Ground observations are often considered as the most reliable and precise source of precipitation data. However, long-term precipitation data from ground observations are lacking in many parts of the world. Gridded precipitation products (GPPs) therefore have emerged as crucial alternatives to ground observations, but it is essential to assess their capability to accurately replicate precipitation patterns. This study aims to evaluate the performance of five GPPs, NASA POWER, TerraClimate, Climate Hazards Group Infrared Precipitation with Climate Data (CHIRPS), GPCC, and Climate Research Unit (CRU), in capturing precipitation and drought patterns from 1981 to 2021 in Yobe, Nigeria. The results indicate that GPCC had good performance at both monthly and annual scales, with high correlation coefficients and low error values. However, it tends to underestimate precipitation amounts in certain areas. Other products also exhibit satisfactory performance with moderate correlations with ground observations. Drought analysis indicates that GPCC outperforms other products in standardised precipitation index-6 calculations, while NASA POWER demonstrates inconsistencies with ground observations, particularly during the early 1980s and mid-2000s. In conclusion, GPCC is the most preferable GPP for precipitation and drought analysis in the Yobe State in Nigeria.

  • Evaluation of five gridded precipitation products (GPPs) in northern Nigeria.

  • GPPs captured monthly precipitation better than annual precipitation.

  • GPPs underestimated precipitation in the western Yobe.

  • GPCC performed better than other GPPs in Kobe, Nigeria.

  • GPCC and Climate Research Unit are suitable for calculating the standardised precipitation index-6 index.

Precipitation is an essential variable for understanding the hydro-climatic changes. As a major component of the hydrological cycle, any significant changes in precipitation can be a sign of climate change (Wu et al. 2019). Accurate precipitation data are required for a wide range of hydrological applications such as drought monitoring (Bahta & Myeki 2021; Orimoloye et al. 2021), flood modelling, and extreme precipitation analysis (Tan 2019; Zhang et al. 2021). Ground observations are often regarded as the most reliable and precise source of precipitation data. However, the lack of resources, uneven distribution of ground observations, political instability, and various other causes particularly make it frequently challenging to get accurate precipitation observations, especially in arid and mountainous regions (Tan et al. 2021; Dhungana et al. 2023). Scientific research from West Africa demonstrates that the limited ground observation networks in the region fail to accurately capture the temporal and geographical climatic features (Ogbu et al. 2020). Funding constraints and a lack of genuine efforts have hampered the development and maintenance of suitable ground observation networks to the World Meteorological Organisation's (WMO) standard (Le Coz & van de Giesen 2020).

High-resolution gridded precipitation products (GPPs) have been developed to provide accurate precipitation to address the issues of ground observations (Schulz et al. 2009). These GPPs can be categorized into gauge-based, satellite-based, reanalysis products, and merged datasets, which are a combination of satellite and gauge products (Sun et al. 2018), for example, gauge-based GPPs like Climate Research Unit (CRU) (Harris et al. 2020) and the Global Precipitation Climatology Centre (GPCC) (Becker et al. 2013); satellite-based GPPs like the Tropical Rainfall Measuring Mission (TRMM) (Huffman et al. 2007) and the Climate Hazards Group Infrared Precipitation with Climate Data (CHIRPS) (Funk et al. 2015); and reanalysis-based GPPs like TerraClimate (Abatzoglou et al. 2018). These products are recognized for their variations in source, spatial and temporal resolutions, domain size, and available timescales. In addition, these products display distinct error bands resulting from deficiencies in rain gauge records, interpolation procedures, and substantial disparities in general climatology (Ayoub et al. 2020). These differences are acknowledged and well documented (Yao et al. 2020), but mostly at the global scale. When selecting reference datasets for catchment size, observational uncertainty and purpose should be carefully taken into account. This might be accomplished by carefully assessing each of the several gridded data packages that are currently available (Lawal et al. 2021).

Before using GPPs in climatological and hydrological impact evaluations, a thorough assessment should be carried out (Sun et al. 2018). Understanding the reliability of GPPs and their ability to simulate hydrological models is crucial for hydrologic analysis (Sun et al. 2018; Setti et al. 2023). Several studies have been conducted worldwide to evaluate the accuracy and suitability of GPPs in hydro-climatic impact studies as well as the ability to detect extreme events in the past decades using various statistical metrics and ground observations as reference data, i.e., China (Li et al. 2023), Malaysia (Ayoub et al. 2020; Tan et al. 2023), Ethiopia (Degefu et al. 2022), and Turkey (Hisam et al. 2023).

The Prediction of Worldwide Energy Resources (POWER) project, developed by the National Aeronautics and Space Administration (NASA), offered long-term climate variables since 1981 at a spatial resolution of 0.5o worldwide and are specifically tailored for the renewable energy and agriculture sectors (Chandler et al. 2013). The NASA POWER project is increasingly gaining popularity as a reliable source for meteorological data input, as evidenced by studies conducted in various parts of the world, i.e., Egypt (Aboelkhair et al. 2019), Oman (Marzouk 2021), and Portugal (Rodrigues & Braga 2021). According to Tan et al. (2023), the NASA POWER product has exhibited a satisfactory level of accuracy in depicting climatological patterns about precipitation, as well as the maximum and minimum temperature in the Kelantan River Basin, Malaysia. When compared to inland mountainous areas, NASA POWER has shown a stronger correlation with ground observations in coastal zones.

