Accuracy of grid precipitation data for Brazil: application in river discharge modelling of the Tocantins catchment

Gridded climate products (GCPs) provide a potential source for representing weather in remote, poor quality or short-term observation regions. The accuracy of three long-term GCPs (Asian Precipitation-Highly-Resolved Observational Data Integration towards Evaluation of Water Resources: APHRODITE, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record: PERSIANN-CDR and National Centers for Environmental Prediction Climate Forecast System Reanalysis: NCEP-CFSR) was analyzed for the Kelantan River Basin (KRB) and Johor River Basin (JRB) in Malaysia from 1983 to 2007. Then, these GCPs were used as inputs into calibrated Soil and Water Assessment Tool (SWAT) models, to assess their capability in simulating streamflow. The results show that the APHRODITE data performed the best in precipitation estimation, followed by the PERSIANN-CDR and NCEP-CFSR datasets. The NCEP-CFSR daily maximum temperature data exhibited a better correlation than the minimum temperature data. For streamflow simulations, the APHRODITE data resulted in strong results for both basins, while the NCEP-CFSR data showed unsatisfactory performance. In contrast, the PERSIANN-CDR data showed acceptable representation of observed streamflow in the KRB, but failed to track the JRB observed streamflow. The combination of the APHRODITE precipitation and NCEP-CFSR temperature data resulted in accurate streamflow simulations. The APHRODITE and PERSIANN-CDR data often underestimated the extreme precipitation and streamflow, while the NCEP-CFSR data produced dramatic overestimations. Therefore, a direct application of NCEP-CFSR data should be avoided in this region. We recommend the use of APHRODITE precipitation and NCEP-CFSR temperature data in modeling of Malaysian water resources.

Section: Discussionsupporting

confidence: 90%

Assessment of Three Long-Term Gridded Climate Products for Hydro-Climatic Simulations in Tropical River Basins

Tan

Gassman

Cracknell

2017

“…Dile and Srinivasan [15] highlighted the benefit of using the CFSR product in sparsely-monitored regions, where CFSR and conventional databases led to minor differences, except for one watershed for which CFSR gave much higher average annual rainfall. Monteiro, et al [16] demonstrated the superiority of one ERA-derived product (WFDEI-WATCH Forcing Data ERA Interim [17]) over CFSR. Finally, in a comparison of several weather input datasets, de Almeida Bressiani, et al [18] concluded that the best option for hydrological simulation is the CFSR product used with ground-based climate data.…”

Section: Introductionmentioning

confidence: 99%

Testing the SWAT Model with Gridded Weather Data of Different Spatial Resolutions

Grusson

Anctil

Sauvage

et al. 2017

This study explored the influence of the spatial resolution of a gridded weather dataset when inputted in the soil and water assessment tool (SWAT) over the Garonne River watershed. Several datasets are compared: ground-based weather stations, the 8-km SAFRAN product (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie), the 0.5 • CFSR product (Climate Forecasting System Reanalysis) and several derived SAFRAN grids upscaled to 16, 32, 64 and 128 km. The SWAT model, calibrated on weather stations, was successively run with each gridded weather dataset. Performances with SAFRAN up to 64 or 128 km were poor, due to a contraction of the spatial variance of daily precipitation. Performances with 8-km SAFRAN are similar to that of the aggregated 16-and 32-km SAFRAN grids. The~30-km CFSR product was found to perform well at some sites, while in others, its performance was considerably inferior because of grid points where precipitation was overestimated. The same problem was found in the calibration, where data at some weather stations did not appear to be representative of the subwatershed in which they are used to compute hydrology. These results suggest that the difference in the representation of the climate was more influential than its spatial resolution, an analysis that was confirmed by similar performances obtained with the SWAT model calibrated on the 16-and 32-km SAFRAN grids. However, the better performances obtained from these two weather datasets than from the ground-based stations' dataset confirmed the advantage of using the SAFRAN product in SWAT modelling.

“…Some studies conducted initial tests on the available data prior to calibration, then chose the data that appeared to perform the best based on certain model efficiency criteria [16,17]. Others studied the sensitivity of model outputs to precipitation ensembles [18,19], and their effects on water resource components of non-calibrated models [14,15,20]. Although all these schemes are important and necessary, in all of them, the prediction uncertainty was based on only one dataset.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Uncertainty of Multiple Input Datasets in the Prediction of Water Resource Components

Kamali

Abbaspour

Yang

2017

Self Cite

A large number of local and global databases for soil, land use, crops, and climate are now available from different sources, which often differ, even when addressing the same spatial and temporal resolutions. As the correct database is unknown, their impact on estimating water resource components (WRC) has mostly been ignored. Here, we study the uncertainty stemming from the use of multiple databases and their impacts on WRC estimates such as blue water and soil water for the Karkheh River Basin (KRB) in Iran. Four climate databases and two land use maps were used to build multiple configurations of the KRB model using the soil and water assessment tool (SWAT), which were similarly calibrated against monthly river discharges. We classified the configurations based on their calibration performances and estimated WRC for each one. The results showed significant differences in WRC estimates, even in models of the same class i.e., with similar performance after calibration. We concluded that a non-negligible level of uncertainty stems from the availability of different sources of input data. As the use of any one database among several produces questionable outputs, it is prudent for modelers to pay more attention to the selection of input data.