspect will be analysed in more detail in the next reporting periods (2014).


-  Regarding the pedological drought, expressed by soil moisture reserves over the 1970-2012, the respective time series at 12 stations (from 34 available) have been analysed in the reported period. The data regarding the rest of stations will considered in the next period (2014). The frequency of droughty months and drought duration over the vegetation period (April-September) has been also analysed. A slightly increasing trend of the frequency of droughty months at all analysed stations for the not irrigated corn crop has been revealed. During the peak water demand period of corn crop (July-August) and winter wheat (May-June), the soil moisture content  was generally located in low and very low limits over extended periods.


2.1.b. General trends and varianbility modes for hydrological extremes

To highlight the dry periods and theirs severity on the Romanian watersheds, the standardized flow index was calculated (Standardized Flow Index - SFI), expressing the hydrological drought. The SFI index is based on a similar procedure as for standardized precipitation index (SPI) but the mean monthly flow series in natural regime are used, instead of monthly total precipitation amounts. The change points in the seasonal SFI have been analysed using the Pettitt test. Significant downward shifts (5% level) in summer and autumn around ‘80 years have been identified for the Jiu, Vedea and Ialomita river basins. For other river basins (Cris, Mures Timis, Olt, Arges and Siret), a 10% statistical significant downward shift between 1963 and 1968 was found. The EOF techniques highlighted that the first two spatial variability modes are similar to those found from precipitation (see 2.1.a.): a monopole structure for EOF1 and a dipole structure for EOF2.


2.2. Homogeneous areas with respect to extreme variability


2.2.1. Climate extremes

Various seasonal temperature and precipitation extreme indices (reported in 2012) have been analysed to identify homogeneous regions for each of these parameters, in terms of their variability characteristics. These indices are: frequency of very warm/wet days, maximum duration of intervals with consecutive very warm/wet days and maximum duration of dry (no rain) intervals. The cluster analysis has been used to reach this objective based on the STATISTICA 8 software. Two clustering methods have been tested (tree clustering and K-means clustering), including various version for each of them (e.g. simple connection, mean connection, Ward method). The Euclidian distance and City block (Manhattan) have been tested to express the distance between e the cluster elements. The homogeneous regions have been identified for each season/index. Even if some similarities have been revealed between them, differences were observed in terms of the number of selected regions and their spatial coherence. The number of homogeneous regions ranges between 4 and 8. Finally, the Ward-Manhattan method has been found to be the optimum one. When the seasonal standardized anomalies are used instead of the original values, the homogeneous zones are more spatial coherent and they are quite similar for all seasons (winter, spring, summer, autumn), suggesting to use the standardized seasonal anomalies together to identify a single clustering for each index that will be carried out in the next reporting period.


2.2.2. Hydrological extremes

Based on the characteristics of four hydro-climatic indices, a new index MEOFIA (Ensemble indices of development multivariate EOF) is proposed. Homogeneous areas are identified based on distribution of monthly discharges recorded at 27 gauging stations in the period 1931-1998 as well as on rotated EOFs for a set of Palmer type indices. In this way, it is highlighted the capacity of each index to capture the spatial-temporal distribution features of hydro-climatic extreme events over the Romanian territory, as well as the ability of their ensemble to maximize the overall specific information for each season.

Finally, the most important genetic factor of extreme events is analysed, mainly the sequence of circulation types on Atlantic-European sector (-50V, 40E, 30N, 65N), in connection with the hydrological-precipitation regime over the Romanian territory, by using a Markov chain model with hidden states (NHMM). The synoptic analysis of typical situations for drought and excessive wet periods (rainfall generating high flow) revealed a dipolar pattern of the sea level pressure (SLP) influencing the hydro-climatic regime in South-Eastern Europe. By conditional analogy method, the SLP patterns associated to drought and non-drought states in Romania have been selected.


