Satellite-based soil moisture provides missing link between summertime precipitation and surface temperature biases in CMIP5 simulations over conterminous United States

Climatological Changes in Soil Moisture during the 21st Century over the Indian Region Using CMIP5 and Satellite Observations

Climate data records of soil moisture (SM) are fundamental for improving our understanding of long-term dynamics in the coupled water, energy, and carbon cycles over land. However, many of these studies rely on models for which the errors are not yet fully understood over a region. This may have a considerable impact on the economic growth of the country if the model’s future predictions are used for studying long-term trends. Here we examined the spatial distribution of past, present, and future predictions of SM studied using the Coupled Model Intercomparison Project Phase5 (CMIP5) simulations for the historical period (1850–2005) and future climate projections (2006–2099) based on Representative Concentration Pathways (RCP-RCP2.6, RCP4.5, RCP6.0, and RCP8.5). Furthermore, the performance of modeled SM with the satellite AMSR-E (Advanced Microwave Scanning Radiometer-Earth observation system) was studied. The modeled SM variations of 38 Global Climate Models (GCMs) show discreteness but still we observed that CESM1-CM5, CSIRO-MK3-6-0, BCC-CSM1-1, and also BCC-CSM1-1-M, NorESM1-M models performed better spatially as well as temporally in all future scenarios. However, from the spatial perspective, a large deviation was observed in the interior peninsula during the monsoon season from model to model. In addition, the spatial distribution of trends was highly diversified from model to model, while the Taylor diagram presents a clear view of the model’s performance with observations over the region. Skill score statistics also give the accuracy of model predictions in comparison with observations. The time series was estimated for the future trend of the SM along with the past few decades, whereas the preindustrial and industrial period changes were involved. Significant positive anomaly trends are noticed in the whole time series of SM during the future projection period of 2021–2099 using CMIP5 SM model datasets.

High-resolution simulation and validation of soil moisture in the arid region of Northwest China

Soil moisture plays an important role in land-atmosphere interactions, agricultural drought monitoring, and water resource management, particularly across arid regions. However, it is challenging to simulate soil moisture of high spatial resolution and to evaluate soil moisture at fine spatial resolution in arid regions in Northwest China due to considerable uncertainties in forcing data and limited in situ measurements. Then, the data set was used to produce the 1 km high-resolution atmospheric forcing datasets and to drive the Community Land Model version 3.5 (CLM3.5) for simulating spatiotemporally continuous surface soil moisture. The capabilities of soil moisture simulation using CLM3.5 forced by the XJLDAS-driven field were validated against data obtained at three soil layers (0-10, 0-20, and 0-50 cm) from 54 soil moisture stations in Xinjiang. Results show that the simulated soil moisture agreed well with the observations [CORR > 0.952], and the intra-annual soil moisture in Xinjiang gradually increased during May through August. The main factors that affect changes in soil moisture across the study region were precipitation and snowmelt. The overall finding of this study is that an XJLDAS, high-resolution forcing data driven CLM3.5 can be used to generate accurate and continuous soil moisture of high resolution (1km) in Xinjiang. This study can help understand the spatiotemporal features of the soil moisture, and provide important input for hydrological studies and agricultural water resources management over the arid region.