Evaluation needs and temporal performance differences of gridded precipitation products in peripheral mountain regions

Abstract

Gridded datasets are of paramount importance to globally derive precipitation quantities for a multitude of scientific and practical applications. However, as most studies do not consider the impacts of temporal and spatial variations of included measurements in the utilized datasets, we conducted a quantitative assessment of the ability of several state of the art gridded precipitation products (CRU, GPCC Full Data Product, GPCC Monitoring Product, ERA-interim, ERA5, MERRA-2, MERRA-2 bias corrected, PERSIANN-CDR) to reproduce monthly precipitation values at climate stations in the Pamir mountains during two 15 year periods (1980–1994, 1998–2012) that are characterized by considerable differences in incorporated observation data. Results regarding the GPCC products illustrated a substantial and significant performance decrease with up to four times higher errors during periods with low observation inputs (1998–2012 with 2 stations on average per 124,000 km2) compared to periods with high quantities of regionally incorporated station data (1980–1994 with 14 stations on average per 124,000 km2). If independent stations were considered, the coefficient of efficiency indicated that only three of the gridded datasets (MERRA–2 bias corrected, GPCC, GPCC MP) performed better than the long term station mean for characterizing surface precipitation. Error patterns and magnitudes show that in complex terrain, evaluation of temporal and spatial variations of included observations is a prerequisite for using gridded precipitation products for scientific applications and to avoid overly optimistic performance assessments.

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

“…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.…”

Suitability of 17 gridded rainfall and temperature datasets for large-scale hydrological modelling in West Africa

“…Usually, the influence of temperature datasets in combination with rainfall datasets is not tested (e.g. Satgé et al, 2019;Camici et al, 2018;Casse et al, 2015;Qi et al, 2016;Zhang et al, 2019), with the exception of a few studies (e.g. Laiti et al, 2018;Lauri et al, 2014), despite the importance of this interaction for evaporation simulation.…”

Suitability of 17 rainfall and temperature gridded datasets for largescale hydrological modelling in West Africa

“…Different temperature datasets are used to allow flexibility in rainfall partitioning into evaporation and runoff because temperature is a key variable for the calculation of potential evaporation (Kirchner and Allen, 2020;Zheng et al, 2019;Van Stan et al, 2020). The hydrological model is recalibrated for each of the 102 combinations of rainfall-temperature datasets (Figure 1).…”

“…Our understanding of environmental systems is underpinned by observational data whose unavailability and uncertainties hinder research and operational applications. Among other factors, atmospheric data quality is of prime importance for the reliability of hydro-meteorological and climatological studies (Ledesma and Futter, 2017;Zandler et al, 2019). Precipitation 35 is one of the major components of the water cycle, which has led to numerous initiatives on understanding its generation, and estimating its amount and variability on Earth (Maidment et al, 2015;Cui et al, 2019).…”