Evaluating the performance of high-resolution climate simulations over the central himalaya and Karakoram - Tika Gurung; Liang Chen; Syed Hammad Ali; Clinton Rowe; Francisco Munoz Arriola; Randall S. Ritzema (Climate Dynamics, 2026)

Assessing the separate contribution of the domain, RCM and GCM to the uncertainty in CORDEX simulations over the overlapped regions that include Jordan - Hebah Alkhasoneh; Clinton Rowe (International Journal of Climatology, 2024)
The Coordinated Regional Downscaling Experiment (CORDEX) offers a framework for regional climate downscaling experiments over continental‐based domains that overlap; this allows for conducting multimodel analysis. However, a question is raised about the uncertainty due to domain selection. This study aims to evaluate the sensitivity of CORDEX simulations to the choices of domain, downscaling regional climate model (RCM) and driving global model (GCM) over a Middle East study region, including Jordan. Understanding these sensitivities helps assess simulation uncertainties and enhance regional climate change projections. Taylor diagrams and variance decomposition analyses were used to analyse seasonal temperature and precipitation climatologies. The results indicate that the domain contribution to variance is negligible, whereas the choice of GCM and RCM strongly influences simulations. Variance in temperature is significantly impacted by the driving model (GCM), whereas RCM has a higher influence on precipitation, which reflects their large‐scale versus local nature. The results of this study support the development of multidomain ensembles since projections produced by the same GCM–RCM model combination are consistent across different overlapping domains. On the other hand, this approach facilitates the consideration as well as a comprehensive quantification of the uncertainties arising from the utilization of multiple GCMs and RCMs within such an ensemble. This improves the reliability of regional climate information, thus facilitating the development of effective adaptation strategies and mitigation plans.

Long-term durability of two HDPE geomembranes formulated with polyethylene of raised temperature resistance (PE-RT) - Matthew Clinton; R. Kerry Rowe (Geotextiles and Geomembranes, 2024)

Geographic and taxonomic variation in adaptive capacity among mountain-dwelling small mammals: Implications for conservation status and actions - Erik A. Beever; Jennifer L. Wilkening; Peter D. Billman; Lindsey L. Thurman; Kristina A. Ernest; David H. Wright; Alisha M. Gill; April C. Craighead; Nolan A. Helmstetter; Leona K. Svancara; Meghan J. Camp; Sabuj Bhattacharyya; et al. (Biological Conservation, 2023)

Feasibility of imaging under structured illumination for evaluation of white striping in broiler breast fillets - Ebenezer Obaloluwa Olaniyi; Yuzhen Lu; Jiaxu Cai; Anuraj Theradiyil Sukumaran; Tessa Jarvis; Clinton Rowe (Journal of Food Engineering, 2023)

Trends of extreme air temperature and precipitation and their impact on corn and soybean yields in Nebraska, USA - Carlos A. C. dos Santos; Christopher M. U. Neale; Mesfin M. Mekonnen; Ivo Zution Gonçalves; Gabriel de Oliveira; Osias Ruiz-Alvarez; Babak Safa; Clinton M. Rowe (Theoretical and Applied Climatology, 2022)

Climate regionalization in Bolivia: A combination of non‐hierarchical and consensus clustering analyses based on precipitation and temperature - Azar M. Abadi; Clinton M. Rowe; Marcos Andrade (International Journal of Climatology, 2020)
Climate regionalization is an inseparable part of many climate change and environmental studies. Delineating climatologically homogeneous regions enhances the utility of such studies and reduces the biases due to the uncertainties associated with climate model outputs at individual grid points which both lead to better understanding of the atmospheric mechanisms affecting a region's climate. Throughout time, researchers and statisticians have developed different methods to perform regionalization in which the techniques are highly dependent on the nature and accessibility of the data. This research aims to divide Bolivia into smaller, coherent climate subdivisions. To achieve this goal, we first apply the non‐hierarchical k ‐means clustering method to climatologies of monthly accumulated precipitation and monthly average temperature separately using a gridded observation dataset for Bolivia spanning from 1979 to 2010. The clustering is performed on the two variables separately to avoid arbitrary attribute scaling and information redundancy as well as to gain a better understanding of these individual variables across Bolivia. Consensus clustering then finds the categorical intersection of the two independent clusters to create homogeneous climate regions. Results from this study show that Bolivia can be divided into 10 climatically distinguishable subdivisions largely explicable by topography and latitude, which are the key climate control factors in the region.

Evaluation of GCMs historical simulations of monthly and seasonal climatology over Bolivia - Azar M. Abadi; Robert Oglesby; Clinton Rowe; Rachindra Mawalagedara (Climate Dynamics, 2018)

An approach to hydrogeological modeling of a large system of groundwater-fed lakes and wetlands in the Nebraska Sand Hills, USA - Nathan R. Rossman; Vitaly A. Zlotnik; Clinton M. Rowe (Hydrogeology Journal, 2018)