This dissertation represents the research and development of new concepts and techniques for modeling the knowledge about the many concepts we as hydrologists must understand such that we can execute models that operate in terms of conceptual abstractions and have those abstractions translate to the data, tools, and models we use every day. This hydrologic knowledge includes conceptual (i.e. semantic) knowledge, such as the hydrologic cycle concepts and relationships, as well as functional (i.e. procedural) knowledge, such as how to compute the area of a watershed polygon, average basin slope or topographic wetness index. This dissertation is presented as three papers and a reference manual for the software created. Because hydrologic knowledge includes both semantic aspects as well as procedural aspects, we have developed, in the first paper, a new form of reasoning engine and knowledge base that extends the general-purpose analysis and problem-solving capability of reasoning engines by incorporating procedural knowledge, represented as computer source code, into the knowledge base. The reasoning engine is able to compile the code and then, if need be, execute the procedural code as part of a query. The potential advantage to this approach is that it simplifies the description of procedural knowledge in a form that can be readily utilized by the reasoning engine to answer a query. Further, since the form of representation of the procedural knowledge is source code, the procedural knowledge has the full capabilities of the underlying language. We use the term "functional ontology" to refer to the new semantic and procedural knowledge models. The first paper applies the new knowledge model to describing and analyzing polygons. The second and third papers address the application of the new functional ontology reasoning engine and knowledge model to hydrologic applications. The second paper models concepts and procedures, including running external software, related to watershed delineation. The third paper models a project scenario that includes integrating several models. A key advance demonstrated in this paper is the use of functional ontologies to apply metamodeling concepts in a manner that both abstracts and fully utilizes computational models and data sets as part of the project modeling process.

Farming communities in the United States and around the world lose billions of dollars every year due to drought. Drought Indices such as the Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI) are widely used by the government agencies to assess and respond to drought. These drought indices are currently monitored at a large spatial resolution (several thousand km2). Further, these drought indices are primarily based on precipitation deficits and are thus good indicators for monitoring large scale meteorological drought. However, agricultural drought depends on soil moisture and evapotranspiration deficits. Hence, two drought indices, the Evapotranspiration Deficit Index (ETDI) and Soil Moisture Deficit Index (SMDI), were developed in this study based on evapotranspiration and soil moisture deficits, respectively. A Geographical Information System (GIS) based approach was used to simulate the hydrology using soil and land use properties at a much finer spatial resolution (16km2) than the existing drought indices. The Soil and Water Assessment Tool (SWAT) was used to simulate the long-term hydrology of six watersheds located in various climatic zones of Texas. The simulated soil water was well-correlated with the Normalized Difference Vegetation Index NDVI (r ~ 0.6) for agriculture and pasture land use types, indicating that the model performed well in simulating the soil water. Using historical weather data from 1901-2002, long-term weekly normal soil moisture and evapotranspiration were estimated. This long-term weekly normal soil moisture and evapotranspiration data was used to calculate ETDI and SMDI at a spatial resolution of 4km ?? 4km. Analysis of the data showed that ETDI and SMDI compared well with wheat and sorghum yields (r > 0.75) suggesting that they are good indicators of agricultural drought. Rainfall is a highly variable input both spatially and temporally. Hence, the use of NEXRAD rainfall data was studied for simulating soil moisture and drought. Analysis of the data showed that raingages often miss small rainfall events that introduce considerable spatial variability among soil moisture simulated using raingage and NEXRAD rainfall data, especially during drought conditions. The study showed that the use of NEXRAD data could improve drought monitoring at a much better spatial resolution.

In this study, Yuvacik Basin, which is located in southeastern part of Marmara Region of T&uuml / rkiye, is selected as the application basin and hydrologic modeling studies are performed for the basin. Basin is divided into three subbasins such as: Kirazdere, Kazandere, and Serindere and each subbasin is modeled with its own parameters. In subbasin and stream network delineation HEC-GeoHMS software is used and for the hydrologic modeling studies the new version of HEC-HMS hydrologic modeling software released in April 2006 is used. Modeling studies consist of four items: event-based hourly simulations, snow period daily simulations, daily runoff forecast using numerical weather prediction data, and runoff scenarios using intensity-duration-frequency curves. As a result of modeling studies, infiltration loss and baseflow parameters of each subbasin are calibrated with both hourly and daily simulations. Hourly parameters are used in spring, summer and fall seasons / daily parameters are used in late fall, winter and early spring (snowfall and snowmelt period) to predict runoff. Observed runoffs are compared with the forecasted runoffs that are obtained using MM5 grid data (precipitation and temperature) in the model. Goodness-of-fit between forecasted and observed runoffs is promising. Hence, the model can be used in real time runoff forecast studies. At last, runoffs that correspond to different return periods and probable maximum precipitation are predicted using intensity-duration-frequency data as input and frequency storm method of HEC-HMS. These runoffs can be used for flood control and flood damage estimation studies.

