Data scarcity is a common problem in hydrological calculations that often makes water resources planning and engineering design challenging. Combining ensemble empirical mode decomposition (EEMD), a radial basis function (RBF) neural network, and an autoregression (AR) model, an improved EEMD prediction model is proposed for runoff series forward prediction, i.e., runoff series extension. In the improved model, considering the decomposition-prediction-reconstruction principle, EEMD was employed for decomposition and reconstruction and the RBF and AR model were used for component prediction. Also, the method of tracking energy differences (MTED) was used as stopping criteria for EEMD in order to solve the problem of mode mixing that occurs frequently in EEMD. The orthogonality index (Ort) and the relative average deviation (RAD) were introduced to verify the mode mixing and prediction performance. A case study showed that the MTED-based decomposition was significantly better than decomposition methods using the standard deviation (SD) criteria and the G. Rilling (GR) criteria. After MTED-based decomposition, mode mixing in EEMD was suppressed effectively (|Ort| < 0.23) and stable orthogonal components were obtained. For this, annual runoff series forward predictions using the improved EEMD-based prediction model were significantly better (RAD < 11.1%) than predictions by the rainfall-runoff method and the AR model method. Thus, this forward prediction model can be regarded as an approach for hydrological series extension, and shows promise for practical applications.

One model structural deficiency is that some dynamic characteristics (such as seasonal dynamics) in catchment conditions are not explicitly represented by hydrological models. This study integrates data mining techniques to develop a clustering preprocessing framework for the subannual calibration of hydrological models to simulate seasonal dynamic behaviors. The proposed framework aims to solve the problems caused by missing processes and deficiencies of hydrological models, providing guidance for future model development. A set of climatic‐land surface indices is provided and preprocessed using the maximal information coefficient and the principal component analysis. Two clustering operations are performed based on the preprocessed climatic index and land‐surface index systems. Hydrological data are clustered into subannual periods for calibration. The parameters are independently optimized for each subperiod using a modified parallel calibration scheme and are then combined to generate a continuous simulation. The framework is applied in calibrating the TOPMODEL. The results show that the performance of the model with a clustering preprocessing framework in the middle‐ and low‐flow conditions is significantly improved without reducing the simulation accuracy for high flows. The transposability of the model parameters from the calibration to validation period has been improved significantly as well. The anomalous parameter values may be attributed in part to the convergence problem when using an optimization algorithm. Though well applied in the TOPMODEL, the framework has the potential to be used in other hydrological models.

Changes in precipitation patterns greatly impact regional drought/flood risk management and utilization of water resources. The main purpose of this paper was to investigate spatio-temporal variability of precipitation concentration in the transitional zone between Qinling Mountains (QDM), Guanzhong Plain (GZP) and the Loess Plateau (LPNS) in China, using monthly-scale precipitation concentration index (PCI) and daily-scale concentration index (CI) from daily rainfall records. The Mann-Kendall method was employed to illustrate the change in trend of PCI and CI, the Kriging interpolation method was adopted to measure spatial distribution, and the Wavelet transforms were used to explore their spatio-temporal correlation with the Arctic Oscillation (AO) & Western Pacific Subtropical High (WPSH) for revealing the potential attribution of precipitation concentration variation. Also, the regional implication of CI was investigated in the zone to provide local knowledge of the index application. Results showed that annual precipitation demonstrated a north-south increasing layered spatial distribution in the zone, representing a generally decreasing trend. The CI change generally exhibited a more significant decreasing trend than did PCI in LPNS and GZP due to AO slowly increasing over time, with a spatially weak layered or radial north-south decay, and an insignificant increasing trend in QDM impacted by the enhancing WPSH, with an obvious layered or radial spatial distribution. The spatiotemporal pattern of PCI variation represented similar characteristics in attribution with CI, but an inverse spatial distribution due to the phase difference (positive and negative effects) of AO and WPSH influencing seasonal precipitation. Regional analysis of CI showed that the CI value with over 0.62 indicated that approximately 80% of precipitation was contributed by 25% of the rainiest days in this zone. Fortunately, the area with this high CI has been getting smaller, implying a positive trend toward regional flash flood and debris flow control.

