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Yaling Liu, Dongdong Chen, Soukayna Mouatadid, Xiaoliang Lu, Min Chen, Yu Cheng, Zhenghui Xie, Binghao Jia, Huan Wu, and Pierre Gentine

Abstract

Soil moisture (SM) links the water and energy cycles over the land–atmosphere interface and largely determines ecosystem functionality, positioning it as an essential player in the Earth system. Despite its importance, accurate estimation of large-scale SM remains a challenge. Here we leverage the strength of neural network (NN) and fidelity of long-term measurements to develop a daily multilayer cropland SM dataset for China from 1981 to 2013, implemented for a range of different cropping patterns. The training and testing of the NN for the five soil layers (0–50 cm, 10-cm depth each) yield R 2 values of 0.65–0.70 and 0.64–0.69, respectively. Our analysis reveals that precipitation and soil properties are the two dominant factors determining SM, but cropping pattern is also crucial. In addition, our simulations of alternative cropping patterns indicate that winter wheat followed by fallow will largely alleviate the SM depletion in most parts of China. On the other hand, cropping patterns of fallow in the winter followed by maize/soybean seem to further aggravate SM decline in the Huang-Huai-Hai region and southwestern China, relative to prevalent practices of double cropping. This may be due to their low soil porosity, which results in more soil water drainage, as opposed to the case that winter crop roots help maintain SM. This multilayer cropland SM dataset with granularity of cropping patterns provides an important alternative and is complementary to modeled and satellite-retrieved products.

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Hanoi Medina, Di Tian, Fabio R. Marin, and Giovanni B. Chirico

Abstract

This study compares the performance of Global Ensemble Forecast System (GEFS) and European Centre for Medium-Range Weather Forecasts (ECMWF) precipitation ensemble forecasts in Brazil and evaluates different analog-based methods and a logistic regression method for postprocessing the GEFS forecasts. The numerical weather prediction (NWP) forecasts were evaluated against the Physical Science Division South America Daily Gridded Precipitation dataset using both deterministic and probabilistic forecasting evaluation metrics. The results show that the ensemble precipitation forecasts performed commonly well in the east and poorly in the northwest of Brazil, independent of the models and the postprocessing methods. While the raw ECMWF forecasts performed better than the raw GEFS forecasts, analog-based GEFS forecasts were more skillful and reliable than both raw ECMWF and GEFS forecasts. The choice of a specific postprocessing strategy had less impact on the performance than the postprocessing itself. Nonetheless, forecasts produced with different analog-based postprocessing strategies were significantly different and were more skillful and as reliable and sharp as forecasts produced with the logistic regression method. The approach considering the logarithm of current and past reforecasts as the measure of closeness between analogs was identified as the best strategy. The results also indicate that the postprocessing using analog methods with long-term reforecast archive improved raw GEFS precipitation forecasting skill more than using logistic regression with short-term reforecast archive. In particular, the postprocessing dramatically improves the GEFS precipitation forecasts when the forecasting skill is low or below zero.

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