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Hydrological climate-change-impact studies depend on climatic variables simulated by climate models. Due to parametrization and numerous simplifications, however, climate-model outputs come with systematic biases compared to the observations. In the past decade, several methods of different complexity and dimensionality for adjustment of such biases were introduced, but their benefits for impact studies and accurate streamflow projections are still debated. In this paper, we evaluated the ability of two state-of-the-art, advanced multivariate bias-adjustment methods to accurately reproduce 16 hydrological signatures, and compared their performance against two parsimonious univariate bias-adjustment methods based on a multi-criteria performance evaluation. The results indicated that all bias-adjustment methods considerably reduced biases and increased the consistency of simulated hydrological signatures. The added value of multivariate methods in maintaining dependence structures between precipitation and temperature was not systematically reflected in the resulting hydrological signatures, as they were generally outperformed by univariate methods. The benefits of multivariate methods only emerged for low-flow signatures in snowmelt-driven catchments. Based on these findings, we identified the most suitable bias-adjustment methods for water-resources management in Nordic regions under a changing climate, and provided practical guidelines for the selection of bias-adjustment methods given specific research targets and hydroclimatic regimes.

High urbanization puts many groundwater resources at risk of quality deterioration. Analyzing all viable potential groundwater contamination scenarios for good decision making requires reliable tool. Coupling several complex models in integrated modelling can often fail to perform in reasonable time. Possible solution in that case could be usage of simplified models in order to speed up long-term continuous calculations and simulations. The paper presents the application of the Cellular Automata (CA) approach in modelling of the contaminant transport under unsteady groundwater conditions. It compares the results obtained using coupled CA models with well-known analytical solutions and standard methods used for pollution transport modelling in groundwater conditions, such as coupled MODFLOW and MT3DMS. Results obtained in this paper show that CA approach can be satisfactorily used for simulations of unsteady groundwater conditions, caused by surface-groundwater interaction, and pollution transport, especially in diffusion dominant cases, gaining the reduction of simulation time up to 10 times.

High urbanization puts many groundwater resources at risk of quality deterioration. Analyzing all viable potential groundwater contamination scenarios for good decision making requires reliable tool. Coupling several complex models in integrated modelling can often fail to perform in reasonable time. Possible solution in that case could be usage of simplified models in order to speed up long-term continuous calculations and simulations. The paper presents the application of the Cellular Automata (CA) approach in modelling of the contaminant transport under unsteady groundwater conditions. It compares the results obtained using coupled CA models with well-known analytical solutions and standard methods used for pollution transport modelling in groundwater conditions, such as coupled MODFLOW and MT3DMS. Results obtained in this paper show that CA approach can be satisfactorily used for simulations of unsteady groundwater conditions, caused by surface-groundwater interaction, and pollution transport, especially in diffusion dominant cases, gaining the reduction of simulation time up to 10 times.

Model-driven forecasting, used for flood risks or big hydropower systems management, can produce results of unsatisfying accuracy even with best-calibrated hydrodynamic models. One of the biggest uncertainty sources is the inflow data, either produced by different hydrological models or obtained using unreliable rating curves. To keep the model in the up-to-date state, data assimilation techniques are used. The aim of the assimilation is to reduce the difference between simulated and observed state of selected variables by updating hydrodynamic model state variables according to observed water levels. The widely used data assimilation method applicable for nonlinear hydrodynamic models is Ensemble Kalman Filter (EnKF). However, this method can often increase the computational time due to complexity of mathematical apparatus, making it less applicable in everyday operations. This paper presents the novel, fast, tailor-made data assimilation method, suitable for 1D open channel hydraulic models, based on control theory. Using Proportional-Integrative-Derivative (PID) controllers, the difference between measured levels and simulated levels obtained by hydrodynamic model is reduced by adding or subtracting the flows in the junctions/sections where water levels are measured. The novel PID control-based data assimilation (PID-DA) is compared to EnKF. Benchmarking shows that PID-DA can be used for data assimilation, even coupled with simplified 1D hydraulic model, without significant sacrifice of stability and accuracy, and with reduction of computational time up to 63 times.