TerraClimate developed by the University of California Merced's Climatology Lab offers monthly climate and water balance information for terrestrial surfaces worldwide. This dataset covers the period from 1958 to 2019 and has a spatial resolution of 0.04o (Abatzoglou et al. 2018). TerraClimate makes a substantial contribution to global hydrological and ecological research, especially for studies that require precipitation data with high spatial resolution and temporal variability (Mabrouk et al. 2022). TerraClimate has been evaluated in many geographical regions, including Ethiopia (Degefu et al. 2022) and Brazil (de Andrade et al. 2022). Araghi et al. (2023) concluded that TerraClimate offered reliable data, about monthly precipitation, solar radiation, and maximum and lowest temperatures over Iran. However, only a small number of studies have assessed NASA POWER and TerraClimate in tropical regions despite their capacity to provide reliable climatic data for hydro-climate applications.

Evaluation of GPPs is relatively limited in Nigeria. Notably, GPCC has been the major GPP of assessment, as evidenced by the works of Ogunjo et al. (2022) and Lawal et al. (2021). Other commonly evaluated GPPs include the CRU and Climate Prediction Centre (CPC) (Lawal et al. 2021), CHIRPS (Ogbu et al. 2020), TRMM (Usman et al. 2018), and PERSIANN-CDR (Ogbu et al. 2020). The findings of these studies showed that GPCC, CHIRPS, and CRU were considered the most trustworthy gridded products in Nigeria because of their excellent performance in properly capturing rainfall patterns across the nation. Yobe is situated in the Lake Chad Basin, which is an area characterized by significant variations in rainfall patterns. The 2009 severe drought in Yobe and its neighbouring regions greatly affected the livestock and agricultural sectors. The drought caused significant damage to crops and livestock deaths, which reduced the amount of food produced overall. Furthermore, throughout the occurrence, the incident caused water to drop to its lowest levels (Hassan et al. 2019). Yobe, like other Sahelian and mountainous areas, faces a shortage of precipitation gauges. Previously, Lawal et al. (2021) assessed gridded products across the entirety of the Lake Chad Basin. The study indicates that the CRU, GPCC, and CPC precipitation data provide superior performance in terms of similarity and are recommended for utilization in hydrological impact assessments in the entire basin. The use of GPPs, such as NASA POWER and TerraClimate, might potentially offer dependable alternative climate data for these locations.

The objective of this study is to assess the accuracy of NASA POWER and TerraClimate datasets in comparison to rain gauge measurements over the Yobe State from 1981 to 2021. In addition, this research also seeks to examine the performance of CHIRPS, GPCC, and CRU datasets in the region during recent years. This is to conduct a comparative analysis of gridded products, specifically focusing on their representation of local-scale (sub-basin) characteristics. This analysis aims to identify both similarities and variations in the variability of monthly precipitation among these products. Moreover, the ability of these products to accurately capture inter-annual rainfall anomalies within the region was also evaluated. This study, which may serve as a guide for researchers and GPP developers, shows the best solutions for modelling precipitation patterns in the area based on the findings.

Study area

Yobe State, sometimes referred to as the Yobe River Basin, is a part of the Lake Chad Basin (Figure 1). Geographically, it is situated within the latitudes of 10°N to 13°N and longitudes of 9.50°E to 13°E. Yobe State encompasses a land area of approximately 47,153 km2 and is estimated to accommodate a population of 3.3 million individuals, as reported by the National Population Commission in 2021. It is situated in the Sudan Sahelian region of Lake Chad, in the northeastern part of Nigeria. The Lake Chad Basin, spanning an expansive area of 2,500,000 km2 (Lawal et al. 2021), is recognized as one of the biggest basins globally. It traverses eight African nations, situated at the boundary between the Sahara Desert and the tropical Sudano Sahelian zone in West Africa.
Figure 1

The distribution of rain gauges over the Yobe State, Nigeria.

Figure 1

The distribution of rain gauges over the Yobe State, Nigeria.

Close modal

Yobe State is characterized by high temperatures, with the northern section experiencing a range of 35–40 °C throughout the year. In contrast, the southern region benefits from a more temperate environment (Abdullahi 2018). March, April, and May are characterized by high temperatures. The duration of the rainy season varies across Yobe State, with around 120 days in the northern part and exceeding 140 days in the southern part. According to Emeka & Abiodun (2022), the precipitation levels in the northern region typically range from 200 to 700 mm/year, while in the southern region it ranges from 500 to 1,000 mm/year. It is worth noting that there is a single rainy season occurring from June to October.

The dominant climatic feature in this region is primarily influenced by the seasonal migration of the Inter-Tropical Convergence Zone (ITCZ) during December and January. The ITCZ is located at a latitude range of 2–5° N. In addition, the area experiences the influence of dry, continental air masses (Emeka & Abiodun 2022). Nonetheless, the beginning and towards the end of the rainy season, which typically occurs from June to October, is when little precipitation, low relative humidity, and high temperatures are typically observed. Furthermore, the ITCZ exhibits a northward migration throughout the period spanning from February to June, coinciding with the onset of substantial precipitation. In addition, the ambient air temperature remains elevated until the arrival of rainfall, typically occurring in June (Emeka & Abiodun 2022).

