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• 21. [Article] A Comparative Assessment of Projected Meteorological and Hydrological Droughts: Elucidating the Role of Temperature

  The changing climate and the associated future increases in temperature are expected to have impacts on drought characteristics and hydrologic cycle. This paper investigates the projected changes in spatiotemporal ...

  Citation × Citation Citation Abstract Copy Title: A Comparative Assessment of Projected Meteorological and Hydrological Droughts: Elucidating the Role of Temperature Author: Ahmadalipour, Ali, Moradkhani, Hamid, Demirel, Mehmet C. Year: 2017 The changing climate and the associated future increases in temperature are expected to have impacts on drought characteristics and hydrologic cycle. This paper investigates the projected changes in spatiotemporal characteristics of droughts and their future attributes over the Willamette River Basin (WRB) in the Pacific Northwest U.S. The analysis is performed using two subsets of downscaled CMIP5 global climate models (GCMs) each consisting of 10 models from two future scenarios (RCP4.5 and RCP8.5) for 30 years of historical period (1970–1999) and 90 years of future projections (2010–2099). Hydrologic modeling is conducted using the Precipitation Runoff Modeling System (PRMS) as a robust distributed hydrologic model with lower computational cost compared to other models. Meteorological and hydrological droughts are studied using three drought indices (i.e. Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, Standardized Streamflow Index). Results reveal that the intensity and duration of hydrological droughts are expected to increase over the WRB, albeit the annual precipitation is expected to increase. On the other hand, the intensity of meteorological droughts do not indicate an aggravation for most cases. We explore the changes of hydrometeolorogical variables over the basin in order to understand the causes for such differences and to discover the controlling factors of drought. Furthermore, the uncertainty of projections are quantified for model, scenario, and downscaling uncertainty. Title: A Comparative Assessment of Projected Meteorological and Hydrological Droughts: Elucidating the Role of Temperature Author: Ahmadalipour, Ali, Moradkhani, Hamid, Demirel, Mehmet C. Year: 2017 The changing climate and the associated future increases in temperature are expected to have impacts on drought characteristics and hydrologic cycle. This paper investigates the projected changes in spatiotemporal characteristics of droughts and their future attributes over the Willamette River Basin (WRB) in the Pacific Northwest U.S. The analysis is performed using two subsets of downscaled CMIP5 global climate models (GCMs) each consisting of 10 models from two future scenarios (RCP4.5 and RCP8.5) for 30 years of historical period (1970–1999) and 90 years of future projections (2010–2099). Hydrologic modeling is conducted using the Precipitation Runoff Modeling System (PRMS) as a robust distributed hydrologic model with lower computational cost compared to other models. Meteorological and hydrological droughts are studied using three drought indices (i.e. Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, Standardized Streamflow Index). Results reveal that the intensity and duration of hydrological droughts are expected to increase over the WRB, albeit the annual precipitation is expected to increase. On the other hand, the intensity of meteorological droughts do not indicate an aggravation for most cases. We explore the changes of hydrometeolorogical variables over the basin in order to understand the causes for such differences and to discover the controlling factors of drought. Furthermore, the uncertainty of projections are quantified for model, scenario, and downscaling uncertainty. Close

• 22. [Article] Watershed Response to Climate Change and Fire-Burns in the Upper Umatilla River Basin Using the Precipitation Runoff Modeling System

  This study provides an analysis of watershed response to climate change and forest fire impacts, to better understand the hydrologic budget and inform water management decisions for present and future ...

