New evidence for the links between the local water cycle and the underground wet sand layer of a mega-dune in the Badain Jaran Desert, China

Scientists and the local government have great concerns about the climate change and water resources in the Badain Jaran Desert of western China. A field study for the local water cycle of a lake-desert system was conducted near the Noertu Lake in the Badain Jaran Desert from 21 June to 26 August 2008. An underground wet sand layer was observed at a depth of 20–50 cm through analysis of datasets collected during the field experiment. Measurements unveiled that the near surface air humidity increased in the nighttime. The sensible and latent heat fluxes were equivalent at a site about 50 m away from the Noertu Lake during the daytime, with mean values of 134.4 and 105.9 W/m2 respectively. The sensible heat flux was dominant at a site about 500 m away from the Noertu Lake, with a mean of 187.7 W/m2, and a mean latent heat flux of only 26.7 W/m2. There were no apparent differences for the land surface energy budget at the two sites during the night time. The latent heat flux was always negative with a mean value of –12.7 W/m2, and the sensible heat flux was either positive or negative with a mean value of 5.10 W/m2. A portion of the local precipitation was evaporated into the air and the top-layer of sand dried quickly after every rainfall event, while another portion seeped deep and was trapped by the underground wet sand layer, and supplied water for surface psammophyte growth. With an increase of air humidity and the occurrence of negative latent heat flux or water vapor condensation around the Noertu Lake during the nighttime, we postulated that the vapor was transported and condensed at the lakeward sand surface, and provided supplemental underground sand pore water. There were links between the local water cycle, underground wet sand layer, psammophyte growth and landscape evolution of the mega-dunes surrounding the lakes in the Badain Jaran Desert of western China.

An evaluation of soil moisture from AMSR-E over source area of the Yellow River, China

In this study,in-situ soil moisture measurements are used to evaluate the accuracy of three AMSR-E soil moisture prod ucts from NASA(National Aeronautics and Space Administration),JAXA(Japanese Aerospace Exploration Agency)and VUA(Vrije University Amsterdam and NASA)over Maqu County,Source Area of the Yellow River(SAYR),China.Re sults show that the VUA soil moisture product performs the best among the three AMSR-E soil moisture products in the study area,with a minimum RMSE(root mean square error)of 0.08(0.10)m3/m3 and smallest absolute error of 0.07(0.08)m3/m3 at the grassland area with ascending(descending)data.Therefore,the VUA soil moisture product is used to describe the spatial variation of soil moisture during the 2010 growing season over SAYR.The VUA soil moisture product shows that soil moisture presents a declining trend from east south(0.42 m3/m3)to west north(0.23 m3/m3),with good agreement with a general precipitation distribution.The center of SAYR presents extreme wetness(0.60 m3/m3)dur ing the whole study period,especially in July,while the head of SAYR presents a high level soil moisture(0.23 m3/m3)in July,August and September.

Estimation of Summer Rainfall over an Arid Area using AMSR-E Measurements:A Case Study in Xinjiang,China

Rainfall estimate in arid region using passive microwave remote sensing techniques has been a complex issue for some time.The main reason for this difficulty is that the high and variable emissivity of land surfaces greatly aggravates the complexity of the signatures from the rain cloud.The Xinjiang area,located in the northwest of China,holds all the typical characteristics of arid climate.A rainfall algorithm has been developed for this region by using the Advanced Microwave Scanning Radiometer for Earth Observing System(AMSR-E) measurements.The algorithm attempts to use all 12 chan-nels on the AMSR-E instrument and a two-step method calibrated over 11 days of hourly rain-gauge data.First,Stepwise Discriminant Analysis(SDA) used to optimally estimate rain pixels based on all 12 channels,although only three channels were found to be necessary.Next,a rain predicator scattering index was used to estimate rain rates.A linear relationship between the rain rates and the scattering index above the threshold of 3.0K was constructed with a simple approximately linear function.The estimated rain rates were compared with the rain-gauge data used to calibrate the method,and a good relationship was found with a root-mean-square error of 2.1mm/h.The numerical calculations and comparisons show that the algorithm works well in the Xinjiang area.

Artificial intelligence for geoscience:Progress,challenges,and perspectives

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection techniques.Traditional models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical processes.However,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world applicability.In contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical knowledge.ML techniques have shown promise in addressing Earth science-related questions.Nevertheless,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into geoscience.The integration of physics-based and data-driven methodologies into hybrid models presents an alternative paradigm.These models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data requirements.This review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and geoscience.It examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in geoscience.The paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

Artificial intelligence is restructuring a new world

Recently,the 2024 Nobel Prizes in Physics and Chemistry were awarded to artificial intelligence(AI)scientists for their groundbreaking contributions.John J.Hopfield and Geoffrey E.Hinton received the Physics Prize for their foundational discoveries and inventions that enabled machine learning through artificial neural networks.David Baker was awarded the Chemistry Prize for his work in computational protein design,while Demis Hassabis and John M.Jumper were recognized for their work in protein structure prediction.