Sensitivity and identifiability of stream flow generation parameters of the SWAT model

Implementation of sensitivity analysis (SA) procedures is helpful in calibration of models and also for their transposition to different watersheds. The reported studies on SA of Soil and Water Assessment Tool (SWAT) model were mostly focused on identifying parameters for pruning or modifying during the calibration process. This paper presents a sensitivity and identifiability analysis of model parameters that influence stream flow generation in SWAT. The analysis was focused on evaluating the sensitivity of the parameters in different climatic settings, temporal scales and flow regimes. The global sensitivity analysis (GSA) technique based on classical decomposition of variance, Sobol', was employed in this study. The results of the study indicate that modeled stream flow show varying sensitivity to parameters in different climatic settings. The results also suggest that the identifiability of a parameter for a given watershed is a major concern in calibrating the model for the specific watershed, as it might lead to equifinality of parameters. The SWAT model parameters show varying sensitivity in different years of simulation suggesting the requirement for dynamic updation of parameters during the simulation. The sensitivity of parameters during various flow regimes (low, medium and high flow) is also found to be uneven, which suggests the significance of a multi‐criteria approach for the calibration of models. Copyright © 2010 John Wiley & Sons, Ltd.     

A Basic Water Quality Model for the River Neckar: Part 2 – model‐based analysis of the oxygen budget and scenarios

In this paper the Basic Water Quality Model (BWQM) for the central part of River Neckar is used to analyse the oxygen budget and to assess the potentials of various measures to prevent or mitigate critical dissolved oxygen (DO) declines. It is shown that the oxygen budget is mainly governed by phytoplankton dynamics. The excessive growth of algae and the sudden break down of the resulting algal blooms may cause episodic DO depressions. Therefore, to stabilise the oxygen budget in a sustainable way, eutrophication has to be controlled within the central part of River Neckar and the upstream regions. The only feasible way to reach this goal appears to be a further drastic reduction of phosphorus emissions. In addition, it is indispensable to hold the very high standards of biochemical oxygen demand and ammonium retention at the wastewater treatment plants. A worse performance of the treatment plants would dramatically aggravate critical DO declines which may be caused by algae dynamics. As long as the oxygen budget is not completely stabilised, weir and turbine aeration can be used to mitigate DO depressions. It could be shown that the potentials of these measures suffice to keep DO at a tolerable level. However, due to the long travel times in River Neckar, it is important to start aeration up to several days before the DO minimum is reached.     

Hydrodynamic-phytoplankton model for short-term forecasts of phytoplankton in Lake Taihu, China

Phytoplankton biomass is an important factor for short-term forecasts of algal blooms. Our new hydrodynamic-phytoplankton model is primarily intended for simulating the spatial and temporal distribution of phytoplankton in Lake Taihu within a time frame of 1-5 days. The model combines two modules: a simple phytoplankton kinetics module for growth and loss; and a mass-transport module, which defines phytoplankton transport horizontally with a two dimensional hydrodynamic model. To adapt field data for model input and calibration, we introduce two simplifications: (a) exclusion of some processes related to phytoplankton dynamics like nutrient dynamics, sediment resuspension, mineralization and nitrification, and (b) use of monthly measured data of the nutrient state. Chlorophyll-α concentration, representing phytoplankton biomass, is the only state variable in the model. A sensitivity analysis was carried out to identify the most sensitive parameter set in the phytoplankton kinetics module. The model was calibrated with field data collected in 2008 and validated with additional data obtained in 2009. A comparison of simulated and observed chlorophyll-α concentration for 33 grid cells achieved an accuracy of 78.7%. However, mean percent error and mean absolute percent error were 13.4% and 58.2%, respectively, which implies that further improvement is necessary, e.g. by reducing uncertainty of the model input and by an improved parameter calibration.     

The identifiability of parameters in a water quality model of the Biebrza River, Poland

The identifiability of model parameters of a steady state water quality model of the Biebrza River and the resulting variation in model results was examined by applying the Monte Carlo method which combines calibration, identifiability analysis, uncertainty analysis, and sensitivity analysis. The water quality model simulates the steady state concentration profiles of chloride, phosphate, ammonium, and nitrate as a function of distance along a river. The water quality model with the best combination of parameter values simulates the observed concentrations very well. However, the range of possible modelled concentrations obtained for other more or less equally eligible combinations of parameter values is rather wide. This range in model outcomes reflects possible errors in the model parameters. Discrepancies between the range in model outcomes and the validation data set are only caused by errors in model structure, or (measurement) errors in boundary conditions or input variables. In this sense the validation procedure is a test of model capability, where the effects of calibration errors are filtered out. It is concluded that, despite some slight deviations between model outcome and observations, the model is successful in simulating the spatial pattern of nutrient concentrations in the Biebrza River.     

