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Revealing the mechanism of hydrological and agricultural drought has been challenging and vital in the environment under extreme weather and water resource shortages. To explore the evolution process from meteorological to hydrological and agricultural drought further, multi-source remote sensing data, including the Gravity Recovery and Climate Experiment (GRACE) product, were collected in the Huaihe River basin of China during 2002–2020. Three machine learning methods, including long short-term memory neural network (LSTM), convolutional neural network (CNN), and categorical boosting (CatBoost), were constructed for hydrological and agricultural drought forecasting. The propagation time from meteorological drought to surface water storage and terrestrial water storage drought, evaluated by the standardized precipitation evapotranspiration index, was 8 and 11 months with Pearson correlation coefficients (R) of 0.68 and 0.48, respectively. Groundwater storage drought was correlated with evapotranspiration and vegetation growth with a 12-month lag time, respectively. In addition, vegetation growth was affected by the drought of soil moisture at depths ranging from 100 to 200 cm with an 8-month lag time with an R of −0.39. Although the forecasting performances of terrestrial water storage drought were better than those of groundwater storage drought and agricultural drought, CNN always performed better than LSTM and CatBoost models, with Nash–Sutclife efficiency values during testing ranging from 0.28 to 0.70, 0.26 to 0.33, and −0.10 to −0.40 for terrestrial water storage drought, groundwater storage drought, and agricultural drought at lead times of 0–3 months, respectively. Furthermore, splitting training and testing data at random significantly improved the performances of CNN and CatBoost methods for drought forecasting rather than in chronological order splitting for non-stationary data.

The streamflow recession analysis, always following a power law, depicts the storage-release relation in catchments. However, the understanding of hydrological recession dynamics is still insufficient. Here, a total of 80 nearly natural catchments from the reference hydrologic networks (RHNs) in the UK were selected to explore the changes and the controlling factors of recession slope curve parameters by utilizing trend analysis methods, the self-organizing map, and k-means clustering algorithms. The results demonstrated that (i) the estimation of event-scale recession parameters was sensitive to different combinations of recession extraction and fitting methods. Particularly, the combination of Brutsaert extraction and the linear regression method always obtained robust estimations of recession parameters. (ii) Changes of annual median recession parameters showed a clear spatial distribution pattern, indicating the climate-driven impact on recession processes. Up to 17 catchments in Scotland showed significant changes in recession processes, always displaying increasing trends in the recession coefficient and decreasing trends in the recession exponent. (iii) The recession exponent strongly depended on the base-flow index, rock permeability, arable area, and temporal distribution of rainfall. Additionally, the spatial patterning of recession processes further provided some insights for facilitating the understanding of the hydrological recession process.

New sequences of nanocomposites including numerous maleic acid-grafted poly(butylene adipate-co-terephthalate) (g-PBAT) and cellulose nanocrystals (CNC) were efficaciously fabricated via transesterification and polycondensation processes with the covalent bonds between the polymer and reinforcing fillers. The grafting interaction of maleic acid onto PBAT was successfully demonstrated using Fourier transform infrared (FTIR) and 13C-nuclear magnetic resonance (NMR) spectra. The morphology of g-PBAT/CNC nanocomposites was investigated by wide-angle X-ray diffraction and transmission electron microscopy. Both results indicate that the CNC was randomly dispersed into the g-PBAT polymer matrix. The storage modulus at −80 and 25 °C was significantly enhanced with the incorporation of CNC into g-PBAT matrix. The crystallization rate of g-PBAT/CNC nanocomposites increased as the loading of CNC increased. With the incorporation of 3 wt% CNC, the half-time for crystallization of the g-PBAT/CNC composite decreased about 50~80% as compared with the same isothermal crystallization of pure polymer matrix. All water vapor permeation (WVP) values of all g-PBAT/CNC nanocomposites decreased as the loading of CNC increased. The decrease in WVP may be attributed to the addition of stiff CNC, causing the increase on the permeation route in the water molecules in the g-PBAT polymer matrix.

