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Accurate estimation of streamflow has a vital importance in water resources engineering, management and planning. In the present study, the abilities of group method of data handling-neural networks (GMDH-NN), dynamic evolving neural-fuzzy inference system (DENFIS) and multivariate adaptive regression spline (MARS) methods are investigated for monthly streamflow prediction. Precipitation, temperature and streamflows from Kalam and Chakdara stations at Swat River basin (mountainous basin), Pakistan, are used as inputs to the applied models in the form of different input scenarios, and models’ performances are evaluated on the basis of root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE) and combined accuracy (CA) indexes. Test results of the Kalam Station show that the DENFIS model provides more accurate prediction results in comparison of GMDH-NN and MARS models with the lowest RMSE (18.9 m 3 /s), MAE (13.1 m 3 /s), CA (10.6 m 3 /s) and the highest NSE (0.941). For the Chakdara Station, the MARS outperforms the GMDH-NN and DENFIS models with the lowest RMSE (47.5 m 3 /s), MAE (31.6 m 3 /s), CA (26.1 m 3 /s) and the highest NSE (0.905). Periodicity (month number of the year) effect on models’ accuracies in predicting monthly streamflow is also examined. Obtained results demonstrate that the periodicity improves the models’ accuracies in general but not necessarily in every case. In addition, the results also show that the monthly streamflow could be successfully predicted using only precipitation and temperature variables as inputs.

In this study, we evaluate elastic net regression (ENR), support vector regression (SVR), random forest (RF) and eXtreme Gradient Boosting (XGB) models and propose a modified multi-model integration method named a modified stacking ensemble strategy (MSES) for monthly streamflow forecasting. We apply the above methods to the Three Gorges Reservoir in the Yangtze River Basin, and the results show the following: (1) RF and XGB present better and more stable forecast performance than ENR and SVR. It can be concluded that the machine learning-based models have the potential for monthly streamflow forecasting. (2) The MSES can effectively reconstruct the original training data in the first layer and optimize the XGB model in the second layer, improving the forecast performance. We believe that the MSES is a computing framework worthy of development, with simple mathematical structure and low computational cost. (3) The forecast performance mainly depends on the size and distribution characteristics of the monthly streamflow sequence, which is still difficult to predict using only climate indices.

Evaluation of the economic costs and benefits of flood disaster risk management projects is crucial. However, current cost-benefit analyses (CBA) often lack reliable estimates of the expected loss reduction from flood control measures and ignore quantitative assessments of resettlement. To address these limitations, this study incorporated a probabilistic risk analysis method and quantitative resettlement benefits assessment into the CBA framework, using the Wuxikou Integrated Flood Management Project (WIFMP) in Jiangxi Province, China, as a case study. The direct economic benefits of flood control were estimated by integrating hydrological statistics, numerical flood inundation simulation, and quantitative damage analysis with exposure and vulnerability data. Furthermore, the resettlement benefits were quantified by measuring the annual income growth of migrants based on assumptions about household employment. Our analysis shows that the total WIFMP investment is RMB 3546.1 million yuan (USD 1 = RMB 6.976 yuan), including loan principal and interest of 244.4 million yuan, and operations and maintenance of 605.5 million yuan at 2020 prices. Annual project benefits are estimated at 351.3 million yuan in flood risk reduction, 155.7–191.9 million yuan from increased resettlement income, and 42.7 million yuan in power and water revenues. Considering the costs and benefits across the entire project lifecycle, the internal rate of return ranges from 13.7 to 14.2%, and the net present value ranges from 31.8 to 352.6 billion yuan. Through improved benefit estimation methodology, this research enables a more reliable and holistic evaluation of costs and benefits for flood risk management projects. It provides insights for policymakers and practitioners involved in similar projects, contributing to more informed decision making and better allocation of resources in flood disaster risk management.

