Assessment of the CHIRPS-Based Satellite Precipitation Estimates

Paredes‐Trejo, Franklin; Barbosa, Humberto Alves; Kumar, T. V. Lakshmi; Thakur, Manoj Kumar; Buriti, Catarina de Oliveira

doi:10.5772/intechopen.91472

Cited by 14 publications

(5 citation statements)

References 42 publications

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“…Weekly rainfall (2005–2019) and temperature (2006–2016) data were obtained from the publicly available [ 153 ] University of California, Santa Barbara Climate Hazards Group InfraRed Precipitation and Temperature with Station dataset (CHIRPS and CHIRTS) products [ 154 , 155 ]. These data were then re-gridded to match each catchment site.…”

Section: Methodsmentioning

confidence: 99%

Inference and dynamic simulation of malaria using a simple climate-driven entomological model of malaria transmission

Ukawuba

Shaman

2022

PLoS Comput Biol

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Given the crucial role of climate in malaria transmission, many mechanistic models of malaria represent vector biology and the parasite lifecycle as functions of climate variables in order to accurately capture malaria transmission dynamics. Lower dimension mechanistic models that utilize implicit vector dynamics have relied on indirect climate modulation of transmission processes, which compromises investigation of the ecological role played by climate in malaria transmission. In this study, we develop an implicit process-based malaria model with direct climate-mediated modulation of transmission pressure borne through the Entomological Inoculation Rate (EIR). The EIR, a measure of the number of infectious bites per person per unit time, includes the effects of vector dynamics, resulting from mosquito development, survivorship, feeding activity and parasite development, all of which are moderated by climate. We combine this EIR-model framework, which is driven by rainfall and temperature, with Bayesian inference methods, and evaluate the model’s ability to simulate local transmission across 42 regions in Rwanda over four years. Our findings indicate that the biologically-motivated, EIR-model framework is capable of accurately simulating seasonal malaria dynamics and capturing of some of the inter-annual variation in malaria incidence. However, the model unsurprisingly failed to reproduce large declines in malaria transmission during 2018 and 2019 due to elevated anti-malaria measures, which were not accounted for in the model structure. The climate-driven transmission model also captured regional variation in malaria incidence across Rwanda’s diverse climate, while identifying key entomological and epidemiological parameters important to seasonal malaria dynamics. In general, this new model construct advances the capabilities of implicitly-forced lower dimension dynamical malaria models by leveraging climate drivers of malaria ecology and transmission.

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Section: Methodsmentioning

confidence: 99%

Inference and dynamic simulation of malaria using a simple climate-driven entomological model of malaria transmission

Ukawuba

Shaman

2022

PLoS Comput Biol

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“…• degrees south [24]. The University of California at Santa Barbara (UCSB) provides CHIRPS precipitation data in monthly, decadal, pentadal, and daily intervals, spanning the period from 1981 to the near present [86]. PERSIANN datasets were developed by the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine, utilizing Gridded Satellite (GridSat-B1) IR data [87].…”

Section: Satellite Datamentioning

confidence: 99%

Comparing Remote Sensing and Geostatistical Techniques in Filling Gaps in Rain Gauge Records and Generating Multi-Return Period Isohyetal Maps in Arid Regions—Case Study: Kingdom of Saudi Arabia

