Drought is one of the most widespread and least understood natural phenomena. Many indices using multiple data types have been created, and their success at identifying periods of extreme wetness and dryness has been well documented. In recent years, researchers have begun to assess the potential of total water storage (TWS) anomalies in drought monitoring method- ologies. The Gravity Recovery and Climate Experiment (GRACE) provides temporally and spatially consistent TWS measurements across the globe, and studies have shown GRACE TWS anomalies are suited to identify drought. GRACE TWS is used with MODIS-derived normalized difference veg- etation index (NDVI) and NOAA/NWS precipitation data to create a new drought index, the Merged-dataset Drought Index (MDI). Each dataset corre- lates with a different type of drought, giving robustness to MDI. MDI is based on dataset deviations from a monthly climatology and is objective and easy to calculate. MDI is studied across Texas, which is broken into five dataset- defined sub-regions. Multiple drought events are identified from 2002 - 2014, with the most severe beginning in October 2010. A new drought severity clas- sification scheme is proposed based on MDI, and it is organized to match the current US Drought Monitor Classification Scheme. MDI shows strong correlation with existing drought indices, notably the Palmer Drought Severity Index (PDSI). MDI consistently identifies droughts in different sub-regions of Texas, but shows better performance in regions with large GRACE TWS signals. MDI performance is enhanced through a weighting scheme that relies more on GRACE TWS. Even with this scheme, MDI and PDSI exhibit occasional behavioral differences. Drought analysis using MDI shows for the first time that GRACE data provides information on a sub-regional scale in Texas, an area with low signal amplitudes. Past studies have shown TWS capable of identifying drought, but MDI is the first index to quantitatively use GRACE TWS in a manner consistent with current practices of identifying drought. MDI also establishes a framework for a future, completely remote-sensing based index that can enable temporally and spatially consistent drought identification across the globe. This study is useful as well for establishing a baseline for the necessary spatial resolution required from future geodetic space missions for use in drought identification at smaller scales. / text

Soil moisture plays a major role in the hydrologic water balance and is the basis for most hydrological models. It influences the partitioning of energy and moisture inputs at the land surface. Because of its importance, it has been used as a key variable for many hydrological studies such as flood forecasting, drought studies and the determination of groundwater recharge. Therefore, spatially distributed soil moisture with reasonable temporal resolution is considered a valuable source of information for hydrological model parameterization and validation. Unfortunately, soil moisture is difficult to measure and remains essentially unmeasured over spatial and temporal scales needed for a number of hydrological model applications. In 2002, the Gravity Recovery And Climate Experiment (GRACE) satellite platform was launched to measure, among other things, the gravitational field of the earth. Over its life span, these orbiting satellites have produced time series of mass changes of the earth-atmosphere system. The subsequent outcome of this, after integration over a number of years, is a time series of highly refined images of the earth's mass distribution. In addition to quantifying the static distribution of mass, the month-to-month variation in the earth's gravitational field are indicative of the integrated value of the subsurface total water storage for specific catchments. Utilization of these natural changes in the earth's gravitational field entails the transformation of the derived GRACE geopotential spherical harmonic coefficients into spatially varying time series estimates of total water storage. These remotely sensed basin total water storage estimates can be routinely validated against independent estimates of total water storage from an atmospheric-based water balance approach or from well calibrated macroscale hydrologic models. The hydrological relevance and implications of remotely estimated GRACE total water storage over poorly gauged, wetland-dominated watershed as well as over a deltaic region underlain by a thick sand aquifer in Western Canada are the focus of this thesis. The domain of the first case study was the Mackenzie River Basin wherein the GRACE total water storage estimates were successfully inter-compared and validated with the atmospheric based water balance. These were then used to assess the WATCLASS hydrological model estimates of total water storage. The outcome of this inter-comparison revealed the potential application of the GRACE-based approach for the closure of the hydrological water balance of the Mackenzie River Basin as well as a dependable source of data for the calibration of traditional hydrological models. The Mackenzie River Basin result led to a second case study where the GRACE-based total water storage was validated using storage estimated from the atmospheric-based water balance P-E computations in conjunction with the measured streamflow records for the Saskatchewan River Basin at its Grand Rapids outlet in Manitoba. The fallout from this comparison was then applied to the characterization of the Prairie-wide 2002/2003 drought enabling the development of a new drought index now known as the Total Storage Deficit Index (TSDI). This study demonstrated the potential application of the GRACE-based technique as a tool for drought characterization in the Canadian Prairies. Finally, the hydroinformatic approach based on the artificial neural network (ANN) enabled the downscaling of the groundwater component from the total water storage estimate from the remote sensing satellite, GRACE. This was subsequently explored as an alternate source of calibration and validation for a hydrological modeling application over the Assiniboine Delta Aquifer in Manitoba. Interestingly, a high correlation exists between the simulated groundwater storage from the coupled hydrological model, CLM-PF and the downscaled groundwater time series storage from the remote sensing satellite GRACE over this 4,000 km2 deltaic basin in Canada.