2.3. Large-scale mechanisms controlling the variability of climate extremes using CCA techniques


By CCA, the optimum linear combination for two multi-dimensional vectors (predictand – the spatial vector of climate extremes and predictor – the spatial vector of certain large-scale variables) and pairs of patterns are selected so that their associated time series be maximum correlated. By construction, the CCA method allows a physical interpretation of the mechanism controlling the variability of the regional climate parameters. In the reported period, a justification for the trend of rain shower frequency (Fr-RS) and thermal stress indices (ITU and IR) presented above (2.1.a.) is analysed by applying the CCA between these predictands and anomalies of various large-scale predictors, selected in terms of each index specific. The work reported in this activity has been carried out by CO (ANM).


For the Fr-RS, the lftx4-Best Lifted Index (4-layer) has been selected as main predictor. Lifted Index LI provides an estimation of the thermodynamic instability in the free atmosphere and represents the difference between environmental temperature at 500mb and the temperature which a parcel will achieve if it is lifted dry-adiabatically from the surface to its lifted condensation level (LCL) and then moist-adiabatically to 500mb. The lftx4 refers to the lowest value of LI, computed using parcels from four different layers near the ground surface. Other predictors refer to large-scale sea level pressure (SLP) and precipitable water (PW) considered as ingredients in generating convective rains. The seasonal (spring and summer) Fr-RS recorded at 81 meteorological stations over 1961-2010, uniformly distributed over Romania have been used as predictands.The CCA supplies the optimum large-scale mechanisms responsible for changes in the frequency of rain showers in Romania, represented by the first CCA pair, showing a strong connection and accounting for an important fraction of the observed variance of the rain shower frequency. The lftx4 pattern exhibits a dipole structure with a nucleus of thermodynamic instability centred over Romania. The combination of more ingredients (lftx4, SLP, PW) accompanying the rain showers' generation, leads to a stronger connection. Results reveal that the dynamic factor given by the surface circulation (represented by the SLP anomalies) is an important additional ingredient for spring, while the precipitable water is important for summer, showing the mixed character of rain showers in spring (large-scale combined with convective) and the strong convective character in summer. The time series associated with this CCA pair exhibit a significant increasing trend, justifying from physical point of view the observed changes in the frequency of rain showers in Romania. These results can further be used in developing skilful statistical downscaling models to project future changes in the rain shower frequency at local scale in Romania, which is not possible from the direct RCM simulations. These findings are included in a paper prepared to be sent for publication in a peer-reviewed journal.


The main large-scale mechanism responsible for the spatial and temporal behaviour of the two stress indices is given by the first CCA pair (CCA1) derived from the connection between the combination of T850 with SLP (thermodynamic and dynamic factors) for IR and the combination T850-SH700 (thermodynamic factors) for ITU. The connection is strong, showing that these results can further be used in developing skilful statistical downscaling models (SDMs) to project future changes in the thermal stress indices in Romania (including their extremes) at local scale, producing high resolution information useful in impact studies on human health and other domains. The time series associated the CCA1 exhibits a significant increasing trend with a shift similar to those found for both indices (2.1.a). This result shows that their increasing trend is mainly explained by the increasing trend of the temperature at 850 hPa covering Romania simultaneously with an increasing trend in zonal circulation (IR) and an increasing trend in specific humidity at 700 hPa (ITU). The results associated with the understanding of the mechanisms controlling the thermal stress index variability has been awarded  as “the best presentation”  for Andreea Dobrinescu at the ANM Annual Stientific Meeting (November 2013).


2.4. Connection between the variability of climate extremes in Romania and large-scale blocking circulation indices


The work associated with this objective was carried out mainly by the UB-FF (P2) partner. An additional technique (compared to those presented in previous report and Busuioc et al., 2013) to justify the increasing trend of the frequency of winter extreme high temperatures over Romania (Frtmax90) was used by analysing the connection between the time series associated to the Frtmax90 EOF1 (PC1) and indices associated to various already established large-scale circulation patterns such as: North Atlantic Oscillation (NAO), West Pacific (WP), East Atlantic (EA), Scandinavian patterns (SCA) and blocking activity in the (20°W-70°E) sector.

