Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile

Zambrano, Francisco; Wardlow, Brian D.; Tadesse, Tsegaye; Lillo‐Saavedra, Mario; Lagos, Octavio

doi:10.1016/j.atmosres.2016.11.006

Cited by 127 publications

(43 citation statements)

References 27 publications

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“…e di erences observed in the SPI1D between rain gauges and CHIRPS can be attributed mainly to the underestimations over the CS region, although all the CWA exhibit the same underestimation pattern by CHIRPS. e lack of zero values can arise from the screening procedure developed to remove "false zeros" in the CHIRPS estimations [50], a bias previously reported by Zambrano et al and Katsanos et al [12,51]. Regarding the remaining timescales and wet and dry categories, CHIRPS estimates and precipitation from rain gauges show a similar behaviour, although with larger regional dispersion considering the SPI6 ( Figure 6).…”

Section: Spi Classification For Wet and Dry Categoriesmentioning

confidence: 65%

“…As described by Funk et al [9], the CHIRPS algorithm (i) is built around a 0.05°c limatology that incorporates satellite information to represent sparsely gauged locations, (ii) incorporates monthly 1981-present 0.05°infrared cold cloud duration-based precipitation estimates, (iii) blends station data to produce a preliminary information product with a latency of about 2 days after the end of a pentad and a nal product with an average latency of about 3 weeks, and (iv) uses a novel blending procedure incorporating the spatial correlation structure of infrared cold cloud duration estimates to assign interpolation weights. is dataset was found to reproduce adequately several characteristics of precipitation over South America [12,17,43] and particularly over the CWA, showing a good agreement for the representation of the seasonal and interannual variability of precipitation and its spatial patterns [24].…”

Section: Advances In Meteorologymentioning

confidence: 72%

“…Based on a comparison among six precipitation-based drought indices, the SPI was selected as the most adequate for analysing meteorological droughts along southern South America [44]. For the calculation of the SPI, time series of 1-month, 3-month, and 6month accumulations were generated and tted to a twoparameter gamma distribution function, following previous recommendations for the study area [22] and in other regions [12,13,27]. Finally, an equiprobability transformation from the cumulative density functions to the standard normal distribution with the mean of 0 and the variance of 1 were performed to obtain the SPI.…”

Section: Methodsmentioning

confidence: 99%

“…is database was mainly used for the assessment of monthly, seasonal, and annual precipitation variability in several regions of the world. Monthly CHIRPS estimations were applied for drought monitoring in Nepal [11], Chile [12], and China [1]. e research of López-Carr et al [13] used 3month accumulations to identify changes in the growing season precipitation patterns over Africa.…”

Section: Introductionmentioning

confidence: 99%

“…Guo et al [27] found that CHIRPS can properly capture the drought characteristics at various timescales with the best performance at the three-month timescale. Zambrano et al [12] concluded that, in order to use the CHIRPS dataset to monitor drought intensity conditions, the product should be calibrated to adjust for the overestimation/underestimation of rainfall geographically. Recently, Gao et al [1] and Zhong et al [28] indicated that CHIRPS successfully captured the spatial patterns of drought over China.…”

Section: Introductionmentioning

confidence: 99%

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Using CHIRPS Dataset to Assess Wet and Dry Conditions along the Semiarid Central-Western Argentina

Rivera

Hinrichs

Marianetti

2019

Advances in Meteorology

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e Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset was conceived as a tool for monitoring drought and environmental change over land. Recent validation efforts along South America have assessed its suitability for reproducing the main spatial and temporal features of precipitation. Nevertheless, little has been done regarding the ability of CHIRPS for the assessment of wet and dry conditions, particularly in areas where in situ precipitation records are scarce. In this paper, we investigated the performance of CHIRPS for monitoring wet and dry events along the semiarid Central-Western Argentina. Using the Standardized Precipitation Index (SPI), we compared the CHIRPS database with records from 49 meteorological stations along the study area for the period 1987-2016. Results indicate that the CHIRPS dataset adequately reproduced the temporal variability of SPI on multiple timescales (1 month, 3 months, and 6 months), particularly in the region dominated by warm season precipitation. e large overestimation of the seasonal precipitation in the region dominated by cold season precipitation can introduce errors that are reflected in the performance of CHIRPS over the western portion of the domain. e frequency of wet and dry classes was accurately reproduced by CHIRPS on timescales larger than 1 month (SPI1), given the existence of a wet bias that produces an underestimation of the frequency of zero values. is bias is further translated to the evaluation of the SPI1 during the spatial and temporal assessment of historical dry (1998) and wet (2016) events, especially for the classification of extreme dry/wet months. e results from the evaluation indicate that CHIRPS is a suitable tool for assessing dry and wet conditions for timescales longer than 1 month and can support decision-making process within the hydrometeorological agencies over the region.

