Availability of precipitation data is very important in every aspect related to hydrology. Readings from the ground stations are reliable and are used in hydrological models to do various analysis. However, the predictions are always associated with uncertainties due to the limited number of ground stations, which requires interpolation of the data. Meanwhile, groundbreaking approach in capturing precipitation events from vantage point through satellites in space has created a platform to not only merge ground data with satellite estimates to produce more accurate result, but also to find the data where ground stations are not available or scarcely available. Nevertheless, the data obtained through these satellite missions needs to be verified on its temporal and spatial resolution as well as the uncertainties associated before we make any decisions on its basis. This study focuses on finding and evaluating data obtained from two multi-satellite precipitation measurements missions: i) Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) ii) Global Precipitation Measurement (GPM) mission. GPM is the latest mission launched on Feb 28, 2014 after the successful completion of TRMM mission which collected valuable data for 17 years since its launch in November 1997. Both near real time and final version precipitation products for TMPA and GPM are considered for this study. Two study areas representing eastern and western parts of the United States of America (USA) are considered: i) Charlotte (CLT) in North Carolina ii) San Francisco (SF) in California. Evaluation is carried out for daily accumulated rainfall estimates and single rainfall events. Statistical analysis and error categorization of daily accumulated rainfall estimates were analyzed in two parts: i) Ten yeas data available for TMPA products were considered for historical analysis ii) Both TMPA and GPM data available for a ten-month common period was considered for GPM Era analysis. To study how well the satellite estimates with their finest temporal and spatial resolution capture single rainfall event and to explore their engineering application potential, an existing model of SF watershed prepared in Infoworks Integrated Catchment Model (ICM) was considered for hydrological simulation. Infoworks ICM is developed and maintained by Wallingford Software in the UK and SF watershed model is owned by San Francisco Public Works (SFPW). The historical analysis of TMPA products suggested overestimation of rainfall in CLT region while underestimation in SF region. This underestimation was largely associated with missed-rainfall events and negative hit events in SF. This inconsistency in estimation was evident in GPM products as well. However, in the study of single rainfall events with higher magnitude of rainfall depth in SF, the total rainfall volume and runoff volume generated in the watershed were over-estimated. Hence, satellite estimates in general tends to miss rainfall events of lower magnitude and over-estimate rainfall events of higher magnitude. From statistical analysis of GPM Era data, it was evident that GPM has been able to correct this inconsistency to some extent where it minimized overestimation in CLT region and minimized negative error due to underestimation in SF. GPM products fairly captured the hydrograph shape of outflow in SF watershed in comparison to TMPA. From this study, it can be concluded that even though GPM precipitation estimates could not quiet completely replace ground rain gage measurements as of now, with the perpetual updating of algorithms to correct its associated error, it holds realistic engineering application potential in the near future.

This thesis presents a new approach to calibration of hydrologic models using distributed computing framework. Distributed hydrologic models are known to be very computationally intensive and difficult to calibrate. To cope with the high computational cost of the process a Surrogate Model Optimization (SMO) technique that is built for distributed computing facilities is proposed. The proposed method along with two analogous SMO methods are employed to calibrate WATCLASS hydrologic model. This model has been developed in University of Waterloo and is now a part of Environment Canada MESH (Environment Canada community environmental modeling system called Modèlisation Environmentale Communautaire (MEC) for Surface Hydrology (SH)) systems. SMO has the advantage of being less sensitive to "curse of dimensionality" and very efficient for large scale and computationally expensive models. In this technique, a mathematical model is constructed based on a small set of simulated data from the original expensive model. SMO technique follows an iterative strategy which in each iteration the surrogate model map the region of optimum more precisely. A new comprehensive method based on a smooth regression model is proposed for calibration of WATCLASS. This method has at least two advantages over the previously proposed methods: a)it does not require a large number of training data, b) it does not have many model parameters and therefore its construction and validation process is not demanding. To evaluate the performance of the proposed SMO method, it has been applied to five well-known test functions and the results are compared to two other analogous SMO methods. Since the performance of all SMOs are promising, two instances of WATCLASS modeling Smoky River watershed are calibrated using these three adopted SMOs and the resultant Nash-Sutcliffe numbers are reported.