耦合敏感参数实时识别的新型数据同化算法研究——以湖泊藻类模拟为例

数据同化是提升复杂机理过程模型精度的关键技术之一,而湖泊藻类模型的敏感参数具有随时间动态变化的特征,导致数据同化过程中无法精准更新某一时段的敏感参数,影响数据同化的模型精度提升效果.针对上述问题,本研究耦合了参数敏感性分析与集合卡尔曼滤波,研发了一种能够实时识别模型敏感参数的新型数据同化算法;为验证研发算法的效率,依托巢湖的高频水质自动监测数据,测试算法对藻类动态模型的精度提升效果.测试结果表明：研发算法能够精准跟踪模型敏感参数的动态变化,并根据监测数据实时更新模型敏感参数,实现了水质高频自动监测数据与藻类动态模型的深度融合,藻类生物量模拟精度提升了55%,即纳什系数(NSE)从0.49提升到0.76,模拟精度提升效果也显著优于传统数据同化算法(NSE=0.63).研发算法可应用于其它水生态环境模型的数据同化,为水生态环境相关要素的精准模拟预测提供关键技术支撑.     

大型深水湖库溶解氧时空变化及驱动因素：以江西仙女湖为例

为探究深水湖库溶解氧的时空分布规律及其主控因素,本文以南方亚热带大型深水湖库——江西省仙女湖为研究对象,基于2008—2021年仙女湖4个国控站点溶解氧的历史数据分析其年际变化的规律及原因;另于2016年水污染事件发生前(2014年5月—2015年4月)及水污染事件结束后(2018年1月—2018年12月)对仙女湖进行加密逐月监测,采用结构方程模型(SEM)分析仙女湖溶解氧的主要驱动因素。研究结果表明,2008—2021年仙女湖水体溶解氧浓度先下降后上升,变化范围为5.1～18.7 mg/L,季节均值为春季>冬季>秋季>夏季。水污染事件发生前高溶解氧区域多出现在舞龙湖湖心区及湖出口位置,水温、叶绿素a浓度和浊度是溶解氧浓度变化的主要驱动因素;水污染事件结束后高溶解氧区域多出现在钤阳湖及舞龙湖枝杈状湖湾位置,叶绿素a浓度及营养盐浓度成为溶解氧浓度变化的主要驱动因素;而pH与溶解氧主要是协同变化的关系。根据对仙女湖最深点(江口)的垂向监测结果,溶解氧的垂向差异为夏季>秋季>春季>冬季,夏、秋季在5 m以下出现低溶解氧(DO<5 mg/L)区域,且夏...     

Multiple objective evaluation of a simple phosphorus transfer model

A simple phosphorus (P) transfer model of the Welland catchment, UK, is evaluated against multiple objective functions using a Monte Carlo approach that combines calibration, identifiability, sensitivity and uncertainty analysis. The model is based on simple conceptual rainfall‐runoff and river routing components, combined with estimates of the daily non‐point source load derived from annual landuse‐based export coefficients, disaggregated as a function of the runoff. The model has limited data requirements, consistent with data availability, and is parsimoneous with respect to the number of parameters identified through inverse modelling. The best performing parameter sets capture the main aspects of the observed flow and total P (TP) concentrations and provide a suitable basis for a decision‐support tool. However, a trade‐off is evident between matching the observed flow peaks, flow recessions and TP concentrations simultaneously, highlighting some limitations of the model structure and/or calibration data. Model analysis indicates that daily non‐point source load cannot be described as a function of near‐surface runoff and land use alone, but that other influences, including seasonality, are important. However, further model development to improve performance is likely to introduce additional complexity (in terms of parameter numbers), and hence additional problems of parameter identifiability and output uncertainty, which in turn raises issues of the information content of the available data. Copyright © 2004 John Wiley & Sons, Ltd.     