Farming communities in the United States and around the world lose billions of dollars every year due to drought. Drought Indices such as the Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI) are widely used by the government agencies to assess and respond to drought. These drought indices are currently monitored at a large spatial resolution (several thousand km2). Further, these drought indices are primarily based on precipitation deficits and are thus good indicators for monitoring large scale meteorological drought. However, agricultural drought depends on soil moisture and evapotranspiration deficits. Hence, two drought indices, the Evapotranspiration Deficit Index (ETDI) and Soil Moisture Deficit Index (SMDI), were developed in this study based on evapotranspiration and soil moisture deficits, respectively. A Geographical Information System (GIS) based approach was used to simulate the hydrology using soil and land use properties at a much finer spatial resolution (16km2) than the existing drought indices. The Soil and Water Assessment Tool (SWAT) was used to simulate the long-term hydrology of six watersheds located in various climatic zones of Texas. The simulated soil water was well-correlated with the Normalized Difference Vegetation Index NDVI (r ~ 0.6) for agriculture and pasture land use types, indicating that the model performed well in simulating the soil water. Using historical weather data from 1901-2002, long-term weekly normal soil moisture and evapotranspiration were estimated. This long-term weekly normal soil moisture and evapotranspiration data was used to calculate ETDI and SMDI at a spatial resolution of 4km ?? 4km. Analysis of the data showed that ETDI and SMDI compared well with wheat and sorghum yields (r > 0.75) suggesting that they are good indicators of agricultural drought. Rainfall is a highly variable input both spatially and temporally. Hence, the use of NEXRAD rainfall data was studied for simulating soil moisture and drought. Analysis of the data showed that raingages often miss small rainfall events that introduce considerable spatial variability among soil moisture simulated using raingage and NEXRAD rainfall data, especially during drought conditions. The study showed that the use of NEXRAD data could improve drought monitoring at a much better spatial resolution.

A surge function is a mathematical function of the form f(x)=axpe-bx. We simplify the surge function by holding p constant at 1 and investigate the simplified form as a potential model to represent the full peak of a stream discharge hydrograph. The previously studied Weibull and gamma distributions are included for comparison. We develop an analysis algorithm which produces the best-fit parameters for every peak for each model function, and we process the data with a MATLAB script that uses spectral analysis to filter year-long, 15-minute, stream-discharge data sets. The filtering is necessary to locate the concave-upward inflection points used to separate the data set into its constituent, individual peaks. The Levenberg-Marquardt algorithm is used to iteratively estimate the unknown parameters for each version of the modeled peak by minimizing the sum of squares of residuals. The results allow goodness-of-fit comparisons between the three model functions, as well as a comparison of peaks at the same gage through the year of record. Application of these methods to five rivers from three distinct hydrologic regions shows that the simple surge function is a special case of the gamma distribution, which is known to be useful as a modeling function for a full-peak hydrograph. The study also confirms that the Weibull distribution produces good fits to 15-minute hydrograph data.

