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The Three‐River Headwater Region, also known as China's water tower, is highly sensitive to climate change and has experienced profound hydrological alterations in the last few decades. This study assessed the potential impacts of climate change on all the important hydrological components such as precipitation, evapotranspiration, streamflow, snow‐melt flow, and soil moisture (SM) content in the region. For this, climate data (i.e., temperature, precipitation, relative humidity, and windspeed) of three Global Climate Models (i.e., CanESM5, MPI‐ESM1.2‐HR, and NorESM2‐MM) was downscaled with the Statistical DownScaling Model (SDSM) and their ensemble was forced into a hydrological model to simulate the hydrological processes for 1981–2100. The screening process, which is central to all downscaling techniques, is very subjective in the SDSM. Therefore, we developed a quantitative screening approach by modifying the method applied by Mahmood and Babel (2013, https://doi.org/10.1007/s00704‐012‐0765‐0) for the selection of a set of logical predictors to cope with multi‐collinearity and their ranking. The analyses were performed for the near future period (NFP, 2021–2060) and far future period (FFP, 2061–2100) relative to the baseline period (BLP, 1981–2020). The results showed that the region will be hotter and wetter in the future, with intensive and frequent floods. For example, temperature, precipitation, evapotranspiration, and streamflow will increase by 1.0–1.5 (1–1.9)°C, 9–21 (15–27)%, 6–17 (9–29)%, and 9–46 (22–64)% in the NFP and by 2.0–2.8 (2.7–4.6)°C, 16–40 (43–87)%, 11–31 (24–73)%, and 20–95 (60–198)% in the FFP, respectively, under SSP2‐4.5 (SSP5‐8.5). Similar projections were explored for other hydrological components. Among all, surface flow showed an unprecedented increase (500%–1,000%) in the FFP. Peak flows will be much higher and will shift forward, and snowmelt will start earlier in the future. The results of the present study can be a good source for understanding the hydrological cycle and be used for the planning and management of water resources of the highly elevated and complex region of the Qinghai Tibetan Plateau. Plain Language Summary: The Three‐River Headwater Region, which is also known as the Sanjiangyuan in Chinese, is located in Qinghai Tibetan Plateau, China. It is considered the water tower of China because it is the source of three giant rivers the Yangtze, Yellow, and Lancang (Mekong). However, its water resources (hydrological cycle) are very sensitive and vulnerable to changing climate. Therefore, we assessed the potential impact of climate change on all the important hydrological components such as precipitation, streamflow, snow melt flow, surface flow, baseflow, soil moisture (SM), and changes in terrestrial water storage. Previous studies mainly focused on precipitation, streamflow, and SM. Global Climate Models (GCMs) are the main tool to assess the future changes in hydrological components under changing climate. Since GCMs have a coarse spatial resolution and biases in their outputs, a statistical downscaling model (SDSM) was applied to fix these issues and used to generate climate data (e.g., temperature and precipitation) for the future (2021–2100) under two scenarios (i.e., SSP2‐4.5 and SSP5‐8.5). These scenarios represent the global development and greenhouse gas emissions in the future. SSP2‐4.5 scenario typically involves moderate greenhouse gas emissions reduction efforts and some adaptation and mitigation measures to address climate change impacts, and SSP5‐8.5 represents high greenhouse gas emissions and limited efforts to mitigate climate change impacts. The screening process, which is central to all downscaling techniques, is very subjective in the SDSM. Therefore, we developed a quantitative screening approach by modifying the method applied by Mahmood and Babel (2013, https://doi.org/10.1007/s00704‐012‐0765‐0) for the selection of a set of logical predictors to cope with multi‐collinearity and their ranking. The downscaled future climate data was used as input to run a hydrological model (HEC‐HMS) to generate hydrological components under both scenarios. The future changes in the hydrological components were obtained for 2021–2060 and 2061–2100 with respect to the baseline period 1981–2020. The results showed that the region will be hotter and wetter in the future, with intensive and frequent floods. Almost all components are