Background: Nitrogen leaching from agricultural lands is a major threat to groundwater and surface waters. This study investigated the relationship between the characteristics of wheat-straw biochar produced at different temperatures and its impact on the uptake of NO3--N. Methods: Three types of biochar were produced from wheat straw at three different ...

Debris floods, as one of the most significant natural hazards, often threaten the lives and property of many people worldwide. Predicting models are essential for flood warning systems to minimize casualties of debris floods. Since HEC-HMS (Hydrologic Engineering Center’s Hydrological Modelling System) cannot simulate debris flow, this study proposes a new hybrid model that uses artificial intelligence models to overcome HEC-HMS’s insufficiency in reflecting the sediment concentration effect on the debris floods. A sediment concentration is an effective factor for evaluating debris flood peak flows. This led to the proposal of new hybrid models for predicting the debris flood peak flows on the basis of hybridization of the artificial intelligence models (Bayesian Network (BN) and Support Vector Regression–Particle Swarm Optimization (SVR-PSO)) and HEC-HMS. To estimate the sediment concentration of floods by using the proposed artificial intelligence models, we nominated an average basin elevation, an average basin slope, a basin area, the current day rainfall, the antecedent rainfall of the past 3 days, and the streamflow of the previous day the previous day as the effective variables. In the validation stage, the average of the Mean Absolute Relative Error (MARE) of the estimated values were 0.024, 0.038, and 0.024 for the typical floods that occurred in the Navrood, Kasilian, and the Amameh basins in the north of Iran, respectively. Similarly, we obtained values of 0.038, 0.073, and 0.040 for the debris flood events for the three respective locations. After predicting the debris flood peak flows by the proposed hybrid HMS-BN and HMS-SVR-PSO models, the average of the MAREs for all debris flood events was reduced to 0.013 and 0.014, respectively. The comparison of MAREs of the examined hybrid models shows that the HMS-BN model results in higher accuracy than the HMS-SVR-PSO model in the prediction of the debris flood peak flows. Generally, the absolute error of prediction by the proposed hybrid model is reduced to one-third of the HEC-HMS. The prediction of the debris flood peak flows using the proposed hybrid model can be examined in the debris flood warning systems to reduce the potential damages and casualties in similar basins.

Reservoir operation in flood conditions on hourly time scales poses significant difficulties due to the inherent uncertainty of inflow forecasts. Hence, incorporating ensemble flood forecasts into real-time reservoir operation optimization has rarely been investigated to effectively address uncertainties in inflow forecasts for flood control, let alone its added value compared to no-forecast operating scheme in multi-objective reservoir operating system. This research first investigates the potential of sequential Long Short-Term Memory networks to forecast one-day-ahead ensemble inflow floods based on numerical weather prediction data. Next, a new framework has been developed to incorporate ensemble inflow forecasts into multi-objective reservoir operation optimization using grid search-based genetic algorithm. Finally, we implemented our proposed ensemble-based operating method on the Dez dam basin in Iran and compared it with the benchmark no-forecast operating approach. The results revealed that the Long Short-Term Memory model reasonably performed well to propagate uncertainty in short-term ensemble inflow forecasts. In addition, the ensemble-based operating method could reduce release peaks and increase reservoir storage more than that of a deterministic (no-forecast) operating approach. Our findings demonstrated that the applicability of a skillful ensemble-based scheme in reservoir flood control operations could be effective in decreasing flood damage and water shortage.