Dataset Information

The perpetual fragility of creeping hillslopes.

ABSTRACT: Soil creeps imperceptibly but relentlessly downhill, shaping landscapes and the human and ecological communities that live within them. What causes this granular material to 'flow' at angles well below repose? The unchallenged dogma is churning of soil by (bio)physical disturbances. Here we experimentally render slow creep dynamics down to micron scale, in a laboratory hillslope where disturbances can be tuned. Surprisingly, we find that even an undisturbed sandpile creeps indefinitely, with rates and styles comparable to natural hillslopes. Creep progressively slows as the initially fragile pile relaxes into a lower energy state. This slowing can be enhanced or reversed with different imposed disturbances. Our observations suggest a new model for soil as a creeping glass, wherein environmental disturbances maintain soil in a perpetually fragile state.

SUBMITTER: Deshpande NS

PROVIDER: S-EPMC8222271 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The perpetual fragility of creeping hillslopes.

Deshpande Nakul S NS Furbish David J DJ Arratia Paulo E PE Jerolmack Douglas J DJ

Nature communications 20210623 1

Soil creeps imperceptibly but relentlessly downhill, shaping landscapes and the human and ecological communities that live within them. What causes this granular material to 'flow' at angles well below repose? The unchallenged dogma is churning of soil by (bio)physical disturbances. Here we experimentally render slow creep dynamics down to micron scale, in a laboratory hillslope where disturbances can be tuned. Surprisingly, we find that even an undisturbed sandpile creeps indefinitely, with rat ...[more]

PMID: 34162848

Similar Datasets

Project description:Nitrogen is the most limiting nutrient for turfgrass growth. Instead of pursuing the maximum yield, most turfgrass managers use nitrogen (N) to maintain a sub-maximal growth rate. Few tools or soil tests exist to help managers guide N fertilizer decisions. Turf growth prediction models have the potential to be useful, but the currently existing turf growth prediction model only takes temperature into account, limiting its accuracy. This study developed machine-learning-based turf growth models using the random forest (RF) algorithm to estimate short-term turfgrass clipping yield. To build the RF model, a large set of variables were extracted as predictors including the 7-day weather, traffic intensity, soil moisture content, N fertilization rate, and the normalized difference red edge (NDRE) vegetation index. In this study, the data were collected from two putting greens where the turfgrass received 0 to 1,800 round/week traffic rates, various irrigation rates to maintain the soil moisture content between 9 and 29%, and N fertilization rates of 0 to 17.5 kg ha-1 applied biweekly. The RF model agreed with the actual clipping yield collected from the experimental results. The temperature and relative humidity were the most important weather factors. Including NDRE improved the prediction accuracy of the model. The highest coefficient of determination (R2) of the RF model was 0.64 for the training dataset and was 0.47 for the testing data set upon the evaluation of the model. This represented a large improvement over the existing growth prediction model (R 2 = 0.01). However, the machine-learning models created were not able to accurately predict the clipping production at other locations. Individual golf courses can create customized growth prediction models using clipping volume to eliminate the deviation caused by temporal and spatial variability. Overall, this study demonstrated the feasibility of creating machine-learning-based yield prediction models that may be able to guide N fertilization decisions on golf course putting greens and presumably other turfgrass areas.

Dataset Information

The perpetual fragility of creeping hillslopes.

Publications

The perpetual fragility of creeping hillslopes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets