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• 1. [Article] Century-Long Variability and Trends in Daily Precipitation Characteristics at Three Finnish Stations

  Long-term variations and trends in a wide range of statistics for daily precipitation characteristics in terms of intensity, frequency and duration in Finland were analysed using precipitation records ...

  Citation × Citation Citation Abstract Copy Title: Century-Long Variability and Trends in Daily Precipitation Characteristics at Three Finnish Stations Author: Irannezhad, Masoud, Marttila, Hannu, De-liang, Chen, Kløve, Bjørn Year: 2016 Long-term variations and trends in a wide range of statistics for daily precipitation characteristics in terms of intensity, frequency and duration in Finland were analysed using precipitation records during 1908－2008 from 3 meteorological stations in the south (Kaisaniemi), center (Kajaani) and north (Sodankylä). Although precipitation days in northern part were more frequent than in central and southern parts, daily precipitation intensity in the south was generally higher than those in the centre and north of the country. Annual sum of very light precipitation (0 mm < daily precipitation ≤ long-term 50th percentile of daily precipitation more than 0 mm) significantly (p Title: Century-Long Variability and Trends in Daily Precipitation Characteristics at Three Finnish Stations Author: Irannezhad, Masoud, Marttila, Hannu, De-liang, Chen, Kløve, Bjørn Year: 2016 Long-term variations and trends in a wide range of statistics for daily precipitation characteristics in terms of intensity, frequency and duration in Finland were analysed using precipitation records during 1908－2008 from 3 meteorological stations in the south (Kaisaniemi), center (Kajaani) and north (Sodankylä). Although precipitation days in northern part were more frequent than in central and southern parts, daily precipitation intensity in the south was generally higher than those in the centre and north of the country. Annual sum of very light precipitation (0 mm < daily precipitation ≤ long-term 50th percentile of daily precipitation more than 0 mm) significantly (p Close

• 2. [Article] The Role of Atmospheric Circulation Patterns in Agroclimate Variability in Finland, 1961-2011

  This study evaluates interannual variations and trends in growing season daily temperature sum and daily precipitation sum in Finland during 1961–2011, and their connections to well known atmospheric circulation ...

  Citation × Citation Citation Abstract Copy Title: The Role of Atmospheric Circulation Patterns in Agroclimate Variability in Finland, 1961-2011 Author: Irannezhad, Masoud, Chen, Deliang, Kløve, Bjørn Year: 2016 This study evaluates interannual variations and trends in growing season daily temperature sum and daily precipitation sum in Finland during 1961–2011, and their connections to well known atmospheric circulation patterns. Changes in summer (June–August) climate partially explain changes in growing season daily temperature sum and daily precipitation sum over Finland, which naturally decreased from south to north. On a national scale, growing season warmed and became wetter during 1961–2011, as growing season daily temperature sum and daily precipitation sum significantly (p < 0.05) increased by 5.01 ± 3.17°C year–1 and 1.39 ± 0.91 mm year–1, respectively. The East Atlantic pattern was the most influential atmospheric circulation pattern for variations in growing season daily temperature sum (rho = 0.40) across Finland and the East Atlantic/West Russia pattern was most influential for growing season daily precipitation sum variability (rho = –0.54). There were significant (p < 0.05) increasing trends in growing season daily temperature sum and daily precipitation sum throughout Finland during 1961–2011. Increased growing season daily temperature sum was mainly observed in northern, central, western, eastern and coastal areas of south-western Finland. This warming was positively associated with the East Atlantic pattern in the north, centre and south, but negatively associated with the East Atlantic/West Russia pattern in eastern Finland. Increased GSP mostly occurred in southern, eastern, western, central, northern and north-western Finland. These wetting trends were positively correlated with the East Atlantic pattern in the north and negatively correlated with the Polar pattern in the south and the East Atlantic/West Russia pattern in the east, west, centre and north-east of Finland. The overall agroclimatic year-to-year variability in Finland between 1961 and 2011 was mostly linked to variations in the East Atlantic and East Atlantic/West Russia patterns. Title: The Role of Atmospheric Circulation Patterns in Agroclimate Variability in Finland, 1961-2011 Author: Irannezhad, Masoud, Chen, Deliang, Kløve, Bjørn Year: 2016 This study evaluates interannual variations and trends in growing season daily temperature sum and daily precipitation sum in Finland during 1961–2011, and their connections to well known atmospheric circulation patterns. Changes in summer (June–August) climate partially explain changes in growing season daily temperature sum and daily precipitation sum over Finland, which naturally decreased from south to north. On a national scale, growing season warmed and became wetter during 1961–2011, as growing season daily temperature sum and daily precipitation sum significantly (p < 0.05) increased by 5.01 ± 3.17°C year–1 and 1.39 ± 0.91 mm year–1, respectively. The East Atlantic pattern was the most influential atmospheric circulation pattern for variations in growing season daily temperature sum (rho = 0.40) across Finland and the East Atlantic/West Russia pattern was most influential for growing season daily precipitation sum variability (rho = –0.54). There were significant (p < 0.05) increasing trends in growing season daily temperature sum and daily precipitation sum throughout Finland during 1961–2011. Increased growing season daily temperature sum was mainly observed in northern, central, western, eastern and coastal areas of south-western Finland. This warming was positively associated with the East Atlantic pattern in the north, centre and south, but negatively associated with the East Atlantic/West Russia pattern in eastern Finland. Increased GSP mostly occurred in southern, eastern, western, central, northern and north-western Finland. These wetting trends were positively correlated with the East Atlantic pattern in the north and negatively correlated with the Polar pattern in the south and the East Atlantic/West Russia pattern in the east, west, centre and north-east of Finland. The overall agroclimatic year-to-year variability in Finland between 1961 and 2011 was mostly linked to variations in the East Atlantic and East Atlantic/West Russia patterns. Close

