Using R for analysing spatio-temporal datasets: a satellite-based precipitation case study

Increasing computer power and the availability of remote-sensing data measuring different environmental variables has led to unprecedented opportunities for Earth sciences in recent decades. However, dealing with hundred or thousands of files, usually in different vectorial and raster formats and measured with different temporal frequencies, impose high computation challenges to take full advantage of all the available data. R is a language and environment for statistical computing and graphics which includes several functions for data manipulation, calculation and graphical display, which are particularly well suited for Earth sciences. In this work I describe how R was used to exhaustively evaluate seven state-of-the-art satellite-based rainfall estimates (SRE) products (TMPA 3B42v7, CHIRPSv2, CMORPH, PERSIANN-CDR, PERSIAN-CCS-adj, MSWEPv1.1 and PGFv3) over the complex topography and diverse climatic gradients of Chile. First, built-in functions were used to automatically download the satellite-images in different raster formats and spatial resolutions and to clip them into the Chilean spatial extent if necessary. Second, the raster package was used to read, plot, and conduct an exploratory data analysis in selected files of each SRE product, in order to detect unexpected problems (rotated spatial domains, order or variables in NetCDF files, etc). Third, raster was used along with the hydroTSM package to aggregate SRE files into different temporal scales (daily, monthly, seasonal, annual). Finally, the hydroTSM and hydroGOF packages were used to carry out a point-to-pixel comparison between precipitation time series measured at 366 stations and the corresponding grid cell of each SRE. The modified Kling-Gupta index of model performance was used to identify possible sources of systematic errors in each SRE, while five categorical indices (PC, POD, FAR, ETS, fBIAS) were used to assess the ability of each SRE to correctly identify different precipitation intensities. In the end, R proved to be and efficient environment to deal with thousands of raster, vectorial and time series files, with different spatial and temporal resolutions and spatial reference systems. In addition, the use of well-documented R scripts made code readable and re-usable, facilitating reproducible research which is essential to build trust in stakeholders and scientific community

The br2 – weighting Method for Estimating the Effects of Air Pollution on Population Health

Uncertainties, limitations and biases may impede the correct application of concentration-response linear functions to estimate the effects of air pollution exposure on population health. The reliability of a prediction depends largely on the strength of the linear correlation between the studied variables. This work proposes the joint use of the coefficient of determination, r2, with the regression slope, b, as an improved measure of the strength of the linear relation between air pollution and its effects on population health. The proposed br2‑weighting method offers more reliable inferences about the potential effects of air pollution on population health, and can be applied universally to other fields of research

The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset

We introduce the first catchment dataset for large sample studies in Chile. This dataset includes 516 catchments; it covers particularly wide latitude (17.8 to 55.0∘ S) and elevation (0 to 6993 m a.s.l.) ranges, and it relies on multiple data sources (including ground data, remote-sensed products and reanalyses) to characterise the hydroclimatic conditions and landscape of a region where in situ measurements are scarce. For each catchment, the dataset provides boundaries, daily streamflow records and basin-averaged daily time series of precipitation (from one national and three global datasets), maximum, minimum and mean temperatures, potential evapotranspiration (PET; from two datasets), and snow water equivalent. We calculated hydro-climatological indices using these time series, and leveraged diverse data sources to extract topographic, geological and land cover features. Relying on publicly available reservoirs and water rights data for the country, we estimated the degree of anthropic intervention within the catchments. To facilitate the use of this dataset and promote common standards in large sample studies, we computed most catchment attributes introduced by Addor et al. (2017) in their Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) dataset, and added several others. We used the dataset presented here (named CAMELS-CL) to characterise regional variations in hydroclimatic conditions over Chile and to explore how basin behaviour is influenced by catchment attributes and water extractions. Further, CAMELS-CL enabled us to analyse biases and uncertainties in basin-wide precipitation and PET. The characterisation of catchment water balances revealed large discrepancies between precipitation products in arid regions and a systematic precipitation underestimation in headwater mountain catchments (high elevations and steep slopes) over humid regions. We evaluated PET products based on ground data and found a fairly good performance of both products in humid regions (r>0.91) and lower correlation (r<0.76) in hyper-arid regions. Further, the satellite-based PET showed a consistent overestimation of observation-based PET. Finally, we explored local anomalies in catchment response by analysing the relationship between hydrological signatures and an attribute characterising the level of anthropic interventions. We showed that larger anthropic interventions are correlated with lower than normal annual flows, runoff ratios, elasticity of runoff with respect to precipitation, and flashiness of runoff, especially in arid catchments. CAMELS-CL provides unprecedented information on catchments in a region largely underrepresented in large sample studies. This effort is part of an international initiative to create multi-national large sample datasets freely available for the community. CAMELS-CL can be visualised from http://camels.cr2.cl and downloaded from https://doi.pangaea.de/10.1594/PANGAEA.894885

Roma e la storia del Canada francese sino alla guerra dei Sette Anni

Presentation of the documents concerning New France and Canada in the Vatican Archive

A coupled hydrology-biogeochemistry model to simulate dissolved organic carbon exports from a permafrost influenced catchment

