Climatologic adjustments to monthly precipitation in Romania

Transcription

1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 31: (2011) Published online 22 February 2010 in Wiley Online Library (wileyonlinelibrary.com) DOI: /joc.2099 Climatologic adjustments to monthly precipitation in Romania Sorin Cheval, a *Mǎdǎlina Baciu, a Alexandru Dumitrescu, a Traian Breza, a David R. Legates b and Viorel Chendeş c a Department of Climatology, National Meteorological Administration, Bucharest, Romania b College of Earth, Ocean, and the Environment, University of Delaware, Newark, Delaware, USA c National Institute of Hydrology and Water Management, Bucharest, Romania ABSTRACT: This article contributes to better understanding of the precipitation data, analyzing several measurement errors in Romania. Based on the influence of wind speed, solid precipitation rate, and wetting losses, we adjusted the monthly amounts registered at 159 weather stations through The results emphasize distinct temporal and spatial distributions of the adjustment magnitude. In general, the correction factors increase with altitude and they have high values in the cold season, as they highly depend on wind speed and solid precipitation percentage. In Romania, bias corrections increase monthly precipitation by less than 10% from June to September, by 10 20% in the transition months, and by higher values during the winter. Copyright 2010 Royal Meteorological Society KEY WORDS precipitation adjustment; Romania; hydrologic modeling Received 3 March 2009; Revised 22 December 2009; Accepted 6 January Introduction and objectives A large number of environmental and economic applications require precipitation as a main input. Any crop forecast, irrigation strategy, or urban sewage plan must account for the available water. Therefore, the accuracy of precipitation measurements and estimations is crucial for the results of such models. At the same time, various factors bias the precipitation amounts measured at rain gauges, introducing systematic and unsystematic errors. The systematic errors include the geographical conditions of the site and the accuracy of the rain gauge. The unsystematic ones refer to human and technical errors, such as inaccurate measuring and recording procedures, or random flaws in the devices. As a result, the gauge-based precipitation amounts almost always underestimate with a magnitude depending on the strength of the influencing factors (Legates, 1987; Adam and Lettenmaier, 2003). For example, in mountainous regions, the high wind speed, frequent snowfalls, and sparse meteorological network can determine noteworthy differences between the measured precipitation amounts and the actual one (Guan et al., 2005). The Carpathian Mountains occupy one third of the Romanian territory and have a major influence on the national environment and society, increasing the interest for accurate precipitation information in the area. * Correspondence to: Sorin Cheval, Department of Climatology, National Meteorological Administration, Bucharest, Romania. sorin.cheval@meteoromania.ro Many scholars have documented the topic during the last decades (Sevruk, 1982, 1984; Legates, 1987; Legates and DeLiberty, 1993; Yang et al., 1998; Allerup et al., 2000; Førland and Hanssen-Bauer, 2000; Bogdanova et al., 2002; Ye et al., 2004; Legates and McCabe, 2005; Sevruk et al., 2009). Through , World Meteorological Organization (WMO) conducted two international programmes dedicated to the accuracy of liquid and solid precipitation measurement, thereby acknowledging the importance of the topic (WMO, 1982, 1998). Most studies focused on mountainous and cold regions because the solid precipitations accentuate the underestimation. This investigation refers to the monthly precipitation amounts in Romania, and it aims to estimate the discrepancy between the measured amounts and the precipitation calculated while taking into account the influence of some bias factors. Daily data may increase the accuracy, but they do not affect the general characteristics that we present here (Legates et al., 2005). 2. Methodology The analysis focuses on monthly data from 159 weather stations over Romania and covers the interval (Figure 1). All the variables were measured at the same site, in WMO standard conditions. The precipitation measurements use Hellmann gauges, placed at 1.5 m above the ground, with an orifice area of 200 cm 2, and not wind shielded. A metal cross is installed in the Hellmann gauges run by the Romanian Met Service to prevent Copyright 2010 Royal Meteorological Society

