Theor Appl Climatol (2010) 101:381–396 DOI 10.1007/s00704-009-0222-x

ORIGINAL PAPER

Precipitation data in a mountainous catchment in : quality assessment and spatiotemporal characteristics

I. Westerberg & A. Walther & J-L. Guerrero & Z. Coello & S. Halldin & C-Y. Xu & D. Chen & L-C. Lundin

Received: 25 April 2008 /Accepted: 1 October 2009 /Published online: 24 October 2009 # Springer-Verlag 2009

Abstract An accurate description of temporal and spatial provided the best results for gap-filling and the universal precipitation variability in is important for kriging method for spatial interpolation. In-homogeneity in local farming, water supply and flood management. Data the time series was the main quality problem, and 22% of quality problems and lack of consistent precipitation data the daily precipitation data were too poor to be used. Spatial impede hydrometeorological analysis in the 7,500 km2 autocorrelation for monthly precipitation was low during basin in central Honduras, encompassing the dry season, and correlation increased markedly when the capital . We used precipitation data from 60 data were temporally aggregated from a daily time scale to daily and 13 monthly stations in 1913–2006 from five local 4–5 days. The analysis manifested the high spatial and authorities and NOAA's Global Historical Climatology temporal variability caused by the diverse precipitation- Network. Quality control routines were developed to tackle generating mechanisms and the need for an improved the specific data quality problems. The quality-controlled monitoring network. data were characterised spatially and temporally, and compared with regional and larger-scale studies. Two gap- filling methods for daily data and three interpolation 1 Introduction methods for monthly and mean annual precipitation were compared. The coefficient-of-correlation-weighting method In Central America, large socio-economic interests, such as hydropower generation, agriculture and flood prevention are dependent on the characteristics of the precipitation regime * : : : : I. Westerberg: ( ) J.-L. Guerrero Z. Coello S. Halldin (Alfaro 2002;Magañaetal.1999). An accurate description C.-Y. Xu L.-C. Lundin of the features of this regime, specifically on a local scale, is Department of Earth Sciences, Uppsala University, dependent on the quality of the database. The creation of Uppsala, Sweden e-mail: [email protected] such a database takes many years of data collection. In addition, a thorough quality control and analysis are needed I. Westerberg before the station data records can be transformed into IVL Swedish Environmental Research Institute, estimates of areal precipitation useful for hydrometeorological Stockholm, Sweden analysis. In Central America in general, and Honduras in A. Walther : D. Chen particular, the need for properly quality controlled precipita- Department of Earth Sciences, University of Gothenburg, tion data has been stated in several previous studies (Aguilar et Gothenburg, Sweden al. 2005; Balairón Pérez et al. 2004; Flambard 2003)of J.-L. Guerrero : Z. Coello which some have been limited by poor quality data. High Universidad Nacional Autónoma de Honduras, quality precipitation data are specifically needed for water Tegucigalpa, Honduras management in the Choluteca River basin in Honduras, encompassing the capital Tegucigalpa. C.-Y. Xu Department of Geosciences, University of Oslo, Precipitation data of high quality are important not only Oslo, Norway for describing the precipitation regime; hydrological models 382 I. Westerberg et al. are sensitive to input data errors, and errors in precipitation 2 Study area data have been shown to be more important than errors in evaporation (Paturel et al. 1995; Xu and Vandewiele 1994; The 7,500 km2 Choluteca River basin is situated in the Xu et al. 2006). There are both random and systematic southern part of Honduras, with a small part of the basin errors in point precipitation measurements (Sevruk 1986); located in (Fig. 1). Elevation in this mountainous random errors can be caused by micro-climatic variations catchment ranges from 0 to 2,300 m above sea level with a around the gauge while systematic errors are related to mean elevation of 890 m. The capital of Honduras, wind, wetting and evaporation losses, etc. On top of these Tegucigalpa, is located in the upper parts and the water errors are human-induced errors like misread and mistyped supply reservoirs for the city affect the hydrological regime records and database inconsistencies. In previous studies on in the basin. This region is also where the precipitation- precipitation-quality control, errors have been identified in monitoring network is most dense. data series as outliers, in-homogeneities and inconsistencies There is a distinct seasonal precipitation variation in the as well as erroneous information about station metadata, area with a marked dry and rainy season; the climate is e.g., coordinates and codes (Aguilar et al. 2005; Eischeid et classified as tropical wet-and-dry in the lowlands and as al. 1995; Feng et al. 2004; Gonzalez-Rouco et al. 2001). In highland climate in the mountainous upper parts of the the Honduran national water-balance study (Balairón Pérez basin (McGregor and Nieuwolt 1998). The regional climate et al. 2004), problems such as non-consistent coding of of Central America is strongly influenced by the surrounding data, erroneous station coordinates, time series data with no oceans, but it also shows a greater spatial and temporal associated codes, and incomplete data were identified. A precipitation variability than might be expected in such a case monitoring network must be carefully designed and (Hastenrath 1967;Portig1976). Orographic lifting that takes managed to prevent these types of errors. Except for place at the high mountain range stretching through the organisational weaknesses in the Honduran hydrometeoro- region, and the orientation of the coastline in relation to logical monitoring network, Flambard (2003) identifies seasonal atmospheric flow patterns (e.g. the trade winds) some technical limiting aspects such as lack of calibration explain a large part of this variability (Hastenrath 1967). The of the equipment, lack of quality control of data and loss of region is located in the North Atlantic trade-wind belt and is sub-daily data as only daily values are stored. In addition to affected by the northward migration of the inter-tropical errors in measured data, the interpolation of point-measured convergence zone (ITCZ), which reaches its maximum precipitation into estimates of the spatial precipitation latitude of about 10°N, in the vicinity of Panama (Amador distribution also introduces uncertainty, especially in et al. 2006;Hastenrath1967). Average annual wind speeds mountainous terrain and if the station density varies over are small and significant seasonal changes in wind direction time. only occur in the western and southern parts of Central The precipitation regime in the Choluteca River basin America, which are affected by the Pacific Ocean (Portig has been addressed in earlier hydrological studies and the 1976). The ITCZ is at its southernmost position, and the need for quality control and a unified approach to easterly trade winds dominate the lower levels of the monitoring has been stressed in most studies (Balairón atmosphere during winter (Hastenrath 1967). The trade Pérez et al. 2004; Díaz Chávez 1984; Flambard 2003; winds are, at times, interrupted by northerly “Nortes” winds Lardizábal Becerra 1976). The majority of the studies have in the first half of winter. The “Nortes” are associated with been made with monthly precipitation data with varying cold-air outbreaks from the North American continent and degrees of quality control performed on the data set. produce precipitation on the windward sides of the mountains Interpolation of precipitation have been made by hand or (Hastenrath 1967;Portig1976). There are large differences with subjectively set weights—in some studies, the effect of in the precipitation regimes of the and Pacific quality problems, such as too-low values, can be seen in coasts; there is no distinct dry season on the Caribbean coast isohyetal maps (e.g. Lardizábal Becerra 1976). while the leeward position with respect to the trade winds The aim of this study was to describe the characteristics and the “Nortes” gives little precipitation on the Pacific coast of the precipitation regime in the Choluteca River basin, during winter (Hastenrath 1967). The rainy season starts and to produce a quality controlled precipitation database around May on the Pacific side and transient weather for future hydrologic modelling and water-resource studies. disturbances such as hurricanes, tropical storms and depres- The objectives were achieved through; (1) gathering and sions, easterly waves and meridional displacements of merging the available data from the different data providers the ITCZ explain the largest part of the synoptic variability in a single database, (2) quality control and a description of in precipitation during this season (Peña and Douglas the temporal characteristics of the data and (3) spatial 2002). Wet spells occur most frequently in May–June and interpolation and analysis of the spatial characteristics of September–October, when the atmospheric influences pre- the precipitation data for different time scales. dominantly come from the Pacific Ocean (Hastenrath 1967), Precipitation data in a mountainous catchment in Honduras 383

