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Journal of the Selva Andina Research Society
versión impresa ISSN 2072-9294versión On-line ISSN 2072-9308
J. Selva Andina Res. Soc. vol.16 no.1 La Paz 2025 Epub 28-Feb-2025
https://doi.org/10.36610/j.jsars.2025.160100028
COMUNICACIÓN CIENTÍFICA
Comparative meteorological data in the Mauri and Coroico river basins, in relation to satellite data
1Public University of El Alto. Department of Agricultural Sciences, Livestock and Natural Resources. Av. Sucre A s/n Villa Esperanza area. City of El Alto. La Paz-Plurinational State of Bolivia.
En algunas estaciones de Bolivia no existen datos meteorológicos o están incompletos, para realizar diversos estudios entre ellos el balance hídrico en cuencas como el Mauri y Coroico, una alternativa son los datos provenientes de satélites que están disponibles y fueron reanalizados, sin embargo, antes de utilizarlos se debe realizar comparaciones y análisis en base a las estaciones in situ, para utilizarlas directamente o realizar algún ajuste. A nivel internacional existen varios estudios sobre la comparación de datos meteorológicos medidos en estaciones en comparación a datos satelitales. Estudios previos en Bolivia señalaron que los datos de Re análisis del Sistema de Previsión Climática, que utiliza el modelo Herramienta de Evaluación del Agua y Suelo provenientes de satélites, son aceptables para la temperatura media y mínima, en la Cuenca Katari utilizaron datos satelitales de precipitación para ajustar los vacíos en algunas estaciones. En esta investigación se obtuvieron datos meteorológicos (temperatura máxima, mínima y precipitación) del Servicio Nacional de Meteorología e Hidrología de Perú y Bolivia, para las estaciones de Chuapalca y Calacoto, ubicados en la cuenca del rio Mauri, y Caranavi en la cuenca del rio Coroico. Los datos fueron comparados estadísticamente, mediante el coeficiente de suficiencia de Nash Sutcliffe, porcentaje de sesgo (PBIAS) y r = CoC (coeficiente de correlación de Pearson), con los datos meteorológicos obtenidos de la NASA (Administración Nacional de Aeronáutica y del Espacio de Estados Unidos de Norte América). El resultado muestra que los datos de la NASA son confiables principalmente para la temperatura máxima y mínima con coeficientes de correlación aceptables superiores a 0.4. Sin embargo, para la precipitación, sobrestiman o subestiman los datos medidos por el SENAMHI, muy probablemente debido a que la precipitación estimado por satélites es afectada por varios factores como, la humedad atmosférica, topografía, montañas y el fenómeno del niño y la niña. Concluyéndose que para la cuenca Mauri en el Altiplano se podría utilizar directamente los datos satelitales re analizados, sin embargo, para la precipitación se debería realizar ajustes antes de utilizarlas. Por otro lado, para la cuenca del rio Coroico en la estación de Caranavi, antes de utilizar las temperaturas y precipitación satelitales reanalizadas se deberías realizar ajustes mediante técnicas como regresión u otras.
Palabras clave: Datos meteorológicos; temperatura; precipitación; estaciones terrestres y satelitales; cuenca Mauri y Coroico
In some stations in Bolivia, there are no meteorological data or they are incomplete, to carry out various studies including the water balance in basins such as the Mauri and Coroico, an alternative is the data from satellites that are available and were reanalyzed, before using them, comparisons and analysis must be made based on the stations in situ, to use them directly or make some adjustment. Internationally, there are several studies on the comparison of meteorological data measured at stations with satellite data. Previous studies in Bolivia indicated that data from the Climate Forecast System Re-analysis, which uses the Water and Soil Assessment Tool model from satellites, is acceptable for mean and minimum temperature, and in the Katari Basin they used satellite precipitation data to adjust for gaps at some stations. In this research, meteorological data (maximum and minimum temperature and precipitation) were obtained from the National Meteorological and Hydrological Service of Peru and Bolivia, for the stations of Chuapalca and Calacoto, located in the Mauri river basin, and Caranavi in the Coroico river basin. The data were statistically compared, using the Nash Sutcliffe coefficient of adequacy, percentage bias (PBIAS), and r = CoC (Pearson's correlation coefficient), with meteorological data obtained from NASA (National Aeronautics and Space Administration of the United States of America). The result shows that NASA data are reliable mainly for maximum and minimum temperatures with acceptable correlation coefficients above 0.4. However, for precipitation, they overestimate or underestimate the data measured by SENAMHI, most likely because satellite-estimated precipitation is affected by several factors such as atmospheric humidity, topography, mountains, and the El Niño and La Niña phenomena. It was concluded that for the Mauri basin in the Altiplano, the re-analyzed satellite data could be used directly, however, precipitation adjustments should be made before using them. On the other hand, for the Coroico river basin at the Caranavi station, adjustments should be made using techniques such as regression or others before using the re-analyzed satellite temperatures and precipitation.
Keywords: Meteorological data; Temperature; precipitation; measured at ground and satellite stations; Mauri and Coroico basin
Introduction
In some stations and localities in Bolivia, it is difficult to access temperature and precipitation data, or there is a need to supplement them with satellite data, however, meteorological data should be analysed before using them in various climatological, hydrological, energy, agricultural and livestock studies, among others. Satellite meteorological data, while evacuating information that should necessarily be contrasted with terrestrial data, and thus improve satellite meteorological devices, or reduce the error through statistics to use them, in this sense, there are studies that compare satellite precipitation data with rainfall gauges in Nepal1, Australia2, China3,4, Brazil5 and Colombia6, among others. As well as temperature7,8, studies carried out in Bolivia9, refer that climate forescat system reanalysis (CFSR) data for the soil and water assessment tool (SWAT) model, coming from satellites, are acceptable for average and minimum temperature. Satellite meteorological data can also be used to complete the historical precipitation series10 or to correct local precipitation series based on satellite data11.
Therefore, the objective of the research was to statistically compare meteorological data from stations of the National Service of Meteorology and Hydrology (SENAMHI) in the Mauri and Coroico river basins, in relation to re-analysed satellite data.
Materials and methods
The study area is located in 2 meteorological stations of the Mauri River basin, this basin is tri-national and transboundary, the Mauri River originates in Peru and flows into the Desaguadero River in Bolivia12 and another part is in Chile, in the same area there are stations such as Calacoto - Bolivia, located at 17.28o LS, 68.63o LO and 3826 m.a.s.l., and Chuapalca - Peru, located at 17.28o LS, 68.63o LO and 3826 m.a.s.l., and Chuapalca - Peru, located at 17.29o LS, 69.63o LO and 4250 m.a.s.l., (Figure 1). The other station at Caranavi in the Coroico river basin, located at 15, 83º LS, 62.28o LO and 600 m.a.s.l. (Figure 2).
Meteorological data. In this research meteorological data (maximum temperature and precipitation) were obtained from the Chuapalca stations in Peru13, the minimum temperature was not taken into account, because there were several unrecorded data.
For the stations of Calacoto and Caranavi (Coroico river basin)14 in Bolivia, the maximum temperature (o C), minimum temperature (o C) and precipitation (mm) were obtained in Excel format from the SENAMHI Bolivia website15.
Satellite meteorological data. They were obtained from POWER Data Access Viewer3 from the National Aeronautics and Space Administration of the United States of America (NASA), these meteorological data in POWER Release 8 were based on products from GMAO (Global Modeling and Assimilation Office) and MERRA-2 (Modern Era Retrospective-Analysis for Research and Applications) and GMAO Direct Processing Instrument Teams (FP-IT)16.
Variables measured. The variables obtained from the Bolivian and Peruvian SENAMHI are presented in Table 1
Table 1 Meteorological data from the Calacoto and Chuapalca stations in the Mauri River Basin, and Caranavi in the Coroico River Basin
| Basin | Station | Meteorological data | Date |
|---|---|---|---|
| Mauri | Calacoto SENAMHI - Bolivia | Tmax (o C), Tmin (o C), pp (mm) | 2000-2020 |
| Chuapalca SENAMHI - Perú | Tmax (o C), pp (mm) | 2000-2014 | |
| Coroico | Caranavi SENAMHI - Bolivia | Tmax (o C), Tmin (o C), y pp (mm) | 2010-2016 |
Tmax (Maximun temperature), Tmin (Minimum temperature), pp (Rainfall)
Statistical analysis. For the statistical analysis, the meteorological data observed in the Bolivian and Peruvian SENAMHI basins were compared with those estimated and re-analysed by NASA, using the following statistics: NSE (Nash and Sutcliffe efficiency coefficient)17, PBIAS (Percentage bias)18,19, and CoC (Pearson's correlation coefficient)20 using Excel and R21.
