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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] Investigating the Impact of Remotely Sensed Precipitation and Hydrologic Model Uncertainties on the Ensemble Streamflow Forecasting

  In the past few years sequential data assimilation (SDA) methods have emerged as the best possible method at hand to properly treat all sources of error in hydrological modeling. However, very few studies ...

  Citation × Citation Citation Abstract Copy Title: Investigating the Impact of Remotely Sensed Precipitation and Hydrologic Model Uncertainties on the Ensemble Streamflow Forecasting Author: Moradkhani, Hamid, Hsu, K., Hong, Y., Sorooshian, S. Year: 2006 In the past few years sequential data assimilation (SDA) methods have emerged as the best possible method at hand to properly treat all sources of error in hydrological modeling. However, very few studies have actually implemented SDA methods using realistic input error models for precipitation. In this study we use particle filtering as a SDA method to propagate input errors through a conceptual hydrologic model and quantify the state, parameter and streamflow uncertainties. Recent progress in satellite-based precipitation observation techniques offers an attractive option for considering spatiotemporal variation of precipitation. Therefore, we use the PERSIANN-CCS precipitation product to propagate input errors through our hydrologic model. Some uncertainty scenarios are set up to incorporate and investigate the impact of the individual uncertainty sources from precipitation, parameters and also combined error sources on the hydrologic response. Also probabilistic measures are used to quantify the quality of ensemble prediction. Title: Investigating the Impact of Remotely Sensed Precipitation and Hydrologic Model Uncertainties on the Ensemble Streamflow Forecasting Author: Moradkhani, Hamid, Hsu, K., Hong, Y., Sorooshian, S. Year: 2006 In the past few years sequential data assimilation (SDA) methods have emerged as the best possible method at hand to properly treat all sources of error in hydrological modeling. However, very few studies have actually implemented SDA methods using realistic input error models for precipitation. In this study we use particle filtering as a SDA method to propagate input errors through a conceptual hydrologic model and quantify the state, parameter and streamflow uncertainties. Recent progress in satellite-based precipitation observation techniques offers an attractive option for considering spatiotemporal variation of precipitation. Therefore, we use the PERSIANN-CCS precipitation product to propagate input errors through our hydrologic model. Some uncertainty scenarios are set up to incorporate and investigate the impact of the individual uncertainty sources from precipitation, parameters and also combined error sources on the hydrologic response. Also probabilistic measures are used to quantify the quality of ensemble prediction. Close

• 3. [Article] Uncertainty Quantification of Satellite Precipitation Estimation and Monte Carlo Assessment of the Error Propagation into Hydrologic Response

  The aim of this paper is to foster the development of an end-to-end uncertainty analysis framework that can quantify satellite-based precipitation estimation error characteristics and to assess the influence ...

  Citation × Citation Citation Abstract Copy Title: Uncertainty Quantification of Satellite Precipitation Estimation and Monte Carlo Assessment of the Error Propagation into Hydrologic Response Author: Moradkhani, Hamid, Hong, Yang, Sorooshian, Soroosh Year: 2006 The aim of this paper is to foster the development of an end-to-end uncertainty analysis framework that can quantify satellite-based precipitation estimation error characteristics and to assess the influence of the error propagation into hydrological simulation. First, the error associated with the satellite-based precipitation estimates is assumed as a nonlinear function of rainfall space-time integration scale, rain intensity, and sampling frequency. Parameters of this function are determined by using high-resolution satellite-based precipitation estimates and gauge-corrected radar rainfall data over the southwestern United States. Parameter sensitivity analysis at 16 selected 5ø ? 5ø latitude-longitude grids shows about 12?16% of variance of each parameter with respect to its mean value. Afterward, the influence of precipitation estimation error on the uncertainty of hydrological response is further examined with Monte Carlo simulation. By this approach, 100 ensemble members of precipitation data are generated, as forcing input to a conceptual rainfall-runoff hydrologic model, and the resulting uncertainty in the streamflow prediction is quantified. Case studies are demonstrated over the Leaf River basin in Mississippi. Compared with conventional procedure, i.e., precipitation estimation error as fixed ratio of rain rates, the proposed framework provides more realistic quantification of precipitation estimation error and offers improved uncertainty assessment of the error propagation into hydrologic simulation. Further study shows that the radar rainfall-generated streamflow sequences are consistently contained by the uncertainty bound of satellite rainfall generated streamflow at the 95% confidence interval. Title: Uncertainty Quantification of Satellite Precipitation Estimation and Monte Carlo Assessment of the Error Propagation into Hydrologic Response Author: Moradkhani, Hamid, Hong, Yang, Sorooshian, Soroosh Year: 2006 The aim of this paper is to foster the development of an end-to-end uncertainty analysis framework that can quantify satellite-based precipitation estimation error characteristics and to assess the influence of the error propagation into hydrological simulation. First, the error associated with the satellite-based precipitation estimates is assumed as a nonlinear function of rainfall space-time integration scale, rain intensity, and sampling frequency. Parameters of this function are determined by using high-resolution satellite-based precipitation estimates and gauge-corrected radar rainfall data over the southwestern United States. Parameter sensitivity analysis at 16 selected 5ø ? 5ø latitude-longitude grids shows about 12?16% of variance of each parameter with respect to its mean value. Afterward, the influence of precipitation estimation error on the uncertainty of hydrological response is further examined with Monte Carlo simulation. By this approach, 100 ensemble members of precipitation data are generated, as forcing input to a conceptual rainfall-runoff hydrologic model, and the resulting uncertainty in the streamflow prediction is quantified. Case studies are demonstrated over the Leaf River basin in Mississippi. Compared with conventional procedure, i.e., precipitation estimation error as fixed ratio of rain rates, the proposed framework provides more realistic quantification of precipitation estimation error and offers improved uncertainty assessment of the error propagation into hydrologic simulation. Further study shows that the radar rainfall-generated streamflow sequences are consistently contained by the uncertainty bound of satellite rainfall generated streamflow at the 95% confidence interval. Close

