Catchment In China Research Articles

Identification and quantitative analysis of flash flood risks for small catchments in China: a new operational modelling approach

Identification and quantitative analysis of flash flood risks for small catchments in China: a new operational modelling approach

Groundwater drought characteristics and its influencing factors with corresponding quantitative contribution over the two largest catchments in China

Investigating the characteristics and influencing factors of groundwater drought, and identifying their difference in the regions with different climate conditions can provide an essential reference for the sustainable management of groundwater resources. As a case study in the two largest and typical catchments in China, the temporal evolution, spatial distribution and trend characteristics of groundwater drought are identified by GRACE-based groundwater drought index in the Yangtze River catchments (YZRC) with a humid climate condition, and Yellow River catchments (YRC) with an arid/semi-arid climate condition. Their influencing factors, which include natural net recharge (P-ET), teleconnection factors (ENSO, PDO and AO), and anthropogenic factors (land use and land cover, LULC), are investigated and quantitatively compared based on Pearson correlation analysis, cross wavelet transform and Random Forest. The results indicate that: (1) The groundwater droughts in the YZRC mainly occur during 2002–2009, which are mostly in the middle and lower reaches of the catchments, and they show a decreasing trend in most regions of the catchments. In the YRC, they mainly occur during 2015–2019, which influence almost the whole catchments, and they show an increasing trend in most regions of the catchments. (2) The P-ET has a significant positive impact on the mitigation of groundwater drought in the YZRC, while it fails to reverse the groundwater drought aggravation in the YRC. The teleconnection factors have obvious influences on groundwater drought evolution, among which Arctic Oscillation (AO) and Pacific Decadal Oscillation (PDO) are the important factors affecting groundwater drought in the YZRC and YRC, respectively. For LULC, the decrease of cropland and grassland could alleviate groundwater drought in the YZRC, and the increase of grassland, forest and urban area in the YRC could aggravate groundwater drought in the catchments. (3) When quantitatively comparing the contributions, the LULC and P-ET are the crucial factors affecting groundwater drought in the two catchments, while in the YRC, the impact of teleconnection factors cannot be ignored, although it is relatively weak. Moreover, groundwater exploitation plays an important role on groundwater drought in the YZRC and YRC. Its contribution in the YZRC and YRC is slightly lower than that of P-ET, but obviously higher than that of teleconnection factors.

Exploring antibiotic consumption between urban and sub-urban catchments using both parent drugs and related metabolites in wastewater-based epidemiology

Consumption of antibiotics leads to the dissemination of antimicrobial resistance worldwide. Better knowledge of temporal and spatial consumption of antibiotics helps public health authorities to control their usage and combat antimicrobial resistance. However, measuring antibiotic consumption with population surveys, sales data, and production statistics remains challenging due to the complexity of prescription preference, patient compliance, and direct disposal of unused drugs. With the approach of wastewater-based epidemiology (WBE), this study aims to evaluate the consumption of eight commonly-used antibiotics between developed urban and developing sub-urban catchments in China and to characterise the ratios of parent drugs to metabolites in studying the consumption. Seven parent antibiotics were detected in all the wastewater samples (n = 56), whereas some metabolites were detected sporadically. The ratios of parent chemicals to metabolites varied among locations and were often higher than the ratios in pharmacokinetic studies. Estimated consumption of antibiotics ranged from 3.2 ± 2.0 mg/day/1000 inhabitants for trimethoprim to 28,400 ± 7800 mg/day/1000 inhabitants for roxithromycin in the studied catchments. Higher consumption of sulfapyridine, sulfadiazine and roxithromycin was observed in urban than suburban catchments, while consumption of sulfamethoxazole, norfloxacin, and trimethoprim was higher in suburban than in urban catchments. Using the literature data, we found more than 95% reduction of antibiotic use in an urban catchment. Our study revealed the geographical pattern in antibiotic use across different urban and suburban catchments via WBE, and the potential of monitoring parent-to-metabolite ratios for WBE in estimating antibiotic use. These results provide a basis for health authorities to plan different drug-specific control policies between urban and suburban catchments, and for future WBE studies to be aware of other sources, such as animal husbandry and disposals of unused drugs, that can influence the estimated consumption.