Ground observations

The Nigerian Meteorological Agency (NIMET) provided the monthly precipitation data for three primary stations (Nguru, Potiskum, and Maiduguri), while the Northeast Arid Zone Development Programme (NEAZDP) provided the data for four local stations (Dapchi, Karasuwa, Garin Alkali, and Yunusari). The distribution of these stations is shown in Figure 1 and Table 1. The NEAZDP initiative is a collaborative effort between the European Union and the Federal Republic of Nigeria, spanning duration of 5 years from 1990 to 1995 (Mukhtar et al. 2017). The four local stations commenced operations in 1992; however, they ceased operations in 2004 as a result of insufficient financing and maintenance. However, according to the guidelines set out by the WMO (2018), a temporal range of 10 years is considered appropriate for conducting this type of inquiry to obtain optimal and meaningful outcomes. Hence, owing to the limited availability of ground observations for validating GPPs, the researcher opts to utilize local station records together with the three long-term records from NIMET, resulting in a total of seven gauge stations for assessment. The selection of datasets was conducted with great consideration given to the criterion of minimizing the presence of missing values.

Table 1

Summary of meteorological stations in Yobe State

Station nameLongitude (N)Latitude (E)Elevation (m)Period
Nguru 10.45 12.88 343 1981–2021 
Potiskum 11.11 11.71 432 1981–2021 
Maiduguri 13.13 11.48 348 1981–2015 
Dapchi 11.50 12.50 343 1992–2004 
Karasuwa 10.84 13.04 335 1992–2004 
Yunusari 11.53 13.14 326 1992–2004 
Garin Alkali 11.05 12.81 338 1992–2004 
Station nameLongitude (N)Latitude (E)Elevation (m)Period
Nguru 10.45 12.88 343 1981–2021 
Potiskum 11.11 11.71 432 1981–2021 
Maiduguri 13.13 11.48 348 1981–2015 
Dapchi 11.50 12.50 343 1992–2004 
Karasuwa 10.84 13.04 335 1992–2004 
Yunusari 11.53 13.14 326 1992–2004 
Garin Alkali 11.05 12.81 338 1992–2004 

Gridded precipitation products

This study uses five GPPs, specifically CRU TS v.4.06, CHIRPS, GPCC, NASA POWER, and TerraClimate, to assess their performance in comparison to gauge-based data. Table 2 presents comprehensive details on the GPPs used.

Table 2

Summary of five gridded precipitation products evaluated in this study.

Data productsTemporal resolutionSpatial resolutionPeriod of recordInput data
CHIRPS Daily 0.05° 1981–present TIR 
CRU TS v.4.06 Monthly 0.5° 1901–present Gauge 
NASA POWER Monthly 0.5° 1981–present Reanalysis 
TerraClimate Monthly 0.04° 1958–present TIR, Gauge Model 
GPCC Monthly 0.5° × 0.5 1901–2020 Gauge 
Data productsTemporal resolutionSpatial resolutionPeriod of recordInput data
CHIRPS Daily 0.05° 1981–present TIR 
CRU TS v.4.06 Monthly 0.5° 1901–present Gauge 
NASA POWER Monthly 0.5° 1981–present Reanalysis 
TerraClimate Monthly 0.04° 1958–present TIR, Gauge Model 
GPCC Monthly 0.5° × 0.5 1901–2020 Gauge 

The CRU TS dataset (Harris et al. 2020) was created through collaborative efforts between the University of East Anglia and the World Meteorological Agency. The dataset provides a comprehensive collection of land-based observations dating back to 1901, with a high level of precision. The CRU TS dataset is widely utilized in climate research and encompasses all land areas of the Earth, except Antarctica. It is characterized by a grid resolution of 0.5° latitude by 0.5° longitude. The derivation of this information involves the process of interpolating monthly climatic anomalies using extensive networks of weather station data, as described by Harris et al. (2020). CRU utilizes data acquired from around 4,000 monitoring stations distributed across various global locations. CRU dataset is available online at https://crudata.uea.ac.uk/cru/data/hrg/. Harris et al. (2020) provided detailed description of the CRU TS version 4 monthly high-resolution gridded product.

The CHIRPS (Funk et al. 2015) comprises comprehensive precipitation climatology data at daily, pentad, and monthly intervals. This dataset is constructed by integrating rainfall estimates from several public data sources, private archives, and national meteorological organizations, as outlined by Ayoub et al. (2020). The development of CHIRPS was undertaken by researchers affiliated with the Climate Hazard Group at the University of California, Santa Barbara, in conjunction with the United States Geological Survey (USGS). Its primary purpose is to facilitate the monitoring of drought conditions and the analysis of rainfall patterns within the data-limited African continent. CHIRPS dataset is available online at https://app.climateengine.org/climateEngine. Funk et al. (2015) provided a thorough overview of the CHIRPS satellite precipitation products.

The GPCC (Becker et al. 2013) is an extensive dataset on precipitation that employs gauge measurements to generate gridded data, offering insights into precipitation patterns over the Earth's land surface on a worldwide scale. The organization was founded in 1989 by a request made by the WMO (Salaudeen et al. 2021). The GPCC has a unique capability to analyse on a daily and monthly basis of precipitation by utilizing measurements obtained from in situ rain gauges. Schneider et al. (2014) stated that the organization provides unlimited access to its gridded monthly and daily precipitation datasets. This access is intended for the specific objectives of climate monitoring and research that is directly related to it. Online access to the GPCC dataset is provided at https://psl.noaa.gov/data/gridded/data.gpcc.html. A thorough explanation of GPCC may be found in the study by Becker et al. (2013).

The TerraClimate dataset (Abatzoglou et al. 2018) was created by the Climatology Lab at the University of California Merced. It employs a method known as climatically aided interpolation to integrate information from many datasets, including WorldClim VI.4, WorldClim V2, CRU TS 4.0, and JRA55. The TerraClimate precipitation datasets, commonly referred to as TERRA, include monthly rainfall data from 1958 to the present. These datasets give information on precipitation with a geographical resolution of 0.04° (4 km) (Ghomlaghi et al. 2022). TerraClimate is available online at https://app.climateengine.org/climateEngine. A detailed description of TerraClimate climate is provided by Abatzoglou et al. (2018).

