Validating CHIRPS-based satellite precipitation estimates in Northeast Brazil

Paredes‐Trejo, Franklin; Barbosa, Humberto Alves; Kumar, T. V. Lakshmi

doi:10.1016/j.jaridenv.2016.12.009

Cited by 201 publications

(130 citation statements)

References 62 publications

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“…The southern part of the region has maximum precipitation in November-December. The semi-arid northern part of NEB has its rainy season between February and May [11,16,47]. The Inter Tropical Convergence Zone (ITCZ) controls the weather system in most of the NEB.…”

Section: Study Areamentioning

confidence: 99%

“…This term refer to the skill of the SWDIS for detection of drought weeks, taking into account a threshold to differentiate the non-drought weeks from drought-weeks. In assessing the SWDIS performance for detection of drought weeks on each RDG grid cell, the Probability of Detection (POD) was used [47,70]. The POD indicates the fraction of the observed drought weeks that were correctly detected; with a threshold equal zero for SWDIS and AWD.…”

Section: Linear Relationship and Drought-weeks Probability Of Detectimentioning

confidence: 99%

“…SWDIS performance for detection of drought weeks on each RDG grid cell, the Probability of Detection (POD) was used [47,70]. The POD indicates the fraction of the observed drought weeks that were correctly detected; with a threshold equal zero for SWDIS and AWD.…”

Section: Spatial and Seasonal Relationship Between The Swdis And Awdmentioning

confidence: 99%

“…In contrast, climatic diagrams for Campo Maior (P = 1300 mm/year) and Esperantinópolis (P = 1378 mm/year) reveal a longer wet period (i.e., December-May, Weeks 1-22, and [48][49][50][51][52], where monthly precipitation is greater than 100 mm. Besides, in spite of show less amount of yearly precipitation, Correntina (P = 1009 mm/year) and Fazendas do Piauí (P = 1202 mm/year) have the most long wet period (i.e., October-April; weeks: 1-17, and [40][41][42][43][44][45][46][47][48][49][50][51][52]. Figure 6 shows a visual comparison of the SWDIS and AWD time series at each site.…”

Section: Behavior Of the Swdis-awd Relationship At Local Scalementioning

confidence: 99%

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Evaluation of the SMOS-Derived Soil Water Deficit Index as Agricultural Drought Index in Northeast of Brazil

Paredes‐Trejo

Barbosa

2017

Water

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Northeast Brazil (NEB) has recently experienced one of its worst droughts in the last decades, with large losses on rainfed agriculture. Soil moisture is the main variable to monitor agricultural drought. The remote sensing approach for drought monitoring has been enriched with the launch of the Soil Moisture and Ocean Salinity (SMOS) in November 2009 by European Space Agency (ESA). In this work, the Soil Water Deficit Index (SWDI) was calculated using the SMOS L2 soil moisture in the NEB. The SMOS-derived SWDI data (SWDIS) were evaluated against the atmospheric water deficit (AWD) calculated from in situ observations. Comparisons were made at seven-day and 0.25 • scales, over the time-span of June 2010 to December 2013. It was found that the SWDIS has a reasonably good overall performance in terms of the drought-weeks detection (skill = 0.986) and capture of the upper soil moisture temporal dynamic (r = 0.652), implying that the SWDIS could be used to track agricultural droughts. Furthermore, SWDIS shows poor performance at sites located in mountains regions affected by severe droughts (−0.10 ≤ r ≤ 0.10). It is also noted that the vegetal cover/use, climate regime, and soil texture have little influence on the AWD-SWDIS coupling.

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Section: Study Areamentioning

confidence: 99%

Section: Linear Relationship and Drought-weeks Probability Of Detectimentioning

confidence: 99%

Section: Spatial and Seasonal Relationship Between The Swdis And Awdmentioning

confidence: 99%

Section: Behavior Of the Swdis-awd Relationship At Local Scalementioning

confidence: 99%

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Evaluation of the SMOS-Derived Soil Water Deficit Index as Agricultural Drought Index in Northeast of Brazil

Paredes‐Trejo

Barbosa

2017

Water

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“…For instance, CHIRPS has been Water 2017, 9, 615 4 of 17 used to quantify the impact of decreasing precipitation trends and increasing temperatures trends in the Greater Horn in Africa [22], and it validated using the data from 21 ground stations in Northeast Brazil [23]. CHIRPS dataset has also been used it to obtain a comprehensive evaluation of eight high spatial resolution precipitation products in an Alpine catchment, the Adige Basin in Italy [24], and to validate a hydrological model to simulate stream flow in a complex topography [25].…”

Section: Precipitation Datasetmentioning

confidence: 99%

Wet Spells and Associated Moisture Sources Anomalies across Danube River Basin

Ciric

Nieto

Ramos

et al. 2017

Water

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Abstract:The Danube River Basin is the second longest catchment basin in Europe and exhibits intense climatological diversity. In recent decades, the frequency and intensity of daily precipitation extremes have suffered from an increment in many parts of the world, including Central and Eastern Europe. Wet spells are defined by the number of consecutive rainy days with different thresholds. The identification of wet spells and their trends in the rainfall time is very important for many sectors, such as agriculture, ecology, hydrology and water resources. Wet spells can lead to extreme events and cause floods and other disasters. In this study, we will attempt to characterise global precipitation in the context of wet spells and associated precipitation depth of wet spells in the Danube River Basin area using daily precipitation data, as well as analysing different approaches to identifying wet spells. The ten most intense wet spells were detected, and the most intense, which occurred on 23 September 1996, was studied in depth in terms of precipitation and associated anomalies, the synoptic situation and the anomalous transport of moisture using a Lagrangian approach. The existence of a marked west-east dipole in the field of sea level pressure between the Atlantic Ocean and the eastern Mediterranean leads to the anomalous moisture transport from the Northern Atlantic Ocean to the Mediterranean Sea, where a higher available amount of moisture existed, and subsequently penetrated within the low positioned over the Danube River Basin. In addition, an Atmospheric River was also responsible for the wet conditions in the Danube River Basin. The combination of all these factors was responsible for the extreme precipitation linked with the wet spell.

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