Short-term flood probability density forecasting using a conceptual hydrological model with machine learning techniques

Making accurate and reliable probability density forecasts of flood processes is fundamentally challenging for machine learning techniques, especially when prediction targets are outside the range of training data. Conceptual hydrological models can reduce rainfall-runoff modelling errors with efficient quasi-physical mechanisms. The Monotone Composite Quantile Regression Neural Network (MCQRNN) is used for the first time to make probability density forecasts of flood processes and serves as a benchmark model, whereas it confronts the drawbacks of overfitting and biased-prediction. Here we propose an integrated model (i.e. XAJ-MCQRNN) that incorporates Xinanjiang conceptual model (XAJ) and MCQRNN to overcome the phenomena of error propagation and accumulation encountered in multi-step-ahead flood probability density forecasts. We consider flood forecasts as a function of rainfall factors and runoff data. The models are evaluated by long-term (2009–2015) 3-hour streamflow series of the Jianxi River catchment in China and rainfall products of the European Centre for Medium-Range Weather Forecasts. Results demonstrated that the proposed XAJ-MCQRNN model can not only outperform the MCQRNN model but also prominently enhance the accuracy and reliability of multi-step-ahead probability density forecasts of flood process. Regarding short-term forecasts in testing stages at four horizons, the XAJ-MCQRNN model achieved higher Nash-Sutcliffe Efficiency but lower Root Mean Square Error values, while improving Coverage Ratio and Relative Bandwidth values in comparison to the MCQRNN model. Consequently, the improvement can benefit the mitigation of the impacts associated with uncertainties of extreme flood and rainfall events as well as promote the accuracy and reliability of flood forecasting and early warning.

Performance dependence of multi-model combination methods on hydrological model calibration strategy and ensemble size

The multi-model combination is a technique to improve the performances of hydrological streamflow simulations. An area that has not been investigated much is the performance dependence of combination techniques on the hydrological model calibration strategy and ensemble size. This study aims at investigating the joint effect of the hydrological models, calibration strategies and ensemble sizes on combination abilities for selecting the most appropriate multi-model combination method. The ensemble members were constructed by applying four hydrological models and four objective functions over 383 catchments in China. The ensemble members were combined by using nine commonly used methods, which are Equal Weights (EWA), Akaike Information Criterion (AICA), Bayes Information Criterion (BICA), Bates and Granger (BGA), Granger Ramanathan A, B, and C (GRA, GRB, and GRC), Bayesian Model Averaging (BMA) and Multi-model Super Ensemble (MMSE). The GRC is found as the best multi-model combination method for hydrological simulations. Adding ensemble members by either multiple hydrological models or calibration strategies could help to improve the simulation abilities. Specifically, the increase of ensemble members can obviously enhance the performance of multi-model combinations when the ensemble size is less than six, while only limited improvement is achieved when the ensemble size is more than nine. The combination of ensemble members with various calibration strategies is hard to compensate for the weakness of hydrological model structures. As well, the application of a single calibration strategy in ensemble members only emphasizes single discharge periods and neglects other important discharge periods. This study found that various models with different objective functions are more robust and efficient. The combination performs better than any individual model in terms of Nash–Sutcliffe efficiency (NSE) for approximately 70% catchments, but the multi-model combination is less efficient in terms of low-flow simulations.