  Citation × Citation Citation Abstract Copy Title: Watershed Response to Climate Change and Fire-Burns in the Upper Umatilla River Basin Using the Precipitation Runoff Modeling System Author: Yazzie, Kimberly Crystal Year: 2016 This study provides an analysis of watershed response to climate change and forest fire impacts, to better understand the hydrologic budget and inform water management decisions for present and future needs. The study site is 2,365 km2, located in the upper Umatilla River Basin (URB) in northeastern Oregon. The Precipitation Runoff Modeling System, a distributed-parameter, physical-process watershed model, was used in this study. Model calibration yielded a Nash Sutcliffe Model Efficiency of 0.73 for both calibration (1995-2010) and validation (2010-2014) of daily streamflow. Ten Global Climate Models using Coupled Model Intercomparison Project Phase 5 experiments with Representative Concentration Pathways 4.5 and 8.5 (RCP), were used to observe hydrologic regime shifts in the 2020s, 2050s, and 2080s. Mean center timing of flow occurs earlier in the year in both pre- and post-fire conditions, where there are increased winter flows and decreased summer flows throughout the 21st century. Change in temperature and percent change in precipitation is more variable in the summer than winter increasing over time, with a slight decrease in winter precipitation in the 2080s in RCP 8.5. Temperature increases 1.6°C in RCP 4.5 and 3.3°C in RCP 8.5 by the end of the 21st century. The ratio of Snow Water Equivalent to Precipitation decreases 96% in the 2080s in RCP 8.5 before forest cover reduction, and decreases 90-99% after forest cover reduction. Potential basin recharge and the base-flow index are both sustained throughout the 21st century with slight declines before forest cover reduction, with an increase in basin recharge and increase in base-flows in the 2080s after fire-burns. However, the simulated sustained base-flows and area-weighted basin recharge in this study, do not take into account the complex geologic structure of the Columbia River Basalt Group (CRBG). A more robust characterization and simulation of URB aquifer recharge would involve coupling the PRMS model with a groundwater model in a future study. Although groundwater recharge in the CRBG in the URB is not well understood, the long-term decline of groundwater storage presents a serious environmental challenge for the Confederated Tribes of the Umatilla Indian Reservation and communities in the URB. Title: Watershed Response to Climate Change and Fire-Burns in the Upper Umatilla River Basin Using the Precipitation Runoff Modeling System Author: Yazzie, Kimberly Crystal Year: 2016 This study provides an analysis of watershed response to climate change and forest fire impacts, to better understand the hydrologic budget and inform water management decisions for present and future needs. The study site is 2,365 km2, located in the upper Umatilla River Basin (URB) in northeastern Oregon. The Precipitation Runoff Modeling System, a distributed-parameter, physical-process watershed model, was used in this study. Model calibration yielded a Nash Sutcliffe Model Efficiency of 0.73 for both calibration (1995-2010) and validation (2010-2014) of daily streamflow. Ten Global Climate Models using Coupled Model Intercomparison Project Phase 5 experiments with Representative Concentration Pathways 4.5 and 8.5 (RCP), were used to observe hydrologic regime shifts in the 2020s, 2050s, and 2080s. Mean center timing of flow occurs earlier in the year in both pre- and post-fire conditions, where there are increased winter flows and decreased summer flows throughout the 21st century. Change in temperature and percent change in precipitation is more variable in the summer than winter increasing over time, with a slight decrease in winter precipitation in the 2080s in RCP 8.5. Temperature increases 1.6°C in RCP 4.5 and 3.3°C in RCP 8.5 by the end of the 21st century. The ratio of Snow Water Equivalent to Precipitation decreases 96% in the 2080s in RCP 8.5 before forest cover reduction, and decreases 90-99% after forest cover reduction. Potential basin recharge and the base-flow index are both sustained throughout the 21st century with slight declines before forest cover reduction, with an increase in basin recharge and increase in base-flows in the 2080s after fire-burns. However, the simulated sustained base-flows and area-weighted basin recharge in this study, do not take into account the complex geologic structure of the Columbia River Basalt Group (CRBG). A more robust characterization and simulation of URB aquifer recharge would involve coupling the PRMS model with a groundwater model in a future study. Although groundwater recharge in the CRBG in the URB is not well understood, the long-term decline of groundwater storage presents a serious environmental challenge for the Confederated Tribes of the Umatilla Indian Reservation and communities in the URB. Close

• 23. [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

• 24. [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

• 25. [Article] Utilizing Humidity and Temperature Data to Advance Monitoring and Prediction of Meteorological Drought

  The fraction of land area over the Continental United States experiencing extreme hot and dry conditions has been increasing over the past several decades, consistent with expectation from anthropogenic ...