This thesis presents a new approach to calibration of hydrologic models using distributed computing framework. Distributed hydrologic models are known to be very computationally intensive and difficult to calibrate. To cope with the high computational cost of the process a Surrogate Model Optimization (SMO) technique that is built for distributed computing facilities is proposed. The proposed method along with two analogous SMO methods are employed to calibrate WATCLASS hydrologic model. This model has been developed in University of Waterloo and is now a part of Environment Canada MESH (Environment Canada community environmental modeling system called Modèlisation Environmentale Communautaire (MEC) for Surface Hydrology (SH)) systems. SMO has the advantage of being less sensitive to "curse of dimensionality" and very efficient for large scale and computationally expensive models. In this technique, a mathematical model is constructed based on a small set of simulated data from the original expensive model. SMO technique follows an iterative strategy which in each iteration the surrogate model map the region of optimum more precisely. A new comprehensive method based on a smooth regression model is proposed for calibration of WATCLASS. This method has at least two advantages over the previously proposed methods: a)it does not require a large number of training data, b) it does not have many model parameters and therefore its construction and validation process is not demanding. To evaluate the performance of the proposed SMO method, it has been applied to five well-known test functions and the results are compared to two other analogous SMO methods. Since the performance of all SMOs are promising, two instances of WATCLASS modeling Smoky River watershed are calibrated using these three adopted SMOs and the resultant Nash-Sutcliffe numbers are reported.

Spatial variability of rainfall is a significant factor in hydrologic and water quality modeling. In recent years, characterizing and analyzing the effect of spatial variability of rainfall in hydrologic applications has become vital with the advent of remotely sensed precipitation estimates that have high spatial resolution. In this study, the effect of spatial variability of rainfall in hourly runoff generation was analyzed using the Soil and Water Assessment Tool (SWAT) for Big Sandy Creek and Walnut Creek Watersheds in North Central Texas. The area of the study catchments was 808 km2 and 196 km2 for Big Sandy Creek and Walnut Creek Watersheds respectively. Hourly rainfall measurements obtained from raingauges and weather radars were used to estimate runoff for the years 1999 to 2003. Results from the study indicated that generated runoff from SWAT showed enormous volume bias when compared against observed runoff. The magnitude of bias increased as the area of the watershed increased and the spatial variability of rainfall diminished. Regardless of high spatial variability, rainfall estimates from weather radars resulted in increased volume of simulated runoff. Therefore, weather radar estimates were corrected for various systematic, range-dependent biases using three different interpolation methods: Inverse Distance Weighting (IDW), Spline, and Thiessen polygon. Runoff simulated using these bias adjusted radar rainfall estimates showed less volume bias compared to simulations using uncorrected radar rainfall. In addition to spatial variability of rainfall, SWAT model structures, such as overland flow, groundwater flow routing, and hourly evapotranspiration distribution, played vital roles in the accuracy of simulated runoff.

The Hydrological Simulation Program - FORTRAN (HSPF) in its personal computer version, release 10.10, was used to perform the hydrological simulation of a sub-watershed of the Occoquan River drainage basin. The sub-watershed selected was the Cub Run Watershed located in the northern area of the Occoquan River catchment. A model in the form of a User Control Input (UCI) file was prepared. The Cub Run Watershed was analyzed considering its geological, edaphic and weather characteristics, and segmented accordingly. The model was calibrated to adjust simulated results to observed data. Several calibration runs were executed and a final run was done considering a further segmented watershed. The simulation results were good even when not all the desired data could be found. The annual percent difference between the best calibration run and the observed results was 21.28%. The ten-month percent difference, excluding June and July, was 5.82 %. The first value is a fair result for hydrologic calibration, the second value is an excellent result for the same type of calibration. Additional segmentation did not further improve the results obtained during the best calibration run. Differences in the calibration when considering just a pervious segment or two segments (one pervious and one impervious) could be noted, indicating the importance of considering impervious surfaces for the simulation. HSPF reacted quite logically to variations in the calibration parameters and the results from those variations could be predicted beforehand. In summary, the PC version of HSPF was demonstrated to be a good management tool for the hydrological simulation of this watershed. / Master of Science

