•A surrogate model for VIC is builded with SKC and LSTM.•The surrogate model alleviates the computing burden of VIC with only slight losses of model fidelity.•The surrogate model keeps partial ...spatial information.
The Variable Infiltration Capacity (VIC) model is a widely used distributed hydrological model. However, VIC is computationally expensive in hydrologic prediction or forecast, which needs tens of thousands of runs of the model. To alleviate the burden of computation and reduce the losses of model fidelity, a new surrogate model (SM) coupling the self-organizing map and K-means clustering algorithm (SKC) with long short-term memory network (LSTM) is proposed. SKC is utilized to divide the subwatershed and select the representative cells in each subwatershed. LSTM is used to simulate the streamflow with the runoff of the representative cells. The new model is successfully applied in the Upper Brahmaputra River (UBR) basin, Southeast China. The results show that the SM-simulated streamflow has little difference from the VIC-simulated streamflow in terms of the Nash-Sutcliffe coefficient efficiency, the main metric of SM performance, 0.9677 at Yangcun Station and 0.9696 at Nuxia Station. For the representative cells, SM retains partial spatial information of the runoff in the study area. The computational savings achieved through the use of SM are over 97% with only slight losses of accuracy in the application to the UBR basin.
Climate and land use changes will affect the hydrological regime, and therefore hydropower. This study which aims to develop a novel modeling framework, does not only determine the changes in ...hydropower generation and sustainability, but also provide robust operating rules for handling uncertainty attributed to both climate and land use changes, using Xinanjiang Reservoir in Eastern China as a case study. Specifically, projections of five bias-corrected and downscaled General Circulation Models (GCMs) and three modeled land uses representing a range of tradeoffs between ecological protection and urban development are employed to drive the Soil and Water Assessment Tool (SWAT) and to predict streamflow under 15 scenarios. We then develop a set of robust rule curves to consider the potential uncertainty in reservoir inflow and to increase hydropower generation, and a baseline rule is presented for comparison. Results show that both robust and baseline rules increase hydropower generation with increasing reservoir inflows in future, but the robust rule yields better hydropower generation, sustainability and efficiency. The streamflow under the rapid urbanization scenarios differs from that under other scenarios, but there are no significant differences in hydropower among scenarios corresponding to the non-linear relationship between streamflow and hydropower change. Our findings highlight the potential to improve water resource utilization in the future, especially based on the robust operating rule considering optimization and uncertainty, and can provide references for future hydropower planning to the other existing plants.
Satellite and reanalysis precipitation products, as new and complementary data sources, are attractive for hydro-meteorological applications, especially in data-sparse areas. This study evaluates the ...accuracy of two satellite precipitation products (TMPA 3B42V7 and PERSIANN-CDR) and one reanalysis precipitation product (NCEP-CFSR) against gauge precipitation observations with four statistical indices over the upstream of the Lancang River Basin (ULRB), Southwest China. The reliability and applicability of these precipitation products as inputs to a hydrological model (Soil and Water Assessment Tool, SWAT) for streamflow and sediment simulations are also assessed. Furthermore, we compare the spatial plots of extreme water yield (99 percentiles) and suspended sediment yield (99 percentiles) driven by the four precipitation sources, and investigate the spatial and temporal variability of water yield and suspended sediment yield over the ULRB. Results show that for direct comparisons with gauge precipitation observations, monthly TMPA 3B42V7 precipitation product performs the best at the basin scale with the smallest error and bias, and the highest correlation, followed by NCEP-CFSR, and PERSIANN-CDR. For modeling-based indirect inference, TMPA 3B42V7 presents great capability for streamflow and sediment simulations in the SWAT model on a monthly time step at the basin outlet, and PERSIANN-CDR also performs well. NCEP-CFSR shows acceptable skills in modeling sediment but unacceptable skills in modeling streamflow. Extreme water yield presents moderate spatial variability over the ULRB while extreme suspended sediment yield presents strong spatial variability. Water yield of this basin shows a decreasing trend during 1998–2008 while there is no obvious trend in suspended sediment yield in this period.
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•The reliability and applicability of three precipitation products are explored.•TMPA 3B42V7 is the best for streamflow and sediment modeling on monthly scale.•Extreme water/suspended sediment yield presents moderate/strong spatial variability•A decreasing/No trend is found for water/sediment yield in the basin during 1998–2008.
•Extreme flow is modeled by SWAT combined with a regional climate model.•SWAT parameter uncertainty under climate change is investigated.•Uncertainty from scenarios, parameters and probability models ...is substantial.
Uncertainty in climate change impact analysis has been widely recognized. Analyzing it becomes an important task particularly when impact analysis results are used for adaptation purposes. A methodology aiming to investigate the impact of climate change and the separate and combined impacts of several uncertainty sources on future extreme flows in the Lanjiang catchment, East China is proposed. A regional climate model PRECIS (Providing REgional Climates for Impacts Studies) is applied to downscale the General Circulation Model (GCM) outputs and the extreme flows are simulated by the SWAT (Soil and Water Assessment Tool) model. Besides emission scenarios and extreme value models, the main uncertainty source, namely SWAT parameters is taken into account and the sequential uncertainty analysis method is employed to analyze the parameter uncertainties. The SWAT model calibration and validation results indicate that the model has a good performance in Lanjiang catchment. The projected future extreme flows show that the design discharges of small return periods are likely larger than those in the baseline period, while those of large return periods will be likely smaller than those in the baseline period at Misai, Quzhou and Lanxi stations. The uncertainty analysis results show that for small return periods such as 5years, the uncertainty introduced from the SWAT model parameters is much larger than those from emission scenarios and extreme value models at Misai and Quzhou stations while for large return periods such as 50years, the uncertainty introduced from all three sources are substantial. However, the already large uncertainty due to SWAT model parameters, emissions scenarios and extreme flow distributions might be dwarfed by GCM uncertainty, which is not concerned in this study.
► An upgraded reliability ensemble averaging method is used for climate projections. ► An inverse distance weighting method with MODWEC is proposed under data scarcity. ► Uncertainty is considered by ...using different emission scenarios and time stages. ► Simulated changes indicate likely more floods in summer and droughts in autumn.
The hydrological cycle has been substantially influenced by climate change and human activities. It is therefore of utmost importance to analyze the impact of climate change on hydrology, particularly on a regional scale, in order to understand potential future changes of water resources and water-related disaster, and provide support for regional water management. However, during the evaluation of climate change impact on hydrology or water resources, large uncertainty exists. In this paper, the Soil Water Assessment Tool (SWAT) model is used to investigate the potential impact of climate change on hydrology of the upper reaches of Qiantang River Basin, East China, for the future period 2011–2100. The uncertainty is considered by employing upgraded reliability ensemble averaged GCM climate projections under three emission scenarios A1B, A2 and B1 for three different stages of the future period. These projections are downscaled and used in the hydrological model. Impact of climate change on precipitation, potential evapotranspiraton and river runoff is then investigated. The model calibration and validation outcomes show reasonable performance of the SWAT model. The final results suggest that annual river runoff will likely decrease almost under all emission scenarios and time stages of the future period. Particularly, at Jinhua Station, substantial decrease of annual river runoff can be noticed, indicating less water resource possibly available for the region in future. Simulated monthly patterns show that the largest decrease will likely occur in winter while increases will occur in summer, implying possible more water-related disasters in this region. However, it is also noticed that the change signs/amount could be different under different emission scenarios and time stages, indicating large uncertainty involved in the impact analysis.
