Spatially distributed long‐term hydrologic simulation using a continuous SCS CN method‐based hybrid hydrologic model

A continuous Soil Conservation Service (SCS) curve number (CN) method that considers time‐varied SCS CN values was developed based on the original SCS CN method with a revised soil moisture accounting approach to estimate run‐off depth for long‐term discontinuous storm events. The method was applied to spatially distributed long‐term hydrologic simulation of rainfall‐run‐off flow with an underlying assumption for its spatial variability using a geographic information systems‐based spatially distributed Clark's unit hydrograph method (Distributed‐Clark; hybrid hydrologic model), which is a simple few parameter run‐off routing method for input of spatiotemporally varied run‐off depth, incorporating conditional unit hydrograph adoption for different run‐off precipitation depth‐based direct run‐off flow convolution. Case studies of spatially distributed long‐term (total of 6 years) hydrologic simulation for four river basins using daily NEXRAD quantitative precipitation estimations demonstrate overall performances of Nash–Sutcliffe efficiency (E_NS) 0.62, coefficient of determination (R²) 0.64, and percent bias 0.33% in direct run‐off and E_NS 0.71, R² 0.72, and percent bias 0.15% in total streamflow for model result comparison against observed streamflow. These results show better fit (improvement in E_NS of 42.0% and R² of 33.3% for total streamflow) than the same model using spatially averaged gauged rainfall. Incorporation of logic for conditional initial abstraction in a continuous SCS CN method, which can accommodate initial run‐off loss amounts based on previous rainfall, slightly enhances model simulation performance; both E_NS and R² increased by 1.4% for total streamflow in a 4‐year calibration period. A continuous SCS CN method‐based hybrid hydrologic model presented in this study is, therefore, potentially significant to improved implementation of long‐term hydrologic applications for spatially distributed rainfall‐run‐off generation and routing, as a relatively simple hydrologic modelling approach for the use of more reliable gridded types of quantitative precipitation estimations.     

Assessment of spatial transferability of process‐based hydrological model parameters in two neighbouring catchments in the Himalayan Region

Estimating the hydrological regime of ungauged catchments in the Himalayan region is challenging due to a lack of sufficient monitoring stations. In this paper, the spatial transferability of the model parameters of the process‐oriented J2000 hydrological model was investigated in 2 glaciated subcatchments of the Koshi river basin in eastern Nepal. The catchments have a high degree of similarity with respect to their static landscape features. The model was first calibrated (1986–1991) and validated (1992–1997) in the Dudh Koshi subcatchment. The calibrated and validated model parameters were then transferred to the nearby Tamor catchment (2001–2009). Sensitivity and uncertainty analyses were carried out for both subcatchments to discover the sensitivity range of the parameters in the two catchments. The model represented the overall hydrograph well in both subcatchments, including baseflow, rising and falling limbs; however, the peak flows were underestimated. The efficiency results according to both Nash–Sutcliffe (ENS) and the coefficient of determination (r²) were above 0.84 in both catchments (1986–1997 in Dudh Koshi and 2001–2009 in Tamor). The ranking of the parameters in respect to their sensitivity matched well for both catchments while taking ENS and log Nash–Sutcliffe (LNS) efficiencies into account. However, there were some differences in sensitivity to ENS and LNS for moderately and less‐sensitive parameters, although the majority (13 out of 16 for ENS and 16 out of 16 for LNS) had a sensitivity response in a similar range. The generalized uncertainty likelihood estimation results suggest that the parameter uncertainty are most of the time within the range and the ensemble mean matches very good (ENS: 0.84) with observed discharge. The results indicate that transfer of the J2000 parameters to a neighbouring catchment in the Himalayan region with similar physiographic landscape characteristics is viable. This indicates the possibility of applying a calibrated process‐based J2000 model to other ungauged catchments in the Himalayan region, which could provide important insights into the hydrological system dynamics and provide much needed information to support water resources planning and management.     