Fig. 1 The Choluteca River basin and the location of precipitation stations that passed quality control

trade winds are weak and the cross-equatorial flow in the mountain station in the upper basin (Nuevo Rosario), the eastern Pacific is strong (Peña and Douglas 2002). The ITCZ coastal station (La Lujosa) has the highest precipitation temporarily shifts southward and the influence of the Pacific amounts in the rainy season. Ocean decreases in July–August; there is then an intensifi- The annual temperature range is small and is exceeded cation of the trade winds which, together with the orographic by the diurnal temperature range (Amador et al. 2006; barrier, create a rainfall maximum on the Caribbean side. At Portig 1976); on a daily basis convective activity and the same time, there is strong subsidence that results in a related precipitation is most likely caused by afternoon relative precipitation minimum on the Pacific coast called the heating (Amador et al. 2006). Maximum annual temperatures midsummer drought (MSD), “veranillo” or “canícula” in most of Central America occur around April before the (Magaña et al. 1999). The intensification of trade winds is start of the rainy season, and the intra-annual variation in associated with the development of the “Intra-Americas temperature is strongly related to the seasonal variation in Low-Level Jet” over the Caribbean and Central America, precipitation (Aguilar et al. 2005; Portig 1976). Several which has a large influence on the regional climate at this previous studies have shown strong correlations between time of the year (Amador 2003;Amadoretal.2006). inter-annual climatic variations in Central America and large Another phenomenon, called “temporales”,thathasalarge scale climate-variability indices, e.g., El Niño Southern impact on the precipitation regime, mainly on the Pacific Oscillation indices and Atlantic sea-surface temperatures coast, are periods of persistent rain lasting from several days (Aguilar et al. 2005;Diazetal.2001; Enfield and Alfaro to more than a week (Peña and Douglas 2002). The 1999). The El Niño effect on precipitation in Central “temporales” originate in the Pacific Ocean and occur America is a reduction in precipitation amounts, and the predominately in June and September–October, causing drying signal is strongest during summer and autumn (Diaz flooding and landslides when high precipitation amounts et al. 2001). La Niña events are associated with precipitation hit large areas (Hastenrath 1967; Portig 1976). The bimodal amounts that are higher than normal. precipitation regime described above is found in most parts of the Pacific Coast of Central America (Hastenrath 1967; Magaña et al. 1999). Mean daily and less noisy mean pentad 3 Methods (5-day accumulations of precipitation, in total 73 pentads per year) climatologies show that the rainy season starts around The study was divided into three parts according to the May with two maxima in June and September–October, the aims: (1) collection and merging of available precipitation MSD occurs around July–August and that the dry season data, (2) quality control and characterisation of data, and (3) extends from December to April (Fig. 2). The station in the spatial interpolation and analysis of spatial characteristics. upper basin that is not mountainous (Tegucigalpa) has lower Data were reformatted and consolidated from several precipitation amounts and a more intense dry season than the sources in the first step. Several specific error types were 384 I. Westerberg et al.

