Since snowmelt runoff is important in the mountainous parts of the world, substantial efforts have been made to develop snowmelt models with many different levels of complexity to simulate the processes at the ground, within the snow, and at the interface with the atmosphere. The land-atmosphere interactions and processing influencing heat transfer to and from a snowpack are largely variable and the conceptual representation of this temporal and spatial variability is difficult. A physically based, two layer point model, is applied to calculate the energy and mass balance of snowmelt in the Upper Karasu Basin, eastern part of Turkey during 2002-2004 snow seasons. The climate data are provided from automated weather stations installed and upgraded to collect quantitative and qualitative data with automated transfer. Each form of energy transfer is evaluated to understand the key processes that have major impact on the snow simulation during accumulation and ablation in two-hourly timesteps. The model performance is evaluated as accurate according to the results, compared with observed snow water equivalents, snow depth and lysimeter runoff yield. In the second part, calculated snowmelt values based on energy and mass balance at the automated stations are related to radiation index model through regression. Then, the spatial patterns of snow water equivalent, solar illumination, albedo and air temperature are used to predict the melt at each grid cell over the whole watershed. The results of distributed model application are evaluated in terms of snow covered area of satellite products, observed snow water equivalent at points through snow pillows and discharge values at the outlet runoff station.

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.

Distributed models of the land surface are essential for global climate models because of the importance of land-atmosphere exchanges of water, energy, momentum. They are also used for high resolution hydrologic simulation because of the need to capture non-linear responses to spatially variable inputs. Continued improvements to these models, and the data which they use, is especially important given ongoing changes in climate and land cover. In hydrologic models, important aspects are sometimes neglected due to the need to simplify the models for operational simulation. For example, operational flash flood models do not consider the role of snow and are often lumped (i.e. do not discretize a watershed into multiple units, and so do not fully consider the effect of intense, localized rainstorms). To address this deficiency, an overland flow model is coupled with a subsurface flow model to create a distributed flash flood forecasting system that can simulate flash floods that involve rain on snow. The model is intended for operational use, and there are extensive algorithms to incorporate high-resolution hydrometeorologic data, to assist in the calibration of the models, and to run the model in real time. A second study, which is designed to improve snow simulation in forested environments, demonstrates the importance of explicitly representing a near canopy environment in snow models, instead of only representing open and canopy covered areas (i.e. with % canopy fraction), as is often done. Our modeling, which uses canopy structure information from Aerial Laser Survey Mapping at 1 meter resolution, suggests that areas near trees have more net snow water input than surrounding areas because of the lack of snow interception, shading by the trees, and the effects of wind. In addition, the greatest discrepancy between our model simulations that explicitly represent forest structure and those that do not occur in areas with more canopy edges. In addition, two value-added Land Cover products (land cover type and maximum green vegetation fraction; MGVF) are developed and evaluated. The new products are good successors to current generation land cover products that are used in global models (many of which rely on 20 year old AVHRR land cover data from a single year) because they are based on 10 years of recent MODIS data. There is substantial spurious interannual variability in the MODIS land cover type data, and the MGVF product can vary substantially from year to year depending on climate conditions, suggesting the importance of using climatologies for land cover data. The new land cover type climatology also agrees better with validation sites, and the MGVF climatology is more consistent with other measures of vegetation (e.g. Leaf Area Index) than the older land cover data.

Hydrologic research is increasingly data and computationally intensive, and often involves hydrologic model simulation and collaboration among researchers. With the development of cyberinfrastructure, researchers are able to improve the efficiency, impact, and effectiveness of their research by utilizing online data sharing and hydrologic modeling functionality. However, further efforts are still in need to improve the capability of cyberinfrastructure to serve the hydrologic science community. This dissertation first presents the evaluation of a physically based snowmelt model as an alternative to a temperature index model to improve operational water supply forecasts in the Colorado River Basin. Then it presents the design of the functionality to share multidimensional space-time data in the HydroShare hydrologic information system. It then describes a web application developed to facilitate input preparation and model execution of a snowmelt model and the storage of these results in HydroShare. The snowmelt model evaluation provided use cases to evaluate the cyberinfrastructure elements developed. This research explored a new approach to advance operational water supply forecasts and provided potential solutions for the challenges associated with the design and implementation of cyberinfrastructure for hydrologic data sharing and modeling.