Search for: All records

We build on previous work which explained the origin of myriad gullies and incised channels on the dry, sandy uplands of northern Lower Michigan by invoking widespread permafrost. Indicators of permafrost (ice-wedge casts and patterned ground) are known from many sites across the region. Our study area, within an extensive reentrant of the retreating Laurentide Ice Sheet, had been particularly well positioned, geographically, for permafrost. Our goal was to characterize the geomorphic characteristics of the gullies on 72 large ridges, to address the hypothesis that they had formed in association with permafrost. Across the study area, thousands of dry, narrow channels and gullies occur in dense networks, typically with channels aligned directly downslope, in parallel drainage patterns. Most of the gullies exhibit only a minimal amount of incision (ca. 2–3 m), a nearly straight longitudinal profile, and lack a clear depositional fan at their mouth. Even where small fans are present, they are subtle and exhibit little down-fan textural sorting, as would be present in larger, more mature fluvial systems. Gully morphologies did not exhibit strong morphological differences as a function of aspect, as we would have expected for an erosional, periglacial system forming on fairly steep slopes. Nonetheless, in these sandy/gravelly sediments, we could find no other scenario that would have allowed for runoff and gully formation, except ice-rich permafrost that limited infiltration and promoted saturation of the active layer, and eventually, runoff. We conclude that the gullies formed via thermo-erosion into ice-rich permafrost, involving mostly fluvial processes but also some slope failure. Even though thermo-erosion can rapidly form deep gullies, our study area has mainly weak gully forms, perhaps because: (1) permafrost existed here only briefly, (2) the landscape was so cold and the permafrost so ice-rich that runoff was rare, (3) the permafrost on the sandy slopes remained somewhat permeable, limiting runoff, and/or (4) the paleoclimate was so dry that little water was available for sediment transport. We could find no evidence that the gullies developed within preexisting polygonal networks, as is happening today in polar regions under a warming climate. Thus, our study has implications for areas of the Arctic and Antarctic that are, today, experiencing rapid hydrological changes. 

Abstract. In the past decades, data-driven machine-learning (ML) models have emerged as promising tools for short-term streamflow forecasting. Among other qualities, the popularity of ML models for such applications is due to their relative ease in implementation, less strict distributional assumption, and competitive computational and predictive performance. Despite the encouraging results, most applications of ML for streamflow forecasting have been limited to watersheds in which rainfall is the major source of runoff. In this study, we evaluate the potential of random forests (RFs), a popular ML method, to make streamflow forecasts at 1 d of lead time at 86 watersheds in the Pacific Northwest. These watersheds cover diverse climatic conditions and physiographic settings and exhibit varied contributions of rainfall and snowmelt to their streamflow. Watersheds are classified into three hydrologic regimes based on the timing of center-of-annual flow volume: rainfall-dominated, transient, and snowmelt-dominated. RF performance is benchmarked against naïve and multiple linear regression (MLR) models and evaluated using four criteria: coefficient of determination, root mean squared error, mean absolute error, and Kling–Gupta efficiency (KGE). Model evaluation scores suggest that the RF performs better in snowmelt-driven watersheds compared to rainfall-driven watersheds. The largest improvements in forecasts compared to benchmark models are found among rainfall-driven watersheds. RF performance deteriorates with increases in catchment slope and soil sandiness. We note disagreement between two popular measures of RF variable importance and recommend jointly considering these measures with the physical processes under study. These and other results presented provide new insights for effective application of RF-based streamflow forecasting.