=Paper=
{{Paper
|id=Vol-3930/paper21
|storemode=property
|title=Performance evaluation of CHIRPS satellite-derived precipitation product at a high-altitude Mediterranean forest
|pdfUrl=https://ceur-ws.org/Vol-3930/paper21.pdf
|volume=Vol-3930
|authors=Stefanos P. Stefanidis,Nikolaos D. Proutsos,Panagiotis S. Stefanidis
|dblpUrl=https://dblp.org/rec/conf/haicta/StefanidisPS24
}}
==Performance evaluation of CHIRPS satellite-derived precipitation product at a high-altitude Mediterranean forest==
https://ceur-ws.org/Vol-3930/paper21.pdf
Performance evaluation of CHIRPS satellite-derived
precipitation product at a high-altitude Mediterranean
forest⋆
Stefanos P. Stefanidis1,†, Nikolaos D. Proutsos2,∗,† and Panagiotis S. Stefanidis3,†
1
Forest Research Institute, Hellenic Agricultural Organization - DIMITRA, Vassilika, Thessaloniki, 57006, Greece
2
Institute of Mediterranean Forest Ecosystems, Hellenic Agricultural Organization - DIMITRA, Terma Alkmanos, Athens,
11528, Greece
3
Laboratory of Mountainous Water Management and Control, Faculty of Forestry and Natural Environment, Aristotle
University of Thessaloniki, Thessaloniki, 54124, Greece
Abstract
The aim of this study is to investigate the accuracy of the Climate Hazards Group Infrared Precipitation
with Stations (CHIRPS) satellite-derived precipitation product against rain gauge data at a high-altitude
Mediterranean forest in Central Greece over a 40-year period (1981-2020). Given the difficulties of
establishing and maintaining rain gauges in forested regions, satellite-derived products such as CHIRPS
may offer valuable information. However, the accuracy of this precipitation product, specifically in
mountainous areas remains underexplored. This study comprised the use of statistical metrics to compare
the monthly, seasonal, and annual precipitation estimates from CHIRPS to those of the observed ground
measures. The findings reveal that CHIRPS effectively captures the pattern of monthly precipitation,
although there is in general a bias towards significantly overpredicting precipitation and strong seasonality
of its performance. Thus, CHIRPS presents significant potential as an efficient source of information in the
absence of ground observations for autumn and possibly summer precipitation in ungauged Mediterranean
mountainous forest sites, though it should be used cautiously for winter and spring.
Keywords
Forest meteorological station, satellite precipitation, Mediterranean, statistical evaluation, Greece 1
1. Introduction
Precipitation plays an essential role in the hydrological cycle and influences multiple disciplines
and applications. Accurate estimation of precipitation in time and space is crucial for supporting the
decision-making process in various water resources, agriculture, climatology, and hydro-energy
scenarios. Although rain gauges remain the most common and reliable instrument for obtaining an
accurate high temporal resolution of precipitation measurement, point scale is limited, and the
spatial variation effectiveness is unpractical in many parts of the world characterized by complex
topology and low-density gauge network [1]. As a result, the conventional approach to areal
precipitation is mainly based on spatial interpolations of the point based rain gauge data. However,
this approach limitations in areas with a sparse gauge network.
Over the past few years, a large number of gridded precipitation products with different spatial
resolutions and coverage periods have been developted [2,3]. These products can generally be
categorized into five major types based on their methodology and attributes: satellite products (e.g.,
GPM, TRMM), radar-based products (e.g., OPERA), reanalysis datasets (e.g., ERA5, CERRA), station-
based gridding (e.g., CRU, GPCC), and hybrid products (e.g., CHIRPS, CMORPH). Notably out of the
⋆ Short Paper Proceedings, Volume I of the 11th International Conference on Information and Communication Technologies in
Agriculture, Food & Environment (HAICTA 2024), Karlovasi, Samos, Greece, 17-20 October 2024.
∗
Corresponding author.
†
These authors contributed equally.
sstefanidis@elgo.gr (S. Stefanidis); np@fria.gr (N. Proutsos); stefanid@for.auth.gr (P. Stefanidis)
0000-0002-7721-7553 (S. Stefanidis); 0000-0002-8270-2991 (N. Proutsos)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
ceur-ws.org 123
ISSN 1613-0073
Proceedings
aforementioned datasets, CHIRPS is known for its fine resolution and wide coverage over time [4].
Several studies have been conducted to evaluate the performance of the CHIRPS precipitation
product. However, these studies typically are based in comparisons with ground data from lowland
precipitation stations [5-9], and only a few of them represent a long analysis for complex
mountainous terrain. These constraints arise from the difficulties involved in instalation,
maintaining and monitoring stations at high elevations, in mountain regions [10].
This study aims to evaluate the performance of CHIRPS precipitation estimates at a high-altitude
Mediterranean forest in Central Greece compared to rain gauge observations over a forty-year period
(1981-2020).
2. Material and methods
The research area is the University Forest of Pertouli, which is situated in the Central Greece's
Pindus Mountain Range. The forest is managed by the University Forests Administration and
Management Fund (UFAMF), which was granted to Aristotle University of Thessaloniki in 1934 for
research and education purposes. It spans approximately 3,290 hectares and extends from longitude
39°32' E to 39°35' E and latitude 21°33' N to 21°38' N (Figure 1). It consists mainly of pure fir stands
(Abies borisii regis), with elevations ranging from 1,100 m to 2,073 m above sea level. A detailed
description of the study area can be found in Stefanidis et al. [11]. Since 1961, a meteorological station
has been installed (1,180 m.a.s.l.) in the forest site and operated by UFAMF. Monthly precipitation
data for the period 1980 – 2020 were utilized for this study.
Figure 1: Location map of the study area.
Additionally, daily estimates of CHIRPS precipitation product, were retrieved through the use of
Google Earth Engine (GEE) cloud computing platform (ee.ImageCollection("UCSB-
CHG/CHIRPS/DAILY) from January 1, 1981, to December 31, 2020.
CHIRPS, developed collaboratively by the Climate Hazards Group at the University of California,
Santa Barbara (UCSB) and the US Geological Survey (USGS), is a gridded precipitation product with
124
quasi-global coverage (50° S–50° N, 180° E–180° W). Operating at a spatial resolution of 0.05°, CHIRPS
provides precipitation estimates from 1981 to near-present, leveraging input parameters such as
monthly precipitation climatology (CHPClim), infrared (IR) sensors from geostationary satellites,
and ground precipitation observations. Notably, CHIRPS incorporates data from the Tropical Rainfall
Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA 3B42) to enhance its
blending process, thereby enhancing its accuracy and reliability.
A variety of statistical metrics were used to evaluate the performance of CHIRPS estimates in
reproducing precipitation, compared to gauged observations at the forest site. These metrics include
correlation coefficient (CC), mean bias error (MBE), mean absolute error (MAE), root-mean-square
error (RMSE) and relative bias (RBIAS). The detailed description of these indices can be found in
previous studies by Stefanidis et al [12] and Alexandridis et al. [13]. Additionally, MBE, MAE and
RMSE were converted to %MBE, %MAE and %RMSE by dividing their initial values with means of
observed values, facilitating comparison of the model’s performance between seasons [14].
3. Results and Discussion
The comparative presentation of the precipitation monthly values measured by rain-gauge and
simulated by CHIRPS is shown in Figure 2a, whereas Figure 2b depicts the monthly averages of all
data of the time period (1981-2020). In both cases the overestimation of precipitation by CHIRPS is
evident. The monthly values present an expected dispersion, with a relatively low slope a (0.668) and
moderate offcet b (22.024) value of the regression line y=ax+b, suggesting an overestimation of about
33% of the monthly values. The coefficient of determination R2 (0.566), can be considered as
adequatefor precipitation assessments suggesting the caution use of the dataset in ungauged
watersheds. The overestimations are clearly shown in Figure 2b being (on absolute) higher in winter
months and lower in summer, with respect to the uneven precipitation distribution that characterizes
the Mediterranean climate [15, 16].
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Figure 2: Regression (a) and averages (b) of monthly precipitation measured (gauge) and estimated
(CHIRPS) values in the forest site of Pertouli, Greece.
The values of the annual precipitation of the two datasets present differences. The CHIRPS
average annual precipitation is 1745 mm, overestimated by a percentage of +21%, compared to the
ground station annual average (1443 mm), which represents an absolute difference of 302 mm. The
average differences is even more variable from year to year as presented in the statistics of Table 1,
where, especially, the correlation coefficient (CC) shows relatively low values (0.55) suggesting a
high desprersion of the annual values.
On a seasonal basis, the differences between the two datasets are even more evident, suggesting
that that the average CHIRPS precipitation of autumn is quite accurate compared to other seasons
125
producing overestimates, but only by +4%, i.e. a precipitation magnitude of about 17 mm, which is a
rather small difference considering that the CHIRPS autumn precipitation is 453 mm, very close to
the respective measured value (436 mm). Compared to all other seasonal statistics presented in Table
1, autumn has the best values of RMSE (34 %), MBE (16.5mm and 4%), MAE (25%) and RBIAS (0.038).
It should be noted, however, that CHIRPS dataset shows an adequate performance in summer
precipitation, producing relatively accurate estimates in our forest site. The summer average
precipitation is limited (132 mm) and rather overestimated by CHIRPS (157 mm) by about +19% (25
mm). However, in summer the comparison between the datasets showed best RMSE (68.4 mm) and
MAE (56.4 mm) values than all other seasons. Finally, the seasonal pattern reveals that spring
precipitation, though highly overestimated (by +33%), presents the best correlation coefficient (CC)
value, indicating the least dispersion of precipitation during the spring months. Following spring in
performance, winter displays the second-best CC value, underscoring its predictive reliability.
Literature supports these findings, particularly for mountainous areas where CHIRPS demonstrated
good correlation with local station data, especially during the winter months [6]. Overall, CHIRPS
can be a good alternative for the autumn and probably summer precipitation in ungauged
Mediterranean mountainous forest sites, however must be used cosiously for the winter and spring
values. The unsatisfactory performance of CHIRPS in spring, particularly in regions with complex
terrain, is also noted by Aksu and Akgül [7], who assessed the performance of CHIRPS against 77
ground station in Turkey for the period 2008-2018 and found that CHIRPS overestitmated monthly
precipitation by 0 to 80 mm/month.
Table 1
Summary of spatial datasets used in this study
Month/ Average (mm) RMSE MBE MAE
Season CHIRPS Gauge (mm) (%) (mm) (%) (mm) (%) RBIAS CC
Monthly
January 205 160 100.4 63 44.5 28 67.4 42 0.277 0.696
February 188 164 76.9 47 23.7 14 57.1 35 0.145 0.679
March 215 131 100.8 77 83.7 64 87.3 67 0.638 0.641
April 138 113 60.5 53 24.4 22 48.9 43 0.215 0.661
May 104 98 44.7 45 6.0 6 33.4 34 0.061 0.608
June 72 45 39.4 88 27.3 61 32.7 73 0.612 0.657
July 47 46 28.8 63 1.2 3 22.7 50 0.026 0.640
August 39 41 36.0 87 -2.7 -7 24.5 59 -0.066 0.552
September 69 95 98.6 104 -25.6 -27 51.2 54 -0.270 0.472
October 164 148 75.2 51 16.5 11 55.6 38 0.112 0.671
November 219 194 104.5 54 25.5 13 79.4 41 0.132 0.492
December 286 209 128.6 62 77.7 37 97.9 47 0.373 0.647
Seasonal
Winter 679 533 240.2 45 146.0 27 182.7 34 0.274 0.545
Autumn 453 436 147.3 34 16.5 4 109.3 25 0.038 0.556
Spring 457 343 147.5 43 114.1 33 127.4 37 0.333 0.583
Summer 157 132 68.4 52 25.7 20 56.4 43 0.195 0.447
Annual 1745 1443 413.4 29 302.2 21 336.1 23 0.209 0.547
The above results are, in general, confirmed by the monthly statistics. The monthly CHIRPS
averages are overestimated in almost all months (by +2% or 1mm in July to +64% or 84 mm in March),
with the exception of August and September, when underestimated by -5% and -27% respectively.
On a monthly basis, the CHIRPS dataset generally performs well, in May, July and August producing
monthly precipitation estimates different by less than 10% on absolut compared to the measured
values. Additionally, during these months the values of the examined statistics indices are best
126
compared to the other months of the year (RMSE=45% and MAE=34% in May; RMSE=28.8 mm,
MBE=1.2 mm, MAE=22.7mm and RBIAS=0.026 in July). However, the poor performance of the
CHIRPS dataset during other months highly affect its performance on the seasonal level. For
example, the excellent performance of CHIRPS at the July and the August precipitation, when its
estimates are quite accurate (with differences less than 5%) is somehow undergraded by the datasets
highly overestimation in June (+60%) resulting to less accurate summer (June to August)
precipitation estimates. However, even in this case CHIRPS performed adequately for summer
precipitation. On the other hand, the very close estimates of autumn CHIRPS precipitation, is
produced by overestimated values in October (by +11%) and November (+13%) amplified by high
underestimates in September (-27%) that resulted to quite accurate seasonal values for the autumn.
This is critical, for monthly climate and hydrological assessments, and can lead to high uncertainties.
4. Conclusions
The evaluation of the CHIRPS precipitation product in a high-altitude forest of Central Greece
reveals significant discrepancies between satellite estimates and rain gauge observations,
characterized primarily by an overestimation of precipitation. Seasonal analysis further delineated
the dataset's performance, showcasing relatively better accuracy in autumn and notable
overestimations in spring and winter. These findings underscore the necessity for cautious
application of CHIRPS data, particularly in ungauged mountainous regions where precipitation
variability is high. Future research should focus on the performance evaluation of multi-source
gridded precipitation across diverse climatic conditions, evaluating also CHIRPS at extreme weather
events and for multiple long-operating meteorological stations. This study highlights the potential
and limitations of using CHIRPS product for environmental research and resource management in
complex forested landscapes.
Acknowledgements
The present work is supported by the project “Bioclima and vegetation of Greece”, funded by the
Hellenic Agricultural Organization – DIMITRA. The meteorological data used in this work were
obtained by the forest meteorological station installed in the University Forest of Pertouli (C. Greece)
that operates and supervised by the Aristotle University Forest Administration and Management
Fund.
Declaration on Generative AI
The author(s) have not employed any Generative AI tools.
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