• 3. [Article] A Multivariate Modeling Approach for Generating Ensemble Climatology Forcing for Hydrologic Applications

  Reliability and accuracy of the forcing data plays a vital role in the Hydrological Streamflow Prediction. Reliability of the forcing data leads to accurate predictions and ultimately reduction of uncertainty. ...

  Citation × Citation Citation Abstract Copy Title: A Multivariate Modeling Approach for Generating Ensemble Climatology Forcing for Hydrologic Applications Author: Khajehei, Sepideh Year: 2015 Reliability and accuracy of the forcing data plays a vital role in the Hydrological Streamflow Prediction. Reliability of the forcing data leads to accurate predictions and ultimately reduction of uncertainty. Currently, Numerical Weather Prediction (NWP) models are developing ensemble forecasts for various temporal and spatial scales. However, it is proven that the raw products of the NWP models may be biased at the basin scale; unlike model grid scale, depending on the size of the catchment. Due to the large space-time variability of precipitation, bias-correcting the ensemble forecasts has proven to be a challenging task. In recent years, Ensemble Pre-Processing (EPP), a statistical approach, has proven to be helpful in reduction of bias and generation of reliable forecast. The procedure is based on the bivariate probability distribution between observation and single-value precipitation forecasts. In the current work, we have applied and evaluated a Bayesian approach, based on the Copula density functions, to develop an ensemble precipitation forecasts from the conditional distribution of the single-value precipitation. Copula functions are the multivariate joint distribution of univariate marginal distributions and are capable of modeling the joint distribution of two variables with any level of correlation and dependency. The advantage of using Copulas, amongst others, includes its capability of modeling the joint distribution independent of the type of marginal distribution. In the present study, we have evaluated the capability of copula-based functions in EPP and comparison is made against an existing and commonly used procedure for same i.e. meta-Gaussian distribution. Monthly precipitation forecast from Climate Forecast System (CFS) and gridded observation from Parameter-elevation Relationships on Independent Slopes Model (PRISM) have been utilized to create ensemble pre-processed precipitation over three sub-basins in the western USA at 0.5-degree spatial resolution. The comparison has been made using both deterministic and probabilistic frameworks of evaluation. Across all the sub-basins and evaluation techniques, copula-based technique shows more reliability and robustness as compared to the meta-Gaussian approach. Title: A Multivariate Modeling Approach for Generating Ensemble Climatology Forcing for Hydrologic Applications Author: Khajehei, Sepideh Year: 2015 Reliability and accuracy of the forcing data plays a vital role in the Hydrological Streamflow Prediction. Reliability of the forcing data leads to accurate predictions and ultimately reduction of uncertainty. Currently, Numerical Weather Prediction (NWP) models are developing ensemble forecasts for various temporal and spatial scales. However, it is proven that the raw products of the NWP models may be biased at the basin scale; unlike model grid scale, depending on the size of the catchment. Due to the large space-time variability of precipitation, bias-correcting the ensemble forecasts has proven to be a challenging task. In recent years, Ensemble Pre-Processing (EPP), a statistical approach, has proven to be helpful in reduction of bias and generation of reliable forecast. The procedure is based on the bivariate probability distribution between observation and single-value precipitation forecasts. In the current work, we have applied and evaluated a Bayesian approach, based on the Copula density functions, to develop an ensemble precipitation forecasts from the conditional distribution of the single-value precipitation. Copula functions are the multivariate joint distribution of univariate marginal distributions and are capable of modeling the joint distribution of two variables with any level of correlation and dependency. The advantage of using Copulas, amongst others, includes its capability of modeling the joint distribution independent of the type of marginal distribution. In the present study, we have evaluated the capability of copula-based functions in EPP and comparison is made against an existing and commonly used procedure for same i.e. meta-Gaussian distribution. Monthly precipitation forecast from Climate Forecast System (CFS) and gridded observation from Parameter-elevation Relationships on Independent Slopes Model (PRISM) have been utilized to create ensemble pre-processed precipitation over three sub-basins in the western USA at 0.5-degree spatial resolution. The comparison has been made using both deterministic and probabilistic frameworks of evaluation. Across all the sub-basins and evaluation techniques, copula-based technique shows more reliability and robustness as compared to the meta-Gaussian approach. Close