Season‐based rainfall–runoff modelling using the probability‐distributed model (PDM) for large basins in southeastern Brazil

Southeastern Brazil is characterized by seasonal rainfall variability. This can have a great social, economic, and environmental impact due to both excessive and deficient water availability. During 2014 and 2015, the region experienced one of the most severe droughts since 1960. The resulting water crisis has seriously affected water supply to the metropolitan region of São Paulo and hydroelectric power generation throughout the entire country. This research considered the upstream basins of the southeastern Brazilian reservoirs Cantareira (2,279 km²; water supply) and Emborcação (29,076 km²), Três Marias (51,576 km²), Furnas (52,197 km²), and Mascarenhas (71,649 km²; hydropower) for hydrological modelling. It made the first attempt at configuring a season‐based probability‐distributed model (PDM‐CEMADEN) for simulating different hydrological processes during wet and dry seasons. The model successfully reproduced the intra‐annual and interannual variability of the upstream inflows during 1985–2015. The performance of the model was very satisfactory not only during the wet, dry, and transitional seasons separately but also during the whole period. The best performance was obtained for the upstream basin of Furnas, as it had the highest quality daily precipitation and potential evapotranspiration data. The Nash–Sutcliffe efficiency and logarithmic Nash–Sutcliffe efficiency were 0.92 and 0.93 for the calibration period 1984–2001, 0.87 and 0.88 for the validation period 2001–2010, and 0.93 and 0.90 for the validation period 2010–2015, respectively. Results indicated that during the wet season, the upstream basins have a larger capacity and variation of soil water storage, a larger soil water conductivity, and quicker surface water flow than during the dry season. The added complexity of configuring a season‐based PDM‐CEMADEN relative to the traditional model is well justified by its capacity to better reproduce initial conditions for hydrological forecasting and prediction. The PDM‐CEMADEN is a simple, efficient, and easy‐to‐use model, and it will facilitate early decision making and implement adaptation measures relating to disaster prevention for reservoirs with large‐sized upstream basins.     

A geomorphology‐based integrated stream–aquifer interaction model for semi‐gauged catchments

Many of the existing stream–aquifer interaction models available in the literature are very complex with limited applicability in semi‐gauged and ungauged catchments. In this study, to estimate the influent and effluent subsurface water fluxes under limited geo‐hydrometeorological data availability conditions, a simple stream–aquifer interaction model, namely, the variable parameter McCarthy–Muskingum (VPMM) hillslope‐storage Boussinesq (hsB) model, has been developed. This novel model couples the VPMM streamflow transport with the hsB groundwater flow transport modules in online mode. In this integrated model, the surface water–groundwater flux exchange process is modelled by the Darcian approach with the variable hydraulic heads between the river stage and groundwater table accounting for the rainfall forcing. Considering the exchange fluxes in the hyporheic zone and lateral overland flow contribution, this approach is field tested in a typical 48‐km stretch of the Brahmani River in eastern India to simulate the streamflow and its depth with the minimum Nash–Sutcliffe efficiency of 94% and 88%; the maximum root mean square error of 134 m³/s and 0.35 m; and the minimum index of agreement of 98% and 97%, respectively. This modelling approach could be very well utilized in data‐scarce world‐river basins to estimate the stream–aquifer exchange flux due to rainfall forcings.     

Multi‐variable and multi‐site calibration and validation of SWAT in a large mountainous catchment with high spatial variability

Many methods developed for calibration and validation of physically based distributed hydrological models are time consuming and computationally intensive. Only a small set of input parameters can be optimized, and the optimization often results in unrealistic values. In this study we adopted a multi‐variable and multi‐site approach to calibration and validation of the Soil Water Assessment Tool (SWAT) model for the Motueka catchment, making use of extensive field measurements. Not only were a number of hydrological processes (model components) in a catchment evaluated, but also a number of subcatchments were used in the calibration. The internal variables used were PET, annual water yield, daily streamflow, baseflow, and soil moisture. The study was conducted using an 11‐year historical flow record (1990–2000); 1990–94 was used for calibration and 1995–2000 for validation. SWAT generally predicted well the PET, water yield and daily streamflow. The predicted daily streamflow matched the observed values, with a Nash–Sutcliffe coefficient of 0·78 during calibration and 0·72 during validation. However, values for subcatchments ranged from 0·31 to 0·67 during calibration, and 0·36 to 0·52 during validation. The predicted soil moisture remained wet compared with the measurement. About 50% of the extra soil water storage predicted by the model can be ascribed to overprediction of precipitation; the remaining 50% discrepancy was likely to be a result of poor representation of soil properties. Hydrological compensations in the modelling results are derived from water balances in the various pathways and storage (evaporation, streamflow, surface runoff, soil moisture and groundwater) and the contributions to streamflow from different geographic areas (hill slopes, variable source areas, sub‐basins, and subcatchments). The use of an integrated multi‐variable and multi‐site method improved the model calibration and validation and highlighted the areas and hydrological processes requiring greater calibration effort. Copyright © 2005 John Wiley & Sons, Ltd.     

Effects of the catchment runoff coefficient on the performance of TOPMODEL in rainfall–runoff modelling

Effects of the catchment runoff coefficient on the performance of TOPMODEL in simulating catchment rainfall–runoff relationships are investigated in this paper, with an aim to improve TOPMODEL's simulation efficiency in catchments with a low runoff coefficient. Application of TOPMODEL in the semi‐arid Yihe catchment, with an area of 2623 km² in the Yellow River basin of China, produced a Nash–Sutcliffe model efficiency of about 80%. To investigate how the catchment runoff coefficient affects the performance of TOPMODEL, the whole observed discharge series of the Yihe catchment is multiplied with a larger‐than‐unity scale factor to obtain an amplified discharge series. Then TOPMODEL is used to simulate the amplified discharge series given the original rainfall and evaporation data. For a set of different scale factors, TOPMODEL efficiency is plotted against the corresponding catchment runoff coefficient and it is found that the efficiency of TOPMODEL increases with the increasing catchment runoff coefficient before reaching a peak (e.g. about 90%); after the peak, however, the efficiency of TOPMODEL decreases with the increasing catchment runoff coefficient. Based on this finding, an approach called the discharge amplification method is proposed to enhance the simulation efficiency of TOPMODEL in rainfall–runoff modelling in catchments with a low runoff coefficient. Copyright © 2004 John Wiley & Sons, Ltd.     

Development of a soil moisture‐based distributed hydrologic model for determining hydrologically based critical source areas

A simple grid cell‐based distributed hydrologic model was developed to provide spatial information on hydrologic components for determining hydrologically based critical source areas. The model represents the critical process (soil moisture variation) to run‐off generation accounting for both local and global water balance. In this way, it simulates both infiltration excess run‐off and saturation excess run‐off. The model was tested by multisite and multivariable evaluation on the 50‐km² Little River Experimental Watershed I in Georgia, U.S. and 2 smaller nested subwatersheds. Water balance, hydrograph, and soil moisture were simulated and compared to observed data. For streamflow calibration, the daily Nash‐Sutcliffe coefficient was 0.78 at the watershed outlet and 0.56 and 0.75 at the 2 nested subwatersheds. For the validation period, the Nash‐Sutcliffe coefficients were 0.79 at the watershed outlet and 0.85 and 0.83 at the 2 subwatersheds. The per cent bias was less than 15% for all sites. For soil moisture, the model also predicted the rising and declining trends at 4 of the 5 measurement sites. The spatial distribution of surface run‐off simulated by the model was mainly controlled by local characteristics (precipitation, soil properties, and land cover) on dry days and by global watershed characteristics (relative position within the watershed and hydrologic connectivity) on wet days when saturation excess run‐off was simulated. The spatial details of run‐off generation and travel time along flow paths provided by the model are helpful for watershed managers to further identify critical source areas of non‐point source pollution and develop best management practices.     

Long‐term runoff forecasting by combining hydrological models and meteorological records

Reservoir operation is generally based on the inflows of the upstream catchment of the reservoir. If the arriving inflows can be forecasted, that can benefit reservoir operation and management. This study attempts to construct a long‐term inflow‐forecasting model by combining a continuous rainfall–runoff model with the long‐term weather outlook from the Central Weather Bureau of Taiwan. The analytical results demonstrate that the continuous rainfall–runoff model has good inflow simulation performance by using 10‐day meteorological and inflow records over a 33‐year period for model calibration and verification. The long‐term inflow forecasting during the dry season was further conducted by combining the continuous rainfall–runoff model and the long‐term weather outlook, which was found to have good performance. Copyright © 2005 John Wiley & Sons, Ltd.     