With growing flood risk due to increased urbanization, flood damage assessment and flood risk management must be reconsidered. To demonstrate and assess the new features and trends of flood risk in urbanized areas, a novel S-shaped function of return period and damage ( R - D ) is proposed. The function contains three parameters, which are defined as the maximum flood damage A , critical return period R c , and integrated loss coefficient k . A basic framework for flood damage assessment was established to evaluate flood damage in the Taihu Basin under various scenarios. The simulation results were used to construct the flood R - D functions. The study results show that the flood R - D model based on the Gompertz function agrees well with the mutability of flood damage in the highly urbanized basin when the flood scale exceeds the defense capability. The R - D function can be utilized for timely and effective flood damage assessment and prediction. It can describe the impacts of socioeconomic development, urbanization degree, and flood control capability improvements well. The turning points of the function curve can be used as gradation criteria for rational strategy development associated with flood hazards.

Estimation of suspended sediments carried by natural rivers is essential for projects related to water resource planning and management. This study proposes a dynamic evolving neural fuzzy inference system (DENFIS) as an alternative tool to estimate the suspended sediment load based on previous values of streamflow and sediment. Several input scenarios of daily streamflow and suspended sediment load measured at two locations of China—Guangyuan and Beibei—were tried to assess the ability of this new method and its results were compared with those of the other two common methods, adaptive neural fuzzy inference system with fuzzy c-means clustering (ANFIS-FCM) and multivariate adaptive regression splines (MARS) based on three commonly utilized statistical indices, root mean square error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE). The data period covers 01/04/2007–12/31/2015 for the both stations. A comparison of the methods indicated that the DENFIS-based models improved the accuracy of the ANFIS-FCM and MARS-based models with respect to RMSE by 33% (32%) and 31% (36%) for the Guangyuan (Beibei) station, respectively. The NSE accuracy for ANFIS-FCM and MARS-based models were increased by 4% (36%) and 15% (19%) using DENFIS for the Guangyuan (Beibei) station, respectively. It was found that the suspended sediment load can be accurately estimated by DENFIS-based models using only previous streamflow data.

Cascade reservoir operation is an effective nonstructural countermeasure for water resources management. In recent years, many metaheuristic algorithms are introduced to handle reservoir optimal operation due to their strong search capability and high efficiency. The butterfly optimization algorithm (BOA) is a newly developed metaheuristic method which has been widely used in solving various optimization problems. But it has local convergence and premature problems. Therefore, this paper proposed an improved version of BOA where three strategies are introduced: (1) the self-adaptive strategy to improve the initial population, (2) the dynamic switch strategy to balance exploration and exploitation, (3) the Levy-flight and standardized fragrance operators for position updating. The feasibility of the BOA, and IBOA are verified and compared with several commonly used algorithms (PSO, SCA, WOA and TSA) based on 19 test functions. Then, these methods are applied to address the optimization of cascade reservoirs that aims to maximize total hydropower generation. The results show that the proposed IBOA produces higher hydropower output and more stable results, indicating better scheduling schemes than BOA and the other four algorithms. In conclusion, IBOA is an effective and robust alternative optimization tool for cascade reservoir operation problems.

Accurate predictions of wind speed and wind energy are essential in renewable energy planning and management. This study was carried out to test the accuracy of two different neuro fuzzy techniques (neuro fuzzy system with grid partition (NF-GP) and neuro fuzzy system with substractive clustering (NF-SC)), and two heuristic regression methods (least square support vector regression (LSSVR) and M5 regression tree (M5RT)) in the prediction of hourly wind speed and wind power using a cross-validation method. Fourfold cross-validation was employed by dividing the data into four equal subsets. LSSVR’s performance was superior to that of the M5RT, NF-SC, and NF-GP models for all datasets in wind speed prediction. The overall average root-mean-square errors (RMSE) of the M5RT, NF-GP, and NF-SC models decreased by 11.71%, 1.68%, and 2.94%, respectively, using the LSSVR model. The applicability of the four different models was also investigated in the prediction of one-hour-ahead wind power. The results showed that NF-GP’s performance was superior to that of LSSVR, NF-SC, and M5RT. The overall average RMSEs of LSSVR, NF-SC, and M5RT decreased by 5.52%, 1.30%, and 15.6%, respectively, using NF-GP.