expected to increase in the future under both scenarios. Among all, surface flow showed an unprecedented increase (500%–1,000%) in the second half of the twenty‐first century (2061–2100). Peak flows are expected to be much higher than the present conditions and to shift forward. Snowmelt will start earlier in the future. This study will be very useful in understanding the hydrological cycle and can be used by policymakers, planners, and stakeholders for proactive adaptation strategies such as water resources planning and management, investments in water infrastructure, land use planning, ecosystem restoration, and community resilience‐building initiatives to mitigate potential risks. Key Points: The region will be hotter and wetter, with intensive and frequent floodsThe hydrological components are expected to increase in the futureSurface flow showed an unprecedented increase of 500%–1,000% [ABSTRACT FROM AUTHOR]

The composition of microbial communities and the functioning of ecosystems are greatly influenced by the nutrient inputs. Despite this fact, our knowledge regarding the impact of phosphorus (P) inputs on soil P availability and microbial community structures in subtropical acidic soils remains limited. We hypothesized that diverse P inputs, incubation temperatures, and soil types could significantly alter soil P availability and microbial communities. To address this gap, we conducted a laboratory incubation experiment, investigating the effects of biochar and inorganic P amendments on soil available P, soil pH, acid phosphatase enzymes, and bacterial abundance. We employed two different incubation temperatures (15 °C and 25 °C) using acidic paddy soil and red soil from the subtropical Southern China region. Our results indicate a notable increase in soil pH, reaching 37% and 39% at 15 °C and 40% and 40.6% at 25 °C, respectively, following the application of biochar and inorganic P amendments in paddy soil. In the case of red soil, we observed pH increases of 41% and 43% at 15 °C and 44% and 45% at 25 °C after the application of biochar and inorganic P amendment, respectively. The addition of inorganic P amendment resulted in the highest available P contents in paddy soil, reaching 111.47 mg/kg at 15 °C and 100.17 mg/kg at 25 °C, respectively. However, Proteobacteria decreased after inorganic P addition, which showed that P might not be the only limiting nutrient for various bacterial communities. Bacterial diversity and richness indices were found to be higher after biochar application in both soils. Gemmatimonadetes, Acidobacteria, and Actinobacteria were found to be strongly influenced by incubation temperatures, whereas most of the top abundant bacterial phyla, such as Gemmatimonadetes, Actinobacteria, Chloroflexi, Acidobacteria, Planctomycetes, Firmicutes, Patescibacteria, and Bacteroidetes, were highly dependent on soil type. At the genus level, various important P solubilizing genera (Pseudomonas, Bradyrhizobium, Streptomyces jietaisiensis, Massilia) significantly increased after biochar and inorganic P addition in both soils. The correlation analysis proved that P-solubilizing genera were significantly associated with changes in soil pH, as well as soil available P after biochar and inorganic P addition. Conclusively, in a short-term incubation experiment, inorganic P amendment greatly increased the soil pH and available phosphorus contents compared to biochar and control treatments; however, the microbial community was observed to be strongly associated with biochar application, soil type, and incubation temperature. [ABSTRACT FROM AUTHOR]

The objective of the study was to configure the Hydrological Modeling System (HEC-HMS) in such a way that it could simulate all-important hydrological components (e.g., streamflow, soil moisture, snowmelt water, terrestrial water storage, baseflow, surface flow, and evapotranspiration) in the Three-River Headwater Region. However, the problem we faced was unsatisfactory simulations of these hydrological components, except streamflow. The main reason we found was the auto-calibration method of HEC-HMS because it generated irrational parameters, especially with the inclusion of Temperature Index Method and Soil Moisture Accounting (an advanced and complex loss method). Similar problems have been reported by different previous studies. To overcome these problems, we designed a comprehensive approach to estimate initial parameters and to calibrate the model manually in such a way that the model could simulate all the important hydrological components satisfactorily. [ABSTRACT FROM AUTHOR]