  Citation × Citation Citation Abstract Copy Title: Utilizing Humidity and Temperature Data to Advance Monitoring and Prediction of Meteorological Drought Author: Behrangi, Ali, Loikith, Paul C., Fetzer, Eric J., Nguyen, Hai M., Granger, Stephanie L. Year: 2015 The fraction of land area over the Continental United States experiencing extreme hot and dry conditions has been increasing over the past several decades, consistent with expectation from anthropogenic climate change. A clear concurrent change in precipitation, however, has not been confirmed. Vapor pressure deficit (VPD), combining temperature and humidity, is utilized here as an indicator of the background atmospheric conditions associated with meteorological drought. Furthermore, atmospheric conditions associated with warm season drought events are assessed by partitioning associated VPD anomalies into the temperature and humidity components. This approach suggests that the concurrence of anomalously high temperature and low humidity was an important driver of the rapid development and evolution of the exceptionally severe 2011 Texas and the 2012 Great Plains droughts. By classification of a decade of extreme drought events and tracking them back in time, it was found that near surface atmospheric temperature and humidity add essential information to the commonly used precipitation-based drought indicators and can advance efforts to determine the timing of drought onset and its severity. Title: Utilizing Humidity and Temperature Data to Advance Monitoring and Prediction of Meteorological Drought Author: Behrangi, Ali, Loikith, Paul C., Fetzer, Eric J., Nguyen, Hai M., Granger, Stephanie L. Year: 2015 The fraction of land area over the Continental United States experiencing extreme hot and dry conditions has been increasing over the past several decades, consistent with expectation from anthropogenic climate change. A clear concurrent change in precipitation, however, has not been confirmed. Vapor pressure deficit (VPD), combining temperature and humidity, is utilized here as an indicator of the background atmospheric conditions associated with meteorological drought. Furthermore, atmospheric conditions associated with warm season drought events are assessed by partitioning associated VPD anomalies into the temperature and humidity components. This approach suggests that the concurrence of anomalously high temperature and low humidity was an important driver of the rapid development and evolution of the exceptionally severe 2011 Texas and the 2012 Great Plains droughts. By classification of a decade of extreme drought events and tracking them back in time, it was found that near surface atmospheric temperature and humidity add essential information to the commonly used precipitation-based drought indicators and can advance efforts to determine the timing of drought onset and its severity. Close

• 26. [Article] Groundwater Recharge of a Landslide : An Isotopic and Meteorological Analysis

  Landslides pose a significant threat to property and personal injury, estimated at 3.5 billion dollars and 25-50 deaths per year respectively (Schuster, 1996). The triggers that cause landslides are well ...