Understanding and quantifying changes in hydrological systems due to human interference are critical for the implementation of adaptive management of global water resources in the changing environment. To explore the implications of hydrological variations for water resources management, the Wuding River Basin (WRB) in the Loess Plateau, China, was selected as a case study. Based on the Budyko-type equation with a time-varying parameter n, a human-induced water–energy balance (HWEB) model was proposed to investigate the hydrological variability in the WRB. The investigation showed that runoff continuously reduced by 0.424 mm/a during 1975–2010, with weakly reducing precipitation and increasing groundwater exploitation causing a decrease in groundwater storage at a rate of 1.07 mm/a, and actual evapotranspiration accounting for more than 90% of precipitation having an insignificantly decreasing trend with a rate of 0.53 mm/a under climate change (decrease) and human impact (increase). Attribution analysis indicated that human-induced underlying surface condition change played a dominant role in runoff reduction by driving an increase in actual evapotranspiration, and that mainly impacted the overall decrease in runoff compounded by climate change during the entire period. It is suggested that reducing the watershed evapotranspiration and controlling groundwater exploitation should receive greater attention in future basin management.

Water competing conflict among water competing sectors from different levels should be taken under consideration during the optimization allocation of water resources. Furthermore, uncertainties are inevitable in the optimization allocation of water resources. In order to deal with the above problems, this study developed a fuzzy max-min decision bi-level fuzzy programming model. The developed model was then applied to a case study in Wuwei, Gansu Province, China. In this study, the net benefit and yield were regarded as the upper-level and lower-level objectives, respectively. Optimal water resource plans were obtained under different possibility levels of fuzzy parameters, which could deal with water competing conflict between the upper level and the lower level effectively. The obtained results are expected to make great contribution in helping local decision-makers to make decisions on dealing with the water competing conflict between the upper and lower level and the optimal use of water resources under uncertainty.

China has played a leading role in global greening efforts over the past few decades (Li et al., 2018;Zhu et al., 2016). Recently, the country sets a goal of achieving peak carbon and carbon neutrality by 2030 and 2060, respectively. This requires a systematic and nature-based strategy to offset anthropogenic carbon dioxide (CO 2 ) emissions. Enhancing China's carbon sink by planting forests in new areas is an appealing option. Afforestation is generally an effective way to sequester CO 2 and to improve environmental conditions through better water conservation, water quality, sand fixation, and other ecosystem services (

The stationarity of observed hydrological series has been broken or destroyed in many areas worldwide due to changing environments, causing hydrologic designs under stationarity assumption to be questioned and placing designed projects under threat. This paper proposed a data expansion approach—namely, the cross-reconstruction (CR) method—for frequency analysis for a step-changed runoff series combined with the empirical mode decomposition (EMD) method. The purpose is to expand the small data on each step to meet the requirements of data capacity for frequency analysis and to provide more reliable statistics within a stepped runoff series. Taking runoff records at three gauges in western China as examples, the results showed that the cross-reconstruction method has the advantage of data expansion of the small sample runoff data, and the expanded runoff data at steps can meet the data capacity requirements for frequency analysis. In addition, the comparison of the expanded and measured data at steps indicated that the expanded data can demonstrate the statistics closer to the potential data population, rather than just reflecting the measured data. Therefore, it is considered that the CR method ought to be available in frequency analysis for step-changed records, can be used as a tool to construct the hydrological probability distribution under different levels of changing environments (at different steps) through data expansion, and can further assist policy-making in water resources management in the future.

Precipitation plays a critical role in water resources management, and trend changes and alterations thereof are crucial to regional or basin water security, disaster prevention, and ecological restoration under a changing environment. In order to explore the implications of precipitation variation for water resources management, taking the Wei River Basin (a transitional zone between the Guanzhong Plain and Loess Plateau) as an example, this paper proposes an index system, namely the index of precipitation alteration (IPA), to evaluate changes in precipitation and investigate their potential influence on water resources management. The system includes 17 indicators gained from observed daily rainfall, involving some structural precipitation indicators describing the precipitation patterns and some functional precipitation indicators influencing utilization of watershed water resources. Non-parametric Mann-Kendall (MK) statistical test is employed to identify the IPA trend change, and range of variability approach is used to evaluate the variation of IPA. The analysis results in Wei River Basin show that IPA varies with different spatial and temporal distributions. Overall, although the annual total precipitation declined in the study area, the frequency of extreme events was increased during 1955–2012. In the face of severe climate change patterns, it is necessary to establish the precipitation index to evaluate the change of precipitation and to provide useful information for future precipitation assessments.