@article{fallahi_nelson_beyene_caldwell_roise_2025, title={A Comparative Assessment of Water Supply Stress Index (WaSSI) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Models for Annual Water Yield Estimation: A Case Study in the Croatan National Forest}, volume={12}, url={https://www.mdpi.com/2076-3298/12/3/89}, DOI={10.3390/environments12030089}, abstractNote={This study conducts a comparison of two ecosystem service models: the Water Supply Stress Index (WaSSI) and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST). It focuses on each model’s capability to estimate annual water yield within the Croatan National Forest (CNF). The Croatan Forest, characterized as a coastal ecosystem with high biodiversity and unique water resource management challenges, provides an opportune setting to examine and compare the accuracy and efficiency of these models in predicting water yield. Utilizing field data and remote sensing, we investigated the capabilities of both models to estimate water yield. The results indicate that both models can serve as useful tools for water resource management in coastal ecosystems, yet there are differences in their accuracy and sensitivity to environmental factors. This study is the first to compare the two ecosystem models, the WaSSI and InVEST, within a coastal forest setting for the calculation of water yield.}, number={3}, journal={Environments}, author={Fallahi, Mahdis and Nelson, Stacy A. C. and Beyene, Solomon and Caldwell, Peter V. and Roise, Joseph P.}, year={2025}, month={Mar} }
 @article{fallahi_nelson_caldwell_roise_beyene_peterson_2025, title={Future Water Yield Projections Under Climate Change Using Optimized and Downscaled Models via the MIDAS Approach}, volume={12}, url={https://doi.org/10.3390/environments12090303}, DOI={10.3390/environments12090303}, abstractNote={Climate change significantly affects hydrological processes in forest ecosystems, particularly in sensitive coastal areas such as the Croatan National Forest (CNF) in North Carolina. Accurate projections of future water yield are essential for managing agriculture, forestry, and natural ecosystems. This study investigates the potential impacts of climate change on water yield using a combination of statistical downscaling and machine learning. Two downscaling methods, a Statistical DownScaling Model (SDSM) and Multivariate Adaptive Constructed Analogs (MACA), were evaluated, with the SDSM providing superior performance for local climate conditions. To improve precipitation input accuracy, twenty ensemble scenarios were generated using the SDSM, and various machine learning algorithms were applied to identify the optimal ensemble. Among these, the Extreme Gradient Boosting (XGBoost) algorithm exhibited the lowest error and was selected for producing high-quality precipitation time series. This methodology is integrated into the MIDAS (Machine Learning-Based Integration of Downscaled Projections for Accurate Simulation) approach, which leverages machine learning to enhance climate input precision and reduce uncertainty in hydrological modeling. Water yield was simulated over the period 1961–2060, combining observed and projected climate data to capture both historical trends and future changes. The results show that combining statistical downscaling with machine learning algorithms can help improve the accuracy of water yield projections under climate change and be useful for water resource planning, forest management, and climate adaptation.}, number={9}, journal={Environments}, author={Fallahi, Mahdis and Nelson, Stacy and Caldwell, Peter and Roise, Joseph and Beyene, Solomon and Peterson, M.}, year={2025}, month={Aug} }