Fig. 2 Mean daily (left panel) 40 90 and mean pentad (5-day, right La Lujosa panel) precipitation in the 35 Nuevo Rosario 80 Choluteca River basin Tegucigalpa (Toncontin) – 1990 2005, calculated for 70 three stations in the upper 30 basin, mountains and 60 coastal area 25 50 20 40 15

Precipitation (mm/day) 30 Precipitation (mm/5 days) 10 20

5 10

0 0 J F M A M J J A S O N D J F M A M J J A S O N D encountered and quality control routines were developed to US National Oceanic and Atmospheric Administration tackle these errors during the second step. The quality- (NOAA) and from SERNA. Only data from the GHCN controlled data were then characterised by calculation of (Vose et al. 1992) have undergone a serious quality control. A climate indices. Suitable time scales for interpolation were great part of the data was reformatted into time series before it analysed by calculation of the spatiotemporal correlation was put into the database. Coordinates of most precipitation structure. The calculation of climate indices enabled stations were checked in the field while, at the same time, we comparison with previous regional studies. Interpolation investigated the measurement equipment and procedures. was then performed in the third step, where two methods for filling short gaps in the data (to obtain more complete 3.2 Quality control and data characterisation data for spatial interpolation) were first evaluated and the best method was used. Three spatial interpolation methods 3.2.1 Quality control were then compared. The spatial characteristics of the interpolated data were finally analysed and the interpolated The stochastic nature of precipitation, in combination with time series compared with the 0.5 degree global CRU TS measurement difficulties, make quality control of this type 2.1 precipitation data for 1901–2002 (Mitchell and Jones of data more problematic than for a spatially continuous 2005) to evaluate the improved local and regional value of variable like temperature. Several different approaches have this dataset compared to easily available global precipitation been used for the quality control of precipitation data. data. Gonzalez-Rouco et al. (2001) use interpolation, homogeni- sation and outlier processing for monthly data from south- 3.1 Data collection and structuring western Europe. The monthly GHCN data are quality controlled with an objective data analysis; outliers are Meteorological stations are operated by several institutions identified first using a temporal check against other values in the area: Secretaría de Recursos Naturales y Ambiente from the same station and then using a spatial-interpolation (SERNA, gov. inst. for natural resources and environment), check against nearby stations (Eischeid et al. 1995). You et Servicio Autónomo Nacional de Acueductos y Alcantar- al. (2007) use an approach based on fitting gamma illados (SANAA, gov. inst. in charge of water works), distributions for quality control of daily precipitation data. Servicio Meteorológico Nacional (SMN, national weather The spatial and temporal consistency of a daily Chinese service), Universidad Nacional Autónoma de Honduras dataset is analysed by Feng et al. (2004); they compare (UNAH) and Empresa Nacional de Energía Electrica outliers to pre-defined high/low extreme values, internal (ENEE, national power company). The US Geological consistency is assessed with a “flat line” check (check for Survey (USGS) operates some automatic weather stations same values, greater than zero, several days in a row), a in co-operation with SERNA. Daily data were collected spatial outliers check is performed using a regression from the above-mentioned institutions. Monthly data were method and the homogeneity of the data is finally assessed retrieved from the Global Historical Climatology Network through three statistical methods. Feng et al. (2004) also (GHCN) version 2 beta dataset (Vose et al. 1992) from the give a more detailed discussion on methods to test the Precipitation data in a mountainous catchment in Honduras 385 homogeneity of a dataset, including subjective visual quality were made as an aid in the decision to remove inspection, station metadata and statistical tests. Construction flagged data. of double-mass curves with nearby stations of good quality is another way to check data homogeneity. Aguilar et al. (2005) 3.2.2 Data characterisation use 200 mm as an outlier criterion and a check for suspiciously long spells of zero precipitation in quality Ten climate indices that have been used in previous studies control of daily Central American precipitation data. of Central American climate (Aguilar et al. 2005; Alfaro A combination of automated tests and visual data inspec- 2002; Magaña et al. 1999; Portig 1976) were computed to tion was used in this study since the amount of data was not characterise the data and enable comparison with regional very large. Four types of quality control were performed: (1) studies (Table 1). Indices were calculated only on time obvious outliers were first removed and data were then series with more than 50% complete years with data in the flagged for three types of homogeneity errors recurrent in the period 1970–2005. The indices for the rainy season start dataset; (2) too-frequently occurring data; (3) sequences of date (SD) and end date (ED), the midsummer drought too-low data and (4) dry months in the rainy season. (MSD), number of dry months (DM) and the percentage of precipitation falling in the rainy season (P_RS) describe (1) Outliers check for all daily values greater than important features of the precipitation regime in Central 100 mm. Only obvious outliers (missing values in America. The SD, ED and MSD were calculated on mean the days before the recording, no high values at other pentad climatologies according to Alfaro (2002)and stations and/or no known storm/hurricane) were Enfield and Alfaro (1999). We used the same index removed. definitions for the R20, RX5d, SDII, CWD and CDD (2) Check for too-frequently occurring data. This type of general indices as Aguilar et al. (2005). error could result from rounding errors (some series The spatiotemporal correlation structure of the data was contained a great number of values where the digits analysed by calculating the correlation between data series expressing the unit parts were too often equal to zero related to distance between stations for different time e.g. 40.7 or 20.3 where it is likely that the measurement scales. This analysis was also made to find a good temporal glasses used were incorrectly read and should have been scale for spatial interpolation. The correlation-coefficient 47 and 23). There were also some series with many values were calculated for all pairs of stations with at least frequently repeated values of 10, 20, 30 and 40 mm 80 concurrent data pairs and with time aggregations ranging giving the data a similar non-stochastic character from 1 to 30 days accumulated precipitation. The data for suggesting that it had been approximated or simply the 1,128 obtained pairs of stations were divided into 24 made up. This suspicion is supported by Flambard distance groups with 47 pairs of stations in each group. The (2003) who states that several lecturers falsify data to mean correlation-coefficient value in each group was cover up that they have missed taking the readings. The plotted against the mean distance between the stations in quality-control routine, developed to address this the group for all time aggregations. problem, flagged data as potentially erroneous if the integer parts of all data (greater than 4 mm and for a 3.3 Spatial interpolation specific year) had more than ten equal values (e.g. if there were more than 10 data of 40.2, 40.5, 40.7… etc, 3.3.1 Patching of missing values where the integer part was 40, all these data were flagged). Two methods were evaluated for the patching of missing daily (3) Check for sequences with too-low data. Series of too- values: the commonly used inverse-distance-weighting low data were flagged for each year if there were less method (IDW) using squared distance and the coefficient-of- than 2 days above a given criterion (here 20 mm) correlation-weighting method (CCWM), which proved to and at least 100 days with values below that criterion work well in a study by Teegavarapu and Chandramouli (to avoid flagging years with a lot of missing data). (2005). The latter is based on the same principle as IDW but (4) Check for dry months during the rainy season. Months correlation coefficients are used instead of distance to with zero precipitation were flagged if the mean of the calculate the weights of the n surrounding stations Eq. 1, other stations was greater than 100 mm. P n q Pi¼1 iRmi All data series were inspected visually on a daily and qm ¼ ð1Þ n R monthly level, and flagged data were removed if deemed i¼1 mi necessary. This process was subjective in the cases where where θm is the missing value to be patched and Rmi is the errors were not obvious. Double-mass curves, scatter plots coefficient of correlation between the i:th station and station and correlation analysis with surrounding stations of good mi missing a value. All available simultaneous data for the 386 I. Westerberg et al.

Table 1 Precipitation–climate indices

Index Name Definition Units

SD Start date of rainy season See Alfaro (2002) Pentad MSD Midsummer drought See Alfaro (2002) Pentad ED End date of rainy season See Alfaro (2002) Pentad DM Number of dry months Dry period defined as not within SD-ED period Months P_RS Rainy season precipitation percentage Percentage of the annual precipitation that falls between the SD and ED % CWD Consecutive wet days Maximum number of consecutive days with precipitation ≥1 mm Days CDD Consecutive dry days Maximum number of consecutive days with precipitation <1 mm Days RX5d Max 5-day precipitation amount Annual maximum consecutive 5-day precipitation Millimeter SDII Simple daily intensity index Annual total precipitation divided by number of days with precipitation ≥1 mm Millimeter per day R20 Number of heavy precipitation days Number of days per year with precipitation >20 mm Days

stations were used for the calculation of the correlation treated as a random variable and an explicitly stated coefficient, with a minimum of 730 concurrent days set as a stationary random-function model (that describes the requirement. In the case of the IDW method, the inverse pattern of spatial continuity) is used for the estimation at squared distance between station m and station i replaces Rmi un-sampled locations (Isaaks and Srivastava 1989). in Eq. 1. The mean absolute error (MAE), the mean As in the study by Goovaerts (2000), an omni- relative error (MRE), the root-mean-square error (RMSE) directional semi-variogram was used because of the limited and the coefficient of determination (R2)wereusedto number of available stations, and the variogram model was evaluate the performance of the two methods at all stations exponential with a nugget effect. In the case of universal with data for the two periods 1985–1995 and 1996–2005. kriging, a trend is modelled as a function of the coordinates Two periods were used to account for possible differences and subtracted where after the residual semi-variogram is in performance because of different spatial distributions of calculated. The Gstat geostatistical software (Pebezma data. The best method was identified and used to patch the and Wesseling 1998) and the Gstat package in R dataset; gaps were only patched if they were shorter than a (Pebezma 2004) were used for the interpolation. The month and if there was at least 1 day with data during any automatic fitting of curves to the sample semi-variograms given month. wereperformedinMatlabtoimproveestimationofthe nugget parameter. The ordinary kriging reduces to a 3.3.2 Spatial interpolation of precipitation simple averaging of data if the semi-variogram is modelled as a pure nugget, i.e. if there is no increase in Spatial interpolation was performed on a monthly time semi-variance with distance all stations get the same scale as more monthly data were available and since weight. Both universal and ordinary kriging were per- monthly correlations were expected to be much higher than formed as block kriging with a block-size of 900 m and a daily correlations. The mean annual and mean monthly discretisation size of 100 points per block. A normalised spatial precipitation distributions were also obtained kriging variance map for each month was calculated by through interpolation to characterise the spatial properties dividing the variance map by the maximum variance. The of the precipitation regime in the basin. mean of the normalised variance for each month was then We used three interpolation methods, all based on used to assess the spatiotemporal coverage of data. weighted linear combinations of the gauged data; a simple method—IDW—and two computationally demanding geo- statistical methods—ordinary kriging (OK) and universal 4 Results kriging (UK) with coordinate base functions. The first method has been shown to produce results comparable to 4.1 Data collection and structuring that of more advanced methods like kriging when sampling density is high (Dirks et al. 1998). Goovaerts (2000) found Data were collected from 60 daily stations and 13 monthly that it is less advantageous to use OK instead of IDW in stations in 1913–2006, reformatted into time series, and situations with low spatial dependence between observa- then imported into a database. Monthly data were available tions. In geostatistics, the variable to be interpolated is at some stations where daily data had been lost for early Precipitation data in a mountainous catchment in Honduras 387 periods. Data losses have been caused both by computer- 4.2 Quality control and data characterisation storage malfunction and flooding during hurricane Mitch— where a large portion of data stored in paper format was 4.2.1 Quality control lost (Flambard 2003). Several erroneous station coordinates were corrected; 14 stations out of 16 from SANAA and Only 48 out of the initial 60 stations with daily precipitation USGS had more than 100 m difference in either the X or Y data remained useable after quality control. At least one coordinate and four stations had a discrepancy greater than daily value was deleted from 26 of these 48 stations. The 700 m. The density of the network was 270 km2/gauge short data series from the six automatic USGS stations were when all of the stations located within 11 km of the deemed unreliable because of high losses caused by wind Choluteca basin were counted. The temporal availability of and were not used. The difficulty in comparing data from station data (Fig. 3) provides an informative description of manual and automatic stations in Honduras is also pointed the data, complementing the spatial description in Fig. 1,and outbyFlambard(2003). The monthly GHCN data shows the number of short series and short gaps in data. contained some temporally misplaced data that were

