Caracterização da variabilidade da precipitação no MATOPIBA, região produtora de soja

A análise sobre a variabilidade dos padrões climatológicos espaciais e temporais das chuvas fornecem informações valiosas para a condução de cultivos agrícolas, principalmente em condições de sequeiro. Assim, o presente estudo objetivou caracterizar a variabilidade da precipitação no MATOPIBA, região produtora de soja, sob influência das fases do ENSO e do gradiente térmico do Atlântico Tropical. Foram utilizados dados diários de precipitação do período de 1980-2013 dispostos em uma grade de espaçamento de 0,25º x 0,25°, abrangendo 963 pontos sobre a região. O acumulado mensal da precipitação foi especializado por meio de sistemas geográficos de informação e da geoestatística. A variabilidade da precipitação foi analisada por meio da aplicação do teste de Mann-Kendall, considerando três cenários de condições meteorológicas (climatologia, favorável-wet e desfavorável-dry) à ocorrência da precipitação. Os volumes de chuvas foram relativamente maiores no cenário da fase fria do ENSO combinado com o gradiente inter-hemisférico apontando para o Sul (favorável-wet), em contrapartida, verificou-se um aumento de condições de risco hídrico nos anos com ocorrência da fase quente do ENSO e o gradiente apontando para o Norte (desfavorável-dry), embora com exceções registradas em algumas áreas no mês de Janeiro e Fevereiro. Tendências positivas e negativas foram identificadas, constatando indícios de alterações nos padrões da variável, previamente para os cenários da climatologia e desfavorável (dry). Os resultados poderão contribuir para o desenvolvimento de soluções e direcionamento na tomada de decisões pelos agentes da cadeia produtiva que visem a mitigação de impactos em decorrência da variabilidade da precipitação na região estudada. Characterization of rainfall variability in the MATOPIBA, soybean producing region AbstractThe analysis of spatial and temporal rainfall patterns variability provides invaluable information for the development of dryland agriculture systems. Therefore, the aim of the present study was to characterize the variability of rainfall in the MATOPIBA, an important soybean producing region, under the influence of ENSO phases and the tropical Atlantic thermal gradient. We used daily rainfall data for the period from 1980-2013 arranged in a 0.25º x 0.25º spacing grid, comprising 963 points over the study region. Monthly accumulated rainfall has been specialized through geographic information systems and geostatistics. Variability rainfall was analyzed by applying the Mann-Kendall test, considering three scenarios of meteorological conditions (climatology, favorable-wet and unfavorable-dry) to the occurrence of precipitation. Rainfall volumes were relatively higher in the ENSO cold phase scenario combined with the southward-favorable interhemispheric gradient. ENSO and the gradient pointing north (unfavorable-dry), although with exceptions recorded in some areas in January and February. Positive and negative trends were identified, showing evidence of changes in the variable's patterns, previously for the climatology and unfavorable (dry) scenarios. The results may contribute to the development of solutions and decision making direction by the agents of the productive chain aiming at mitigating impacts due to the variability of precipitation in the studied region.Keywords: Climate variability; ENSO; Agrometeorology

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Characterization of sub-daily rainfall properties in three raingauges located in northeast Brazil

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-364-345-2014 ◽

2014 ◽

Vol 364 ◽

pp. 345-350 ◽

Cited By ~ 4

Author(s):

J. V. Coutinho ◽

C. D. N. Almeida ◽

A. M. F. Leal ◽

L. R. Barbosa

Keyword(s):

Daily Rainfall ◽

Northeast Brazil ◽

Rainfall Events ◽

Event Time ◽

Accumulated Rainfall ◽

Semi Arid Region ◽

Definition Of ◽

The Right ◽

Experimental Basins

Abstract. This paper aims to evaluate the characteristics of rainfall events of three experimental basins located in northeast Brazil. The study areas are located, one in Ceará State and two in Paraíba State. Thus, the definition of rainfall events was based on two characteristics: minimum inter-event time and minimum event depth. Then, they were classified according to the shape of the hyetograph: to the left rectangular, triangular, and triangular with peak, and to the right, bimodal and unshaped. Evaluation of the percentages of each type of hyetograph and the main characteristics of rainfall events (peak, duration and intensity) was carried out. The results shows that the two experimental basins located in the semi-arid region have similar characteristics, and shapeless events have significant accumulated rainfall.