Lumped surface and sub‐surface runoff for erosion modeling within a small hilly watershed in northern Vietnam

Developing models to predict on‐site soil erosion and off‐site sediment transport at the agricultural watershed scale represent an on‐going challenge in research today. This study attempts to simulate the daily discharge and sediment loss using a distributed model that combines surface and sub‐surface runoffs in a small hilly watershed (< 1 km²). The semi‐quantitative model, Predict and Localize Erosion and Runoff (PLER), integrates the Manning–Strickler equation to simulate runoff and the Griffith University Erosion System Template equation to simulate soil detachment, sediment storage and soil loss based on a map resolution of 30 m × 30 m and over a daily time interval. By using a basic input data set and only two calibration coefficients based, respectively, on water velocity and soil detachment, the PLER model is easily applicable to different agricultural scenarios. The results indicate appropriate model performance and a high correlation between measured and predicted data with both Nash–Sutcliffe efficiency (Ef) and correlation coefficient (r²) having values > 0.9. With the simple input data needs, PLER model is a useful tool for daily runoff and soil erosion modeling in small hilly watersheds in humid tropical areas. Copyright © 2013 John Wiley & Sons, Ltd.     

Use of a parsimonious rainfall–run‐off model for predicting hydrological response in ungauged basins

A new parameter parsimonious rainfall–run‐off model, the Distance Distribution Dynamics (DDD) model, is used to simulate hydrological time series at ungauged sites in the Lygne basin in Norway. The model parameters were estimated as functions of catchment characteristics determined by geographical information system. The multiple regression equations relating catchment characteristics and model parameters were trained from 84 calibrated catchments located all over Norway, and all model parameters showed significant correlations with catchment characteristics. The significant correlation coefficients (with p‐value < 0.05) ranged from 0.22 to 0.55. The suitability of DDD for predictions in ungauged basins was tested for 17 catchments not used to estimate the multiple regression equations. For ten of the 17 catchments, deviations in Nash–Sutcliffe efficiency (NSE) criteria between the calibrated and regionalised model were less than 0.1, and for two calibrated catchments within the Lygne basin, the deviations were less than 0.08. The median NSE for the regionalized DDD for the 17 catchments for two time series was 0.66 and 0.72. Deviations in NSE between calibrated and regionalised models are well explained by the deviations between calibrated and regressed parameters describing spatial snow distribution and snowmelt respectively. The quality of the simulated run‐off series for the ungauged sites in the Lygne basin was assessed by comparing flow indices describing high, medium and low flow estimated from observed run‐off at the 17 catchments and for the simulated run‐off series. The indices estimated for the simulated series were generally well within the ranges defined by the 17 observed series. Copyright © 2014 John Wiley & Sons, Ltd.     

Water and salt balance modelling to predict the effects of land-use changes in forested catchments. 3. The large catchment model

This paper presents an application of a long-term, large catchment-scale, water balance model developed to predict the effects of forest clearing in the south-west of Western Australia. The conceptual model simulates the basic daily water balance fluxes in forested catchments before and after clearing. The large catchment is divided into a number of sub-catchments (1–5 km² in area), which are taken as the fundamental building blocks of the large catchment model. The responses of the individual subcatchments to rainfall and pan evaporation are conceptualized in terms of three inter-dependent subsurface stores A, B and F, which are considered to represent the moisture states of the subcatchments. Details of the subcatchment-scale water balance model have been presented earlier in Part 1 of this series of papers. The response of any subcatchment is a function of its local moisture state, as measured by the local values of the stores. The variations of the initial values of the stores among the subcatchments are described in the large catchment model through simple, linear equations involving a number of similarity indices representing topography, mean annual rainfall and level of forest clearing. The model is applied to the Conjurunup catchment, a medium-sized (39·6 km²) catchment in the south-west of Western Australia. The catchment has been heterogeneously (in space and time) cleared for bauxite mining and subsequently rehabilitated. For this application, the catchment is divided into 11 subcatchments. The model parameters are estimated by calibration, by comparing observed and predicted runoff values, over a 18 year period, for the large catchment and two of the subcatchments. Excellent fits are obtained.     