It was shown that the East Atlantic Oscillation controls a significant part of inter-annual extreme high temperature variability over Romania via advection of warm air from the west. In addition, a strong relationship between blocking activity and frequency of extreme high temperature events in Romania was found. High blocking activity in the (20°W-70°E) sector is related with relatively strong advection of cold air over the country during winter. On the other hand, low blocking activity in the same sector is related with weak advection of relatively cold air in the region. Moreover, the blocking frequency in this sector is modulated mainly by the East Atlantic Oscillation. However, the dominant pattern of Frtmax90 index from Romania also showed weak connection with the NAO, SCA, and WP. This is not so surprisingly, especially in the context of recent findings of other paper, which show that NAO centers of action are influenced by the phase of the EA and SCA patterns. These results are included in an article submitted for publication to an international peer-reviewed journal (Rimbu et al., 2013, under review).


Similar work was carried to explain the mechanism controlling the variability of summer Frpp90 (frequency of very wet days). In this respect, it was found that enhanced blocking activity over the 0°E-40°E sector is associated with high frequency of summer extreme precipitation events due to enhanced synoptic scale activity associated to advection of relatively high potential vorticity from northeast toward Romanian region as well as with enhanced synoptic scale activity in the Mediterranean region. Enhanced blocking activity in the 50°E-70°E sector favours an eastward extension of Atlantic jet and the associated instabilities are related to extreme precipitation events over large parts of central and Eastern Europe. It was argued that these two distinct blocking patterns explain a substantial part of extreme precipitation variability in Romania during summer.


Some preliminary results have been obtained by CO (ANM) with respect to the link between the annual values of the mean/maximum/minimum discharge at Birlad hydrological station (representative for the selected Birlad pilot basin) and

and Palmer index (expressing the hydrological drought). The analysis has been carried out for the interval 1961-2010. It was found a significant positive correlation of 0.63 for mean discharge and of 0.54 for maximum discharge. A weaker correlation (but still statistical significant) has been revealed for the minimum discharge.


3. Developing of statistical downscaling models

Two activities have been planned within this objective both of them carried out by CO: developing of high resolution (6 hours-time step, 1kx1km-spatial resolution) statistical downscaling models for selected hydrological basins (3.1); developing of statistical downscaling models for seasonal extreme indices over the entire country at station scale (3.2). In the following, the results obtained in the reported period are summarised. In 3.1 are used the gridded temperature and precipitation presented at 1.2.


3.1. Developing of high resolution (6 hours-time step, 1kx1km-spatial grid) statistical downscaling models


Within this activity, three types of models are developed, as following:

- Conditional stochastic model (CSM) to generate 6-hours precipitation at a spatial grid of 1kmx1km over a selected Hydrological pilot basin (Birlad in the reported period). This model is a combination between the statistical downscaling model based on CCA and a Markov chain model of the first order. In the CLIMHYDEX project, an improved version of this model was developed compared to those developed by CO in the FP6 EU ENSEMBLES project: high spatial and temporal resolution (6-hour time step, 1kmx1km spatial resolution); generation of precipitation time series at all grid points in a single model run. The precipitation either occurs or it does not (the two states) and the conditional probability of precipitation occurrence depends only on the occurrence on the previous 6- hours interval. There are two parameters describing the precipitation occurrence process: the transition probability p01, the probability of a wet interval following a dry interval, and p11, the probability of a wet interval following a wet interval. As a wet interval, the case of 6-hour precipitation amount > 0.1 mm is used in this study. The variation of precipitation amount on wet 6-hour interval is described by the two gamma distribution parameters. In the reported period, the software packages related to this model have been modified to solve the project objectives presented above. These software packages have been tested with observed data at some stations placed in Birlad basin showing that the software packages correctly perform.


- Linear model based on CCA, developed in previous studies for seasonal averages at station scale, has been adapted to be developed at same spatial and temporal resolution over the Birlad basin as presented above for the CSM. The NCEP/NCAR reanalysis data of temperature at same spatial and temporal resolution over the aria (20E-30E, 40N-50N) has been considered as predictor. In reported period, the predictor and predictand files has been updated to the standard format needed for the model input and then standardised versions for all 6-hours data have been considered together. To remove the serial correlation of data, it was decided to stratify the predictor/predictand data on each hour (1,7,13,19) and season.