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Characterization of Monsoonal Rainfall in the Sudano-Sahelian Zone of Northwestern Nigeria

Indonesian Journal of Earth Sciences ◽

10.52562/injoes.v1i2.261 ◽

2021 ◽

Vol 1 (2) ◽

pp. 123-135

Author(s):

Abdullahi Umar ◽

Saadu Umar Wali ◽

Ibrahim Mustapha Dankani

Keyword(s):

Wavelet Transform ◽

Wavelet Analysis ◽

Daily Rainfall ◽

Kendall Test ◽

Morlet Wavelet ◽

Rainfall Data ◽

Seasonal Rainfall ◽

Rainfall Characteristics ◽

Sahelian Zone

Wavelet transform has been underutilized in characterization of rainfall (Real Onset Dates and Real Cessation Dates) in the study area. This study aims at the characterization of monsoonal rainfall. Daily rainfall data of four stations for the period 1981-2018 were collected from Nigerian Meteorological Agency. The Intra-seasonal Rainfall Monitoring Index (IRMI) was generated and used in determining the RODs and RCDs. The Mann–Kendall test was used to detect trends of the rainfall characteristics. Wavelet transform was used in modelling RODs and RCDs. Findings revealed that RODs vary between stations. There is low (0.3 Spearman’s Rank r) correlation between latitudes and Early Cessations (ECs) of rains. The Morlet wavelet analysis revealed that from 1999 to 2018, there were more of EOs and NOs especially in Kano station. We conclude that from 1981 to 2018 there has been a minimal increase in the retreat dates of rainfall in the study area.

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Characterization of daily rainfall variability in Hong Kong: A nonlinear dynamic perspective

International Journal of Climatology ◽

10.1002/joc.6891 ◽

2020 ◽

Cited By ~ 1

Author(s):

Zhenru Shu ◽

Pak Wai Chan ◽

Qiusheng Li ◽

Yuncheng He ◽

Bowen Yan

Keyword(s):

Hong Kong ◽

Nonlinear Dynamic ◽

Rainfall Variability ◽

Daily Rainfall ◽

Dynamic Perspective

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Rainfall Variability, Drought Characterization, and Efficacy of Rainfall Data Reconstruction: Case of Eastern Kenya

Advances in Meteorology ◽

10.1155/2015/380404 ◽

2015 ◽

Vol 2015 ◽

pp. 1-16 ◽

Cited By ~ 15

Author(s):

M. Oscar Kisaka ◽

M. Mucheru-Muna ◽

F. K. Ngetich ◽

J. N. Mugwe ◽

D. Mugendi ◽

...

Keyword(s):

Rainfall Variability ◽

Daily Rainfall ◽

Rainfall Data ◽

Kriging Interpolation ◽

Study Region ◽

Daily Rainfall Data ◽

Kriging Interpolation Method ◽

Dry Spell ◽

Interpolation Techniques ◽

Mean Square Errors

This study examined the extent of seasonal rainfall variability, drought occurrence, and the efficacy of interpolation techniques in eastern Kenya. Analyses of rainfall variability utilized rainfall anomaly index, coefficients of variance, and probability analyses. Spline, Kriging, and inverse distance weighting interpolation techniques were assessed using daily rainfall data and digital elevation model using ArcGIS. Validation of these interpolation methods was evaluated by comparing the modelled/generated rainfall values and the observed daily rainfall data using root mean square errors and mean absolute errors statistics. Results showed 90% chance of below cropping threshold rainfall (500 mm) exceeding 258.1 mm during short rains in Embu for one year return period. Rainfall variability was found to be high in seasonal amounts (CV = 0.56, 0.47, and 0.59) and in number of rainy days (CV = 0.88, 0.49, and 0.53) in Machang’a, Kiritiri, and Kindaruma, respectively. Monthly rainfall variability was found to be equally high during April and November (CV = 0.48, 0.49, and 0.76) with high probabilities (0.67) of droughts exceeding 15 days in Machang’a and Kindaruma. Dry-spell probabilities within growing months were high, (91%, 93%, 81%, and 60%) in Kiambere, Kindaruma, Machang’a, and Embu, respectively. Kriging interpolation method emerged as the most appropriate geostatistical interpolation technique suitable for spatial rainfall maps generation for the study region.