  Citation × Citation Citation Abstract Copy Title: Groundwater Recharge of a Landslide : An Isotopic and Meteorological Analysis Author: Allen, Bryan Year: 2016 Landslides pose a significant threat to property and personal injury, estimated at 3.5 billion dollars and 25-50 deaths per year respectively (Schuster, 1996). The triggers that cause landslides are well understood and include intense precipitation events, seismic shaking, destabilizing activities, and the over-steepening of slopes. However, these triggers were absent when the Silt Creek Landslide initiated, and determining this landslide’s subsurface hydrologic conditions is the focus of this study. Located on the western slopes of the Cascade mountain range in Oregon and within an active logging area, the landslide reactivated during the summer of 2014 at a site that had previous landslides dating back to at least 1994. The area is underlain by poorly welded tuffs capped with basaltic flows. Considering the near-drought to drought conditions that existed 3 years’ prior, it has a relatively high rate of slip of approximately 15 m/yr. On March 4th, 2016 I obtained twelve water samples from surface flow and springs on the landslide for isotope analysis of oxygen (18O) and deuterium (D). Comparing the resultant mean, -10.09‰ δ18O, against well-defined linear elevation/ δ18O relationships for western Oregon and the Global Meteoric Water Line for δ18O / δD the source of surface flow and groundwater likely originates from a pond above the head scarp. Analysis of precipitation data over a 42-year period prior to reactivation confirms the drought-like conditions existing at time of reactivation. These conditions align with the ‘bathtub’ model wherein an older landslide creates the conditions for repeated failures by aligning platy clay minerals such that the hydraulic conductivity is reduced to the point that a perched aquifer forms. This provides lubrication for subsequent landslides even during periods of low precipitation. Understanding the mechanisms operating on this landslide can inform future assessments of hazard and risk as well as appropriate mitigation strategies. Title: Groundwater Recharge of a Landslide : An Isotopic and Meteorological Analysis Author: Allen, Bryan Year: 2016 Landslides pose a significant threat to property and personal injury, estimated at 3.5 billion dollars and 25-50 deaths per year respectively (Schuster, 1996). The triggers that cause landslides are well understood and include intense precipitation events, seismic shaking, destabilizing activities, and the over-steepening of slopes. However, these triggers were absent when the Silt Creek Landslide initiated, and determining this landslide’s subsurface hydrologic conditions is the focus of this study. Located on the western slopes of the Cascade mountain range in Oregon and within an active logging area, the landslide reactivated during the summer of 2014 at a site that had previous landslides dating back to at least 1994. The area is underlain by poorly welded tuffs capped with basaltic flows. Considering the near-drought to drought conditions that existed 3 years’ prior, it has a relatively high rate of slip of approximately 15 m/yr. On March 4th, 2016 I obtained twelve water samples from surface flow and springs on the landslide for isotope analysis of oxygen (18O) and deuterium (D). Comparing the resultant mean, -10.09‰ δ18O, against well-defined linear elevation/ δ18O relationships for western Oregon and the Global Meteoric Water Line for δ18O / δD the source of surface flow and groundwater likely originates from a pond above the head scarp. Analysis of precipitation data over a 42-year period prior to reactivation confirms the drought-like conditions existing at time of reactivation. These conditions align with the ‘bathtub’ model wherein an older landslide creates the conditions for repeated failures by aligning platy clay minerals such that the hydraulic conductivity is reduced to the point that a perched aquifer forms. This provides lubrication for subsequent landslides even during periods of low precipitation. Understanding the mechanisms operating on this landslide can inform future assessments of hazard and risk as well as appropriate mitigation strategies. Close

• 27. [Article] Precipitation Intensity Trend Detection using Hourly and Daily Observations in Portland, Oregon

  The intensity of precipitation is expected to increase in response to climate change, but the regions where this may occur are unclear. The lack of certainty from climate models warrants an examination ...

  Citation × Citation Citation Abstract Copy Title: Precipitation Intensity Trend Detection using Hourly and Daily Observations in Portland, Oregon Author: Cooley, Alexis, Chang, Heejun Year: 2017 The intensity of precipitation is expected to increase in response to climate change, but the regions where this may occur are unclear. The lack of certainty from climate models warrants an examination of trends in observational records. However, the temporal resolution of records may affect the success of trend detection. Daily observations are often used, but may be too coarse to detect changes. Sub-daily records may improve detection, but their value is not yet quantified. Using daily and hourly records from 24 rain gages in Portland, Oregon (OR), trends in precipitation intensity and volume are examined for the period of 1999–2015. Daily intensity is measured using the Simple Daily Intensity Index, and this method is adapted to measure hourly scale intensity. Kendall’s tau, a non-parametric correlation coefficient, is used for monotonic trend detection. Field significance and tests for spatial autocorrelation using Moran’s Index are used to determine the significance of group hypothesis tests. Results indicate that the hourly data is superior in trend detection when compared with daily data; more trends are detected with hourly scale data at both the 5% and 10% significance levels. Hourly records showed a significant increase in 6 of 12 months, while daily records showed a significant increase in 4 of 12 months at the 10% significance level. At both scales increasing trends were concentrated in spring and summer months, while no winter trends were detected. Volume was shown to be increasing in most months experiencing increased intensity, and is a probable driver of the intensity trends observed. Title: Precipitation Intensity Trend Detection using Hourly and Daily Observations in Portland, Oregon Author: Cooley, Alexis, Chang, Heejun Year: 2017 The intensity of precipitation is expected to increase in response to climate change, but the regions where this may occur are unclear. The lack of certainty from climate models warrants an examination of trends in observational records. However, the temporal resolution of records may affect the success of trend detection. Daily observations are often used, but may be too coarse to detect changes. Sub-daily records may improve detection, but their value is not yet quantified. Using daily and hourly records from 24 rain gages in Portland, Oregon (OR), trends in precipitation intensity and volume are examined for the period of 1999–2015. Daily intensity is measured using the Simple Daily Intensity Index, and this method is adapted to measure hourly scale intensity. Kendall’s tau, a non-parametric correlation coefficient, is used for monotonic trend detection. Field significance and tests for spatial autocorrelation using Moran’s Index are used to determine the significance of group hypothesis tests. Results indicate that the hourly data is superior in trend detection when compared with daily data; more trends are detected with hourly scale data at both the 5% and 10% significance levels. Hourly records showed a significant increase in 6 of 12 months, while daily records showed a significant increase in 4 of 12 months at the 10% significance level. At both scales increasing trends were concentrated in spring and summer months, while no winter trends were detected. Volume was shown to be increasing in most months experiencing increased intensity, and is a probable driver of the intensity trends observed. Close

