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• 1. [Article] A Probabilistic Drought Forecasting Framework: A Combined Dynamical and Statistical Approach

  In order to improve drought forecasting skill, this study develops a probabilistic drought forecasting framework comprised of dynamical and statistical modeling components. The novelty of this study is ...

  Citation × Citation Citation Abstract Copy Title: A Probabilistic Drought Forecasting Framework: A Combined Dynamical and Statistical Approach Author: Yan, Hongxiang, Moradkhani, Hamid, Zarekarizi, Mahkameh Year: 2017 In order to improve drought forecasting skill, this study develops a probabilistic drought forecasting framework comprised of dynamical and statistical modeling components. The novelty of this study is to seek the use of data assimilation to quantify initial condition uncertainty with the Monte Carlo ensemble members, rather than relying entirely on the hydrologic model or land surface model to generate a single deterministic initial condition, as currently implemented in the operational drought forecasting systems. Next, the initial condition uncertainty is quantified through data assimilation and coupled with a newly developed probabilistic drought forecasting model using a copula function. The initial condition at each forecast start date are sampled from the data assimilation ensembles for forecast initialization. Finally, seasonal drought forecasting products are generated with the updated initial conditions. This study introduces the theory behind the proposed drought forecasting system, with an application in Columbia River Basin, Pacific Northwest, United States. Results from both synthetic and real case studies suggest that the proposed drought forecasting system significantly improves the seasonal drought forecasting skills and can facilitate the state drought preparation and declaration, at least three months before the official state drought declaration. Title: A Probabilistic Drought Forecasting Framework: A Combined Dynamical and Statistical Approach Author: Yan, Hongxiang, Moradkhani, Hamid, Zarekarizi, Mahkameh Year: 2017 In order to improve drought forecasting skill, this study develops a probabilistic drought forecasting framework comprised of dynamical and statistical modeling components. The novelty of this study is to seek the use of data assimilation to quantify initial condition uncertainty with the Monte Carlo ensemble members, rather than relying entirely on the hydrologic model or land surface model to generate a single deterministic initial condition, as currently implemented in the operational drought forecasting systems. Next, the initial condition uncertainty is quantified through data assimilation and coupled with a newly developed probabilistic drought forecasting model using a copula function. The initial condition at each forecast start date are sampled from the data assimilation ensembles for forecast initialization. Finally, seasonal drought forecasting products are generated with the updated initial conditions. This study introduces the theory behind the proposed drought forecasting system, with an application in Columbia River Basin, Pacific Northwest, United States. Results from both synthetic and real case studies suggest that the proposed drought forecasting system significantly improves the seasonal drought forecasting skills and can facilitate the state drought preparation and declaration, at least three months before the official state drought declaration. Close

• 2. [Article] Ensemble Combination of Seasonal Streamflow Forecasts

  Various hydrologic models with different complexities have been developed to represent the characteristics of river basins, improve streamflow forecasts such as seasonal volumetric flow predictions, and ...

  Citation × Citation Citation Abstract Copy Title: Ensemble Combination of Seasonal Streamflow Forecasts Author: Najafi, Mohammad Reza, Moradkhani, Hamid Year: 2015 Various hydrologic models with different complexities have been developed to represent the characteristics of river basins, improve streamflow forecasts such as seasonal volumetric flow predictions, and meet other demands from different stakeholders. Because no single hydrologic model is able to perfectly simulate the observed flow, multimodel combination techniques are developed to combine forecasts obtained from different models and to quantify the uncertainties with the goal of improving upon single-model performance. In this study, a comprehensive set of multimodel ensemble averaging techniques with varying complexities are investigated for operational forecasting over four river basins in the Western United States. Ensemble merging models are divided into three categories of simple, intermediate, and complex, and comparison is made between each class by using a bootstrap approach. Analysis suggests that model combination effectively improves most of the individual seasonal forecasts and can outperform the best forecast model. Simple average, median, Bates-Granger, constrained linear regression, and Bayesian model averaging optimized by expectation maximization showed better results compared with other methods over three basins. For the Rogue River basin, the intermediate and complex models outperformed most of the individual forecasts and the simple methods. Multimodeling techniques based on information criteria showed similar performances. Title: Ensemble Combination of Seasonal Streamflow Forecasts Author: Najafi, Mohammad Reza, Moradkhani, Hamid Year: 2015 Various hydrologic models with different complexities have been developed to represent the characteristics of river basins, improve streamflow forecasts such as seasonal volumetric flow predictions, and meet other demands from different stakeholders. Because no single hydrologic model is able to perfectly simulate the observed flow, multimodel combination techniques are developed to combine forecasts obtained from different models and to quantify the uncertainties with the goal of improving upon single-model performance. In this study, a comprehensive set of multimodel ensemble averaging techniques with varying complexities are investigated for operational forecasting over four river basins in the Western United States. Ensemble merging models are divided into three categories of simple, intermediate, and complex, and comparison is made between each class by using a bootstrap approach. Analysis suggests that model combination effectively improves most of the individual seasonal forecasts and can outperform the best forecast model. Simple average, median, Bates-Granger, constrained linear regression, and Bayesian model averaging optimized by expectation maximization showed better results compared with other methods over three basins. For the Rogue River basin, the intermediate and complex models outperformed most of the individual forecasts and the simple methods. Multimodeling techniques based on information criteria showed similar performances. Close

• 3. [Article] Centennial Drought Outlook Over the CONUS Using NASA-NEX Downscaled Climate Ensemble

  Drought is a natural hazard developing slowly and affecting large areas which may have severe consequences on society and economy. Due to the effects of climate change, drought is expected to exacerbate ...

  Citation × Citation Citation Abstract Copy Title: Centennial Drought Outlook Over the CONUS Using NASA-NEX Downscaled Climate Ensemble Author: Ahmadalipour, Ali, Moradkhani, Hamid, Svoboda, Mark Year: 2017 Drought is a natural hazard developing slowly and affecting large areas which may have severe consequences on society and economy. Due to the effects of climate change, drought is expected to exacerbate in various regions in future. In this study, the impact of climate change on drought characteristics is assessed, and statistical methods are employed to analyse the significance of projections. This is the first study utilizing 21 recently available downscaled global climate models generated by NASA (NEX-GDDP) to evaluate drought projections over various regions across the United States. Drought is investigated through a multi-model dual-index dual-scenario approach to probabilistically analyse drought attributes while characterizing the uncertainty in future drought projections. Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index values at the seasonal scale (3 months) are used to project and analyse meteorological drought conditions from 1950 to 2099 at 0.25° spatial resolution. Two future concentration pathways of RCP4.5 and RCP8.5 are considered for this analysis. Accounting for the combined effects of precipitation and temperature variations reveals a considerable aggravation in severity and extent of future drought in the western United States and a tendency toward more frequent and intense summer droughts across the Contiguous United States. Title: Centennial Drought Outlook Over the CONUS Using NASA-NEX Downscaled Climate Ensemble Author: Ahmadalipour, Ali, Moradkhani, Hamid, Svoboda, Mark Year: 2017 Drought is a natural hazard developing slowly and affecting large areas which may have severe consequences on society and economy. Due to the effects of climate change, drought is expected to exacerbate in various regions in future. In this study, the impact of climate change on drought characteristics is assessed, and statistical methods are employed to analyse the significance of projections. This is the first study utilizing 21 recently available downscaled global climate models generated by NASA (NEX-GDDP) to evaluate drought projections over various regions across the United States. Drought is investigated through a multi-model dual-index dual-scenario approach to probabilistically analyse drought attributes while characterizing the uncertainty in future drought projections. Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index values at the seasonal scale (3 months) are used to project and analyse meteorological drought conditions from 1950 to 2099 at 0.25° spatial resolution. Two future concentration pathways of RCP4.5 and RCP8.5 are considered for this analysis. Accounting for the combined effects of precipitation and temperature variations reveals a considerable aggravation in severity and extent of future drought in the western United States and a tendency toward more frequent and intense summer droughts across the Contiguous United States. Close

• 4. [Article] Combined Assimilation of Streamflow and Satellite Soil Moisture with the Particle Filter and Geostatistical Modeling

  Assimilation of satellite soil moisture and streamflow data into a distributed hydrologic model has received increasing attention over the past few years. This study provides a detailed analysis of the ...