N2O emission and its influencing factors in subtropical streams, China

BackgroundRivers and streams are one of the primary sources of nitrous oxide (N2O) which is an important greenhouse gas with great global warming potential. Yet, over the past century, human activities have dramatically increased reactive nitrogen loadings into and consequently led to increased N2O emission from the river ecosystems. Here, we carried out a study in two subtropical rivers, i.e., Jinshui River and Qi River with slight and intense human disturbance in their respective catchments in China. The study intended to explore spatial variability and seasonality in N2O emissions, and the relative importance of physicochemical variables, nitrification and denitrification potentials, and functional genes abundance influencing N2O emissions.ResultsN2O concentration, N2O saturation, and N2O flux of Jinshui River peaked in high flow season. N2O concentration, N2O saturations, and N2O flux in Qi River and downstream of Jinshui River were significantly higher than that in other areas in normal and low flow seasons. N2O concentration was positively correlated with water temperature, water NO3−, and DOC, negatively correlated with water NH4+ and DOC/NO3− (the ratio of dissolved organic carbon to NO3− in water), and positively correlated with potential nitrification rate in high flow season, but not correlated with functional genes abundance. Both rivers had lower N2O saturation and flux than many freshwater systems, and their EFr-5 (N2O emission factor for river) was lower than the recommended values of IPCC.ConclusionsWhile the two rivers were moderate sources of N2O and N2O emissions in river systems were normally elevated in the summer, areas with intense human disturbance had higher N2O concentration, N2O saturations, and N2O flux than those with slight human disturbance. Physicochemical variables were good indicators of N2O emissions in the river ecosystems.

Nitrogen emission and deposition budget in an agricultural catchment in subtropical central China

The study of emissions and depositions of atmospheric reactive nitrogen species (Nrs) in a region is important to uncover the sources and sinks of atmospheric Nrs in the region. In this study, atmospheric total Nrs depositions including both wet-only and dry deposition were monitored simultaneously across major land-use types in a 105 km2 catchment called Jinjing River Catchment (JRC) in subtropical central China from 2015 to 2016. Based on activity data and emission factors for the main Nrs emission sources, ammonia (NH3) and nitrogen oxides (NOx) emission inventories for the catchment were also compiled. The estimated total Nrs deposition in JRC was 35.9 kg N ha−1 yr−1, with approximately 49.7 % attributed to reduced compounds (NHx), and 40.5 % attributed to oxidized (NOy). The total Nrs emission rate in JRC was 80.4 kg N ha−1 yr−1, with 61.5 and 18.9 kg N ha−1 yr−1 from NH3 and NOx emissions, respectively. Livestock excretion and fertilization were the two main contributing emission sources for NH3, while vehicle sources contributed the bulk of NOx emissions. The net atmospheric budgets of Nrs in paddy field, forest, and tea field were +3.7, −36.1, and +23.8 kg N ha−1 yr−1, respectively. At the catchment scale, the net atmospheric budget of Nrs was +47.7 kg N ha−1 yr−1, with +43.7 kg N ha−1 yr−1 of NHx and +4.0 kg N ha−1 yr−1 of NOy, indicating that the subtropical catchment was net sources of atmospheric Nrs. Considering that excessive atmospheric Nr emissions and deposition may cause adverse effects on the environment, effects should be conducted to mitigate the Nrs emissions from agriculture and transportation, and increasing the area of forest is good for reducing the net positive budget of atmospheric Nrs in the subtropical catchments in China.

