Persistence in surface overflow of Andean rivers

The temporal structure of both deficit (negative) and excess (positive) periods in surface overflow of Andean rivers were analyzed by studying the runs. In Southern areas (in the province of Neuquén and the Southern part of the province of Mendoza), positive and negative groups of anomalies have been found to diminish geometrically over the years. In the Northern areas (in the province of San Juan and Northern part of the province of Mendoza) persistence occurs in negative runs only. This behavior is produced by the influence of the basins located in an arid zone, because of the heterogeneity in the structure of the precipitation in this region.En este trabajo se analiza el comportamiento de la persistencia en el escurrimiento superficial de los ríos andinos, usando el método de rachas de eventos con anomalías positivas o negativas. Se ha encontrado que las rachas positivas o negativas tienen en la zona más austral (provincia de Neuquén y sur de Mendoza) un decaimiento de tipo geométrico con los años; en cambio, hacia la zona más septentrional (provincia de San Juan y norte de Mendoza) aparece la persistencia solamente en las rachas negativas o de sequías. Este comportamiento no se encuentra en la estructura de la precipitación que cae en la cuenca, por lo tanto, se infiere que el mismo sería debido a la regulación que ejercen las cuencas del norte, inmersas en una región de clima árido.Material digitalizado en SEDICI gracias a la colaboración de la Facultad de Ciencias Astronómicas y Geofísicas (UNLP).Asociación Argentina de Geofísicos y Geodesta

Análisis de autocorrelaciones en series hidrológicas andinas

Se ha realizado una evaluación de los procesos temporales contenidos en series hidrológicas de ríos andinos. Analizando los correlogramas de las series en estudio, se han encontrado en ellos dependencias importantes entre los escurrimientos de un ciclo hidrológico y su consecutivo, y otras de rezagos 7 y 11-12 años, particularmente los ríos Jáchal, San Juan, Mendoza y Tunuyán. El 33,4 % de las series muestran un comportamiento definidamente aleatorio, y ninguna de ellas parece responder a un proceso autorregresivo de tipo Markov. Las series de precipitación regional en Chile, entre latitudes de 32.5° y 35.5° S, muestran correlogramas semejantes a los de los ríos del Norte de la zona de Cuyo en los rezagos de 11-12 años.An evaluation of the hydrological processes involved in the time series of the andean rivers runoff is done. Series correlograras are analysed; iraportant dependences between one hydrological annual cycle runoff with its consecutive one are found. Other significant autocorrelation at lag 7 and 11-12 years, in particular for the Jáchal, San Juan, Mendoza and Tunuyán rivers, are also found. It has been observed that 33,l+ % of series are random and all of them do not behave as Autorregresive Processes like Markov processes. Regional rainfall series between 32.5° and 35.5° S in Chile and the North Cuyan runoff series are compared showing similar correlograms at lags 11-12 years.Asociación Argentina de Geofísicos y Geodesta

The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields

Large-scale crop monitoring and yield estimation are important for both scientific research and practical applications. Satellite remote sensing provides an effective means for regional and global cropland monitoring, particularly in data-sparse regions that lack reliable ground observations and reporting. The conventional approach of using visible and near-infrared based vegetation index (VI) observations has prevailed for decades since the onset of the global satellite era. However, other satellite data encompass diverse spectral ranges that may contain complementary information on crop growth and yield, but have been largely understudied and underused. Here we conducted one of the first attempts at synergizing multiple satellite data spanning a diverse spectral range, including visible, near-infrared, thermal and microwave, into one framework to estimate crop yield for the U.S. Corn Belt, one of the world\u27s most important food baskets. Specifically, we included MODIS Enhanced VI (EVI), estimated Gross Primary Production based on GOME-2 solar-induced fluorescence (SIF-GPP), thermal-based ALEXI Evapotranspiration (ET), QuikSCAT Ku-band radar backscatter, and AMSR-E X-band passive microwave Vegetation Optical Depth (VOD) in this study, benchmarked on USDA county-level crop yield statistics. We used Partial Least Square Regression (PLSR), an effective statistical model for dimension reduction, to distinguish commonly shared and unique individual information from the various satellite data and other ancillary climate information for crop yield estimation. In the PLSR model that includes all of the satellite data and climate variables from 2007 to 2009, we assessed the first two major PLSR components and found that the first component (an integrated proxy of crop aboveground biomass) explained 82% variability of modelled crop yield, and the second component (dominated by environmental stresses) explained 15% variability of modelled crop yield. We found that most of the satellite derived metrics (e.g. SIF-GPP, radar backscatter, EVI, VOD, ALEXI-ET) share common information related to aboveground crop biomass (i.e. the first component). For this shared information, the SIF-GPP and backscatter data contain almost the same amount of information as EVI at the county scale. When removing the above shared component from all of the satellite data, we found that EVI and SIF-GPP do not provide much extra information; instead, Ku-band backscatter, thermal-based ALEXI-ET, and X-band VOD provide unique information on environmental stresses that improves overall crop yield predictive skill. In particular, Ku-band backscatter and associated differences between morning and afternoon overpasses contribute unique information on crop growth and environmental stress. Overall, using satellite data from various spectral bands significantly improves regional crop yield predictions. The additional use of ancillary climate data (e.g. precipitation and temperature) further improves model skill, in part because the crop reproductive stage related to harvest index is highly sensitive to environmental stresses but they are not fully captured by the satellite data used in our study. We conclude that using satellite data across various spectral ranges can improve monitoring of large-scale crop growth and yield beyond what can be achieved from individual sensors. These results also inform the synergistic use and development of current and next generation satellite missions, including NASA ECOSTRESS, SMAP, and OCO-2, for agricultural applications