Journal of Hydrologic Engineering

In recent years, there has been an increase in interest in estimating probable maximum precipitation (PMP) by numerical weather model (NWM)-based precipitation maximization methods, mainly, the moisture optimization method, storm transposition method, and their combination. This study addresses a quantitative comparison of the effectiveness of each precipitation maximization method regarding the increase in precipitation depths and a discussion on their functions for maximizing precipitation depths. To achieve such a comparison, this study conducted a numerical experiment using the moisture optimization method, which proportionally increases relative humidity using the integrated water vapor transport criterion, and the storm transposition method, which geospatially shifts atmospheric boundary conditions in the weather research and forecasting (WRF) model to maximize atmospheric river (AR)-induced precipitation depths over the Columbia River Basin. Our findings suggest that the storm transposition method should also be considered an essential method in the NWM-based precipitation maximization rather than relying solely on optimizing atmospheric moisture. This study also found that even for ARs with historically small amounts of precipitation over a specified basin, it is possible to increase precipitation depths significantly over the basin by using the storm transposition method. This finding implies that, for maximizing precipitation using the storm transposition method, it is important to select storms that historically did not directly hit the basin but were located around the basin as well, unlike the conventional approach of selecting storms solely based on precipitation depth over the basin. This finding also suggests that the storm transposition method is essential, particularly for estimating long-duration PMP that may cover a whole season, given the contribution of precipitation increase in each single storm event to the snowpack accumulation and reservoir storage for a long duration. Lastly, this study has shown that including the transposition method together with the moisture optimization method can increase precipitation amounts and give a higher upper bound for NWM-based precipitation maximization, especially in AR-dominated regions.

The storage–discharge ($S\text{-}Q$) relation, widely employed in hydrology and hydraulics, is often derived from empirical data and is found to be hysteretic or looped, although approximated by a straight line or a curve in hydrologic modeling. The nature of hysteresis depends on geometric and hydraulic conditions. Despite its ubiquitous use, an analytical treatment discussing what causes the storage–discharge relationship to exhibit a looped behavior does not seem to have been reported. This study analyzes the $S\text{-}Q$ relation analytically using kinematic wave approximation for a watershed represented by a plane, considering simultaneously rainfall, infiltration, and surface runoff or overland flow. For purposes of simplicity and tractability of analytical solutions, both rainfall intensity and infiltration rate are assumed to be constant. Depending on the duration of rainfall, two cases—equilibrium and partial equilibrium—are distinguished. The hysteretic $S\text{-}Q$ relationship is different for these two cases and requires close scrutiny, which is pursued in this study. It is emphasized that the assumptions of constant rainfall intensity and infiltration rate, rectangular geometry, and kinematic wave approximation undermine the dynamics of hysteresis.

The trophic state index (TSI) is generally accepted as an indicator of the trophic status of inland water bodies. Regular monitoring of the trophic status of a water body is vital for understanding the health and functioning of its ecosystem. The band ratios (BR) and normalized difference (ND) indices are customarily used for classifying the land uses, but their capacity for quality monitoring of a water body has not been fully explored. The present study explores the possibility of using ND and BR of the bands of Sentinel 2 multispectral imagery (MSI) (S2) imagery for mapping the TSI of an inland water body. The data of Milford Lake, Kansas, were used to derive TSI. Three atmospherically corrected data sets obtained from Case 2 regional coast color processor (C2RCC), Acolite, and Sentinel 2 Correction (Sen2Cor) were used to derive ND and BR indices. The results of the correlation analyses of these indices with TSI reinforced the importance of atmospheric correction on any algorithm. TSI derived from turbidity measurements was observed to be well correlated to ND and BR indices of green (B3) and red bands (B4, B5, and B6) derived from the C2RCC data set (correlation coefficient $>0.8$). Thresholds of BR and ND indices of green and red bands for delineating the hypereutrophic areas from the total area of interest were also identified. The regression relations and threshold criteria were validated with the data from five other lakes in the US and one lake in India to verify the compliance of these relations to other areas. TSI could be estimated with a mean absolute percentage error (MAPE) of 9% from these lakes by using ND34, ND35, and ND45. The threshold criteria for BR34, BR46, and BR56 for delineating hypereutrophic areas were also validated for these lakes. The validations confirm that these computationally simple indices could be utilized for quality monitoring in areas of different geographic and climatic characteristics.

Daily water level prediction for rivers is of great significance in flood prevention and enhanced water resources supervision. In order to accurately predict daily water level time series without sufficient data despite the need for large training data sets for neural networks, this paper proposes an innovative daily water level forecasting model, ECRBM-GRU-SSA, which combines the enhanced continuous restricted Boltzmann machine (ECRBM), the gated recurrent neural unit (GRU), and the sparrow search algorithm (SSA). The ECRBM extracts input features and then cooperates with the ensemble strategy to increase the generalization ability of the final model. SSA adjusts model parameters. The contribution of each component to the final prediction result is analyzed using daily water level meteorological data from the Qingxi River. The accuracy of the proposed model is verified by comparing it with basic prediction models like support vector machine (SVM), random forest (RF), and GRU and with improved models such as ECRBM-GRU and GRU-SSA. The indicators $RMSE$, $MAE$, $R$ and $NSE$ are improved from 11.5% to 57.3%, 9.3% to 73.6%, 0.5% to 4.6%, and 5.6% to 31.9%, respectively. Therefore, the proposed model provides technical support for staff managing water resources.

Spatial and temporal distribution of nitrate, phosphate, and salinity of complex soil and water system is essential for land and water resources management. In this study, by aggregating a flow [SWAP (Soil-Water-Air-Plant)] and a transport [ANIMO (Agricultural Nitrogen Model)] model, the regional distributions of salinity, nitrate, and phosphate for an irrigation district, west of Iran, were simulated. Precise sensitivity analysis, calibration, and validation were performed based on the observed values. The results revealed that the soil salinity did not have considerable change with depth, but nitrate and phosphate decreased by about 42% and 92%, respectively. The effects of irrigation, fertilization, and seasonal weather changes on concentration of concerned parameters was also simulated well in time series. The mass balance results indicated that more salt had outflows from and more nitrate and phosphate remained in the soil, which could be a sign of soil deterioration. This spatial-temporal distributed model of unsaturated zone could be used for evaluation of long-term irrigation management scenarios while considering soil and water quantity and quality indicators.