  Citation × Citation Citation Abstract Copy Title: Combined Assimilation of Streamflow and Satellite Soil Moisture with the Particle Filter and Geostatistical Modeling Author: Yan, Hongxiang, Moradkhani, Hamid Year: 2016 Assimilation of satellite soil moisture and streamflow data into a distributed hydrologic model has received increasing attention over the past few years. This study provides a detailed analysis of the joint and separate assimilation of streamflow and Advanced Scatterometer (ASCAT) surface soil moisture into a distributed Sacramento Soil Moisture Accounting (SAC-SMA) model, with the use of recently developed particle filter-Markov chain Monte Carlo (PF-MCMC) method. Performance is assessed over the Salt River Watershed in Arizona, which is one of the watersheds without anthropogenic effects in Model Parameter Estimation Experiment (MOPEX). A total of five data assimilation (DA) scenarios are designed and the effects of the locations of streamflow gauges and the ASCAT soil moisture on the predictions of soil moisture and streamflow are assessed. In addition, a geostatistical model is introduced to overcome the significantly biased satellite soil moisture and also discontinuity issue. The results indicate that: (1) solely assimilating outlet streamflow can lead to biased soil moisture estimation; (2) when the study area can only be partially covered by the satellite data, the geostatistical approach can estimate the soil moisture for those uncovered grid cells; (3) joint assimilation of streamflow and soil moisture from geostatistical modeling can further improve the surface soil moisture prediction. This study recommends that the geostatistical model is a helpful tool to aid the remote sensing technique and the hydrologic DA study. Title: Combined Assimilation of Streamflow and Satellite Soil Moisture with the Particle Filter and Geostatistical Modeling Author: Yan, Hongxiang, Moradkhani, Hamid Year: 2016 Assimilation of satellite soil moisture and streamflow data into a distributed hydrologic model has received increasing attention over the past few years. This study provides a detailed analysis of the joint and separate assimilation of streamflow and Advanced Scatterometer (ASCAT) surface soil moisture into a distributed Sacramento Soil Moisture Accounting (SAC-SMA) model, with the use of recently developed particle filter-Markov chain Monte Carlo (PF-MCMC) method. Performance is assessed over the Salt River Watershed in Arizona, which is one of the watersheds without anthropogenic effects in Model Parameter Estimation Experiment (MOPEX). A total of five data assimilation (DA) scenarios are designed and the effects of the locations of streamflow gauges and the ASCAT soil moisture on the predictions of soil moisture and streamflow are assessed. In addition, a geostatistical model is introduced to overcome the significantly biased satellite soil moisture and also discontinuity issue. The results indicate that: (1) solely assimilating outlet streamflow can lead to biased soil moisture estimation; (2) when the study area can only be partially covered by the satellite data, the geostatistical approach can estimate the soil moisture for those uncovered grid cells; (3) joint assimilation of streamflow and soil moisture from geostatistical modeling can further improve the surface soil moisture prediction. This study recommends that the geostatistical model is a helpful tool to aid the remote sensing technique and the hydrologic DA study. Close

• 5. [Article] Detecting Non-Stationary Hydrologic Model Parameters in a Paired Catchment

  Non-stationarity represents one of the major challenges facing hydrologists. There exists a need to develop modelling systems that are capable of accounting for potential catchment changes, in order to ...