Fig. 3 Chronogram showing the temporal availability of daily precipitation data before quality control 388 I. Westerberg et al. identified by comparison with daily data. An example of 4.3 Spatial interpolation findings from the quality control of the Las Botijas station is shown in Fig. 4. 4.3.1 Patching of missing values By comparing the temporal availability before and after the quality control, around 20% of the 1970 to 2005 data The CCWM method was a better gap-filling method than were seen to have bad quality (Fig. 5). In total, 22% of the the IDW method according to all performance measures daily dataset was rejected because of quality problems. The (Table 4). This was true both for patched daily values and rejected data consisted of 3% erroneous zeros, 11% too-low patched daily values converted to monthly values. values, 42% too-frequent values whereas the remaining The CCWM method was thus used for patching. In total, 44% had other (mainly homogeneity) problems. 250 additional months with data were obtained after the The mean monthly precipitation of the quality-controlled patching. The mean patched gap length was 4.6 days for all dataset of stations having at least 50% complete years of stations. data in 1970–2005 is presented in Table 2. 4.3.2 Spatial interpolation 4.2.2 Data characterisation The mean annual precipitation distribution for the years The impact of data quality errors on the mean values for all 1975–1985 was interpolated based on data from 28 stations stations of climate indices was small (mean indices only and in 1990–2005 based on 34 stations. The lack of stations shown for quality-controlled data in Table 3) but errors could in the mountainous parts of the upper basin has a clear greatly influence indices for specific areas and time periods. effect on the interpolated map for the earlier period in The index values did not have any relation to the station comparison to the later period (Fig. 8). elevation, but some indices were related to the distance to Precipitation was highest close to the coast and around the coast (Fig. 6). A higher percentage of the precipitation the mountains in the upper part of the basin in the years falls during the rainy season close to the coast and this is 1990–2005. There was no great difference between maps also where the dry season is most intense (high number of created by the three interpolation methods, but the CDD) and the precipitation is most heavy (RX5d, R20 and smoothing effect of kriging compared with IDW and the SDII are high). There was a weak relationship between SD trend effect in UK were apparent. Since all stations close to and the distance to the coast but no relationship for MSD or the coast have high precipitation, the maps produced by UK ED. and IDW appear more realistic than the one for OK in Daily correlation coefficients between the stations were 1990–2005. OK gave the best results in 1975–85 and UK in low and decreased rapidly with distance between stations 1990–2005 in the cross-validation (Table 5). (Fig. 7). The increase in correlation moving from daily to The mean monthly precipitation for the period 1990– 4–5 days accumulated data was substantial, and this change 2005 was higher in the upper parts of the basin in the dry was greater than the increase in correlation moving from season December–April, with the opposite situation in the 4–5 days to monthly accumulations. rainy season May–November (Fig. 9). The area around the

Fig. 4 Results of quality control 100 of daily precipitation data from Las Botijas precipitation Las Botijas; small dots mark 90 Too-frequent data too-low data and crosses mark Too-low data too-frequent data flagged as 80 potentially erroneous 70

60

50

40

Precipitation [mm/day] 30

20

10

0 1970 1980 1990 Precipitation data in a mountainous catchment in Honduras 389

Fig. 5 Number of precipitation 50 stations with data over time, Daily data before QC before and after the quality 40 Daily data after QC control (QC) and (lower panel) percentage of data rejected in Monthly data after QC and gap-filling QC 30

20 Number of stations 10

0 1920 1940 1960 1980 2000 100

50

0 1950 1960 1970 1980 1990 2000 2010 Data removed in QC [%] highest mountain in the northern part has the highest 45% of the months, and the interpolation then resulted in a precipitation amounts throughout the dry season. The leeward simple averaging of the data. The lack of spatial autocor- position of the coastal areas with respect to the easterly and relation frequently occurred in the dry season (around 65% northerly winds during winter is reflected in the precipitation of the months) and less frequently in the rainy season distribution from November to February–March. (around 25% of the months) in the period 1970–2005. The The mean cross-validation errors were in general lower UK method was chosen over IDW for the CRU TS 2.1-data for the UK and IDW methods for spatial interpolation of comparison since the areal mean and not the spatial monthly time series in 1970–2005. The difference was most distribution was of primary interest. The UK method was less clearly seen in the correlation coefficient (Table 6). biased and the statistics of the interpolated data were closer to Practically no spatial autocorrelation was estimated from the observed in the cross-validation (Table 7). the data with the universal (ordinary) kriging semi- An integrated measure of the spatiotemporal data variograms (the effective range was shorter than 1 km) for coverage in the basin during the interpolation period

Table 2 Mean monthly precipitation in mm for 1970–2005 for stations having at least 50% complete years of data