Funded by Natural Environment Research Council. Grant Number: NE/K000268/1 Natural Sciences and Engineering Research Council of CanadaPeer reviewedPublisher PD

Identifying streamgage networks for maximizing the effectiveness of regional water balance modeling

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98795/1/wrcr20233.pd

Precipitation Constrains Amphibian Chytrid Fungus Infection Rates in a Terrestrial Frog Assemblage in Jamaica, West Indies

We model Batrachochytrium dendrobatidis (Bd) infection rates in Jamaican frogs—one of the most threatened amphibian fauna in the world. The majority of species we surveyed were terrestrial direct‐developing frogs or frogs that breed in tank bromeliads, rather than those that use permanent water bodies to breed. Thus, we were able to investigate the climatic correlates of Bd infection in a frog assemblage that does not rely on permanent water bodies. We sampled frogs for Bd across all of the major habitat types on the island, used machine learning algorithms to identify climatic variables that are correlated with infection rates, and extrapolated infection rates across the island. We compared the effectiveness of the machine learning algorithms for species distribution modeling in the context of our study, and found that infection rate rose quickly with precipitation in the driest month. Infection rates also increased with mean temperature in the warmest quarter until 22 °C, and remained relatively level thereafter. Both of these results are in accordance with previous studies of the physiology of Bd . Based on our environmental results, we suggest that frogs occupying high‐precipitation habitats with cool rainy‐season temperatures, though zcurrently experiencing low frequencies of infection, may experience an increase in infection rates as global warming increases temperatures in their habitat.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106115/1/btp12093.pd

Precipitation regionalization, anomalies and drought occurrence in the Yucatan Peninsula, Mexico

© 2020 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. Climate change projections have identified the Yucatan Peninsula as being vulnerable to increasing drought. Understanding spatial and temporal precipitation variability and drought occurrence are therefore important. Drought monitoring in Mexico has been carried out only relatively recently and often without considering the long-term variability in both droughts and precipitation. This research explores the spatio-temporal variability of precipitation and occurrence of droughts at a much finer spatial resolution and over a longer temporal period than previous studies. Using statistical (cluster analysis and standardized precipitation index) and geostatistical (kriging) techniques, maps of precipitation and droughts are generated for the period 1980–2011. These show that whilst many previous studies have regarded the Yucatan Peninsula as a homogenous region with respect to precipitation, there are actually four distinctive clusters of precipitation amount, showing climatic variability across the Peninsula. The analyses also show that droughts in the Peninsula are regionalised. Twelve-month Standardized Precipitation Indices (SPI), calculated for individual stations and for precipitation surfaces, reveal distinct patterns of spatial and temporal variability. SPI surfaces indicate the occurrence of major droughts in 1981, 1986–1987, 1994, 1996, 2003, 2004 and 2009, but these rarely affect the whole Yucatan Peninsula uniformly. Wetter years, such as 1983, 1984, 1988, 1992, 1995, 2002 and 2005 sometimes reflect the impact of individual extreme events, such as hurricane Isidore in 2002. Our results show that drought can be regionalised, thus enhancing the quality of information about droughts in the area and providing evidence and support for future drought mitigation and environmental protection. These methods could usefully be applied elsewhere

Modeling the vacuolar storage of malate shed lights on pre- and post-harvest fruit acidity

Background: Malate is one of the most important organic acids in many fruits and its concentration plays a critical role in organoleptic properties. Several studies suggest that malate accumulation in fruit cells is controlled at the level of vacuolar storage. However, the regulation of vacuolar malate storage throughout fruit development, and the origins of the phenotypic variability of the malate concentration within fruit species remain to be clarified. In the present study, we adapted the mechanistic model of vacuolar storage proposed by Lobit et al. in order to study the accumulation of malate in pre and postharvest fruits. The main adaptation concerned the variation of the free energy of ATP hydrolysis during fruit development. Banana fruit was taken as a reference because it has the particularity of having separate growth and post-harvest ripening stages, during which malate concentration undergoes substantial changes. Moreover, the concentration of malate in banana pulp varies greatly among cultivars which make possible to use the model as a tool to analyze the genotypic variability. The model was calibrated and validated using data sets from three cultivars with contrasting malate accumulation, grown under different fruit loads and potassium supplies, and harvested at different stages. Results: The model predicted the pre and post-harvest dynamics of malate concentration with fairly good accuracy for the three cultivars (mean RRMSE = 0.25-0.42). The sensitivity of the model to parameters and input variables was analyzed. According to the model, vacuolar composition, in particular potassium and organic acid concentrations, had an important effect on malate accumulation. The model suggested that rising temperatures depressed malate accumulation. The model also helped distinguish differences in malate concentration among the three cultivars and between the pre and post-harvest stages by highlighting the probable importance of proton pump activity and particularly of the free energy of ATP hydrolysis and vacuolar pH. Conclusions: This model appears to be an interesting tool to study malate accumulation in pre and postharvest fruits and to get insights into the ecophysiological determinants of fruit acidity, and thus may be useful for fruit quality improvement. (Résumé d'auteur