No abstract

Many forested steeplands in the western United States display a legacy of disturbances due to timber harvest, mining or wildfires, for example. Such disturbances have caused accelerated hillslope erosion, leading to increased sedimentation in fish-bearing streams. Several restoration techniques have been implemented to address these problems in mountain catchments, many of which involve the removal of abandoned roads and re-establishing drainage networks across road prisms. With limited restoration funds to be applied across large catchments, land managers are faced with deciding which areas and problems should be treated first, and by which technique, in order to design the most effective and cost-effective sediment reduction strategy. Currently most restoration is conducted on a site-specific scale according to uniform treatment policies. To create catchment-scale policies for restoration, we developed two optimization models -dynamic programming and genetic algorithms -to determine the most cost-effective treatment level for roads and stream crossings in a pilot study basin with approximately 700 road segments and crossings. These models considered the trade-offs between the cost and effectiveness of different restoration strategies to minimize the predicted erosion from all forest roads within a catchment, while meeting a specified budget constraint. The optimal sediment reduction strategies developed by these models performed much better than two strategies of uniform erosion control which are commonly applied to road erosion problems by land managers, with sediment savings increased by an additional 48 to 80 per cent. These optimization models can be used to formulate the most cost-effective restoration policy for sediment reduction on a catchment scale. Thus, cost savings can be applied to further restoration work within the catchment. Nevertheless, the models are based on erosion rates measured on past restoration sites, and need to be updated as additional monitoring studies evaluate long-term basin response to erosion control treatments. Figure 2. Three treatment options for road crossings. (A) No treatment: drainage structure and road fill remain in stream channel. (B) Basic excavation: culvert and road fill are excavated from stream channel. (C) Total excavation: culvert, road fill and excess sediment are excavated from stream channel and stream banks are reshaped extensively. In this example a mulch of wood and branches was applied on the newly excavated stream banks.focused treatments on the steeper, lower hillslopes where erosion risks are usually highest. However, for crossing treatments, the DP prescribed 'total excavation' slightly more frequently as a technique for crossing removal, whereas the GA used a greater mix of crossing treatments. With a US$250 000 budget, the GA prescribed 'no treatment' or 'rip and drain' for many road segments located on gentle slopes far from the stream (upper and some middle hillslope sites), whereas the DP recommended at least partial outsloping on middle hillslo...

This article describes the collaborative modeling process and the resulting water resources planning model developed to evaluate water management scenarios in the transboundary Rio Grande basin. The Rio Grande is a severely water stressed basin that faces numerous management challenges as it crosses numerous jurisdictional boundaries. A collaborative process was undertaken to identify and model water management scenarios to improve water supply for stakeholders, the environment, and international obligations of water delivery from Mexico to the United States. A transparent and open process of data collection, model building, and scenario development was completed by a project steering committee composed of university, nongovernmental, and governmental experts from both countries. The outcome of the process was a planning model described in this article, with data and operations that were agreed on by water planning officials in each country. Water management scenarios were created from stakeholder input and were modeled and evaluated for effectiveness with the planning model.(KEY TERMS: Rio Grande; decision support systems; planning; geographic information system; water resources management.)

(NSF and IES 2018) describe reproducibility as a continuum (Fig. 1). The goal is to push work up the continuum to make data, models, code, directions, and other digital artifacts used in the research available for others to reuse (availability). Then, use shared artifacts to exactly reproduce published results (reproducibility, sometimes called bit or computational reproducibility). Finally, use artifacts with existing and new data sets to replicate findings across sites or domains (replicability). For example, the Journal of Water Resources Planning and Management policy to specify the availability of data, models, and code (Rosenberg and Watkins 2018) primarily targets availability in the reproducibility continuum. This Fig. 1. Reproducibility is a continuum.

No abstract

No abstract

No abstract

No abstract