2 CLIMATOLOGIC ADJUSTMENTS TO MONTHLY PRECIPITATION 705 Figure 1. Geographical distribution of the weather stations and altitudinal background of Romania. snow to be blown out of the gauge. WMO (1998) provides more details about the performance of the Hellmann gauges compared with other types. Sevruk et al. (2009) reported that the Hellmann unshielded gauges catch only 20% of the snow measured by the Double-Fence International Reference (DFIR) gauges at wind speed >6 m/s. The wind speed is measured by anemometers positioned at 10 m height, any mercury barometers situated inside weather station buildings supply air pressure data. Hair hygrometers are placed in the thermometer screens and provide relative humidity data. To get a complete dataset and avoid the unsystematic biases, we used climatol contributed package (Guijarro, 2004) to R statistical system (R Development Core Team, 2008) to fill the missing data, and we applied no homogenization procedure. The adjustment of monthly precipitation utilizes the equation proposed by Legates (1987), from which we have deleted the evaporation loss term once we agree that, in midlatitudes, they are significantly lower than the other factors (Sevruk, 1982; Adam and Lettenmaier, 2003) or it can be assimilated to wetting loss (Ye et al., 2004): P a = (1 R)K r (P g + P wr ) + RK s (P g + P ws ) (1) where P a is the adjusted precipitation, P g is the measured precipitation, P w represents the wetting losses, K is the factor for wind-induced losses, and R represents the proportion of precipitation that falls in solid form. The subscripts r and s stand for the liquid and solid components of each quantity, respectively. The meteorological data used to calculate P a include the measured precipitation (monthly amounts in millimeters and number of days), mean atmospheric sea-level pressure (kpa), air and dew point temperature ( C), mean air humidity (kpa), mean wind speed (m/s), and logarithmic coefficient of wind speed profile. There are also nonmeteorological data included in the equation: sheltering coefficient, average vertical angle of the obstacles, and roughness length at gauge site (m). The following paragraphs refer to the procedure for computing the wind-induced losses (a), wetting losses (b), and solid precipitation (c). (a) Wind-induced losses (K) represent the dominant meteorological factor that affects the precipitation (Legates et al., 2005; Yang et al., 2005), and it is computed separately for rainfall (K r ) and solid precipitation (K s ), respectively. K r = 1 + (0.011 m 2 u w2 hp ) (2) where m u is the coefficient of transfer from standard atmosphere to its actual condition and w hp is the mean