Station Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Sabanagrande 1 3 18 48 277 271 97 170 373 204 41 4 Güinope 8 7 8 32 167 162 87 125 198 154 36 8 La Ermita 20 17 18 41 157 107 81 98 179 172 63 28 La Venta 18 13 15 38 125 155 97 107 166 176 70 28 Lepaterique SERNA 4 5 14 46 182 224 103 153 259 163 26 6 Potrerillos 8 5 9 33 174 158 104 127 176 182 48 12 Reitoca 1 7 12 81 342 365 138 278 464 257 56 3 San Antonio de Flores 11 7 14 32 181 193 117 162 216 177 36 11 San Lucas 12 6 13 43 238 236 128 200 299 217 47 14 Teupasenti 22 9 16 33 142 165 125 134 179 167 74 34 Tegucigalpa (Toncontin) 5 4 11 35 142 140 73 104 163 123 34 11 Texiguat 2 2 10 45 222 187 78 136 222 161 28 4 Zambrano 11 9 10 49 156 169 84 124 193 155 40 18 El_Coyolar 4 8 9 31 128 168 63 95 189 141 33 8 El_Zamorano 8 7 12 33 152 158 120 153 199 120 44 20 Flores SERNA 3 6 15 39 114 157 97 135 177 124 24 8 Rio Abajo 32 27 20 30 135 134 133 132 185 179 91 41 Flores SMN 3 8 20 30 102 172 92 138 197 129 27 9 Liure 1 2 17 38 218 183 72 145 234 178 35 6 390 I. Westerberg et al.

Table 3 Climate indices 1970–2005 calculated for the 19 stations having at least 50% complete years with data; abbreviations are explained in Table 1

SD pentad MSD pentad ED pentad DM P_RS R20 RX5d CDD CWD SDII (date) (date) (date) months [%] days mm days days mm/day

Max 30 (26–30/5) 46 (14–18/8) 62 (2-6/11) 7.7 94 36 244 82 15 22 Mean 28 (16–20/5) 41 (15–19/7) 60 (23–27/10) 6.7 81 17 157 57 10 12 Min 24 (26–30/4) 37 (30/6–4/7) 56 (3–7/10) 5.8 72 12 117 31 7 9

1970–2005 was obtained by calculating the normalised TS 2.1 data was done for 1970–2005 for five 0.5˚×0.5˚ kriging variance for each month and then averaging all pixels covering the basin (Fig. 11). these monthly maps. The mean normalised variance map There were considerable deviations between the two (where low values represent areas with good spatiotemporal datasets in the wettest months; the precipitation during data coverage) revealed the low temporal coverage of high- hurricane Mitch in October 1998 was 433 mm in the CRU altitude stations and lack of data coverage in the southern TS 2.1 data compared with 692 mm in the interpolated data and easterly parts of the watershed (Fig. 10). series. The mean (131 mm CRU TS 2.1, 101 mm UK areal The comparison between measured mean monthly mean) and standard deviation (117 mm CRU TS 2.1, precipitation, interpolated with the UK method, and CRU 104 mm UK areal mean) were higher for the global data

48

30 46 62

44 28 60 42

26 40 58 SD (pentad) ED (pentad) MSD (pentad) 38 24 36 56

0 50 100 150 0 50 100 150 0 50 100 150 8 0.95 250 35 7.5 0.9 30 7 200 0.85 25 6.5 P RS (%)

0.8 R20 (days) RX5d (mm)

DM (months) 20 150 6 0.75 15 5.5 0.7 0 50 100 150 0 50 100 150 0 50 100 150 0 50 100 150

16 80 14 20 70

60 12 15 50

CDD (days) 10 CWD (days) SDII (mm/day) 40 8 10 30 0 50 100 150 0 50 100 150 0 50 100 150 Distance to coast (km) Distance to coast (km) Distance to coast (km)

Fig. 6 Index values (calculated on stations with at least 50% complete 500 m.a.s.l. The latest starting dates are found furthest from the coast years with data in 1970–2005) plotted against distance to the coast. and a larger percentage of the precipitation falls in the rainy season at The black circles represent the stations located above 1,000 m.a.s.l., stations located close to the coast the grey stations 500–1,000 m.a.s.l. and the white stations below Precipitation data in a mountainous catchment in Honduras 391

Fig. 7 Correlation between pairs of precipitation stations against 1 distance between them for different time aggregations (from 1 to 30 days). The data 0.9 were divided into 24 distance groups, and each point in the plot represents the mean of the 47 0.8 correlation-coefficient values in the distance group 0.7

0.6 Correlation

0.5

0.4

0.3

0.2 0 30 50 20 25 100 10 15 150 0 5

Distance [km] Time aggregation [days] than for the interpolated station data. The correlation controlled, gap-filled, and spatially interpolated dataset. between the two datasets was 0.82 for the whole time The data, collected and structured from many institutions period but only 0.17 for the driest (February) and 0.54 for into a single database, provided an overview of the the wettest (September) months (Figs. 11 and 12). availability of precipitation data and through the quality Precipitation in the rainy season was greater in the CRU control a better basis for future hydrometeorological TS 2.1 data and the correlation was notably low in analysis. The analysis of the data has confirmed many September and October when precipitation reaches a characteristics of the precipitation regime described in maximum. The maximum difference in annual precipitation earlier studies, e.g. the low percentage of annual precipitation was 864 mm and the mean 364 mm between the two series. that occurs in the dry season on the Pacific coast. However, the variability has been shown to be substantially larger on the local scale than what can be identified from the regional scale. 5 Discussion and conclusions The large range of climate indices, the spatiotemporal correlation structure, and the interpolation results from this The precipitation regime of the Choluteca River basin was study all manifest the high spatial variability created by the characterised through creation and analysis of a quality- precipitation-generating mechanisms.

