Evaluation of Precipitation Estimates CMORPH-CRT on Regions of Mexico with Different Climates

Flores, José Luis Bruster; Ortíz-Gómez, Ruperto; Fierro, Adrián Leonardo Ferriño; Guerra-Cobián, Víctor Hugo; Burgos-Flores, Dagoberto; Lizárraga-Mendiola, Liliana

doi:10.3390/w11081722

Cited by 13 publications

(3 citation statements)

References 16 publications

(29 reference statements)

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“…Both SPPs tend to overestimate low-intensity rain and underestimate high-intensity rain. Such tendency at the sub-daily scale was also reported for other regions with different climatic characteristics and varied topography, e.g., in Mexico (Bruster-Flores et al, 2019), Myanmar (Yuan et al, 2019), Ethiopia (Haile et al, 2013), and Tibetian Plateau (Li et al, 2021). The same tendency still persist at a daily time scale in different mountainous regions around the world (Derin et al, 2016).…”

Section: Discussionsupporting

confidence: 73%

Dynamic rainfall thresholds for landslide early warning in Progo Catchment, Java, Indonesia

Satyaningsih¹,

Jetten²,

Ettema³

et al. 2023

Preprint

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High spatiotemporal resolution satellite data have been available to provide rainfall estimates with global coverage and relatively short latency. On the other hand, a rain gauge measures the actual rain that falls to the surface, but its network density is commonly sparse, particularly those that record at sub-daily records. These datasets are extensively used to define rainfall thresholds for landslides. This study aims to investigate the use of GSMaP-GNRT and CMORPH-CRT data along with automatic rain station data to determine rainfall thresholds for landslides in Progo Catchment, Indonesia, as the basis for landslide early warning in the area. Using the frequentist method, we derived the thresholds based on 213 landslide occurrences for 2012-2021 in the Progo Catchment. Instead of relying on a fixed time window to determine rainfall events triggering landslides, we consider a dynamic window, enabling us to adapt to the rainfall event responsible for landslides by extending or shortening its duration depending on the persistence of the rainfall signal. Results indicate that both GSMaP-GNRT and CMORPH-CRT products fail to capture high-intensity rainfall in Progo Catchment and overestimate light rainfall measured by rain gauge observations.Nevertheless, when accumulated to define the rainfall threshold, the overall performance of GSMaP-GNRT and automatic rain station data in Progo Catchment is comparable. The rainfall measured at the stations performed slightly better than the estimated rainfall from GSMaP-GNRT, particularly at a probability exceedance level below 15%. In contrast, CMORPH -CRT performed the worst for all exceedance probabilities. The suitable exceedance probability for early warning purposes in Progo Catchment is 10% if it is based on the automatic rain station data. At this exceedance probability level, the threshold can adequately discriminate triggering/non-triggering rainfall conditions and produces the minimum false alarms and missed events.

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Section: Discussionsupporting

confidence: 73%

Dynamic rainfall thresholds for landslide early warning in Progo Catchment, Java, Indonesia

Satyaningsih¹,

Jetten²,

Ettema³

et al. 2023

Preprint

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“…In the case of precipitation derived from the Tropical Rainfall Measuring Mission (TRMM), Ávila-Carrasco et al [16] indicate a good approximation in the summer and autumn seasons and a strong dependence on the type of climate and topography on a portion of the Santiago River basin; whereas [17] recommend its use to fill in missing data in time series of weather stations. With respect to the database Climate Prediction Center morphing technique with corrected bias (CMORPH-CRT), Bruster-Flores et al [18] conclude that there is an overestimation of precipitation throughout the country, with a correlation of weak to moderate.…”

Section: Introductionmentioning

confidence: 99%

Potential of the CHIRPS Database for Extreme Precipitation Risk Studies. Assessment in the State of Jalisco (Mexico)

