Urban flooding has become a global issue due to climate change, urbanization and limitation in the capacity of urban drainage infrastructures. To tackle the growing threats, it is crucial to understand urban surface flood resilience, i.e., how urban drainage catchments can resist against and recover from flooding. This study proposes a grid cell based resilience metric to assess urban surface flood resilience at the urban drainage catchment scale. The new metric is defined as the ratio of the number of unflooded grid cells to the total grid cell number in an urban drainage catchment. A two-dimensional Cellular Automata based model CADDIES is used to simulate urban surface flooding. This methodology is demonstrated using a case study in Dalian, China, which is divided into 31 urban drainage catchments for flood resilience analysis. Results show the high resolution resilience assessment identifies vulnerable catchments and helps develop effective adaptation strategies to enhance urban surface flood resilience. Comparison of the new metric with an existing metric reveals that new metric has the advantage of fully reflecting the changing process of system performance. Effectiveness of adaptation strategies for enhancing urban surface flood resilience is discussed for different catchments. This study provides a new way to characterize urban flood resilience and an in-depth understanding of flood resilience for urban drainage catchments of different characteristics, and thus help develop effective intervention strategies for sustainable sponge city development.

This paper addresses how much flood water can be conserved for use after the flood season through the operation of reservoir by taking into account the residual flood control capacity (the difference between flood conveyance capacity and the expected inflow in a lead time). A two‐stage model for dynamic control of the flood‐limited water level (the maximum allowed water level during the flood season, DC‐FLWL) is established considering forecast uncertainty and acceptable flood risk. It is found that DC‐FLWL is applicable when the reservoir inflow ranges from small to medium levels of the historical records, while both forecast uncertainty and acceptable risk in the downstream affect the feasible space of DC‐FLWL. As forecast uncertainty increases (under a given risk level) or as acceptable risk level decreases (under a given forecast uncertainty level), the minimum required safety margin for flood control increases, and the chance for DC‐FLWL decreases. The derived hedging rules from the modeling framework illustrate either the dominant role of water conservation or flood control or the trade‐off between the two objectives under different levels of forecast uncertainty and acceptable risk. These rules may provide useful guidelines for conserving water from flood, especially in the area with heavy water stress. The analysis is illustrated via a case study with a real‐world reservoir in northeastern China.

The applicability of six fine-resolution precipitation products, including precipitation radar, infrared, microwave and gauge-based products, using different precipitation computation recipes, is evaluated using statistical and hydrological methods in northeastern China. In addition, a framework quantifying uncertainty contributions of precipitation products, hydrological models, and their interactions to uncertainties in ensemble discharges is proposed. The investigated precipitation products are Tropical Rainfall Measuring Mission (TRMM) products (TRMM3B42 and TRMM3B42RT), Global Land Data Assimilation System (GLDAS)/Noah, Asian Precipitation – Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and a Global Satellite Mapping of Precipitation (GSMAP-MVK+) product. Two hydrological models of different complexities, i.e. a water and energy budget-based distributed hydrological model and a physically based semi-distributed hydrological model, are employed to investigate the influence of hydrological models on simulated discharges. Results show APHRODITE has high accuracy at a monthly scale compared with other products, and GSMAP-MVK+ shows huge advantage and is better than TRMM3B42 in relative bias (RB), Nash–Sutcliffe coefficient of efficiency (NSE), root mean square error (RMSE), correlation coefficient (CC), false alarm ratio, and critical success index. These findings could be very useful for validation, refinement, and future development of satellite-based products (e.g. NASA Global Precipitation Measurement). Although large uncertainty exists in heavy precipitation, hydrological models contribute most of the uncertainty in extreme discharges. Interactions between precipitation products and hydrological models can have the similar magnitude of contribution to discharge uncertainty as the hydrological models. A better precipitation product does not guarantee a better discharge simulation because of interactions. It is also found that a good discharge simulation depends on a good coalition of a hydrological model and a precipitation product, suggesting that, although the satellite-based precipitation products are not as accurate as the gauge-based products, they could have better performance in discharge simulations when appropriately combined with hydrological models. This information is revealed for the first time and very beneficial for precipitation product applications.

