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It will be inappropriate to tell which package is best without any work done on the same. One way to come up with which package to use is to see which articles have used them (or based on which published article it has been developed) and how relevant those articles are for your research work. Consider it only as a suggestion. All the best.
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Hydrological sciences / hddtools: Hydrological Data Discovery Tools in R
« on: November 25, 2018, 11:13:58 AM »
hddtools stands for Hydrological Data Discovery Tools. This R package is an open source project designed to facilitate access to a variety of online open data sources relevant for hydrologists and, in general, environmental scientists and practitioners.
This typically implies the download of a metadata catalogue, selection of information needed, a formal request for dataset(s), de-compression, conversion, manual filtering and parsing. All those operations are made more efficient by re-usable functions.
Depending on the data license, functions can provide offline and/or online modes. When redistribution is allowed, for instance, a copy of the dataset is cached within the package and updated twice a year. This is the fastest option and also allows offline use of package's functions. When re-distribution is not allowed, only online mode is provided.
Link to manual:
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Reliable meteorological data are a basic requirement for hydrological and ecological studies at the landscape scale. Given the large spatial variation of meteorology over complex terrains, meteorological records from a single weather station are often not representative of entire landscapes. Studies made on multiple sites over a landscape require different meteorological series for each site; and other studies may require meteorological data series for all grid cells of a landscape, in a continuous way. In these cases, spatial correlation between the meteorology series of different sites or cells must be taken into account. For example, the sequence of days with rain of contiguous cells will normally be the same or very similar, even if precipitation amounts may differ. Finally, studies addressing the impacts of climate change on forests and landscapes require downscaling coarse-scale predictions of global or regional climate models to the landscape scale. When downscaling predictions for several locations in a landscape, spatial correlation of predictions is also important.

With the aim to assist research of climatic impacts on forests, the R package meteoland provides utilities to estimate daily weather variables at any position over complex terrains:

 1. Spatial interpolation of daily weather records from meteorological stations.

 2. Statistical correction of meteorological data series (e.g. from climate models).

 Spatial interpolation is required when meteorology for the area and period of interest cannot be obtained from local sensors. The nearest weather station may not have data for the period of interest or it may be located too far away to be representative of the target area. Correcting the biases of a meteorological data series containing biases using a more accurate meteorological series is necessary when the more accurate series does not cover the period of interest and the less accurate series does. The less accurate series may be at coarser scale, as with climate model predictions or climate reanalysis data. In this case one can speak of statistical correction adn downscaling. However, one may also correct the predictions of climate models using reanalysis data estimated at the same spatial resolution.

Link to paper:

Link to R Manual:

Link to User Guide:
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Programming / Re: Downscaling
« on: October 18, 2017, 05:01:31 PM »

We have discussed a lot about downscaling climate variables in our forum. A simple search would have yielded desired results on the same.

For e.g.
An R package for assessment of statistical downscaling methods for hydrological climate change impact,1307.msg3022.html#msg3022
Documentation of Musica R package:

Discussion on Statistical Downscaling,1216.msg3050.html#msg3050,1239.msg2919.html#msg2919 (Article reference is also given, do read it for better understanding)

Also, It would be well appreciated if the question asked precisely mentions the problem faced in the research activity.
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Due to inherent bias the climate model simulated precipitation and temperature cannot be used to drive a hydrological model without pre-processing e statistical downscaling. This often consists of reducing the bias in the climate model simulations (bias correction) and/or transformation of the observed data in order to match the projected changes (delta change). The validation of the statistical downscaling methods is typically limited to the scale for which the transformation was calibrated and the driving variables (precipitation and temperature) of the hydrological model. The paper introduces an R package ”musica” which provides ready to use tools for routine validation of statistical downscaling methods at multiple time scales as well as several advanced methods for statistical downscaling. The musica package is used to validate simulated runoff. It is shown that using conventional methods for downscaling of precipitation and temperature often leads to substantial biases in simulated runoff at all time scales.

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go through this paper, tough its not a simple statistical downscaling, it will give you the clarity of downscaling monthly precipitation

Downscaling Precipitation to River Basin in India for IPCC SRES Scenarios Using Support Vector Machine, Anandhi, A, V.V. Srinivas, R.S. Nanjundiah, D. Nagesh Kumar, International Journal of Climatology, Wiley InterScience on behalf of Royal Meteorological Society (RMetS), Vol. 28, No. 3, March 2008, pp. 401-420, DOI: 10.1002/joc.1529
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