VARIABILIDAD DE LA VEGETACIÓN CON EL ÍNDICE DE DIFERENCIA NORMALIZADA (NDVI) EN LATINOAMÉRICA

La vegetación es uno de los componentes más relevantes del ciclo hidrológico y es, además, un factor clave para evaluar la biodiversidad dentro de una región geográfica determinada. Su interacción con el clima y su relación con el cambio climático, es de interés mundial. En Latinoamérica, la dinámica de la vegetación requiere ser entendida para garantizar el manejo sustentable del elemento natural suelo, con especial énfasis en la conservación del patrimonio forestal, en la gestión integral de los recursos hídricos y en el aprovechamiento, manejo y conservación de todos los componentes de la diversidad biológica. Este artículo tiene por objeto describir e interpretar información hemerobibliográfica sobre la variabilidad de la vegetación en Latinoamérica con el empleo de series temporales del índice de vegetación de diferencia normalizada (NDVI),  el cual es de gran importancia en el campo del sensoriamiento remoto, para conocer la fenología de los ecosistemas existentes en esta región de América y saber cómo se ha manejado la vigilancia de eventos climáticos como las sequías e inundaciones, el monitoreo de áreas verdes, las pérdidas forestales por deforestación o quema y la administración de áreas protegidas. Se encontró que la evaluación de la dinámica de la vegetación a partir de series temporales del NDVI ha tomado gran importancia para modelar el clima y monitorear la respuesta de la vegetación ante el cambio climático global en diversas regiones de Latinoamérica; sin embargo, a pesar de su gran potencial, es un área de investigación incipiente en Venezuela.    

Validation of Satellite (TMPA and IMERG) Rainfall Products with the IMD Gridded Data Sets over Monsoon Core Region of India

This work presents the validation of satellite (TMPA and IMERG) rainfall products against the India Meteorological Department (IMD) gridded data sets (0.25° × 0.25°) of dense network of rain gauges distributed over the monsoon core region of India. The validation uses the data sets covering the 20 years (1998–2017) and detects the time series bias; inter annual variations and Intra Seasonal Oscillations (ISO). The bias in the two data sets is found to be very less over the core region compared to whole India. The correlation between daily rainfall IMD and satellite is found to be +0.88 which is of 99% confidence level. The dominant periodicities in the rainfall patterns of IMD and satellite are Madden Julie Oscillation (30–60 days) and local oscillations (less than 20 days) are conspicuous and the normalized power varies from year to year. During the El Niño and La Niña years, the normalized power of rainfall pattern is low and high in satellite data sets which infer the suppressed and strongest activity of MJO over Indian Ocean that modulates the rainfall pattern over India

Assessing the spatiotemporal patterns and impacts of droughts in the Orinoco river basin using earth observations data and surface observations

Droughts impact the water cycle, ecological balance, and socio-economic development in various regions around the world. The Orinoco River Basin is a region highly susceptible to droughts. The basin supports diverse ecosystems and supplies valuable resources to local communities. We assess the spatiotemporal patterns and impacts of droughts in the basin using remote sensing data and surface observations. We use monthly precipitation (P), air temperature near the surface (T2M), enhanced vegetation index (EVI) derived from Earth observations, and average daily flow (Q) data to quantify drought characteristics and impacts. We also investigated the association between drought and global warming by correlating the drought intensity and the percentage of dry area with sea surface temperature (SST) anomalies in the Pacific (Niño 3.4 index), Atlantic (North Atlantic Index [NATL]), and South Atlantic Index [SATL]) oceans. We evaluate the modulating effect of droughts on the hydrological regime of the most relevant tributaries by calculating the trend and significance of the regional standardized precipitation index (SPI) and percentage area affected by dry conditions. El Niño events worsen the region’s drought conditions (SPI vs. Niño 3.4 index, r = −0.221), while Atlantic SST variability has less influence on the basin’s precipitation regime (SPI vs. NATL and SATL, r = 0.117 and −0.045, respectively). We also found that long-term surface warming trends aggravate drought conditions (SPI vs. T2M anomalies, r = −0.473), but vegetation greenness increases despite high surface temperatures (SPI vs. EVI anomalies, r = 0.284). We emphasize the irregular spatial-temporal patterns of droughts in the region and their profound effects on the ecological flow of rivers during prolonged hydrological droughts. This approach provides crucial insights into potential implications for water availability, agricultural productivity, and overall ecosystem health. Our study underlines the urgent need for adaptive management strategies to mitigate the adverse effects of droughts on ecosystems and human populations. The insights derived from our study have practical implications for developing strategies to address the impacts of droughts and ensure the protection of this ecologically significant region