The NASA POWER project was developed by the National Aeronautics and Space Administration (NASA). The organization has consistently provided extensive support for satellite systems and research initiatives that provide crucial data for the analysis of climate and climatic phenomena under its Earth Science research program. The NASA POWER platform provides a web interface that allows users to conveniently access a diverse range of global climate data (Tan et al. 2023). These data are derived from the assimilation products of the Modern Period Retrospective Analysis for Research and Applications (MERRA-2). The primary constituents of the system encompass the GEOS atmospheric model and the gridpoint statistical interpolation (GSI) analysis tool (Gelaro et al. 2017). Sparks (2018) provided an overview of NASA POWER products, which is freely available online at https://power.larc.nasa.gov/data-access-viewer/.

Accuracy assessment

Continuous statistical metrics, namely, the correlation coefficient (CC), root-mean-square error (RMSE), mean absolute error (MAE), and mean error (ME) were utilized to evaluate the performance of CRU, CHIRPS, NASA POWER, TerraClimate, and GPCC against ground observations data from the period of 1981–2021. In addition, the standardised precipitation index (SPI) was used as a basis for the investigation of drought monitoring. The evaluation at monthly and annual timescales was conducted using continuous statistical indicators, whereas the assessment of inter-annual rainfall anomalies was conducted, i.e., SPI.

CC quantifies the degree of the linear association between the gridded product and the estimates obtained from ground observations (Asuero et al. 2006). Values close to 1 indicate a strong correlation between the GPP and observed estimations. RMSE is a statistical measure that quantifies the variability between a GPP and ground observation by calculating the standard deviation of their differences. According to Willmott & Matsuura (2005), a larger value of RMSE signifies a significant disparity between the observed and gridded rainfall data. ME is a metric used to quantify the average discrepancy between ground observations and gridded rainfall data. A positive number indicates an overestimation of GPPs, whereas a negative value indicates an underestimation. MAE is the calculated average of the absolute differences between the GPPs and the observed values, taking into account the weights assigned to each discrepancy (Willmott & Matsuura 2005; Robeson & Willmott 2023).

Standardized precipitation index

SPI was used to examine the efficacy of NASA POWER, CRU, CHIRPS, GPCC, and TerraClimate datasets in replicating observed inter-annual variations in rainfall anomalies (McKee et al. 1993). The SPI is a metric used to quantify the level of variability in rainfall patterns over an extended duration in relation to the average values (Chukwudi & Adebowale 2018). SPI values for moderately, severely, and extremely dry conditions ranged from −1.49 to −1, −1.99 to −1.5, and less than −2, respectively. SPI values above 2, 1.5 to 1.99, and 1 to 1.49 indicate extremely, very, and moderately wet conditions. SPI values in the range of −0.99 to 0.99 represent a normal climate condition (WMO 2012; Suhana et al. 2023).

According to the World Meteorological Organization (WMO 2012), the SPI was suggested as the most effective method for monitoring drought in operational activities. It is a tool utilized to assess the likelihood of precipitation occurring over various timescales, such as 3, 6, 12, and 24 months (McKee et al. 1993). The usefulness of the GPPs in tracking precipitation anomalies across 6-month accumulation periods – a crucial aspect of medium-term impact analysis – was investigated using a 6-month SPI (SPI-6). SPI-6 is linked to variations in reservoir levels or streamflow for the purpose of monitoring the water supply. The investigation specifically focused on three prominent meteorological stations within the region. The formula of SPI is stated as follows:
formula
where represents the observed precipitation in millimetres for timescale k in month j, with j ranging from 1 to 12 of year i; μi and σj represent population parameters, specifically, the expected value and standard deviation of precipitation in month j, respectively. For further details, please refer to the study by Alsenjar et al. (2022).

Monthly precipitation

Table 3 shows the magnitudes of errors in the GPPs derived from observed data across various locations. The results of the study established that the GPPs well represented both long-term trends and peak values at all of the locations. Notably, the products displayed substantial error deviations, ranging from 25.46 to 43.02 mm/month for RMSE and from 7.02 to 25.23 mm/month for MAE. The result supported the findings of Ogbu et al. (2020), which stated that GPPs successfully documented the latitudinal fluctuations of ITCZ as it transitioned from lower to higher latitudes, resulting in convective occurrences.

Table 3

Summary of statistics metrics for monthly evaluation of gridded precipitation products over the Yobe State, Nigeria