Modelling spatial variations in dissolved oxygen in fine‐grained streams under uncertainty

This paper presents a novel triple‐layer model, called VART DO‐3L, for simulation of spatial variations in dissolved oxygen (DO) in fine‐grained streams, characterized by a fluid mud (fluff or flocculent) layer (an advection‐dominated storage zone) as the interface between overlying stream water and relatively consolidated streambed sediment (a diffusion‐dominated storage zone). A global sensitivity analysis is conducted to investigate the sensitivity of VART DO‐3L model input parameters. Results of the sensitivity analysis indicate that the most sensitive parameter is the relative size of the advection‐dominated storage zones (A_s/A), followed by a lumped reaction term (R) for the flocculent layer, biological reaction rate (μ_o) in diffusive layer and biochemical oxygen demand concentration (L) in water column. In order to address uncertainty in model input parameters, Monte Carlo simulations are performed to sample parameter values and to produce various parameter combinations or cases. The VART DO‐3L model is applied to the Lower Amite River in Louisiana, USA, to simulate vertical and longitudinal variations in DO under the cases. In terms of longitudinal variation, the DO level decreases from 7.9 mg l at the Denham Springs station to about 2.89 mg l^?1 at the Port Vincent station. In terms of vertical variation, the DO level drops rapidly from the overlying water column to the advection‐dominated storage zone and further to the diffusive layer. The DO level (C_F) in the advective layer (flocculent layer) can reach as high as 40% of DO concentration (C) in the water column. The VART DO‐3L model may be applied to similar rivers for simulation of spatial variations in DO level. Copyright © 2014 John Wiley & Sons, Ltd.     

EFFECT OF MODEL STRUCTURE ON THE ACCURACY AND UNCERTAINTY OF RESULTS FROM WATER QUALITY MODELS

Eight one-dimensional steady-state models with different complexity, which describe the phosphate concentration as a function of the distance along a river, were examined with respect to accuracy and uncertainty of the model results and identifiability of the model parameters by means of combined calibration and sensitivity analysis using Monte Carlo simulations. In addition, the models were evaluated by the Akaike information criterion (AIC). All eight models were calibrated on the same data set from the Biebrza River, Poland. Although the accuracy increases with model complexity, the percentage of explained variance is not significantly improved in comparison with the model that describes the phosphate concentration by means of three parameters. This model also yields the minimum value of the AIC and the parameters could be well identified. Identification of the model parameters becomes poorer with increasing model complexity; in other words the parameters become increasingly correlated. This scarcely affects the uncertainty of the model results if correlation is taken into account. If correlation is not taken into account, the uncertainty of model results increases with model complexity. © 1997 by John Wiley & Sons, Ltd.     

Assessing parameter identifiability for multiple performance criteria to constrain model parameters

ABSTRACT

Reliable simulations of hydrological models require that model parameters are precisely identified. In constraining model parameters to small ranges, high parameter identifiability is achieved. In this study, it is investigated how precisely model parameters can be constrained in relation to a set of contrasting performance criteria. For this, model simulations with identical parameter samplings are carried out with a hydrological model (SWAT) applied to three contrasting catchments in Germany (lowland, mid-range mountains, alpine regions). Ten performance criteria including statistical metrics and signature measures are calculated for each model simulation. Based on the parameter identifiability that is computed separately for each performance criterion, model parameters are constrained to smaller ranges individually for each catchment. An iterative repetition of model simulations with successively constrained parameter ranges leads to more precise parameter identifiability and improves model performance. Based on these results, a more consistent handling of model parameters is achieved for model calibration.     

Monitoring and modeling dissolved oxygen dynamics through continuous longitudinal sampling: a case study in Wen‐Rui Tang River,Wenzhou, China