Evaluation of Eight Global Precipitation Datasets in Hydrological Modeling

The number of global precipitation datasets (PPs) is on the rise and they are commonly used for hydrological applications. A comprehensive evaluation on their performance in hydrological modeling is required to improve their performance. This study comprehensively evaluates the performance of eight widely used PPs in hydrological modeling by comparing with gauge-observed precipitation for a large number of catchments. These PPs include the Global Precipitation Climatology Centre (GPCC), Climate Hazards Group Infrared Precipitation with Station dataset (CHIRPS) V2.0, Climate Prediction Center Morphing Gauge Blended dataset (CMORPH BLD), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN CDR), Tropical Rainfall Measuring Mission multi-satellite Precipitation Analysis 3B42RT (TMPA 3B42RT), Multi-Source Weighted-Ensemble Precipitation (MSWEP V2.0), European Center for Medium-range Weather Forecast Reanalysis 5 (ERA5) and WATCH Forcing Data methodology applied to ERA-Interim Data (WFDEI). Specifically, the evaluation is conducted over 1382 catchments in China, Europe and North America for the 1998-2015 period at a daily temporal scale. The reliabilities of PPs in hydrological modeling are evaluated with a calibrated hydrological model using rain gauge observations. The effectiveness of PPs-specific calibration and bias correction in hydrological modeling performances are also investigated for all PPs. The results show that: (1) compared with the rain gauge observations, GPCC provides the best performance overall, followed by MSWEP V2.0; (2) among the eight PPs, the ones incorporating daily gauge data (MSWEP V2.0 and CMORPH BLD) provide superior hydrological performance, followed by those incorporating 5-day (CHIRPS V2.0) and monthly (TMPA 3B42RT, WFDEI, and PERSIANN CDR) gauge data. MSWEP V2.0 and CMORPH BLD perform better than GPCC, underscoring the effectiveness of merging multiple satellite and reanalysis datasets; (3) regionally, all PPs exhibit better performances in temperate regions than in arid or topographically complex mountainous regions; and (4) PPs-specific calibration and bias correction both can improve the streamflow simulations for all eight PPs in terms of the Nash and Sutcliffe efficiency and the absolute bias. This study provides insights on the reliabilities of PPs in hydrological modeling and the approaches to improve their performance, which is expected to provide a reference for the applications of global precipitation datasets.

A novel hybrid XAJ-LSTM model for multi-step-ahead flood forecasting

AbstractThe conceptual hydrologic model has been widely used for flood forecasting, while long short-term memory (LSTM) neural network has been demonstrated a powerful ability to tackle time-series predictions. This study proposed a novel hybrid model by combining the Xinanjiang (XAJ) conceptual model and LSTM model (XAJ-LSTM) to achieve precise multi-step-ahead flood forecasts. The hybrid model takes flood forecasts of the XAJ model as the input variables of the LSTM model to enhance the physical mechanism of hydrological modeling. Using the XAJ and the LSTM models as benchmark models for comparison purposes, the hybrid model was applied to the Lushui reservoir catchment in China. The results demonstrated that three models could offer reasonable multi-step-ahead flood forecasts and the XAJ-LSTM model not only could effectively simulate the long-term dependence between precipitation and flood datasets, but also could create more accurate forecasts than the XAJ and the LSTM models. The hybrid model maintained similar forecast performance after feeding with simulated flood values of the XAJ model during horizons to . The study concludes that the XAJ-LSTM model that integrates the conceptual model and machine learning can raise the accuracy of multi-step-ahead flood forecasts while improving the interpretability of data-driven model internals.

The underlying causes and effects of phytoplankton seasonal turnover on resource use efficiency in freshwater lakes.

The extent of intra‐annual turnover in phytoplankton communities is directly associated with the overall diversity. However, our understanding of the underlying causes and effects of intra‐annual turnover remains limited. In this study, we performed a two‐season investigation of the phytoplankton composition in the lakes of the Yangtze River catchment in China in spring and summer 2012, which covered a regional spatial scale. We analyzed the Sørensen pairwise dissimilarity (βsor) between the two seasons, their driving factors, and effects on resource use efficiency in phytoplankton. The results showed that the changes in phytoplankton composition from spring to summer were not synchronous among these lakes. The spatial environmental characteristics, temporal changes in environmental variables and the initial phytoplankton composition together explained the variation in βsor for phytoplankton, and their explanatory powers and primary driving variables depended on the phytoplankton taxonomic groups. Among the driving variables, increased nitrogen level and seasonal temperature difference will promote spring–summer community turnover and then improve the phosphorus use efficiency of phytoplankton community. The species diversity of the initial community might increase its stability and slow the spring–summer shift in phytoplankton, especially in cyanobacteria and Chlorophyta. Our study highlights the understanding of the patterns and underlying causes of temporal beta diversity in freshwater phytoplankton communities.