Table 4 Mean values of daily – – and monthly error measures at Mean values at all stations 1985 95 Mean values at all stations 1996 05 all stations for the two evaluation periods and two IDW CCWM IDW CCWM gap-filling methods 2 R daily 0.28 0.36 0.32 0.37 2 R monthly 0.80 0.84 0.82 0.84

MAEdaily (mm) 3.4 3.1 3.5 3.3 2 R coefficient of determination, MAEmonthly (mm) 41 34 41 36 MAE mean absolute error, MRE MRE (%) 2.9 2.5 2.7 2.3 mean relative error, RMSE root daily mean square error, IDW inverse MREmonthly (%) 1.6 1.4 1.9 1.6 distance weighting, CCWM RMSEdaily (mm) 8.1 7.5 8.5 8.1 coefficient-of-correlation- RMSEmonthly (mm) 64 53 65 58 weighting method 392 I. Westerberg et al.

Fig. 8 Mean annual precipita- tion (millimeter) 1975–1985 (upper row, for the 28 stations with more than 50% complete years with monthly data during that period) and 1990–2005 (bottom row, for the 34 stations with more than 50% complete years with monthly data during the period) interpolated with inverse distance weighting (left), universal kriging (middle) and ordinary kriging (right)

5.1 Quality control and data characteristics false. The application of double-mass curves and other comparisons with neighbouring stations helped in these The main data-quality problems were related to in- decisions. Some errors (such as temporally misplaced data) homogeneity of the time-series data, and the proportion of were identified in the quality-controlled GHCN dataset poor data was high and relatively consistent from year to when compared with daily data in this study. year. This high percentage of bad-quality data emphasises The characteristics of the precipitation regime agreed the need for quality control of climate data in this region, well with results from regional studies but also showed something that has been brought to attention in earlier considerable local-scale variation. As an example, SD, ED studies (Aguilar et al. 2005; Balairón Pérez et al. 2004; and MSD values agreed with regional calculations (Alfaro Flambard 2003). Too-low values and erroneous zeros were 2002), and also showed considerable local-scale variation. easily identifiable quality problems that can produce large Large variation was found in SD and P_RS, with the errors if not removed; too-frequent data were mainly earliest start dates and the highest percentage of precipita- identifiable on a daily time scale and lowered the spatial tion in the rainy season in the southern parts of the basin. correlation between stations. This type of error could result The estimated CDD values were likely too high since many from incorrectly rounded data as well as false data, the stations were totally dry in the dry season while stations known latter case being the most important to remove. The largest to be reliable had small precipitation amounts. The percentage risk of making a type I error (erroneously removing good of precipitation that falls in the rainy season was comparable to data) was in the subjective decision to remove too-frequent figures given by Portig (1976) for southern Honduras. data—i.e., to decide whether the data were incorrectly Correlation between stations decreased rapidly with rounded (which could still be acceptable) or if they were distance on a daily time scale; this was not surprising given

Table 5 Cross-validation results – – for interpolation of mean annual Time period 1975 1985 1990 2005 precipitation with ordinary kriging (OK), universal Interpolation method OK UK IDW OK UK IDW kriging (UK) and inverse distance weighting (IDW), Correlation coefficient 0.84 0.78 0.80 0.43 0.51 0.48 errors calculated as Mean error (mm) 11 23 20 8 8 34 — observed interpolated Max. positive error (mm) 619 628 734 591 551 559 Min. negative error (mm) −317 −330 −347 −347 −374 −288 Mean absolute error (mm) 163 187 185 165 161 159 Mean relative error (%) 0.12 0.14 0.14 0.14 0.14 0.13 Root mean square error (mm) 209 240 243 218 209 215 Mean areal precipitation (mm) 1,170 1,190 1,200 1,150 1,190 1,140 Precipitation data in a mountainous catchment in Honduras 393

Fig. 9 Mean monthly precipita- tion (millimeter) interpolated with IDW for stations with more than 50% complete years of monthly data in the period 1990–2005. January through April in the upper row (left to right), May through August in the second row (left to right) and September through December in the bottom row (left to right)

the convective nature of precipitation in the area and the all pointed to UK being the best spatial interpolation influence of topography on the generation of precipitation. method in this area. IDW was a better interpolator than The marked increase in correlation when data were temporally UK in the dry season when spatial dependence was low aggregated from a daily time scale to 4–5 days showed that the most of the time. Goovaerts (2000) states that it is less smallest feasible time scale for a meaningful interpolation is advantageous to use kriging instead of IDW in such cases. around 4–5 days. The availability of monthly precipitation The low spatial autocorrelation for monthly precipitation in data favours monthly analysis and interpolation the dry season was likely associated with precipitation generated on windward sides of the mountains during 5.2 Spatial interpolation northerly and easterly winds. Other interpolation methods that use elevation as an auxiliary variable (after the trend with The evaluation of gap-filling methods showed that the higher precipitation close to the coast has been removed) CCWM, and not the IDW method, was best in filling short could be considered. However, there was not a strong gaps in precipitation time series. The results of patching relationship between precipitation and elevation (because of were reliable when aggregated to a monthly scale but less the diverse precipitation-generating mechanisms) and the reliable for daily data. Spatial trends apparent on yearly and number of stations was low, which makes estimation of more monthly time scales together with cross-validation results parameters uncertain. The scattered temporal coverage of the stations indicated that errors in the spatial distribution of Table 6 Cross-validation results for spatial interpolation of monthly precipitation data from 1970–2005 with ordinary kriging (OK), Table 7 Mean statistics of interpolated and observed data in the universal kriging (UK) and inverse distance weighting (IDW) cross-validation of interpolation of monthly precipitation data from – Error measures 1970–2005 OK UK IDW 1970 2005 with ordinary kriging (OK), universal kriging (UK) and inverse distance weighting (IDW) Correlation coefficient 0.26 0.40 0.43 OK UK IDW Observed Mean absolute error (mm) 34 32 32 Mean error (mm) 0.66 0.86 2.46 Min 68 42 63 30 Max. positive error (mm) 134 126 133 Max 150 176 164 248 Min. negative error (mm) −73 −78 −71 Mean 100 100 98 100 Mean relative error (%) 1.5 1.3 1.2 Median 96 96 93 89 Root mean square error (mm) 45 43 44 Standard deviation 21 32 24 52

