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.

The influence of climate change and anthropogenic activities on precipitation–runoff relationships (PRRs) has been widely discussed. Traditional models assuming stationary conditions can lead to inaccurate streamflow predictions. To address this issue, we propose a Driving index for changes in Precipitation–Runoff Relationships (DPRR), identified as key PRR influencers, involving climate forcing, groundwater, vegetation dynamics, and anthropogenic influences. According to the quantitative results of inputting the candidate driving factors into a holistic conceptual model, the possible process explanations for changes in the PRR were deduced. This framework is validated across five sub-basins in the Wei River basin. Moreover, non-stationary hydrological processes were initially detected, and the nonlinear correlations among the factors were assessed. The results show that baseflow emerges as the primary factor positively influencing the PRR (enhancing the PRR) but with high uncertainty. Potential evapotranspiration plays a dominant role in driving negative PRR changes in the sub-basins which are characterized by a semi-arid climate and minor human interference. Vegetation dynamics negatively influence the PRR, with driving levels correlating with the scale of soil and water conservation engineering, displaying lower uncertainty. Anthropogenic influences, represented by impervious surface ratio (ISR), night-time light (NTL), and population density (POP), exhibit varying driving levels, with ISR having the strongest and most direct impact, closely linked to urbanization processes and scales. The temporal dynamics of driving factors computed by dynamic DPRR generally correspond with hydrological regime shifts in non-stationary environments. The study's findings offer a comprehensive understanding of hydrological processes, enabling informed decision-making for the development of sustainable hydrological models.