Hydrological models require a complete and accurate time series of weather inputs to adequately represent watershed-scale responses. The Global Historical Climatology Network (GHCN) is the most comprehensive ground-based global weather database and is often used in hydrological modeling studies. Since higher density, lower reliability precipitation measurements from private citizens collected by the Community Collaborative Rain, Hail, and Snow (CoCoRaHS) network data were integrated into the GHCN, hydrological modelers in the U.S. have access to a much greater amount of weather data. However, the benefit of using CoCoRaHS data has not been assessed. The objectives of this work were to develop a method for generating a complete weather data time series based on the combination of data from multiple GHCN monitors and to assess several methods for estimation of missing weather data. Weather data from GHCN monitors located within a specific radius of a watershed were obtained and interpolated using three estimation methods (Inverse Distance Weighting (IDW), Inverse Distance and Elevation Weighting (IDEW), and Closest Station), creating a seamless time-series of weather observations. To evaluate the performance of the methodologies, weather data obtained from each estimation method was used to force the Soil and Water Assessment Tool (SWAT) models of 21 U.S. Department of Agriculture-Conservation Effects Assessment Project watersheds in different climate regions to simulate daily streamflow for 2010-2021. Except for three watersheds, all SWAT models had Nash-Sutcliffe Efficiency above 0.5, the ratio of the root mean square error to the standard deviation of observations below 0.7, and percent bias from -25% to 25% with a satisfactory performance rating. Overall, IDEW and IDW performed similarly, and the Closest Station method resulted in the poorest streamflow simulation. A comparison with published SWAT model results further corroborated improved model performance using newly combined GHCN data with all Closest Station, IDW, and IDEW methods.

Watershed scale models are essential for determining best management practices (BMPs), but they contain many parameters that modelers cannot directly measure. Modelers commonly estimate these parameters through a calibration process based on observed streamflow and nutrient data. However, a lack of long-term streamflow records makes watershed model parameter estimation in low data environments (LDE) challenging for hydrologists. To reliably estimate parameters in LDE, a new calibration technique, simultaneous multi-basin calibration (SMC), was developed to estimate the parameters of several SWAT model initializations for newly instrumented USGS gages in the Lake Champlain Basin of Vermont, USA (Little Otter Creek-Monkton, West Branch Dead Creek, and East Branch Dead Creek). In SMC, SWAT models of each watershed were initialized following standard methods. Then, in order to increase information content, the simulated flow from each model and the corresponding measured flow were combined, and calibrated as one model using a differential evolution algorithm DEoptim. We compared the results obtained from the new technique with one of the most commonly used approaches for calibration in LDE: the similarity-based regionalization (SBR) based on a calibration of a nearby watershed with similar characteristics. In the SBR method, the calibrated parameters from a watershed with a more extended period of recorded data (donor watershed, Little Otter Creek-Ferrisburg) transfer to the LDE watersheds (receptor watersheds). We show that in SBR the uncertainty of the donor watershed model propagates through the receptor watershed model, this propagation does not occur in SMC. We demonstrated that the agreement between simulated and observed streamflow, via the Nash-Sutcliffe efficiency (NSE) improved model performance from 1-20% using the SMC technique. Moreover, the calibrated soil storage parameters, including soil depth, available water capacity, and soil saturated hydraulic conductivity obtained from individual SMC and SBR models, were compared to the SSURGO soil database, where the SMC method provided parameter estimates that more closely matched SSURGO. This study demonstrated that a SMC method can outperform SBR in low data environments.