• 4. [Article] Improving Hydrological Process Modeling Using Optimized Threshold-Based Wavelet De-Noising Technique

  The extent of the noise on hydrological data is inevitable, which reduces the efficiency of Data-Driven Models (DDMs). Despite of this fact that the DDMs such as Artificial Neural Network (ANN) are capable ...

  Citation × Citation Citation Abstract Copy Title: Improving Hydrological Process Modeling Using Optimized Threshold-Based Wavelet De-Noising Technique Author: Abbaszadeh, Peyman Year: 2016 The extent of the noise on hydrological data is inevitable, which reduces the efficiency of Data-Driven Models (DDMs). Despite of this fact that the DDMs such as Artificial Neural Network (ANN) are capable of nonlinear functional mapping between a set of input and output variables, but refining of the time series through data pre-processing methods can provide with the possibility to increase the performance of these set of models. The main objective of this study is to propose a new method called Optimized Threshold-Based Wavelet De-noising technique (OTWD) to de-noise hydrological time series and improve the prediction accuracy while the DDM is being used. For this purpose, in the first step, Wavelet-ANN (WNN) model was developed for identifying suitable wavelet function and maximum decomposition level. Afterward, sub-signals of original precipitation time series which were determined in the first step were de-noised by using of OTWD technique. Therefore, these clean sub-signals of precipitation time series were imposed as input data to the ANN to predict the precipitation one time step ahead. The results showed that OTWD technique could improve the efficiency of WNN model dramatically; this outcome was reported by the different efficiency criterions such as Nash-Sutcliffe Efficiency (NSE = 0.92), Root Mean Squared Error (RMSE = 0.0103), coefficient correlation of linear regression (R = 0.93), Peak Value Criterion (PVC = 0.021) and Low Value Criterion (LVC = 0.026). The best fitted WNN model in comparison by proposed model showed weaker performance by the NSE, RMSE, R, PVC and LVC values of 0.86, 0.043, 0.87, 0.034 and 0.045, respectively. Title: Improving Hydrological Process Modeling Using Optimized Threshold-Based Wavelet De-Noising Technique Author: Abbaszadeh, Peyman Year: 2016 The extent of the noise on hydrological data is inevitable, which reduces the efficiency of Data-Driven Models (DDMs). Despite of this fact that the DDMs such as Artificial Neural Network (ANN) are capable of nonlinear functional mapping between a set of input and output variables, but refining of the time series through data pre-processing methods can provide with the possibility to increase the performance of these set of models. The main objective of this study is to propose a new method called Optimized Threshold-Based Wavelet De-noising technique (OTWD) to de-noise hydrological time series and improve the prediction accuracy while the DDM is being used. For this purpose, in the first step, Wavelet-ANN (WNN) model was developed for identifying suitable wavelet function and maximum decomposition level. Afterward, sub-signals of original precipitation time series which were determined in the first step were de-noised by using of OTWD technique. Therefore, these clean sub-signals of precipitation time series were imposed as input data to the ANN to predict the precipitation one time step ahead. The results showed that OTWD technique could improve the efficiency of WNN model dramatically; this outcome was reported by the different efficiency criterions such as Nash-Sutcliffe Efficiency (NSE = 0.92), Root Mean Squared Error (RMSE = 0.0103), coefficient correlation of linear regression (R = 0.93), Peak Value Criterion (PVC = 0.021) and Low Value Criterion (LVC = 0.026). The best fitted WNN model in comparison by proposed model showed weaker performance by the NSE, RMSE, R, PVC and LVC values of 0.86, 0.043, 0.87, 0.034 and 0.045, respectively. Close

