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ABSTRACT:Water pollution has become a growing threat to human society and natural ecosystems in the recent decades. Assessment of seasonal changes in water quality is important for evaluating temporal variations of river pollution. In this study, seasonal variations of chemical characteristics of surface water for the Chehelchay watershed in northeast of Iran was investigated. Various multivariate statistical techniques, including multivariate analysis of variance, discriminant analysis, principal component analysis and factor analysis were applied to analyze river water quality data set containing 12 parameters recorded during 13 years within 1995-2008. The results showed that river water quality has significant seasonal changes. Discriminant analysis identified most important parameters contributing to seasonal variations of river water quality. The analysis rendered a dramatic data reduction using only five parameters: electrical conductivity, chloride, bicarbonate, sulfate and hardness, which correctly assigned 70.2 % of the observations to their respective seasonal groups. Principal component analysis / factor analysis assisted to recognize the factors or origins responsible for seasonal water quality variations. It was determined that in each season more than 80 % of the total variance is explained by three latent factors standing for salinity, weathering-related processes and alkalinity, respectively. Generally, the analysis of water quality data revealed that the Chehelchay River water chemistry is strongly affected by rock water interaction, hydrologic processes and anthropogenic activities. This study demonstrates the usefulness of multivariate statistical approaches for analysis and interpretation of water quality data, identification of pollution sources and understanding of temporal variations in water quality for effective river water quality management.

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Hydrologic models are simplified representations of natural hydrologic systems. Since these models rely on assumptions and simplifications to capture some aspects of hydrological processes, calibration of parameters is unavoidable. However, utilizing the philosophy of a recent modelling framework proposed by Bahremand (2016), we show how calibration of most model parameters can be avoided by allocating or presetting these parameters utilizing knowledge gained from sensitivity analyses, field observations and a priori specifications as a part of a parameter allocation procedure. This paper details the simulation of daily river flow of the Shemshak‐Roudak watershed performed using the Python version of the WetSpa model. The WetSpa‐Python model is a distributed model of hydrological processes applied at the watershed scale. The model was applied to the Shemshak‐Roudak watershed of Iran with parameter allocation. Model calibration involved only two parameters. Straightforward methods were proposed for allocating model parameters, including three baseflow‐related parameters and the determination of maximum active groundwater storage using a mass curve technique. Also, the Budyko curve was used to constrain a correction factor for potential evapotranspiration. The WetSpa‐Python model was extended to include the influence of snowmelt. A failure to include snow in the hydrological processes of the WetSpa‐Python model creates a significant discrepancy between the observed and simulated hydrographs during the spring. The results of daily simulations for 12 years (2002–2014) are in good agreement with observations of discharge (Kling‐Gupta Efficiency = 0.84). These results demonstrate that it is feasible to simulate hydrographs with limited calibration given a knowledge of hydrological processes and an understanding of relationships between catchment characteristics and model parameters.

The utility of an unsaturated zone soil moisture model is not only its ability to describe the soil moisture dynamics at a given point but also the possibility to generalize the results to larger areas. In this study we investigated the predictive performance of a one‐dimensional unsaturated zone soil moisture model when applied at point, field, response unit, and catchment scales, using detailed field observations from a 0.42‐km2 catchment in the Netherlands. Our main question was how model parameterization and model performance could be compared across these scales. We considered two different calibration–validation schemes and three performance statistics. In all cases we applied the same Levenberg–Marquardt optimization scheme. Differences between calibration–validation schemes (interpolation vs. extrapolation) were surprisingly small. Using one particular model parameterization across the various aggregation levels, the optimal Mualem–van Genuchten parameters for a coarser aggregation level can be derived from an underlying level by simple arithmetic averaging. The different performance indices (RMSE, index of agreement, and Nash–Sutcliffe coefficient) were highly variable between observation locations and for different aggregation levels. Overall, the indices were more favorable at higher aggregation levels, and in correspondence with errors reported in comparable studies. The unsaturated‐zone model did not, however, provide satisfactory predictions of independent flux observations, in this case daily catchment discharge. Moreover we did not succeed in deriving a meta‐model to scale model performance indices with aggregation level. Our case study therefore supports the view that multiscale calibration studies that use both state and flux observations are required to compare results from unsaturated zone models at different aggregation levels.