Synoptic water sampling at a fixed site monitoring station provides only limited ‘snap‐shots’ of the complex water quality dynamics within a surface water system. However, water quality often changes rapidly in both spatial and temporal dimensions, especially in highly polluted urban rivers. In this study, we designed and applied a continuous longitudinal sampling technique to monitor the fine‐scale spatial changes of water quality conditions, assess water pollutant sources, and determine the assimilative capacity for biochemical oxygen demand (BOD) in an urban segment of the hypoxic Wen‐Rui Tang River in eastern China. The continuous longitudinal sampling was capable of collecting dissolved oxygen (DO) data every 5 s yielding a ~11 m sampling interval with a precision of ±0.1 mg L^?1. The Streeter and Phelps BOD‐DO model was used to calculate: (1) the oxygen consumption coefficient (K₁) required for calibration of water quality models, (2) BOD assimilative capacity, and (3) BOD source and load identification. In the 2014 m river segment sampled, the oxygen consumption coefficient (K₁) was 0.428 d^?1 (20°C), the total BOD discharge was 916 kg d^?1, and the BOD assimilative capacity was 382 kg d^?1 when the minimum DO level was set to 2 mg L^?1. In addition, the longitudinal analysis identified eight major drainage outlets (BOD point sources), which were verified by field observations. This new approach provides a simple, cost‐effective method of evaluating BOD‐DO dynamics over large spatial areas with rapidly changing water quality conditions, such as urban environments. It represents a major breakthrough in the development and application of water quality sampling techniques to obtain spatially distributed DO and BOD in real time. Copyright © 2012 John Wiley & Sons, Ltd.     

Are Models Too Simple? Arguments for Increased Parameterization

The idea that models should be as simple as possible is often accepted without question. However, too much simplification and parsimony may degrade a model's utility. Models are often constructed to make predictions; yet, they are commonly parameterized with a focus on calibration, regardless of whether (1) the calibration data can constrain simulated predictions or (2) the number and type of calibration parameters are commensurate with the hydraulic property details on which key predictions may depend. Parameterization estimated through the calibration process is commonly limited by the necessity that the number of calibration parameters be smaller than the number of observations. This limitation largely stems from historical restrictions in calibration and computing capability; we argue here that better methods and computing capabilities are now available and should become more widely used. To make this case, two approaches to model calibration are contrasted: (1) a traditional approach based on a small number of homogeneous parameter zones defined by the modeler a priori and (2) regularized inversion, which includes many more parameters than the traditional approach. We discuss some advantages of regularized inversion, focusing on the increased insight that can be gained from calibration data. We present these issues using reasoning that we believe has a common sense appeal to modelers; knowledge of mathematics is not required to follow our arguments. We present equations in an Appendix, however, to illustrate the fundamental differences between traditional model calibration and a regularized inversion approach.     

白洋淀浮游植物群落的时空变化及其与环境因子的关系

浮游植物和环境因子是水生态中重要的组成部分,研究浮游植物与环境因子的相关关系可为白洋淀水资源管理及水生态保护提供理论基础.本研究于2018年非汛期(5月)和汛期(8月)分别对白洋淀淀区8个采样点的浮游植物及环境因子进行调查分析.采用Pearson相关性分析法筛选主要环境因子,分析白洋淀浮游植物群落结构变化和主要环境因子的分布特征,以及两者间的相互关系.结果表明,汛期主要环境因子为溶解氧(DO)、高锰酸盐指数(CODMn)、总氮(TN)和总磷(TP),非汛期主要环境因子为DO、CODMn、氨氮(NH_3-N)和TP.汛期和非汛期检出浮游植物分别为5门38种和6门43种,浮游植物丰度分别为415.30×10~5～1018.14×10~5cells/L和249.62×10~5～454.21×10~5cells/L,优势种分别为6种和10种,且基本为蓝藻和绿藻.浮游植物群落Shannon-Wiener多样性指数(H')、Margalef物种丰富度指数(d M)、Pielou均匀度指数(J)和物种多样性阈值4项指数均表明汛期浮游植物多样性小于非汛期.浮游植物群落特征与水质关联性较强,水质较好区域(如淀区中心) H'和J均较高,反之在水质较差区域(如府河、孝义河等汇入口) H'和J较低.TP和DO是影响汛期浮游植物群落特征的关键因素,CODMn和TP是影响非汛期浮游植物群落特征关键因素.水质评价结果表明白洋淀水质整体处于富营养状态,与2005年以来对白洋淀进行的3次浮游植物生态调查结果相比,淀区浮游植物多样性与均匀度显著下降,表明淀区富营养化程度持续加深.     

