Drought forecasts using data reported by the CHIRPS tool of the CHANLUD
satellite weather station
Pronósticos
de sequías mediante datos reportados por la herramienta CHIRPS de la estación
meteorológica satelital CHANLUD
Published Instituto Tecnológico Superior Edwards Deming. Quito
- Ecuador Periodicity July - September Vol. 1, Num. 26, 2025 pp.
51-69 http://centrosuragraria.com/index.php/revista Dates of receipt Received: April 02, 2025 Approved: June 11, 2025 Correspondence author Creative Commons License Creative Commons License,
Attribution-NonCommercial-ShareAlike 4.0 International.https://creativecommons.org/licenses/by-nc-sa/4.0/deed.es
Ruth Laura Barrera-Basantes1
Luis Patricio Tello-Oquendo2
Electronics and computer engineer, National University of Chimborazo. https://orcid.org/0000-0002-5656-6594 Electronics and computer engineer, National University of Chimborazo. https://orcid.org/0000-0002-5274-666X
Key words: Droughts, stochastic model, ARIMA, forecasts.
Resumen: El estudio de las características y los tipos de sequía observados en los últimos años son elementos indispensables al momento de identificar problemas relacionados al cambio climático. Las tendencias climáticas visibilizan con precisión zonas áridas y semiáridas las mismas que desencadenan cambios exponenciales en: los ciclos de cultivos que afectan directamente la seguridad alimentaria, en la economía agrícola y en el funcionamiento de infraestructuras para recursos hídricos. La propuesta de estudio contribuye a la construcción de un modelo estocástico que aporte al pronóstico de sequias en el Ecuador, la técnica utiliza información proporcionada por la herramienta CHIRPS ubicada en la estación meteorológica satelital de CHANLUD, los datos de CHIRPS se validaron a través de métricas estadísticas para un modelo ARIMA (2,0,1). Los resultados evidenciaron la presencia de sequías en categoría normal (escases de precipitaciones) y sequías extremadamente húmedas (suficientes precipitaciones pero problemas de distribución de agua), de estas durante el periodo de análisis 2000-2023 las sequías normales varían entre -0.99 y 0.94, correspondiendo a los meses de enero 2000 hasta marzo de 2012 y abril 2013 a noviembre 2023, la sequía extremadamente húmeda esta entre 2,38 y 4,32, para los meses de abril 2012 a marzo de 2013; además los eventos de sequias no presentaron patrones de periodicidad.
Palabras clave: Sequías, modelo estocástico, ARIMA, pronósticos.
Introduction
The
climatological and meteorological changes of the last decades report serious
problems in moorlands and water demarcations, especially in the national
territory where there are 31 hydrological systems formed by 79 basins coming
from watersheds that drain towards the Pacific Ocean (Patiño et al., 2023).
Among the significant triggers are the impact on agricultural production,
migration of human communities and reduction of structural spaces associated
with industry, manufacturing and, in the last annual period, the massive impact
was on the production of electricity. According to the National Electricity
Operator CENACE, it was impossible to provide Ecuadorian homes with electricity
since the water reservoirs reached their minimum levels, the damage reached all
fields from education, economy, technology to social coexistence itself, all
thanks to the fact that the dry season or drought in the eastern basin began
earlier than planned (Primicias, 2024).
Researchers
such as Espín and Soria (2021) characterized the
information of Andean ecosystems as the presence of a pattern of increased
temperature and evapotranspiration, conditioning factors for the presence or
absence of a drought. However, the World Meteorological Organization (2017)
highlighted that the evolution of drought in Ecuador is complex when considering
the existing relationship between El Niño Southern Oscillation (ENSO) and the spatio-temporal variability of droughts in the territory.
With
the aforementioned background, several analyses have shown that the trend of
droughts in the country triggers a constant risk due to the fact that the
cyclicity standards of the hydrological phenomenon constantly go from a
moderate category to a high category. At the same time, researcher Vergara
(2023) presented reports on behalf of the United Nations Convention to Combat
Drought where it was estimated that about 29% of Ecuador has experienced some
degree of "significant" degradation due to the impact of droughts.
In
this context, the Ministry of Environment, Water and Ecological Transition of
Ecuador in June 2021 drafted the National Drought Plan in order to propose and
formulate territorial strategies related to the phenomenon of drought in areas
with high levels of susceptibility. Considering the background information
provided, the losses related to the decrease or lack of precipitation amounted
to approximately US$424 million. The effects generated by natural phenomena are
not only caused by the absence of precipitation but also by an excessive
increase in precipitation (Ministry of the Environment, Water and Ecological
Transition, 2021).
Regarding
the monitoring of the phenomenon, the country has recorded measurements of
climatological variables in a conventional manner, capturing observations from
ground stations that, although accurate in regions located at high altitudes,
have limitations due to limited coverage and representativeness. Spatial
interpolation could become a solution; however, its usefulness generates
uncertainty in places where measurement stations are dispersed (Mena et al.,
2021).
This
is why the proposed prediction model allows the development of mitigation and
adaptation strategies to generate a contribution in the water sector,
especially in vulnerable regions. The contribution to biodiversity is
unavoidable since it is possible to control the alteration of ecosystems and
reduce the impact on species. From the technological perspective, the advance
of remote sensing highlights its capacity to offer satellite precipitation
estimates with great coverage, high resolution and public accessibility after
monitoring droughts, floods and landslides worldwide. This is intended to
highlight the effectiveness of Climate Hazards Group Infrared Precipitation
with Station (CHIRPS), a long-term satellite precipitation product designed for
drought monitoring using the Standardized Precipitation Index (SPI) as an
indicator of multiple droughts and space-time accuracy in terms of the
geographic area of droughts.
Methodology
The design of the
study was experimental considering the data collection process that used as a
technique the satellite observation and conversion of raster files captured at
the CHANLUD meteorological station into numerical information. The quantitative
methodology allowed the construction of the standardized precipitation index (SPI)
and the proposal of prediction models related to the manipulation of time
series. With respect to the depth of the object of study, it was descriptive
since the findings allowed characterizing the level of severity of drought and
comparing it with previous studies; in terms of time, it was a cross-sectional
study by monitoring the variable in the period 2000 - 2023.
Collective
The study
population consisted of satellite precipitation reports taken from the CHANLUD
meteorological station located in the Machángara
River micro-basin for the period January 2000 to December 2023.
Data Collection
Techniques and Instruments
The research used
the observation technique and the collection instrument was the CHIRPS digital
log downloaded from the CHANLUD weather station; the CHIRPS precipitation
satellite product information combines satellite measurements and
meteorological data provided on the ground for the generation of accurate
precipitation estimates. The CHIRPS product used "smart"
interpolation techniques to estimate high-resolution precipitation based on
infrared observations over a long-term period.
The downloaded
data corresponded to daily precipitation stored in .TIF extension raster files,
the necessary elements for data extraction were knowledge of the exact
coordinates of the weather station focal points to ensure reporting accuracy.
Data Processing
and Interpretation Techniques
Data processing
used methodologies such as descriptive statistics, precipitation indicator
calculation and stochastic prediction models to characterize and predict
information on precipitation values that determine the presence of droughts
under the following detail:
a. Descriptive
Statistics
The univariate
description of the climatological variable "precipitation" used
libraries such as library(dplyr), library(lubridate), library(xts),
library(ggplot2), library(ggpubr), library(ggh4x),
library(janitor), library(SPEI), library(SEI) and library(ggpubr),
by calculating numerical indicators (measures of central tendency, dispersion and
shape). In addition, graphical representations such as box plots and
oscillation plots that referred the behavior of the feature on a timeline were
performed.
b. Determination
of the Standard Precipitation Index (SPI)
The calculation
and weighting of the SPI used libraries such as SPEI from the satellite station
reports, the back-up methodology was the one proposed by (Edwards & McKee,
1997).
Table1 . Standardized Precipitation Index (SPI)
Values
|
Drought Category
or Severity |
SPI Value |
|
|
Extremely Wet |
≥ 2.0 |
|
|
Very Wet |
1.5 a 1.99 |
|
|
Moderately Humid |
1.0 a 1.49 |
|
|
Normal or
approximately normal |
-0.99 a 0.99 |
|
|
Moderately dry |
-1.0 a -1.49 |
|
|
Severely dry |
-1.5 a -1.99 |
|
|
Extremely dry |
≤ -2 |
|
Source: (Edwards & McKee, 1997).
c. Stochastic prediction model
A model is
defined as autoregressive where the response variable of a period t is
explained by the observations of itself in previous periods plus an error term;
in addition all Y_t can be expressed as a linear
combination of its historical values in the same way plus the error.
Autoregressive models are represented by the word AR which indicates the order
of the model: AR (1), AR (2),....etc. The order of the model expresses the
number of lagged observations of the analyzed time series. Thus, for example, an
AR (1) model would have the expression shown in equation 1:
The error term of
models of this type is generally called white noise when it meets the three
traditional basic assumptions: zero mean, constant variance and zero covariance
between errors corresponding to different observations. The generic expression
of an autoregressive AR(p) model would be as shown below:
In abbreviated
form the model would be:
Where
If the process is
replicated successively p times it delays the value in p periods as referred to
in the equation:
Data processing
was performed in R programming language, for organization of tables and other
second order results an Excel spreadsheet was used.
Results
Descriptive
Statistics
The following
information corresponds to the univariate description of the precipitation
variable from the reports of the CHANLUD satellite meteorological station; at
this stage of the study, numerical indicators and comparative graphs were used
throughout the years.
Table 2. Numerical indicators of
"Precipitation" period 2000-2023
|
Precipitation
Indicators |
|
|
Average |
61,42 |
|
Standard error |
3,30 |
|
Median |
47,56 |
|
Mode |
amodal |
|
Standard deviation |
75,03 |
|
Sample variance |
5629,77 |
|
Kurtosis |
92,32 |
|
Skewness
coefficient |
8,26 |
|
Range |
981,88 |
|
Minimum |
9,8 |
|
Maximum |
991,68 |
Source: Authors.
The report of 23
years showed that the annual rainfall was 61.42 ml/m2 variation was 75.03
ml/m2, the kurtosis value of 92.32 reflected the presence of a leptokurtic
distribution or in turn, in reinforcement to the above mentioned, the maximum
value of 991.68 ml/m2 represented the presence of very intense rains during the
interval. Likewise, the minimum value of 9.8 ml/m2 reflected the minimum
presence of rainfall, the extremes of the variable evidenced both the presence
of extremely strong storms and the presence of droughts, which reveals a
disorderly climatic change related to an unusual climatic pattern.
The following
multiple plots show the comparative trend of precipitation at the satellite
level.
Figure 1. Monthly interannual comparison between 2000 and
2023 satellite stations.
Source: Author
The behavior of
the satellite information lacks a defined trend, the time series that collects
the interannual monthly values of the 23 years reflected a variation in the
cyclicity over time, an inescapable unimodal distribution was reported where
the months with the highest precipitation were February and with a minimal
difference March; those with the lowest precipitation were June, July, August
and September. Jiménez et al. (2024) validated the findings constructed from
satellite data due to characteristics such as a wider coverage when monitoring
large areas that are difficult to access for ground stations. In relation to
technology, satellites, thanks to radars and infrared sensors, detect humidity
levels and rainfall intensity with greater precision.
On the other
hand, Perez et al., (2020) in a climate trend study period 1976 to 2017
determined that according to the multi-year monthly averages there was a dry
climate in the months of June to September and a wet climate in two intervals
(February - May and October - December). (2004) in the South American Amazon
determined that the wave analysis of the Amazonian hydrological regime shows a
high temporal instability with intermittent interannual and interdecadal
oscillations during the years 1903-1998; therefore, on interannual scales,
precipitation can be high and low and these changes can be associated with the
El Niño phenomenon.
In contrast to
these results, Oñate and Bosque (2001) reported that in the southern zone of
Ecuador and northern Peru, a report of 40 meteorological stations from 1970 to
2000 showed decreasing trends in the highlands and increasing trends in the
lowlands, a contribution that was not taken into account by the authorities in
charge of hydroelectric plants after warning of a significant decrease in
precipitation and a possible increase in extreme events.
Table 3. Statistical metrics of the satellite report
|
Metric |
Value |
|
Relative bias |
0.52 |
|
Pearson correlation |
0.75 |
|
Root
mean square error (RMSE) |
65.23 |
|
Fractional RMSE |
0.39 |
Source: Author
The relative bias
allowed validating the precision and accuracy of the reports, a value of 0.52
revealed that the estimator overestimated the true precipitation value, which
evidenced the lack of quality control processes of the equipment used for data
collection. With respect to the mean square error, notable discrepancies were
observed between the possible predictions and their real value, which suggests
the use of time series tools that do not involve the association between two
characteristics, but work with their own outdated information. The RMSE
indicator may detract from the modeling if one considers the information of its
variant Fractional RMSE indicator which indicated that the average model error
is approximately 39%.
Standardized
Precipitation Index (SPI) Construction
The SPI was
constructed under the SPEI functions package whose transformation of the
variable was in monthly periods, then its results were discretized through a frequency
distribution to identify the category of droughts that could be of different
nature according to the categories proposed in Table 1 of the document.
Table 4. Standardized precipitation index classification
|
Category or severity of drought |
Frequency |
Percentage |
|
Extremely wet |
16 |
5,6 |
|
Moderately Humid |
1 |
0,3 |
|
Moderately Dry |
1 |
0,3 |
|
Normal |
270 |
93,8 |
|
Total |
288 |
100 |
Source: Author
93.8% of the SPI
ruled out the absence of droughts since according to the indicator the
precipitation described a normal climatic behavior, 5.6% were extremely humid
droughts, only 0.3% was the percentage of representativeness for the moderately
humid and moderately dry categories.
Stochastic model
for the standardized precipitation index SPI
For the
construction of the stochastic model, the indexes were organized in a numerical
vector of information that represented the standardization of each of the
precipitation values in the 23 years of historical analysis.
Figure 2 . SPI time series period 2000-2023.
Source: Author
Figure 3. SPI time series
Source: Author
The creation of
the time series graph allows evaluating the presence of stationarity in order
to facilitate the analysis and modeling of the indicator and thereby ensure
that statistical properties such as the mean and variance are constant over
time. The visual report appeared that the SPI values are stationary; however,
the Dickey Fuller test at 5% significance was used under the following
hypotheses:
H0: The time
series is not stationary.
H1: The time
series is stationary.
The probability
value was 0.01 which verified compliance with stationarity (p-value ≤ α: Reject H0).
Additionally to
evaluate the characteristics of the time series.
Figure 4. SPI time series characteristics.
Source: Author
The composite
plot demonstrated the presence of a constant long-term trend, the seasonality
evidenced the fulfillment of monthly patterns in terms of constant increase or
decrease of precipitation, the random component (noise) guaranteed the
variability of the data.
Figure 5 shows
the base series for the construction of the model.
Figure 5. SPI stationary time series.
Source: Author
To define the
model parameters, the autocorrelation function and the partial autocorrelation
function were calculated in order to define the number of autoregressives
and moving averages used in the ARIMA model.
Figure 6. Autocorrelation function and partial
autocorrelation function of the SPI.
Source: Author
According to the
graphical report of the ACF and PACF functions, the proposed model used 2
autoregressive, zero differences because the series was stationary from its
origin file and an ARIMA moving average (2,0,1), each of the parameters are
shown in Figure 6.
The model under
the parameters would be according to the equation
For the
validation of the model, the diagnostic graph was constructed in order to
evaluate the presence of white noise and guarantee the prediction of
standardized precipitation index values in the coming years.
Figure 7. Characteristics of the SPI ARIMA (2,0,1) model.
Source: Author
Although the
graph showed the presence of white noise, the Ljung Box inferential test was
performed at 5% significance under the following hypotheses:
H0: There is
white noise in the SPI series.
H1: There is no
white noise in the SPI series.
The probability
value was 0.05 which verified the presence of white noise in the series
(p-value ≤ α: Reject H0), this validates the constant variance, mean equal to zero
and the absence of correlations to handle data independence.
Calculation of
forecasts
Figure 8. Model forecasts
Source: Author
The prediction
model requested the construction of 12 SPI values corresponding to the year
2024, each of the forecasts presented confidence intervals at 80% and 95% of
significance, the positive or negative sign of the interval limits represent
the increase or decrease of the SPI index over time.
The information
in the table is shown in the following value extension graph.
Figure 9. SPI time series and model forecasts
The present
research used precipitation reports from the CHANLUD weather station for SPI
calculation and the results found are reason for contrast with the stochastic
model proposed in the research of Saquisili (2019). In that research, a
stochastic model was proposed for drought prediction using climate and
reanalysis data for the Machangara river sub-basin
but with the difference that the precipitation values for SPI calculation were
captured from the CHANLUD ground weather station.
The comparative
study developed the proposal of 12 ARIMA models fitted on the time series of
the standardized precipitation index with the parameters described in Figure
10.
Figure 10. ARIMA models proposed in the comparative study.
Source:
(Saquisili, 2019).
Saquisili (2019)
mentioned that the proposed models in most cases are not stationary which
invalidates the calculation of forecasts with any of the models however the
present research built an ARIMA (10,0,1) model that guaranteed stationarity and
white noise. This showed that data from satellites collect more information
compared to terrestrial information; on the other hand, data collection of
climatic variables is more accurate in satellite information due to the
location of the meteorological station.
On the other
hand, the author Saquisili (2019) mentions "The SPI3 values for the
analysis period 1965-2015, show that moderate, severe and extreme drought
events, have taken place in the study area. For the labrado
station, moderate drought varies between -1 and -1.45, corresponding to the
months of December 1966 and August 1992; severe drought ranges between -1.52
and -1.96, for the month of June 2014 and December 1989; and extreme drought is
between -2.02 and - 2.84, for the month of August 2012 and November 2010,
respectively. While, in the CHANLUD station, the moderate drought ranges
between -1 and -1.48, corresponding to the months of February 1969 and August
2003; the severe drought fluctuates between -1.51 and -1.78, recorded in the
month of October 2001 and May 1967, respectively; and the minimum extreme
drought is recorded in April 2013 with a value of -2 and the maximum is
recorded in December 2000, with a value of -3.08. All drought events have a
duration of 1 to 4 months. Also, it could be identified that, throughout the
period of analysis, there are no drought events that show a certain
periodicity".
In this research,
the SPI for the period of analysis 1981-2023, identifies normal, moderately dry
and extremely wet drought events. The normal droughts vary between -0.99 and
0.94, corresponding to the months from January to December from 1981 to 2023
with a total of 422 months of the 516 months observed in the 42 years; the
moderately dry drought ranges between -1.00 and -1.47, for the months of April,
June August, November and December of the years 1982, 1985, 1986, 1987, 1988,
1989, 1990, 1991 and 2005; and extremely wet drought is between 2.38 and 4.32,
for the months of August to December 1999, January to April in 2000, April to
December 2012 and January to March 2013. The drought events did not show
patterns of periodicity.
Conclusions
The usefulness of
the CHIRPS tool guarantees reliability in the climate data collected thanks to
an almost global coverage since 1981 with a resolution of 0.05° between 50°S
and 50°N, its information matrix stores precipitation values from satellite
estimates of great value and these were validated through metrics such as
Pearson Correlation Index, Relative Bias, Pearson Correlation, Root Mean Square
Error (RMSE) and Fractional RMSE.
The Standardized
Precipitation Index (SPI) indicates, according to its percentage distribution,
that 93.8% of the values of the indicator denote the presence of droughts with
normal behavior and 5.6% correspond to extremely wet droughts.
With reference to
the SPI values during the observation period 2000-2023, there were normal and
extremely humid drought events. Normal droughts vary between -0.99 and 0.94,
these appeared in the months of January 2000 to March 2012 in a continuous
monthly basis and then reappeared in April 2013 to November 2023; this means
that during the indicated months Ecuador presents a shortage of rainfall so
that the natural climatic condition negatively affects water resources,
agriculture and the environment. On the other hand, the extremely humid drought
is between 2.38 and 4.32, it was present in the months of April 2012 to March
2013; which indicates that despite having sufficient rainfall in general terms,
there are problems of water shortage due to factors such as uneven distribution
of rainfall, poor water management or lack of adequate infrastructure.
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