Hydrologic models are important for effective water resources management. They vary in complexity from parsimonious, spatially lumped, to physically-based, fully distributed models, which are generally expected to outperform the former. Wide applications of complex models are limited due to high data and computational demands. Therefore, a new approach based on well-balanced model complexity is needed to obtain reasonable simulation results with low data requirements. This paper presents a novel 3DNet-Catch hydrologic model, developed to represent key processes in sloped catchments under a temperate climate with modest data requirements. 3DNet-Catch includes runoff simulations within computational units by employing the interception, snow and soil routines, as well as runoff and channel routing. The soil routine, which is the key model feature, combines the SCS-CN method, an analytically integrated nonlinear outflow equation and the Brooks-Corey relation for unsaturated conductivity in an innovative manner. To advance runoff routing in 3DNet-Catch, an approach for analytical integration of the linear and nonlinear outflow equations is implemented. Most model parameters are physically meaningful, thus facilitating model calibration. The model structure can be adjusted according to soil and groundwater flow data, and it can include hydraulic structures, thereby providing adaptability to local conditions. A comprehensive hydrologic evaluation framework is established and conducted to examine whether 3DNet-Catch is adequately parameterised and can accurately reproduce catchment hydrologic response. The model parameterisation is evaluated by sensitivity, identifiability and correlation analyses. Model efficiency is quantified in terms of performance measures, hydrologic signatures and plausibility of the simulated hydrological processes. The results show high sensitivity of the hydrologic variables and performance measures to the model parameters, particularly to those o

Hydrologic models are important for effective water resources management. They vary in complexity from parsimonious, spatially lumped, to physically-based, fully distributed models, which are generally expected to outperform the former. Wide applications of complex models are limited due to high data and computational demands. Therefore, a new approach based on well-balanced model complexity is needed to obtain reasonable simulation results with low data requirements. This paper presents a novel 3DNet-Catch hydrologic model, developed to represent key processes in sloped catchments under a temperate climate with modest data requirements. 3DNet-Catch includes runoff simulations within computational units by employing the interception, snow and soil routines, as well as runoff and channel routing. The soil routine, which is the key model feature, combines the SCS-CN method, an analytically integrated nonlinear outflow equation and the Brooks-Corey relation for unsaturated conductivity in an innovative manner. To advance runoff routing in 3DNet-Catch, an approach for analytical integration of the linear and nonlinear outflow equations is implemented. Most model parameters are physically meaningful, thus facilitating model calibration. The model structure can be adjusted according to soil and groundwater flow data, and it can include hydraulic structures, thereby providing adaptability to local conditions. A comprehensive hydrologic evaluation framework is established and conducted to examine whether 3DNet-Catch is adequately parameterised and can accurately reproduce catchment hydrologic response. The model parameterisation is evaluated by sensitivity, identifiability and correlation analyses. Model efficiency is quantified in terms of performance measures, hydrologic signatures and plausibility of the simulated hydrological processes. The results show high sensitivity of the hydrologic variables and performance measures to the model parameters, particularly to those o

Hydrologic models are important for effective water resources management. They vary in complexity from parsimonious, spatially lumped, to physically-based, fully distributed models, which are generally expected to outperform the former. Wide applications of complex models are limited due to high data and computational demands. Therefore, a new approach based on well-balanced model complexity is needed to obtain reasonable simulation results with low data requirements. This paper presents a novel 3DNet-Catch hydrologic model, developed to represent key processes in sloped catchments under a temperate climate with modest data requirements. 3DNet-Catch includes runoff simulations within computational units by employing the interception, snow and soil routines, as well as runoff and channel routing. The soil routine, which is the key model feature, combines the SCS-CN method, an analytically integrated nonlinear outflow equation and the Brooks-Corey relation for unsaturated conductivity in an innovative manner. To advance runoff routing in 3DNet-Catch, an approach for analytical integration of the linear and nonlinear outflow equations is implemented. Most model parameters are physically meaningful, thus facilitating model calibration. The model structure can be adjusted according to soil and groundwater flow data, and it can include hydraulic structures, thereby providing adaptability to local conditions. A comprehensive hydrologic evaluation framework is established and conducted to examine whether 3DNet-Catch is adequately parameterised and can accurately reproduce catchment hydrologic response. The model parameterisation is evaluated by sensitivity, identifiability and correlation analyses. Model efficiency is quantified in terms of performance measures, hydrologic signatures and plausibility of the simulated hydrological processes. The results show high sensitivity of the hydrologic variables and performance measures to the model parameters, particularly to those o