Station nameProductsCCRMSE (mm/month)MAE (mm/month)ME (mm/month)
Nguru CRU 0.77 34.47 16.28 1.25 
CHIRPS 0.78 34.36 15.02 −57.48 
NASA POWER 0.71 39.66 17.79 16.88 
TerraClimate 0.72 38.41 16.58 −48.4 
GPCC 0.83 30.53 8.72 −68.8 
Potiskum CRU 0.81 38.54 19.57 49.79 
CHIRPS 0.83 32.12 17.76 16.62 
NASA POWER 0.71 47.90 24.17 78.23 
TerraClimate 0.81 36.73 17.54 0.96 
GPCC 0.88 29.48 12.07 −36.47 
Maiduguri CRU 0.75 39.05 19.56 20.83 
CHIRPS 0.78 36.28 16.82 −43.87 
NASA POWER 0.59 50.12 25.23 −52.65 
TerraClimate 0.78 36.29 17.04 −1.67 
GPCC 0.89 25.46 7.02 −20.1 
Dapchi CRU 0.73 37.13 17.55 31.51 
CHIRPS 0.73 36.80 16.64 6.26 
NASA POWER 0.72 37.69 19.36 50.76 
TerraClimate 0.74 36.20 16.86 29.86 
GPCC 0.74 36.78 18.53 39.29 
Garin Alkali CRU 0.68 37.79 17.93 40.48 
CHIRPS 0.70 36.03 16.72 −63.00 
NASA POWER 0.68 36.96 17.58 −65.45 
TerraClimate 0.68 36.78 18.67 −17.86 
GPCC 0.64 40.87 18.68 −138.67 
Karasuwa CRU 0.67 39.92 17.91 −20.33 
CHIRPS 0.67 40.07 18.49 −24.92 
NASA POWER 0.71 37.25 18.61 20.56 
TerraClimate 0.65 40.69 18.40 −16.49 
GPCC 0.63 43.02 18.88 −87.44 
Yunusari CRU 0.51 40.03 19.74 141.09 
CHIRPS 0.46 40.07 17.49 98.07 
NASA POWER 0.45 42.80 19.44 132.16 
TerraClimate 0.52 36.96 17.26 89.07 
GPCC 0.48 37.14 17.49 −35.91 
Station nameProductsCCRMSE (mm/month)MAE (mm/month)ME (mm/month)
Nguru CRU 0.77 34.47 16.28 1.25 
CHIRPS 0.78 34.36 15.02 −57.48 
NASA POWER 0.71 39.66 17.79 16.88 
TerraClimate 0.72 38.41 16.58 −48.4 
GPCC 0.83 30.53 8.72 −68.8 
Potiskum CRU 0.81 38.54 19.57 49.79 
CHIRPS 0.83 32.12 17.76 16.62 
NASA POWER 0.71 47.90 24.17 78.23 
TerraClimate 0.81 36.73 17.54 0.96 
GPCC 0.88 29.48 12.07 −36.47 
Maiduguri CRU 0.75 39.05 19.56 20.83 
CHIRPS 0.78 36.28 16.82 −43.87 
NASA POWER 0.59 50.12 25.23 −52.65 
TerraClimate 0.78 36.29 17.04 −1.67 
GPCC 0.89 25.46 7.02 −20.1 
Dapchi CRU 0.73 37.13 17.55 31.51 
CHIRPS 0.73 36.80 16.64 6.26 
NASA POWER 0.72 37.69 19.36 50.76 
TerraClimate 0.74 36.20 16.86 29.86 
GPCC 0.74 36.78 18.53 39.29 
Garin Alkali CRU 0.68 37.79 17.93 40.48 
CHIRPS 0.70 36.03 16.72 −63.00 
NASA POWER 0.68 36.96 17.58 −65.45 
TerraClimate 0.68 36.78 18.67 −17.86 
GPCC 0.64 40.87 18.68 −138.67 
Karasuwa CRU 0.67 39.92 17.91 −20.33 
CHIRPS 0.67 40.07 18.49 −24.92 
NASA POWER 0.71 37.25 18.61 20.56 
TerraClimate 0.65 40.69 18.40 −16.49 
GPCC 0.63 43.02 18.88 −87.44 
Yunusari CRU 0.51 40.03 19.74 141.09 
CHIRPS 0.46 40.07 17.49 98.07 
NASA POWER 0.45 42.80 19.44 132.16 
TerraClimate 0.52 36.96 17.26 89.07 
GPCC 0.48 37.14 17.49 −35.91 

When compared to other GPPs, Table 3 demonstrates that GPCC correlated the best with ground observations, with the lowest error values at the Nguru, Potiskum, Maiduguri, and Dapchi stations. While CHIRPS, CRU, and TerraClimate are also performed remarkably well at these stations. CHIRPS outperformed other GPPs at the Garin Alkali station, while NASA POWER exhibited the highest correlation values at Karasuwa station, followed by CRU, CHIRPS, TerraClimate, and GPCC. These findings align with the conclusions drawn by Ogunjo et al. (2022), indicating that GPCC exhibits the highest level of performance. Nevertheless, GPPs had lower correlation values ranging from 0.45 to 0.52 at the Yunusari station, with NASA POWER demonstrating the lowest correlation value of 0.45. The aridity of the area may be attributed to the impact of climate change since it experiences the least amount of precipitation in the whole region (Ibrahim et al. 2022).

The performance of all GPPs in reflecting the regional distribution of rainfall was significantly moderate, as indicated by the CC values shown in Table 3 and the literature (Usman et al. 2018; Degefu et al. 2022). However, most products accurately depict locations with high precipitation levels in the southern and western regions, while showing limited agreement in low-precipitation areas, especially in the northern regions at the Yunusari station (Figure 2(g)). The aforementioned finding can be attributed to the local precipitation system, as indicated in Figure 2.
Figure 2

Comparison of gauges and GPPs in estimating monthly precipitation for the (a) Nguru, (b) Potiskum, (c) Maiduguri, (d) Dapchi, (e) Garin Alkali, (f) Karasuwa, and (g) Yunusari stations.

Figure 2

Comparison of gauges and GPPs in estimating monthly precipitation for the (a) Nguru, (b) Potiskum, (c) Maiduguri, (d) Dapchi, (e) Garin Alkali, (f) Karasuwa, and (g) Yunusari stations.