  Citation × Citation Citation Abstract Copy Title: Detecting Non-Stationary Hydrologic Model Parameters in a Paired Catchment Author: Pathiraja, Sahani, Marshall, Lucy, Sharma, Ashish, Moradkhani, Hamid Year: 2016 Non-stationarity represents one of the major challenges facing hydrologists. There exists a need to develop modelling systems that are capable of accounting for potential catchment changes, in order to provide useful predictions for the future. Such changes may be due to climatic temporal variations or human induced changes to land cover. Extensive research has been undertaken on the impacts of land-use change on hydrologic behaviour, however, few studies have examined this issue in a predictive modelling context. In this paper, we investigate whether a time varying model parameter estimation framework that uses the principles of Data Assimilation can improve prediction for two pairs of experimental catchments in Western Australia. All catchments were initially forested, but after three years one catchment was fully cleared whilst another had only 50% of its area cleared. Their adjacent catchments remained unchanged as a control. Temporal variations in parameters were detected for both treated catchments, with no comparable variations for the control catchments. Improved streamflow prediction and representation of soil moisture dynamics were also seen for the time varying parameter case, compared to when a time invariant parameter set from the calibration period was used. While we use the above mentioned catchments to illustrate the usefulness of the approach, the methods are generic and equally applicable in other settings. This study serves as an important validation step to demonstrate the potential for time varying model structures to improve both predictions and modelling of changing catchments. Title: Detecting Non-Stationary Hydrologic Model Parameters in a Paired Catchment Author: Pathiraja, Sahani, Marshall, Lucy, Sharma, Ashish, Moradkhani, Hamid Year: 2016 Non-stationarity represents one of the major challenges facing hydrologists. There exists a need to develop modelling systems that are capable of accounting for potential catchment changes, in order to provide useful predictions for the future. Such changes may be due to climatic temporal variations or human induced changes to land cover. Extensive research has been undertaken on the impacts of land-use change on hydrologic behaviour, however, few studies have examined this issue in a predictive modelling context. In this paper, we investigate whether a time varying model parameter estimation framework that uses the principles of Data Assimilation can improve prediction for two pairs of experimental catchments in Western Australia. All catchments were initially forested, but after three years one catchment was fully cleared whilst another had only 50% of its area cleared. Their adjacent catchments remained unchanged as a control. Temporal variations in parameters were detected for both treated catchments, with no comparable variations for the control catchments. Improved streamflow prediction and representation of soil moisture dynamics were also seen for the time varying parameter case, compared to when a time invariant parameter set from the calibration period was used. While we use the above mentioned catchments to illustrate the usefulness of the approach, the methods are generic and equally applicable in other settings. This study serves as an important validation step to demonstrate the potential for time varying model structures to improve both predictions and modelling of changing catchments. Close

• 6. [Article] Time Varying Parameter Models for Catchments with Land Use Change: the Importance of Model Structure

  Rapid population and economic growth in South-East-Asia has been accompanied by extensive land use change with consequent impacts on catchment hydrology. Modelling methodologies capable of handling changing ...

  Citation × Citation Citation Abstract Copy Title: Time Varying Parameter Models for Catchments with Land Use Change: the Importance of Model Structure Author: Pathiraja, Sahani, Anghileri, Daniela, Burlando, Paolo, Sharma, Ashish, Marshall, Lucy, Moradkhani, Hamid Year: 2017 Rapid population and economic growth in South-East-Asia has been accompanied by extensive land use change with consequent impacts on catchment hydrology. Modelling methodologies capable of handling changing land use conditions are therefore becoming ever more important, and are receiving increasing attention from hydrologists. A recently developed Data Assimilation based framework that allows model parameters to vary through time in response to signals of change in observations is considered for a medium sized catchment (2880 km²) in Northern Vietnam experiencing substantial but gradual land cover change. We investigate the efficacy of the method as well as the importance of the chosen model structure in ensuring the success of time varying parameter methods. The framework was utilized with two conceptual models (HBV and HyMOD) that gave good quality streamflow predictions during pre-change conditions. Although both time varying parameter models gave improved streamflow predictions under changed conditions compared to the time invariant parameter model, persistent biases for low flows were apparent in the HyMOD case. It was found that HyMOD was not suited to representing the modified baseflow conditions, resulting in extreme and unrealistic time varying parameter estimates. This work shows that the chosen model can be critical for ensuring the time varying parameter framework successfully models streamflow under changed land cover conditions. It also serves as an effective tool for separating the influence of climatic and land use change in retrospective studies where the lack of a paired control catchment precludes such an assessment. Title: Time Varying Parameter Models for Catchments with Land Use Change: the Importance of Model Structure Author: Pathiraja, Sahani, Anghileri, Daniela, Burlando, Paolo, Sharma, Ashish, Marshall, Lucy, Moradkhani, Hamid Year: 2017 Rapid population and economic growth in South-East-Asia has been accompanied by extensive land use change with consequent impacts on catchment hydrology. Modelling methodologies capable of handling changing land use conditions are therefore becoming ever more important, and are receiving increasing attention from hydrologists. A recently developed Data Assimilation based framework that allows model parameters to vary through time in response to signals of change in observations is considered for a medium sized catchment (2880 km²) in Northern Vietnam experiencing substantial but gradual land cover change. We investigate the efficacy of the method as well as the importance of the chosen model structure in ensuring the success of time varying parameter methods. The framework was utilized with two conceptual models (HBV and HyMOD) that gave good quality streamflow predictions during pre-change conditions. Although both time varying parameter models gave improved streamflow predictions under changed conditions compared to the time invariant parameter model, persistent biases for low flows were apparent in the HyMOD case. It was found that HyMOD was not suited to representing the modified baseflow conditions, resulting in extreme and unrealistic time varying parameter estimates. This work shows that the chosen model can be critical for ensuring the time varying parameter framework successfully models streamflow under changed land cover conditions. It also serves as an effective tool for separating the influence of climatic and land use change in retrospective studies where the lack of a paired control catchment precludes such an assessment. Close