-Statistical model based on neural network have been developed for 6 hours-temperature at 8 stations placed in Birlad basin using the same predictor data set as for the CCA model presented above. The model was calibrated for the half of data (randomly selected) and validated over the rest of data. The results were very good showing correlation coefficients between observed and estimated data ranging between 0.77 and 0.97.


3.2.  Developing of statistical downscaling models for seasonal extreme indices

This activity, carried out by the CO (ANM), refers to the statistical downscaling models (SDMs) mainly based on CCA (canonical correlation analysis). As a novelty, in this project, this type of SDM is applied to the combination of various climate extremes as predictand by using the results presented in previous CLIMHYDEX report and Busuioc et al. (2014a). In the present report, as a test, the combination between the winter Frpp90, Dmax90 and Dmaxpp0 over a homogeneous region placed in south-western Romania (see 2.2.1) has been considered. As large-scale predictors, the SLP, T850 and SH700 have been used, either alone or in combination. It was found that the SLP is the best predictor for the simultaneous variability of the analysed precipitation extremes, the combination with the T850 gives a lower SDM skill but it is still skillful. The highest skill was obtained for Frpp90 for the entire region and the lowest one for the Dmaxpp90, with a skillful SDM only for some stations.

Another test was carried out for the thermal stress indices (ITU and IR) over the entire country. It was found that for both indices the CCA model is skillful over the entire country. T850 is a good predictor for both indices. The combination T850-SLP (for IR) and T850-SH700 (for ITU) gives a higher skill only for IR. These results have been presented at two scientific conferences (Dobrinescu and Busuioc, 2014a, b).


For the first time in Romania, a SDM based on artificial neural networks from the Multi-Layer Perceptron family was tested for various seasonal temperature and precipitation indices. Even if reasonable results have been achieved for the temperature indices, this type of SDM is mainly recommended to be used for daily/sub-daily time series, due to quite short seasonal time series used for the model calibration. This activity will continue in 2014.


4. Identify and assess the spatial and temporal characteristics of flood and hydrological drought phenomena in selected river basins


Within this objective, three activities were planned, all made by partner P1. The synthesis of results is presented below.


4.1. Spatial characteristics extrapolation of extreme hydrological events


To achieve the scenarios which analysis the hydrological response of the watershed according to land use change, the SWAT model will be applied. In this respect, the hydrological data and physical-geographic characteristics for the Moneasa-Ciurea representative basin were prepared. The daily discharges were loaded in electronic format for the period 1985-1995 and for the period 1995 – 2005 this activity is in progress. For Moneasa basin a total of six stations and two rainfall gauging stations are used, while for Ciurea basin, the recorded data were collected from five gauging stations and one rainfall post. Also, for this last representative basin, detailed Digital Terrain Models using topographic information at scale 1: 5,000 was created. In this case, the contours are of 2.5 m spacing.


To identify dependencies between climatic elements, morphological and physic-geographical catchment characteristics and the hydrological extremes, the monthly minimum and maximum discharges from gauging stations of the Crisul Alb and Barlad basins are analysed.


4.2. Analysis of solid flows to the input and output of Tungujei reservoir during hydrological extremes events


For this stage, an analysis regarding the evolution of liquids and solids (in suspension) discharges transited through the Tungujei reservoirs is stipulated. Thus, analysing the first hydrological parameter, during the period 1988 - 2012, it is found that their volumetric evolution is complies with the type of dominant hydrological regime in the area and/or the operating mode of reservoir. In detail, in years with poor hydrologic flow regime the priority is accumulation into the reservoir, while during the excess hydrological regime the passing flow by reservoir is preferred.

Regarding the analysis of solid flow, this is focused on determining the transit of sediments upstream and downstream of reservoir and also on the knowledge of the sediment accumulation. The analysis of this hydrologic parameter evolution shows silt retention in basin as a priority, confirming the role of sediment trap for all lakes. These analyses will continue with evaluation of sediment volume size on corresponding periods of extreme hydrological regime phases.