Close modal

GPPs tended to overestimate precipitation values in the Yunusari region, which is located at the lowest altitude. Specifically, the CRU product exhibits an overestimation of over 35%, whereas the GPCC dataset underestimated rainfall at the same station by around 11%. All GPPs underestimated precipitation values in August, which is also the peak of precipitation at the Karasuwa and Dapchi stations (Figure 2), and this can be attributed to the influence of the convective system. However, the GPPs were able to accurately reproduce the annual cycle of the mean monthly rainfall pattern at most of the locations. GPPs exhibited moderate MAE values across all stations, with a range of 7.02 to 19.57 mm/month.

Figure 3 presents a scatter plot depicting the degree of concurrence between the evaluated GPPs and rain gauges in the cumulative monthly analysis across various locations for the 1981–2021 period. The GPCC dataset had a substantial correlation coefficient of 0.95, indicating a strong relationship with the reference data. In addition, the RMSE and MAE values for GPCC were considerably lower at 44.25 and 17.26 mm/month, respectively, in comparison to the other datasets. The study revealed a strong correlation between GPCC, CHIRPS, and CRU datasets. In addition, TerraClimate, CHIRPS, CRU, and other products have shown a significant degree of consistency.
Figure 3

Scatter plot between gauges and gridded precipitation products at monthly scale for three stations from 1981 to 2021.

Figure 3

Scatter plot between gauges and gridded precipitation products at monthly scale for three stations from 1981 to 2021.

Close modal

Annual precipitation

In this section, Table 4 reveals the inter-annual variability of annual precipitation for all seven locations across the region. These datasets were then used to compute the aggregate precipitation amounts for every seasonal cycle (annual) throughout 40 years, spanning from 1981 to 2021 for two locations. In addition, data from 1981 to 2015 were considered for one location, and data from 1992 to 2004 were considered for four other locations based on the availability of gauge datasets in the study area. Nevertheless, a limited level of agreement was identified between GPPs and gauge observations, as evidenced by a decrease in correlation values (Table 4) in comparison to the monthly assessment. This suggests that monthly precipitation total errors were neither symmetric nor random. Thus, the temporal aggregates did not exhibit a nullifying effect, therefore failing to enhance the correlation between the products and the observed data (Duan et al. 2016). The sub-cloud evaporation phenomenon, in which water evaporates before reaching the surface, may also be one of the reasons contributed to lower correlations of GPPs in annual precipitation estimation (Aksu & Akgül 2020).

Table 4

Summary of statistics metrics for annual evaluation of gridded precipitation products over the Yobe State

Station NameProductsCCRMSE (mm/y)MAE (mm/y)ME (mm/y)
Nguru CRU 0.39 140.74 105.30 1.26 
CHIRPS 0.28 167.49 105.02 −63.26 
NASA POWER 0.27 170.52 115.48 −61.25 
TerraClimate 0.24 164.46 110.05 −48.34 
GPCC 0.46 146.83 79.17 60.81 
Potiskum CRU 0.33 146.58 107.90 49.59 
CHIRPS 0.38 114.22 93.55 16.63 
NASA POWER 0.15 210.52 165.19 66.36 
TerraClimate 0.34 132.75 101.34 1.39 
GPCC 0.65 93.50 51.93 34.39 
Maiduguri CRU 0.51 117.32 84.99 20.84 
CHIRPS 0.35 138.31 97.39 −43.84 
NASA POWER 0.02 214.73 175.48 −52.09 
TerraClimate 0.55 109.48 75.32 −1.67 
GPCC 0.84 68.14 37.30 −20.11 
Dapchi CRU 0.57 97.95 75.03 31.51 
CHIRPS 0.45 109.30 88.02 6.27 
NASA POWER 0.09 159.54 120.10 50.81 
TerraClimate 0.47 108.70 86.16 36.86 
GPCC 0.41 118.72 95.61 48.19 
Garin Alkali CRU 0.22 135.44 116.26 40.48 
CHIRPS 0.23 141.16 109.49 −67.99 
NASA POWER 0.24 140.89 122.99 −65.45 
TerraClimate 0.19 130.32 110.97 −17.88 
GPCC 0.26 185.67 140.46 −138.72 
Karasuwa CRU 0.13 163.73 131.36 −20.33 
CHIRPS 0.13 163.61 128.05 −24.09 
NASA POWER 0.22 154.58 134.60 20.64 
TerraClimate 0.08 169.41 137.16 −16.08 
GPCC 0.12 188.46 143.93 −87.55 
Yunusari CRU 0.19 195.95 170.53 141.13 
CHIRPS 0.17 168.36 153.44 98.09 
NASA POWER 0.47 171.88 151.72 132.19 
TerraClimate 0.20 161.28 135.98 89.07 
GPCC 0.35 126.13 102.77 35.89 
Station NameProductsCCRMSE (mm/y)MAE (mm/y)ME (mm/y)
Nguru CRU 0.39 140.74 105.30 1.26 
CHIRPS 0.28 167.49 105.02 −63.26 
NASA POWER 0.27 170.52 115.48 −61.25 
TerraClimate 0.24 164.46 110.05 −48.34 
GPCC 0.46 146.83 79.17 60.81 
Potiskum CRU 0.33 146.58 107.90 49.59 
CHIRPS 0.38 114.22 93.55 16.63 
NASA POWER 0.15 210.52 165.19 66.36 
TerraClimate 0.34 132.75 101.34 1.39 
GPCC 0.65 93.50 51.93 34.39 
Maiduguri CRU 0.51 117.32 84.99 20.84 
CHIRPS 0.35 138.31 97.39 −43.84 
NASA POWER 0.02 214.73 175.48 −52.09 
TerraClimate 0.55 109.48 75.32 −1.67 
GPCC 0.84 68.14 37.30 −20.11 
Dapchi CRU 0.57 97.95 75.03 31.51 
CHIRPS 0.45 109.30 88.02 6.27 
NASA POWER 0.09 159.54 120.10 50.81 
TerraClimate 0.47 108.70 86.16 36.86 
GPCC 0.41 118.72 95.61 48.19 
Garin Alkali CRU 0.22 135.44 116.26 40.48 
CHIRPS 0.23 141.16 109.49 −67.99 
NASA POWER 0.24 140.89 122.99 −65.45 
TerraClimate 0.19 130.32 110.97 −17.88 
GPCC 0.26 185.67 140.46 −138.72 
Karasuwa CRU 0.13 163.73 131.36 −20.33 
CHIRPS 0.13 163.61 128.05 −24.09 
NASA POWER 0.22 154.58 134.60 20.64 
TerraClimate 0.08 169.41 137.16 −16.08 
GPCC 0.12 188.46 143.93 −87.55 
Yunusari CRU 0.19 195.95 170.53 141.13 
CHIRPS 0.17 168.36 153.44 98.09 
NASA POWER 0.47 171.88 151.72 132.19 
TerraClimate 0.20 161.28 135.98 89.07 
GPCC 0.35 126.13 102.77 35.89 

