Jun Xia's research while affiliated with Wuhan University and other places

The rapid development of the city leads to the continuous updating of the land use allocation ratio, particularly during the flood season, which will exacerbate the significant changes in the spatial and temporal patterns of urban flooding, increasing the difficulty of urban flood forecasting and early warning. In this study, the spatial and temporal evolution of flooding in a high-density urban area was analyzed based on the Mike Flood model, and the influence mechanisms of different rainfall peak locations and infiltration rate scenarios on the spatial and temporal characteristics of urban waterlogging were explored. The results revealed that under the same return period, the larger the rainfall peak coefficient, the larger the peak value of inundation volume and inundation area. When the rainfall peak coefficient is small, the higher the return period is, and the larger the peak lag time of the inundation volume is, in which P = 50a, r = 0.2, the peak lag time of the inundation volume reached 32 min and 45 min for the inundation depths H > 0.03 m and H > 0.15 m, respectively. There are also significant differences in the peak lag time of waterlogging inundation volume for different inundation depths. The greater the inundation depth, the longer the peak lag time of the inundation volume, and the higher the return period, the more significant the effect of lag time prolongation. It is worth noting that the increase in infiltration rate may lead to an advance in the peak time of inundation volume and inundation area, and the peak time of the inundation area is overall more obvious than that of inundation volume. The effect of infiltration rate on the peak time of inundation volume for larger inundation depths was relatively large; the peak times of inundation volume and inundation area were advanced by 4–6 min and 4–8 min for H > 0.03 m and H > 0.15 m, respectively, after the increase in infiltration rate, and the higher the rainfall return period, the longer the advance time. The spatial and temporal characteristics of waterlogging under different peak rainfall locations and infiltration capacities obtained in this study can help provide a new perspective for temporal forecasting and warning of urban waterlogging.

The rapid development of the city leads to the continuous updating of the ratio of land use allocation, especially during the flood season, which will exacerbate the significant changes in the spatial and temporal patterns of urban flooding, increasing the difficulty of urban flood forecasting and early warning. In this study, the spatial and temporal evolution of flooding in a high-density urban area was analyzed based on the Mike Flood model, and the influence mechanisms of different rainfall peak locations and infiltration rate scenarios on the spatial and temporal characteristics of urban waterlogging were explored. The results revealed that under the same return period, the larger the rainfall peak coefficient, the larger the peak value of inundation volume and inundation area. When the rainfall peak coefficient is small, the higher the return period is, and the larger the peak lag time of the inundation volume is, in which P = 50a, r = 0.2, the delay time of the inundation volume for the inundation depths H > 0.03 m and H > 0.15 m reached 32 min and 45 min, respectively, At the same time, there are also significant differences in the peak lag time of waterlogging inundation volume in different inundation depths. The greater the inundation depth, the longer the peak lag time of waterlogging inundation volume, and the higher the return period, the more significant the effect of lag time prolongation. It is worth noting that the increase in infiltration rate will lead to the advance of the peak time of inundation volume and inundation area, and the peak time of the inundation area is overall more obvious than that of inundation volume. The peak times of inundation volume and inundation area were advanced by 4 ~ 8 min and − 2 ~ 9 min for H > 0.03 m and H > 0.15 m, respectively, after the increase in infiltration rate; and the higher the return period, the smaller the rainfall peak coefficient and the longer the advance time. The spatial and temporal characteristics of waterlogging under different peak rainfall locations and infiltration capacities obtained in this study can help provide a new perspective for temporal forecasting and warning of urban waterlogging.

Water quality parameters are key indicators of quality of water and can indicate the algal biomass and eutrophication in lakes. Therefore, this study spectrally inverted and evaluated the water quality of the Poyang Lake in China by analysing the differences between the measured water quality parameters and the observed image spectra of Sentinel‐2 remote sensing. This analysis was done using statistical regression models (SRMs) and various machine learning models (e.g., support vector machine [SVM] and random forest [RF]). The following major conclusions were drawn: (1) nutrients accumulated more in summer (June–August) than in winter (December–February). For example, in local areas of the main river, total nitrogen (TN) concentration reached 3.4 mg/L in summer of 2016, whereas total phosphorus (TP) concentrations were below 0.052 mg/L in winters of 2016 and 2017. (2) The three models, SRM, RF and SVM, achieved good results in the inversions of Secchi depth and permanganate index. However, differences were noted in the inversions of other parameters. For example, the goodness‐of‐fit ( r ² ) between the inversion and measured values of TN concentration from RF was 0.81, while that from SR was 0.73. (3) The spatial distribution patterns of the water quality parameters showed differences. The ammoniacal nitrogen concentration was higher in the central region than that in the western region of the lake, whereas TP concentration was higher at the shoreline of the lake at 0.07 mg/L on 2 April 2017 but was relatively low in the main channel.

The storm water management model (SWMM) has been used extensively to plan, implement, control, and evaluate low impact development facilities and other drainage systems to solve storm-related problems in sponge cities. However, the calibration of SWMM involves a variety of sensitive parameters and may bring significant uncertainties. Here we incorporated the distributed time variant gain model (DTVGM), a model with a simple structure and few parameters, into the SWMM (called DTVGM-SWMM) to reduce the complexity but keep the mechanistic representation of the hydrological process. The DTVGM runoff module parameters were calibrated and validated using the Nash–Sutcliffe efficiency (NSE), based on measured data and the results of SWMM. It was then coupled with the SWMM routing module to estimate catchment runoffs and outflows. Finally, the performance was evaluated using NSE (0.57~0.94), relative errors of the flow depth (−7.59~19.79%), and peak flow rate (−33.68~54.37%) under different storm events. These implied that the DTVGM-SWMM simulations were generally consistent with those of the control group, but underperformed in simulating peak flows. Overall, the proposed framework could reasonably estimate the runoff, especially the outflow process in the urban catchment. This study provides a simple and reliable method for urban stormwater simulation.