Modelling surface runoff to evaluate the effects of wildfires in multiple semi‐arid,shrubland‐dominated catchments

Wildfires change the infiltration properties of soil, reduce the amount of interception and result in increased runoff. A wildfire at Northeast Attica, Central Greece, in August 2009, destroyed approximately one third of a study area consisting of a mixture of shrublands, pastures and pines. The present study simultaneously models multiple semi‐arid, shrubland‐dominated Mediterranean catchments and assesses the hydrological response (mean annual and monthly runoff and runoff coefficients) during the first few years following wildfires. A physically based, hydrological model (MIKE SHE) was chosen. Calibration and validation results of mean monthly discharge presented very good agreement with the observed data for the pre‐wildfire and post‐wildfire period for two subcatchments (Nash–Sutcliffe Efficiency coefficient of 79.7%). The model was then used to assess the pre‐wildfire and post‐wildfire runoff responses for each of seven catchments in the study area. Mean annual surface runoff increased for the first year and after the second year following the wildfires increased by 112% and 166%, respectively. These values are within the range observed in similar cases of monitored sites. This modelling approach may provide a way of prioritizing catchment selection with respect to post‐fire remediation activities. Additionally, this modelling assessment methodology would be valuable to other semi‐arid areas because it provides an important means for comprehensively assessing post‐wildfire response over large regions and therefore attempts to address some of the scaled issues in the specific literature field of research. Copyright © 2015 John Wiley & Sons, Ltd.     

Input data resolution‐induced uncertainty in watershed modelling

The temporal‐spatial resolution of input data‐induced uncertainty in a watershed‐based water quality model, Hydrologic Simulation Program‐FORTRAN (HSPF), is investigated in this study. The temporal resolution‐induced uncertainty is described using the coefficient of variation (CV). The CV is found to decrease with decreasing temporal resolution and follow a log‐normal relation with time interval for temperature data while it exhibits a power‐law relation for rainfall data. The temporal‐scale uncertainties in the temperature and rainfall data follow a general extreme value distribution and a Weibull distribution, respectively. The Nash‐Sutcliffe coefficient (NSC) is employed to represent the spatial resolution induced uncertainty. The spatial resolution uncertainty in the dissolved oxygen and nitrate‐nitrogen concentrations simulated using HSPF is observed to follow a general extreme value distribution and a log‐normal distribution, respectively. The probability density functions (PDF) provide new insights into the effect of temporal‐scale and spatial resolution of input data on uncertainties involved in watershed modelling and total maximum daily load calculations. This study exhibits non‐symmetric distributions of uncertainty in water quality modelling, which simplify weather and water quality monitoring and reducing the cost involved in flow and water quality monitoring. Copyright © 2011 John Wiley & Sons, Ltd.     

Comparing theoretically supported rainfall‐runoff erosivity factors at the Sparacia (South Italy) experimental site

Interpreting rainfall‐runoff erosivity by a process‐oriented scheme allows to conjugate the physical approach to soil loss estimate with the empirical one. Including the effect of runoff in the model permits to distinguish between detachment and transport in the soil erosion process. In this paper, at first, a general definition of the rainfall‐runoff erosivity factor REF_e including the power of both event runoff coefficient Q_R and event rainfall erosivity index EI₃₀ of the Universal Soil Loss Equation (USLE) is proposed. The REF_e factor is applicable to all USLE‐based models (USLE, Modified USLE [USLE‐M] and Modified USLE‐M [USLE‐MM]) and it allows to distinguish between purely empirical models (e.g., Modified USLE‐M [USLE‐MM]) and those supported by applying theoretical dimensional analysis and self‐similarity to Wischmeier and Smith scheme. This last model category includes USLE, USLE‐M, and a new model, named USLE‐M based (USLE‐MB), that uses a rainfall‐runoff erosivity factor in which a power of runoff coefficient multiplies EI₃₀. Using the database of Sparacia experimental site, the USLE‐MB is parameterized and a comparison with soil loss data is carried out. The developed analysis shows that USLE‐MB (characterized by a Nash–Sutcliffe Efficiency Index NSEI equal to 0.73 and a root mean square error RMSE = 11.7 Mg ha^?1) has very similar soil loss estimate performances as compared with the USLE‐M (NSEI = 0.72 and RMSE = 12.0 Mg ha^?1). However, the USLE‐MB yields a maximum discrepancy factor between predicted and measured soil loss values (176) that is much lower than that of USLE‐M (291). In conclusion, the USLE‐MB should be preferred in the context of theoretically supported USLE type models.     