• 4. [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

• 5. [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

• 6. [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

• 7. [Article] Statistical Comparisons of Watershed-Scale Response to Climate Change in Selected Basins across the United States

  In an earlier global climate-change study, air temperature and precipitation data for the entire twenty-first century simulated from five general circulation models were used as input to precalibrated ...

  Citation × Citation Citation Abstract Copy Title: Statistical Comparisons of Watershed-Scale Response to Climate Change in Selected Basins across the United States Author: Risley, John, Moradkhani, Hamid, Hay, Lauren, Markstrom, Steve Year: 2011 In an earlier global climate-change study, air temperature and precipitation data for the entire twenty-first century simulated from five general circulation models were used as input to precalibrated watershed models for 14 selected basins across the United States. Simulated daily streamflow and energy output from the watershed models were used to compute a range of statistics. With a side-by-side comparison of the statistical analyses for the 14 basins, regional climatic and hydrologic trends over the twenty-first century could be qualitatively identified. Low-flow statistics (95% exceedance, 7-day mean annual minimum, and summer mean monthly streamflow) decreased for almost all basins. Annual maximum daily streamflow also decreased in all the basins, except for all four basins in California and the Pacific Northwest. An analysis of the supply of available energy and water for the basins indicated that ratios of evaporation to precipitation and potential evapotranspiration to precipitation for most of the basins will increase. Probability density functions (PDFs) were developed to assess the uncertainty and multimodality in the impact of climate change on mean annual streamflow variability. Kolmogorov?Smirnov tests showed significant differences between the beginning and ending twenty-first-century PDFs for most of the basins, with the exception of four basins that are located in the western United States. Almost none of the basin PDFs were normally distributed, and two basins in the upper Midwest had PDFs that were extremely dispersed and skewed. Title: Statistical Comparisons of Watershed-Scale Response to Climate Change in Selected Basins across the United States Author: Risley, John, Moradkhani, Hamid, Hay, Lauren, Markstrom, Steve Year: 2011 In an earlier global climate-change study, air temperature and precipitation data for the entire twenty-first century simulated from five general circulation models were used as input to precalibrated watershed models for 14 selected basins across the United States. Simulated daily streamflow and energy output from the watershed models were used to compute a range of statistics. With a side-by-side comparison of the statistical analyses for the 14 basins, regional climatic and hydrologic trends over the twenty-first century could be qualitatively identified. Low-flow statistics (95% exceedance, 7-day mean annual minimum, and summer mean monthly streamflow) decreased for almost all basins. Annual maximum daily streamflow also decreased in all the basins, except for all four basins in California and the Pacific Northwest. An analysis of the supply of available energy and water for the basins indicated that ratios of evaporation to precipitation and potential evapotranspiration to precipitation for most of the basins will increase. Probability density functions (PDFs) were developed to assess the uncertainty and multimodality in the impact of climate change on mean annual streamflow variability. Kolmogorov?Smirnov tests showed significant differences between the beginning and ending twenty-first-century PDFs for most of the basins, with the exception of four basins that are located in the western United States. Almost none of the basin PDFs were normally distributed, and two basins in the upper Midwest had PDFs that were extremely dispersed and skewed. Close

• 8. [Article] Identification of Appropriate Lags and Temporal Resolutions for Low Flow Indicators in the River Rhine to Forecast Low Flows with Different Lead Times

  The aim of this paper is to assess the relative importance of low flow indicators for the River Rhine and to identify their appropriate temporal lag and resolution. This is done in the context of low flow ...