With global warming, hydrological regimes in the headwater basins of the Tibetan Plateau (TP) have significantly changed. Investigating the responses of hydrological processes to climate change in TP ...has become more and more important to make robust strategies for water resources management. However, using just a few GCMs may constrain the uncertainty in assessment of climate impacts. Therefore, a framework is proposed in this study to generate ensemble climate change scenarios and then investigate changes of hydrological processes under climate change in the upper reaches of Yarlung Zangbo River basin (UYZR) and Lancang River basin (ULR). Firstly, the Latin Hypercube Simulation (LHS) is used to generate an ensemble of future climate change scenarios by resampling change factors of meteorological variables from 18 GCMs under emission scenarios RCP2.6 and RCP8.5. The inherent dependence structures of change factors, i.e. the correlations of change factors among 12 months for different meteorological variables, are also considered in ensembles. Secondly, the HBV hydrological model coupled with a degree-day snowmelt model is applied to explore the potential change of runoff in the future period 2041–2070. Results show that: 1) the resampling method is effective and can provide a wide ensemble of climate change scenarios. 2) Precipitation, temperature and potential evapotranspiration in the UYZR and ULR basins are expected to increase under the two scenarios, particularly under RCP8.5. 3) The total runoff also shows a moderately upward trend in two basins, both mainly due to increased precipitation. In the UYZR basin, fast runoff accounts for a larger proportion in total runoff than slow runoff, while in ULR, both almost play the same role in total runoff. Furthermore, snowmelt-induced runoff in both basins would be less and rainfall-induced runoff will probably become more important in the future.
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•The LHS method can well resample change factors to reflect GCMs' uncertainty.•Future precipitation, temperature and total runoff probably increase in UYZR and ULR.•More rainfall-induced runoff and less snowmelt-induced runoff possibly occur in both basins.•Hydrological regimes are different between UYZR and ULR.
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•A new adaptive recursive (AR) multi-step-ahead forecast model is proposed.•An improved quantum-inspired version of Grey Wolf Optimizer (QGWO) is proposed.•QGWO is both tested on ...mathematical benchmark problems and parameters optimization problems.•AR model displays superior performance than the non-adaptive and non-recursive models.
It remains a challenge to obtain an accurate multi-step-ahead forecast of reservoir water availability for island areas. A novel hybrid multi-step-ahead forecast model is introduced to solve this problem. Partial autocorrelation function (PACF) is first used to analyze the characteristics of the target time series for extracting the appropriate input variables. Least-square support vector machine (LSSVM) with recursive mechanism is then explored for modelling multi-step-ahead forecasts, in which the forecast model is adjusted adaptively as long as new information is updated. The parameters in LSSVM are optimized based on an improved quantum-inspired version of Grey Wolf Optimizer (QGWO). The QGWO fortifies against the stagnation of the wolves at an optimal local point using three strategies including the quantum operator, non-linear convergence factor, and dynamic weighting. In this paper, the performance of QGWO was first demonstrated with that of other meta-heuristic (MH) algorithms in solving eight mathematical benchmark problems. Two time series of reservoir water availability in Zhoushan Islands, China, were then provided and analyzed to validate the performance of the advanced forecast approach in multi-step-ahead forecasts. The forecasts were compared with those obtained by the non-adaptive (NA) and non-recursive (NR) models. Results indicate that (1) QGWO displays superior performance on model parameters optimization than other comparative MH algorithms; (2) The proposed hybrid model in consideration of the nearest anterior feedback, as well as the adaptive mechanism, not only outperforms the two comparative models (NA, NR) but significantly enhances the accuracy of multi-step-ahead forecasts for non-stationary time series and low or high volume events, even as the forecast time horizon increases.
One of the most notable characteristics of synaptic transmission is the wide variation in synaptic strength in response to identical stimulation. In hippocampal neurons, approximately one-third of ...axonal mitochondria are highly motile, and some dynamically pass through presynaptic boutons. This raises a fundamental question: can motile mitochondria contribute to the pulse-to-pulse variability of presynaptic strength? Recently, we identified syntaphilin as an axonal mitochondrial-docking protein. Using hippocampal neurons and slices of syntaphilin knockout mice, we demonstrate that the motility of axonal mitochondria correlates with presynaptic variability. Enhancing mitochondrial motility increases the pulse-to-pulse variability, whereas immobilizing mitochondria reduces the variability. By dual-color live imaging at single-bouton levels, we further show that motile mitochondria passing through boutons dynamically influence synaptic vesicle release, mainly by altering ATP homeostasis in axons. Thus, our study provides insight into the fundamental properties of the CNS to ensure the plasticity and reliability of synaptic transmission.
