Hydrological Evaluation of PERSIANN-CDR Rainfall over Upper Senegal River and Bani River Basins

Bâ, Khalidou M.; Balcázar, Luis; Diaz, Vitali; Ortiz, Febe; Albores, Miguel Ángel Gómez; Díaz-Delgado, Carlos

doi:10.3390/rs10121884

Cited by 12 publications

(19 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, Ashouri et al [38] established the proficiency of PERSIANN-CDR in terms of long-term rainfall-runoff modelling with low bias. In recent years, there has been growing literature in the performance of PERSIANN-CDR daily rainfall product to acquire rainfall information in Asian mountainous regions and assess its efficiency in terms of long-term performance for rainfall and hydrological modelling [32,37,[65][66][67][68][69].…”

Section: Appropriateness Of Gridded Datasets In Compared To Observed mentioning

confidence: 99%

An Analysis of Long-Term Rainfall Trends and Variability in the Uttarakhand Himalaya Using Google Earth Engine

et al. 2020

View full text Add to dashboard Cite

This paper analyses the spatio-temporal trends and variability in annual, seasonal, and monthly rainfall with corresponding rainy days in Bhilangana river basin, Uttarakhand Himalaya, based on stations and two gridded products. Station-based monthly rainfall and rainy days data were obtained from the India Meteorological Department (IMD) for the period from 1983 to 2008 and applied, along with two daily rainfall gridded products to establish temporal changes and spatial associations in the study area. Due to the lack of more recent ground station rainfall measurements for the basin, gridded data were then used to establish monthly rainfall spatio-temporal trends for the period 2009 to 2018. The study shows all surface observatories in the catchment experienced an annual decreasing trend in rainfall over the 1983 to 2008 period, averaging 15.75 mm per decade. Analysis of at the monthly and seasonal trend showed reduced rainfall for August and during monsoon season as a whole (10.13 and 11.38 mm per decade, respectively); maximum changes were observed in both monsoon and winter months. Gridded rainfall data were obtained from the Climate Hazard Infrared Group Precipitation Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). By combining the big data analytical potential of Google Earth Engine (GEE), we compare spatial patterns and temporal trends in observational and modelled precipitation and demonstrate that remote sensing products can reliably be used in inaccessible areas where observational data are scarce and/or temporally incomplete. CHIRPS reanalysis data indicate that there are in fact three significantly distinct annual rainfall periods in the basin, viz. phase 1: 1983 to 1997 (relatively high annual rainfall); phase 2: 1998 to 2008 (drought); phase 3: 2009 to 2018 (return to relatively high annual rainfall again). By comparison, PERSIANN-CDR data show reduced annual and winter precipitation, but no significant changes during the monsoon and pre-monsoon seasons from 1983 to 2008. The major conclusions of this study are that rainfall modelled using CHIRPS corresponds well with the observational record in confirming the decreased annual and seasonal rainfall, averaging 10.9 and 7.9 mm per decade respectively between 1983 and 2008, although there is a trend (albeit not statistically significant) to higher rainfall after the marked dry period between 1998 and 2008. Long-term variability in rainfall in the Bhilangana river basin has had critical impacts on the environment arising from water scarcity in this mountainous region.

show abstract

Section: Appropriateness Of Gridded Datasets In Compared To Observed mentioning

confidence: 99%

An Analysis of Long-Term Rainfall Trends and Variability in the Uttarakhand Himalaya Using Google Earth Engine

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Furthermore, the spatial distribution of rain gauges is often highly uneven in practice, with few gauges in remote areas, in less developed regions, in areas with complicated terrain or in forested areas [8]. As a result, in situ rain gauge data seldom matches the needs of applications that require precipitation data with high spatiotemporal resolution [8,9]. This is particularly true across vast swaths of the African continent [8,10].…”

Section: Introductionmentioning

confidence: 99%

“…Recent SPPs also provide adequate spatial (< 0.1°*0.1°) and temporal (daily to sub-hourly, depending on the product) resolution, allowing for credible precipitation estimates in data-scarce environments or at ungauged locations [2,11,12]. They have been used in a variety of applications, including hydrological modeling, extreme event analysis, and water resource management [8,9,13].…”

Section: Introductionmentioning

confidence: 99%

Reliability of Satellite-Based Rainfall Products for Water Man-agement Studies: the Case of The Ankavia River Basin in Madagascar