Assessment of the CHIRPS-Based Satellite Precipitation Estimates

At present, satellite rainfall products, such as the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) product, have become an alternative source of rainfall data for regions where rain gauge stations are sparse, e.g., Northeast Brazil (NEB). In this study, continuous scores (i.e., Pearson’s correlation coefficient, R; percentage bias, PBIAS; and unbiased root mean square error, ubRMSE) and categorical scores (i.e., probability of detection, POD; false alarm ratio, FAR; and threat score, TS) were used to assess the CHIRPS rainfall estimates against ground-based observations on a pixel-to-station basis, during 01 January 1981 to 30 June 2019 over NEB. Results showed that CHIRPS exhibits better performance in inland regions (R, PBIAS, and ubRMSE median: 0.51, −3.71%, and 9.20 mm/day; POD, FAR, and TS median: 0.59, 0.44, and 0.40, respectively) than near the coast (R, PBIAS, and ubRMSE median: 0.36, −5.66%, and 12.43 mm/day; POD, FAR, and TS median: 0.32, 0.42, and 0.26, respectively). It shows better performance in the wettest months (i.e., DJF) than in the driest months (i.e., JJA) and is sensitive to both the warm-top stratiform cloud systems and the sub-cloud evaporation processes. Overall, the CHIRPS rainfall data set could be used for some operational purposes in NEB

Tendencia de la precipitación estacional e influencia de El Niño-Oscilación Austral sobre la ocurrencia de extremos pluviométricos en la cuenca del lago de Valencia, Venezuela

La cuenca del lago de Valencia (CELV) es la cuenca endorreica de mayor tamaño en Venezuela. Por su elevada densidad poblacional e industrial es susceptible a los extremos pluviométricos. Se sabe que el fenómeno ENOA (El Niño-Oscilación Austral) modula las lluvias en el territorio venezolano, pero no se ha explorado su incidencia en detalle en la CELV. En este estudio se analiza la tendencia espacial y temporal de la precipitación estacional y se explora la asociación entre la ocurrencia de meses con extremos pluviométricos y las fases de ENOA (El Niño, La Niña, neutro) en la CELV. Se seleccionaron ocho estaciones climáticas con buena calidad de registros. Los periodos 1934-2005 y 1966- 1992 se adoptan para los análisis a escalas local y regional. Se identificaron los meses de la temporada seca y húmeda. En cada estación se calculó la precipitación acumulada estacional y se evaluó su tendencia de largo plazo utilizando la prueba de Mann-Kendall. Se categorizó la precipitación mensual local y estacional en extrema seca (ES), no extrema (NE) y extrema húmeda (EH), usando como umbrales los percentiles 10 y 90. Se analizó la ocurrencia probabilística espacial y simultaneidad de un mes ES, NE y EH, según la temporada y fases de ENOA. La asociación entre ENOA y la precipitación estacional se explora con una prueba Chi-Cuadrado. Se encontró lo siguiente: no existen tendencias locales de largo plazo en la precipitación total estacional; la ocurrencia de extremos pluviométricos estacionales está parcialmente asociada con los eventos El Niño/La Niña; la incidencia de extremos pluviométricos podría estar vinculada con factores climáticos locales

