The Red River Campaign, 1864

The Red River Campaign in the spring of 1864 was the disastrous culmination of the Union high command’s persistent efforts to conquer Louisiana and Texas. Abraham Lincoln ordered Maj. Gen. Nathaniel P. Banks, commander of the Department of the Gulf, to lead a large force from New Orleans up the Red River Valley, capture Shreveport (the Confederacy’s Trans-Mississippi capital and major commercial center), and invade Texas. Lincoln delayed an important campaign against Mobile and diverted significant manpower from the western theater and Arkansas, along with a large fleet of naval vessels, to support Banks in order to accomplish sweeping economic, political, and foreign policy goals. Mismanaged by Banks from the start, the campaign suffered defeat before reaching Shreveport, but it created havoc in the Red River Valley by allowing many slaves to flee to Union forces, compelling many civilians to flee with their slaves to Texas for safety, and inducing defeated Union soldiers to destroy a vast array of civilian properties and towns. As a result, northern Louisiana suffered economically for many years, while Texas emerged from the war continuing to grow into an economic powerhouse.

Machine Learning for Predicting Field Soil Moisture Using Soil, Crop, and Nearby Weather Station Data in the Red River Valley of the North

Precise soil moisture prediction is important for water management and logistics of on-farm operations. However, soil moisture is affected by various soil, crop, and meteorological factors, and it is difficult to establish ideal mathematical models for moisture prediction. We investigated various machine learning techniques for predicting soil moisture in the Red River Valley of the North (RRVN). Specifically, the evaluated machine learning techniques included classification and regression trees (CART), random forest regression (RFR), boosted regression trees (BRT), multiple linear regression (MLR), support vector regression (SVR), and artificial neural networks (ANN). The objective of this study was to determine the effectiveness of these machine learning techniques and evaluate the importance of predictor variables. The RFR and BRT algorithms performed the best, with mean absolute errors (MAE) of <0.040 m3 m−3 and root mean square errors (RMSE) of 0.045 and 0.048 m3 m−3, respectively. Similarly, RFR, SVR, and BRT showed high correlations (r2 of 0.72, 0.65 and 0.67 respectively) between predicted and measured soil moisture. The CART, RFR, and BRT models showed that soil moisture at nearby weather stations had the highest relative influence on moisture prediction, followed by 4-day cumulative rainfall and PET, subsequently followed by bulk density and Ksat.

The first record of the monospecific genus Rhinopalpa (Lepidoptera: Nymphalidae) from China

The family Nymphalidae is the largest group of butterflies with high species richeness. Rhinopalpa polynice (Cramer, [1779]), a forest species, was discovered in the mid-stream of the Yuanjiang-Red River Valley of Yunnan Province for the first time, which represents the first record of the genus Rhinopalpa in China. The species R. polynice (Cramer, [1779]) is the first record of the genus Rhinopalpa from China. The specimen was collected in the mid-stream of the Yuanjiang-Red River Valley of Yunnan Province. The female genitalia are described for the first time.

Physical-based hydrological modelling for real-time forecasting of soil moisture in a mesoscale catchment

&lt;p&gt;Soil moisture is highly variable in space and time. Climate change is expected to increase the variation in precipitation that may cause more frequent extremes in soil moisture. This will have major impacts on agriculture and infrastructure. Hence, forecasting can help mitigate the impacts of soil moisture extremes by providing warning about upcoming extreme events. Accurate soil moisture forecasting will provide policymakers, farmers and other stakeholders more reliable information on crop yield potential and flood risk to improve decision making. &amp;#160;Real-time soil moisture monitoring and forecasting can be accomplished by utilizing a numerical modelling approach that consolidates various sources of weather and hydrological data to simulate soil moisture levels. Soil water movement is difficult to describe numerically for fine-textured soils. Additionally, soil water behaviour during freeze/thaw events are generally weakly described by numerical tools. This study addresses both problems and evaluates how soil moisture can be forecasted under the hydrologically challenging conditions of the Red River Valley using the Brunkild catchment within the Red River basin.&amp;#160; The Brunkild catchment represents a highly variable landscape cross-section that includes heavy clay soils of the Red River Valley through to the coarse-textured soils of the adjacent escarpment. Soil moisture levels were continuously monitored from June &amp;#8211; August 2020 using Sentek sensors which were installed at depths of 10 to 90 cm with 10 cm spacing, and with POGO sensors that were used to manually measure surface soil moisture levels at monthly intervals from June to August 2020. Climate variables were obtained from the RISMA (Real-time In-situ Soil Monitoring for Agriculture) stations present inside the catchment.&amp;#160; In addition to soil moisture data, surface water flow and groundwater data will also be used to aid with calibration and validation of a fully-integrated HydroGeoSphere (HGS) surface water &amp;#8211; groundwater model of the catchment. Preliminary results using MERRA 2 data as climate forcing showed a strong fit for all observations in sandy soils and a good fit for all observation in clay. The next simulations will use the observed weather data. The model will be recalibrated and then being used to forecast soil moisture in the Brunkild catchment for the coming 14 days for the 2021 growing season.&lt;/p&gt;

Impact of Controlled Drainage and Subirrigation on Water Quality in the Red River Valley

The potential impact of controlled drainage (CD), which limits drainage outflow, and subirrigation (SI), which provides supplemental water through drain tile, on surface water quality are not well known in the Red River Valley (RRV). In this study, water samples were collected and analyzed for chemical concentrations from a tile-drained field that also has controlled drainage and subirrigation modes in the RRV of southeastern North Dakota from 2012–2018. A decreasing trend in overall nutrient load loss was observed because of reduced drainage outflow, though some chemical concentrations were found to be above the recommended surface water quality standards in this region. For example, sulfate was recommended to be below 750 mg/L but was reported at a mean value of 1971 mg/L during spring free drainage. The chemical composition of the subirrigation water was shown to have an impact on drainage water and the soil, specifically on salinity-related parameters, and the impact varied between years. This variation largely depended on the amount of subirrigation applied, soil moisture, and soil properties. Overall, the results of this study show the benefits of controlled drainage on nutrient loss reduction from agricultural fields.