ABSTRACT:

Accurate prediction of the event-based runoff coefficient (ERC) is fundamental to understanding watershed response, yet achieving reliable predictions across diverse climatic and geophysical regions remains a significant challenge. This study presents a robust, data-driven framework for predicting ERC at the continental scale across the Contiguous United States (CONUS). Utilizing a large-sample hydrology approach, we harmonized a comprehensive dataset of 479,872 rainfall-runoff events from USGS-gauged watersheds. The model integrates sub-daily atmospheric forcing from the NLDAS-2 dataset with static watershed attributes, including topography, land cover, and soil characteristics. To address spatial heterogeneity, we implemented a regime-based modeling approach that utilizes K-means clustering to categorize watersheds into distinct hydrological regimes before training. Predictive performance was evaluated using advanced machine learning algorithms, specifically eXtreme Gradient Boosting (XGBoost) and Support Vector Regression (SVR). Our results demonstrate that the regime-based ML framework significantly outperforms traditional global modeling approaches, particularly in regions with high seasonal variability. Sensitivity analysis reveals that antecedent moisture conditions and land-use patterns are the primary drivers of ERC variability at the continental scale. To support community research and reproducibility, the processed dataset and model scripts are made publicly available via HydroShare. This work provides a scalable tool for predicting watershed response in ungauged basins and offers new insights into the process controls governing runoff generation across the CONUS.

ABSTRACT:

Spatial interpolation techniques play an important role in hydrology as many point observations need to be interpolated to create continuous surfaces. Despite the availability of several tools and methods for interpolating data, not all of them work consistently for hydrologic applications. One of the techniques, Laplace Equation, which is used in hydrology for creating flownets, has rarely been used for interpolating hydrology data. The objective of this study is to examine the efficiency of Laplace formulation (LF) in interpolating hydrologic data and compare it with other widely used methods such as the inverse distance weighting (IDW), natural neighbor, and ordinary kriging. Comparison is performed quantitatively for using root mean square error (RMSE) and R2, visually for creating reasonable surfaces and computationally for ease of operation and speed. Data related to surface elevation, river bathymetry, precipitation, temperature, and soil moisture are used for different areas in the United States. RMSE and R2 results show that LF is comparable to other methods for accuracy. LF is easy to use as it requires fewer input parameters compared to IDW and Kriging. Computationally, LF is faster than other methods in terms of speed when the datasets are not large. Overall, LF offers a robust alternative to existing methods for interpolating various hydrology data. Further work is required to improve its computational efficiency.

ABSTRACT:

This resource includes hourly precipitation data collected by National Oceanic and Atmospheric Administration's (NOAA's) and downloaded from the National Climate Data Center (NCDC) from station located in Beltsville, MD. These data were collected to with the purpose of obtain important inputs for some further research about hydrologic modeling. Samples were collected automatically through code in Python. Methods implemented for sample collection and analysis are described within the resource.

ABSTRACT:

This resource includes hourly precipitation data collected by National Oceanic and Atmospheric Administration's (NOAA's) and downloaded from the National Climate Data Center (NCDC) from station located in Beltsville, MD. These data were collected to with the purpose of obtain important inputs for some further research about hydrologic modeling. Samples were collected automatically through code in Python. Methods implemented for sample collection and analysis are described within the resource.