Jianqun Wang

Recycled water is commonly considered an environmentally friendly alternative water source for urban water systems, which can not only serve as a solution for water scarcity, but also reduce wastewater discharge from sewage systems. However, owing to the high degree of energy consumption during recycled water production, the utilization of recycled water may be detrimental to greenhouse gas emission reduction. In this work, we conduct a detailed investigation into greenhouse gas emissions from different sources in a typical multisource urban water system in China. Furthermore, we develop an optimization model for water resource allocation based on the rime optimization algorithm and regret theory. The results show that although greenhouse gas emissions from recycled water exceed those from other sources, their impact can be eliminated through rational water resource allocation. Specifically, compared with the original water resource allocation, the optimal results effectively reduce pollutant emissions by 7.6~11.1% without excessively increasing water resource shortages and greenhouse gas emissions. Additionally, both subjective preferences and recycled water utilization conditions have significant impacts on the optimization results, which should be carefully selected according to practical situations and technologies. Overall, the methods developed in this study provide a new general framework for the water resource allocation of multisource urban water systems in the context of greenhouse gas emission reduction and recycled water utilization, which can be employed in other areas.

The South China Sea spans the tropics and subtropics. Tropical cyclones in the area are extremely active, due to the features of its marine environment, such as wide water-depth profile, complex topography and hydrology. The maximum wave heights along the coast of China are normally generated by typhoons. Especially in the context of global warming, extreme weather events have significantly increased in the recent years, leading to more frequent strong and super typhoons. With the development of resources and energy in China expanding into the deep sea, extreme waves have caused serious damage to sea projects, endangering people’s lives and properties. Selecting the accurate typhoon gradient model to calculate various extreme waves, including the probable maximum tropical cyclone (PMTC) wave, is of significance for the safety of marine engineering construction and disaster prevention and mitigation. In this paper, we first proposed a wind field fusion model suitable for the South China Sea by superimposing an empirical typhoon model with the background wind field, and further verified it using the measured typhoon data. Secondly, the fused wind field was used as the input wind field of the SWAN (Simulating Waves Nearshore) model, and the wave fields of typhoons “Usagi” and “Mangosteen” were used to verify the model. The relevant parameters of PMTC were calculated using the Pearson Type III frequency fitting method, while the verified SWAN model was used to calculate the probable maximum typhoon wave, and P-III frequency analysis was carried out by direction of the extrapolated result of typhoon waves to obtain the design wave elements of each return period. Finally, a model for calculating the probable maximum typhoon wave suitable for this sea area was proposed to derive the characteristic parameters and time-histories of the probable maximum typhoon wave and the wave heights and their corresponding frequencies of various extreme waves.

As an important reference of reservoir regulation, more and more attention has been paid to the numeric precipitation forecast. Due to the uncertainty of meteorological prediction, reservoir regulation based on precipitation forecasts may lead to flood control risks. Therefore, the reliability of precipitation forecasts is crucial to the formulation of reservoir regulation strategy based on it. In this paper, a reliability assessment model for a continuous precipitation forecast is proposed based on the first-passage problem and fuzzy mathematics. The uncertainty of precipitation forecast is described by the generalized Bayesian model, and the fuzzy reliability of a continuous precipitation forecast can be obtained by the first-passage fuzzy probability model (FFPM). Due to the importance of a no-rain period in flood resource utilization, the no-rain forecasts from four different forecast centers in the Meishan basin are used as an example. The results show that the fuzzy mathematics is helpful in describing the uncertainty of the boundary for the no-rain set, and the fuzzy reliability of the no-rain forecast is affected by the selection of the range for the no-rain forecast, while the influence of the membership function is limited. Furthermore, due to the downward trend of fuzzy reliability as the lead time increases, there is a contradiction between excess water storage of the reservoir and the fuzzy reliability of the no-rain forecast. A longer continuous no-rain period means more excess water storage, but it also faces lower reliability. In actual reservoir regulation, the results of FFPM can be combined with more information to formulate better strategies for reservoir regulation. As an important reference of reservoir regulation, more and more attention has been paid to the numeric precipitation forecast. Due to the uncertainty of meteorological prediction, reservoir regulation based on precipitation forecasts may lead to flood control risks. Therefore, the reliability of precipitation forecasts is crucial to the formulation of reservoir regulation strategy based on it. In this paper, a reliability assessment model for a continuous precipitation forecast is proposed based on the first-passage problem and fuzzy mathematics. The uncertainty of precipitation forecast is described by the generalized Bayesian model, and the fuzzy reliability of a continuous precipitation forecast can be obtained by the first-passage fuzzy probability model (FFPM). Due to the importance of a no-rain period in flood resource utilization, the no-rain forecasts from four different forecast centers in the Meishan basin are used as an example. The results show that the fuzzy mathematics is helpful in describing the uncertainty of the boundary for the no-rain set, and the fuzzy reliability of the no-rain forecast is affected by the selection of the range for the no-rain forecast, while the influence of the membership function is limited. Furthermore, due to the downward trend of fuzzy reliability as the lead time increases, there is a contradiction between excess water storage of the reservoir and the fuzzy reliability of the no-rain forecast. A longer continuous no-rain period means more excess water storage, but it also faces lower reliability. In actual reservoir regulation, the results of FFPM can be combined with more information to formulate better strategies for reservoir regulation.

