Jing Tian

Global warming is widely expected to intensify the hydrological cycle. However, observational evidence for accelerated evapotranspiration (ET), a key terrestrial water cycle and energy balance component, remains ambiguous. Here, we constrained diverse datasets using basin-scale ET estimates covering 47% of global land to reveal a hidden asymmetry. We found a small global change in ET [−0.06 ± 0.44 mm year ⁻² (millimeters per year squared), P > 0.10] from 2000 to 2022, resulting from strongly opposing hemispheric trends. The Northern Hemisphere shows noticeable ET increases (0.91 ± 0.46 mm year ⁻² , P < 0.05), primarily driven by vegetation greening, whereas the Southern Hemisphere exhibits strong declines (−2.63 ± 0.46 mm year ⁻² , P < 0.05) due to precipitation deficits. Our observation-constrained projections indicate that this asymmetry is likely to persist through 2050. The small global ET trend masks a marked compensation between northern greening and southern drying, suggesting that current models may overestimate homogenized water cycle intensification. Our study provides profound implications for global food security, carbon sequestration, drought risks, and regional climate adaptation.

The air urban heat island (UHI) poses significant challenges to urban environments. While vegetation is a recognized mitigation strategy, the in-situ cooling effects of different vegetation types, such as forests and grasslands, across diverse climatic regions remain poorly quantified. This study addresses this gap using a station-pair regression approach to evaluate vegetation's cooling efficacy in three major Chinese cities: Beijing, Shanghai, and the Mega-city Cluster in the Pearl River Delta (MCPRD). We developed multiple linear regression models using summer data from 2016 to 2020. These models predict the air temperature difference between paired meteorological sites based on spatial differences in vegetation conditions, derived from high-resolution Sentinel-2 imagery, distance to large water body, wind speed, and elevation. The model performed well, achieving coefficient of determination of 0.71, 0.58 and 0.82 for Beijing, Shanghai and MCPRD, respectively. Crucially, our results show that compared to completely urbanized land, existing vegetation significantly lowers air temperature by 2.43 °C in Beijing, 0.58 °C in Shanghai, and 2.74 °C in the MCPRD. This research quantifies the variable cooling benefits of urban vegetation across different climate zones, providing an empirical basis for optimizing green infrastructure in urban planning to enhance thermal comfort.

Groundwater storage is vital for managing water resources, especially as global water scarcity intensifies. Estimating groundwater levels regionally is challenging due to natural heterogeneity. We employed a large groundwater observation sample, along with Global Land Data Assimilation System (GLDAS) and Gravity Recovery and Climate Experiments (GRACE) datasets, to develop a random forest model for predicting groundwater levels in China’s Yellow River Basin. The model showed robustness, achieving an R² of 0.95 in calibration and an R² of 0.91 ± 0.009 in 10-fold cross-validation with 100 repetitions. Temporal predictability was lower, with an R² of 0.72 for April–May 2023; however, the temporal prediction is preliminary and limited by the short validation period (April–May 2023), which should be interpreted with caution. Spatial maps revealed significant seasonal declines in fall and winter, particularly in the middle and lower reaches. This study highlights the potential of machine learning with extensive observations to estimate regional groundwater levels and supports groundwater analysis with robust data.

Accurate quantification of global water-cycle components remains a major challenge in Earth system science. In this study, we refine historical and future estimates of global river flow by applying an emergent constraint (EC) approach, which integrates outputs from 26 Earth System Models (ESMs) with observed streamflow from 50 large river basins (covering 26.9% of global land area). For the historical period (1980–2014), we estimate global river flow at 39.1±5.4×103 km3 yr-1 and a river flow-to-precipitation ratio of 0.35±0.03 , both lower than previous assessments. Land evapotranspiration is estimated at 73.4±6.2×103 km3 yr-1. Under future climate change, the EC-constrained projection indicates a global river flow increase of ,7.8±5.5 mm yr-1 K-1, which is 9.3% lower than the ESM ensemble mean and reduces inter-model uncertainty by 66%. Our results highlight a systematic overestimation of river flow increases in current ESMs, underscoring the importance of incorporating observational constraints and human impacts to improve the reliability of hydrological projections. This study provides a benchmark for global water-cycle partitioning and supports more accurate water resource planning under climate change.This work is supported by the National Natural Science Foundation of China (Grant No. 42330506 and 42361144709), the Talent Program of the Ministry of Science and Technology of China, and the PIFI outstanding international team project by the Chinese Academy of Sciences.

