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HighlightsSimulation of overland (AnnAGNPS) and ephemeral gully (REGEM) erosion underestimated watershed sediment yield.Three methods were used to disaggregate contribution of streambank sediment loads to daily streamflow events.A rainfall-based method, with scaling factor and threshold term, had good performance and low bias in all watersheds.Calibrated model results indicated that most sediment yield was from streambank sources (78% to 93%).Abstract. Models used to guide watershed management must account for sediment from all erosion sources (interrill and rill, ephemeral gully (EG), and streambank), each of which operates at different spatial and temporal scales. Our hypothesis was that the use of separate models to explicitly simulate sediment yield contributions for each of these three sources would improve model agreement with measured watershed sediment yield data. We tested this hypothesis using AnnAGNPS (overland flow/erosion model), REGEM (EG erosion model), and field-measured streambank erosion disaggregated to an event basis using three methods in three watersheds (North Fork, Main Stem, and Irish Creek, Kansas). AnnAGNPS alone and in combination with REGEM underestimated sediment yields and had a poorly calibrated performance in all three watersheds, which reinforced the need to include sediment loads from stream sources. The three streambank sediment disaggregation methods each included two calibration terms: a scaling factor and a threshold term, which disaggregated 2-year-total streambank erosion measurements into event-based values based on rainfall, streamflow, or stream power. All three methods gave “very good” sediment-yield calibration results (event-based Ef = 0.6, PBIAS near 0%) for Main Stem and Irish Creek but gave “satisfactory” results only for the rainfall-based streambank disaggregation method in the evaluation watershed (North Fork). Calibrated model results indicated that most of the measured outlet sediment yields in the study watersheds were from streambank sources (78% to 93%), with less from overland erosion (7% to 22%) and little from EG erosion (0% to 7%). These methods could be equally effective in scaling and disaggregating stream-based sediment contributions without measured streambank erosion, but the calibration terms would lose physical meaning in reference to measured streambank values. The skill demonstrated by all three streambank disaggregation thresholds may provide a foundation for building simple, process-based streambank sediment yield simulation models. Keywords: Ephemeral gully erosion, Interrill and rill erosion, Modeling, Streambank erosion, Watershed.

Potential changes in future climate in the Texas Plains region were investigated in the context of agriculture by analyzing three climate model projections under the A2 climate scenario (medium–high emission scenario). Spatially downscaled historic (1971–2000) and future (2041–2070) climate datasets (rainfall and temperature) were downloaded from the North American Regional Climate Change Assessment Program (NARCCAP). Climate variables predicted by three regional climate models (RCMs) namely the Regional Climate Model Version3–Geophysical Fluid Dynamics Laboratory (RCM3-GFDL), Regional Climate Model Version3–Third Generation Coupled Global Climate Model (RCM3-CGCM3), and Canadian Regional Climate Model–Community Climate System Model (CRCM-CCSM) were evaluated in this study. Gaussian and Gamma distribution mapping techniques were employed to remove the bias in temperature and rainfall data, respectively. Both the minimum and maximum temperatures across the study region in the future showed an upward trend, with the temperatures increasing in the range of 1.9 to 2.9 °C and 2.0 to 3.2 °C, respectively. All three climate models predicted a decline in rainfall within a range of 30 to 127 mm in majority of counties across the study region. In addition, they predicted an increase in the intensity of extreme rainfall events in the future. The frost-free season as predicted by the three models showed an increase by 2.6–3.4 weeks across the region, and the number of frost days declined by 17.9 to 30 %. Overall, these projections indicate considerable changes to the climate in the Texas Plains region in the future, and these changes could potentially impact agriculture in this region.