4.3. The variability of extreme hydrological events in Romania and the relationship with the large-scale atmospheric circulation


The most important genetic factors of considered extreme events are analysed: sequence of circulation types on Euro-Atlantic sector (50 V - 40 E, 30 N - 65 N) in interrelation with hydro-rainfall regime in Romania through an inhomogeneous Markov chain model with hidden states (NHMM). Interestingly, the analysis of typical synoptic situations for excessive periods of drought and wet (heavy rainfall generating high flow rates) led to the identification of a dipolar structure of the distribution of surface atmospheric pressure on the Euro –Atlantic sector, influencing the south east Europe hydro-climatic regime. By conditional analogy method were selected also the baric characteristic situations, of drought and non-drought states in Romanian area.


Seasonal variability of droughts in the Danube basin and its connection with large-scale atmospheric circulation for the spring season are investigated through a statistical analysis of indices scPDSI and SPEI and of global climate anomalies fields. EOFs analysis was used to study the spatial and temporal variability of droughts in the Danube basin. Analysis of composite maps (corresponding to EOF1) reveals that in spring, dry conditions throughout the basin, are determined by a pattern of sea surface temperature (SST) anomalies characterized by positive anomalies on the east coast of the United States and the west Europe and negative anomalies of SST in the central and northern part of the Atlantic. The connection with the atmospheric circulation is characterized by negative anomalies of geopotential height at 500 mb (Z500) over Greenland and positive Z500 anomalies over the central part of Atlantic extending across Europe. Dry (wet) periods over the west (east) of the Danube basin, on the second mode of variability (EOF2) are related to a regional pattern of sea surface temperature anomalies characterized by negative SST anomalies on the east coast the United States and the Mediterranean Sea, and positive SST anomalies on the western coast of Europe. The relationship with the Z500 anomalies is characterized by a positive anomaly center Z500 over the British Isles and north-west part of Europe and negative anomalies of Z500 in the south of Europe.





5. Development of improved hydrological models at different spatial-temporal scales for the two selected pilot basins


5.1. Analysis of groundwater resources and the exchange between surface water and groundwater


According to the project implementation plan, during the year 2013, the processing of available data in the Crisul Alb basin was made in a format necessary to be used as inputs in the MIKESHE model.  The exchange between the surface water and groundwater is achieved only in areas where Quaternary alluvial deposits are signalled. Consequently, a detailed knowledge of geological and hydrogeological conditions of the groundwater in Crisul Alb basin was the main activity in this period.


5.1.1. Geological factor Influence in water resources distribution

Based on the activities carried out in 2012, 13 lithological columns of representative network of wells and 6 hydrogeological cross-sections through the Vărşand, Cil, Zărand, Ineu, Bocsig, Chişineu Cris alignments were performed. Quaternary alluvial deposits, which are "responsible" for the exchange between surface water and groundwater, occur at north and south of the Crisul Alb, between the towns Varsand and Gurahont.


5.1.2. Hydrogeological considerations on phreatic aquifer

The map of the groundwater piezometric surface was made based on the levels calculated for each piezometric level measured in October 2012 and the absolute values of the Crisul Alb surface water quotas, measured in the same period to the hydrological stations Chisineu Cris, Ineu and Gurahont. From piezometric map analysis results that the general direction of groundwater flow is from SE to NW, with local changes of flow direction near the Crisul Alb River. Regarding the relationship between surface water and groundwater, the Crisul Alb River drains the groundwater of the analyzed area except a small area, located immediately upstream of Ineu, where the river feeds the aquifer. In the reported period, the data regarding the evolution of piezometric levels in the wells of National Hydrogeological Network was analysed. The results show a clear trend of increasing in the first 4 months, then a decreasing one until September, followed by an upward until the end of the year. Evolution of piezometric levels for wells having a continuous series of data will be analysed in the next stage.


5.2. Calibration of hydrological models with distributed and concentrated parameters in selected river basins


 5.2.1. Calibration of the hydrological model with concentrated parameters in the Barlad riverbasin

To estimate the impact of climate change and variability on flow regime in Barlad river basin, the CONSUL model with concentrated parameters was used. The model calibration was made by the flow simulation during 1975 - 2010 for the 56 sub-basins. Average precipitation and air temperature (used as input data in hydrological model) for each sub-basin was performed using a pre-processing program of meteorological data from original rectangular grid nodes (e.g. 1kmx1km grid points presented in 1.2) corresponding to the Bârlad river basin, averaging being achieved as weighted values based on the representativeness of these nodes for each analyzed sub-basin.