• 5. [Article] Modelling Rainwater-Harvesting System Reliability Based on Historical Precipitation Data for Portland

  Rainwater harvesting can ease the water availability problems for many parts of the world where water is not easily available. It is also a solution for minimizing stormwater runoff and better managing ...

  Citation × Citation Citation Abstract Copy Title: Modelling Rainwater-Harvesting System Reliability Based on Historical Precipitation Data for Portland Author: Mathur, Mrinali Year: 2014 Rainwater harvesting can ease the water availability problems for many parts of the world where water is not easily available. It is also a solution for minimizing stormwater runoff and better managing sewers by preventing combined sewers to overflow. The goal of this project is to calculate the reliability of rainwater harvesting systems and provide optimum size of the storage tank using historical records for Portland specific rain pattern. A simple water balance approach for historical data is used to calculate the reliability by which rainwater harvesting system can be used as a means of irrigating gardens in Portland. The size of the storage units needs to be adequate with the area of the roof and the desired water use rate. The maximum storage required to attain 100% reliability is more than 20m3 for lower end of irrigation demand and more than 100m3 for upper end of the irrigation demand for specific catchment area. The reliability with which Portland backyard gardens can be irrigated is approximately 20%- 50%, with lower end of range representing 1m3 storage and the upper end of range representing 5m3 of storage tank. Roof top runoff inputs to the combined sewer system are reduced by approximately 15% and potable water saved is 38% with storage tank volume of 5m3. A cost benefit analysis suggest that for a tank of 5m3 it takes 15 years to recover the investment done for installing rainwater harvesting system. Title: Modelling Rainwater-Harvesting System Reliability Based on Historical Precipitation Data for Portland Author: Mathur, Mrinali Year: 2014 Rainwater harvesting can ease the water availability problems for many parts of the world where water is not easily available. It is also a solution for minimizing stormwater runoff and better managing sewers by preventing combined sewers to overflow. The goal of this project is to calculate the reliability of rainwater harvesting systems and provide optimum size of the storage tank using historical records for Portland specific rain pattern. A simple water balance approach for historical data is used to calculate the reliability by which rainwater harvesting system can be used as a means of irrigating gardens in Portland. The size of the storage units needs to be adequate with the area of the roof and the desired water use rate. The maximum storage required to attain 100% reliability is more than 20m3 for lower end of irrigation demand and more than 100m3 for upper end of the irrigation demand for specific catchment area. The reliability with which Portland backyard gardens can be irrigated is approximately 20%- 50%, with lower end of range representing 1m3 storage and the upper end of range representing 5m3 of storage tank. Roof top runoff inputs to the combined sewer system are reduced by approximately 15% and potable water saved is 38% with storage tank volume of 5m3. A cost benefit analysis suggest that for a tank of 5m3 it takes 15 years to recover the investment done for installing rainwater harvesting system. Close

• 6. [Article] Multi-Model Ensemble Analysis of Runoff Extremes for Climate Change Impact Assessments

  In this study multi-model ensemble analysis of extreme runoff is performed based on eight regional climate models (RCMs) provided by the North American Regional Climate Change Assessment Program (NARCCAP). ...