• 7. [Article] Towards an Improved Ensemble Precipitation Forecast: A Probabilistic Post-Processing Approach

  Recently, ensemble post-processing (EPP) has become a commonly used approach for reducing the uncertainty in forcing data and hence hydrologic simulation. The procedure was introduced to build ensemble ...

  Citation × Citation Citation Abstract Copy Title: Towards an Improved Ensemble Precipitation Forecast: A Probabilistic Post-Processing Approach Author: Khajehei, Sepideh, Moradkhani, Hamid Year: 2017 Recently, ensemble post-processing (EPP) has become a commonly used approach for reducing the uncertainty in forcing data and hence hydrologic simulation. The procedure was introduced to build ensemble precipitation forecasts based on the statistical relationship between observations and forecasts. More specifically, the approach relies on a transfer function that is developed based on a bivariate joint distribution between the observations and the simulations in the historical period. The transfer function is used to post-process the forecast. In this study, we propose a Bayesian EPP approach based on copula functions (COP-EPP) to improve the reliability of the precipitation ensemble forecast. Evaluation of the copula-based method is carried out by comparing the performance of the generated ensemble precipitation with the outputs from an existing procedure, i.e. mixed type meta-Gaussian distribution. Monthly precipitation from Climate Forecast System Reanalysis (CFS) and gridded observation from Parameter-Elevation Relationships on Independent Slopes Model (PRISM) have been employed to generate the post-processed ensemble precipitation. Deterministic and probabilistic verification frameworks are utilized in order to evaluate the outputs from the proposed technique. Distribution of seasonal precipitation for the generated ensemble from the copula-based technique is compared to the observation and raw forecasts for three sub-basins located in the Western United States. Results show that both techniques are successful in producing reliable and unbiased ensemble forecast, however, the COP-EPP demonstrates considerable improvement in the ensemble forecast in both deterministic and probabilistic verification, in particular in characterizing the extreme events in wet seasons. Title: Towards an Improved Ensemble Precipitation Forecast: A Probabilistic Post-Processing Approach Author: Khajehei, Sepideh, Moradkhani, Hamid Year: 2017 Recently, ensemble post-processing (EPP) has become a commonly used approach for reducing the uncertainty in forcing data and hence hydrologic simulation. The procedure was introduced to build ensemble precipitation forecasts based on the statistical relationship between observations and forecasts. More specifically, the approach relies on a transfer function that is developed based on a bivariate joint distribution between the observations and the simulations in the historical period. The transfer function is used to post-process the forecast. In this study, we propose a Bayesian EPP approach based on copula functions (COP-EPP) to improve the reliability of the precipitation ensemble forecast. Evaluation of the copula-based method is carried out by comparing the performance of the generated ensemble precipitation with the outputs from an existing procedure, i.e. mixed type meta-Gaussian distribution. Monthly precipitation from Climate Forecast System Reanalysis (CFS) and gridded observation from Parameter-Elevation Relationships on Independent Slopes Model (PRISM) have been employed to generate the post-processed ensemble precipitation. Deterministic and probabilistic verification frameworks are utilized in order to evaluate the outputs from the proposed technique. Distribution of seasonal precipitation for the generated ensemble from the copula-based technique is compared to the observation and raw forecasts for three sub-basins located in the Western United States. Results show that both techniques are successful in producing reliable and unbiased ensemble forecast, however, the COP-EPP demonstrates considerable improvement in the ensemble forecast in both deterministic and probabilistic verification, in particular in characterizing the extreme events in wet seasons. Close