The Texas High Plains (THP) region contributes to about 25% of the US cotton production. Dwindling groundwater resources in the underlying Ogallala aquifer, future climate variability and frequent occurrences of droughts are major concerns for cotton production in this region. Assessing the impacts of climate change on cotton production enables development and evaluation of irrigation strategies for efficient utilization of groundwater resources in this region. In this study, the CROPGRO-Cotton module within the Cropping System Model (CSM) that is distributed with the Decision Support System for Agrotechnology Transfer (DSSAT) was evaluated for the THP region using measured data from cotton water use efficiency experiments at Halfway over a period of four years (2010–2013). Simulated seed cotton yield matched closely with observed yield during model calibration (average percent error of 0.1) and validation (average percent error of 6.5). The evaluated model was able to accurately simulate seed cotton yield under various irrigation strategies over the four growing seasons. The evaluated CROPGRO-Cotton model was later used to simulate the seed cotton yield under historic (1971–2000) and future (2041–2070) climate scenarios projected by three climate models. On an average, when compared to historic seed cotton yield, simulated future seed cotton yield across the THP decreased within a range of 4–17% when carbon dioxide (CO2) concentration was assumed to be constant at the current level (380 ppm) under three climatic model scenarios. In contrast, when the CO2 concentration was assumed to increase from 493 ppm (in year 2041) to 635 ppm (in year 2070) according to the Intergovernmental Panel on Climate Change (IPCC) A2 emission scenario, the simulated future average seed cotton yield in the THP region increased within a range of 14–29% as compared to historic average yield. When the irrigation amount was reduced by 40% (from 100% to 60%), the average (2041–2070) seed cotton yield decreased by 37% and 39% under the constant and increasing CO2 concentration scenarios, respectively. These results imply that cotton is sensitive to atmospheric CO2 concentrations, and cotton production in the THP could potentially withstand the effects of future climate variability under moderate increases in CO2 levels if irrigation water availability remains at current levels.

Cotton is one of the major crops cultivated in the Texas Rolling Plains region, and it is a major contributor to the regional economy. Cotton cultivation in this region is facing severe challenges due to an increase in the frequency of droughts and a projected decrease in rainfall in the future. Development and evaluation of deficit irrigation strategies for this region could potentially conserve water while maintaining cotton yields. In this study, the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model (CSM) CROPGRO-Cotton was extensively tested and then used for evaluating various deficit irrigation strategies for this region. The model inputs were obtained from field experiments conducted at Chillicothe, Texas, during four growing seasons: 2008-2010 and 2012. The model was first calibrated using the data from a 100% evapotranspiration (ET) replacement irrigation scheduling experiment conducted in 2012 and then validated on three other irrigation scheduling treatments (75% ET replacement, soil moisture based, and tensiometer based) conducted in the same year. The model was further evaluated using the data from cotton tillage and irrigation experiments conducted in an adjacent field during 2008-2010. The model calibration, validation, and evaluation results were satisfactory except under dry conditions (0% ET replacement and 33% ET replacement). Simulated maximum seed cotton yields under normal and dry weather conditions were achieved at 100% and 110% ET replacement, respectively. Percentage decrease in seed cotton yield was marginal (3.5% to 8.8%) when the amount of irrigation water applied was decreased from 100% to 66% ET replacement under normal rainfall conditions. However, under less than normal rainfall (drier) conditions, the percentage decrease in seed cotton yield was substantial (about 17.5%) when the irrigation strategy was switched from 100% to 70% ET replacement. The simulations demonstrate that adopting deficit irrigation practices under normal weather conditions can conserve water without adversely affecting seed cotton yields. However, under dry conditions, there is a risk of increased yield loss, and therefore producers should consider that risk when adopting deficit irrigation strategies.

To improve wildfire responses by making and implementing safe, effective and efficient management decision, predictive services has been created to better preparing and responding to wildfires. Recently, efforts had been made to strengthen the capabilities on collecting and integrating timely weather information for improving the predictive services products. Previously, the weather information source for generating fire danger risk adjective by the National Fire Danger Rating System (NFDRS) model were from Remote Automated Weather Stations (RAWS) observation network. Recently, a cost-effective method has been proposed by integrating the Automated Surface Observing System (ASOS) timely observation into the weather information data source, GIS programs have been developed by feeding the National Fire Danger Rating System through the Weather Information Management System (WIMS) alternative gateway automatically with the ASOS weather stations. As a result, the map accuracy has been improved for the predictive service map products. Adopting the advanced technology from geographic information systems and information technologies, daily updated fire dangers maps for predictive services has been greatly enhanced thus better positioned wildfire managers and firefighters towards wildfires risks.