Hydrological simulation in karst areas is of great importance and challenge. It is a practical way to enhance the performance of existing hydrological models in karst areas by coupling karst modules that represent hydrological processes in these areas. The near-surface critical zone structure affects runoff generation in karst areas significantly and its complex hydrological processes could be simplified with threshold behaviors. This study proposed a three-thresholds-based karst runoff generation module (3T-KRGM), which used three reservoirs to represent water storage in the soil zone, soil–epikarst interface, and epikarst zone. The 3T-KRGM is coupled with the Xinanjiang (XAJ) model to extend the applicability of the model to karst areas. Both the improved XAJ model and the original XAJ model were used in the Shibantang watershed, which is a typical karst watershed located in southwest China. The results indicate that the performance of daily discharge simulations was obviously improved by introducing the 3T-KRGM. In addition, both the parameter sensitivity analysis and baseflow simulation demonstrate that the 3T-KRGM is rational in structure. The 3T-KRGM could also be easily coupled into other hydrological models, thus benefiting the hydrological simulation in karst areas.

The equivalent frequency regional composition (EFRC) method is an important and commonly used tool to determine the design flood regional composition at various sub-catchments in natural conditions. One of the cases in the EFRC method assumes that the exceedance probabilities of design flood volume at upstream and downstream sites are equal, and the corresponding flood volume at intermediate catchment equals the gap between the volumes of upstream and downstream floods. However, the relationship between the exceedance probability of upstream and downstream flood volumes P and that of corresponding intermediate flood volume C has not been clarified, and whether P>C or P ≤ C has not been theoretically proven. In this study, based on the normal, extreme value type I and Logistic distributions, the relationship between C and P is deduced via theoretical derivations, and based on the Pearson type III, two-parameter lognormal and generalized extreme value distributions, the relationship between C and P is investigated using Monte Carlo experiments. The results show that C is larger than P in the context of the design flood, whereas P is larger than C in the context of low-flow runoff. Thus, the issue of exceedance probability corresponding flood is further theoretically clarified using the EFRC method.

Climate variability and human activities are two main contributing attributions for runoff changes in the Yellow River, China. In the loess hilly-gully regions of the middle Yellow River, water shortage has been a serious problem, and this results in large-scale constructions of soil and water conservation (SWC) measures in the past decades in order to retain water for agricultural irrigation and industrial production. This disturbed the natural runoff characteristics. In this paper, we focused on a typical loess hilly-gully region (Wudinghe and Luhe River basins) and investigated the effects of SWC measures and climate variability on runoff during the period of 1961–2013, while the SWC measures were the main representative of human activities in this region. The nonparametric Mann-Kendall test was used to analyze the changes of annual precipitation, air temperature, potential evapotranspiration (PET), and runoff. The analysis revealed the decrease in precipitation, significant rise in temperature, and remarkable runoff reduction with a rate of more than 0.4 mm per year. It was found that runoff capacity in this region also decreased. Using the change point detection methods, the abrupt change point of annual runoff series was found at 1970, and thus, the study period was divided into the baseline period (1961–1970) and changed period (1971–2013). A conceptual framework based on four statistical runoff methods was used for attribution analysis of runoff decline in the Wudinghe and Luhe River basins (−37.3 and −56.4%, respectively). Results showed that runoff reduction can be explained by 85.2–90.3% (83.3–85.7%) with the SWC measures in the Wudinghe (Luhe) River basin while the remaining proportions were caused by climate variability. The findings suggested that the large-scale SWC measures demonstrated a dominant influence on runoff decline, and the change of precipitation extreme was also a promoting factor of the upward trending of SWC measures’ contribution to runoff decline. This study enhances our understanding of runoff changes caused by SWC measures and climate variability in the typical semiarid region of Loess Plateau, China.