Sistema para la alerta temprana de sequías meteorológicas en Venezuela

Las sequías ocurren cuando las lluvias disminuyen o cesan durante varios días, meses o años. En el último quinquenio, sucedieron en Venezuela,varias sequías meteorológicas que impactaron negativamente los sectores hidrológico, hidroeléctricoy agrícola. Con el objeto de proveer a las instituciones que administran los recursos hídricos, una herramienta que alertetempranamenteeste fenómeno climático y así confrontar sus impactos, se desarrolló y validó un modelo probabilístico condicional que advierte anticipadamente su ocurrencia en el país. Se usaron las series temporales pluviométricas de 632 estaciones administradas por entes públicos y privados. ElÍndice de Precipitación Estandarizado, SPI (por sus siglas en inglés:Standardized Precipitation Index) de McKee et al. (1993), se usó en la identificación de los eventos secos. Un Análisis de Componentes Principales juntoa un Sistema de Información Geográfica (SIG) se usaron paradelimitar Subregiones Homogéneas (SH) geográficamente continuas, según el SPI. En cada SH se seleccionó una estación representativa (Estación de Referencia, ER). Se aplicóun análisis de correlación cruzadaa las series de SPI en las ER y las series temporales de ciertas anomalías;esta última, representada por 10 índices asociadosa igual número de Variables Macroclimáticas (VM).Así, se identificó el desfase para el cual ocurrela mayorcorrelación lineal entre dichas series. Las 4 VM desfasadas, con mayor correlación lineal en cada ER, se organizaronen tres categorías (-1, 0 y +1), usando los cuartiles Q2 y Q4 como valores de truncamiento; las series SPI se catalogaronencuatro clases:No Seca (NS), Moderadamente Seca (MS), Severamente Seca (SS) o Extremadamente Seca (ES)definidas por McKee et al. (1993). Se determinó la probabilidad condicional de ocurrencia (reglas de Bayes) de las cuatro clases del SPI mencionadas, según cada una de las 81 combinaciones que pueden presentar las 4 VM desfasadas. Los modelos generados en cada ER, se validaron con las series de SPI provenientes de20 estaciones pluviométricas delServicio de Meteorología de la Fuerza Aérea Venezolana (no usadas anteriormente). Los resultados indican que los modelos tienen un porcentaje de acierto,promedio, del 83%. Engeneral, el porcentaje de aciertosde los eventos ES, tiende a ser directamente proporcional a la longitud de los registros considerados en el desarrollo del modelo. Por tanto, los modelos deben ser recalibrados en la medida que se cuente con nuevas mediciones, mejorando así, la asociación entre la estructura que presentan las señales de las VMsy la probabilidad de ocurrencia de los posibles estados del sistema (NS, MS, SS y ES)

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

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

Assessment of SM2RAIN-Derived and State-of-the-Art Satellite Rainfall Products over Northeastern Brazil