Errors calculated as observed minus interpolated All units are given in millimeter 394 I. Westerberg et al.

0.9 interpolated monthly precipitation would not be stationary in space or time—i.e. there will be time-varying biases in the 0.8 interpolated areal means. The areal precipitation, e.g. will be underestimated during periods with no data in the mountainous 0.7 northern part of the basin. The time-varying biases will 0.6 present a real problem if maps of monthly precipitation are needed. The magnitudes of the monthly biases depend on (1) 0.5 the inter-annual precipitation variability (e.g. El Niño/La Niña), (2) the spatial configuration of the stations in the 0.4 dataset and (3) the possibilities of long-term trends in the monthly precipitation data. However, no significant trend for 0.3 regional annual precipitation was found in a previous study by Aguilar et al. (2005), wherefore the first two causes are likely 0.2 to be most important. The high inter-annual precipitation variability, with scattered temporal coverage of data, com- Fig. 10 Mean normalised universal kriging variances of monthly plicated the process of achieving a representative mean precipitation for 1970–2005 annual distribution. Here 50% complete years of data was set

Fig. 11 Comparison of interpo- February lated monthly areal precipitation 60 800 (millimeter) with CRU TS 2.1 CRU data for 1970–2003 for the 40 UK areal mean 700 driest and wettest months 600 (February and September); the 20

scatter plot (right panel) was Precipitation [mm] made for the entire time series 500 0 1970 1975 1980 1985 1990 1995 2000 400 September 500 300 400 CRU precipitation [mm]

300 200

200 100 100 Precipitation [mm]

0 0 1970 1975 1980 1985 1990 1995 2000 0 200 400 600 800 UK areal mean precipitation [mm]

Fig. 12 Monthly precipitation 1 400 climatologies for UK- Monthly coefficient of correlation interpolated areal mean and UK areal mean CRU TS 2.1 data for the Mean CRU period 1970–2003, and the 0.8 monthly correlations between 300 the two series

0.6

200

0.4 Coefficient of correlation 100 Mean monthly precipitation [mm] 0.2

0 0 0 2 4 6 8 10 12 Month Precipitation data in a mountainous catchment in Honduras 395 as a criterion for the interpolation and index calculations, evaluation measurements performed at ground level to which was a compromise between getting representative data assess the bias in the measurements and enable calculation for the stations and getting a sufficient number of stations. of correction factors. Reliable wind information may also The interpolated mean annual precipitation regime was very improve precipitation interpolation (e.g. Johansson and different for 1975–85 compared with 1995–2005, and it can Chen 2005). The most immediate action should, however, be concluded that neither dataset gives a sufficient description be to address the quality of data from existing rainfall of the spatial precipitation regime. In the first period there stations—so the approximately 20% of stations having were more stations close to the coast, and in the later period unreliable data today could function correctly in the future. there were few stations there but more in the upper catchment. Systematic quality control including visualisation of the However, even with more data available in the mountainous time series data in combination with automated checks is upper areas in the later period, the spatial variability is most proposed as an effective way to identify these quality likely highly underestimated. The “bull's eyes” around the problems. Organisational weaknesses in the precipitation mountain stations illustrate this problem, if there were more monitoring in Honduras have been identified by Flambard stations in these areas the high spatial variability would be (2003) and include decreased resources in later years, more realistically described. The very dry area around the competence level and loss of capacitated personnel at the Texiguat station in the lower parts of the basin found in some institutions, organisation and data security, decentralised other studies (but not here) seemed to be linked to the fact responsibility (several institutions involved), lack of ex- that there were several years of quality problems with too- change and cooperation between the institutions and the low data (Häggström 1990;LardizábalBecerra1976). employment of permanent observers. These issues must be Effects of sea breeze and configuration of the river addressed in combination with technical problems to ensure valley and coastline can be important in the southern part of a well-functioning monitoring network in the future. Pentad the catchment, where there were high precipitation amounts or higher time scales are recommended for future hydro- in the area close to the coast. These mesoscale effects were meteorological studies. The data-quality problems and the largest in the wettest months when the influences of the high spatial and temporal variability make uncertainty Pacific Ocean were greatest. estimation a vital part of any future studies of water The CRU TS 2.1 data showed considerable bias balance and water resources in the basin. compared to interpolated station data, specifically for months with high precipitation amounts. This was probably Acknowledgements This work was funded by the Swedish Inter- caused by the different station density used in the global national Development Cooperation Agency grant number 75007349 and SWE-2005-296. The authors would like to thank the staff at data and our interpolation. The global dataset includes a SANAA, SERNA, UNAH and SMN for their kind assistance in smaller number of stations than our dataset. A few high- providing data for the study. 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