Watershed water cycles undergo profound changes under changing environments. Analyses of runoff evolution characteristics are fundamental to our understanding of the evolution of water cycles under changing environments. In this study, linear regression, moving average, Mann–Kendall, Pettitt, accumulative anomaly, STARS, wavelet analysis, and CEEMDAN methods were used to analyze the trends, mutations, and periodic and intrinsic dynamic patterns of runoff evolution using long-term historical data. The intra-annual distribution of runoff in the Dawen River Basin was uneven, with an overall decreasing trend and mutations in 1975–1976. The main periods of runoff were 1.9 and 2.2 years, and the strongest oscillations in the study period occurred in 1978–1983. The runoff decomposition high-frequency term (intra-annual fluctuation term) had a stronger fluctuation frequency, with a period of 0.51–0.55 years, while the low-frequency term (interannual fluctuation term) had a period of 1.55–2.26 years. The trend term for the runoff decomposition tended to decrease throughout the monitoring period and gradually stabilized at the end of the monitoring period, while the period gradually decreased from upstream to downstream. In summary, we used multiple methods to analyze the evolutionary characteristics of runoff, which are of great relevance to the adaptive management of water resources under changing environments.

The development of industrialization and urbanization has intensified the coupling of human activities and hydrological processes and promoted the emergence of socio-hydrology. This paper addresses the issue of socio-hydrology due to new development and social demand for hydrological sciences and sustainable development. Four key scientific issues are identified through systematic analysis and summary of the relative research and international progress, i.e., (1) the long-term dynamic process of socio-hydrological system evolution; (2) quantitative description and driving mechanism analysis of socio-hydrological coupling system; (3) prediction of the trajectories of socio-hydrological system co-evolution, and (4) integrated water resource management from the perspective of water systems. Moreover, opportunities and challenges for developing socio-hydrology are emphasized, including (1) strengthening the research of interdisciplinary theoretical systems; (2) improving and broadening socio-hydrological research technical methods, and (3) supporting integrated water resources management (IWRM) for sustainable utilization goals (SDGs). The review is expected to provide a reference for the future development of socio-hydrology discipline.

The optimal implementation of low impact development (LID) practices remains an open research issue. Multiobjective optimization (MOO) based on physically-based models facilitates LID sizing but requires large computational budgets. We developed and validated a surrogate MOO framework based on machine learning (ML) models to obtain satisfactory optimal solutions with an affordable computational cost. First, a calibrated and validated storm water management model (SWMM) was driven by a 2-year, 120 min Chicago pattern storm with random LID areas for generating 10000 samples. Second, ML models, including multiple linear regression (MLR), generalized regression neural network (GRNN), and backpropagation neural network (BPNN), were trained and validated to predict catchment total outflow (CTO) and catchment peak outflow (CPO). Third, using the outperformed ML models as surrogates, the LID areas were optimized by non-dominated sorting genetic algorithm-II to minimize construction costs, CTO, and CPO. The results obtained for an urban catchment in Fengxi, China, demonstrated that: (1) BPNN outperformed MLR and GRNN in terms of accuracy, while the training time (∼40 min) was ∼6857 times and ∼706 times that of MLR (∼0.35 s) and GRNN (3.4 s) models, respectively. (2) The optimization process using BPNN models as surrogates converged well, indicating the efficiency of the proposed framework. (3) Under the marginal effect, the permeable pavement was identified as the leading practice for most Pareto solutions, followed by bioretention cell and green roof. Moreover, a subcatchment with a higher runoff coefficient was deemed preferable for LID implementation. (4) Outflow reduction by the LIDs was significant (CTO and CPO were reduced by 28.6–56.9 % and 27.5–48.3 %, respectively) but negligible for retarding the time of CPO. (5) The optimization time was reduced by ∼95.82% using the surrogate-based MOO instead of the SWMM-based model when the training time of the BPNN models was ignored. This study provides insights into the efficient optimization of LIDs.

The campus is facing the problems of waterlogging and runoff pollution. To mitigate these phenomena, this study constructs a SWMM model using a campus as the study area. The reduction effects of runoff volume and pollutant load under four LID facility combination scenarios were simulated. Eleven evaluation indicators were selected from environmental, economic and social aspects, and the weights of each indicator were calculated by applying the CRITIC method. The relative percentage of different rainfall recurrence periods under the same evaluation indicator was obtained. The composite index method is used to calculate the composite index for each scenario. The combination of rain garden, permeable pavement, bioretention and green roof is determined as the optimal scenario. The Fuzzy pattern recognition method is used to select three representative rainfall events and simulate the reduction effect of runoff volume and pollutant load under four scenarios to further verify the optimal scenario. The results show that the reduction rates of runoff volume and pollutant load under scenario 4 are 2.7%, 4.2% and 4.7% higher than those under scenarios 1, 2 and 3. Based on the above study, the optimal LID facility combination selected in this paper will provide a reference for the construction of sponge facilities similar to this study area.