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Possibility of global gridded streamflow dataset correction: applications of large-scale watersheds with different climates

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Abstract

Previous studies depicted that the global gridded hydroclimatic products mostly lack precision and are inconsistent throughout the real-world water cycle. The current study evaluates the efficiency of the eight streamflow datasets (FLDAS, GLDAS 2.1, GLDAS 2.0, ERA5, TERRA, ERA5_Land, MERRA2, and GRUN) in two large-scale watersheds with different climate conditions (Karkhe River basin in Iran and Rio Itapecuru River basin in Brazil). To correct the products via different statistical metrics, two tuning procedures (including scale factors and the network-based Muskingum method) have been used. Based on the results showing the paramount impacts on the correction procedures of the products, GRUN, ERA_land, and GLDAS 2 perform the best accuracy in the Karkhe River basin; however, the worst product in the watershed is GLDAS 2.1. In the Rio Itapecuru River basin, MERRA2 and TERRA have the best and worst performance, respectively. In the first watershed, the KGE and TaylorS in GLDAS 2.1 improved by 0.85 and 0.23 in the case of correction using scale factor, and these statistics also significantly increased using coupled scale factor and Muskingum routing methods by 0.84 and 0.21, respectively. In the second watershed, these statistics increased by 9.16 and 0.56 in the worst case using scale factors; these metrics also levelled up by 9.32 and 0.62 via coupled scale factor and Muskingum routing methods. In addition, it appeared that the corrected products could better simulate the streamflow in terms of oscillation time series and the extreme temporal values of the watersheds.

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Data availability

The datasets used during the current study are available from the corresponding author on reasonable request.

Code availability

The codes developed during the current study are available from the corresponding author on reasonable request.

Abbreviations

AMSR-E:

Advanced Microwave Scanning Radiometer for EOS

CaMa-Flood:

Catchment-based Macro-scale Floodplain

CHIRPS:

Climate Hazards Group InfraRed Precipitation with Station

CLM:

Community Land Model

CRU:

Climatic Research Unit

ECMWF:

European Centre for Medium-Range Weather Forecasts

ERA5:

ECMWF Reanalysis 5

FLDAS:

FEWS NET Land Data Assimilation System

FEWS NET:

The Famine Early Warning Systems Network

GLDAS:

Global Land Data Assimilation System

EOS :

Earth Observing System

GEOS:

Goddard Earth Observing System Model

GGPs:

Global Gridded Products

GGSPs:

Global Gridded Streamflow Products

GloFAS:

Global Flood Awareness System

GMAO:

Global Modeling and Assimilation Office

GRDC:

Global Runoff Data Centre

GRUN:

Global Runoff Reconstruction

GSWP3:

Global Soil Wetness Project Phase 3

IRF:

Impulse Response Function

JRA55:

Japanese 55-year Reanalysis

KGE:

Kling–Gupta Efficiency

LSMs:

Land Surface Models

MERRA:

The Modern-Era Retrospective Analysis for Research and Applications

LRR:

Linear Reservoir Routing

MP:

Multi-parameterization

NASA:

National Aeronautics and Space Administration

NSE:

Nash–Sutcliffe Efficiency

RAPID:

Routing Application for Parallel Computation of Discharge

RF:

Random forest

RMSE:

Root mean square error

SMC:

Soil Moisture Mean Seasonal Cycles

SMOS:

Soil Moisture Ocean and Salinity

SVE:

Saint Venant Equation

TaylorS:

Taylor skill

TS:

Time-series

TWS:

Terrestrial water storage

VIC:

Variable infiltration capacity

WRF:

Weather Research and Forecasting

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Acknowledgements

We appreciate the Global Runoff Data Centre (GRDC) for providing us access to river discharge observations in the Rio Itapecuru River basin (https://www.bafg.de/GRDC/EN/04_spcldtbss/43_GRfN/refDataset_node.html).

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Authors and Affiliations

  1. School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Hesam Barkhordari & Mohsen Nasseri

  2. Department of Civil Engineering and Water Engineering, University of Laval, Quebec City, QC, Canada

    Hamidreza Rezazadeh

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Conceptualization: Mohsen Nasseri. Methodology: Mohsen Nasseri and Hesam Barkhordari. Formal analysis and investigation: Hesam Barkhordari and Hamidreza Rezazadeh. Writing—original draft preparation: Hesam Barkhordari. Writing—review and editing: Hesam Barkhordari and Mohsen Nasseri. Supervision: Mohsen Nasseri.

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Correspondence to Mohsen Nasseri.

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Barkhordari, H., Nasseri, M. & Rezazadeh, H. Possibility of global gridded streamflow dataset correction: applications of large-scale watersheds with different climates. Theor Appl Climatol 152, 627–647 (2023). https://doi.org/10.1007/s00704-023-04388-2

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