Evaluation of 22 precipitation and 23 soil moisture products over a semiarid area

Precipitation and soil moisture are rigorously measured or estimated from a variety of sources. Here we evaluate 22 precipitation and 23 soil moisture products against long-term daily observed precipitation (Pobs) and July-September daily observationally constrained soil moisture (SM) datasets over a densely monitored 150 km2 watershed in southeastern Arizona, USA. Gauge/radar precipitation products perform best, followed by satellite and reanalysis products, and the median correlations of annual precipitation from these three categories with Pobs are 0.83, 0.46, and 0.68, respectively. The CMIP5 model precipitation results are the worst, including their overestimate of cold season precipitation and the lack of significant correlation of annual precipitation with Pobs from all (except one) CMIP5 models. Satellite soil moisture products perform best, followed by land data assimilation systems and reanalyses, and the CMIP5 model results are the worst. For instance, the median unbiased RMSD values of July-September soil moisture compared with SM are 0.0070, 0.011, 0.014, and 0.029 m3m-3 for these four product categories, respectively. All 17 (except 3) precipitation [15 (except 2) soil moisture] products with at least 20 years of data agree with Pobs (SM) without significant trends. The uncertainties associated with the scale mismatch between Pobs and coarser resolution products are addressed using two 4 km gauge/radar precipitation products, and their impact on the results presented in this study is overall small. These results identify strengths and weaknesses of each product for future improvement; they also emphasize the importance of using multiple gauge/radar and satellite products along with their uncertainties in evaluating reanalyses and models. In this presentation, we will discuss the above results. Time permitting, we will also discuss the evaluation of the new GPM precipitation and SMAP soil moisture products and discuss how to adjust reanalysis precipitation to better fit in situ observations.