<p>In this dissertation, a Hydrologic Data Assimilation System (HDAS) relying on the Duke Coupled surface-groundwater Hydrology Model (DCHM) and various data assimilation techniques including EnKF (Ensemble Kalman Filter), the fixed-lag EnKS (Ensemble Kalman Smoother) and the Asynchronous EnKF (AEnKF) was developed to 1) investigate the hydrological predictability of precipitation-induced natural hazards (i.e. floods and landslides) in the Southern Appalachians in North Carolina, USA, and 2) to characterize the seasonal (wet/dry) and inter-annual variability of surface-groundwater interactions with implications for water resource management in the Upper Zambezi River Basin (UZRB) in southern Africa. The overarching research objective is to improve hydrologic predictability of precipitation-induced natural hazards and water resources in regions of complex terrain. The underlying research hypothesis is that hydrologic response in mountainous regions is governed by surface-subsurface interaction mechanisms, specifically interflow in soil-mantled slopes, surface-groundwater interactions in recharge areas, and wetland dynamics in alluvial floodplains at low elevations. The research approach is to investigate the modes of uncertainty propagation from atmospheric forcing and hydrologic states on processes at multiple scales using a parsimonious uncalibrated hydrologic model (i.e. the DCHM), and Monte Carlo and Data-Assimilation methods. In order to investigate the coupled surface-groundwater system and assess the predictability of precipitation-induced natural hazards (i.e. floods and landslides) in headwater basins, including the propagation of uncertainty in QPE/QPF (Quantitative Precipitation Estimates/Forecasts) to QFE/QFF (Quantitative Flood Estimates/Forecasts), the DCHM model was implemented first at high spatial resolution (250m) in the Southern Appalachian Mountains (SAM) in North Carolina, USA. The DCHM modeling system was implemented subsequently at coarse resolution (5 km) in the Upper Zambezi River Basin (UZRB) in southern Africa for decadal-scale simulations (i.e. water years from 2002 to 2012). </p><p>The research in the SAM showed that joint QPE-QFF distributions for flood response at the headwater catchment scale are highly non-linear with respect to the space-time structure of rainfall, exhibiting strong dependence on basin physiography, initial soil moisture conditions (transient basin storage capacity), the space-time organization of runoff generation and conveyance mechanisms, and in particular interflow dynamics. The errors associated with QPEs and QPFs were characterized using rainfall observations from a dense raingauge network in the Pigeon River Basin, resulting in a simple linear regression model for adjusting/improving QPEs. Deterministic QFEs simulated by the DCHM agree well with observations, with Nash–Sutcliffe (NS) coefficients of 0.8~0.9. Limitations with state-of-the-science operational QPF and the impact of even limited improvements in rainfall forcing was demonstrated through an experiment consisting of nudging satellite-like observations (i.e. Adjusted QPEs) into operational QPE/QPF that showed significant improvement in QFF performance, especially when the timing of satellite overpass is such that it captures transient episodes of heavy rainfall during the event. The research further showed that the dynamics of subsurface hydrologic processes play an important role as a trigger mechanism of shallow landslides through soil moisture redistribution by interflow. Specifically, transient mass fluxes associated with the temporal-spatial dynamics of interflow govern the timing of shallow landslide initiation, and subsequent debris flow mobilization, independently of storm characteristics such as precipitation intensity and duration. Interflow response was shown to be dominant at high elevations in the presence of deep soils as well as in basins with large alluvial fans or unconsolidated debris flow deposits. In recharge areas and where subsurface flow is an important contribution to streamflow, subsurface-groundwater interactions determine initial hydrologic conditions (e.g. soil moisture states and water table position), which in turn govern the timing and magnitude of flood response at the event scale. More generally, surface-groundwater interactions are essential to capture low flows in the summer season, and generally during persistent dry weather and drought conditions. Future advances in QFF and landslide monitoring remain principally constrained by progress in QPE and QPF at the spatial resolution necessary to resolve rainfall-interflow dynamics in mountainous regions.</p><p>The predictability of QFE/QFF was further scrutinized in a complete operational environment during the Intense Observing Period (IOP) of the Integrated Precipitation and Hydrology Experiment (IPHEx-IOP), in order to investigate the predictability of floods (and flashfloods) in headwater catchments in the Southern Appalachians with various drainage sizes. With the DCHM, a variety of operational QPEs were used to produce hydrological hindcasts for the previous day, from which the final states were used as initial conditions in the hydrological forecast for the current day. Although the IPHEx operational testbed results were promising in terms of not having missed any of the flash flood events during the IOP with large lead times of up to 6 hours, significant errors of overprediction or underprediction were identified that could be traced back to the QPFs and subgrid-scale variability of radar QPEs. Furthermore, the added value of improving QFE/QFF through assimilating discharge observations into the DCHM was investigated for advancing flood forecasting skills in the operational mode. Both the flood hindcast/forecast results were significantly improved by assimilating the discharge observations into the DCHM using the EnKF (Ensemble Kalman Filter), the fixed-lag EnKS (Ensemble Kalman Smoother) and Asynchronous EnKF (AEnKF). The results not only demonstrate the utility of discharge assimilation in operational forecasts, but also reveal the importance of initial water storage in the basin for issuing flood forecasts. Specifically, hindcast NSEs as high as 0.98, 0.71 and 0.99 at 15-min time-scales were attained for three headwater catchments in the inner mountain region, demonstrating that assimilation of discharge observations at the basin’s outlet can reduce the errors and uncertainties in soil moisture. Success in operational flood forecasting at lead times of 6, 9, 12 and 15hrs was also achieved through discharge assimilation, with NSEs of 0.87, 0.78, 0.72 and 0.51, respectively. The discharge assimilation experiments indicate that the optimal assimilating time window not only depends on basin properties but also on the storm-specific space-time-structure of rainfall within the basin, and therefore adaptive, context-aware configurations of the data assimilation system should prove useful to address the challenges of flood prediction in headwater basins.</p><p>A physical parameterization of wetland hydrology was incorporated in the DCHM for water resource assessment studies in the UZRB. The spatial distribution of wetlands was introduced in the model using probability occurrence maps generated by logistic regression models using MODIS reflectance-based indices as predictor variables. Continuous model simulations for the 2002-2012 period show that the DCHM with wetland parameterization was able to reproduce wetland hydrology processes adequately, including surface-groundwater interactions. The modelled regional terrestrial water storage anomaly (TWSA) captured very well the inter- and intra-annual variability of the system water storage changes in good agreement with the NASA’s GRACE (Gravity Recovery and Climate Experiment) TWSA observations. Specifically, the positive trend of TWSA documented by GRACE was simulated independently by the DCHM. Furthermore, it was determined that the TSWA positive trend results from cumulative water storage in the sandy soils of the Cuando-Luana sub-basin when shifts in storm tracks move rainfall to the western sector of the Angolan High Plateau. </p><p>Overall, the dissertation study demonstrates the capability of the DCHM in predicting specific characteristics of hydrological response to extreme events and also the inter- and intra-annual variability of surface-groundwater interactions at a decadal scale. The DCHM, coupled with slope stability module and wetland module featuring surface-groundwater interaction mechanism, not only is of great potential in the context of developing a regional warning system for natural hazards (i.e. flashfloods and landslides), but also is promising in investigating regional water budgets at decadal scale. In addition, the DCHM-HDAS demonstrated the ability to reduce forecasting uncertainty and errors associated with forcing data and the model proper, thus significantly improving the predictability of natural hazards. The HDAS could also be used to investigate the regional water resource assessment especially in poorly-gauged regions (e.g. southern Africa), taking advantage of satellite observations.</p> / Dissertation