Flash floods in mountainous catchments are often caused by the rainstorm, which may result in more severe consequences than plain area floods due to less timescale and a fast-flowing front of water and debris. Flash flood forecasting is a huge challenge for hydrologists and managers due to its instantaneity, nonlinearity, and dependency. Among different methods of flood forecasting, data-driven models have become increasingly popular in recent years due to their strong ability to simulate nonlinear hydrological processes. This study proposed a Support Vector Regression (SVR) model, which is a powerful artificial intelligence-based model originated from statistical learning theory, to forecast flash floods at different lead times in a small mountainous catchment. The lagged average rainfall and runoff are identified as model input variables, and the time lags associated with the model input variables are determined by the hydrological concept of the time of response. There are 69 flash flood events collected from 1984 to 2012 in a mountainous catchment in China and then used for the model training and testing. The contribution of the runoff variables to the predictions and the phase lag of model outputs are analyzed. The results show that: (i) the SVR model has satisfactory predictive performances for one to three-hours ahead forecasting; (ii) the lagged runoff variables have a more significant effect on the predictions than the rainfall variables; and (iii) the phase lag (time difference) of prediction results significantly exists in both two- and three-hours-ahead forecasting models, however, the input rainfall information can assist in mitigating the phase lag of peak flow.

Droughts and heat waves both are natural extreme climate events occurring in most parts of the world. To understand the spatio-temporal characteristics of droughts and heat waves in China, we examine changes in droughts, heat waves, and the compound of both during 1961–2017 based on high resolution gridded monthly sc_PDSI and daily temperature data. Results show that North China and Northwest China are the two regions that experience the most frequent droughts, while Central China is the least drought-affected region. Significant drought decreasing trends were mostly observed Qinghai, Xinjiang, and Tibet provinces, while the belt region between Yunnan and Heilongjiang provinces experienced significant drought increasing trends. Heat waves occur more frequently than droughts, and the increase of heat wave occurrence is also more obvious. The increasing of heat wave occurrence since the 2000s has been unprecedented. The compound droughts and heat waves were mild from the 1960s to 1980s, and began to increase in 1990s. Furthermore, the significant increasing trends of the percentage of compound droughts and heat waves to droughts are observed in entire China, and more than 90% drought occurrences are accompanied by one or more heat waves in the 2010s. The results highlight the increased percentage of compound droughts and heat waves and call for improved efforts on assessing the impact of compound extremes, especially in an era of changing climate.

This paper presents a new Two Stage Bayesian Stochastic Dynamic Programming (TS‐BSDP) model for real time operation of cascaded hydropower systems to handle varying uncertainty of inflow forecasts from Quantitative Precipitation Forecasts. In this model, the inflow forecasts are considered as having increasing uncertainty with extending lead time, thus the forecast horizon is divided into two periods: the inflows in the first period are assumed to be accurate, and the inflows in the second period assumed to be of high uncertainty. Two operation strategies are developed to derive hydropower operation policies for the first and the entire forecast horizon using TS‐BSDP. In this paper, the newly developed model is tested on China's Hun River cascade hydropower system and is compared with three popular stochastic dynamic programming models. Comparative results show that the TS‐BSDP model exhibits significantly improved system performance in terms of power generation and system reliability due to its explicit and effective utilization of varying degrees of inflow forecast uncertainty. The results also show that the decision strategies should be determined considering the magnitude of uncertainty in inflow forecasts. Further, this study confirms the previous finding that the benefit in hydropower generation gained from the use of a longer horizon of inflow forecasts is diminished due to higher uncertainty and further reveals that the benefit reduction can be substantially mitigated through explicit consideration of varying magnitudes of forecast uncertainties in the decision‐making process.