3 706 S. CHEVAL et al. wind speed at the gauge orifice (m/s). m u = p t p p e p (3) where p is the mean atmospheric sea-level pressure (kpa), t is air temperature ( C), and e p stands for mean air humidity (kpa) t d e p = e t d (4) where t d is the dew point temperature ( C). w hp = k L w p m (5) where k L is the logarithmic coefficient of wind speed profile, w p is the mean wind speed at anemometer height (m/s), and m is the sheltering coefficient calculated as (90 α)/90, ranging between 0 for completely obstructed horizon and 1 for completely free horizon, and α is the average vertical angle of obstacles (degrees). The values of m were estimated based on photogrammetric images provided by the National Agency for Cadastre and Land Registration ( k L = log(h/z 0) (6) log(h a /z 0 ) where h is gauge orifice height (m), H a is height of the anemometer (m), and z 0 is roughness length (m) at gauge site. For the gauges from the weather stations administrated by the Romanian Meteorological Administration, h is 1.50 m and H a is 10.0 m. The values of z 0 were estimated based on photogrammetric images. (b) Wetting losses ( P wr ). The amount of wetting loss depends on the type of precipitation, the frequency of gauge emptying, the geometry of the gauge, and its materials (Legates, 1987). P wr = a M (7) where a is the empirical coefficient of average wetting loss per precipitation event and M is the number of precipitation days in the month (Sevruk, 1984). The values of a range between 0.15 and 0.3 (Legates, 1987), and we used the maximum value. (c) Precipitations falling in solid form strongly influence the measured amounts (Fuchs et al., 2001; Adam and Lettenmaier, 2003). For wind speed ranging between 2.5 and 4 m/s at gauge height, the correction factor can be 2 3 times higher for solid precipitation than for liquid ones (Fuchs et al., 2001). The proportion of solid precipitation (R), wind-induced losses (K s ), and wetting losses ( P ws ) were used for snowfall adjustment: 100 K s = [ w hp (whp 2 )] (8) P ws = 0.15 M (9) The solid and liquid precipitations were differentiated based on the formula from Legates (1987): R = t (10) where R is the percentage of monthly precipitation falling in solid form (0 R 1) and t is the average monthly air temperature ( C). The proportion of monthly precipitation falling in liquid form is (1 R). For interpolating the precipitation data, we used the kriging with external drift within R environment; the auxiliary predictors were altitude, latitude, and longitude (Pebesma, 2004; R Development Core Team, 2008). Water balance methodology was applied to validate the results (Legates and McCabe, 2005). For two river catchments placed in different geographic conditions, we compared the surface runoff (SR) resulted from measured discharge (SR) to the SR derived from measured (SRM) and adjusted precipitation (SRA), respectively. The monthly discharge data were provided by the Romanian National Institute of Hydrology and Water Management. We calculated the SRM and SRA for two hydrologic catchments using the Thornthwaite monthly water balance model (McCabe and Markstrom, 2007), obtained via Internet from U.S. Geological Survey. The inputs to the model are air temperature, precipitation, latitude, runoff factor, direct runoff factor, soil moisture storage capacity, rain temperature threshold, snow temperature threshold, and maximum snow melt rate of the snow storage. Further, the comparisons SR SRM and SR SRA were investigated through four indicators: Pearson correlation coefficient (R 2 ), annual averages (mm), root mean square error (RMSE), and mean bias error (MBE). 3. Results and validation The monthly precipitations fluctuate with a magnitude M a that depends temporally and regionally on the variability of the influencing factors. Its relative value was calculated as percentage of the adjusted precipitation (P a ) from the measured amounts (P g ): M a = P a P g P g 100 (11) For most situations, the adjusted monthly precipitations are less than 20% higher than the measured amounts, and in almost 12% of the cases M a is above 30% (Figure 2). The highest values occur at mountain stations and they overpass 100% through November April (Table I). The proximity of the Black Sea, with low roughness and strong winds can explain the record value registered at Sulina in August. Figure 3 summarizes the spatial distribution and temporal variations of M a in Romania, with weather stations organized by altitude, each row representing one weather station. Apparently, the monthly precipitations are more

4 CLIMATOLOGIC ADJUSTMENTS TO MONTHLY PRECIPITATION 707 Figure 2. Relative differences between the adjusted and measured monthly precipitation in Romania. underestimated at higher altitudes and during winter, consistent with the findings of other studies (Fuchs et al., 2001; Yang et al., 2005). The diverse topography and meteorological influences determine a considerable interannual variability of the correction in all regions. Since the wind-induced losses are the most significant bias (Sevruk, 1982; Michelsohn, 2004), the local topography can influence the adjustment accordingly, overcoming the elevation control. This can explain both the major differences between P a and P g recorded at certain low levels, and the small values noticed at some high elevations. The wind speed strongly controls the difference P a P g all the year, while the influence of the altitude and proportion of solid precipitation are significant only in some specific months. It is noteworthy that the bias of wind speed and solid precipitation tends to a seesaw pattern: while the former is higher during the cold season and lower by summer, the latter has the lowest values through December February (Figure 4). We assume that the wind speed instability specific to the summer weather regime over Romania contributes to a small decreasing of the r values. However, the proportion of the solid precipitation is more significant for the measurements during the transition seasons, when the alternations from the liquid to solid aggregation form are frequent. The results can support the amendment of precipitation maps according to the correction values. For example, Figures 5 7 present the interpolation of the M a values for the wettest and the driest months in Romania (June and October), and for the annual amounts. The good correlation between M a and wind speed stimulates the very well regional fitting of the two variables. One could expect that more precipitation events in a month increase the probability of significant deviations of Table I. Maximum relative differences between the adjusted and measured monthly precipitation. Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Ma (%) Station Ceahlau Toaca Ceahlau Toaca Ceahlau Toaca Vf. Omu Vf. Omu Tarcu Tarcu Sulina Ceahlau Toaca Ceahlau Toaca Ceahlau Toaca Ceahlau Toaca Altitude (m)