The aim of the research undertaken in this dissertation was to use medium-range to seasonal precipitation forecasts for hydrologic applications for catchments in the core North American Monsoon (NAM) region. To this end, it was necessary to develop a better understanding of the physical and statistical relationships between runoff processes and the temporal statistics of rainfall. To achieve this goal, development of statistically downscaled estimates of warm season precipitation over the core region of the North American Monsoon Experiment (NAME) were developed. Currently, NAM precipitation is poorly predicted on local and regional scales by Global Circulation Models (GCMs). The downscaling technique used here, the K-Nearest Neighbor (KNN) model, combines information from retrospective GCM forecasts with simultaneous historical observations to infer statistical relationships between the low-resolution GCM fields and the locally-observed precipitation records. The stochastic nature of monsoon rainfall presents significant challenges for downscaling efforts and, therefore, necessitate a regionalization and an ensemble or probabilistic-based approach to quantitative precipitation forecasting. It was found that regionalization of the precipitation climatology prior to downscaling using KNN offered significant advantages in terms of improved skill scores.Selected output variables from retrospective ensemble runs of the National Centers for Environmental Predictions medium-range forecast (MRF) model were fed into the KNN downscaling model. The quality of the downscaled precipitation forecasts was evaluated in terms of a standard suite of ensemble verification metrics. This study represents the first time the KNN model has been successfully applied within a warm season convective climate regime and shown to produce skillful and reliable ensemble forecasts of daily precipitation out to a lead time of four to six days, depending on the forecast month.Knowledge of the behavior of the regional hydrologic systems in NAM was transferred into a modeling framework aimed at improving intra-seasonal hydrologic predictions. To this end, a robust lumped-parameter computational model of intermediate conceptual complexity was calibrated and applied to generate streamflow in three unregulated test basins in the core region of the NAM. The modeled response to different time-accumulated KNN-generated precipitation forcing was investigated. Although the model had some difficulty in accurately simulating hydrologic fluxes on the basis of Hortonian runoff principles only, the preliminary results achieved from this study are encouraging. The primary and most novel finding from this study is an improved predictability of the NAM system using state-of-the-art ensemble forecasting systems. Additionally, this research significantly enhanced the utility of the MRF ensemble forecasts and made them reliable for regional hydrologic applications. Finally, monthly streamflow simulations (from an ensemble-based approach) have been demonstrated. Estimated ensemble forecasts provide quantitative estimates of uncertainty associated with our model forecasts.