The identifiability analysis for setting up measuring campaigns in integrated water quality modelling

Identifiability analysis enables the quantification of the number of model parameters that can be assessed by calibration with respect to a data set. Such a methodology is based on the appraisal of sensitivity coefficients of the model parameters by means of Monte Carlo runs. By employing the Fisher Information Matrix, the methodology enables one to gain insights with respect to the number of model parameters that can be reliably assessed. The paper presents a study where identifiability analysis is used as a tool for setting up measuring campaigns for integrated water quality modelling. Particularly, by means of the identifiability analysis, the information about the location and the number of the monitoring stations in the integrated system required for assessing a specific group of model parameters were gained. The analysis has been applied to a real, partially urbanised, catchment containing two sewer systems, two wastewater treatment plants and a river. Several scenarios of measuring campaigns have been considered; each scenario was characterised by different monitoring station locations for the gathering of quantity and quality data. The results enabled us to assess the maximum number of model parameters quantifiable for each scenario i.e. for each data set. The methodology resulted to be a powerful tool for designing measuring campaign for integrated water quality modelling. Indeed, the crucial cross sections throughout the integrated wastewater system were detected optimizing both human and economic efforts in the gathering of field data. Further, a connection between the data set and the number of model parameters effectively assessable has been established leading to much more reliable model results.     

Conceptual modelling to assess the influence of hydro-climatic variability on runoff processes in data scarce semi-arid Andean catchments

Using hydro-meteorological time series of 50 years and in situ measurements, the dominant runoff processes in perennial Andean headwater catchments in Chile were determined using the hydrological model HBV light. First, cluster analysis was used to identify dry, wet and intermediate years. From these, sub-periods were identified with contrasting seasonal climatic influences on streamflow. By calibrating the model across different periods, impacts on model performance, parameter sensitivity and identifiability were investigated, providing insights into differences in hydrological processes. The modelling approach suggested that, independently of a dry or wet period of calibration, the streamflow response is mostly consistent with flux from groundwater storage, while only a small fraction comes from direct routing of snowmelt. The variation of model parameters, such as the groundwater rate coefficient, was found to be consistent with differing recharge in wet and dry years. The resulting snowmelt–groundwater model is a realistic hypothesis of the hydrological operation of such complex, data scarce and semi-arid Andean catchments. This model may also be a useful tool for predictions of seasonal water availability and a basis for further field studies.     

Sensitivity-guided reduction of parametric dimensionality for multi-objective calibration of watershed models

Problem complexity for watershed model calibration is heavily dependent on the number of parameters that can be identified during model calibration. This study investigates the use of global sensitivity analysis as a screening tool to reduce the parametric dimensionality of multi-objective hydrological model calibration problems while maximizing the information extracted from hydrological response data. This study shows that by expanding calibration problem formulations beyond traditional, statistical error metrics to also include metrics that capture indices or signatures of hydrological function, it is possible to reduce the complexity of calibration while maintaining high quality model predictions. The sensitivity-guided calibration is demonstrated using the Sacramento Soil Moisture Accounting (SAC-SMA) conceptual rainfall–runoff model of moderate complexity (i.e., up to 14 freely varying parameters). Using both statistical and hydrological metrics, optimization results demonstrate that parameters controlling at least 20% of the model output variance (through individual effects and interactions) should be included in the calibration process. This threshold generally yields 30–40% reductions in the number of SAC-SMA parameters requiring calibration – setting the others to a priori values – while maintaining high quality predictions. Two parameters are recommended to be calibrated in all cases (percent impervious area and lower zone tension water storage), three parameters are needed in drier watersheds (additional impervious area, riparian zone vegetation, and percent of percolation going to tension storage), and the lower zone parameters are crucial unless the watershed is very dry. Overall, this study demonstrates that a coupled, multi-objective sensitivity and calibration analysis better captures differences between watersheds during model calibration and serves to maximize the value of available watershed response time series. These contributions are particularly important given the ongoing development of more complex integrated models, which will require new tools to address the growing discrepancy between the information content of hydrological data and the number of model parameters that have to be estimated.     

Global sensitivity analyses for a complex hydrological model applied in an Alpine watershed

The increasing complexity of hydrological models results in a large number of parameters to be estimated. In order to better understand how these complex models work, efficient screening methods are required in order to identify the most important parameters. This is of particular importance for models that are used within an operational real‐time forecasting chain such as HQsim. The objectives of this investigation are to (i) identify the most sensitive parameters of the complex HQsim model applied in the Alpine Lech catchment and (ii) compare model parameter sensitivity rankings attained from three global sensitivity analysis techniques. The techniques presented are the (i) regional sensitivity analysis, (ii) Morris analysis and (iii) state‐dependent parameter modelling. The results indicate that parameters affecting snow melt as well as processes in the unsaturated soil zone reveal high significance in the analysed catchment. The snow melt parameters show clear temporal patterns in the sensitivity whereas most of the parameters affecting processes in the unsaturated soil zone do not vary in importance across the year. Overall, the maximum degree day factor (meltfunc_max) has been identified to play a key role within the HQsim model. Although the parameter sensitivity rankings are equivalent between methods for a number of parameters, for several key parameters differing results were obtained. An uncertainty analysis demonstrates that a parameter ranking attained from only one method is subjected to large uncertainty. Copyright © 2012 John Wiley & Sons, Ltd.     