The three rivers' source region (TRSR) located in the Qinghai-Tibetan Plateau is an important ecological region of China for its location and high elevation. However, the ecological condition of the region is critical to water conservation and ecological security in China. Therefore, the present study focused on determining the environmental flow requirements (EFRs) of the Lancang (above Xiangda streamflow gauge), Yangtze (Zhimenda), and Yellow (Tangnaihai) Rivers, which will be helpful in the sustainable development of the region. Because of lacking ecological data, we used four hydrological methods (i.e., flow duration curve shifting, Tennant, flow duration curve analysis, and low flow index method) to assess the EFRs for the minimum and optimum eco-environmental conditions of these rivers. Since there was missing data on most gauges and we had streamflow data of only one gauge (Zhimenda) in the Yangtze headwater region, a hydrological model was applied to complete the missing data and simulate the streamflow data for the Tuotuo, DamChu, Chumar, and Tongtian Rivers for the estimation of the EFRs insides the region. The environmental flows of 118 m3/s (76% of mean annual flow, MAF), 312 m3/s (72% of MAF), 484 m3/s (77% of MAF) were determined for the conservation of the optimum riverine ecosystem and 67 m3/s (43% of MAF), 155 m3/s (36% of MAF), and 290 m3/s (47% of MAF) for the minimum eco-environmental conditions for the Lancang, Yangtze, Yellow Rivers, respectively. It was also noticed that low flow months (November to April) required more proportions of mean monthly flow than high flow months (May to October). These preliminary results will be very supportive of the water resources managers and policymakers in the case of any sustainable socio-economic development in the region. Unlabelled Image • Environmental flows were assessed in the headwater regions of the Yangtze, Yellow and Lancang Rivers. • HEC-HMS was applied to simulate discharge in main tributaries of Yangtze River. • 70–78% of mean annual flow was explored as optimal environmental flow requirements. • 30–47% of mean annual flow was explored as the minimum environmental flow requirements. • Low flow months requires more proportions of mean annual flow than high flow months. [ABSTRACT FROM AUTHOR]

Investigating trends in water use efficiency (WUE) and its causality is critical for understanding ecosystem behaviors. Although WUE has shown nonlinear changes in the last several decades across most global ecosystems, the majority of available studies have focused on its linear trend. This study attempted to accurately attribute the linear and nonlinear variations in WUE using normalized driving factors in the Partial Derivative (PD) equation to develop a Normalized Partial Derivative model (NPD-model). Two linear trends obtained from the Linear Regression (LR) and Non-Parametric (NP) and a nonlinear trend obtained from the Ensemble Empirical Mode Decomposition (EEMD) are employed in the NPD-model for attributing WUE change in China. The individual and relative responses of driving factors to WUE change during 2000-2018 are quantified using the China version of the PML-V2 evapotranspiration and gross primary productivity products. The results show that the nonlinear EEMD-based NPD-model with an R 2 of 0.83, performs best compared to the LR- and NP-based NPD-models, which have R 2 values of 0.64 and 0.7, respectively. WUE increased monotonically in most areas of all vegetation types, with high variability observed in grassland and shrubland. The EEMD-based attribution analysis indicates that leaf area index is the leading factor in regulating WUE in China, followed by CO 2 and climate. The relative contributions revealed that increased WUE in most of China is dominated by the combination of vegetation and environmental factors, covering more than 80% of the study area. These contribution results, however, are largely different from those obtained using the LR- and NP-based NPD-models, as 52% of the study area exhibits cyclic variation in WUE. Therefore, the nonlinear EEMD-based NPD-model provides excellent spatiotemporal attribution of WUE through its driving factors, which is crucial for understanding the ecosystem response to changing environments, potentially assisting in ecosystem and water resource management., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)