Hydrologic models are important for effective water resources management. They vary in complexity from parsimonious, spatially lumped, to physically-based, fully distributed models, which are generally expected to outperform the former. Wide applications of complex models are limited due to high data and computational demands. Therefore, a new approach based on well-balanced model complexity is needed to obtain reasonable simulation results with low data requirements. This paper presents a novel 3DNet-Catch hydrologic model, developed to represent key processes in sloped catchments under a temperate climate with modest data requirements. 3DNet-Catch includes runoff simulations within computational units by employing the interception, snow and soil routines, as well as runoff and channel routing. The soil routine, which is the key model feature, combines the SCS-CN method, an analytically integrated nonlinear outflow equation and the Brooks-Corey relation for unsaturated conductivity in an innovative manner. To advance runoff routing in 3DNet-Catch, an approach for analytical integration of the linear and nonlinear outflow equations is implemented. Most model parameters are physically meaningful, thus facilitating model calibration. The model structure can be adjusted according to soil and groundwater flow data, and it can include hydraulic structures, thereby providing adaptability to local conditions. A comprehensive hydrologic evaluation framework is established and conducted to examine whether 3DNet-Catch is adequately parameterised and can accurately reproduce catchment hydrologic response. The model parameterisation is evaluated by sensitivity, identifiability and correlation analyses. Model efficiency is quantified in terms of performance measures, hydrologic signatures and plausibility of the simulated hydrological processes. The results show high sensitivity of the hydrologic variables and performance measures to the model parameters, particularly to those o

The authors present a detailed procedure for modelling of mean monthly flow time-series using records of the Great Morava River (Serbia). The proposed procedure overcomes a major challenge of other available methods by disaggregating the time series in order to capture the main properties of the hydrologic process in both long-run and short-run, The main assumption of the conducted research is that a time series of monthly flow rates represents a stochastic process comprised of deterministic, stochastic and random components, the former of which can be further decomposed into a composite trend and two periodic components (short-term or seasonal periodicity and long-term or multi-annual periodicity). In the present paper, the deterministic component of a monthly flow time-series is assessed by spectral analysis, whereas its stochastic component is modelled using cross-correlation transfer functions, artificial neural networks and polynomial regression. The results suggest that the deterministic component can be expressed solely as a function of time, whereas the stochastic component changes as a nonlinear function of climatic factors (rainfall and temperature). For the calibration period, the results of the analysis infers a lower value of Kling-Gupta Efficiency in the case of transfer functions (0.736), whereas artificial neural networks and polynomial regression suggest a significantly better match between the observed and simulated values, 0.841 and 0.891, respectively. It seems that transfer functions fail to capture high monthly flow rates, whereas the model based on polynomial regression reproduces high monthly flows much better because it is able to successfully capture a highly nonlinear relationship between the inputs and the output. The proposed methodology that uses a combination of artificial neural networks, spectral analysis and polynomial regression for deterministic and stochastic components can be applied to forecast monthly or seasonal flow rates.

The authors present a detailed procedure for modelling of mean monthly flow time-series using records of the Great Morava River (Serbia). The proposed procedure overcomes a major challenge of other available methods by disaggregating the time series in order to capture the main properties of the hydrologic process in both long-run and short-run, The main assumption of the conducted research is that a time series of monthly flow rates represents a stochastic process comprised of deterministic, stochastic and random components, the former of which can be further decomposed into a composite trend and two periodic components (short-term or seasonal periodicity and long-term or multi-annual periodicity). In the present paper, the deterministic component of a monthly flow time-series is assessed by spectral analysis, whereas its stochastic component is modelled using cross-correlation transfer functions, artificial neural networks and polynomial regression. The results suggest that the deterministic component can be expressed solely as a function of time, whereas the stochastic component changes as a nonlinear function of climatic factors (rainfall and temperature). For the calibration period, the results of the analysis infers a lower value of Kling-Gupta Efficiency in the case of transfer functions (0.736), whereas artificial neural networks and polynomial regression suggest a significantly better match between the observed and simulated values, 0.841 and 0.891, respectively. It seems that transfer functions fail to capture high monthly flow rates, whereas the model based on polynomial regression reproduces high monthly flows much better because it is able to successfully capture a highly nonlinear relationship between the inputs and the output. The proposed methodology that uses a combination of artificial neural networks, spectral analysis and polynomial regression for deterministic and stochastic components can be applied to forecast monthly or seasonal flow rates.