Helmi,

Farouk,

Hassan

et al. 2024

Water

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Arid regions are susceptible to flash floods and severe drought periods, therefore there is a need for accurate and gap-free rainfall data for the design of flood mitigation measures and water resource management. Nevertheless, arid regions may suffer from a shortage of precipitation gauge data, whether due to improper gauge coverage or gaps in the recorded data. Several alternatives are available to compensate for deficiencies in terrestrial rain gauge records, such as satellite data or utilizing geostatistical interpolation. However, adequate assessment of these alternatives is mandatory to avoid the dramatic effect of using improper data in the design of flood protection works and water resource management. The current study covers 75% of the Kingdom of Saudi Arabia’s area and spans the period from 1967 to 2014. Seven satellite precipitation datasets with daily, 3-h, and 30-min temporal resolutions, along with 43 geostatistical interpolation techniques, are evaluated as supplementary data to address the gaps in terrestrial gauge records. The Normalized Root Mean Square Error by the mean value of observation (NRMSE) is selected as a ranking criterion for the evaluated datasets. The geostatistical techniques outperformed the satellite datasets with 0.69 and 0.8 NRMSE for the maximum and total annual records, respectively. The best performance was found in the areas with the highest gauge density. PERSIANN-CDR and GPM IMERG V7 satellite datasets performed better than other satellite datasets, with 0.8 and 0.82 NRMSE for the maximum and total annual records, respectively. The spatial distributions of maximum and total annual precipitation for every year from 1967 to 2014 are generated using geostatistical techniques. Eight Probability Density Functions (PDFs) belonging to the Gamma, Normal, and Extreme Value families are assessed to fit the gap-filled datasets. The PDFs are ranked according to the Chi-square test results and Akaike information criterion (AIC). The Gamma, Extreme Value, and Normal distribution families had the best fitting over 56%, 34%, and 10% of the study area gridded data, respectively. Finally, the selected PDF at each grid point is utilized to generate the maximum annual precipitation for 2, 5, 10, 25, 50, and 100-year rasters that can be used directly as a gridded precipitation input for hydrological studies.

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“…Hal ini diartikan bahwa semakin dekat dengan garis pantai estimasi kejadian hujan data CHIPRS semakin baik, kebalikannya semakin jauh jarak stasiun dengan garis pantai, nilai false CHIRPS dalam estimasi kejadian hujan semakin tinggi. Hasil yang sama juga ditunjukkan oleh Paredes -Trejo, et al, (2021) bahwa estimasi CHIRPS lebih akurat pada daerah datar yang dekat dengan pantai.…”

Section: Validasi Kategorialunclassified

Validasi Curah Hujan Harian Chirps Precipitation Satellite Product Di Provinsi Kalimantan Barat

Suryanto

Amprin

Anisum

2023

JRPB

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Data curah hujan produk satelit dapat digunakan sebagai alternatif keterbatasan pengukuran curah hujan menggunakan penakar hujan. Akurasi data hujan satelit sangat bervariasi antar wilayah karena faktor lingkungan yang beragam, sehingga validasi hujan data satelit sangat diperlukan. Penelitian ini bertujuan untuk menguji akurasi data hujan harian Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) pada 7 stasiun hujan di Provinsi Kalimantan Barat. Metode point-to-pixel digunakan untuk membandingkan curah hujan harian pengamatan Badan Meteorologi Klimatologi dan Geofisika (BMKG) sepanjang 20 tahun (2002 – 2021) dengan data curah hujan CHIRPS yang bersesuaian dengan lokasi stasiun hujan. Validasi data CHIPRS menggunakan dua jenis validasi yaitu validasi kontinu dan validasi kategorial. Validasi kontinu diperoleh rata-rata korelasi Pearson (R), percent bias (Pbias), mean error (ME), mean absolute error (MAE), dan root mean square error (RMSE) adalah 0,25, 9,92 %, 0,68 mm, 12,17 mm, dan 19,82 mm. Berdasarkan nilai rata-rata percent bias dan korelasi Pearson, estimasi hujan data CHIRPS sangat baik, namun mempunyai korelasi lemah dengan data pengamatan. Validasi kategorial diperoleh nilai rata-rata probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), frequency bias index (FBI) dan Heidke skill score (HSS) adalah 0,72, 0,44, 056, 1,01, dan 0,27. Validasi kategorial menunjukkan bahwa data CHIRPS sangat baik dalam mengestimasi kejadian hujan di Kalimantan Barat.

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Assessment of the CHIRPS-Based Satellite Precipitation Estimates

Cited by 14 publications

References 42 publications

Inference and dynamic simulation of malaria using a simple climate-driven entomological model of malaria transmission

Inference and dynamic simulation of malaria using a simple climate-driven entomological model of malaria transmission

Comparing Remote Sensing and Geostatistical Techniques in Filling Gaps in Rain Gauge Records and Generating Multi-Return Period Isohyetal Maps in Arid Regions—Case Study: Kingdom of Saudi Arabia

Validasi Curah Hujan Harian Chirps Precipitation Satellite Product Di Provinsi Kalimantan Barat

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