Rainfall‐runoff modelling using artificial neural networks technique: a Blue Nile catchment case study

A rainfall‐runoff model based on an artificial neural network (ANN) is presented for the Blue Nile catchment. The best geometry of the ANN rainfall‐runoff model in terms of number of hidden layers and nodes is identified through a sensitivity analysis. The Blue Nile catchment (about 300 000 km²) in the Nile basin is selected here as a case study. The catchment is classified into seven subcatchments, and the mean areal precipitation over those subcatchments is computed as a main input to the ANN model. The available daily data (1992–99) are divided into two sets for model calibration (1992–96) and for validation (1997–99). The results of the ANN model are compared with one of physical distributed rainfall‐runoff models that apply hydraulic and hydrologic fundamental equations in a grid base. The results over the case study area and the comparative analysis with the physically based distributed model show that the ANN technique has great potential in simulating the rainfall‐runoff process adequately. Because the available record used in the calibration of the ANN model is too short, the ANN model is biased compared with the distributed model, especially for high flows. Copyright © 2005 John Wiley & Sons, Ltd.     

Nonlinear bacterial load‐streamflow response on a marine beach

Bacterial concentration (Escherichia coli) is used as the key indicator for marine beach water quality in Hong Kong. For beaches receiving streamflow from unsewered catchments, water quality is mainly affected by local nonpoint source pollution and is highly dependent on the bacterial load contributed from the catchment. As most of these catchments are ungauged, the bacterial load is generally unknown. In this study, streamflow and the associated bacterial load contributed from an unsewered catchment to a marine beach, Big Wave Bay, are simulated using a modelling approach. The physically based distributed hydrological model, MIKE‐SHE, and the empirical watershed water quality model (Hydrological Simulation Program – Fortran) are used to simulate streamflow and daily‐averaged E. coli concentration/load, respectively. The total daily derived loads predicted by the model during calibration (June–July 2007) and validation (July–October 2008) periods agree well with empirical validation data, with a percentage difference of 3 and 2%, respectively. The simulation results show a nonlinear relationship between E. coli load and rainfall/streamflow and reveal a source limiting nature of nonpoint source pollution. The derived load is further used as an independent variable in a multiple linear regression (MLR) model to predict daily beach water quality. When compared with the MLR models based solely on hydrometeorological input variables (e.g. rainfall and salinity), the new model based on bacterial load predicts much more realistic E. coli concentrations during rainstorms. Copyright © 2014 John Wiley & Sons, Ltd.     

A conceptual model of supra‐glacial lake formation on debris‐covered glaciers based on GPR facies analysis

Supra‐glacial lakes and ponds can create hotspots of mass loss on debris‐covered glaciers. While much research has been directed at understanding lateral lake expansion, little is known about the rates or processes governing lake deepening. To a large degree, this knowledge gap persists due to sparse observations of lake beds. Here we report on the novel use of ground penetrating radar (GPR) surveys to simultaneously collect supra‐glacial lake bathymetry and bottom composition data from Spillway Lake (surface area of 2.4 × 10⁵ m²; volume of 9.5 × 10⁴ m³), which is located in the terminus region of the Ngozumpa Glacier in the Khumbu region of the Nepal Himalaya. We identified two GPR bottom signals corresponding to two sedimentary facies of (1) sub‐horizontal layered fine sediment drape and (2) coarse blocky diamict. We provide an understanding of the changes in subaqueous debris distribution that occur through stages of lake expansion by combining the GPR results with in situ observations of shoreline deposits matching the interpreted facies. From this, we present an updated conceptual model of supra‐glacial lake evolution, with the addition of data on the evolving debris environment, showing how dominant depositional processes can change as lakes evolve from perched lakes to multi‐basin base‐level lakes and finally onto large moraine‐dammed lakes. Throughout lake evolution, processes such as shoreline steepening, lakebed collapse into voids and conduit interception, subaerial and subaqueous calving and rapid areal expansion alter the spatial distribution and makeup of lakebed debris and sediments forcing a number of positive and negative feedbacks on lake expansion. Copyright © 2016 John Wiley & Sons, Ltd.     