• 8. [Article] Accounting For Downscaling and Model Uncertainty in Fine-Resolution Seasonal Climate Projections Over the Columbia River Basin

  Climate change is expected to have severe impacts on natural systems as well as various socio-economic aspects of human life. This has urged scientific communities to improve the understanding of future ...

  Citation × Citation Citation Abstract Copy Title: Accounting For Downscaling and Model Uncertainty in Fine-Resolution Seasonal Climate Projections Over the Columbia River Basin Author: Ahmadalipour, Ali, Moradkhani, Hamid, Rana, Arun Year: 2017 Climate change is expected to have severe impacts on natural systems as well as various socio-economic aspects of human life. This has urged scientific communities to improve the understanding of future climate and reduce the uncertainties associated with projections. In the present study, ten statistically downscaled CMIP5 GCMs at 1/16th deg. spatial resolution from two different downscaling procedures are utilized over the Columbia River Basin (CRB) to assess the changes in climate variables and characterize the associated uncertainties. Three climate variables, i.e. precipitation, maximum temperature, and minimum temperature, are studied for the historical period of 1970–2000 as well as future period of 2010–2099, simulated with representative concentration pathways of RCP4.5 and RCP8.5. Bayesian Model Averaging (BMA) is employed to reduce the model uncertainty and develop a probabilistic projection for each variable in each scenario. Historical comparison of long-term attributes of GCMs and observation suggests a more accurate representation for BMA than individual models. Furthermore, BMA projections are used to investigate future seasonal to annual changes of climate variables. Projections indicate significant increase in annual precipitation and temperature, with varied degree of change across different sub-basins of CRB. We then characterized uncertainty of future projections for each season over CRB. Results reveal that model uncertainty is the main source of uncertainty, among others. However, downscaling uncertainty considerably contributes to the total uncertainty of future projections, especially in summer. On the contrary, downscaling uncertainty appears to be higher than scenario uncertainty for precipitation. © 2017 Springer-Verlag Berlin Heidelberg Title: Accounting For Downscaling and Model Uncertainty in Fine-Resolution Seasonal Climate Projections Over the Columbia River Basin Author: Ahmadalipour, Ali, Moradkhani, Hamid, Rana, Arun Year: 2017 Climate change is expected to have severe impacts on natural systems as well as various socio-economic aspects of human life. This has urged scientific communities to improve the understanding of future climate and reduce the uncertainties associated with projections. In the present study, ten statistically downscaled CMIP5 GCMs at 1/16th deg. spatial resolution from two different downscaling procedures are utilized over the Columbia River Basin (CRB) to assess the changes in climate variables and characterize the associated uncertainties. Three climate variables, i.e. precipitation, maximum temperature, and minimum temperature, are studied for the historical period of 1970–2000 as well as future period of 2010–2099, simulated with representative concentration pathways of RCP4.5 and RCP8.5. Bayesian Model Averaging (BMA) is employed to reduce the model uncertainty and develop a probabilistic projection for each variable in each scenario. Historical comparison of long-term attributes of GCMs and observation suggests a more accurate representation for BMA than individual models. Furthermore, BMA projections are used to investigate future seasonal to annual changes of climate variables. Projections indicate significant increase in annual precipitation and temperature, with varied degree of change across different sub-basins of CRB. We then characterized uncertainty of future projections for each season over CRB. Results reveal that model uncertainty is the main source of uncertainty, among others. However, downscaling uncertainty considerably contributes to the total uncertainty of future projections, especially in summer. On the contrary, downscaling uncertainty appears to be higher than scenario uncertainty for precipitation. © 2017 Springer-Verlag Berlin Heidelberg Close

• 9. [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

• 10. [Article] Improving Soil Moisture Profile Prediction With the Particle Filter-Markov Chain Monte Carlo Method

  Satellite soil moisture estimates have received increasing attention over the past decade. This paper examines the applicability of estimating soil moisture states and soil hydraulic parameters through ...