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Understanding the variability of the rainfall dipole in West Africa using the EC-Earth last millennium simulation

Climate Dynamics ◽

10.1007/s00382-021-05696-x ◽

2021 ◽

Author(s):

Qiong Zhang ◽

Ellen Berntell ◽

Qiang Li ◽

Fredrik Charpentier Ljungqvist

Keyword(s):

West Africa ◽

Model Simulation ◽

Rainfall Variability ◽

Low Pressure ◽

Last Millennium ◽

Tropical Atlantic ◽

Gulf Of Guinea ◽

Dipole Pattern ◽

Sahel Region ◽

Heat Low

AbstractThere is a well-known mode of rainfall variability associating opposite hydrological conditions over the Sahel region and the Gulf of Guinea, forming a dipole pattern. Previous meteorological observations show that the dipole pattern varies at interannual timescales. Using an EC-Earth climate model simulation for last millennium (850–1850 CE), we investigate the rainfall variability in West Africa over longer timescales. The 1000-year-long simulation data show that this rainfall dipole presents at decadal to multidecadal and centennial variability and long-term trend. Using the singular value decomposition (SVD) analysis, we identified that the rainfall dipole present in the first SVD mode with 60% explained variance and associated with the variabilities in tropical Atlantic sea surface temperature (SST). The second SVD mode shows a monopole rainfall variability pattern centred over the Sahel, associated with the extra-tropical Atlantic SST variability. We conclude that the rainfall dipole-like pattern is a natural variability mode originated from the local ocean–atmosphere-land coupling in the tropical Atlantic basin. The warm SST anomalies in the equatorial Atlantic Ocean favour an anomalous low pressure at the tropics. This low pressure weakens the meridional pressure gradient between the Saharan Heat Low and the tropical Atlantic. It leads to anomalous northeasterly, reduces the southwesterly moisture flux into the Sahel and confines the Gulf of Guinea's moisture convergence. The influence from extra-tropical climate variability, such as Atlantic multidecadal oscillation, tends to modify the rainfall dipole pattern to a monopole pattern from the Gulf of Guinea to Sahara through influencing the Sahara heat low. External forcing—such as orbital forcing, solar radiation, volcanic and land-use—can amplify/dampen the dipole mode through thermal forcing and atmosphere dynamical feedback.

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Modeling and forecasting of rainfall reoccurrence changes using Markov Switching in Iran

SN Applied Sciences ◽

10.1007/s42452-021-04728-9 ◽

2021 ◽

Vol 3 (8) ◽

Author(s):

Majid Javari

Keyword(s):

Environmental Planning ◽

Rainfall Variability ◽

Markov Switching ◽

Daily Rainfall ◽

First Order ◽

Markov Switching Models ◽

Markov Transition Matrix ◽

Switching Models ◽

Markov Transition ◽

Modeling And Forecasting

AbstractThis paper represents the recurrence (reoccurrence) changes in the rainfall series using Markov Switching models (MSM). The switching employs a dynamic pattern that allows a linear model to be combined with nonlinearity models a discrete structure. The result is the Markov Switching models (MSM) reoccurrence predicting technique. Markov Switching models (MSM) were employed to analyze rainfall reoccurrence with spatiotemporal regime probabilities. In this study, Markov Switching models (MSM) were used based on the simple exogenous probability frame by identifying a first-order Markov process for the regime probabilities. The Markov transition matrix and regime probabilities were used to analyze the rainfall reoccurrence in 167 synoptic and climatology stations. The analysis results show a low distribution from 0.0 to 0.2 (0–20%) per day spatially from selecting stations, probability mean of daily rainfall recurrence is 0.84, and a different distribution based on the second regime was found to be more remarkable to the rainfall variability. The rainfall reoccurrence in daily rainfall was estimated with relatively low variability and strong reoccurrence daily with ranged from 0.851 to 0.995 (85.1–99.5%) per day based on the spatial distribution. The variability analysis of rainfall in the intermediate and long variability and irregular variability patterns would be helpful for the rainfall variability for environmental planning.