5 708 S. CHEVAL et al. Figure 3. Monthly deviations of the adjusted precipitation against measured amounts. the adjusted amounts to the measured ones. Nevertheless, in the wettest month of the year, namely June, M a surpasses 20% of the measured precipitations only in few mountainous spots, while similar values are much more extended in October (Figures 5 and 6). The annual adjustments sum up the monthly values, so that they are more homogeneous over the territory (Figure 7). Commonly, the few spatial outliers are caused by the complex terrain of the Carpathians, with rapid alternation of peaks, valleys, and depressions. Site errors the rain gauge is not representative for the surroundings may also occur (Michelson, 2004), and influence the interpolation. The corrections applied to the measured amounts induce substantial modifications in certain areas, and higher resolution views underline better the effect on precipitation interpolation. Figures 8 and 9 highlight the distribution of the annual precipitation amounts in a mountainous area in the Romanian Carpathians, retrieved from measured and adjusted data, respectively. There are major changes in the quantitative distribution of the precipitation, but the spatial pattern remains stable. Based on the amounts measured at the weather stations, the area of interest receives mm/year, while the adjusted average amount reaches mm/year. To validate the results, we have compared the SR resulted from measured discharges, and the SR derived from measured (SRM) and adjusted precipitation (SRA), respectively, in the hydrologic basins Arieş and Scheia (Figure 10), within different geographic backgrounds.

6 CLIMATOLOGIC ADJUSTMENTS TO MONTHLY PRECIPITATION 709 Figure 4. Linear correlation coefficients (r) between the altitude, wind speed, proportion of solid precipitation, and the relative difference P a P g. Figure 5. Average differences between the adjusted and measured precipitation June. Arieş watershed lies over 641 km 2, between 500 and 1800 m a.s.l., mainly in a mountainous environment, and highly afforested (cca. 70%). Scheia basin has 29.7 km 2, in a hilly area, between 300 and 500 m a.s.l., quite homogeneous as land use (approximately 80% arable land). Different climatic patterns describe the two areas. Excepting very few months, the Arieş catchment is cooler and wetter than Scheia throughout the year. Tables II and III illustrate the comparison between different SR instances, as exemplified by correlation, average differences, RMSE, and MBE. For all these indicators, the adjusted precipitations lead to runoff values (SRA) closer to the ones resulted from the measured discharges (SR) than in the case of measured precipitations (SRM). At the same time, it seems that the model returns better outputs in small and homogenous

7 710 S. CHEVAL et al. Figure 6. Average differences between the adjusted and measured precipitation October. Figure 7. Average differences between the adjusted and measured precipitation annual amounts.

8 CLIMATOLOGIC ADJUSTMENTS TO MONTHLY PRECIPITATION 711 Figure 8. Average annual precipitation as derived from measured precipitation amounts in the Curvature Carpathians ( ). Table II. Pearson correlation coefficients (R 2 ) between SR and SRM, and between SR and SRA, respectively. Average values of SR, SRM, and SRA. Hydrographic R 2 Average (mm) catchment SR SRM SR SRA SR SRM SRA Arieş Scheia P g, up to the situation when the average SRA is remarkably close to SR, for Scheia catchment (Table II). Further, the RMSE and MBE values are considerably lower for the differences SR SRA than to SR SRM (Table III), confirming that the P a enforce better results of the Thornthwaite water balance model than the P g. Although finer tuning of the model or using more complex approaches could provide more accurate results, such an enterprise is beyond the goals of this study. Table III. Root mean square error (RMSE) and mean bias error (MBE) values of the differences between SR and SRM, and between SR and SRA, respectively. Hydrographic catchment RMSE (%) MBE(%) SR SRM SR SRA SR SRM SR SRA Arieş Scheia basins: the values from Scheia basin are all superior to the ones from Arieş. Since adjusted (P a ) and measured precipitations (P g ) are very well correlated, SRA and SRM correlation coefficients to SR have similar values. However, P a enhances notably the absolute modeled SR values compared with 4. Discussions and conclusions The findings of this research are consistent with the results obtained in other studies, concluding that the magnitude of the precipitation adjustment varies considerably intra-annual, owing primarily to fluctuations of wind speed and air temperature (Yang et al., 1998; Ye et al., 2004; Legates et al., 2005). For the studied territory, bias corrections increase monthly precipitation by less than 10% from June to September, by 10 20% in the transition months, and by higher values during the winter. Considering the low but existing influence of some factors not included in this study (i.e. evaporation loss, mixed precipitation), and the use of some expert assumptions (i.e. roughness), the adjusted amounts are reliable, and probably still underestimated. Allerup et al. (2000) argue that precipitation correction in a certain spot can use wind speed data up to 50 km,

