Integrating data-driven ecological models in an expert-based decision support system for water management in the Du river basin (Vietnam)

Spatially Explicit River Basin Models for Cost-Benefit Analyses to Optimize Land Use

Recently, a wide range of models have been used in analyzing the costs and benefits of land utilization in river basins. Despite these advances, there is not enough information on how to select appropriate models to perform cost-benefit analyses. A literature search in the Web of Science (WOS) online database was implemented and resulted in the selection of 27 articles that utilized models to perform cost-benefit analyses of river basins. The models reviewed in these papers were categorized into five types: process-based, statistical, probabilistic, data-driven, and modeling frameworks or integrated models. Twenty-six models were reviewed based on their data and input variable needs and user convenience. A SWOT analysis was also performed to highlight the strengths, weaknesses, opportunities, and threats of these models. One of the main strengths is their ability to perform scenario-based analyses while the main drawback is the limited availability of data impeding the use of the models. We found that, to some extent, there is an increase in model applicability as the number of input variables increases but there are exceptions to this observation. Future studies should explicitly report on the necessary time needed for data collection, model development and/or training, and model application. This information is highly valuable to users and modelers when choosing which model to use in performing a particular cost-benefit analysis. These models can be developed and applied to assist sustainable development as well as the sustainable utilization of agricultural parcels within a river basin, which can eventually reduce the negative impacts of intensive agriculture and minimize habitat degradation on water resources.

A Hierarchical Binary Process Model to Assess Deviation from Desired Ecological Condition across a Broad Forested Landscape in Alabama

This work describes the development and analysis of a spatially explicit environmental model to estimate the current, ecological, condition class of a managed forest landscape in the southern United States. The model could be extendable to other similar temperate forest landscapes, yet is characterized as a problem-specific, hierarchical, binary process model given the explicit relationships it recognizes between the management of southern United States pine-dominated natural forests and historical ecological conditions. The model is theoretical, based on informed proposals of the landscape processes that influence the ecological condition, and their relationship to perceived ecological condition. The modeling effort is based on spatial data that describe the historical forest community classes, forest plan provisions, fire history, silvicultural treatments, and current vegetation conditions, and six potential ecological condition classes (ECC) are assigned to lands. A case study was provided involving a large national forest, and validation of the outcomes of the modelling effort suggested that the overall accuracy when predicting the exact ecological condition class was about 46%, while the overall accuracy ±1 class was about 81%. For large, heterogeneous forest areas, issues remain in estimating the input variables relatively accurately, particularly the pine basal area.