Journal of Hydrologic Engineering

Accurate risk forecasting of agricultural water shortages has important implications for the prevention and reduction of disasters in regional agricultural production. Encountering effective precipitation ($P_e$) and crop water requirements ($E{T}_c$) can determine the regional agricultural water shortage risk under natural precipitation supply conditions. The Guanzhong Plain (GP) of China, divided into the Eastern Guanzhong Plain (EGP) and the Western Guanzhong Plain (WGP), is sensitive to water shortage due to the local semihumid and drought-prone climate. Based on the daily meteorological data at six representative weather stations in the GP from 1962 to 2016, $P_e$ and $E{T}_c$ (marked as $P_{ey}$, $P_{ew}$, and $P_{es}$, and $E{T}_{cy}$, $E{T}_{cw}$, and $E{T}_{cs}$ over a hydrological year, winter wheat growth period, and summer maize growth period, respectively) were computed, and copula functions were employed to model the joint distribution of $P_e$ and $E{T}_c$. The results showed that the mean values of $P_{ey}$, $P_{ew}$, and $P_{es}$ (533, 245, and 287 mm) in the EGP were smaller than those of $P_{ey}$, $P_{ew}$, and $P_{es}$ (599, 274, and 325 mm) in the WGP. However, the mean values of $E{T}_{cy}$, $E{T}_{cw}$, and $E{T}_{cs}$ (997, 494, and 502 mm) in the EGP were greater than those of $E{T}_{cy}$, $E{T}_{cw}$, and $E{T}_{cs}$ (895, 417, and 477 mm) in the WGP. The Frank copula was identified as the most suitable model for the joint modeling of $P_e$ and $E{T}_c$ series. According to the joint probability distribution, the asynchronous encounter probability of $P_e$ and $E{T}_c$ was around three or fourfold of the synchronous encounter probability in the GP. The occurrence probabilities of severe and extreme agricultural water shortage under natural precipitation supply condition were 0.24–0.27 and 0.04–0.06 in the EGP, while the corresponding values were 0.20–0.23 and 0.03–0.04 in the WGP. This study proved that natural precipitation could not meet crop water demand, and the joint encounter probability of low effective precipitation and high crop water demand was high, so irrigation and optimum agricultural water resource allocation are thus necessary for agricultural production in the semihumid and drought-prone region.

Annual flood peaks frequently stem from distinct flood-producing mechanisms. However, most inference procedures rely on a single distributional model, which can lead to ill-posed inferences of its upper-tail behavior. For addressing this problem, this paper explores a Bayesian mixture model, which combines the Gamma and generalized Pareto distributions under the concept of penalized complexity prior distribution (PCPD) for the tail index, for modeling annual flood peaks. The proposed approach was applied in two catchments in the western US, in which empirical evidence of mixed population exists and historical and paleoflood information is available for validation purposes. The results suggested that despite the increased complexity of the mixture model, describing flood events with distinct distributions was beneficial for the goodness of fit and for extrapolating to large return periods. In addition, the PCPD proved effective in constraining the tail index inference because narrower credible intervals compared with well-established models were obtained for most flood quantiles. Overall, the proposed approach seems feasible for reconciling distinct flood-generating mechanisms and reliability in statistical estimation in flood frequency analysis.

One of the key factors for flood modeling and control is the flood hydrograph, which is not always available due to lack of flood discharge observations. In reverse flow routing, hydraulic or hydrological calculations are performed from the downstream end to the upstream end. In the present study, a reverse flood routing approach is developed based on the Muskingum model. The storage function is conceptualized as linear and five different nonlinear forms. The Euler and the fourth-order Runge–Kutta numerical methods are used for solving the storage models. The shuffled complex evolution (SCE) algorithm is used for optimization of the flood routing parameters. The models are calibrated and validated with theoretical and actual hydrographs. The results indicate that the proposed methodology could substantially (up to almost 82%) improve comparison with observed inflows. The practical applicability of the proposed methodology is also validated in real river systems.

Accurate forecasting of daily runoff plays an important role in water resource management. This paper presents a feed-forward neural network interval-mapping-model-based clustering analysis technique and the and whale optimization algorithm (C-BPELM-WOAM) for the prediction intervals of the daily runoff series. The proposed model is composed of two parts. One part is the daily runoff point prediction. In this part, a combination of the error unequal-weight coefficient with the error back-propagation training and extreme learning machine algorithms was applied to construct a feed-forward neural network (BPELM), which can improve the performance of the prediction model. The second part is a clustering interval-mapping prediction model based on the whale optimization algorithm (WOA). In this part, $k$-means clustering was used to classify the daily runoff series data into several groups. Then the interval-mapping coefficients corresponding to each group of data were optimized by the WOA so that the prediction interval could be obtained. Finally, the daily runoff data for the Astor River basin were used to verify the efficiency of the C-BPELM-WOAM for daily runoff prediction intervals. The results showed that the C-BPELM-WOAM model obtained higher quality daily runoff prediction intervals.

Popular models currently available for estimation of design intensities in the wet mountainous Western Ghat areas of South India are not those based on analysis of local rainfall records. Possibilities of development of $\mathrm{i}\text{-}\mathrm{d}\text{-}\mathrm{f}$ curves applicable in the region are also remote due to the constraint of short length of records. The present work, taken up in this background, presents the novel idea of substituting daily rainfall depth for frequency in the popular i-d-f curves and furnishes a reliable model for the Western Ghat regions of the state of Karnataka. The procedure advocated for the development of the model relies on the fact that in areas characterized by nearly continuous falls and long spells of rain, as here, the intensities are well associated with total rainfall and hence the i-d relationships are functions of daily depth. This work proposes to use all the available subhourly data from different stations across supposedly homogenous regions and to use envelop curves to reduce sampling variations. i-d curves for individual days falling within specified ranges of daily depth are grouped together, and each group is represented by an envelop curve. Power functions, forced through constant exponents, are developed for the different envelop curves. The association between the varying parameter of such curves and the daily depth (D) is modeled by a power function, resulting in i-d-D relationships of the form ${\mathrm{i}=\mathrm{a}(\mathrm{D}}^{\mathrm{b}}/{\mathrm{d}}^{\mathrm{c}})$. The model applicable for the study area has been derived using intensity—duration data of 4–5 years from 33 stations spread across the study area, divided into four regions based on normal annual rainfall. The model has been validated and found reliable. The model results are discussed and used to test the validity of the method presently being followed in the region, the one advocated by the Central Water Commission (CWC). Pitfalls in the CWC method have been brought out. It is concluded that the model presented forms a reliable tool for estimating design intensities until a more authentic one is developed using further extensive data.