Abstract:Satellite-based and reanalysis quantitative precipitation estimates are attractive for hydrologic prediction or forecasting and reliable water resources management, especially for ungauged regions. This study evaluates three widely used global highresolution precipitation products [Precipitation Estimation from Remotely Sensed Information using Artificial Neural NetworksClimate Data Record (PERSIANN-CDR), Tropical Rainfall Measuring Mission 3B42 Version 7 (TRMM 3B42V7), and National Centers for Environment Prediction-Climate Forecast System Reanalysis (NCEP-CFSR)] against gauge observations with seven statistical indices over two humid regions in China. Furthermore, the study investigates whether the three precipitation products can be reliably utilized as inputs in Soil and Water Assessment Tool, a semi-distributed hydrological model, to simulate streamflows. Results show that the precipitation estimates derived from TRMM 3B42V7 outperform the other two products with the smallest errors and bias, and highest correlation at monthly scale, which is followed by PERSIANN-CDR and NCEP-CFSR in this rank. However, the superiority of TRMM 3B42V7 in errors, bias, and correlations is not warranted at daily scale. PERSIANN-CDR and 3B42V7 present encouraging potential for streamflow prediction at daily and monthly scale respectively over the two humid regions, whilst the performance of NCEP-CFSR for hydrological applications varies from basin to basin. Simulations forced with 3B42V7 are the best among the three precipitation products in capturing daily measured streamflows, whilst PERSIANN-CDR-driven simulations underestimate high streamflows and high streamflow simulations driven by NCEP-CFSR mostly are overestimated significantly. In terms of extreme events analysis, PERSIANN-CDR often underestimates the extreme precipitation, so do extreme streamflow simulations forced with it. NCEP-CFSR performs just the reverse, compared with PERSIANN-CDR. The performance pattern of TRMM 3B42V7 on extremes is not certain, with coexisting underestimation and overestimation.

[1] Defining highly variable freshwater plume area from space is important for characterizing the dynamics of biogeochemical properties and understanding the effects of climate change and human activities on plume-related processes. The absorption coefficient of colored dissolved organic matter (a CDOM ) from satellite ocean color data can be used to estimate the salinity and thus the plume area in coastal oceans if a robust conservative salinity and a CDOM relationship and an accurate satellite a CDOM algorithm can be established. In this paper, tight relationships between surface water salinity and in situ a CDOM were found during several cruises covering all seasons and the full salinity range in the East China Sea. Thus, a salinity inversion model from a CDOM was developed and validated with an independent data set, in which 73.6% of the data were within the absolute salinity error of AE1 and 87.1% were within AE1.5. Factors influencing the conservative behavior of colored dissolved organic matter are analyzed, with a particular focus on the effect of the phytoplankton-induced autochthonous colored dissolved organic matter. In addition, several satellite a CDOM algorithms were compared and validated with our in situ data. Monthly satellite-derived salinity images were mapped in August from 2008 to 2010 and showed the significant interannual variability in the plume coverage. This study demonstrated that the salinity derived from satellite-derived a CDOM can provide a reliable and good synoptic view of the plume area, and help with biogeochemical studies, in particular, those properties related to the interannual variability of plume coverage, although the development of a localized satellite algorithm of a CDOM is still desirable.

No abstract

Satellite-based and reanalysis precipitation products provide a practical way to overcome the shortage of gauge precipitation data because of their high spatial and temporal resolution. This study compared two reanalysis precipitation datasets (the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS), the National Centers for Environment Prediction Climate Forecast System Reanalysis (NCEP-CFSR)) and two satellite-based datasets (the Tropical Rainfall Measuring Mission 3B42 Version 7 (3B42V7) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR)) with observed precipitation in the Xiang River basin in China at two spatial (grids and the whole basin) and two temporal (daily and monthly) scales. These datasets were then used as inputs to a SWAT model to evaluate their usefulness in hydrological prediction. Bayesian model averaging was used to discriminate dataset performance. The results show that: (1) for daily timesteps, correlations between reanalysis datasets and gauge observations are >0.55, better than satellite-based datasets; The bias values of satellite-based datasets are <10% at most evaluated grid locations and for the whole baseline. PERSIANN-CDR cannot detect the spatial distribution of rainfall events; the probability of detection (POD) of PERSIANN-CDR at most evaluated grids is <0.50; (2) CMADS and 3B42V7 are better than PERSIANN-CDR and NCEP-CFSR in most situations in terms of correlation with gauge observations; satellite-based datasets are better than reanalysis datasets in terms of bias; and (3) CMADS and 3B42V7 simulate streamflow well for both daily (The Nash-Sutcliffe coefficient (NS) > 0.70) and monthly (NS > 0.80) timesteps; NCEP-CFSR is worst because it substantially overestimates streamflow; PERSIANN-CDR is not good because of its low NS (0.40) during the validation period.

No abstract