Both GPCC and CRU demonstrated a strong ability to faithfully replicate the annual precipitation patterns at the Maiduguri, Potiskum, and Nguru stations as shown in Figure 4 and Table 4. This is supported by the high and moderate correlation values of 0.84 at Maiduguri station and 0.65 and 0.46 for Potiskum and Nguru stations, respectively, as recorded by the GPCC. In general, a significant portion of the northern region was effectively captured by the GPCC, NASA POWER, and CRU datasets (Figure 4). However, TerraClimate, NASA POWER, and CHIRPS products exhibited comparatively lower performance across most of the stations at the annual scale (Table 4).
Figure 4

Comparison gauges and GPPs in estimating annual precipitation at the (a) Nguru, (b) Potiskum, (c) Maiduguri, (d) Dapchi, (e) Garin Alkali, (f) Karasuwa, and (g) Yunusari stations.

Figure 4

Comparison gauges and GPPs in estimating annual precipitation at the (a) Nguru, (b) Potiskum, (c) Maiduguri, (d) Dapchi, (e) Garin Alkali, (f) Karasuwa, and (g) Yunusari stations.

Close modal

The RMSE values for all GPPs at various stations were found to be higher, and it is noteworthy that the GPCC product exhibited comparatively lower RMSE values at the Maiduguri (68.14 mm/year) and Potiskum (93.50 mm/year) stations. In a similar vein, the analysis showed that the GPCC product had an MAE value of 37.30 mm/year, ahead of CRU with 75.03 mm/year, TerraClimate with 75.32 mm/year, and CHIRPS with 88.02 mm/year. Nevertheless, the recorded negative values for ME in Table 4 suggest that the majority of GPPs have underestimated annual precipitation when compared to gauge measurements. Figure 4 demonstrates that GPPs tend to underestimate precipitation levels at nearly all stations, with the notable exception of Yunusari. The TerraClimate product had the lowest ME value of −1.67 mm/year, while the GPCC product displayed the highest ME value of −138.72 mm/year.

Drought assessment

The capability of CRU, CHIRPS, NASA POWER, TerraClimate, and GPCC products to accurately reproduce changes in annual rainfall and drought was evaluated at three stations in the given region over a period spanning from 1981 to 2015. The calculated SPI-6 for the three stations, Nguru, Potiskum, and Maiduguri, are depicted in Figure 5. GPCC demonstrated superior performance compared to other GPPs in accurately representing the inter-annual variability of rainfall anomalies across when compared to the rain gauges as observed in the 34-year rainfall series. Most of the GPPs showed an increasing linear trend of SPI-6 from 1981 to 2015. The increasing trend of SPI captured by gauge and CHIRPS was also reported by Mianabadi et al. (2022) over most of the regions in south-eastern, Iran. In contrast, NASA POWER is the only GPP that showed a decreasing linear trend of SPI over Yobe State. Similarly, NASA POWER also showed some biases in the computations of extreme precipitation in other tropical regions (Tan et al. 2023), i.e., underestimations of days with little or no precipitation, ranging from 0 to 1 mm/day, and days with intense precipitation, over 50 mm/day.
Figure 5

Temporal changes of SPI-6 over the Yobe state from 1918 to 2015 as measured by (a) Gauge, (b) CRU, (c) CHIRPS, (d) NASA POWER, (e) TerraClimate, and (f) GPCC.

Figure 5

Temporal changes of SPI-6 over the Yobe state from 1918 to 2015 as measured by (a) Gauge, (b) CRU, (c) CHIRPS, (d) NASA POWER, (e) TerraClimate, and (f) GPCC.

Close modal
With a CC value of 0.85, GPCC had the strongest correlation with gauges in the SPI-6 computations, followed by NASA POWER (0.41), CRU (0.75), TerraClimate (0.74), and CHIRPS (0.73). In terms of drought detection capability, Figure 6 shows the probability of moderately, severely, and extremely dry conditions across Yobe State between 1981 and 2015 using gauges and several GPPs based on SPI-6. In general, Yobe State experienced moderately dry conditions, followed by severely and extremely dry conditions based on ground observations, where the pattern is captured by most of the GPPs, except CHIRPS and NASA POWER. CHIRPS has shown a considerable good correlation with ground observations in the SPI-6 calculations, but it detected more extremely dry conditions than moderately dry conditions. The overestimation of extremely drought conditions may be contributed to the underestimation of precipitation by CHIRPS over the Yobe State. Similarly, CHIRPS also underestimated precipitation over inland Turkey due to the shadow effects of mountainous areas (Aksu & Akgül 2020). The findings show that GPCC and CRU are better options for monitoring droughts in Yobe State.
Figure 6

Drought detection probability over Yobe State from 1918 to 2015 as measured by gauges and different gridded precipitation products based on SPI-6.

