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Every Civil War veteran had a story to tell. But few stories top the one lived by Wright Stephen Batchelor. Like most North Carolina farmers, Batchelor eschewed slaveholding. He also opposed secession and war, yet he fought on both sides of the conflict. During his time in each uniform, Batchelor barely avoided death at the Battle of Gettysburg, was captured twice, and survived one of the war's most infamous prisoner-of-war camps. He escaped and, after walking hundreds of miles, rejoined his comrades at Petersburg, Virginia, just as the Union siege there began. Once the war ended, Batchelor returned on foot to his farm, where he took part in local politics, supported rights for freedmen, and was fatally involved in a bizarre hometown murder. Michael K. Brantley's story of his great-great-grandfather's odyssey blends memory and Civil War history to look at how the complexities of loyalty and personal belief governed one man's actions-and still influence the ways Americans think about the conflict today.

Bioretention cells are widely used as an infiltration-based stormwater control measure to reduce the negative impacts of urban stormwater runoff. Two sets of cells were monitored at Rocky Mount and at Nashville to measure the effects that underdrain configuration, media depth, surface storage volume, and underlying soil type had on hydrologic and water quality performance. Both sites are located in the Upper Coastal Plain of North Carolina, where insitu soils tend to have a high sand content. The two bioretention cells at Rocky Mount were designed with an internal water storage (IWS) zone and had varying degrees of sandy underlying soils. The underlying soils for these two cells were sand (Sand cell) and sandy clay loam (SCL cell). After the first year, the IWS zone depth was reduced by lowering the outlet. While the Sand cell, with its sandy underlying soils and deep IWS zone provided greater outflow reduction, it had minimal nitrogen treatment because of a short hydraulic residence time (less than three hours). On the other hand, the SCL cell had a longer hydraulic residence time (up to seven days), and it had significant concentration reductions for all forms of nitrogen, including nitrate. Fill media is a major expense, so a study objective from the Nashville site was to evaluate the impact of varying media depth (0.6 m versus 0.9 m). A post-construction objective was to analyze the impact of under-sizing the surface storage zone. Construction and design errors resulted in the surface storage volumes of the bioretention cells to be approximately one-third of the design volume; moreover, the surface was clogged, which limited infiltration. After one year of monitoring, the clogging layer was removed, which doubled the surface storage volume. Deeper media depth promoted more exfiltration and met a low impact development goal of outflow reduction twice as often. Also, despite being relatively undersized, the repaired cells were able to treat nearly 90 percent of runoff, suggesting that current design guidance may be over-sizing the surface storage zone. Also in Nashville, another bioretention cell was installed in series with a pervious concrete system that included a subsurface storage zone. These two practices in series had excellent peak flow and outflow reduction. For low impact development (LID) practices in series, serious consideration should be taken to balance the returns of flow rate and outflow reduction vis-avis cost. The bioretention cell was installed at a site with a high water table, so this impact was quantified. Because of the intercepted groundwater, the site exported 63 percent more total nitrogen than what was present in the runoff load. Overall, bioretention cells can be designed and constructed with a variety of specifications, among them are media depth, underdrain configuration, media composition, drainage area to bioretention area ratio, and surface storage volume. One way to quantify various designs is using a long-term model. The hydrology data from the field sites were used to calibrate and validate DRAINMOD, a widely-accepted, long-term drainage model. The measured and predicted (modeled) results were in good agreement during both the calibration and validation periods; Nash-Sutcliffe coefficients for runoff, drainage, overflow, and exfiltration/evapotranspiration commonly exceeded 0.8. These results proved that DRAINMOD can be reliably used to simulate the hydrologic response of runoff entering a bioretention cell. With a reliable long-term bioretention model, designers and regulators will be able to shift from the current “one size fits all” design approaches and establish a \"flexible\" bioretention design methodology that is based on underlying soil type, design specifications, and climate. Finally, in an attempt to improve construction practices, the effects of construction activity on underlying soils were explored. The effects of soil type, soil moisture, and excavation technique were tested. The results showed that excavating using the teeth of the bucket to scarify the surface (rake method) would maintain a more permeable surface than using the back of the bucket (scoop method).

We review previous approaches to nowcasting earthquakes and introduce new approaches based on deep learning using three distinct models based on recurrent neural networks and transformers. We discuss different choices for observables and measures presenting promising initial results for a region of Southern California from 1950–2020. Earthquake activity is predicted as a function of 0.1-degree spatial bins for time periods varying from two weeks to four years. The overall quality is measured by the Nash Sutcliffe efficiency comparing the deviation of nowcast and observation with the variance over time in each spatial region. The software is available as open source together with the preprocessed data from the USGS.

Precise assessment of suspended sediment load (SSL) is vital for many applications in hydrological modeling and hydraulic engineering. In this study, a smoothed long short-term memory (SM-LSTM) model was used to predict day-to-day SSL at two stations over two rivers namely Thebes station on the Mississippi River and Omaha station on the Missouri River. The model first removes the interference factors in the SSL time series by Fourier Transformation (FT) de-noising and then feeds into a long short-term memory (LSTM) network to forecast the SSL. Before de-noising, missing data in the time series is computed using the Monte Carlo multiple imputation technique. LSTM networks are a type of recurrent neural network (RNN) that incorporates memory cells, which makes them well-suited for learning temporal associations over the previous time steps. The model was built using daily observed time series of SSL in the Mississippi and Missouri rivers in the United States. The developed model was then assessed and compared to LSTM and RNN. These models were trained using 4 different time lags of the SSL time series as inputs. The SM-LSTM model with 12 lagged inputs outperformed the other models with the lowest root mean square errors (RMSE) = 32254 ton and mean absolute errors (MAE) = 19517 ton, and the highest Nash–Sutcliffe efficiency (NSE) = 0.99 for the Thebes Station while the model with 3 lagged inputs acted as the best with the lowest RMSE = 2244 ton and MAE = 1370 ton, and the highest NSE = 0.989 for the Omaha Station. The comparison of prediction accuracies showed that the SM-LSTM model can more satisfactorily predict daily SSL time series compared to LSTM and RNN.

