Aquatic Plant and Dissolved Oxygen Changes in a Reference‐Condition Prairie Stream Subjected to Experimental Nutrient Enrichments

Effects of controlled nutrient additions on a prairie stream were studied using a before‐after‐control‐impact paired design. The site is in a reference condition with low soluble nitrate (NO₃) and phosphate (soluble reactive phosphorus [SRP]) in summer (3 μg NO₃‐N/L, 4 μg SRP/L). Nutrients were added to two reaches over the growing season at two levels (Low Dose — 39 μg NO₃‐N/L and 4.4 SRP/L; High Dose — 119 μg NO₃‐N/L and 15.6 μg SRP/L). Continuously measured dissolved oxygen (DO) and changes in aquatic flora were compared to an upstream Control. Enrichment led microalgae and filamentous algae to increase in density, areal coverage, and thickness, and the magnitude of the changes were largely concordant with dosing (more in the High Dose); algal growth also suppressed macrophytes in the High Dose. Enrichment caused significant increases in diel DO swings whose magnitudes were consistent with dosing level. In the High Dose, benthic algae flourished in the growing season and then senesced en masse in fall. The decomposing algae led DO to crash (ca. 0 mg/L on the bottom), but DO impacts were out‐of‐sync with peak algal growth and photosynthesis, which occurred weeks earlier. This finding provides a plausible explanation as to why high DO delta in streams impacts aquatic life even when concurrently measured DO is not low. When DO crashed, DO was longitudinally patchy, some areas having low DO near the bottom, others near saturation. Geomorphology and exposure to wind may have caused this pattern.     

Runoff Storage Potential of Drained Upland Depressions on the Des Moines Lobe of Iowa

We present estimates of the volumetric storage capacities of currently drained upland depressions and catchment depressional specific storage and runoff storage indices for the Des Moines Lobe of Iowa (DML‐IA) subregion of the Prairie Pothole Region of North America. Storage capacities were determined using hydrologically enforced Light Detection and Ranging‐derived digital elevation models, and a unique geoprocessing algorithm. Depressional specific storage was estimated for each 12‐digit Hydrologic Unit Code (HUC12) watershed in the region from total catchment‐specific depressional storage volume and catchment area. Runoff storage indices were calculated using catchment depressional specific storage values and estimates of the amount of rainfall likely to fall within each watershed during sub‐annual and 1‐, 2‐, 5‐, and 10‐year 24‐h events. The 173,171 identified drained depressions in the DML‐IA can store up to 903.5 Mm³ of runoff. Most of this capacity is in depressions located in the north of the region. Specific storage varies from nearly 109 mm in the younger landscapes to <10 mm in older more eroded areas. For 95% of the HUC12 watersheds comprising the region, depressional storage will likely be exhausted by rainfall‐derived runoff in excess of a 1‐year 24‐h event. Rainfall amounts greater than a 5‐year 24‐h event will exceed all available depressional storage. Therefore, the capacity of drained depressions in the DML‐IA to mitigate flooding resulting from infrequent, but large, storm events is limited.     

Error Assessment for Height Above the Nearest Drainage Inundation Mapping

Real‐time flood inundation mapping is vital for emergency response to help protect life and property. Inundation mapping transforms rainfall forecasts into meaningful spatial information that can be utilized before, during, and after disasters. While inundation mapping has traditionally been conducted on a local scale, automated algorithms using topography data can be utilized to efficiently produce flood maps across the continental scale. The Height Above the Nearest Drainage method can be used in conjunction with synthetic rating curves (SRCs) to produce inundation maps, but the performance of these inundation maps needs to be assessed. Here we assess the accuracy of the SRCs and calculate statistics for comparing the SRCs to rating curves obtained from hydrodynamic models calibrated against observed stage heights. We find SRCs are accurate enough for large‐scale approximate inundation mapping while not as accurate when assessing individual reaches or cross sections. We investigate the effect of terrain and channel characteristics and observe reach length and slope predict divergence between the two types of rating curves, and SRCs perform poorly for short reaches with extreme slope values. We propose an approach to recalculate the slope in Manning’s equation as the weighted average over a minimum distance and assess accuracy for a range of moving window lengths.     

Ignoring the irrelevant: auditory tolerance of audible but innocuous sounds in the bat-detecting ears of moths

Noctuid moths listen for the echolocation calls of hunting bats and respond to these predator cues with evasive flight. The African bollworm moth, Helicoverpa armigera, feeds at flowers near intensely singing cicadas, Platypleura capensis, yet does not avoid them. We determined that the moth can hear the cicada by observing that both of its auditory receptors (A1 and A2 cells) respond to the cicada's song. The firing response of the A1 cell rapidly adapts to the song and develops spike periods in less than a second that are in excess of those reported to elicit avoidance flight to bats in earlier studies. The possibility also exists that for at least part of the day, sensory input in the form of olfaction or vision overrides the moth's auditory responses. While auditory tolerance appears to allow H. armigera to exploit a food resource in close proximity to acoustic interference, it may render their hearing defence ineffective and make them vulnerable to predation by bats during the evening when cicadas continue to sing. Our study describes the first field observation of an eared insect ignoring audible but innocuous sounds.     

A remote sensing tool for near real-time monitoring of harmful algal blooms and turbidity in reservoirs

Harmful algal blooms (HABs) diminish the utility of reservoirs for drinking water supply, irrigation, recreation, and ecosystem service provision. HABs decrease water quality and are a significant health concern in surface water bodies. Near real-time monitoring of HABs in reservoirs and small water bodies is essential to understand the dynamics of turbidity and HAB formation. This study uses satellite imagery to remotely sense chlorophyll-a concentrations (chl-a), phycocyanin concentrations, and turbidity in two reservoirs, the Grand Lake O′ the Cherokees and Hudson Reservoir, OK, USA, to develop a tool for near real-time monitoring of HABs. Landsat-8 and Sentinel-2 imagery from 2013 to 2017 and from 2015 to 2020 were used to train and test three different models that include multiple regression, support vector regression (SVR), and random forest regression (RFR). Performance was assessed by comparing the three models to estimate chl-a, phycocyanin, and turbidity. The results showed that RFR achieved the best performance, with R² values of 0.75, 0.82, and 0.79 for chl-a, turbidity, and phycocyanin, while multiple regression had R² values of 0.29, 0.51, and 0.46 and SVR had R² values of 0.58, 0.62, and 0.61 on the testing datasets, respectively. This paper examines the potential of the developed open-source satellite remote sensing tool for monitoring reservoirs in Oklahoma to assess spatial and temporal variations in surface water quality.     

Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making.     

Safety climate

Safety climate, the objective measurement of attitudes and perceptions toward Occupational Health & Safety (OH&S) issues, has been largely ignored and measures such as lost time and frequency rate have been used to determine the efficacy of OH&S programs. Yet, it is clear that measuring the precursors of accidents identified in a safety climate analysis provides a powerful proactive management tool. This paper reports on safety climate factors found in clerical and service organizations in Australia. Concurrent validity studies are reported. In contradistinction to published studies, it is argued that safety climate factors are not stable across organizations. The significance of this for planning and assessing the efficacy of OH&S programs is discussed. It is concluded that although safety climate factors are not stable, the safety climate factor pattern identified in organizations provides a powerful management tool for designing OH&S programs.