In recent years, flash droughts with a rapid onset and strong intensity have attracted extensive attention due to their impact on agriculture and ecosystems. However, there is still no feasible method for flash drought forecasting and early warning. This paper employs the thresholds of several meteorological variables to identify flash droughts in Zhejiang Province, China, and build a probabilistic flash drought forecasting model through numeric weather forecast (NWF) and the generalized Bayesian model (GBM). The results show that the northern part of Zhejiang Province has the highest risk of flash drought. The NWF is a viable method to provide future information for flash drought forecasting and early warning, but its forecasting accuracy tends to decline with the increase in the lead time and is very limited when the lead time is over 5 days, especially for the precipitation forecast. Due to the low performance of the NWF, the flash drought forecast based on the raw NWF may be unreliable when the lead time is over 5 days. To solve this problem, probabilistic forecasting based on GBM is employed to quantify the uncertainty in the NWF and is tested through an example analysis. In the example analysis, it was found that the probability of a flash drought exceeds 30% from the probabilistic forecasting when the lead time is 12 days, while the deterministic forecasting via the raw NWF cannot identify a flash drought when the lead time is over 5 days. In conclusion, probabilistic forecasting can identify a potential flash drought earlier and can be used to evaluate the risk of a flash drought, which is conducive for the early warning of flash droughts and the development of response measures.

With the rapid development of meteorological models, numerical weather prediction is increasingly used in flood forecasting and reservoir regulation, but its forecasting ability is limited by the large amount of uncertainty from meteorological systems. In this paper, a new, hybrid framework is developed to improve numerical precipitation forecasting by combining the multimodel ensemble and probabilistic postprocessing methods. The results show that the multimodel ensemble method used in this paper is an efficient way to reduce prediction errors, especially missing alarm errors. In a comparison of the probabilistic postprocessors based the generalized Bayesian model (GBM) and bivariate probabilistic model (BPM), the GBM shows better performance from the aspects of indicators and is more suitable for real-time applications. Meanwhile, the assessment of probabilistic results shows that the skill of probabilistic precipitation forecasts is related to the quality of their inputs. According to these results, a new hybrid framework is proposed by taking the results from multimodel ensemble as the input of probabilistic postprocessor. Compared to using the raw numerical in GBM, the hybrid framework improves the accuracy, sharpness, reliability, and resolution ability from different lead times by 2–13%, 1–22%, and 0–12% respectively, especially when the lead time is less than 4 d, the improvement can reach 9–13%, 10–22%, and 5–12% respectively. In conclusion, the hybrid two-step framework can provide a more skillful precipitation forecast, which can be useful for flood forecasting and reservoir regulation.

The comprehensive assessment of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) V05B is important for benchmarking the product’s continued improvement and future development. The performance of IMERG V05B precipitation products was systematically evaluated using 542 precipitation gauges at multiple spatiotemporal scales from March 2014 to February 2017 over China. Moreover, IMERG V05B was compared with IMERG V04A, the Tropical Rainfall Measuring Mission (TRMM) 3B42, and the Climate Prediction Center Morphing technique (CMORPH)-CRT in this study. Categorical verification techniques and statistical methods are used to quantify their performance. Results illustrate the following. (1) Except for IMERG V04A’s severe underestimation over the Tibetan Plateau (TP) and Xinjiang (XJ) with high negative relative biases (RBs) and CMORPH-CRT’s overestimation over XJ with high positive RB, the four satellite-based precipitation products generally capture the same spatial patterns of precipitation over China. (2) At the annual scale over China, the IMERG products do not show an advantage over its predecessor (TRMM 3B42) in terms of RMSEs, RRMSEs, and Rs; meanwhile, the performance of IMERG products is worse than TRMM 3B42 in spring and summer according to the RMSE, RRMSE, and R metrics. Between the two IMERG products, IMERG V05B shows the anticipated improvement (over IMERG V04A) with a decrease in RMSE from 0.4496 to 0.4097 mm/day, a decrease of RRMSE from 16.95% to 15.44%, and an increase of R from 0.9689 to 0.9759 during the whole study period. Similar results are obtained at the seasonal scale. Among the four satellite products, CMORPH-CRT shows the worst seasonal performance with the highest RMSE (0.6247 mm/day), RRMSE (23.55%), and lowest R (0.9343) over China. (3) Over XJ and TP, IMERG V05B clearly improves the strong underestimation of precipitation in IMERG V04A with the RBs of 5.2% vs. −21.8% over XJ, and 2.78% vs. −46% over TP. Results at the annual scale are similar to those obtained at the seasonal scale, except for summer results over XJ. While, over the remaining subregions, the two IMERG products have a close performance; meanwhile, IMERG V04A slightly improves IMERG V05B’s overestimation according to RBs (except for HN) at the annual scale. However, all four products are unreliable over XJ at both an annual and seasonal scale. (4) Across all products, TRMM 3B42 best reproduces the probability density function (PDF) of daily precipitation intensity. (5) According to the categorical verification technique in this study, both IMERG products yield better results for the detection of precipitation events on the basis of probability of detection (POD) and critical success index (CSI) categorical evaluations compared to TRMM 3B42 and CMORPH-CRT over China and across most of the subregions. However, all four products have room for further improvement, especially in high-latitude and dry climate regions. These findings provide valuable feedback for both IMERG algorithm developers and data set users.

Recently, the use of the numerical rainfall forecast has become a common approach to improve the lead time of streamflow forecasts for flood control and reservoir regulation. The control forecasts of five operational global prediction systems from different centers were evaluated against the observed data by a series of area-weighted verification and classification metrics during May to September 2015–2017 in six subcatchments of the Xixian Catchment in the Huaihe River Basin. According to the demand of flood control safety, four different ensemble methods were adopted to reduce the forecast errors of the datasets, especially the errors of missing alarm (MA), which may be detrimental to reservoir regulation and flood control. The results indicate that the raw forecast datasets have large missing alarm errors (MEs) and cannot be directly applied to the extension of flood forecasting lead time. Although the ensemble methods can improve the performance of rainfall forecasts, the missing alarm error is still large, leading to a huge hazard in flood control. To improve the lead time of the flood forecast, as well as avert the risk from rainfall prediction, a new ensemble method was proposed on the basis of support vector regression (SVR). Compared to the other methods, the new method has a better ability in reducing the ME of the forecasts. More specifically, with the use of the new method, the lead time of flood forecasts can be prolonged to at least 3 d without great risk in flood control, which corresponds to the aim of flood prevention and disaster reduction.