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Section: Spi Classification For Wet and Dry Categoriesmentioning

confidence: 65%

Section: Advances In Meteorologymentioning

confidence: 72%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using CHIRPS Dataset to Assess Wet and Dry Conditions along the Semiarid Central-Western Argentina

Rivera

Hinrichs

Marianetti

2019

Advances in Meteorology

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Assessment of rainfall variability and future change in Brazil across multiple timescales

Alves

Chadwick

Moise

et al. 2020

Intl Journal of Climatology

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Rainfall variability change under global warming is a crucial issue that may have a substantial impact on society and the environment, as it can directly impact biodiversity, agriculture, and water resources. Observed precipitation trends and climate change projections over Brazil indicate that many sectors of society are potentially highly vulnerable to the impacts of climate change. The purpose of this study is to assess model projections of the change in rainfall variability at various temporal scales over sub-regions of Brazil. For this, daily data from 30 CMIP5 models for historical (1900-2005) and future (2050-2100) experiments under a high-emission scenario are used. We assess the change in precipitation variability, applying a band-pass filter to isolate variability on daily, weekly, monthly, intra-seasonal, and El Nino Southern Oscillation (ENSO) time scales. For historical climate, simulated precipitation is evaluated against observations to establish model reliability. The results show that models largely agree on increases in variability on all timescales in all subregions, except on ENSO timescales where models do not agree on the sign of future change. Brazil will experience more rainfall variability in the future that is, drier or more frequent dry periods and wetter wet periods on daily, weekly, monthly, and intra-seasonal timescales, even in sub-regions where future changes in mean rainfall are currently uncertain. This may provide useful information for climate change adaptation across, for example, the agriculture and water resource sectors in Brazil.

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Development and evaluation of pre and post integration techniques for enhancing drought predictions over India

Kolluru

2021

Intl Journal of Climatology

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Accurate estimation and prediction of drought events are highly essential for implementing effective planning and management strategies to handle this complex natural phenomenon. Application of machine learning algorithms (MLAs) for integrating satellite precipitation products (SPPs), unlike gauge observations, can furnish precise drought estimations. In this study, we have proposed and tested two approaches (pre and post‐integration of SPPs) that deal with the prediction of drought employing 13 MLAs. Three SPPs are integrated under four combinations (involves two and three datasets integration) employing pre and post integration approaches to predict Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index at various temporal scales (1, 3, 6 and 12‐month). From the overall results, Approach‐2 that involves estimation of drought before integration using MLAs proved effective than Approach‐1 (prediction of drought post‐integration). Neural Networks based Bayesian Regularization (NBR) under three dataset integration outperformed at all temporal scales and climatic zones of India when compared to the other 12 MLAs and two dataset integration combinations. The blended product (NBR) manifested enhancements in statistical results at all temporal scales and climatic zones. European Centre for Medium‐Range Weather Forecasts ReAnalysis (ERA‐5) dataset performed better in predicting drought events in more climatic zones compared to Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Climate Data Record (PERSIANN‐CDR) and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) when compared against Indian Meteorological Department (IMD) dataset. In contrast, PERSIANN‐CDR proved effective in predicting drought at the country scale. ERA‐5 could be suitable for real‐time drought monitoring and prediction, whereas PERSIANN‐CDR can be used for retrospective drought analysis. The proposed approach and the best‐performed algorithm (NBR) can be extended and applied in any climatic region for enhancing the drought predictions where remotely sensed information are accessible even in regions with finite ground data availability.

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Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile

Cited by 127 publications

References 27 publications

Using CHIRPS Dataset to Assess Wet and Dry Conditions along the Semiarid Central-Western Argentina

Using CHIRPS Dataset to Assess Wet and Dry Conditions along the Semiarid Central-Western Argentina

Assessment of rainfall variability and future change in Brazil across multiple timescales

Development and evaluation of pre and post integration techniques for enhancing drought predictions over India

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