New framework for natural-artificial transport paths and hydrological connectivity analysis in an agriculture-intensive catchment

The impacts of human activities on hydrological connectivity disturb the network topology of transport paths, which has gradually evolved from natural terrain features to dual natural-artificial features. In this study, a new framework is proposed to extract information from natural-artificial transport paths and related hydrological connectivity dominated by agricultural practices and ditch networks. Graph theory and connectivity indexes are integrated for the comprehensive classification and the parallel processing of potential flow transport networks and their upstream drainage areas. Based on high-resolution remote sensing data and detailed field investigations, this new framework, which combines graph theory and connectivity indexes, is applied to a typical agriculture-intensive catchment in China. The results show that artificial factors greatly influence the transport paths and the related drainage areas. With the development of ditch construction, the hydrological transport paths become shorter and more fragmented. In addition, key ditch segments are identified by connectivity indexes, and recommendations are given for future planning. This new framework offers an approach for the hydrological connectivity analysis of complex networks and provides effective strategies for agricultural development.

Comparing Three Hydrological Models for Flash Flood Simulations in 13 Humid and Semi-humid Mountainous Catchments

Flash flood disaster ranks top among all the natural hazards across the world due to its high frequency, severity and fatality. However, flash flood simulation is still challenging in small and medium-sized catchments with complex orography, flashy hydrological responses and poor observations. Three distributed hydrological models, i.e., TOPModel, HEC and CNFF, are selected to simulate flash floods in seven humid and six semi-humid catchments in China, with consideration of water balance (RER), peak flow rate (REQ) and its occurrence time (TP), hydrograph variation (SNSE) and model uncertainty. Influences of five catchment attributes are further investigated on individual model performances. All three models perform satisfactorily in humid catchments, but less satisfactorily in semi-humid catchments. Water balance is well obtained by CNFF, followed by HEC and TOPModel. Peak flow rate and its occurrence time are most accurately captured by CNFF and HEC, respectively. Hydrograph variations are well reproduced by HEC and CNFF. TOPModel performs well for picking peak flow and hydrograph variation in humid catchments. Uncertainty interval is narrowest for HEC with average relative interval length at 95% confidence level being 0.78 ~ 2.53. Most observations are bracketed by uncertainty intervals for TOPModel (64.79% ~ 91.91% of total). Three model performance indices (i.e., RER, REQ, and SNSE) are mainly affected by drainage area and forest ratio across humid and semi-humid catchments, while TP performance is mainly affected by mean slope in humid catchments.

Human activities' fingerprint on multitrophic biodiversity and ecosystem functions across a major river catchment in China.

Human-induced global change dramatically alters individual aspects of river biodiversity, such as taxonomic, phylogenetic or functional diversity, and is predicted to lead to losses of associated ecosystem functions. Understanding these losses and dependencies are critical to human well-being. Until now, however, most studies have only looked either at individual organismal groups or single functions, and little is known on the effect of human activities on multitrophic biodiversity and on ecosystem multifunctionality in riverine ecosystem. Here we profiled biodiversity from bacteria to invertebrates based on environmental DNA (hereafter, 'eDNA') samples across a major river catchment in China, and analysed their dependencies with multiple ecosystem functions, especially linked to C/N/P-cycling. Firstly, we found a spatial cross-taxon congruence pattern of communities' structure in the network of the Shaying river, which was related to strong environmental filtering due to human land use. Secondly, human land use explained the decline of multitrophic and multifaceted biodiversity and ecosystem functions, but increased functional redundancy in the riverine ecosystem. Thirdly, biodiversity and ecosystem function relationships at an integrative level showed a concave-up (non-saturating) shape. Finally, structural equation modeling suggested that land use affects ecosystem functions through biodiversity-mediated pathways, including biodiversity loss and altered community interdependence in multitrophic groups. Our study highlights the value of a complete and inclusive assessment of biodiversity and ecosystem functions for an integrated land-use management of riverine ecosystems.