Novel and biodegradable acrylic acid-grafted poly(1,4-butylene adipate-co-terephthalate)/organically modified layered double hydroxide (g-PBAT/m-LDH) nanocomposites were synthesized through the polycondensation and transesterification process, with the covalent linkages between the polymer and the inorganic materials. X-ray diffraction and transmission electron microscopy were used to characterize the structure and morphology of the g-PBAT/m-LDH nanocomposites. The experimental results show that the m-LDH was exfoliated and widely distributed in the g-PBAT matrix. The addition of m-LDH into the g-PBAT extensively improved the storage modulus at −90 °C, when compared to that of the pure g-PBAT matrix. The effects of the minor comonomer of the butylene terephthalate (BT) unit and the addition of m-LDH on the crystallization behavior, and the polymorphic crystals of the g-PBAT at numerous crystallization temperatures, were examined, using a differential scanning calorimeter (DSC). The data indicate that the minor comonomer of the BT unit into g-PBAT can significantly change the starting formation temperatures of the α-form and ꞵ-form crystals, while a change in the starting formation temperatures of the α-form and ꞵ-form crystals using the addition of m-LDH into g-PBAT is not evident.

Urbanization is one of the main factors altering hydrological processes. To understand the influence of urbanization on flood characteristics including runoff, inundation and streamflow, it is essential to quantify the level of urbanization and analyze the corresponding impacts on flood. This study proposed an integrated and systematic framework to assess how urbanization affects flood characteristics in Hangzhou, China. Land use changes in 2000–2020 representing the level of urbanization were extracted from Landsat images with an ensemble machine learning method and future scenarios were designed with Patch-generating Land Use Simulation (PLUS). The runoff, inundation and streamflow under 24 h design rainfall with 100 year return period were assessed using the integrated hydrologic-hydraulic model for land use of different years. Finally, the relationship between land use changes and flood characteristics were identified and analyzed. It was found that the study area underwent a rapid urbanization process with artificial surface increasing from 6.93% to 29.12% during 2000–2020. Total runoff volume and total inundation volume showed a growing trend with a percentage change of 5.96% and 12.33% respectively under the design rainfall. This study highlights the significance of water body area in influencing flood characteristics. The results of this study can improve understanding of flood responses to land use and provide useful insight to decision makers in developing urban flood management measures.

The change in movable beds is related to the mechanisms of sediment transport and hydrodynamics. Numerical modelling with empirical equations and the simplified momentum equation is the common means to analyze the complicated sediment transport processing in river channels. The optimization of parameters is essential to obtain the proper results. Inadequate parameters would cause errors during the simulation process and accumulate the errors with long-time simulation. The optimized parameter combination for numerical modelling, however, is rarely discussed. This study adopted the ensemble method to simulate the change in the river channel, with a single model combined with multiple parameters. The optimized parameter combinations for a given river reach are investigated. Two river basins, located in Taiwan, were used as study cases, to simulate river morphology through the SRH-2D, which was developed by the U.S. Bureau of Reclamation. The input parameters related to the sediment transport module were randomly selected within a reasonable range. The parameter sets with proper results were selected as ensemble members. The concentration of sedimentation and bathymetry elevation was used to conduct the calibration. Both study cases show that 20 ensemble members were good enough to capture the results and save simulation time. However, when the ensemble members increased to 100, there was no significant improvement, but a longer simulation time. The result showed that the peak concentration and the occurrence of time could be predicted by the ensemble size of 20. Moreover, with consideration of the bed elevation as the target, the result showed that this method could quantitatively simulate the change in bed elevation. With both cases, this study showed that the ensemble method is a suitable approach for river morphology numerical modelling. The ensemble size of 20 can effectively obtain the result and reduce the uncertainty for sediment transport simulation.