9 712 S. CHEVAL et al. Figure 9. Average annual precipitation as derived from adjusted precipitation amounts in the Curvature Carpathians ( ). Figure 10. Geographic positions and relief of the hydrographic catchments: Arieş (A) and Scheia (B).

10 CLIMATOLOGIC ADJUSTMENTS TO MONTHLY PRECIPITATION 713 Figure 11. Differences of the precipitation adjustment calculated in this study and those reported by Legates (1987), for annual (line), June (interrupted line) and October (dotted line) amounts. rain intensity up to 75 km, and information concerning the mixture of snow/rain up to 100 km from the station. In our case, the geographic distribution of the stations, well balanced over the territory, pledges for the spatial meaning of the output. As a supplementary control of the results, we compared them with those reported by Legates (1987) at 76 locations from Romania (Figure 11). For the June, October, and annual datasets, most differences do not exceed 10%, and the largest discrepancies occur at stations with high wind speeds. The comparison accentuates the major role of the wind speed and proportion of snowfall in measuring precipitation. Recognizing their utility both for fundamental and applied research, one should also take into account the shortcomings of the outputs. The temporal resolution may induce certain flaws, and it gathers the main criticism for this methodology. The computation aggregates monthly average wind speed and precipitation amounts, despite the fact that in reality the precipitation might occur both at high speed and at calm. However, daily information might enhance the accuracy of the adjustment, but they would not modify the spatial and the temporal pattern (Legates et al., 2005). Figure 12 illustrates the precipitation annual regime at four locations in Romania, in diverse geographic conditions, as resulted from rain gauge measured monthly amounts (P g ), adjusted monthly amounts (P a ), and monthly amounts calculated from daily adjusted precipitations (P ad ). The P ad increase the monthly sums due to the better fitting between precipitation fall and wind speed, and the overall pattern is consistent with the monthly data. Generally, the difference between P ad and P a is less than 5 mm. There are also some other factors that usually bias the precipitation records. For example, this research does not refer to false precipitation added by the blowing snow (Bogdanova et al., 2002). We treated the precipitations as liquid or solid, separated by theoretical thermal limits, whereas in reality the two phases can interfere. The splash effect or rain intensity was not considered here. Besides, the analysis uses the average roughness and shelter effect, although they change over time and their influence on precipitation measurements varies consequently. Changes in instrumentation and measurement procedures can also add imperfection to the correction coefficients. The validation procedure founded on comparing the SR values resulted from measured discharges (a), on one hand, and from measured precipitations (b), and adjusted precipitations (c), has demonstrated that the adjustment of the rain gauge amounts according to the bias of external factors like wind and snow can generate values closer to hydrologic needs. Eventually, when based on adjusted precipitations, the Thornthwaite water balance model turns out SRs closer to the values calculated from measured discharges, than using the rain gauge amounts. We still acknowledge the need for meticulous validation of the results. We take into consideration to test different methodologies, focus on finer spatial and temporal resolutions, using more hydrologic information, and perform field experiments. Nevertheless, these outputs already enhance our current understanding of the water budget available in Romania. One can expect that further investigations to bring more precision, while the spatial and temporal pattern as well as the magnitude of the adjustments would not modify substantially. Acknowledgements The research was motivated by the participation in the EU FP6 Project HYDRATE (Hydrometeorological data resources and technologies for effective flash flood forecasting), and in the COST Action ES0601-advances in homogenization methods of climate series: an integrated approach (HOME). The authors kindly thank Dr José A. Guijarro (Agencia Estatal de Meteorología, Delegación Territorial en Illes Baleares, Spain) for his valuable assistance in filling in

