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Concurrent compound dry and hot events (CDHEs) amplified more damange on the ecosystems and human society than individual extremes. Under climate change, compound dry and hot events become more frequent on a global scale. This paper proposes a mathematical method to quantitatively attribute changes of CDHEs to changes of precipitation, change in temperature and change in the dependence between precipitation and temperature. The attribution is achieved by formulating the total differential equation of the return period of CDHEs among Meta-gaussian model. A case study of China is devised based on monthly precipitation and temperature data during the period from 1921 to 2020 for 80 major river basins. It is found that temperature is the main driving factor of increases in CDHEs for 49 major river basins in China, except for the upper and middle reaches of the Yangtze River. In West China, precipitation changes drove the increase in CDHEs in 18 river basins (23%), particularly in parts of North Xinjiang, Qinghai and Gansu. On the other hand, dependence between precipitation and temperature dominated changes of CDHEs in 13 river basins (16%) of China with other factors, including parts of South China, East China and Northwestern China. Furthermore, changes in both the mean and spread of precipitation and temperature can also contribute to changes in CDHEs. [ABSTRACT FROM AUTHOR]

Afforestation to control soil erosion has been implemented throughout China over the past few decades. The long‐term hydrological effects, such as total water yield and baseflow, of this large‐scale anthropogenic activity remain unclear. Using six decades of hydrologic observations and remote sensing data, we explore the hydrological responses to forest expansion in four basins with contrasting climates across China. No significant change in runoff was found for the period 1970–2012 for the cold and dry Hailar River Basin in northeastern China. However, both forest expansion and reduced precipitation contributed to the runoff reduction after afforestation since the late 1990s. Similarly, afforestation and drying climate since the mid‐1990s induced a significant decrease in runoff for the Weihe River Basin in semi‐arid northwestern China. In contrast, the two wet basins in the humid southern China, Ganjiang River Basin and Dongjiang River Basin, showed insignificant changes in total runoff during their study periods. However, the baseflow in the winter dry seasons in these two watersheds significantly increased since the 1950s. Our results highlight the long‐term variable effects of forest expansion and local climatic variability on basin hydrology in different climatic regions. This study suggests that landuse change in the humid study watersheds did not cause dramatic change in river flow and that region‐specific afforestation policy should be considered to deal with forestation‐water quantity trade‐off. Conclusions from this study can help improve decision‐making for ecological restoration policies and water resource management in China and other countries where intensive afforestation efforts are taking place. Key Points: Both precipitation and afforestation explained reduction in runoff for dry basinsForest recovery did not result in significant changes in total runoff for two wet basinsDry season baseflow in two wet basins had an upward trend [ABSTRACT FROM AUTHOR]

This study evaluates the future change in hydropower potential and sustainability of the Tibetan Plateau (TP) in 21st century under climate change, using the Lancang River hydropower base (LRHB) in the upper Mekong basin (UMB) as a case study. Future climate projections simulated by five different global climate models (GCMs) individually and the variable infiltration capacity (VIC) distributed hydrological model coupled with a reservoir model are used to project the future hydropower outputs. Results present a generally ideal prospect for hydropower development in the UMB, as most GCMs illustrate overall increasing hydropower outputs of the plants along with the increasing reservoir inflow. The sustainability of the hydropower is also improved in most GCMs, with generally higher reliability and lower vulnerability; however, due to the large impact of increased climate variability, some GCMs show poorer sustainability for the hydropower plants in the future scenarios, even though its overall hydropower outputs are increased. Therefore, the negative influence of the increased variability of some passive GCM projections still indicates the risks for hydropower development in the TP and thus requires consideration. This study is expected to provide reference for further hydropower planning and development over the TP under climate change. • First assessment of future hydropower changes in the Tibetan Plateau (TP) is presented. • Overall increased hydropower output is found for the upper Mekong basin. • Increased sustainability is commonly found except for some Global Climate Model (GCM) projection cases. • The risk in hydropower induced by increased climate variability in TP should not be ignored. [ABSTRACT FROM AUTHOR]