  Citation × Citation Citation Abstract Copy Title: Improving Soil Moisture Profile Prediction With the Particle Filter-Markov Chain Monte Carlo Method Author: Yan, Hongxiang, DeChant, Caleb Matthew, Moradkhani, Hamid Year: 2015 Satellite soil moisture estimates have received increasing attention over the past decade. This paper examines the applicability of estimating soil moisture states and soil hydraulic parameters through two particle filter (PF) methods: The PF with commonly used sampling importance resampling (PF-SIR) and the PF with recently developed Markov chain Monte Carlo sampling (PF-MCMC) methods. In a synthetic experiment, the potential of assimilating remotely sensed near-surface soil moisture measurements into a 1-D mechanistic soil water model (HYDRUS-1D) using both the PF-SIR and PF-MCMC algorithms is analyzed. The effects of satellite temporal resolution and accuracy, soil type, and ensemble size on the assimilation of soil moisture are analyzed. In a real data experiment, we first validate the Advanced Microwave Scanning Radiometer--Earth Observing System (AMSR-E) soil moisture products in the Oklahoma Little Washita Watershed. Aside from rescaling the remotely sensed soil moisture, a bias correction algorithm is implemented to correct the deep soil moisture estimate. Both the ascending and descending AMSR-E soil moisture data are assimilated into the HYDRUS-1D model. The synthetic assimilation results indicated that, whereas both updating schemes showed the ability to correct the soil moisture state and estimate hydraulic parameters, the PF-MCMC scheme is consistently more accurate than PR-SIR. For real data case, the quality of remotely sensed soil moisture impacts the benefits of their assimilation into the model. The PF-MCMC scheme brought marginal gains than the open-loop simulation in RMSE at both surface and root-zone soil layer, whereas the PF-SIR scheme degraded the open-loop simulation. Title: Improving Soil Moisture Profile Prediction With the Particle Filter-Markov Chain Monte Carlo Method Author: Yan, Hongxiang, DeChant, Caleb Matthew, Moradkhani, Hamid Year: 2015 Satellite soil moisture estimates have received increasing attention over the past decade. This paper examines the applicability of estimating soil moisture states and soil hydraulic parameters through two particle filter (PF) methods: The PF with commonly used sampling importance resampling (PF-SIR) and the PF with recently developed Markov chain Monte Carlo sampling (PF-MCMC) methods. In a synthetic experiment, the potential of assimilating remotely sensed near-surface soil moisture measurements into a 1-D mechanistic soil water model (HYDRUS-1D) using both the PF-SIR and PF-MCMC algorithms is analyzed. The effects of satellite temporal resolution and accuracy, soil type, and ensemble size on the assimilation of soil moisture are analyzed. In a real data experiment, we first validate the Advanced Microwave Scanning Radiometer--Earth Observing System (AMSR-E) soil moisture products in the Oklahoma Little Washita Watershed. Aside from rescaling the remotely sensed soil moisture, a bias correction algorithm is implemented to correct the deep soil moisture estimate. Both the ascending and descending AMSR-E soil moisture data are assimilated into the HYDRUS-1D model. The synthetic assimilation results indicated that, whereas both updating schemes showed the ability to correct the soil moisture state and estimate hydraulic parameters, the PF-MCMC scheme is consistently more accurate than PR-SIR. For real data case, the quality of remotely sensed soil moisture impacts the benefits of their assimilation into the model. The PF-MCMC scheme brought marginal gains than the open-loop simulation in RMSE at both surface and root-zone soil layer, whereas the PF-SIR scheme degraded the open-loop simulation. Close