Responses of Precipitation and Runoff to Climate Warming and Implications for Future Drought Changes in China

AbstractThe Clausius‐Clapeyron relationship holds that the atmospheric water vapor content enhances with warming temperatures, suggesting intensifications of precipitable water and also altering runoff generation. Drought conditions are determined by variations in water fluxes such as precipitation and runoff, which tightly connect with temperature scaling characteristics. However, whether and how water fluxes' scaling with temperatures may affect the evolution of droughts under climate change has not yet been systematically investigated. This study develops a cascade modeling chain consisting of the climate model ensemble, bias correction technique, and hydrological models to investigate the precipitation and runoff scaling relationships with warming temperatures under the current (1961–2005) and future periods (2011–2055 and 2056–2100), as well as their implications on future drought changes across 151 catchments in China. The results show that (1) precipitation (runoff) scaling relationships with temperatures are stable during different time periods; (2) return level analysis indicates drought risks are projected to become (1–10 times) more severe across central and southern catchments, where the precipitation (runoff) strengthens with rising temperatures up to a peak point and then decline in a hotter environment. The northeastern and western catchments, where a monotonic increasing scaling type dominated, are accompanied by drought mitigations for two future periods; (3) future changes in hydrological droughts relative to the baseline are (1–5 times) larger than those in meteorological droughts. These results imply that changes in future drought risks are highly dependent on the present precipitation (runoff)‐temperature relationships, suggesting a meaningful implication of scaling types for future drought prediction.

Quantitative identification of non-point sources of nitrate in urban channels based on dense in-situ samplings and nitrate isotope composition

Quantitative identification of non-point sources of nitrate in urban channels plays a critical role in effective nutrient management in urban regions. This is an emerging issue due to fast urbanization and the resultant complicated hydrological and hydraulic conditions in urban areas. In this study, we examine spatial-temporal characteristics of nitrogen concentration in urban channels based on dense in-situ samplings during a one-year period over a small urban catchment in China. We quantitatively identify nitrate sources into urban channels based on dual-isotope analyses and Bayesian isotope mixing model. Results show that nitrogen concentration peaks in winter as well as in urban channels and land surfaces in the urban core region. Sewage (47%) is the dominate contributor to NO3−-N in urban channels, followed by NH4+ in fertilizer (30%) as the second contributor. Sewage (NH4+ in fertilizer) contributes more NO3−-N to channels in winter (summer) with the proportion of 65% (44%), and more NO3−-N to urban core (suburban) channels with the proportion of 59% (42%). The rainfall and distribution of rainwater drains explain the monthly and spatial variations of contribution of NO3−-N sources well, respectively. In addition, less NO3−-N in the urban channels derives from nitrification, which is consistent with the results of high properties of NH4+-N/TN in this region. Our results highlight the key roles of land use types and rainfall in NO3−-N source apportionment, and provide support for the nitrogen management practices in urbanized regions.

A Statistical Vertically Mixed Runoff Model for Regions Featured by Complex Runoff Generation Process

Hydrological models for regions characterized by complex runoff generation process been suffer from a great weakness. A delicate hydrological balance triggered by prolonged wet or dry underlying condition and variable extreme rainfall makes the rainfall-runoff process difficult to simulate with traditional models. To this end, this study develops a novel vertically mixed model for complex runoff estimation that considers both the runoff generation in excess of infiltration at soil surface and that on excess of storage capacity at subsurface. Different from traditional models, the model is first coupled through a statistical approach proposed in this study, which considers the spatial heterogeneity of water transport and runoff generation. The model has the advantage of distributed model to describe spatial heterogeneity and the merits of lumped conceptual model to conveniently and accurately forecast flood. The model is tested through comparison with other four models in three catchments in China. The Nash–Sutcliffe efficiency coefficient and the ratio of qualified results increase obviously. Results show that the model performs well in simulating various floods, providing a beneficial means to simulate floods in regions with complex runoff generation process.

Data-Driven Modeling and the Influence of Objective Function Selection on Model Performance in Limited Data Regions.