Soil moisture plays a major role in the hydrologic water balance and is the basis for most hydrological models. It influences the partitioning of energy and moisture inputs at the land surface. Because of its importance, it has been used as a key variable for many hydrological studies such as flood forecasting, drought studies and the determination of groundwater recharge. Therefore, spatially distributed soil moisture with reasonable temporal resolution is considered a valuable source of information for hydrological model parameterization and validation. Unfortunately, soil moisture is difficult to measure and remains essentially unmeasured over spatial and temporal scales needed for a number of hydrological model applications. In 2002, the Gravity Recovery And Climate Experiment (GRACE) satellite platform was launched to measure, among other things, the gravitational field of the earth. Over its life span, these orbiting satellites have produced time series of mass changes of the earth-atmosphere system. The subsequent outcome of this, after integration over a number of years, is a time series of highly refined images of the earth's mass distribution. In addition to quantifying the static distribution of mass, the month-to-month variation in the earth's gravitational field are indicative of the integrated value of the subsurface total water storage for specific catchments. Utilization of these natural changes in the earth's gravitational field entails the transformation of the derived GRACE geopotential spherical harmonic coefficients into spatially varying time series estimates of total water storage. These remotely sensed basin total water storage estimates can be routinely validated against independent estimates of total water storage from an atmospheric-based water balance approach or from well calibrated macroscale hydrologic models. The hydrological relevance and implications of remotely estimated GRACE total water storage over poorly gauged, wetland-dominated watershed as well as over a deltaic region underlain by a thick sand aquifer in Western Canada are the focus of this thesis. The domain of the first case study was the Mackenzie River Basin wherein the GRACE total water storage estimates were successfully inter-compared and validated with the atmospheric based water balance. These were then used to assess the WATCLASS hydrological model estimates of total water storage. The outcome of this inter-comparison revealed the potential application of the GRACE-based approach for the closure of the hydrological water balance of the Mackenzie River Basin as well as a dependable source of data for the calibration of traditional hydrological models. The Mackenzie River Basin result led to a second case study where the GRACE-based total water storage was validated using storage estimated from the atmospheric-based water balance P-E computations in conjunction with the measured streamflow records for the Saskatchewan River Basin at its Grand Rapids outlet in Manitoba. The fallout from this comparison was then applied to the characterization of the Prairie-wide 2002/2003 drought enabling the development of a new drought index now known as the Total Storage Deficit Index (TSDI). This study demonstrated the potential application of the GRACE-based technique as a tool for drought characterization in the Canadian Prairies. Finally, the hydroinformatic approach based on the artificial neural network (ANN) enabled the downscaling of the groundwater component from the total water storage estimate from the remote sensing satellite, GRACE. This was subsequently explored as an alternate source of calibration and validation for a hydrological modeling application over the Assiniboine Delta Aquifer in Manitoba. Interestingly, a high correlation exists between the simulated groundwater storage from the coupled hydrological model, CLM-PF and the downscaled groundwater time series storage from the remote sensing satellite GRACE over this 4,000 km2 deltaic basin in Canada.