11 714 S. CHEVAL et al. Figure 12. Monthly precipitation amounts ( ) at four weather stations from different geographic conditions: Campeni N, E, 591 m; Suceava N, E, 350 m; Lugoj N, E, 123 m; and Stolnici 44 34,24 48, 209 m. The black columns represent the rain gauge measured amounts (a), the gray columns stand for adjusted monthly amounts (b), and the white columns are monthly averages calculated from daily adjusted precipitations (c). the missing data. We are also grateful to Dr Enric Aguilar (Center on Climate Change, University Rovira i Virgili de Tarragona) for his helpful suggestions. Last, but not least, we would like to thank the anonymous reviewers for their comments and recommendations. References Adam JC, Lettenmaier DP Adjustment of global gridded precipitation for systematic bias. Journal of Geophysical Research 108(D9): 4257, DOI: /2002JD Allerup P, Madsen H, Vejen F Correction of precipitation based on off-site weather information. Atmospheric Research 53: Bogdanova EG, Ilyin BM, Dragomilova IV Application of a comprehensive bias-correction model to precipitation measured at Russian north pole drifting stations. Journal of Hydrometeorology 3: Førland EJ, Hanssen-Bauer I Increased precipitation in the Norwegian Arctic: true or false? Climatic Change 46: Fuchs T, Rapp J, Rubel F, Rudolf B Correction of synoptic precipitation observations due to systematic measuring errors with special regard to precipitation phases. Physics and Chemistry of the Earth (B) 26(9): Guan H, Wilson JL, Makhnin O Geostatistical mapping of mountain precipitation incorporating autosearched effects of terrain and climatic characteristics. Journal of Hydrometeorology 6(6): Guijarro JA CLIMATOL: Software libre para la depuración y homogeneización de datos climatológicos. In El clima, entre el Mar y la Montaña, A-4, García Codron JC, Diego Liaño C, Fdez. de Arróyabe Hernáez P, Garmendia Pedraja y C, Rasilla Álvarez D. (eds). Asociación Española de Climatología: Legates DR A climatology of global precipitation. Publications in Climatology 40(1): 86 pp. Legates DR, DeLiberty TL Precipitation measurement biases in the United States. Water Resources Bulletin 29: Legates DR, McCabe GJ A re-evaluation of the average annual global water balance. Physical Geography 26(6): Legates DR, Daqing Y, Quiring S, Freeman K, Bogart A Bias adjustment to arctic precipitation: a comparison of daily versus monthly bias adjustments. Paper read at 8 th Conference on Polar Meteorology and Oceanography, Jan, at San Diego, CA. McCabe GJ, Markstrom SL A Monthly Water-Balance Model Driven by a Graphical User Interface. U.S. Geological Survey Open- File Report , 12 pp. Michelson DB Systematic correction of precipitation gauge observations using analyzed meteorological variables. Journal of Hydrology 290: Pebesma EJ Multivariable geostatistics in S: the Gstat package. Computers & Geosciences 30: R Development Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN Sevruk B Methods of correction for systematic error in point precipitation measurement for operational use. WMO Operational Hydrology Report 21(589): 91 pp. Sevruk B Comparison of Evaporation Losses from Standard Precipitation Gauges. Proceedings of TECEMO/WMO Sevruk B, Ondrás M, Chvíla B The WMO precipitation measurement intercomparisons. Atmospheric Research 92: WMO Methods of Correction for Systematic Error in Point Precipitation Measurement for Operational Use. Operational Hydrology Report 21, WMO-No WMO Instrument and Observing Methods. WMO Solid Precipitation Measurement Intercomparison Final Report 67, WMO/TD-872, 212 pp. Yang D, Goodison BE, Ishida S Adjustment of daily precipitation data at 10 climate stations in Alaska: application of world meteorological organization intercomparison results. Water Resources Research 34(2): Yang D, Kane D, Zhang Z, Legates DR, Goodison B Bias corrections of long-term ( ) daily precipitation data over the northern regions. Geophysical Research Letters 32: L19501, DOI: /2005GL Ye B, Yang D, Ding Y, Han T, Koike T A bias-corrected precipitation climatology for China. Journal of Hydrometeorology 5: