Authors :
Dumisani Elia Siwinda; Edwin G Nyirenda
Volume/Issue :
Volume 8 - 2023, Issue 10 - October
Google Scholar :
https://tinyurl.com/35cccnnz
Scribd :
https://tinyurl.com/ycku3856
DOI :
https://doi.org/10.5281/zenodo.10074915
Abstract :
Satellite-based rainfall estimates offer a
valuable alternative for rainfall data collection,
particularly in developing countries like Malawi, which
face challenges due to limited ground gauge station
networks. However, these estimates often exhibit biases
and systematic errors, necessitating validation against
ground station data. The Climate Hazards Group
Infrared Precipitation with Station Data (CHIRPS) v2 is
one such product that has demonstrated promising
performance worldwide and is accessible in Malawi.
In this study, we evaluated CHIRPS monthly
rainfall estimates from January 1981 to December 2021
against ground station data from twenty locations in
Malawi. Our assessment considered CHIRPS'
performance in different seasons (wet and dry) and
geographic regions (high altitude, medium altitude, low
altitude, and the lakeshore). We used both continuous
(Coefficient of Correlation (R), Percent Bias (PBias), and
unbiased Root Mean Square Error (ubRMSE)) and
categorical scores (Probability of Detection (POD), False
Alarm Ratio (FAR), and Threat Score (TS)) for
evaluation.
Our results revealed that CHIRPS outperformed
during the wet season in comparison to the dry season,
considering both continuous and categorical scores. In
terms of geographic locations, CHIRPS exhibited the
highest R in the mid-altitude areas during both wet and
dry seasons, while low altitude areas had the poorest
performance. Additionally, CHIRPS displayed low bias
in the mid and low altitude areas during the wet season,
with poor performance observed at high altitudes and
the lakeshore. In the dry season, mid-altitude areas
maintained a good R performance. CHIRPS showed the
least error in high altitude areas in both seasons in terms
of ubRMSE. Furthermore, all locations achieved a good
POD of at least 0.957 during the wet season, while the
lakeshore had the highest mean POD of 0.369 during the
dry season. All regions exhibited a good FAR during the
wet season, with high altitudes performing well in the
dry season (mean FAR of 0.250). The Lakeshore
reported the highest mean TS of 0.932, while high
altitudes had the lowest (mean TS of 0.887).
In conclusion, CHIRPS demonstrates superior
performance in Malawi during the wet season compared
to the dry season. Geographically, there is no single
station that excels in all assessments; however, mid-
altitude areas consistently perform better in most
evaluations. Thus, CHIRPS can be a valuable resource
for water management and agricultural operations in
Malawi.
Keywords :
CHIRPS Dataset; Rainfall Analysis; Seasonal Variability; Geographic Locations; Performance Metrics; Malawi; Satellite-based Estimates; Precipitation Estimation.
Satellite-based rainfall estimates offer a
valuable alternative for rainfall data collection,
particularly in developing countries like Malawi, which
face challenges due to limited ground gauge station
networks. However, these estimates often exhibit biases
and systematic errors, necessitating validation against
ground station data. The Climate Hazards Group
Infrared Precipitation with Station Data (CHIRPS) v2 is
one such product that has demonstrated promising
performance worldwide and is accessible in Malawi.
In this study, we evaluated CHIRPS monthly
rainfall estimates from January 1981 to December 2021
against ground station data from twenty locations in
Malawi. Our assessment considered CHIRPS'
performance in different seasons (wet and dry) and
geographic regions (high altitude, medium altitude, low
altitude, and the lakeshore). We used both continuous
(Coefficient of Correlation (R), Percent Bias (PBias), and
unbiased Root Mean Square Error (ubRMSE)) and
categorical scores (Probability of Detection (POD), False
Alarm Ratio (FAR), and Threat Score (TS)) for
evaluation.
Our results revealed that CHIRPS outperformed
during the wet season in comparison to the dry season,
considering both continuous and categorical scores. In
terms of geographic locations, CHIRPS exhibited the
highest R in the mid-altitude areas during both wet and
dry seasons, while low altitude areas had the poorest
performance. Additionally, CHIRPS displayed low bias
in the mid and low altitude areas during the wet season,
with poor performance observed at high altitudes and
the lakeshore. In the dry season, mid-altitude areas
maintained a good R performance. CHIRPS showed the
least error in high altitude areas in both seasons in terms
of ubRMSE. Furthermore, all locations achieved a good
POD of at least 0.957 during the wet season, while the
lakeshore had the highest mean POD of 0.369 during the
dry season. All regions exhibited a good FAR during the
wet season, with high altitudes performing well in the
dry season (mean FAR of 0.250). The Lakeshore
reported the highest mean TS of 0.932, while high
altitudes had the lowest (mean TS of 0.887).
In conclusion, CHIRPS demonstrates superior
performance in Malawi during the wet season compared
to the dry season. Geographically, there is no single
station that excels in all assessments; however, mid-
altitude areas consistently perform better in most
evaluations. Thus, CHIRPS can be a valuable resource
for water management and agricultural operations in
Malawi.
Keywords :
CHIRPS Dataset; Rainfall Analysis; Seasonal Variability; Geographic Locations; Performance Metrics; Malawi; Satellite-based Estimates; Precipitation Estimation.