Hydrologic data assimilation using particle Markov chain Monte Carlo simulation: Theory,concepts and applications

During the past decades much progress has been made in the development of computer based methods for parameter and predictive uncertainty estimation of hydrologic models. The goal of this paper is twofold. As part of this special anniversary issue we first shortly review the most important historical developments in hydrologic model calibration and uncertainty analysis that has led to current perspectives. Then, we introduce theory, concepts and simulation results of a novel data assimilation scheme for joint inference of model parameters and state variables. This Particle-DREAM method combines the strengths of sequential Monte Carlo sampling and Markov chain Monte Carlo simulation and is especially designed for treatment of forcing, parameter, model structural and calibration data error. Two different variants of Particle-DREAM are presented to satisfy assumptions regarding the temporal behavior of the model parameters. Simulation results using a 40-dimensional atmospheric “toy” model, the Lorenz attractor and a rainfall–runoff model show that Particle-DREAM, P-DREAM_(VP) and P-DREAM_(IP) require far fewer particles than current state-of-the-art filters to closely track the evolving target distribution of interest, and provide important insights into the information content of discharge data and non-stationarity of model parameters. Our development follows formal Bayes, yet Particle-DREAM and its variants readily accommodate hydrologic signatures, informal likelihood functions or other (in)sufficient statistics if those better represent the salient features of the calibration data and simulation model used.     

The comparative study of multi‐site uncertainty evaluation method based on SWAT model

With the recent development of distributed hydrological models, the use of multi‐site observed data to evaluate model performance is becoming more common. Distributed hydrological model have many advantages, and at the same time, it also faces the challenge to calibrate over‐do parameters. As a typical distributed hydrological model, problems also exist in Soil and Water Assessment Tool (SWAT) parameter calibration. In the paper, four different uncertainty approaches – Particle Swarm Optimization (PSO) techniques, Generalized Likelihood Uncertainty Estimation (GLUE), Sequential Uncertainty Fitting algorithm (SUFI‐2) and Parameter Solution (PARASOL) – are taken to a comparative study with the SWAT model applied in Peace River Basin, central Florida. In our study, the observed river discharge data used in SWAT model calibration were collected from the three gauging stations at the main tributary of the Peace River. Behind these approaches, there is a shared philosophy; all methods seek out many parameter set to fit the uncertainties due to the non‐uniqueness in model parameter evaluation. On the basis of the statistical results of four uncertainty methods, difficulty level of each method, the number of runs and theoretical basis, the reasons that affected the accuracy of simulation were analysed and compared. Furthermore, for the four uncertainty method with SWAT model in the study area, the pairwise correlation between parameters and the distributions of model fit summary statistics computed from the sampling over the behavioural parameter and the entire model calibration parameter feasible spaces were identified and examined. It provided additional insight into the relative identifiability of the four uncertainty methods Copyright © 2014 John Wiley & Sons, Ltd.     

Hydrological modelling of Ethiopian catchments using limited data

The hydrological component of the soil and water assessment tool (SWAT) model is adapted for two Ethiopian catchments based on primary knowledge of the coherence spectrum between rainfall and stream flow data. Spectrum analysis using the available nearby climatic data is made to limit the temporal and spatial scales (inverse rate coefficients) subject to the calibration of compartmentalized runoff models. The exclusion of unwarranted time scales in the calibration implies that the model efficiency (r² values) decrease only moderately between calibration and validation, and the optimization is focused on warranted problems. On the basis of the available data for the two Ethiopian catchments, the implication is that only periods longer than about 50 days can be reliably evaluated in the model. The model structure of SWAT for the surface runoff and groundwater flow response is modified to make the time scales consistent with the results of the spectrum analysis. An optimization algorithm is developed to constrain and combine the model parameters with the spectrum analysis results. Copyright © 2009 John Wiley & Sons, Ltd.