Abstract
The lack of meteorological observation data limits the hydro-climatic analysis and modeling, especially for the ungauged or data-limited regions, while satellite and reanalysis products can provide potential data sources in these regions. In this study, three daily products, including two satellite products (Tropic Rainfall Measuring Mission Multi-Satellite Precipitation Analysis, TMPA 3B42 and 3B42RT) and one reanalysis product (China Meteorological Assimilation Driving Datasets for the SWAT Model, CMADS), were used to assess the capacity of hydro-climatic simulation based on the statistical method and hydrological model in Ganjiang River Basin (GRB), a humid basin of southern China. CAMDS, TMPA 3B42 and 3B42RT precipitation were evaluated against ground-based observation based on multiple statistical metrics at different temporal scales. The similar evaluation was carried out for CMADS temperature. Then, eight scenarios were constructed into calibrating the Soil and Water Assessment Tool (SWAT) model and simulating streamflow, to assess their capacity in hydrological simulation. The results showed that CMADS data performed better in precipitation estimation than TMPA 3B42 and 3B42RT at daily and monthly scales, while worse at the annual scale. In addition, CMADS can capture the spatial distribution of precipitation well. Moreover, the CMADS daily temperature data agreed well with observations at meteorological stations. For hydrological simulations, streamflow simulation results driven by eight input scenarios obtained acceptable performance according to model evaluation criteria. Compared with the simulation results, the models driven by ground-based observation precipitation obtained the most accurate streamflow simulation results, followed by CMADS, TMPA 3B42 and 3B42RT precipitation. Besides, CMADS temperature can capture the spatial distribution characteristics well and improve the streamflow simulations. This study provides valuable insights for hydro-climatic application of satellite and reanalysis meteorological products in the ungauged or data-limited regions.
Highlights
Precipitation plays an important role in the hydrological cycle and is the main input to hydrological and eco-hydrological models [1,2,3]
In order to analyze the effect of different precipitation inputs on hydrological simulation, the accuracy of satellite or reanalysis precipitation should be firstly evaluated against ground-based observations
(−218.55 mm) for CMADS were worse than results of values with CC (0.90), Root-Mean-Square Error (RMSE) (188.27 mm) and Mean Error (ME) (58.13 mm) for 3B42 at the annual scale, which is contrary to the performance of these two products at daily and monthly scales
Summary
Precipitation plays an important role in the hydrological cycle and is the main input to hydrological and eco-hydrological models [1,2,3]. There is quite a sparse monitoring network of climate stations in many regions, in these difficult-to-access environments and developing countries in which inadequate funds are available for installation and maintenance of equipment related with precipitation and temperature monitoring [14]. These observations from sparse monitoring networks can provide accurate estimation of precipitation and temperature at a single spot in these regions, it may be inadequate to deal with hydrological and meteorological heterogeneity and complexity across the whole region [15]
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