  Citation × Citation Citation Abstract Copy Title: Multi-Model Ensemble Analysis of Runoff Extremes for Climate Change Impact Assessments Author: Najafi, Mohammad Reza, Moradkhani, Hamid Year: 2015 In this study multi-model ensemble analysis of extreme runoff is performed based on eight regional climate models (RCMs) provided by the North American Regional Climate Change Assessment Program (NARCCAP). Hydrologic simulation is performed by driving the Variable Infiltration Capacity (VIC) model over the Pacific Northwest region, for historical and future time periods. Extreme event analysis is then conducted using spatial hierarchical Bayesian modeling (SHB). Ensemble merging of extreme runoff is carried out using Bayesian Model Averaging (BMA) in which spatially distributed weights corresponding to each regional climate model are obtained. Comparison of the residuals before and after the multi-model combination shows that the merged signal generally outperforms the best individual signal. The climate model simulations show close performance regarding maximum and minimum temperature and wind speed, however, the differences are more pronounced for precipitation and runoff. Between-model variances increase for the future time series compared to the historical ones indicating larger uncertainties in climate change projections. The combined model is then used to predict projected seasonal runoff extremes and compare them with historical simulations. Ensemble average results suggest that seasonal extreme runoff will increase in most regions in particular the Rockies and west of the Cascades. Title: Multi-Model Ensemble Analysis of Runoff Extremes for Climate Change Impact Assessments Author: Najafi, Mohammad Reza, Moradkhani, Hamid Year: 2015 In this study multi-model ensemble analysis of extreme runoff is performed based on eight regional climate models (RCMs) provided by the North American Regional Climate Change Assessment Program (NARCCAP). Hydrologic simulation is performed by driving the Variable Infiltration Capacity (VIC) model over the Pacific Northwest region, for historical and future time periods. Extreme event analysis is then conducted using spatial hierarchical Bayesian modeling (SHB). Ensemble merging of extreme runoff is carried out using Bayesian Model Averaging (BMA) in which spatially distributed weights corresponding to each regional climate model are obtained. Comparison of the residuals before and after the multi-model combination shows that the merged signal generally outperforms the best individual signal. The climate model simulations show close performance regarding maximum and minimum temperature and wind speed, however, the differences are more pronounced for precipitation and runoff. Between-model variances increase for the future time series compared to the historical ones indicating larger uncertainties in climate change projections. The combined model is then used to predict projected seasonal runoff extremes and compare them with historical simulations. Ensemble average results suggest that seasonal extreme runoff will increase in most regions in particular the Rockies and west of the Cascades. Close

• 7. [Article] Daily Discharge Forecasting Using Least Square Support Vector Regression and Regression Tree

  Prediction of river flow is one of the main issues in the field of water resources management. Because of the complexity of the rainfall-runoff process, data-driven methods have gained increased importance. ...

  Citation × Citation Citation Abstract Copy Title: Daily Discharge Forecasting Using Least Square Support Vector Regression and Regression Tree Author: Sahraei, Shahram, Andalani, Saeed Zare, Zakermoshfegh, Mohammad, Sisakht, Babak Nikeghbal, Talebbeydokhti, Nasser, Moradkhani, Hamid Year: 2015 Prediction of river flow is one of the main issues in the field of water resources management. Because of the complexity of the rainfall-runoff process, data-driven methods have gained increased importance. In the current study, two newly developed models called Least Square Support Vector Regression (LSSVR) and Regression Tree (RT) are used. The LSSVR model is based on the constrained optimization method and applies structural risk minimization in order to yield a general optimized result. Also in the RT, data movement is based on laws discovered in the tree. Both models have been applied to the data in the Kashkan watershed. Variables include (a) recorded precipitation values in the Kashkan watershed stations, and (b) outlet discharge values of one and two previous days. Present discharge is considered as output of the two models. Following that, a sensitivity analysis has been carried out on the input features and less important features has been diminished so that both models have provided better prediction on the data. The final results of both models have been compared. It was found that the LSSVR model has better performance. Finally, the results present these models as a suitable models in river flow forecasting. Title: Daily Discharge Forecasting Using Least Square Support Vector Regression and Regression Tree Author: Sahraei, Shahram, Andalani, Saeed Zare, Zakermoshfegh, Mohammad, Sisakht, Babak Nikeghbal, Talebbeydokhti, Nasser, Moradkhani, Hamid Year: 2015 Prediction of river flow is one of the main issues in the field of water resources management. Because of the complexity of the rainfall-runoff process, data-driven methods have gained increased importance. In the current study, two newly developed models called Least Square Support Vector Regression (LSSVR) and Regression Tree (RT) are used. The LSSVR model is based on the constrained optimization method and applies structural risk minimization in order to yield a general optimized result. Also in the RT, data movement is based on laws discovered in the tree. Both models have been applied to the data in the Kashkan watershed. Variables include (a) recorded precipitation values in the Kashkan watershed stations, and (b) outlet discharge values of one and two previous days. Present discharge is considered as output of the two models. Following that, a sensitivity analysis has been carried out on the input features and less important features has been diminished so that both models have provided better prediction on the data. The final results of both models have been compared. It was found that the LSSVR model has better performance. Finally, the results present these models as a suitable models in river flow forecasting. Close