Este trabalho compreende a modelagem hidrológica da bacia hidrográfica do rio Muriaé (BHRM). As simulações hidrológicas foram realizadas com o modelo hidrológico TOPMODEL com medições de precipitação de estimativa de vazão da rede telemétrica da Rede Hidrometeorológica Nacional (RHN). Utilizou-se também as respectivas estimativas de precipitação por satélite com o método CMORPH, e a análise de precipitação integrada entre a precipitação medida pela telemetria e a estimada por satélite por meio da análise objetiva estatística (ANOBES). A calibração e a validação do modelo TOPMODEL foram realizadas para eventos hidrológicos entre 2016 e 2018. A calibração do modelo TOPMODEL com as séries de dados de precipitação acima foi avaliada por meio do coeficiente de Nash-Sutcliffe (NSE), que variou entre 0,7 e 0,9. A validação do modelo TOPMODEL com séries independentes resultou em NSE de -0,8 a 0,3. Este resultado em grande parte se deve ao pequeno número de eventos hidrológicos desde o início das medições telemétricas na BHRM. O TOPMODEL também foi utilizado para simular vazões em séries com período anual entre 2009 e 2013. A calibração e validação com séries anuais resultaram em NSE ~ 0,6. Notadamente, as simulações com CMORPH tendem a subestimar as vazões, enquanto que com ANOBES o desempenho foi melhor, em especial para períodos de cheia. Portanto, os resultados sugerem a aplicabilidade do modelo TOPMODEL para simulações hidrológicas da Bacia do Rio Muriaé, com os melhores resultados obtidos quando a modelagem iniciou em um período de estiagem e o dado de precipitação representou a variabilidade espacial da chuva. / This study comprises the hydrological modeling of the Muriaé river basin. Hydrologic simulations were performed with the TOPMODEL hydrological model, with precipitation measurements and discharge estimation from the Brazilian Hydrometeorology Network (RHN). It was also used satellite precipitation estimates with the CMORPH method, and the integrated precipitation analysis between the precipitation measured by the telemetry and the estimated by satellite through objective statistical analysis (SOAS). The calibration and validation of the TOPMODEL model were performed for hydrological events between 2016 and 2018. The calibration of the TOPMODEL model with the above precipitation data series was evaluated using the Nash-Sutcliffe coefficient (NSE), which ranged from 0,7 and 0,9. Validation of the TOPMODEL model with independent series resulted in NSE from -0,8 to 0,3. This result is largely due to the small number of hydrological events since the beginning of telemetry measurements at the Muriaé river basin. TOPMODEL was also used to simulate flows in series with annual period between 2009 and 2013. Calibration and validation with annual series resulted in NSE ~ 0.6. Notably, CMORPH simulations tend to underestimate flow rates, while with SOAS the performance was better, especially for flood periods. Therefore, the results suggest the applicability of the TOPMODEL model for hydrological simulations of the Muriaé river basin, with the best results obtained when the modeling started in a drought period and the rainfall data represented the spatial variability of the rainfall.

Numeric hydrologic models can aid in water resource management by providing predictive simulations of water behavior. As computers become more advanced, the models developed also become more complex using more data to represent larger areas for forecasting hydrologic behavior. Unfortunately, as the simulations use more data, the output often becomes difficult to manage and share without investing time and effort into setting up server environments or decreasing the quality of the output to compensate for an efficient and effective user experience. A proposed solution to facilitate the accessibility of massive hydrologic model output is through the web-based visualization tool developed at Carnegie Mellon University called Time Machine. For a more efficient and automated workflow, a Python tool named TMAPS was developed from this research for rendering hydrologic model results, geoprocessing the rendered output, and generating Time Machines seamlessly. The tool can be installed from the CI-WATER GitHub repository and allows the user to 1) select the output parameters and visualization settings desired to be rendered, 2) run the code on a local or HPC setup, and 3) use a web browser interface to view the tiled transient results seamlessly while maintaining high quality. Currently, the only hydrologic model supported is ADHydro - a large-scale high-resolution multi-physics watershed simulation. In an effort to facilitate organizing the library of Time Machine products, an app was created through Tethys - a server-based Django application designed to aid in the development and sharing of water resource engineering apps.

