Miralha, L., Wissler, A. D., Segura, C., & Bladon, K. D. (2023). Characterizing stream temperature hysteresis in forested headwater streams. Hydrological Processes, 37(1), e14795. 

This study aimed to (a) quantify the variability of stream temperature (Ts) hysteresis during storms across seasons in different sub-regions and (b) investigate the relationship between the hysteretic response and catchment characteristics. We found that the hysteretic behaviour of Ts varied across seasons—the greatest HI occurred during spring and summer. We posit that the drivers of Ts response during storms are likely dependent on catchment physiographic characteristics. Our study also illustrated the potential utility of stream temperature as a tracer for improving the understanding of hydrologic connectivity and shifts in the dominant runoff contributions to streamflow during storm events.

Miralha, L., Sidique, S., & Muenich, R. L. (2022). The spatial organization of CAFOs and its relationship to water quality in the United States. Journal of Hydrology, 613, 128301. 

In this article, Miralha et al. investigated the influence of  spatial aggregation of CAFOs on water quality conditions in the United States. Overall, they found that watersheds with significant clustering patterns were associated with higher TP and TN levels. This study also brings insights into new water quality modeling approaches and supports future policy decisions.

Miralha et al. (2021) Spatiotemporal land use change and environmental degradation surrounding CAFOs in Michigan and North Carolina. Science of The Total Environment, 800, 149391.

In this research, Miralha et al investigated whether the presence of regulated liquid waste CAFOs is associated with land-use change over time and space as well as degraded environmental conditions surrounding those facilities. They found that cropland extent increased while savanna and forest decreased near CAFOs. Similar observations did not occur outside of areas influenced by CAFOs.

Scavia et al. (2021) Quantifying uncertainty cascading from climate, watershed, and lake models in harmful algal bloom predictions. Science of the Total Environment  759, 143487

Uncertainty analysis across model types suggest that the largest sources of error for the projections come from uncertain HAB model parameters and climate models, followed by HAB model structure and the SWAT models. It also suggests that uncertainties in the HAB models are driven roughly evenly between HAB measurement errors and model prediction errors.

Miralha et al. (2021) Bias correction of climate model outputs influences watershed model nutrient load predictions. Science of The Total Environment 759, 143039

In this research, Lorrayne and her collaborators investigated how bias correcting climate model products that served as inputs impact nutrient load predictions using a SWAT model calibrated for the Maumee Basin in the Great Lakes Region. They found that the choice of bias correction techniques influences the nutrient load predictions.

Miralha, L., Kim, D.(2018) Accounting for and predicting the influence of spatial autocorrelation (SAC) in water quality modeling. ISPRS International Journal of Geo-Information 7 (2), 64

Our study demonstrates that analyzing SAC in water quality modeling provides benefits beyond just improvements in model outcomes (R2 and rSAC): it can potentially lead to a better understanding of the extent of the spatial organization of water quality variables, as well as serve as a useful screening technique to anticipate the predictability of the spatial pattern in the independent variable used in a spatially explicit model.