Heavy rains and tropical storms often result in floods, which are expected to increase in frequency and intensity. Flood prediction models and inundation mapping tools provide decision-makers and emergency responders with crucial information to better prepare for these events. However, the performance of models relies on the accuracy and timeliness of data received from in situ gaging stations and remote sensing; each of these data sources has its limitations, especially when it comes to real-time monitoring of floods. This study presents a vision-based framework for measuring water levels and detecting floods using computer vision and deep learning (DL) techniques. The DL models use time-lapse images captured by surveillance cameras during storm events for the semantic segmentation of water extent in images. Three different DL-based approaches, namely PSPNet, TransUNet, and SegFormer, were applied and evaluated for semantic segmentation. The predicted masks are transformed into water level values by intersecting the extracted water edges, with the 2D representation of a point cloud generated by an Apple iPhone 13 Pro lidar sensor. The estimated water levels were compared to reference data collected by an ultrasonic sensor. The results showed that SegFormer outperformed other DL-based approaches by achieving 99.55 % and 99.81 % for intersection over union (IoU) and accuracy, respectively. Moreover, the highest correlations between reference data and the vision-based approach reached above 0.98 for both the coefficient of determination (R2) and Nash–Sutcliffe efficiency. This study demonstrates the potential of using surveillance cameras and artificial intelligence for hydrologic monitoring and their integration with existing surveillance infrastructure.

The current study analyses the effect of root zone soil moisture in the calibration and validation of Soil and Water Assessment Tool (SWAT) model. A multi-algorithm, genetically adaptive multi-objective method (AMALGAM) is used for the calibration of the model. The multi-variable calibration considering both streamflow and soil moisture is compared with a single-variable calibration considering streamflow and then analysed the effectiveness of root zone soil moisture in the calibration of SWAT. The results of the analysis show that the root zone soil moisture significantly influences the simulation of evapotranspiration component in SWAT. The SOL_AWC and SOL_K are found to be the key parameters for the simulation of hydrological fluxes in SWAT. The multi-variable calibration at the watershed outlet ensures a better process representation and spatial prediction in SWAT compared to the single-variable calibration approach.

Sustained pumping of groundwater can lead to declining water levels in wellfields and concerns regarding the sustainability of groundwater resources. Aquifer Storage and Recovery (ASR) is a promising approach for maintaining water levels in wells and increasing the sustainability of groundwater resources. Herein, an analytical model relying on superposition of the Theis equation is used to resolve water levels at 40 wells in three vertically stacked ASR wellfields operating in the Denver Basin Aquifers, Colorado (USA). Fifteen years of dynamic recovery/recharge data are used to estimate aquifer and well properties, which are then used to predict water levels at individual wells. Close agreement between modeled and observed water levels supports the validity of the analytical model for ASR wellfield applications. During the study period, 45 million m3 of groundwater is produced and 11 million m3 is recharged, leading to a net withdrawal of 34 million m3 of groundwater. To quantify the benefits of recharge, the analytical model is applied to predict water levels at wells absent the historical recharge. Results indicate that during recovery and no-flow periods, recharge has increased water levels at wells up to 60 m compared to the no-recharge scenario. On average, the recharge increased water levels during the study period by 3, 4, and 11 m for wells in the Denver, Arapahoe, and Laramie Fox-Hills Aquifers, respectively. This study demonstrates the utility of analytical modeling to quantify the effects of long-term ASR at wells.

Optimal restoration and management of coastal wetland are contingent on reliable assessment of hydrological processes. In this study, we used the Soil and Water Assessment Tool (SWAT) model to assess the impacts of a proposed coastal wetland restoration plan on the water balance components of the Heeia watershed (Hawaii). There is a need to optimize between water needs for taro cultivation and accompanying cultural practices, wetland ecosystem services, and streamflow that feeds downstream coastal fishponds and reefs of the Heeia watershed. For this, we completed two land use change scenarios (conversion of an existing California grassland to a proposed taro field and mangroves to a pond in the wetland area) with several irrigation water diversion scenarios at different percent of minimum streamflow values in the reach. The irrigation water diversion scenarios aimed at achieving sustainable growth of the taro crop without compromising streamflow value, which plays a vital role in the health of a downstream fishpond and coastal environment of the watershed. Findings generally suggest that the conversion of a California grassland to a patched taro field is expected to decrease the baseflow value, which was a major source of streamflow for the study area, due to soil layer compaction, and thus decrease in groundwater recharge from the taro field. However, various taro irrigation water application and management scenarios suggested that diverting 50% of the minimum streamflow value for taro field would provide sustainable growth of taro crop without compromising streamflow value and environmental health of the coastal wetland and downstream fishponds.

A key area of research in hydrologic modeling is the prediction of flood response in complex urban basins with hydraulic structures such as pump stations, canals, culverts, and spillways. The prediction of the basin's response to heavy rainfall is needed in order to assess the impacts of potential watershed management decisions, especially during high flow periods. In this study, the HEC-HMS model was adopted to predict the accumulated discharges in a small urban basin located in West Palm Beach, Florida, USA. The model was calibrated based on seven flood events and validated using seven independent events spanning a 5-year period. The results show that the accumulated flow of water released from the basin was simulated with high accuracy, and that the model can be used for various management scenarios involving high flow conditions in the south Florida urban basin.