The efficient management of water resources is critical for sustainable agricultural practices, particularly in arid and semi-arid regions. This study aims to assess crop yield and water use efficiency (WUE) for maize and wheat in Northern China, with a focus on irrigation management, using the Agricultural Production Systems sIMulator (APSIM). APSIM, a widely used crop modeling tool, provides a robust framework for simulating crop growth, yield, and water consumption under different climatic and management scenarios.Our research integrates historical climate data and crop management practices to evaluate how irrigation strategies influence crop water consumption and yield in the region. By simulating different irrigation regimes, including deficit and full irrigation, we explore their impacts on crop water use efficiency (WUE) and overall yield. The results indicate that optimal irrigation scheduling can significantly enhance water use efficiency, reducing water consumption while maintaining crop productivity. Moreover, the model highlights the sensitivity of crop yield to varying water availability, demonstrating the importance of timely and appropriate irrigation interventions.The study underscores how crop water consumption can be better managed to enhance WUE and achieve sustainable agricultural production. Future research will focus on refining the model to account for the effects of soil salinity and other environmental factors, further enhancing its applicability for water resource management in arid regions.

Root zone soil moisture (RZSM) serves as a crucial metric for assessing water stored in the soil. Modeling approaches are commonly employed in estimating RZSM. However, modelled RZSM often deviate from true RZSM values due to errors from model input data and parameters. Machine learning methods and data fusion techniques can enhance simulation accuracy. In this study, we conducted a comparative analysis of three methods for RZSM data fusion: random forest (RF), extended triple collocation (ETC), and Bayes Three Cornered Hat (BTCH).Soil moisture observation data from 2018 to 2022 were collected at 2121 sites across China from the China Meteorological Administration (Fig.1). Daily average data were calculated by arithmetically averaging hourly data and used in the analysis. Six RZSM datasets were utilized, including SMAP Level 4, GLDAS-NOAH2.1, GLDAS-Catchment2.2, ERA5, MERRA2, and CRSR. All these data were resampled to 0.25° to maintain the same spatial resolution and were arithmetically averaged as daily averages. Additionally, some parameters related to soil, climate, and vegetation were used to build a machine learning model, specifically a random forest model. Fig. 1 Distribution of soil moisture sites and daily soil moisture (m3/m3) at depths ranging from 0–50 cm across China during the period from 2018 to 2019To investigate the impact of different inputs on the performance of the RF method, three groups of inputs were employed. The specifics of the inputs used for the three methods are outlined in Table 1. The evaluation of the RF method results was carried out using a five-fold cross-validation approach.Model Inputs RFmodel1 NOAH, SMAP, ERA5, MERRA2, CFSR, CLSM, LAI, Soil properties, Meteorological data RFmodel2 NOAH, LAI, Soil properties, Meteorological data RFmodel3 NOAH, SMAP, ERA5, MERRA2, CFSR, CLSM BTCH NOAH, SMAP, ERA5, MERRA2, CFSR, CLSM ETC NOAH, MERRA2, CLSM The boxplots show RFmodel1 performs best, emphasizing the need for comprehensive information in machine learning models. RFmodel2, superior to RFmodel3, highlights the significance of LAI, soil properties, and meteorological data in RZSM estimation. ETC and BTCH outperform individual RZSM datasets, especially in the absence of true data. The superior performance of ETC over BTCH is attributed to ETC's inputs, namely NOAH, MERRA2, and CLSM, which exhibit better accuracy compared to SMAP, ERA5, and CFSR, the inputs used by BTCH.Fig.2 Boxplots of the Pearson coefficient (R), Root Mean Square Error (RMSE), and bias between in situ root zone soil moisture (RZSM) and its estimates from the three random forest models, Bayes Three Cornered Hat (BTCH), and Extended Triple Collocation (ETC) methodsIn summary, the random forest method outperforms BTCH and ETC in the fusion of root zone soil moisture (RZSM) data, highlighting the importance of including leaf area index (LAI), soil properties, and meteorological data in the construction of the random forest model. Both BTCH and ETC demonstrate utility in enhancing RZSM estimates, making them valuable options when true data is unavailable.

Irrigation plays a crucial role in bolstering crop productivity and ameliorating the adverse impacts of drought. Despite its significance, existing studies have not extensively incorporated irrigation into agricultural drought indicators. In this study, we introduce a novel agricultural drought index, the Standardized Irrigation Water Deficit Index (SIWDI) that is quantified using meteorological, phenological, and runoff inputs. To test its robustness, we calculated the irrigation water deficit for three major crops across various time scales in the Yangtze River Basin over the past 23 years. Our analysis reveals that the irrigation water deficit in this region follows a norminvgauss probability distribution. Drawing a mathematical parallel to the Standardized Precipitation Evapotranspiration Index (SPEI), the SIWDI is compared to the SPEI across the Yangtze River Basin. Results underscore the SIWDI’s notable advantage in evaluating drought conditions in agriculturally concentrated regions, alleviating the impact of non-growing season droughts by incorporating crop growth processes and spatial distribution. This innovative index provides monitoring outcomes closely aligned with actual conditions, empowering farmers to respond more effectively to the looming threat of drought.