Across federally managed rangelands in the Intermountain West, streams and riparian areas are limited but frequently used by cattle for drinking and seeking refuge from heat and weather, creating persistent management challenges for agencies tasked with conserving freshwater ecosystems. The Bureau of Land Management (BLM) collaborates with partner organizations to collect standardized stream monitoring data that informs management decisions about grazing and other disturbances on public lands. However, while field-based indicators describe discrete stream monitoring locations, there remains an opportunity to link these data with landscape-wide spatial datasets to generate a network simulation of stream conditions. Modeling with Fuzzy Inference Systems (FIS) is well suited for handling ecological data with vague or qualitative components because it integrates expert knowledge with quantitative predictors to produce interpretable outputs. Here, we apply a FIS to generate a susceptibility index that ranks stream reaches across Nevada by their predicted likelihood of cattle access, which can inform targeted grazing management. We use five spatially derived ecological predictors widely recognized in the literature as influencing cattle distribution across rangeland environments: proximity to upland water sources, cattle-specific heat stress index (HSI), riparian vegetation density, stream-adjacent hillslope, and proximity to upland shade. Preliminary outputs generate continuous susceptibility estimates along stream networks, revealing spatial patterns in cattle–stream interactions. We will test this preliminary model with a field-based disturbance indicator from the BLM’s Multiple Indicator Monitoring (MIM) dataset. This collaborative approach between the BLM and its partners highlights how standardized monitoring data and interpretable modeling tools can serve both agency reporting needs and broader scientific applications that generate actionable insights for streams and rivers on public lands.