Adolfo Posadas

Precipitation characteristics of the South American monsoon system derived from multiple datasets

TheSouthAmericanmonsoon system(SAMS) isthemostimportant climaticfeature in South America and is characterized by pronounced seasonality in precipitation during the austral summer. This study compares several statistical properties of daily gridded precipitation from different data (1998‐2008): 1) Physical Sciences Division (PSD), Earth System Research Laboratory [1.08 and 2.58 latitude (lat)/longitude (lon)]; 2) Global Precipitation Climatology Project (GPCP; 18 lat/lon); 3) Climate Prediction Center (CPC) unified gauge (CPC-uni) (0.58 lat/lon); 4) NCEP Climate Forecast System Reanalysis (CFSR) (0.58 lat/lon); 5) NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis (0.58 lat/0.38 lon); and 6) Tropical Rainfall Measuring Mission (TRMM) 3B42 V6 data (0.258 lat/lon). The same statistical analyses are applied to data in 1) a common 2.58 lat/lon grid and 2) in the original resolutions of the datasets. Alldatasetsconsistentlyrepresentthelarge-scalepatternsoftheSAMS.Theonset,demise,anddurationof SAMS are consistent among PSD, GPCP, CPC-uni, and TRMM datasets, whereas CFSR and MERRA seem to have problems in capturingthe correct timing of SAMS. Spectral analyses show that intraseasonal variance is somewhat similar in the six datasets. Moreover, differences in spatial patterns of mean precipitation are small among PSD, GPCP, CPC-uni, and TRMM data, while some discrepancies are found in CFSR and MERRA relative to the other datasets. Fitting of gamma frequency distributions to daily precipitation shows differences in the parameters that characterize the shape, scale, and tails of the frequency distributions. This suggests that significant uncertainties exist in the characterization of extreme precipitation, an issue that is highly important in the context of climate variability and change in South America.

TRMM rainfall correction over the Andean Plateau using wavelet multi-resolution analysis

Quantifying rainfall from remotely sensed data is crucial for regions where meteorological stations are scarce. This might be one of the only options for analysing rainfall patterns at different temporal and spatial scales in data-scarce environments, particularly in developing countries. The Tropical Rainfall Measuring Mission (TRMM) provides rainfall estimation over the tropics. Rainfall estimates from the TRMM satellite exhibit inaccuracies over topographically complex regions, thus warranting suitable corrections. Multi-resolution analysis (MRA) was applied to improve TRMM 3B42 daily rainfall estimation at 19 meteorological stations located over the Andean Plateau. The detailed signal from each meteorological station was added to the trend signal of each TRMM data cell. Comparing raw and corrected TRMM with gauged rainfall revealed that wavelet-based correction of TRMM 3B42 on average improved several metrics: entropy difference (15.45−1.32), determination coefficient (0.07−0.92), bias (0.68−1.01) and relative mean absolute error (RMAE, 0.86−0.59). The entropy difference of corrected TRMM and gauged rainfall was less than 5%, even when TRMM correction was performed with noise from a station located up to 565 km away from the TRMM cell. This entropy difference corresponded to an average bias of less than 10% in the rainfall estimation.

Multifractal characterization of the spatial distribution of ulexite in a Bolivian salt flat

Understanding spatial patterns is a critical and under‐explored aspect of remote sensing. This paper describes how multifractal theory can be applied to characterize these heterogeneous patterns in remotely sensed data as well as to determine the operational scale. An example based on the characterization of ulexite distribution on the worlds largest salt flat (10 000 km2), located in Bolivia, using a binarized Landsat Thematic Mapper (TM) 4/7 ratio image, is used to describe the step‐by‐step procedure. Distribution was well characterized by the multifractal parameters and expressed through the f–α, τ–q and D–q relationships. Moments from q = −2 to 5 showed a linear trend in scales from approximately 0.007 to 10 000 km2. This implies that the attribute analysed could be measured at different scales, within defined boundaries, and up‐ and down‐scaled using the multifractal parameters. In addition, the asymmetry shown by the f–α spectrum indicates the presence of clusters with high probability of finding ulexite, and large areas where the mineral might be found in small patches. The areas with a high probability of finding ulexite were mapped to guide any future field survey. Using the maximum entropy concept, the operational scale to determine the mineral was obtained at 1062 m.