This work explores the use of hydroinformatics tools to provide a user friendly and accessible interface for executing and visualizing the output of distributed hydrological models for Iowa. It uses an IFIS-based web environment for graphical displays and it communicates with the ASYNCH ODE solver to provide input parameters and to gather modeling outputs. The distributed hydrologic models used here are based on the segmentation of the terrain into hillslope-link hydrologic units, for which water flow processes are represented by sets of nonlinear ordinary differential equations. This modeling strategy has shown promising results in in modeling extreme flood events in the state of Iowa – USA. The usage and evaluation of outputs from hillslope-link models (HLM) has been limited to a restrict group of academics due to the demand of high processing capability and the number of customized tools needed to visualize model outputs. HLM-based models provide abundant output information on rainfall-runoff processes of the hydrological cycle, including estimates of discharge for all streams in the state of Iowa, and for all conceptual vertical layers of water storage in soils. The interfaces and methodologies developed in this thesis respond to the constant demand for communicating effectively water-related information from academic communities to the public using hydroinformatics tools to provide an accessible portal to the information generated by complex hydrological models. It also facilitates model development and evaluation by allowing rapid development of what-if scenarios. This work represents a significant advance in this direction, and the results have been made publicly available online under the URL http://ifis.iowafloodcenter.org/ifis/sc/modelplus/.

Intensive human-environment interactions are taking place in Midwestern agricultural systems. An integrated modeling framework is suitable for predicting dynamics of key variables of the socio-economic, biophysical, hydrological processes as well as exploring the potential transitions of system states in response to changes of the driving factors. The purpose of this dissertation is to address issues concerning the interacting processes and consequent changes in land use, water balance, and water quality using an integrated modeling framework. This dissertation is composed of three studies in the same agricultural watershed, the Clear Creek watershed in East-Central Iowa. In the first study, a parsimonious hydrologic model, the Threshold-Exceedance-Lagrangian Model (TELM), is further developed into RS-TELM (Remote Sensing TELM) to integrate remote sensing vegetation data for estimating evapotranspiration. The goodness of fit of RS-TELM is comparable to a well-calibrated SWAT (Soil and Water Assessment Tool) and even slightly superior in capturing intra-seasonal variability of stream flow. The integration of RS LAI (Leaf Area Index) data improves the model's performance especially over the agriculture dominated landscapes. The input of rainfall datasets with spatially explicit information plays a critical role in increasing the model's goodness of fit. In the second study, an agent-based model is developed to simulate farmers' decisions on crop type and fertilizer application in response to commodity and biofuel crop prices. The comparison between simulated crop land percentage and crop rotations with satellite-based land cover data suggest that farmers may be underestimating the effects that continuous corn production has on yields (yield drag). The simulation results given alternative market scenarios based on a survey of agricultural land owners and operators in the Clear Creek Watershed show that, farmers see cellulosic biofuel feedstock production in the form of perennial grasses or corn stover as a more risky enterprise than their current crop production systems, likely because of market and production risks and lock in effects. As a result farmers do not follow a simple farm-profit maximization rule. In the third study, the consequent water quantity and quality change of the potential land use transitions given alternative biofuel crop market scenarios is explored in a case study in the Clear Creek watershed. A computer program is developed to implement the loose-coupling strategy to couple an agent-based land use model with SWAT. The simulation results show that watershed-scale water quantity (water yield and runoff) and quality variables (sediment and nutrient loads) decrease in values as switchgrass price increases. However, negligence of farmers risk aversions towards biofuel crop adoption would cause overestimation of the impacts of switchgrass price on water quantity and quality.

This thesis utilizes a distributed hydrologic model to predict hydrologic effects of Low Impact Development (LID), and also analyzes runoff from small sub-areas within the watershed. City planners and developers rely on accurate hydrologic models, which enable them to design flood-proof developments and effectively mitigate flooding downstream. Common hydrologic models use a lumped approach, which averages the physical characteristics of basins for model calculations, limiting their ability to estimate runoff within the basin. In contrast, distributed hydrologic models, which divide the watershed into a grid system, can be used to predict runoff at any location within the watershed. The fully distributed hydrologic model, VfloTM, is used to model stormwater runoff in The Woodlands, TX watershed, and to demonstrate the effectiveness of the master planned community. This thesis also suggests that a calibrated VfloTM model can accurately predict stormwater runoff from small sub-areas within a watershed.