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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]

Rivers originating from the Tibetan Plateau (TP) provide water to more than 1 billion people living downstream. Almost 40% of the TP is currently underlain by permafrost, which serves as both an ice reserve and a flow barrier and is expected to degrade drastically in a warming climate. The hydrological impacts of permafrost thaw across the TP, however, remain poorly understood. Here, we quantify the permafrost change on the TP over 1980–2100 and evaluate its hydrological impacts using a physically‐based cryospheric‐hydrological model at a high spatial resolution. Using the ensemble mean of 38 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), the near‐surface permafrost area and the total ground ice storage are projected to decrease by 86.4% and 61.6% during 2020–2100 under a high‐emission scenario, respectively. The lowering of the permafrost table and removal of permafrost as a flow barrier would enhance infiltration and raise subsurface storage capacity. The diminished water supply from ground ice melt and enhanced subsurface storage capacity could jointly reduce annual runoff and lead to exacerbated regional water shortage when facing future droughts. If the most severe 10‐year drought in the historical period occurs again in the future, the annual river runoff will further decrease by 9.7% and 11.3% compared with the historical dry period due to vanishing cryosphere in the source area of Yellow and Yangtze River. Our findings highlight the importance to get prepared for the additional water shortage risks caused by pervasive permafrost thaw in future water resources management across the TP. Plain Language Summary: Permafrost is the subsurface material that stays frozen for at least two consecutive years. It is both an ice reserve and a flow barrier, the degradation of which will lead to mobilized meltwater and enhanced infiltration at the same time. Here, we employ a physically‐based, cryospheric‐hydrological model to examine the changes in permafrost hydrology across the TP in a warming climate. As permafrost degradation progresses, the deepening of permafrost table and the complete thaw of permafrost would result in enhanced hydraulic connectivity and subsurface storage capacity. These changes could lead to reduced runoff and exacerbated water shortage when facing future droughts, calling for adaptive water management measures to address the potential challenges resulting from the vanishing cryosphere across the Third Pole region. Key Points: Pervasive permafrost degradation is projected in the future on the Tibetan Plateau using a physically‐based cryospheric hydrological modelThe near‐surface permafrost area and ground ice storage are expected to decrease by 86.4% and 61.6% during 2020–2100 under a warm scenarioThe diminished water supply and enhanced subsurface storage capacity could jointly exacerbate water shortage when facing future droughts [ABSTRACT FROM AUTHOR]

It has been found that a decreased fraction of precipitation falling as snow in a warmer climate significantly affects streamflow. Here, based on observations from 631 catchments over the contiguous United States, we proposed a framework to investigate the first‐order and second‐order hydro‐climatic relationships between annual streamflow and snowfall fraction. Results show that in addition to the mean annual streamflow, the interannual streamflow variability will also decrease with lower snowfall fraction in the context of warming. The positive relationship between streamflow variability and snowfall fraction may result from the asymmetric hydrological effects of snowfall in the wet and dry years. To our knowledge, it is the first attempt to detect the role of snowfall on the streamflow variability. This study provides new understandings of the second‐order hydro‐climatic effects, and these findings will facilitate decisions for water resource management in a changing climate. Plain Language Summary: With persistent global warming, more precipitation will land on the earth's surface as rainfall instead of snowfall. The observed evidence has shown that the precipitation phase shift (PPS) would reduce mean annual streamflow, leading to less water resources available for human utilization. However, the impacts of this PPS on the interannual variability of river flows have yet to be understood. Here, we employ a parsimonious model to show that both the mean annual streamflow and interannual streamflow variability would decrease concurrently due to temperature rising. We show observed evidence that the snowfall leads to streamflow increases in the wet year and decreases in the dry year. This asymmetric hydrological effect of snowfall results in higher interannual streamflow variability in the snowy regions. These findings contribute to the understanding of the possible changes in hydrological processes at snow‐affected catchments under a changing climate. Key Points: An observation‐driven approach was developed to investigate the second‐order relationship between streamflow and snowfall fractionBoth the mean annual streamflow and interannual streamflow variability will decrease with less snowfall fraction in the context of warmingThe asymmetric effects of snowfall fraction on streamflow in the wet and dry years amplify the interannual streamflow variability [ABSTRACT FROM AUTHOR]

Abstract: We develop a Bayesian model to predict the maximum thickness of seasonally frozen ground (MTSFG) using historical air temperature and precipitation observations. We use the Stefan solution and meteorological data from 11 stations to estimate the MTSFG changes from 1961 to 2016 in the Yellow River source region of northwestern China. We employ an antecedent precipitation index model to estimate changes in the liquid soil water content. The marginal posterior probability distributions of the antecedent precipitation index parameters are estimated using Markov chain Monte Carlo sampling methods. We compare the results of our stochastic method with those obtained from the traditional deterministic method and find that they are consistent in general. The stochastic approach is effective for estimating the historical changes in the frozen ground depth (root‐mean‐square errors = 0.13–0.35 m), and it provides more information on model uncertainty regarding soil moisture variations. Additionally, simulation shows that the MTSFG has decreased by 0.31 cm per year over the last 56 years on the northeastern Qinghai‐Tibet Plateau. This decrease in frost depth accelerated in the 1990s and 2000s. Considering the lack of data on seasonally frozen soil monitoring, the Bayesian method provides a pragmatic approach to statistically model frozen ground changes using available meteorological data. [ABSTRACT FROM AUTHOR]

Core Ideas: Liquid soil moisture controls seasonal variations in soil albedo.Solar zenith angle determines diurnal variations in soil albedo.Soil optical parameter values can be reasonably estimated from soil composition.The simulated surface energy budget in summer is considerably improved. Soil albedo controls the surface energy budget on the Tibetan Plateau (TP); however, estimates of the soil albedo include substantial uncertainties. This study presents a physically based soil albedo parameterization that includes the effects of the solar zenith angle and liquid soil moisture. The parameter values are calibrated and validated using the measurements from the Global Energy and Water Cycle Experiment–Asian Monsoon Experiment and the Coordinated Enhanced Observing Period Asia–Australia Monsoon Project (CAMP‐Tibet). Because few measurements of albedo are available from the TP, a transfer equation was built to estimate the parameter values through their inverse physical links with the soil composition. Compared with previous parameterizations, the parameter values from the transfer equation reduce the simulated mean bias and RMSE values by nearly 50%. Comparisons of the observed albedos and simulated values obtained using the new parameterization show that the correlation values are >0.5 and that the absolute errors (dimensionless) are within ±0.05. The new parameterization captures the diurnal and seasonal variations better than the previous schemes and shows that the diurnal and seasonal variations in soil albedo are controlled by the solar zenith angle and liquid soil moisture, respectively. This new parameterization scheme was incorporated into the Geomorphology‐Based Eco‐Hydrological Model and run for the CAMP‐Tibet stations. The inclusion of this new parameterization significantly improved the simulated surface energy budget in summer; the deviations in the upward shortwave radiation decreased by 81%, and the accuracy of the simulated Bowen ratio increased substantially. [ABSTRACT FROM AUTHOR]