The influence of river‐to‐lake backflow on the hydrodynamics of a large floodplain lake system (Poyang Lake,China)

Backflow, the temporary reversal of discharge at the outlet of a lake, is an important mechanism controlling flow and transport in many connected river–lake systems. This study used statistical methods to examine long‐term variations and primary causal factors of backflow from the Yangtze River to a laterally connected, large floodplain lake (Poyang Lake, China). Additionally, the effects of backflow on the lake hydrology were explored using a physically based hydrodynamic model and a particle‐tracking model. Although backflow into Poyang Lake occurs frequently, with an average of 16 backflow events per year, and varies greatly in magnitude between years, statistical analysis indicates that both the frequency and magnitude of backflow reduced significantly during 2001–2010 relative to the previous period of 1960–2000. The ratio of Poyang Lake catchment inflows to Yangtze River discharge can be used as an indication of the daily occurrence of backflow, which is most likely to occur during periods when this ratio is lower than 5%. Statistical analysis also indicates that the Yangtze River discharge is the main controlling factor of backflow during July to October, rather than catchment inflows to the lake. Hydrodynamic modelling reveals that, in general, backflow disturbs the normal northward water flow direction in Poyang Lake and transports mass ~20 km southward into the lake. The effects of backflow on flow direction, water velocities and water levels propagate to virtually its upstream extremity. The current study represents a first attempt to explore backflow and causal factors for a highly dynamic floodplain lake system. An improved understanding of Poyang Lake backflow is critical for guiding future strategies to manage the lake, its water quality and ecosystem value, given proposals to modify the lake–river connectivity. Copyright © 2016 John Wiley & Sons, Ltd.     

Storm‐event rainfall–runoff modelling approach for ungauged sites in Taiwan

This work develops a top‐down modelling approach for storm‐event rainfall–runoff model calibration at unmeasured sites in Taiwan. Twenty‐six storm events occurring in seven sub‐catchments in the Kao‐Ping River provided the analytical data set. Regional formulas for three important features of a streamflow hydrograph, i.e. time to peak, peak flow, and total runoff volume, were developed via the characteristics of storm event and catchment using multivariate regression analysis. Validation of the regional formulas demonstrates that they reasonably predict the three features of a streamflow hydrograph at ungauged sites. All of the sub‐catchments in the study area were then adopted as ungauged areas, and the three streamflow hydrograph features were calculated by the regional formulas and substituted into the fuzzy multi‐objective function for rainfall–runoff model calibration. Calibration results show that the proposed approach can effectively simulate the streamflow hydrographs at the ungauged sites. The simulated hydrographs more closely resemble observed hydrographs than hydrographs synthesized using the Soil Conservation Service (SCS) dimensionless unit hydrograph method, a conventional method for hydrograph estimation at ungauged sites in Taiwan. Copyright © 2008 John Wiley & Sons, Ltd.     

Inorganic nitrogen retention in acid‐sensitive lakes in southern Norway and southern Ontario,Canada—a comparison of mass balance data with an empirical N retention model

In‐lake retention of inorganic nitrogen species (nitrate and ammonium) was estimated from mass balances in five acid‐sensitive lakes in southern Norway and eight in southern Ontario, Canada, to evaluate an empirical in‐lake N retention (R_N) model. This model is included in the First‐order Acidity Balance (FAB) model, which currently is used for calculation of critical acid loads and exceedances in many countries. To estimate in‐lake R_N, the FAB model uses a recommended mass transfer coefficient (S_N) of 5 m year⁻¹, which mainly is derived from NO₃⁻ mass balances in Canadian lakes. To date, the in‐lake R_N model has not been evaluated for large parts of Europe. At the Norwegian study sites receiving the highest N deposition (>120 meq m⁻² year⁻¹) the net in‐lake retention of inorganic N (TIN) exceeded the corresponding terrestrial retention by a factor of 1·1–2·6. Despite differences in N loading and hydrology at the Norwegian and Canadian sites, both the mean mass transfer coefficients for NO₃⁻ (S_NO3; 6·5 versus 5·6 m year⁻¹) and TIN (S_TIN; 7·9 versus 7·0 m year⁻¹) were of comparable magnitude. Both mean values and ranges of S_NO3 suggest that the default S_N value presently recommended for FAB model applications seems valid over a large range in N inputs and areal water loads (q_s). However, owing to the relatively few data available for lakes with high q_s values (15–150 m year⁻¹), it is recommended that more lakes within this range be included in future studies to obtain a more precise prediction of in‐lake N retention over a wide q_s gradient. Also, when considering that the FAB model treats all inorganic N leaching from a catchment as NO₃⁻, it seems reasonable to use a default S_TIN value instead of just S_NO3 when estimating in‐lake R_N. In that case, the in‐lake R_N presently calculated by the FAB model might be slightly underestimated. Copyright © 2003 John Wiley & Sons, Ltd.