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TEMPORAL RAINFALL DISAGGREGATION BY A SIMPLE RANDOM CASCADE MODEL

Jurnal Teknologi ◽

10.11113/jt.v80.10957 ◽

2018 ◽

Vol 80 (6) ◽

Author(s):

Siti Mariam Saad ◽

Abdul Aziz Jemain ◽

Noriszura Ismail

Keyword(s):

Rainfall Variability ◽

Daily Rainfall ◽

Poisson Model ◽

Rain Gauge ◽

Peninsular Malaysia ◽

Cascade Model ◽

Statistical Measures ◽

Hourly Rainfall ◽

Cascade Models ◽

Log Normal

This study evaluates the utility and suitability of a simple discrete multiplicative random cascade model for temporal rainfall disaggregation. Two of a simple random cascade model, namely log-Poisson and log-Normal models are applied to simulate hourly rainfall from daily rainfall at seven rain gauge stations in Peninsular Malaysia. The cascade models are evaluated based on the capability to simulate data that preserve three important properties of observed rainfall: rainfall variability, intermittency and extreme events. The results show that both cascade models are able to simulate reasonably well the commonly used statistical measures for rainfall variability (e.g. mean and standard deviation) of hourly rainfall. With respect to rainfall intermittency, even though both models are underestimated, the observed dry proportion, log-Normal model is likely to simulate number of dry spells better than log-Poisson model. In terms of rainfall extremes, it is demonstrated that log-Poisson and log-Normal models gave a satisfactory performance for most of the studied stations herein, except for Dungun and Kuala Krai stations, which both located in the east part of Peninsula.

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Stochastic daily rainfall generation on tropical islands with complex topography

10.5194/hess-2021-453 ◽

2021 ◽

Author(s):

Lionel Benoit ◽

Lydie Sichoix ◽

Alison D. Nugent ◽

Matthew P. Lucas ◽

Thomas W. Giambelluca

Keyword(s):

Probabilistic Models ◽

Conceptual Models ◽

Rainfall Variability ◽

Daily Rainfall ◽

French Polynesia ◽

High Elevation ◽

Time Behavior ◽

Tropical Islands ◽

Orographic Effects ◽

Spatial Gradients

Abstract. Stochastic rainfall generators are probabilistic models of rainfall space-time behavior. During parameterization and calibration, they allow the identification and quantification of the main modes of rainfall variability. Hence, stochastic rainfall models can be regarded as probabilistic conceptual models of rainfall dynamics. As with most conceptual models in Earth Sciences, the performance of stochastic rainfall models strongly relies on their adequacy in representing the rain process at hand. On tropical islands with high elevation topography, orographic rain enhancement challenges most existing stochastic models because it creates localized rains with strong spatial gradients, which break down the stationarity of rain statistics. To allow for stochastic rainfall modeling on tropical islands, despite non-stationarity, we propose a new stochastic daily rainfall generator specifically for areas with significant orographic effects. Our model relies on a preliminary classification of daily rain patterns into rain types based on rainfall space and intensity statistics, and sheds new light on rainfall variability at the island scale. Within each rain type, the spatial distribution of rainfall through the island is modeled following a meta-Gaussian approach combining empirical spatial copulas and a Gamma transform function, which allows us to generate realistic daily rain fields. When applied to the stochastic simulation of rainfall on the islands of O‘ahu (Hawai‘i, United States of America) and Tahiti (French Polynesia) in the tropical Pacific, the proposed model demonstrates good skills in jointly simulating site specific and island scale rain statistics. Hence, it provides a new tool for stochastic impact studies in tropical islands, in particular for watershed water resources management and downscaling of future precipitation projections.

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