The identification of unit hydrographs and component flows from rainfall, evapotranspiration and streamflow data (IHACRES) model has been proven to be an efficient yet basic model to simulate rainfall–runoff processes due to the difficulty in obtaining the comprehensive data required by physical models, especially in data-scarce, semi-arid regions. The success of a calibration process is tremendously dependent on the objective function chosen. However, objective functions have been applied largely in over daily and monthly scales and seldom over sub-daily scales. This study, therefore, implements the IHACRES model using ‘hydromad’ in R to simulate flood events with data limitations in Zhidan, a semi-arid catchment in China. We apply objective function constraints by time aggregating the commonly used Nash–Sutcliffe efficiency into daily and hourly scales to investigate the influence of objective function constraints on the model performance and the general capability of the IHACRES model to simulate flood events in the study watershed. The results of the study demonstrated the advantage of the finer time-scaled hourly objective function over its daily counterpart in simulating runoff for the selected flood events. The results also indicated that the IHACRES model performed extremely well in the Zhidan watershed, presenting the feasibility of the use of the IHACRES model to simulate flood events in data scarce, semi-arid regions.

Simulating the change of precipitation-runoff relationship during drought years in the eastern monsoon region of China

Most hydrological simulation and prediction methods assume that the precipitation-runoff relationship was stationary. However, this assumption was found to be questionable during drought years: the annual runoff coefficients (the ratio of annual runoff to annual precipitation) during drought years tend to be smaller than those during non-drought years. However, little is known about the spatial distribution of the magnitude of runoff coefficient change (RCchange) during drought years, and which factors dominate the spatial pattern of RCchange over a large spatial scale. To answer these questions, this study investigated the RCchange in 265 catchments in China that cover a broad range of climate and landscape conditions. We identified the significant factors affecting RCchange from ten catchment characteristics and developed a multivariate generalized additive model to simulate the spatial pattern of RCchange across the eastern monsoon region of China. Results indicated that the RCchange showed an increasing trend from north to south of China, with values ranging from −67.1% to −0.3%, with the average being −26.4%. The lower RCchange (corresponding to more significant runoff reduction) in drought years was more likely to occur in catchments with dryer climate and lower elevation. The simulated RCchange by the multivariate generalized additive model demonstrated a good agreement with observed RCchange, and the values of Nash-Sutcliffe efficiency between observed and simulated RCchange were 0.77 for training catchments and 0.72 for testing catchments. Finally, we applied the model to extrapolate RCchange to the entire eastern monsoon region of China. The result would benefit water resources management during drought years.

Comprehensive modelling of runoff-generated debris flow from formation to propagation in a catchment

This study aimed to develop an integrated model of the runoff-generated debris flow that considers the initial conditions, movement mechanisms, and entrainment effect. The study focused on the formation and propagation processes of debris flow within a catchment, and the process is divided into three stages: rainfall infiltration, runoff, and debris flow routing. Soil saturation, rainfall, and entrainment are the main factors that influence the debris flow formation and propagation processes. Existing models for each stage, including Richards’s equations, shallow water equations, and two-phase debris flow equations, were coupled. The tridiagonal matrix algorithm and finite volume method were applied to solve these equations. Finally, several experimental cases and the 2010 debris flow event in the Hongchun catchment in China were simulated by using the proposed model. The results showed that the proposed model could effectively describe the behaviours of each stage during the debris flow formation and propagation processes. Although several aspects of the model require further improvement, the physical-parameter-based prediction of runoff-generated debris flows from formation to propagation is effectively performed by the model.

Tracking faecal microorganisms using the qPCR method in a typical urban catchment in China.

Faecal microorganisms represent a key threat to human health. Potential origins of faecal microbial contamination in a typical urban-representative micro-scale were evaluated. The quantitative polymerase chain reaction (qPCR) method was used in this study. The Bacteroidetes is selected as the indicative microorganism in runoff samples that are collected during four representative stormwater events in north China. The principal component analysis (PCA) method indicated the distribution feature of the environmental factors. The largest contributor is dog, followed by bird and human to the faecal pollution in stormwater runoff. The output of human and dog faecal pollutants in response to the first flush effect of nonpoint source pollution while the transmit time of bird faecal pollutant is relatively longer. In addition, the number of antecedent drying days represents the key factor for dog faecal pollution, while human faecal pollution is impacted by more factors. The results of this study will provide sound evidence for the tracking and management of nonpoint source faecal pollution in urban catchment areas.