Sustainable water resources management is facing a rigorous challenge due to global climate change. Nowadays, improving streamflow predictions based on uneven precipitation is an important task. The main purpose of this study is to integrate the ensemble technique concept into artificial neural networks for reducing model uncertainty in hourly streamflow predictions. The ensemble streamflow predictions are built following two steps: (1) Generating the ensemble members through disturbance of initial weights, data resampling, and alteration of model structure; (2) consolidating the model outputs through the arithmetic average, stacking, and Bayesian model average. This study investigates various ensemble strategies on two study sites, where the watershed size and hydrological conditions are different. The results help to realize whether the ensemble methods are sensitive to hydrological or physiographical conditions. Additionally, the applicability and availability of the ensemble strategies can be easily evaluated in this study. Among various ensemble strategies, the best ESP is produced by the combination of boosting (data resampling) and Bayesian model average. The results demonstrate that the ensemble neural networks greatly improved the accuracy of streamflow predictions as compared to a single neural network, and the improvement made by the ensemble neural network is about 19–37% and 20–30% in Longquan Creek and Jinhua River watersheds, respectively, for 1–3 h ahead streamflow prediction. Moreover, the results obtained from different ensemble strategies are quite consistent in both watersheds, indicating that the ensemble strategies are insensitive to hydrological and physiographical factors. Finally, the output intervals of ensemble streamflow prediction may also reflect the possible peak flow, which is valuable information for flood prevention.

Due to nonuniform rainfall distribution in Taiwan, groundwater is an important water source in certain areas that lack water storage facilities during periods of drought. Therefore, groundwater recharge is an important issue for sustainable water resources management. The mountainous areas and the alluvial fan areas of the Jhuoshui River basin in Central Taiwan are considered abundant groundwater recharge regions. This study aims to investigate the interactive mechanisms between surface water and groundwater through statistical techniques and estimate groundwater level variations by a combination of artificial intelligence techniques and the Gamma test (GT). The Jhuoshui River basin in Central Taiwan is selected as the study area. The results demonstrate that: (1) More days of accumulated rainfall data are required to affect variable groundwater levels in low-permeability wells or deep wells; (2) effective rainfall thresholds can be properly identified by lower bound screening of accumulated rainfall; (3) daily groundwater level variation can be estimated effectively by artificial neural networks (ANNs); and (4) it is difficult to build efficient models for low-permeability wells, and the accuracy and stability of models is worse in the proximal-fan areas than in the mountainous areas.

In this research, we propose recurrent neural networks (RNNs) to build a relationship between rainfalls and water level patterns of an urban sewerage system based on historical torrential rain/storm events. The RNN allows signals to propagate in both forward and backward directions, which offers the network dynamic memories. Besides, the information at the current time-step with a feedback operation can yield a time-delay unit that provides internal input information at the next time-step to effectively deal with time-varying systems. The RNN is implemented at both gauged and ungauged sites for 5-, 10-, 15-, and 20-min-ahead water level predictions. The results show that the RNN is capable of learning the nonlinear sewerage system and producing satisfactory predictions at the gauged sites. Concerning the ungauged sites, there are no historical data of water level to support prediction. In order to overcome such problem, a set of synthetic data, generated from a storm water management model (SWMM) under cautious verification process of applicability based on the data from nearby gauging stations, are introduced as the learning target to the training procedure of the RNN and moreover evaluating the performance of the RNN at the ungauged sites. The results demonstrate that the potential role of the SWMM coupled with nearby rainfall and water level information can be of great use in enhancing the capability of the RNN at the ungauged sites. Hence we can conclude that the RNN is an effective and suitable model for successfully predicting the water levels at both gauged and ungauged sites in urban sewerage systems.

The over extraction of groundwater in central-western and southwestern Taiwan has resulted in serious land subsidence for decades. For making countermeasures in response to land subsidence, this study collects long-term hydrological data to explore the relationships between surface water and groundwater in various monitoring stations, and then constructs one-month-ahead forecast models by using data-driven techniques for the water resources management of the Zhuoshui River basin in Taiwan. The results demonstrate that the constructed models can accurately forecast monthly groundwater levels. The sensitivity analysis is next conducted on the input variables of the constructed models by using the partial derivative method. The analysis results reveal that streamflow is a predominant factor for groundwater level variation, and therefore streamflow management made by the upstream weir of the river would influence groundwater level variations. This study further implements several scenario analyses based on the interactive mechanism between groundwater and surface water in response to future climatic conditions and weir discharge management, respectively. The results of scenario analyses indicate that the groundwater recharge zone spreads along the Zhuoshui River while lateral and vertical recharge sources would cause different quantities and distribution patterns of groundwater recharge. Besides, an increase in weir discharge would improve groundwater recharge quantities with groundwater level variations at 0.12 m and 0.06 m in wet and dry seasons, respectively. As a consequence, the operation of weir discharge would play an import role in sustainable development of water resources management in the study area.