Abstract
Inadequate climate data stations often make hydrological modelling a rather challenging task in data-sparse regions. Gridded climate data can be used as an alternative; however, their accuracy in replicating the climatology of the region of interest with low levels of uncertainty is important to water resource planning. This study utilised several performance metrics and multi-criteria decision making to assess the performance of the widely used gridded precipitation and temperature data against quality-controlled observed station records in the Lake Chad basin. The study’s findings reveal that the products differ in their quality across the selected performance metrics, although they are especially promising with regards to temperature. However, there are some inherent weaknesses in replicating the observed station data. Princeton University Global Meteorological Forcing precipitation showed the worst performance, with Kling–Gupta efficiency of 0.13–0.50, a mean modified index of agreement of 0.68, and a similarity coefficient SU = 0.365, relative to other products with satisfactory performance across all stations. There were varying degrees of mismatch in unidirectional precipitation and temperature trends, although they were satisfactory in replicating the hydro-climatic information with a low level of uncertainty. Assessment based on multi-criteria decision making revealed that the Climate Research Unit, Global Precipitation Climatology Centre, and Climate Prediction Centre precipitation data and the Climate Research Unit and Princeton University Global Meteorological Forcing temperature data exhibit better performance in terms of similarity, and are recommended for application in hydrological impact studies—especially in the quantification of projected climate hazards and vulnerabilities for better water policy decision making in the Lake Chad basin.
Highlights
The CRU, Global Precipitation Climatology Centre (GPCC), and Princeton University Global Meteorological Forcing (PGF) datasets showed a better performance at 53.3%, 33.3%, and 13.3%, respectively, while CRU, PGF, and University of Delaware (UDEL) showed a better performance, at 41.7%, 33.3, and 25.0% of the precipitation and temperature stations in the study area, respectively
This paper evaluated the performance of a long-term time series of high-resolution gridded precipitation and temperature datasets and their suitability for hydro-climatic studies in the data-scarce Lake Chad basin
The emphasis in this assessment was to employ multiple performance metrics to evaluate the ability of the selected datasets to replicate the qualitycontrolled observed meteorological station records available, and to provide methodological guidance based on multi-criteria decision making as to the choice of reference dataset suitable for climate and hydrological impact studies in data-scarce regions
Summary
Reliable and long-term observed meteorological datasets are sparse and unavailable in some regions—especially sub-Saharan Africa and the Mediterranean—making hydrological studies a challenging task [1,2]. High-resolution gridded data have been developed to address these shortcomings; an understanding of their limitations in terms of observational uncertainties and reliability is important in order to address the twin issues of choice of dataset and suitability to represent basin features. Some climate data products are more appropriate than others in their applications for climate change impact studies across different regions; careful and adequate assessment of their strengths and limitations is required in order to provide guidance for future climate and hydrological studies—especially in data-sparse basins. An accurate hydro-climatic impact study requires climate data at high temporal and spatial resolutions
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