Detection of bacterial wilt infection caused by Ralstonia solanacearum in potato (Solanum tuberosum L.) through multifractal analysis applied to remotely sensed data

Potato bacterial wilt, caused by the bacterium Ralstonia solanacearum race 3 biovar 2 (R3bv2), affects potato production in several regions in the world. The disease becomes visually detectable when extensive damage to the crop has already occurred. Two greenhouse experiments were conducted to test the capability of a remote sensing diagnostic method supported by multispectral and multifractal analyses of the light reflectance signal, to detect physiological and morphological changes in plants caused by the infection. The analysis was carried out using the Wavelet Transform Modulus Maxima (WTMM) combined with the Multifractal (MF) analysis to assess the variability of high-resolution temporal and spatial signals and the conservative properties of the processes across temporal and spatial scales. The multispectral signal, enhanced by multifractal analysis, detected both symptomatic and latently infected plants, matching the results of ELISA laboratory assessment in 100 and 82%, respectively. Although the multispectral method provided no earlier detection than the visual assessment on symptomatic plants, the former was able to detect asymptomatic latent infection, showing a great potential as a monitoring tool for the control of bacterial wilt in potato crops. Applied to precision agriculture, this capability of the remote sensing diagnostic methodology would provide a more efficient control of the disease through an early and full spatial assessment of the health status of the crop and the prevention of spreading the disease.

Multi‐scale assessment of spatial precipitation variability over complex mountain terrain using a high‐resolution spatio‐temporal wavelet reconstruction method

Studying precipitation variability in the Peruvian Andes is a challenge given the high topographic variability and the scarcity of weather stations. Yet previous research has shown that a near-linear relationship exists between precipitation and vegetation in the semi-arid central Andes. We exploit this relationship by developing a new, spatially highly resolved spatio-temporal precipitation reconstruction method, using daily precipitation time series from in situ weather stations, and dekadal (10 calendar days) normalized difference vegetation index (NDVI) fields. The two data sets are combined through a wavelet decomposition method. A 4° x 4° region around Quelccaya ice cap (QIC), the worlds largest tropical ice cap located in the central Peruvian Andes, was selected as study area, due to its importance for climatic, glaciologic and paleoclimatic research. The reconstructed end product, a ~1 km2 gridded precipitation data set at dekadal temporal resolution, was validated against independent rain gauge data and compared with the Tropical Rainfall Measuring Mission (TRMM) 3B42 version 7 product. This validation showed a better overall performance of our own reconstruction than the TRMM data. Additionally, a comparison of our precipitation product with snowfall measurements at the QIC summit (5670 m) shows a regionally coherent signal at the dekadal scale, suggesting that the precipitation falling at QIC is driven by regional- rather than local-scale convective activity. We anticipate that this methodology and the type of data generated in this study will be useful for hydrological and glaciological studies, as well as for validation of high-resolution downscaling products in mountain regions.

Multifractal Downscaling of Rainfall Using Normalized Difference Vegetation Index (NDVI) in the Andes Plateau

In this paper, a multifractal downscaling technique is applied to adequately transformed and lag corrected normalized difference vegetation index (NDVI) in order to obtain daily estimates of rainfall in an area of the Peruvian Andean high plateau. This downscaling procedure is temporal in nature since the original NDVI information is provided at an irregular temporal sampling period between 8 and 11 days, and the desired final scale is 1 day. The spatial resolution of approximately 1 km remains the same throughout the downscaling process. The results were validated against on-site measurements of meteorological stations distributed in the area under study.

Embedding spatial variability in rainfall field reconstruction

ABSTRACT This manuscript provides a methodology for the reconstruction of a rainfall field when there are scarce rain-gauge stations available. This situation typically arises when measurements are taken from meteorological stations across time, and the information for the complete field is required as an input for larger scale models. The proposed method is based on a wavelet reconstruction technique that requires no distributional assumptions, but relies on the relation between rainfall and normalized difference vegetation index to account for the unobserved spatial variability of the field. The methodology is applied over a region of the southern Peruvian Andes where data gathered from meteorological stations provide enough statistical significance. A comparison with respect to an alternative source of spatial variability and common practices is provided.

Rendimiento y absorción de algunos nutrimentos en plantas de camote cultivadas con estrés hídrico y salino

Sweet potato (Ipomoea batatas L.) is a low production cost crop that is grown during almost the whole year, mainly in developing countries. In arid and semiarid regions, the presence of salinity and water stress can generate yield reductions and losses in the quality of the tuber roots. To address these issues, an experiment was performed to determine yield, N, P, K, Ca, Mg and Na uptake and proline content in plants belonging to two sweet potato cultivars with a different degree of salt tolerance, grown under three (0, 8 and 14 mmol NaCl) salt and two watering regimes (watering after two and four days), during summer-fall conditions of 2009. Salinity and watering frequencies were controlled by using the soilless culture technique. Both water and salt stresses reduced tuber root yields. The yield reduction is explained by a reduction in the uptake of N, P, K, Ca and Mg with the water stress treatment, and an increased Na uptake in the high salinity treatment. The salt and water stress adjustments were reflected in an increment in proline content in leaves and tuber roots. The results confirm the tolerant cultivar as a hardy variety adaptable to abiotic stresses, whereas the non-tolerant variety had lower yield and nutrient uptake. The results did not support the hypothesis that changes in proline content might be used as fast screening tools to discriminate between tolerant and susceptible sweet potato cultivars. ADDITIONAL