Rainfall forecasts are an integral part of hydrological forecasting systems at sub-seasonal to seasonal timescales. In seasonal forecasting, global climate models (GCMs) are now the go-to source for rainfall forecasts. For hydrological applications however, GCM forecasts are often biased and unreliable in uncertainty spread, and calibration is therefore required before use. There are sophisticated statistical techniques for calibrating monthly and seasonal aggregations of the forecasts. However, calibration of seasonal forecasts at the daily time step typically uses very simple statistical methods or climate analogue methods. These methods generally lack the sophistication to achieve unbiased, reliable and coherent forecasts of daily amounts and seasonal accumulated totals. In this study, we propose and evaluate a Rainfall Post-Processing method for Seasonal forecasts (RPP-S), which is based on the Bayesian joint probability modelling approach for calibrating daily forecasts and the Schaake Shuffle for connecting the daily ensemble members of different lead times. We apply the method to post-process ACCESS-S forecasts for 12 perennial and ephemeral catchments across Australia and for 12 initialisation dates. RPP-S significantly reduces bias in raw forecasts and improves both skill and reliability. RPP-S forecasts are also more skilful and reliable than forecasts derived from ACCESS-S forecasts that have been post-processed using quantile mapping, especially for monthly and seasonal accumulations. Several opportunities to improve the robustness and skill of RPP-S are identified. The new RPP-S postprocessed forecasts will be used in ensemble sub-seasonal to seasonal streamflow applications. [ABSTRACT FROM AUTHOR]

Hydropower plant operation alters natural streamflow regimes, which leads to tradeoff between hydropower benefits and the needs of downstream river ecosystems. This study proposes a novel approach for evaluating how hydropower-ecology tradeoff will evolve under future climate change based on Pareto optimal fronts, and demonstrated its efficacy using a case study of the cascade hydropower plants and downstream river ecosystems of the upper Mekong basin (UMB), a representative transboundary river basin. Future climate projections from multiple global climate models (GCMs) are used. Results show that, although GCMs project a future overall increase in streamflow, the hydropower-ecology conflict will likely be exacerbated by increased streamflow variability. Nearly one third of the GCMs indicate the alleviated conflict between hydropower and ecosystems, one third show little apparent change, and the final third show aggravated conflicts. According to the pessimistic GCMs, maintaining ecological impact at historical levels in the future could result in a hydropower deficit for which thermal power would need to compensate, generating additional 1.33 MMT CO2e in greenhouse gas emissions per year. These results reveal the potential challenges facing hydropower and ecological development in the UMB in the future, and emphasize the importance of developing adaptive mitigation techniques under climate change. • A novel approach evaluating hydropower-ecology (H-E) tradeoff under climate change is proposed. • H-E tradeoff evolution of upper Mekong basin shows high uncertainty under climate change. • Enhanced streamflow variability increases the projection uncertainty of H-E tradeoff. [ABSTRACT FROM AUTHOR]

This study investigated the strength and limitations of two widely used multi-model averaging frameworks—Bayesian model averaging (BMA) and reliability ensemble averaging (REA), in post-processing runoff projections derived from coupled hydrological models and climate downscaling models. The performance and weight distributions of five model ensembles were thoroughly compared, including simple equal-weight averaging, BMA, and REAs optimizing mean (REA-mean), maximum (REA-max), and minimum (REA-min) monthly runoff. The results suggest that REA and BMA both can synthesize individual models' diverse skills with comparable reliability, despite of their different averaging strategies and assumptions. While BMA weighs candidate models by their predictive skills in the baseline period, REA also forces the model ensembles to approximate a convergent projection towards the long-term future. The type of incorporation of the uncertain future climate in REA weighting criteria, as well as the differences in parameter estimation (i.e., the expectation maximization (EM) algorithm in BMA and the Markov Chain Monte Carlo sampling method in REA), tend to cause larger uncertainty ranges in the weight distributions of REA ensembles. Moreover, our results show that different averaging objectives could cause much larger discrepancy than that induced by different weighting criteria or parameter estimation algorithms. Among the three REA ensembles, REA-max most resembled BMA because the EM algorithm of BMA converges to the minimum aggregated error, and thus emphasize the simulation of high flows. REA-min achieved better performance in terms of inter-annual temporal pattern, yet at the cost of compromising accuracy in capturing mean behaviors. Caution should be taken to strike a balance among runoff features of interest. [ABSTRACT FROM AUTHOR]