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Precipitation and Co-precipitation01:17

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Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
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Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
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The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
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A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Coprecipitation is the contamination of a precipitate by otherwise soluble species and occurs via different processes. In colloidal precipitates, coprecipitation occurs via surface adsorption. For instance, barium sulfate has a primary layer of adsorbed barium ions and a secondary layer of nitrate counterions. This results in contamination of the precipitate by barium nitrate.
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A framework for developing a spatial high-resolution daily precipitation dataset over a data-sparse region.

Farhad Yazdandoost1, Sogol Moradian1, Ardalan Izadi2

  • 1Department of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran.

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|October 7, 2020
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Summary

This study developed a hybrid precipitation dataset for Iran's Sistan and Baluchestan province, improving local rainfall data accuracy and timeliness. The new dataset offers reliable, up-to-date daily precipitation information.

Keywords:
Atmospheric scienceEarth sciencesEnvironmental scienceGeophysicsHydrologyMerged datasetOptimally weighted dataPrecipitation evaluationSistan and Baluchestan

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Area of Science:

  • Hydrology
  • Remote Sensing
  • Climate Science

Background:

  • Sistan and Baluchestan province in Iran is a poorly rain-gauged area, necessitating improved precipitation data.
  • Existing gridded precipitation products have varying accuracy and timeliness, impacting local hydrological studies.

Purpose of the Study:

  • To develop a reliable and up-to-date local precipitation dataset for Sistan and Baluchestan province.
  • To evaluate and compare the performance of eight global gridded precipitation products.
  • To create a hybrid dataset by optimally combining the most suitable products.

Main Methods:

  • The Global Precipitation Climatology Project (GPCC) data was used as a reference for evaluating eight gridded precipitation products (CHIRPS, CMORPH-RAW, ERA5, ERA-Interim, GPM-IMERG, GSMaP-MVK, PERSIANN, TRMM3B42) from 1982-2016.
  • Statistical and machine learning approaches (NSGA II, ETROPY, TOPSIS) were employed to estimate daily weights for the selected ensemble members, maximizing correlation and minimizing error.
  • The performance of the developed hybrid precipitation dataset was assessed for accuracy and timeliness.

Main Results:

  • The study identified GPM-IMERG, GSMaP-MVK, and PERSIANN as the most suitable ensemble members for the hybrid dataset based on performance and minimal time delay.
  • The developed hybrid dataset, operational from 2014 to present, provides accurate local daily precipitation data at a 0.25° spatial resolution.
  • The hybrid dataset demonstrates a significant reduction in time delay compared to other available precipitation datasets.

Conclusions:

  • The proposed hybrid precipitation dataset offers a reliable and timely solution for hydrological applications in poorly rain-gauged regions like Sistan and Baluchestan.
  • Optimal weighting of ensemble members using advanced statistical/machine learning techniques enhances the accuracy of precipitation estimations.
  • This framework can be adapted for developing similar high-resolution precipitation datasets in other data-scarce areas worldwide.