To determine the initial values of the CONSUL model parameters, the generalization relationships of these parameters depending on morphometric characteristics of sub-basins or river sectors were used. Calibration of model parameters was performed in two stages: (i) individual (based on the 25 rainfall-runoff events) and (ii) globally (by continuous flow simulation on considered calibration period).


5.2.2 Calibration of the NOAH model with distributed parameters in the Barlad river basins

For detailed simulation of the hydrological processes in the Barlad sub-basins, the NOAH hydrological model with distributed parameters is used. This model is composed of several sub-models that simulate the energy and water balance in the soil, evapotranspiration, snow cover evolution, surface hypodermic and basic flow, runoff propagation in river beds, etc.


In this reporting period, the NOAH hydrological model with distributed parameters was calibrated for Barlad River Basin using the historical data supplied by CO (activity 1.1) and dependencies between model parameters and physical-geographical characteristics of the basin. NOAH distributed hydrological model parameters was configured in the basin Bârlad to simulate rainfall-runoff processes at a spatial resolution of 1 km, with a resolution of 100 m for the simulation of water propagation on slopes and in the river, respectively, a computing time step of rainfall-runoff processes of 30 min.



Publications (including under review)


1. Adler, M.J., Corbus, C., Mic, R., Chelcea, S., Borcan, M., 2013. Climate Change and Its Impact in Water Resources, Belgrade AIHS Volume (under print), Conference Proceedings of Climate Variability and Change – Hydrological Impacts, Belgrade, October 2013


2.Adler, M.J., Corbus, C., Mic, R., Chelcea, S., 2013. Climate Change and its Impact in extremes, Proceedings of the Annual Conference of the University of Bucharest, Phaculty of Physics, 21 June, 2013


3) Birsan, M.-V. and Dumitrescu, A., 2013. Snow variability in Romania in connection to large-scale atmospheric circulation. Int. J. Climatol.. doi:10.1002/joc.3671

4) Busuioc A., Dobrinesc, A., Birsan, M-V., Dumitrescu, A., Orzan, A., 2013. Spatial and temporal variability of climate extremes in Romania and associated large-scale mechanisms. Int. J. Climatol (under review)

5) Chelcea, S.,  Adler, M.J., 2013. Legătura dintre seceta meteorologică şi seceta hidrologică pe râul Bârlad, Hidrotehnica, vol. 58, 4-5, pag. 22, ISSN 0439-0962.

6) Cheval S., 2013. The Standardized Precipitation Index – An Overview. Int. J. Climatol (under review)

7) Cheval S., Busuioc,A.,  Dumitrescu, A.,  Birsan, M-V: Spatiotemporal variability of the meteorological drought in Romania using the Standardized Precipitation Index (SPI),. Climate Research (under review).

8) Dumitrescu, A., 2013.  Spatio-temporal interpolation of the hourly precipitation values over Romania for 1975-2010, International Journal of Geographical Information Science (under review)

9) Chelcea, S., M. Ionita, 2013: Extreme value analysis of the Barlad river time series, Ovidius” University Annals of Constanta - Series CIVIL ENGINEERING (2013) journal, Constanţa – in print.

10) Rimbu, N., S. Stefan, C. Necula, C. Grigoras and A. Orzan, 2013: Winter temperature extremes variability in Romania and its relationship with large-scale atmospheric circulation. Theoretical and Applied Climatology (under review).

11) Matreata, S., Baciu, O., Apostu D., Matreata M., 2013, Evaluation of the Romanian flash flood forecasting system – case study in the Calnau river basin. Die Bodenkultur - Journal for Land Management, Food and Environment. 64. Band/Heft 3-4/ISSN 006-5471

12) Stefanescu, V., Stefan, S., Georgescu, F., 2013: Spatial distribution of heavy precipitation in Romania between 1980 and 2009. Meteorological Application. Article first published online: 2 JUL 2013, DOI:10.1002/met.1391


Dr. Aristita Busuioc

Project Director