The obtained values of the coefficient of NSE and CoC = r22 can be interpreted according to Table 2 and 3.
Table 2 Interpretation of NSE values-Nash and Sutcliffe sufficiency coefficient and Pearson correlation coefficient r = CoC)
| NSE values | Interpretation | r values | Interpretation |
|---|---|---|---|
|
NSE < .36 .36 < NSE< .75 NSE > .75 |
No quilified Qualified Good |
-.3-.3 ±.3-±.7 ±.7-± 1 |
Weak Moderate Strong |
Table 3 Interpretation of PBIAS values
| PBIAS (%) | Interpretation |
|---|---|
|
0-10 10-15 15-25 >25 |
Very good Good Fail Inadecuado |
The values of PBIAS (percentage bias) can be qualified as follows (Table 3).
Results
In this research, meteorological data (maximum and minimum temperature and precipitation) were obtained from the stations of Chuapalca in Peru and Calacoto in Bolivia, located in the Mauri River basin12, and the station of Caranavi in the Coroico river basin14.
Stations in the Mauri River basin.Figure 3a and b, present the correlation between the maximum temperature for SENAMHI versus NASA data for the Calacoto and Chuapalca station in the Mauri River basin.
Figure a left and b right
Figure 3 Comparison of the maximum temperature of the SENAMHI stations versus NASA (a) Calacoto from 2000 to 2020, (b) Chuapalca from 2000 to 2014
Table 4 and Figure 3a and b, present the comparison of the maximum temperatures, measured by SENAMHI versus NASA data, the NSE for the Chuapalca station in Peru shows a value of 0.6. Regarding the PBIAS the Chuapalca station shows a value of 1.58, and the Calacoto station a value of 11.5. For the correlation coefficient Chuapalca stands out with 0.7 and Calacoto with 0.4.
Table 4 and Figure 3a and b, show the comparison of maximum and minimum temperatures and precipitation measured by SENAMHI at Calacoto and Chuapalca stations, compared to NASA data.
Regarding the comparison of minimum temperature in Table 4, it shows for Calacoto an NSE of 0.2, the PBIAS 211.2 and CoC of 0.61. On the other hand, precipitation for Calacoto shows an NSE of -2.75, PBIAS -2.78 and CoC 0.1, as well as for Chuapalca the NSE, PBIAS and CoC were 0.1, 44.32 and 0.37 respectively. The correlation coefficients of precipitation for Calacoto and Chuapalca are presented in Figure 4a and b.
Table 4 Nash Sutcliffe efficiency coefficient (NSE), bias percentage (PBIAS) and correlation coefficient (r), for the Calacoto stations from 2000 to 2020 and Chuapalca from 2000 to 2014, compared with satellite data
| Calacoto station | Chuapalca station | ||
|---|---|---|---|
| Maximun Temperature (o C) | NSE | -1.19 | .60 |
| PBIAS | 11.50 | 1.58 | |
| r=CoC | .40 | .78 | |
| Minimium Temperature (o C) | NSE | .20 | - |
| PBIAS | 211.20 | - | |
| r =CoC | .61 | - | |
| Rainfall (mm) | NSE | -2.75 | .10 |
| PBIAS | -2.78 | 44.32 | |
| r =CoC | .10 | .37 |
Figure 4 Comparison of SENAMHI and NASA precipitation for (a) Calacoto from 2000 to 2020 and (b) Chuapalca from 2000 to 2014
Coroico River Basin station.Table 5 presents the comparison statistics between SENAMHI Caranavi station and NASA for maximum temperature, minimum temperature and precipitation, where for maximum temperature the NSE was -11, PBIAS 30 % and r 0.52, as can also be seen in Figure 4a.Table 5 also shows that the SENAMHI precipitation in Caranavi in relation to NASA are not correlated with a value of r 0.2, however, it presents a PBIAS of 8 % and an NSE of -0.3.
Table 5 Nash Sutcliffe efficiency coefficient (NSE), bias percentage (PBIAS) and correlation coefficient (r=CoC), for the SENAMHI - Caranavi station from 2010 to 2016, compared with satellite data
| Tmax (oC) | Tmin (oC) | pp(mm) | ||||||
|---|---|---|---|---|---|---|---|---|
| NSE | PBIAS | r=CoC | NSE | PBIAS | r=CoC | NSE | PBIAS | r=CoC |
| -11 | 30 | .52 | -3.4 | 21 | .74 | -.3 | 8 | .2 |
Figure 5 presents the comparison of meteorological data for the SENAMHI Caranavi station in relation to NASA. It is observed that the minimum temperature data from NASA with an average of 15oC, are below those measured by SENAMHI with an average of 20o C, although they present the same trend (Figure 4b), which is also corroborated with an NSE of -3.40, PBIAS 21 % and CoC 0.74 (Table 5).
Discussion
For the stations of the Mauri River basin such as Calacoto and Chuapalca, the NSE was 0.6 in Chuapalca, there was a qualified correlation, Table 2, between the maximum temperature measured by SENAMHI and NASA, this was also corroborated by the CoC whose value 0.78, means that there is a strong positive correlation23, also the PBIAS had a value of 1.58 % was categorised as very good24,25, this percentage means that the variation between SENAMHI and NASA maximum temperature data is minimal and acceptable (Figure 3b).
In Calacoto the maximum temperature measured by SENAMHI in relation to NASA presented a moderate correlation (0.4)23, it is also visible with PBIAS with a value of 11.50 %, value categorized as good24,25, although it also means that NASA slightly underestimates the maximum temperature in Calacoto in relation to SENAMHI, it is very likely that for this reason the NSE was -1.19, which according to Table 2, is much lower than 0.36, which categorizes it as unqualified24,25. Studies in Brazil reported high correlation of satellite data with those measured at stations for temperatures26. One application in river basins is that meteorological data (maximum temperature) is needed to calculate evapotranspiration27, water balance and flow generation28, but often there is no or no continuous field-measured data29. An alternative could be to obtain the maximum temperature from NASA (MERRA 2) to complement this scarce or missing information30 in the areas where this information is needed.
Regarding the minimum temperature at the Calacoto station, the NSE with a value of -0.2 is categorised as unqualified, as it is below (0.36)23,31, in relation to PBIAS with a value of 105.20 % means that there is a big difference between the two, mainly in winter, as NASA with an average of 0.2o C, overestimated the minimum temperature in Calacoto in relation to SENAMHI with an average of -3.5, while the CoC was 0.4, which was categorised as moderate22. A study carried out in Peru32, found that there is indeed variation (overestimates and underestimates) between satellite data and those measured at the ground station for minimum temperature, the same author32, recommends that before using satellite data for minimum temperature, corrections should be made. This difference is most likely due to the fact that the dynamics of minimum temperature processes change significantly with altitude33.
Regarding precipitation, the values for NSE for both Calacoto and Chuapalca, are below (0.36)23, for PBIAS Calacoto -2.78 % was reported, categorized as very good, however they also indicate that the NASA data overestimate the precipitation for example in some dates like 04/01/2000 the SENAMHI reported 1. 6 mm and NASA 13.58 mm, a factor that affects the accuracy of satellites in the estimation of precipitation is the altitude, so a study on the island of Bali in Taiwan, tended to overestimate rainfall events at high altitudes34. Regarding the CoC the value was weak, as it is below (0.3)22 as observed in Figure 4a. In Chuapalca the PBIAS was 44.32 % value categorised as inadequate24, also this value refers that NASA data underestimates precipitation with a daily average of 0.7 mm and for SENAMHI a daily average of 1.3 mm, on the CoC 0.37 was moderate22,35.
Studies in African conditions, where precipitation data are scarce, NASA data had seasonal similarity with measured precipitation, but the values were higher than measured10, studies in Colombia, satellite data showed correlations of 0.7, but only in some regions and in others they were negative6, as reported in this research in Calacoto and Chuapalca. These differences could be attributed to the mountains, high humidity environmental sites and the El Niño and La Niña phenomenon6, studies in China, in some seasons such as summer, when there is a localised climate due to topography that is special, make it difficult for satellites to estimate the correct precipitation3, and in winter they tend to overestimate or underestimate precipitation depending on the satellite data used35. Other studies indicate that satellite data underestimate rainfall peaks and the presence of sporadic rainfall or showers due to frequent variations in soil moisture31. Therefore, prior to using satellite data for rainfall, corrections should be made using regression or other techniques.
In the Coroico river basin, the Caranavi station, in relation to the maximum temperature, the value of 30 % of the PBIAS estimated at the Caranavi station indicated that the SENAMHI station data exceeded the NASA data, in other words the NASA data underestimate with an average of 24o C compared to 34o C from SENAMHI, which is also corroborated by the NSE which was -11 categorized as unqualified since it is below (0. 36)23, studies conducted for MERRA-2 also tend to underestimate the real temperature values for Peruvian conditions25. On the other hand, CoC was 0.52 according to Table 2 was moderate, indicating that SENAMHI and NASA data have the same seasonal trend, studies in other scenarios show that indeed the data provided by satellites for temperature have some reliability in their trend36, it was also in this study for the minimum temperature with a CoC of 0.74 categorized as strong Table 2, however the PBIAS 21 % value categorized as insufficient means that the NASA data despite having the same trend as SENAMHI underestimate the minimum temperature with an average of 15o C compared to 20o C of SENAMHI, the NSE resulted in -3.4 categorized as inadequate according to Table 2. For the temperature there is moderate to strong correlation, this allows us to use the regression equations36 to correct the data obtained from the satellites at the Caranavi station.
For precipitation in Caranavi both the CoC and NSE presented values of weak and improper according to Table 2, however, the PBIAS is categorised as very good with a value of -8 %, this means that there is a slight underestimation of the NASA precipitation with a daily average of 3.11 mm versus 2.88 mm from SENAMHI, these differences could most likely be due to the relatively rugged topography in Caranavi, which creates certain microclimates that the satellites cannot access due to the distance37.
The research will be used for flow modelling in the Mauri and Coroico basins. According to White16,38, meteorological data are relevant in the SWAT model, as precipitation is the main component, which generates surface runoff, infiltration and deep percolation that feed the flow of a river within the basin39, and temperatures are important for estimating evapotranspiration27. Apart from climatic data, river flow also depends on the state of catchment management and catchment cover40. Other utilities of this research are that it could be used for flood and drought risk forecasting and assessment, rainfall forecasting for dry periods, desertification, climate change studies and water resources planning, drought assessment in agriculture41, assessment of precipitation trends for hydrological analysis42, historical lake level mapping43 and application and analysis of hydrological models44,45.
It can be concluded that, for Altiplano stations (Calacoto and Chuapalca) in the Mauri River basin, maximum temperature data from NASA (MERRA 2) could be used directly, if ground-based data are not available or need to be supplemented. However, for minimum temperature and precipitation, due to over- or underestimation of satellite data, corrections by linear regression, dynamic downscaling, statistical or other techniques should be made before using NASA data (MERRA 2).
For subtropical conditions such as Caranavi in the Coroico river basin, for maximum and minimum temperature and precipitation due to over- or underestimation of satellite data, corrections by linear regression, downscaling or other techniques are recommended before using satellite data.
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Acknowledgements The author thanks SENAMHI of Peru and Bolivia, as well as the Area of Agricultural Sciences, Livestock and RRNN.
Ethical considerations The research was approved by the Ethics Committee of the Department of Agricultural Sciences and Natural Resources of the Public University of El Alto.
Limitations in the research In this study, we wanted to evaluate other meteorological data apart from precipitation and maximum and minimum temperature, such as wind speed and atmospheric humidity, however, in the SENAMHI stations these variables were not recorded or were incomplete, on the other hand, the minimum temperature of the Chuapalca station of SENAMHI Peru could not be evaluated, because it was incomplete.
Editor's Note: Journal of the Selva Andina Research Society (JSARS) remains neutral with respect to jurisdictional claims published in maps and institutional affiliations, and all claims expressed in this article are solely those of the authors, and do not necessarily represent those of their affiliated organizations, or those of the publisher, editors, and reviewers. Any product that may be evaluated in this article or claim that its manufacturer may make is neither guaranteed nor endorsed by the publisher.
Received: May 01, 2024; Revised: September 01, 2024; Accepted: December 01, 2024
Este es un artículo publicado en acceso abierto bajo una licencia Creative Commons