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•Axonal mitochondrial motility correlates with the pulse-to-pulse EPSC variability•Motile mitochondria passing by boutons contribute to variation of SV release•Presynaptic mitochondria maintain SV release during sustained synaptic activity•Altered ATP homeostasis is one of the primary sources for presynaptic variability
One of the most notable characteristics in synaptic physiology is the wide pulse-to-pulse variability of synaptic strength in response to identical stimulation. A long-standing question is how this variability arises. In this study, Sheng and colleagues reveal that dynamic transport of axonal mitochondria, either passing through or pausing at boutons, is one of the primary mechanisms underlying the pulse-to-pulse variability of synaptic strength in the CNS. Altered ATP homeostasis at axonal terminals contributes to this variability.
Stochastic nature of streamflow poses significant challenges in attaining a reliable forecasting model. In general, variational mode decomposition (VMD) can improve the forecast performance but ...easily expose the sub-signals to boundary effect. Furthermore, one model is not able to adapt all properties of different sub-series. Accordingly, we have two aims in this study. One is to propose an adaptive weight combined forecasting model to improve the middle and long-term streamflow forecast skill. It adapts the boundary effect in such a way that its inputs come from decomposition during calibration sets, and outputs are extracted from decomposition during the entire series. The other one is to link system performance improvement, i.e., the forecast skill, to the forecast value to address the gap in methodologies appropriate for data-limited locations (only hydrological time series collected). Four artificial intelligence-based models coupled with adaptive appendant and parameter optimization are developed as hybrid adaptive (HA) for forecasting each sub-signal decomposed by VMD in the proposed forecast model. The multi-objective grey wolf optimization (MOGWO) algorithm is then employed to combine individual forecasts based on performance-based weighting strategies and provide the Pareto-optimal ensemble forecasts. The proposed model is applied to forecast streamflow 1 to 6 months ahead of two stations in the Yellow River, China, and the results show that the ensemble forecasts can increase the values of Nash-Sutcliffe efficiency coefficient by 0.10 ~ 7.96% and reducing the values of root mean squared error by 1.08 ~ 32.11% compared to the HA model. The relationship between the forecast skill and its value can be strongly affected by decision-makers priorities, but the relative improvement in hydropower generation obtained by the compromised forecasts going from 0.02% to 3.39% indicates that improved forecasts are potentially valuable for informing strategic decisions.
Summary
We explored the prognostic factors for children with very high‐risk (VHR) Philadelphia chromosome (Ph) negative B‐cell acute lymphoblastic leukaemia (B‐ALL) and compared the therapeutic ...effects of intensive chemotherapy and unmanipulated haploidentical haematopoietic stem cell transplantation (haplo‐HSCT) as post‐remission treatment in these patients undergoing first complete remission (CR1). A total of 104 paediatric patients with VHR B‐ALL in CR1 were retrospectively enrolled in this study, including 42 receiving unmanipulated haplo‐HSCT (Group A) and 62 receiving ongoing chemotherapy (Group B). Estimated 3‐year overall survival (OS), disease‐free survival (DFS) and cumulative incidence of relapse (CIR) at 36·2 months median follow‐up were 69·5 ± 4·7%, 63·5 ± 4·8% and 32·4 ± 4·7%, respectively. Maintenance of persistent positive or conversion from negative to positive of measurable residual disease (MRD) and chemotherapy were independent risk factors associated with inferior long‐term survival and higher CIR. OS, DFS, and CIR differed significantly between the groups in patients with persistent positive or negative‐to‐positive MRD. Haplo‐HSCT may be an option for children with VHR Ph‐negative B‐ALL in CR1, especially for patients with persistent positive or negative‐to‐positive MRD, and could achieve better survival than intensive chemotherapy as post‐remission treatment.
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