Garcia¹,

Hernández²,

Aguilar³

et al. 2023

IJSDP

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The Assessment of extreme precipitation as a natural hazard for society requires high temporal and spatial resolutions data. In those terms, satellite-based precipitation products such as CHIRPS (Rainfall Estimates from Rain Gauge and Satellite Observations) usually are an adequate data sources, not only because the resolution, but also due to the open access and low latency of the information. However, teleconnection efficiency is usually related to aspects such as the physical-geographical characteristics of terrains and the algorithms and parameterizations used in the process, so it is important to carry out a validation process before using this type of data source. Thus, this research aims to evaluate the potential of CHIRPS database in the risk studies associated with extreme precipitation through the study case of Jalisco state (Mé xico). CHIRPS data and in situ data, obtained from the Network of Conventional Climatic Stations of CONAGUA, are compared between 1981-2020, applying several descriptive and correlational statisticians, for this analysis, the daily data and pentads in the study period are used. For comparisons with reference stations, CHIRPS pixels containing those stations were used. The final value representing CHIRPS was obtained from a weighted mean interpolation between each of the pixel vertices and the location of the in situ station, and a series of extreme precipitation index proposed by the IPC C (Intergovernmental Panel on Climate Change). It was found that CHIRPS database is not recommended for the analysis of the maximum annual precipitation in 24 hours, as well as for consecutive days with rain and the total annual days with accumulated above the 95 percentiles. Nevertheless, results indicate a good adjustment for the annual distribution of rainfall and especially for the maximum annual precipitation in five days, useful for recurrence products and agrometeorological indicators. The importance of using CHIRPS data in this work is due to the lack of official information and the importance of combining data from satellite sensors with in situ stations, thus allowing a correction with real data. In the case of the Mexican territory, this product shows an acceptable behavior and its use has been recommended as an auxiliary database in studies related to droughts and average conditions.

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“…Results indicated that CHIRPS provided a more detailed spatial representation than had ever been obtained before. Bruster-Flores et al (2019) assessed the performance of the CMORPH product with corrected bias (CMORPH-CRT) in 14 sites in Mexico covering different climates and based on weather automatic stations. The evaluation was performed on two temporal scales, 30 min and daily based on two approaches: categorical metrics and statistical indicators.…”

Section: Introductionmentioning

confidence: 99%

Evaluating reanalysis and satellite-based precipitation at regional scale: A case study in southern Mexico

Morales-Velázquez

Herrera

Aparicio³

et al. 2021

ATM

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Accurate precipitation data is essential for any hydrometeorological study, particularly for calibration and simulation of hydrological models. In this paper, we evaluate the precipitation of two different reanalysis products (the ERA5 and GLDAS), and two satellite-based precipitation products (TRMM 3B42 and CHIRPS) over the La Sierra river basins in Southern Mexico, on regional and daily time scales, from 2008 to 2010. We compare the collocated gridded precipitation data against in-situ precipitation measurements in each gauge station, as well as the mean areal precipitation (MAP) over the catchments in the study area for the different products. The Pearson correlation coefficient, the root mean square error, and the multiplicative bias metrics suggest that CHIRPS and ERA5 are the highest quality precipitation products over the study area. CHIRPS performs better on the grid to point comparison, estimating better precipitation events from 10-50 mm, above 100 mm, and for all the values without threshold. ERA5 does better for precipitation from 0-10 and 50-100 mm. These two datasets also have better performance on representing the spatial rainfall variability according to the mean annual precipitation and MAP analysis, showing statistical values close to each other.

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Evaluation of Precipitation Estimates CMORPH-CRT on Regions of Mexico with Different Climates

Cited by 13 publications

References 16 publications

Dynamic rainfall thresholds for landslide early warning in Progo Catchment, Java, Indonesia

Dynamic rainfall thresholds for landslide early warning in Progo Catchment, Java, Indonesia

Potential of the CHIRPS Database for Extreme Precipitation Risk Studies. Assessment in the State of Jalisco (Mexico)

Evaluating reanalysis and satellite-based precipitation at regional scale: A case study in southern Mexico

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