Climate variability is strongly influencing hydrological processes under complex weather conditions, and it should be considered to forecast reservoir inflow for efficient dam operation strategies. Large-scale climate indices can provide potential information about climate variability, as they usually have a direct or indirect correlation with hydrologic variables. This study aims to use large-scale climate indices in monthly reservoir inflow forecasting for considering climate variability. For this purpose, time series and artificial intelligence models, such as Seasonal AutoRegressive Integrated Moving Average (SARIMA), SARIMA with eXogenous variables (SARIMAX), Artificial Neural Network (ANN), Adaptive Neural-based Fuzzy Inference System (ANFIS), and Random Forest (RF) models were employed with two types of input variables, autoregressive variables (AR-) and a combination of autoregressive and exogenous variables (ARX-). Several statistical methods, including ensemble empirical mode decomposition (EEMD), were used to select the lagged climate indices. Finally, monthly reservoir inflow was forecasted by SARIMA, SARIMAX, AR-ANN, ARX-ANN, AR-ANFIS, ARX-ANFIS, AR-RF, and ARX-RF models. As a result, the use of climate indices in artificial intelligence models showed a potential to improve the model performance, and the ARX-ANN and AR-RF models generally showed the best performance among the employed models.

This study evaluates five bias correction and statistical downscaling (BCSD) techniques for daily precipitation and examines their impacts on the projected changes in flood extremes (i.e., 1%, 0.5%, and 0.2% floods). We use climate model outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to conduct hydrologic simulations across watersheds in Iowa and determine historical and future flood extreme estimates based on generalized extreme value distribution fitting. Projected changes in these extremes are examined with respect to four Shared Socioeconomic Pathways (SSPs) alongside five BCSD techniques. We find the magnitude of the estimates of future annual exceedance probabilities (AEPs) are expected to increase under all SSPs, especially for the emission scenarios with higher greenhouse gases concentrations (i.e., SSP370 and SSP585). Our results also suggest the choice of BCSD impacts the magnitude of the projected changes, with the SSPs that play a more limited role compared to the choice of downscaling method. The variability in projected flood changes across Iowa is similar across the downscaling technique but increases as the AEP increases. Our findings provide insights into the impact of downscaling techniques on flood extremes' projections and useful information for climate planning across the state.

Artificial neural networks (ANNs) have been extensively used to forecast monthly precipitation for water resources management over the past few decades. Efforts to produce more accurate and stable forecasts face ongoing challenges as the so‐called single‐ANN (S‐ANN) approach has several limitations, particularly regarding uncertainty. Many attempts have been made to deal with different types of uncertainties by applying ensemble approaches. Here, we propose a new ANN ensemble model (ANN‐ENS) dealing with uncertainty in model structure and input variable selection to provide a more accurate and stable forecasting performance. This model is structured by generating various input layers, considering all the candidate input variables (i.e.,large‐scale climate indices and lagged precipitation). We developed a modified backward elimination method to select the preliminary input variables from all the candidate input variables. Then, we tested and validated the proposed ANN‐ENS using observed monthly precipitation from 10 meteorological stations in the Han River basin, South Korea. Our results demonstrated that the ANN‐ENS enhanced the forecasting performance in terms of both accuracy and stability. Although a significant uncertainty was introduced by using all the candidate input variables, the forecasting result outperformed S‐ANNs for all employed stations. Additionally, the ANN‐ENS provided a more stable forecasting performance in comparison with S‐ANNs, which are highly sensitive. Moreover, the generated ensemble members were slightly biased at some stations but were generally reliable.

The proper assessment of design flood is a major concern for many hydrological applications in small urban watersheds. A number of approaches can be used including statistical approach and the continuous simulation and design storm methods. However, each method has its own limitations and assumptions being applied to the real world. The design storm method has been widely used for a long time because of the simplicity of the method, but three critical assumptions are made such as the equality of the return periods between the rainfall and corresponding flood quantiles and the selections of the rainfall hyetograph and antecedent soil moisture conditions. Continuous simulation cannot be applied to small urban catchments with quick responses of runoff to rainfall. In this paper, a new flood frequency analysis for the simulated annual peak flows (FASAP) is proposed. This method employs the candidate rainfall events selected by considering a time step order of five minutes and a sliding duration without any assumptions about the conventional design storm method in an urban watershed. In addition, the proposed methodology was verified by comparing the results with the conventional method in a real urban watershed.