Figure 6

Drought detection probability over Yobe State from 1918 to 2015 as measured by gauges and different gridded precipitation products based on SPI-6.

Close modal

GPCC consistently showed stronger correlations and lower error values compared to other GPPs in most places, both the monthly and annual scales (Tables 3 and 4). The result is consistent with the findings of Ogunjo et al. (2022), which stated that GPCC-gridded products demonstrate superior performance over Nigeria. GPCC has been validated as one of the most accurate GPPs and used as an alternative to gauge measurements in several regions worldwide. For example, Nguyen-Duy et al. (2023) used GPCC products as reference data for evaluating CMIP6 GCMs in Vietnam.

At the monthly evaluation, the CHIRPS product showed strong correlations across many stations (Table 3), which is also consistent with the findings from previous studies (Ogbu et al. 2020; Gebretsadkan et al. 2023). The overall performance of CHIRPS is good because it can predict rainfall through cold cloud duration using satellite thermal infrared recordings. On the other hand, TerraClimate performance was moderate among the evaluated GPPs based on the analysis of the monthly and annual statistical metrics presented in Tables 3 and 4, as well as for SPI-6. Moreover, it underestimated precipitation in four out of the seven stations on both the monthly and annual scales.

NASA POWER exhibited reasonable performance at the monthly scale, with overall high correlation values across the stations, except for the Yunusari station (Table 4). In contrast, NASA POWER had a considerable lower performance among the GPPs at the annual scale assessment. It displayed the lowest correlation values, ranging from 0.02 to 0.47, which are considered insignificant or relatively low CC values (Asuero et al. 2006). In addition, it demonstrated the highest values for RMSE and MAE, ranging from 140.89 to 214.73 mm/year and 115.48 to 175.48 mm/year, respectively. The observed favourable performance across most stations on the monthly scale aligns with the findings of prior research as reported by Tayyeh & Mohammed (2023), which stated that NASA POWER has shown a strong agreement with gauge measurements in the Euphrates River basin. Tan et al. (2023) also found a strong correlation between NASA POWER and gauge data in terms of monthly precipitation, with a moderate correlation at the annual scale.

Figure 6 depicts the temporal fluctuations of the SPI across a 6-month timescale over the three specified geographical areas. The GPCC demonstrates superior performance, matching the upward and downward patterns quite well, with the CC value of 0.73. While CRU, TerraClimate, and CHIRPS demonstrated moderate correlation with gauges in estimating SPI-6, with the CC values ranging from 0.52 to 0.55. In contrast, NASA POWER was unable to capture the SPI-6 pattern and trend accurately as shown in Figure 6(d). Therefore, gridded products are suitable for evaluating drought conditions, as demonstrated by Mabrouk et al. (2022) using TerraClimate monthly precipitation data.

The limitation of this study is the inadequate dataset, which only includes monthly precipitation data, as well as a lack of appropriate gauges in the northeast arid zone of Nigeria. More ground observations should be installed to enhance the climate assessment of desert regions. In addition, future research should incorporate additional climatic variables such as temperature, relative humidity, and wind speed.

GPPs have proven to be a viable substitute for rainfall data in a range of hydro-climatic applications at both global and regional scales, hence contributing to water management practices and policy formulations. The evaluation of the accuracy of GPPs in comparison to ground observations is necessary at the local scales, notwithstanding the advantages of the former in terms of spatial and temporal resolution and data availability. This study evaluated the performance of five GPPs, namely, CRU, CHIRPS, NASA POWER, TerraClimate, and GPCC, in Yobe, Nigeria, from 1981 to 2021. The assessment included continuous statistical metrics and the SPI as evaluation criteria.

Among the five precipitation products assessed in this investigation, the GPCC demonstrated superior performance compared to other GPPs in reproducing precipitation patterns at both the monthly and annual timescales over the Yobe State from 1981 to 2021. The performance of GPPs is better in estimating monthly precipitation than annual ones, which may be contributed by the calibration of gauge-based reference data at the monthly scale. The gridded product outputs exhibit a tendency to underestimate precipitation amounts in areas that normally receive higher precipitation. Among the evaluated GPPs, NASA POWER exhibits the highest tendency in the underestimation of high precipitation amounts. In terms of drought assessment, the GPCC product has a higher level of performance compared to other products in estimating SPI-6. While CRU, TerraClimate, and CHIPRS demonstrated a moderate correlation with gauges in estimating SPI-6. In contrast, NASA POWER is not suitable for drought assessment over the Yobe State.

This work was supported by the Ministry of Higher Education (MOHE) Malaysia, Fundamental Research Grant Scheme (Grant number: FRGS/1/2022/SS07/USM/01/3).

The raw data used in this study are available to download from https://www.dwd.de/EN/ourservices/gpcc/gpcc.html (GPCC), https://power.larc.nasa.gov/ (NASA POWER), https://app.climateengine.org/climateEngine (CHIRPS and TerraClimate), and https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/ (CRU).

The authors declare there is no conflict.

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