A critical evaluation of the newly released precipitation data set is very important for both the end users and data developers. Meanwhile, the evaluation may provide a benchmark for the product’s continued development and future improvement. To these ends, the four precipitation estimates including IMERG (the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement) V04A, IMERG V03D, CMORPH (the Climate Prediction Center Morphing technique)-CRT and TRMM (the Tropical Rainfall Measuring Mission) 3B42 are systematically evaluated against the gauge precipitation estimates at multiple spatiotemporal scales from 1 June 2014 to 30 November 2015 over three different topographic and climatic watersheds in China. Meanwhile, the statistical methods are utilized to quantize the performance of the four satellite-based precipitation estimates. The results show that: (1) over the Tibetan Plateau cold region, among all products, IMERG V04A underestimates precipitation with the largest RB (−46.98%) during the study period and the similar results are seen at the seasonal scale. However, IMERG V03D demonstrates the best performance according to RB (7.46%), RMSE (0.44 mm/day) and RRMSE (28.37%). Except for in summer, TRMM 3B42 perform better than CMORPH according to RMSEs, RRMSEs and Rs; (2) within the semi-humid Huaihe River Basin, IMERG V04A has a slight advantage over the other three satellite-based precipitation products with the lowest RMSE (0.32 mm/day) during the evaluation period and followed by IMERG V03D, TRMM 3B42 and CMORPH orderly; (3) over the arid/semi-arid Weihe River Basin, in comparison with the other three products, TRMM 3B42 demonstrates the best performance with the lowest RMSE (0.1 mm/day), RRMSE (8.44%) and highest R (0.92) during the study period. Meanwhile, IMERG V03D perform better than IMERG V04A according all the statistical indicators; (4) in winter, IMERG V04A and IMERG V03D tend to underestimate the total precipitation with RBs (−70.62% vs. −6.47% over the Tibetan Plateau, −46.92% vs. −0.66% over the Weihe River Basin, respectively); and (5) overall, except for IMERG V04A in Tibetan Plateau, all satellite-based precipitation captured the gauge-based precipitation well over the three regions according to RRMSEs, Rs and Rbs during the study period. IMERG V03D performs better than its predecessors-TRMM 3B42 and CMORPH over the Tibetan Plateau region and the Huaihe River Basin, while IMERG V04A only does so over the latter. Between the two IMERG products, IMERG V04A does not show an advantage over IMERG V03D over the Tibetan Plateau region and the Weihe River Basin. In particular, over the former, IMERG V04A performs far worse than IMERG V03D. These findings provide valuable feedback for both IMERG algorithm developers and data users.

This study aimed to statistically and hydrologically assess the performance of the four latest and widely used satellite–gauge combined precipitation estimates (SGPEs), namely CRT (CMORPH CRT), BLD (CMORPH BLD), CDR (PERSIANN CDR), 3B42 (TMPA 3B42 version 7) over the upper yellow river basins (UYRB) in china during 2001–2012 time period. The performances of the SGPEs were compared with the Chinese Meteorological Administration (CMA) datasets using the hydrologic model called Variable Infiltration Capacity (VIC) which is known as a land surface hydrologic model. Results indicated that irrespective of the slight underestimation in the western mountains and overestimation in the southeast, the four SGPEs could generally captured the spatial distribution of precipitation well. Although 3B42 exhibited a better performance in capturing the spatial distribution of daily average precipitation, BLD agreed best with CMA in the time series of watershed average precipitation, which resulted in BLD having a comparable performance to the CMA in the long-term hydrological simulations. Moreover, the potential for disastrous heavy rain mainly occurs in southeastern corner of the basin, and CRT and BLD comparisons showed to be closer to the CMA in the distribution of extreme precipitation events while 3B42 and CDR overestimated the extreme precipitation especially over the southeast of UYRB region. Therefore, CRT and BLD were able to match the high peak discharges very well for the wet seasons, while 3B42 and CDR overrated the high peak discharges. In addition, the four SGPEs performed well for the 2005 flood event but exhibited poorly when tested for the 2012 flood event. Results indicate that the application of the four SGPEs should be used with caution in simulating massive flood events over UYRB region.