• 28. [Article] High-Performance Computing for Drought Prediction

  In recent decades, there has been considerable interest in using satellite soil moisture data to examine the global water-energy cycle and manage water resources. Current satellites are limited in their ...

  Citation × Citation Citation Abstract Copy Title: High-Performance Computing for Drought Prediction Author: Cooney, Henry Year: 2016 In recent decades, there has been considerable interest in using satellite soil moisture data to examine the global water-energy cycle and manage water resources. Current satellites are limited in their sensing depth, and can only directly measure top soil layers. Using a particle filter, this data may be fused with the output of a hydrologic simulation to improve simulation results, and characterize a hydrologic system at the watershed level. However, this approach increases computational requirements dramatically, and requires rethinking to accommodate data scaling and achieve good performance. We present a detailed performance study of several alternative implementations of the hybrid approach. We combined the Precipitation Runoff Modeling System (PRMS), a widely used hydrologic model published by the U.S. Geologic Survey, with satellite data. This problem is representative of many science efforts in that it incorporates software used across the scientific domain (PRMS) with novel functionality by integrating satellite data. Title: High-Performance Computing for Drought Prediction Author: Cooney, Henry Year: 2016 In recent decades, there has been considerable interest in using satellite soil moisture data to examine the global water-energy cycle and manage water resources. Current satellites are limited in their sensing depth, and can only directly measure top soil layers. Using a particle filter, this data may be fused with the output of a hydrologic simulation to improve simulation results, and characterize a hydrologic system at the watershed level. However, this approach increases computational requirements dramatically, and requires rethinking to accommodate data scaling and achieve good performance. We present a detailed performance study of several alternative implementations of the hybrid approach. We combined the Precipitation Runoff Modeling System (PRMS), a widely used hydrologic model published by the U.S. Geologic Survey, with satellite data. This problem is representative of many science efforts in that it incorporates software used across the scientific domain (PRMS) with novel functionality by integrating satellite data. Close

• 29. [Article] Wavelet-Coupled Machine Learning Methods for Drought Forecast Utilizing Hybrid Meteorological and Remotely-Sensed Data

  In this study, a statistical drought early warning method is proposed using novel machine learning algorithms, with the inclusion of multiple drought-related attributes from precipitation, satellite-derived ...

