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The world's rangelands and drylands are undergoing rapid change, and consequently are becoming more difficult to manage. Big data and digital technologies (digital tools) provide land managers with a means to understand and adaptively manage change. An assortment of tools—including standardized field ecosystem monitoring databases; web‐accessible maps of vegetation change, production forecasts, and climate risk; sensor networks and virtual fencing; mobile applications to collect and access a variety of data; and new models, interpretive tools, and tool libraries—together provide unprecedented opportunities to detect and direct rangeland change. Accessibility to and manager trust in and knowledge of these tools, however, have failed to keep pace with technological advances. Collaborative adaptive management that involves multiple stakeholders and scientists who learn from management actions is ideally suited to capitalize on an integrated suite of digital tools. Embedding science professionals and experienced technology users in social networks can enhance peer‐to‐peer learning about digital tools and fulfill their considerable promise. 

Physically based hydrologic models require significant effort and extensive information for development, calibration, and validation. The study explored the use of the random forest regression (RFR), a supervised machine learning (ML) model, as an alternative to the physically based Soil and Water Assessment Tool (SWAT) for predicting streamflow in the Rio Grande Headwaters near Del Norte, a snowmelt-dominated mountainous watershed of the Upper Rio Grande Basin. Remotely sensed data were used for the random forest machine learning analysis (RFML) and RStudio for data processing and synthesizing. The RFML model outperformed the SWAT model in accuracy and demonstrated its capability in predicting streamflow in this region. We implemented a customized approach to the RFR model to assess the model’s performance for three training periods, across 1991–2010, 1996–2010, and 2001–2010; the results indicated that the model’s accuracy improved with longer training periods, implying that the model trained on a more extended period is better able to capture the parameters’ variability and reproduce streamflow data more accurately. The variable importance (i.e., IncNodePurity) measure of the RFML model revealed that the snow depth and the minimum temperature were consistently the top two predictors across all training periods. The paper also evaluated how well the SWAT model performs in reproducing streamflow data of the watershed with a conventional approach. The SWAT model needed more time and data to set up and calibrate, delivering acceptable performance in annual mean streamflow simulation, with satisfactory index of agreement (d), coefficient of determination (R2), and percent bias (PBIAS) values, but monthly simulation warrants further exploration and model adjustments. The study recommends exploring snowmelt runoff hydrologic processes, dust-driven sublimation effects, and more detailed topographic input parameters to update the SWAT snowmelt routine for better monthly flow estimation. The results provide a critical analysis for enhancing streamflow prediction, which is valuable for further research and water resource management, including snowmelt-driven semi-arid regions.