Railroad bridges and culverts in the United States are often subject to extreme floods, which have been known to washout sections of track and ultimately lead to derailments. The potential for these events is particularly high in the western U.S. due to the lack of data, inadequate radar coverage, and the high spatial and temporal variability of storm events and terrain. In this work, a hydrologic model is developed that is capable of effectively describing the rainfall-runoff relationship of extreme thunderstorms in arid and semi-arid regions. The model was calibrated and validated using data from ten storms at the semi-arid Walnut Gulch Experimental Watershed. A methodology is also proposed for reducing the amount of raingages required to provide acceptable inputs to the hydrologic model, and also determining the most appropriate placement location for these gages. Results show that the model is capable of reproducing peak discharges, peak timings, and total volumes to within 22.1%, 12 min, and 32.8%, respectively. Results of the gage reduction procedure show that a decrease in the amount of raingages used to drive the model results in a disproportionally smaller decrease in model accuracy. Results also indicate that choosing gages using the minimization of correlation approach that is described herein will lead to an increase in model accuracy as opposed to selecting gages on a random basis.

This thesis evaluates regional hydrology using output from the Canadian Regional Climate Model (CRCM 4.1) and examines changes in the hydrological processes over the Churchill River Basin (CRB) by employing the Variable Infiltration Capacity (VIC) hydrology model. The CRCM evaluation has been performed by combining the atmospheric and the terrestrial water budget components of the hydrological cycle. The North American Regional Reanalysis (NARR) data are used where direct observations are not available. The outcome of the evaluation reveals the potential of the CRCM for use in long-term hydrological studies. The CRCM atmospheric moisture fluxes and storage tendencies show reasonable agreement with the NARR. The long-term moisture flux over the CRB was found to be generally divergent during summer. A systematic bias is observed in the CRCM precipitation and temperature. A quantile-based mapping of the cumulative distribution function is applied for precipitation adjustments. The temperature correction only involves shifting and scaling to adjust mean and variance. The results indicate that the techniques employed for correction are useful for hydrological studies. Bias-correction is also applied to the CRCM future climate. The CRCM bias-corrected data is then used for hydrological modeling of the CRB. The VIC-simulated streamflow exhibits acceptable agreement with observations. The VIC model's internal variables such as snow and soil moisture indicate that the model is capable of simulating internal process variables adequately. The VIC-simulated snow and soil moisture shows the potential of use as an alternative dataset for hydrological studies. Streamflow along with precipitation and temperature are analyzed for trends. No statistically significant trend is observed in the daily precipitation series. Results suggest that an increase in temperature may reduce accumulation of snow during fall and winter. The flow regime may be in transition from a snowmelt dominated regime to a rainfall dominated regime. Results from future climate simulations of the A2 emission scenario indicate a projected increase of streamflow, while the snow depth and duration exhibit a decrease. Soil moisture response to future climate warming shows an overall increase with a greater likelihood of occurrences of higher soil moisture.

This thesis evaluates regional hydrology using output from the Canadian Regional Climate Model (CRCM 4.1) and examines changes in the hydrological processes over the Churchill River Basin (CRB) by employing the Variable Infiltration Capacity (VIC) hydrology model. The CRCM evaluation has been performed by combining the atmospheric and the terrestrial water budget components of the hydrological cycle. The North American Regional Reanalysis (NARR) data are used where direct observations are not available. The outcome of the evaluation reveals the potential of the CRCM for use in long-term hydrological studies. The CRCM atmospheric moisture fluxes and storage tendencies show reasonable agreement with the NARR. The long-term moisture flux over the CRB was found to be generally divergent during summer. A systematic bias is observed in the CRCM precipitation and temperature. A quantile-based mapping of the cumulative distribution function is applied for precipitation adjustments. The temperature correction only involves shifting and scaling to adjust mean and variance. The results indicate that the techniques employed for correction are useful for hydrological studies. Bias-correction is also applied to the CRCM future climate. The CRCM bias-corrected data is then used for hydrological modeling of the CRB. The VIC-simulated streamflow exhibits acceptable agreement with observations. The VIC model's internal variables such as snow and soil moisture indicate that the model is capable of simulating internal process variables adequately. The VIC-simulated snow and soil moisture shows the potential of use as an alternative dataset for hydrological studies. Streamflow along with precipitation and temperature are analyzed for trends. No statistically significant trend is observed in the daily precipitation series. Results suggest that an increase in temperature may reduce accumulation of snow during fall and winter. The flow regime may be in transition from a snowmelt dominated regime to a rainfall dominated regime. Results from future climate simulations of the A2 emission scenario indicate a projected increase of streamflow, while the snow depth and duration exhibit a decrease. Soil moisture response to future climate warming shows an overall increase with a greater likelihood of occurrences of higher soil moisture.