Evaluation of precipitation datasets against local observations in southwestern Iran

Maraghi, Ali Fallah; Rakhshandehroo, Gholamreza; Berg, Peter; Sungmin, O; Orth, René

doi:10.1002/joc.6445

Cited by 74 publications

(48 citation statements)

References 63 publications

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“…At present, with its long time-series, good spatiotemporal resolution, and large number of parameters available [99], ERA5 is one of the best and complete global-gridded reanalysis meteorological datasets [34,[100][101][102][103]. However, its derived precipitation is still far from "state-of-the-art" conditions [104][105][106][107]. As a result, our validation implicitly showed that dataset resolution is still insufficient to capture precipitation heterogeneity in small catchments by smoothing flood peaks.…”

Section: Resultsmentioning

confidence: 92%

Global BROOK90 R Package: An Automatic Framework to Simulate the Water Balance at Any Location

Vorobevskii

Kronenberg

Bernhofer

2020

Water

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The number of global open-source hydrometeorological datasets and models is large and growing. However, with a constantly growing demand for services and tools from stakeholders, not only in the water sector, we still lack simple solutions, which are easy to use for nonexperts. The new R package incorporates the BROOK90 hydrologic model and global open-source datasets used for parameterization and forcing. The aim is to estimate the vertical water fluxes within the soil–water–plant system of a single site or of a small catchment (<100 km2). This includes data scarce regions where no hydrometeorological measurements or reliable site characteristics can be obtained. The end-user only needs to provide a location and the desired period. The package automatically downloads the necessary datasets for elevation (Amazon Web Service Terrain Tiles), land cover (Copernicus: Land Cover 100 m), soil characteristics (ISRIC: SoilGrids250), and meteorological forcing (Copernicus: ERA5 reanalysis). Subsequently these datasets are processed, specific hydrotopes are created, and BROOK90 is applied. In a last step, the output data of all desired variables on a daily scale as well as time-series plots are stored. A first daily and monthly validation based on five catchments within various climate zones shows a decent representation of soil moisture, evapotranspiration, and runoff components. A considerably better performance is achieved for a monthly scale.

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Section: Resultsmentioning

confidence: 92%

Global BROOK90 R Package: An Automatic Framework to Simulate the Water Balance at Any Location

Vorobevskii

Kronenberg

Bernhofer

2020

Water

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“…There were small changes related to vegetation modelling. For a region with a low density of gauges in Iran, Fallah et al (2020) showed that ERA5 precipitation is closer to local observations than ERA-Interim but that GPCCv8…”

Section: Previous Analysesmentioning

confidence: 80%

WFDE5: bias adjusted ERA5 reanalysis data for impact studies

Cucchi¹,

Weedon²,

Amici³

et al. 2020

Preprint

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Abstract. The WFDE5 dataset (C3S, 2020) has been generated using the WATCH Forcing Data (WFD) methodology applied to surface meteorological variables from the ERA5 reanalysis. The WFDEI dataset had previously been generated by applying the WFD methodology to ERA-Interim. The WFDE5 is provided at 0.5° spatial resolution, but has higher temporal resolution (hourly) compared to WFDEI (3-hourly). It also has higher spatial variability since it was generated by aggregation of the higher-resolution ERA5 rather than by interpolation of the lower resolution ERA-Interim data. Evaluation against meteorological observations at 13 globally distributed FLUXNET2015 sites shows that, on average, WFDE5 has lower mean absolute error and higher correlation than WFDEI for all variables. Bias-adjusted monthly precipitation totals of WFDE5 results in more plausible global hydrological water balance components as analyzed in an uncalibrated hydrological model (WaterGAP) than use of raw ERA5 data for model forcing. The dataset, which can be downloaded from https://doi.org/10.24381/cds.20d54e34 (C3S, 2020), is distributed by the Copernicus Climate Change Service (C3S) through its Climate Data Store (CDS, Copernicus Climate Change Service, 2020) and currently spans from the start of January 1979 to the end of 2018. The dataset has been produced using a number of CDS Toolbox applications, whose source code is available with the data – allowing users to re-generate part of the dataset or apply the same approach on other data. Future updates are expected spanning from 1950 to the most recent year. A sample of the complete dataset, which covers the whole 2016 year, is accessible without registration to the CDS at https://doi.org/10.21957/935p-cj60.

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“…Diverse results are more common in data sparse regions or in regions where data are not generally available to all data sets. It is therefore difficult to determine which is closer to the truth in a global assessment like this, and more detailed regional studies, such as Fallah et al (2020), are needed.…”

Section: Discussionmentioning

confidence: 99%

HydroGFD3.0: a 25 km global near real-time updated precipitation and temperature data set

Berg

Almén

Bozhinova

2020

Preprint

Self Cite

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Abstract. HydroGFD (Hydrological Global Forcing Data) is a data set of bias adjusted reanalysis data for daily precipitation, and minimum, mean, and maximum temperature. It is mainly intended for large scale hydrological modeling, but is also suitable for other impact modeling. The data set has an almost global land area coverage, excluding the Antarctic continent, at a horizontal resolution of 0.25°, i.e. about 25 km. It is available for the complete ERA5 reanalysis time period; currently 1979 until five days ago. This period will be extended back to 1950 once the back catalogue of ERA5 is available. The historical period is adjusted using global gridded observational data sets, and to acquire real-time data, a collection of several reference data sets is used. Consistency in time is attempted by relying on a background climatology, and only making use of anomalies from the different data sets. Precipitation is adjusted for mean bias as well as the number or wet days in a month. The latter is relying on a calibrated statistical method with input only of the monthly precipitation anomaly, such that no additional input data about the number of wet days is necessary. The daily mean temperature is adjusted toward the monthly mean of the observations, and applied to 1 h timesteps of the ERA5 reanalysis. Daily mean, minimum and maximum temperature are then calculated. The performance of the HydroGFD3 data set is on par with other similar products, although there are significant differences in different parts of the globe, especially where observations are uncertain. Further, HydroGFD3 tends to have higher precipitation extremes, partly due to its higher spatial resolution. In this paper, we present the methodology, evaluation results, and how to access to the data set at https://doi.org/10.5281/zenodo.3871707.

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Evaluation of precipitation datasets against local observations in southwestern Iran

Cited by 74 publications

References 63 publications

Global BROOK90 R Package: An Automatic Framework to Simulate the Water Balance at Any Location

Global BROOK90 R Package: An Automatic Framework to Simulate the Water Balance at Any Location

WFDE5: bias adjusted ERA5 reanalysis data for impact studies

HydroGFD3.0: a 25 km global near real-time updated precipitation and temperature data set

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