Ramahaimandimby¹,

Randriamaherisoa²,

Jonard³

et al. 2022

Preprint

View full text Add to dashboard Cite

Hydrological modeling for water management in large watersheds requires accurate spatially-distributed rainfall time series. In case of low coverage density of ground-based measurements, satellite precipitation products (SPP) constitute an attractive alternative, the quality of which must nevertheless be verified. The objective of this study was to evaluate, at different time scales, the reliability of six SPPs against a 2-year record from a network of 14 rainfall gauges located in the Ankavia catchment (Madagascar). The SPPs considered in this study are the African Rainfall Estimate Climatology (ARC2), the Climate Hazards group Infrared Precipitation with Station data (CHIRPS), the ECMWF Reanalysis (ERA5), the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the African Rainfall Estimation (REF2) products. The results suggest that IMERG (R² = 0.63, slope of linear regression a = 0.96, root mean square error RMSE = 12 mm/day, mean absolute error MAE = 5.5 mm/day) outperforms other SPPs at the daily scale, followed by REF2 (R² = 0.41, a = 0.94, RMSE = 15 mm/day, MAE = 6 mm/day) and ARC2 (R² = 0.30, a = 0.88, RMSE = 16 mm/day, MAE = 6.7 mm/day). All SPPs, with the exception of the ERA5, overestimate the ‘no rain’ class (0 – 0.2 mm/day). ARC2, IMERG, PERSIANN, and REF2 all underestimate rainfall occurrence in the 0.2 – 150 mm/day rainfall range, whilst CHIRPS and ERA5 overestimate it. Only CHIRPS and PERSIANN could estimate extreme rainfall (&gt;150 mm/day) satisfactorily. According to the Critical Success Index (CSI) categorical statistical measure, IMERG performs quite well in detecting rain events in the range 2-150 mm/day, whereas PERSIANN outperforms IMERG for rain events larger than 150 mm/day. Because it performs best at daily scale, only IMERG was evaluated for time scales other than daily. At the yearly and monthly time scales, the performance is good with R² = 0.97 and 0.87, respectively. At the event time scale, the probability distribution function PDF of rain gauge values and IMERG data show good agreement. However, at hourly time scale, the correlation between ground-based measurements and IMERG data becomes poor (R2 = 0.20). Overall, the IMERG product can be regarded as the most reliable satellite precipitation source at monthly, daily and event time scales for hydrological applications in the study area, but the poor agreement at hourly time scale and the inability to detect extreme rainfall >200 mm/day may nevertheless restrict its use.

show abstract

“…Rainfall variability at the Sahel is dynamically related to the variability of atmospheric circulation, Hadley cells, and West African Monsoon (WAM) circulation [6]. WAM is a coupled atmosphere-ocean-land system [7] responsible for summer rainfall in the Sahel from May to October [4,6], with the most significant rainfall happening in July-August-September (JAS) [8,9]. The WAM flows as a shallow moist surface air layer from the Gulf of Guinea, overlaid by the main northeast trade winds, which blow from the Sahara and which is known as Harmattan.…”

Section: Introductionmentioning

confidence: 99%

Development and Assessment of Seasonal Rainfall Forecasting Models for the Bani and the Senegal Basins by Identifying the Best Predictive Teleconnection

et al. 2022

Self Cite

View full text Add to dashboard Cite

The high variability of rainfall in the Sahel region causes droughts and floods that affect millions of people every year. Several rainfall forecasting models have been proposed, but the results still need to be improved. In this study, linear, polynomial, and exponential models are developed to forecast rainfall in the Bani and Senegal River basins. All three models use Atlantic sea surface temperature (SST). A fourth algorithm using stepwise regression was also developed for the precipitation estimates over these two basins. The stepwise regression algorithm uses SST with covariates, mean sea level pressure (MSLP), relative humidity (RHUM), and five El Niño indices. The explanatory variables SST, RHUM, and MSLP were selected based on principal component analysis (PCA) and cluster analysis to find the homogeneous region of the Atlantic with the greatest predictive ability. PERSIANN-CDR rainfall data were used as the dependent variable. Models were developed for each pixel of 0.25° × 0.25° spatial resolution. The second-order polynomial model with a lag of about 11 months outperforms all other models and explains 87% of the variance in precipitation over the two watersheds. Nash–Sutcliffe efficiency (NSE) values were between 0.751 and 0.926 for the Bani River basin and from 0.175 to 0.915 for the Senegal River basin, for which the lowest values are found in the driest area (Sahara). Results showed that the North Atlantic SST shows a more robust teleconnection with precipitation dynamics in both basins.

show abstract

Hydrological Evaluation of PERSIANN-CDR Rainfall over Upper Senegal River and Bani River Basins

Cited by 12 publications

References 33 publications

An Analysis of Long-Term Rainfall Trends and Variability in the Uttarakhand Himalaya Using Google Earth Engine

An Analysis of Long-Term Rainfall Trends and Variability in the Uttarakhand Himalaya Using Google Earth Engine

Reliability of Satellite-Based Rainfall Products for Water Man-agement Studies: the Case of The Ankavia River Basin in Madagascar

Development and Assessment of Seasonal Rainfall Forecasting Models for the Bani and the Senegal Basins by Identifying the Best Predictive Teleconnection

Contact Info

Product

Resources

About