Microwave-based satellite rainfall products offer an opportunity to assess rainfall-related events for regions where rain-gauge stations are sparse, such as in Northeast Brazil (NEB). Accurate measurement of rainfall is vital for water resource managers in this semiarid region. In this work, the SM2RAIN-CCI rainfall data obtained from the inversion of the microwave-based satellite soil moisture (SM) observations derived from the European Space Agency (ESA) Climate Change Initiative (CCI), and ones from three state-of-the-art rainfall products (Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), Climate Prediction Center Morphing Technique (CMORPH), and Multi-SourceWeighted-Ensemble Precipitation (MSWEP)) were evaluated against in situ rainfall observations under different bioclimatic conditions at the NEB (e.g., AMZ, Amazônia; CER, Cerrado; MAT, Mata Atlântica; and CAAT, Caatinga). Comparisons were made at daily, 5-day, and 0.25° scales, during the time-span of 1998 to 2015. It was found that 5-day SM2RAIN-CCI has a reasonably good performance in terms of the correlation coefficient over the CER biome (R median: 0.75). In terms of the root mean square error (RMSE), it exhibits better performance in the CAAT biome (RMSE median: 12.57 mm). In terms of bias (B), the MSWEP, SM2RAIN-CCI, and CHIRPS datasets show the best performance in MAT (B median: −8.50%), AMZ (B median: −0.65%), and CER (B median: 0.30%), respectively. Conversely, CMORPH poorly represents the rainfall variability in all biomes, particularly in the MAT biome (R median: 0.43; B median: −67.50%). In terms of detection of rainfall events, all products show good performance (Probability of detection (POD) median > 0.90). The performance of SM2RAIN-CCI suggests that the SM2RAIN algorithm fails to estimate the amount of rainfall under very dry or very wet conditions. Overall, results highlight the feasibility of SM2RAIN-CCI in those poorly gauged regions in the semiarid region of NEB

Evaluation of the Performance of SM2RAIN-Derived Rainfall Products over Brazil

Microwave-based satellite soil moisture products enable an innovative way of estimating rainfall using soil moisture observations with a bottom-up approach based on the inversion of the soil water balance Equation (SM2RAIN). In this work, the SM2RAIN-CCI (SM2RAIN-ASCAT) rainfall data obtained from the inversion of the microwave-based satellite soil moisture (SM) observations derived from the European Space Agency (ESA) Climate Change Initiative (CCI) (from the Advanced SCATterometer (ASCAT) soil moisture data) were evaluated against in situ rainfall observations under different bioclimatic conditions in Brazil. The research V7 version of the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TRMM TMPA) was also used as a state-of-the-art rainfall product with an up-bottom approach. Comparisons were made at daily and 0.25° scales, during the time-span of 2007−2015. The SM2RAIN-CCI, SM2RAIN-ASCAT, and TRMM TMPA products showed relatively good Pearson correlation values (R) with the gauge-based observations, mainly in the Caatinga (CAAT) and Cerrado (CER) biomes (R median > 0.55). SM2RAIN-ASCAT largely underestimated rainfall across the country, particularly over the CAAT and CER biomes (bias median < −16.05%), while SM2RAIN-CCI is characterized by providing rainfall estimates with only a slight bias (bias median: −0.20%), and TRMM TMPA tended to overestimate the amount of rainfall (bias median: 7.82%). All products exhibited the highest values of unbiased root mean square error (ubRMSE) in winter (DJF) when heavy rainfall events tend to occur more frequently, whereas the lowest values are observed in summer (JJA) with light rainfall events. The SM2RAIN-based products showed larger contribution of systematic error components than random error components, while the opposite was observed for TRMM TMPA. In general, both SM2RAIN-based rainfall products can be effectively used for some operational purposes on a daily scale, such as water resources management and agriculture, whether the bias is previously adjusted

Incidencia de las sequías sobre las cuencas aportantes a los grandes embalses en Venezuela

Desde el año 2000 el territorio venezolano ha sido afectado por sequías recurrentes que han causado una merma significativa en las reservas hídricas almacenadas en sus grandes embalses. Se evaluaron las características más relevantes de las sequías severas que afectaron las cuencas aportantes de 32 grandes embalses entre 1960-2005. Se empleó el índice de precipitación estandarizada para medir la anomalía de la precipitación acumulada a escala anual (SPI-12). El patrón subyacente que configura las sequías en el contexto espacio-temporal se identificó aplicando un análisis por conglomerados. Entre los resultados, destacan: a) las sequías son eventos relativamente frecuentes que se alternan con periodos predominantemente húmedos; b) los grandes embalses se agrupan en ocho subregiones geográficas de acuerdo a su exposición a las sequías; c) el inicio y la finalización de las sequías en las cuencas aportantes pueden ser abruptas o graduales en el tiempo