  Citation × Citation Citation Abstract Copy Title: Wavelet-Coupled Machine Learning Methods for Drought Forecast Utilizing Hybrid Meteorological and Remotely-Sensed Data Author: Tan, R., Perkowski, Marek Year: 2015 In this study, a statistical drought early warning method is proposed using novel machine learning algorithms, with the inclusion of multiple drought-related attributes from precipitation, satellite-derived land cover vegetation indices, and surface discharge. The forecast is made for the long-term hydrological drought in the region of Central Valley, California. The wavelet transform analysis is employed in combination with support vector regression and artificial neural network algorithms for improving the drought prediction effectiveness. The performance of the drought prediction is evaluated using three statistical metrics: Coefficient of Determination (R2 ), Root-Mean-Square Error (RMSE), and Mean-Absolute-Error (MAE). The results clearly indicate that using hybrid precipitation and satellite remotelysensed data, the proposed wavelet-coupled machine learning method can effectively predict long-term drought in the area of Central Valley California, over a lead time of 3 to 6 months, which is crucial for agricultural planning, reservoir management, and authorities’ allocation of water resources. Title: Wavelet-Coupled Machine Learning Methods for Drought Forecast Utilizing Hybrid Meteorological and Remotely-Sensed Data Author: Tan, R., Perkowski, Marek Year: 2015 In this study, a statistical drought early warning method is proposed using novel machine learning algorithms, with the inclusion of multiple drought-related attributes from precipitation, satellite-derived land cover vegetation indices, and surface discharge. The forecast is made for the long-term hydrological drought in the region of Central Valley, California. The wavelet transform analysis is employed in combination with support vector regression and artificial neural network algorithms for improving the drought prediction effectiveness. The performance of the drought prediction is evaluated using three statistical metrics: Coefficient of Determination (R2 ), Root-Mean-Square Error (RMSE), and Mean-Absolute-Error (MAE). The results clearly indicate that using hybrid precipitation and satellite remotelysensed data, the proposed wavelet-coupled machine learning method can effectively predict long-term drought in the area of Central Valley California, over a lead time of 3 to 6 months, which is crucial for agricultural planning, reservoir management, and authorities’ allocation of water resources. Close

• 30. [Article] Carbon and Oxygen Isotope Fractionation in Laboratory-Precipitated, Inorganic Calcite

  Carbon and oxygen isotopes in calcite crystals provide a record of the environmental conditions under which the crystals formed. To investigate the influence of temperature, pH, and growth rate on isotope ...

  Citation × Citation Citation Abstract Copy Title: Carbon and Oxygen Isotope Fractionation in Laboratory-Precipitated, Inorganic Calcite Author: Baker, Evan Year: 2015 Carbon and oxygen isotopes in calcite crystals provide a record of the environmental conditions under which the crystals formed. To investigate the influence of temperature, pH, and growth rate on isotope discrimination by calcite, we measured carbon and oxygen isotope fractionation through a series of calcite precipitation experiments at T = 25C and pH = 7.5 - 9.3. We observe that neither the carbon nor oxygen isotope compositions correspond to the theoretical equilibrium isotope fractionation between calcite and solution. We also demonstrate that the fractionation of oxygen isotopes between calcite and water decreases with increasing pH, consistent with available data from experiments in which the enzyme carbonic anhydrase was used. Finally, we compare the carbon and oxygen isotopes of our calcite crystals to those of biogenic carbonates. This thesis includes previously unpublished co-authored material. Title: Carbon and Oxygen Isotope Fractionation in Laboratory-Precipitated, Inorganic Calcite Author: Baker, Evan Year: 2015 Carbon and oxygen isotopes in calcite crystals provide a record of the environmental conditions under which the crystals formed. To investigate the influence of temperature, pH, and growth rate on isotope discrimination by calcite, we measured carbon and oxygen isotope fractionation through a series of calcite precipitation experiments at T = 25C and pH = 7.5 - 9.3. We observe that neither the carbon nor oxygen isotope compositions correspond to the theoretical equilibrium isotope fractionation between calcite and solution. We also demonstrate that the fractionation of oxygen isotopes between calcite and water decreases with increasing pH, consistent with available data from experiments in which the enzyme carbonic anhydrase was used. Finally, we compare the carbon and oxygen isotopes of our calcite crystals to those of biogenic carbonates. This thesis includes previously unpublished co-authored material. Close