  Citation × Citation Citation Abstract Copy Title: Identification of Appropriate Lags and Temporal Resolutions for Low Flow Indicators in the River Rhine to Forecast Low Flows with Different Lead Times Author: Demirel, Mehmet, Booij, Martijn, Hoekstra, Arjen Year: 2012 The aim of this paper is to assess the relative importance of low flow indicators for the River Rhine and to identify their appropriate temporal lag and resolution. This is done in the context of low flow forecasting with lead times of 14 and 90 days. First, the Rhine basin is subdivided into seven sub-basins. By considering the dominant processes in the sub-basins, five low flow indicators were selected: precipitation, potential evapotranspiration, groundwater storage, snow storage and lake storage. Correlation analysis was then carried out to determine the relationship between observed low flows and preselected indicators with varying lags (days) and temporal resolutions (from 1 day to 7 months). The results show that the most important low flow indicators in the Alpine sub-basins for forecasts with a lead time of 14 days are potential evapotranspiration with a large lag and temporal resolution, and lake levels with a small lag and temporal resolution. In the other sub-basins groundwater levels with a small lag and temporal resolution are important in addition to potential evapotranspiration with a large lag and temporal resolution. The picture is slightly different for forecasts with a lead time of 90 days. The snow storage in the Alpine sub-basins and the precipitation in the other sub-basins also become relevant for low flows. Consequently, the most important low flow indicators in the Alpine sub-basins for forecasts with a lead time of 90 days are potential evapotranspiration with a large lag and temporal resolution, lake levels with a small lag and temporal resolution and snow storage with a small lag and large temporal resolution. The resultant correlation maps provide appropriate lags and temporal resolutions for indicators to forecast low flows in the River Rhine with different lead times. Title: Identification of Appropriate Lags and Temporal Resolutions for Low Flow Indicators in the River Rhine to Forecast Low Flows with Different Lead Times Author: Demirel, Mehmet, Booij, Martijn, Hoekstra, Arjen Year: 2012 The aim of this paper is to assess the relative importance of low flow indicators for the River Rhine and to identify their appropriate temporal lag and resolution. This is done in the context of low flow forecasting with lead times of 14 and 90 days. First, the Rhine basin is subdivided into seven sub-basins. By considering the dominant processes in the sub-basins, five low flow indicators were selected: precipitation, potential evapotranspiration, groundwater storage, snow storage and lake storage. Correlation analysis was then carried out to determine the relationship between observed low flows and preselected indicators with varying lags (days) and temporal resolutions (from 1 day to 7 months). The results show that the most important low flow indicators in the Alpine sub-basins for forecasts with a lead time of 14 days are potential evapotranspiration with a large lag and temporal resolution, and lake levels with a small lag and temporal resolution. In the other sub-basins groundwater levels with a small lag and temporal resolution are important in addition to potential evapotranspiration with a large lag and temporal resolution. The picture is slightly different for forecasts with a lead time of 90 days. The snow storage in the Alpine sub-basins and the precipitation in the other sub-basins also become relevant for low flows. Consequently, the most important low flow indicators in the Alpine sub-basins for forecasts with a lead time of 90 days are potential evapotranspiration with a large lag and temporal resolution, lake levels with a small lag and temporal resolution and snow storage with a small lag and large temporal resolution. The resultant correlation maps provide appropriate lags and temporal resolutions for indicators to forecast low flows in the River Rhine with different lead times. Close

• 9. [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

• 10. [Article] Improving the Characterization of Initial Condition for Ensemble Streamflow Prediction Using Data Assimilation

  Within the National Weather Service River Forecast System, water supply forecasting is performed through Ensemble Streamflow Prediction (ESP). ESP relies both on the estimation of initial conditions and ...

  Citation × Citation Citation Abstract Copy Title: Improving the Characterization of Initial Condition for Ensemble Streamflow Prediction Using Data Assimilation Author: DeChant, Caleb Matthew, Moradkhani, Hamid Year: 2011 Within the National Weather Service River Forecast System, water supply forecasting is performed through Ensemble Streamflow Prediction (ESP). ESP relies both on the estimation of initial conditions and historically resampled forcing data to produce seasonal volumetric forecasts. In the western US, the accuracy of initial condition estimation is particularly important due to the large quantities of water stored in mountain snowpack. In order to improve the estimation of snow quantities, this study explores the use of ensemble data assimilation. Rather than relying entirely on the model to create single deterministic initial snow water storage, as currently implemented in operational forecasting, this study incorporates SNOTEL data along with model predictions to create an ensemble based probabilistic estimation of snow water storage. This creates a framework to account for initial condition uncertainty in addition to forcing uncertainty. The results presented in this study suggest that data assimilation has the potential to improve ESP for probabilistic volumetric forecasts but is limited by the available observations. Title: Improving the Characterization of Initial Condition for Ensemble Streamflow Prediction Using Data Assimilation Author: DeChant, Caleb Matthew, Moradkhani, Hamid Year: 2011 Within the National Weather Service River Forecast System, water supply forecasting is performed through Ensemble Streamflow Prediction (ESP). ESP relies both on the estimation of initial conditions and historically resampled forcing data to produce seasonal volumetric forecasts. In the western US, the accuracy of initial condition estimation is particularly important due to the large quantities of water stored in mountain snowpack. In order to improve the estimation of snow quantities, this study explores the use of ensemble data assimilation. Rather than relying entirely on the model to create single deterministic initial snow water storage, as currently implemented in operational forecasting, this study incorporates SNOTEL data along with model predictions to create an ensemble based probabilistic estimation of snow water storage. This creates a framework to account for initial condition uncertainty in addition to forcing uncertainty. The results presented in this study suggest that data assimilation has the potential to improve ESP for probabilistic volumetric forecasts but is limited by the available observations. Close