This study determines the relationships between water flow and water quality in three types of channels in southern Florida, USA: Shark River Slough, Peace River, and Hillsboro Canal. Peace River most resembles a natural channel with floodplain connectivity, sinuosity, and uninhibited flow. Shark River Slough has a natural, shallow channel with sheet flow, while the Hillsboro Canal is the most modified channel due to dredging, straightening, and regulated flow. Hydrologic indices for each channel were estimated to characterize flow regimes and flow variability, while concentration–discharge (C–Q) relationships were determined to quantify the impact of flow regime on water quality. The greatest variability in flow occurred at the Hillsboro Canal, followed by Peace River and Shark River Slough. Connectivity to floodplains and long durations of low and high flow pulses at Peace River and Shark River Slough contributed to the dilution of water quality constituent concentrations at higher flows. Conversely, the channelized characteristics of the Hillsboro Canal resulted in an enrichment of constituents, especially during high flows. This study suggests that C–Q relationships can be used in canal discharge management to prevent water quality degradation of sensitive downstream wetland and aquatic ecosystems.

Conventional methods of measuring total suspended sediments (TSS) and total particulate phosphorus (TPP) are typically low-resolution and miss critical processes that impact their exports in aquatic environments. To create high-resolution TSS and TPP estimates, echo intensity (EI), a biproduct of velocity measurements from acoustic devices, was utilized. An acoustic Doppler velocimeter (ADV) and an acoustic Doppler current profiler (ADCP) were deployed in three locations in the L-29 Canal in South Florida, USA, to obtain estimates near the canal bed and in the water column, respectively. Corrections for transmission losses from the ADCP proved unnecessary due to the low vertical variability in the measured EI. EI calibrations were performed using artificially created TSS obtained from bed sediments (ADV) and gravimetrically measured TSS from water samples that matched the depths and times of the ADCP deployments. The measured TSS values were then analyzed for total phosphorus and converted to TPP estimates. The results showed that high TSS and TPP were caused by the rapid discharge releases typical of managed canals. This work demonstrates that high-resolution estimates are imperative for assessing the effects of such swift hydrologic changes on the potential export of sediments and nutrients to delicate ecosystems downstream.

Accurate estimates of soil total phosphorus (TP) concentrations are essential for sustainable nutrient management, food security, and water quality protection. This study predicts and maps the spatial distribution of TP in the top 5 cm and C horizon of soils across the conterminous USA (CONUS) using data from the Geochemical and Mineralogical Data for Soils of the Conterminous United States. We compare the performances of random forest (RF) and inverse distance weighting (IDW) to model and generate soil TP predictions. The RF incorporates 19 predictor variables, including spatial coordinates, climate, soil properties, and topography, while IDW relies solely on coordinates and interpolates between soil TP observations. Models are evaluated using five‐fold cross‐validation. The RF models outperform the IDW models and explain 52% (RMSE = 0.22 log ₁₀ mg kg ⁻¹ ) and 56% (RMSE = 0.26 log ₁₀ mg kg ⁻¹ ) of the variance in soil TP for the top 5 cm and C horizon, respectively. As expected, both model types identify higher TP concentrations in the top 5 cm than in the C horizon, particularly in agricultural regions, reflecting anthropogenic influences. Furthermore, the RF‐generated maps show more realistic spatial patterns that capture the heterogeneity of the CONUS and avoid the bullseye patterns often characteristic of IDW‐generated maps. Additional insights from the RF models show that coordinates, soil texture, pH, and climate are top predictors of soil TP. Increased availability of variables, such as iron and aluminum, that can bind with phosphorus in soils, could improve RF model performance.