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The Priestley–Taylor (PT) coefficient (α) is generally set as a constant value or is fitted as an empirical function of environmental variables, and it can bias the evaporation estimation or hydrological projections under global warming. By using an atmospheric boundary layer model, this study derives a theoretical and parameter-free equation for estimating α as a function of air temperature (T) and specific humidity (Q). With observations from several waterbodies and non-water-limited land sites, we demonstrate that, in addition to estimating the value of α well, the derived expressions can also capture the sensitivity of α to T and Q , that is, dα/dT and dα/dQ. α is generally negatively associated with T and Q , in which regard T plays a more fundamental role in controlling α behaviors. Based on climate model data, we further show that this negative relationship between α and T is of great importance for long-term hydrological predictions. We also provide a lookup graph for practical and broad uses to directly find the values of dα/dT and dα/dQ under specific conditions. Overall, the derived expression gives a physically clear and straightforward approach to quantify changes in α , which is essential for PT-based hydrological simulation and projections. [ABSTRACT FROM AUTHOR]

The Yellow River source region is located in the transition region between permafrost and seasonally frozen ground on the northeastern Qinghai-Tibet Plateau. The region has experienced severe climate change, especially air temperature increases, in past decades. In this study, we employed a geomorphology-based eco-hydrological model (GBEHM) to assess the impacts of climate change on the frozen ground and eco-hydrological processes in the region. Based on a long-term simulation from 1981 to 2015, we found that the areal mean maximum thickness of seasonally frozen ground ranged from 1.1-1.8m and decreased by 1.2cm per year. Additionally, the ratio of the permafrost area to the total area decreased by 1.1% per year. These decreasing trends are faster than the average in China because the study area is on the sensitive margin of the Qinghai-Tibet Plateau. The annual runoff exhibited variations similar to those of the annual precipitation (R2=0.85), although the annual evapotranspiration (ET) exhibited an increasing trend (14.3mm/10a) similar to that of the annual mean air temperature (0.66°C/10a). The runoff coefficient (annual runoff divided by annual precipitation) displayed a decreasing trend because of the increasing ET, and the vegetation responses to climate warming and permafrost degradation were manifested as increases in the leaf area index (LAI) and ET at the start of the growing season. Furthermore, the results showed that changes to the frozen ground depth affected vegetation growth. Notably, a rapid decrease in the frozen ground depth (< -3.0cm/a) decreased the topsoil moisture and then decreased the LAI. This study showed that the eco-hydrological processes in the headwater area of the Yellow River have changed because of permafrost degradation, and these changes could further influence the water resources availability in the middle and lower reaches of the basin.

The Three Gorges Reservoir (TGR) is one of the world's largest hydropower projects and plays an important role in water resources management in the Yangtze River. For the sake of disaster prevention and catchment management, it is crucial to understand the regulation capacity of the TGR on extreme hydrological events and its impact on flow regime in a changing climate. This study obtains historical inflows of the TGR from 1961 to 2019 and uses a distributed hydrological model to simulate the future inflows from 2021 to 2070. These data are adopted to drive a machine learning‐based TGR operation model to obtain the simulated outflow with TGR operation, which are then compared with the natural flow without TGR operation to assess the impact of TGR. The results indicate that the average flood peaks and total flooding days in the historical period could have been reduced by 29.2% and 53.4% with the operation of TGR. The relative declines in drought indicators including duration and intensity were generally less than 10%. Faced with more severe extreme hydrological events in the future, the TGR is still expected to alleviate floods and droughts, but cannot bring them down to historical levels. The impact of TGR operation on flow regime will also evolve in a changing climate, potentially altering the habitats of river ecosystems. This study proposes feasible methods for simulating the operation of large reservoirs and quantifying the impact on flow regime, and provides insights for integrated watershed management in the upper Yangtze River basin. Plain Language Summary: The Three Gorges Reservoir (TGR) is located in the upper Yangtze River basin and is one of the world's largest hydropower projects. This study combines hydrological modeling and machine learning methods to quantify the effects of TGR operation on flow regime in the historical and future periods. The results indicate that the operation of TGR could play an important role in mitigating historical droughts and floods. Specifically, the flood peak in 1998 could have been reduced by 24.7% if the TGR was in operation. In the future, the extreme hydrological events will become more severe. While the TGR will continue to play a significant role, it cannot fully control floods and droughts to the same extent as it did in the historical period. In a changing climate, the Yangtze River basin will still face higher flood and drought risks with the operation of TGR. Under future climate scenarios, the operation of TGR will also alter the flow regime metrics and have new implications on the river ecosystem environment. Key Points: An ensemble learning model based on two neural networks is constructed to simulate Three Gorges Reservoir (TGR) operationsTGR can mitigate floods and droughts, but this capability might be weakened by more severe future extreme eventsTGR can offset most flow regime changes caused by climate change, but adaptive operation strategies are still needed [ABSTRACT FROM AUTHOR]

To overcome rubber tree (RT) tissue culture explant source limitations, the current study aimed to establish a new Hevea brasiliensis somatic embryogenesis (SE) system, laying the technical foundation for the establishment of an axillary-bud-based seedling regeneration system. In this study, in vitro plantlets of Hevea brasiliensis Chinese Academy of Tropical Agricultural Sciences 917 (CATAS 917) were used as the experimental materials. Firstly, the optimum conditions for axillary bud swelling were studied; then, the effects of phenology, the swelling time of axillary buds (ABs), and medium of embryogenic callus induction were studied. Plantlets were obtained through somatic embryogenesis. Flow cytometry, inter-simple sequence repeat (ISSR molecular marker) and chromosome karyotype analysis were used to study the genetic stability of regenerated plants along with budding seedlings (BSs) and secondary somatic embryo seedlings (SSESs) as the control. The results show that the rubber tree's phenology period was mature, and the axillary bud induction rate was the highest in the 2 mg/L 6-benzyladenine (6-BA) medium (up to 85.83%). Later, 3-day-old swelling axillary buds were used as explants for callogenesis and somatic embryogenesis. The callus induction rate was optimum in MH (Medium in Hevea) + 1.5 mg/L 2,4-dichlorophenoxyacetic acid (2,4-D) + 1.5 mg/L 1-naphthalene acetic acid (NAA) + 1.5 mg/L Kinetin (KT) + 70 g/L sucrose (56.55%). The regenerated plants were obtained after the 175-day culture of explants through callus induction, embryogenic callus induction, somatic embryo development, and plant regeneration. Compared with the secondary somatic embryo seedling control, axillary bud regeneration plants (ABRPs) were normal diploid plants at the cellular and molecular level, with a variation rate of 7.74%. [ABSTRACT FROM AUTHOR]