Headwater streams are the primary sources of water in a drainage network and serve as a critical hydrologic link between the surrounding landscape and larger, downstream surface waters. Many states, including North Carolina, regulate activity in and near headwater streams for the protection of water quality and aquatic resources. A fundamental tool for regulatory management is an accurate representation of streams on a map. Limited resources preclude field mapping every headwater stream and its origin across a large region. It is more practical to develop a model for headwater streams based on a sample of field data that can then be extrapolated to a larger area of interest. The North Carolina Division of Water Quality has developed a costeffective method for modeling and mapping the location, length, and flow classification (intermittent and perennial) of headwater streams. We used a multiple logistic regression approach that combined field data and terrain derivatives for watersheds located in the Triassic Basins ecoregion. Field data were collected using a standard methodology for identifying headwater streams and origins. Terrain derivatives were generated from digital elevation models interpolated from bare-earth Light Detection and Range data. Model accuracies greater than 80% were achieved in classifying stream presence and absence, stream length and perennial stream length, but were not as consistent in predicting intermittent stream length.(KEY TERMS: hydrology; hydrologic model; headwater streams; stream mapping; logistic regression model; LiDAR; GIS; stream regulation.) Russell, Periann P., Susan M. Gale, Breda Muñoz, John R. Dorney, and Matthew J. Rubino, 2015. A Spatially Explicit Model for Mapping Headwater Streams.

In Thailand, the world's largest rice exporter, rice constitutes a major export on which the economy of the whole country depends. Climate change could affect rice growth and development and thus jeopardize Thailand's wealth. Current climatic conditions in Thailand are compared to predictions from four general circulation models (GCMs). Temperature predictions correlate well with the observed values. Predictions of monthly rainfall correlate poorly. Virtually all models agree that significant increases in temperature (from 1 to 7 ~ will occur in the region including Thailand following a doubling in atmospheric carbon dioxide (CO2) concentration. The regional seasonality and extent of the rise in temperature varies with each model. Predictions of changes in rainfall vary widely between models. Global warming should in principle allow a northward expansion of rice-growing areas and a lengthening of the growing season now constrained by low temperatures. The expected increase in water-use efficiency due to enhanced CO 2 might decrease the water deficit vulnerability of dryland rice areas and could make it possible to slightly expand them.

No abstract

The objective of this study was to assess the performance of form-based and process-based models, and of local-scale and reach-scale models, used to examine bank retreat and sediment transport in stream restoration. The evaluated models were the Bank Erosion Hazard Index (BEHI), Bank Assessment for Nonpoint Source Consequences of Sediment (BANCS), Bank Stability and Toe Erosion Model (BSTEM), and HEC River Analysis System (HEC-RAS 1D). Model-to-model assessments were conducted to quantify the impact of model selection when predicting applied stress and geomorphic change in a restored stream in North Carolina, USA. Results indicated that the mobility of the bed dictated model selection at the reach-scale. The process-based HEC-RAS 1D was needed to accurately analyze the sand-bed stream, predicting amounts of geomorphic change comparable to measured data and up to three orders of magnitude higher than those from local-scale models. At the local-scale, results indicated that the bank retreat mechanism and flow variability constrained model selection. The form-based BEHI and BANCS did not directly account for geotechnical failure nor capture severe floods, underpredicting amounts of geomorphic change by an order of magnitude when compared to the process-based BSTEM, and failing to characterize erosion potential and applied stresses after short-term morphodynamic adjustments.