•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.
Rivers originating in the Tibetan Plateau are crucial to the population in Asia. However, research about quantifying seasonal catchment memory of these rivers is still limited. Here, we propose a ...model able to accurately estimate terrestrial water storage change (TWSC), and characterize catchment memory processes and durations using the memory curve and the influence/domination time, respectively. By investigating eight representative basins of the region, we find that the seasonal catchment memory in precipitation-dominated basins is mainly controlled by precipitation, and that in non-precipitation-dominated basins is strongly influenced by temperature. We further uncover that in precipitation-dominated basins, longer influence time corresponds to longer domination time, with the influence/domination time of approximately six/four months during monsoon season. In addition, the long-term catchment memory is observed in non-precipitation-dominated basins. Quantifying catchment memory can identify efficient lead times for seasonal streamflow forecasts and water resource management.
Informing reservoirs with forecasts is highly important for real‐time flood control. This study proposed a forecast‐informed methodology framework for reservoir flood control operation under ...uncertainty. A new combination of two post‐processing methods, that is, the Cloud model and error‐based copula functions, were developed to merge individual AI‐based forecasts to ensemble flood forecasts, so called stochastic errors‐based Cloud (SE‐Cloud). A multi‐objective robust optimization model (MRO) integrating the risk, resilience, and vulnerability was then proposed to tackle flood control problems under ensemble forecasts; for comparison, a two‐objective stochastic optimization model (TSO) was developed to minimize the expected highest reservoir level and peak release. The proposed methodology was applied to the Lishimen reservoir in the Shifeng River subbasin, China, aiming to comprehensively verify the relationships among deterministic forecasts, ensemble forecasts, and flood control performance. Results showed that the Cloud model could effectively integrate different models and improve forecast accuracy. But a higher deterministic forecast quality did not consistently result in improved flood control performance. SE‐Cloud could capture the peak flow and effectively characterize forecast uncertainties and increased hypervolume values by 13.14%–39.65% compared to the Cloud model, indicating the superiority of ensemble forecasts in generating robust solutions over individual deterministic forecasts. MRO released more inflow than TSO, decreasing the expected highest water level by 0.05 m and incrementing the expected peak release by 4.29%. However, with downstream resilience value remaining at zero, it is demonstrated that MRO improving upstream vulnerability did not necessarily diminish resilience. The enhanced robustness highlights the potential of AI‐based ensemble forecasts in flood control.
Key Points
Flood scenarios characterized by forecast errors were developed using Cloud model and Copula functions based on individual AI‐based forecasts
A multi‐objective robust optimization model integrating the risk, resilience, and vulnerability was proposed to enhance model robustness
Ensemble forecasts can be more valuable than deterministic forecasts in the flood control operation
•Mathematical relationship between correlation coefficient index and abrupt change was established.•Significance of abrupt changes was graded as: no, weak, moderate, strong, dramatic.•Statistical ...characters of a series influence the significance of its abrupt changes.•Human activities contributed much more to abrupt changes in runoff in north China.
Abrupt changes are an important manifestation of hydrological variability. How to accurately detect the abrupt changes in hydrological time series and evaluate their significance is an important issue, but methods for dealing with them effectively are lacking. In this study, we propose an approach to evaluate the significance of abrupt changes in time series at five levels: no, weak, moderate, strong, and dramatic. The approach was based on an index of correlation coefficient calculated for the original time series and its abrupt change component. A bigger value of correlation coefficient reflects a higher significance level of abrupt change. Results of Monte-Carlo experiments verified the reliability of the proposed approach, and also indicated the great influence of statistical characteristics of time series on the significance level of abrupt change. The approach was derived from the relationship between correlation coefficient index and abrupt change, and can estimate and grade the significance levels of abrupt changes in hydrological time series.
Application of the proposed approach to ten major watersheds in China showed that abrupt changes mainly occurred in five watersheds in northern China, which have arid or semi-arid climate and severe shortages of water resources. Runoff processes in northern China were more sensitive to precipitation change than those in southern China. Although annual precipitation and surface water resources amount (SWRA) exhibited a harmonious relationship in most watersheds, abrupt changes in the latter were more significant. Compared with abrupt changes in annual precipitation, human activities contributed much more to the abrupt changes in the corresponding SWRA, except for the Northwest Inland River watershed.
Conventional calibration methods used in hydrological modelling are based on runoff observations at the basin outlet. However, calibration with only runoff often produces reasonable runoff but poor ...results for other hydrological variables. Multi-variable calibration with both runoff and remote sensing-based evapotranspiration (ET) is developed naturally, due to the importance of ET and its data availability. This study compares two main calibration schemes: (1) calibration with only runoff (Scheme I) and (2) multi-variable calibration with both runoff and remote sensing-based ET (Scheme II). ET data are obtained from three remote sensing-based ET datasets, namely Penman–Monteith–Leuning (PML), FLUXCOM, and the Global Land Evaporation Amsterdam Model (GLEAM). The aforementioned calibration schemes are applied to calibrate the parameters of the Distributed Hydrology Soil Vegetation Model (DHSVM) through ε-dominance non-dominated sorted genetic algorithm II (ε-NSGAII). The results show that all three ET datasets have good performance for areal ET in the study area. The DHSVM model calibrated based on Scheme I produces acceptable performance in runoff simulation (Kling–Gupta Efficiency, KGE = 0.87), but not for ET simulation (KGE < 0.7). However, reasonable simulations can be achieved for both variables based on Scheme II. The KGE value of runoff simulation can reach 0.87(0.91), 0.72(0.85), and 0.75(0.86) in the calibration (validation) period based on Scheme II (PML), Scheme II (FLUXCOM), and Scheme II (GLEAM), respectively. Simultaneously, ET simulations are greatly improved both in the calibration and validation periods. Furthermore, incorporating ET data into all three Scheme II variants is able to improve the performance of extreme flow simulations (including extreme low flow and high flow). Based on the improvement of the three datasets in extreme flow simulations, PML can be utilized for multi-variable calibration in drought forecasting, and FLUXCOM and GLEAM are good choices for flood forecasting.
Abstract
Investigation of the role of multiple general circulation model (GCM) ensembles in obtaining comprehensive knowledge of hydrological responses across the Yellow River Basin (YRB), China, is ...still of substantial importance. This study evaluates the performance of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating the hydrological regime in the YRB and compares the results with those from CMIP 5 (CMIP5). The comparison is performed between 21 GCMs from CMIP6 under three Shared Socioeconomic Pathway scenarios and 18 GCMs from CMIP5 under three Representative Concentration Pathway scenarios. Raw CMIP outputs are first corrected and downscaled by the Bias Correction and Spatial Disaggregation methods, and the bias-corrected GCM outputs are then employed to drive the Soil and Water Assessment Tool hydrological model and project streamflow. After correction and downscaling, areal averages for future changes (relative to 1971–2000) of temperature and precipitation are found larger in CMIP6 than in CMIP5. The emblematic annual mean temperature of CMIP6 increases by 1.64–2.20 and 2.31–5.29 °C for the future period of 2026–2055 and 2066–2095, while the counterpart of CMIP5 is 1.92–2.39 and 1.68–4.76 °C, respectively. In terms of precipitation, for CMIP6, it increases by 3.45–4.70 and 6.77–15.40%, and for CMIP5 by 2.58–2.96 and 3.83–9.95%. It is further concluded that: (1) future streamflow will probably decrease less under CMIP6 than that under CMIP5 in most cases, and climate changes of this kind will affect regional water supply and security in the YRB; (2) uncertainty in the projected streamflow is dominated by GCMs uncertainty with the contribution rate of >75%; (3) the streamflow is more sensitive to precipitation changes in comparison with temperature changes in the near future. In contrast, streamflow reduction is more attributed to an increase in temperature with a contribution rate of almost >60% than in precipitation in the far future.
The Upper Mekong River Basin (UMRB), Southwest China.
With climate change unfolding and climate change knowledge evolving over time, it is imperative to investigate whether the latest CMIP6 models ...offer enhanced utility in climate change impact studies compared to their predecessors. This study strengthens the comparison between CMIP5 and CMIP6 models in assessing hydrological responses to future climate change. This was achieved utilizing the Soil and Water Assessment Tool, driven by downscaled CMIP5/CMIP6 model outputs under RCP8.5/SSP5–8.5. Both streamflow and sediment responses, encompassing the spatial and temporal changes, and the relationships between streamflow and sediment loads, were carefully evaluated and compared between CMIP5 and CMIP6.
CMIP6 indicates a stronger warming in 2071–2100 over the UMRB compared to CMIP5. Mean annual precipitation/streamflow is projected to increase by 22.7%/26.3% using CMIP5 and 28.4%/34.4% using CMIP6. Mean annual sediment load changes, however, show a discrepancy between CMIP5 (−3.7%) and CMIP6 (+13.8%). CMIP6 exhibits larger inter-model variability in both climate and hydrological projections. Regarding future spatial distributions of mean annual water and sediment yields, a considerable agreement is demonstrated between CMIP5 and CMIP6, despite CMIP6 showing larger projections over most subbasins. Additionally, both ensembles exhibit approximate relationships between streamflow and sediment loads, indicating a comparable decline in watershed sediment generation and transport capacity under future climate change. Overall, CMIP6 suggests more severe climate change impacts on streamflow and sediment loads in the UMRB than CMIP5, highlighting the need to update climate change adaptation and mitigation policies based on the latest insights derived from CMIP6.
Display omitted
•CMIP6 projects greater climate change impacts on streamflow and sediment loads.•Larger inter-model variability is found in CMIP6 projections compared to CMIP5.•CMIP5 and CMIP6 show similar spatial patterns of water and sediment yields.•CMIP5 and CMIP6 indicate comparable changes in streamflow-sediment relationships.•Specific CMIP6 GCMs and their CMIP5 counterparts may not generate approximate projections.
Abstract
Hydrological and climatic data at finer temporal resolutions are considered essential to model hydrological processes, especially for short duration flood events. Parameter transferability ...is an essential approach to obtain sub-daily hydrological simulations at many regions without sub-daily data. In this study, the objective is to investigate temporary dependency of parameter sensitivity for different flood types, which contributes to research into parameter transferability. This study is conducted in a medium-sized basin using a distributed hydrological model, DHSVM. Thirty-six flood events in the period of 04/12/2006–07/01/2013 in the Jinhua River basin, China, are classified into three flood types (FF: flash flood, SRF: short rainfall flood and LRF: long rainfall flood) by using the fuzzy decision tree method. The results show that SRF is the dominant flood type in the study area, followed by LRF and FF. Runoff simulations of FF and SRF are more sensitive to parameter perturbations than those of LRF. Sensitive parameters are highly dependent on temporal resolutions. The temporary dependency of LRF is the highest, followed by SRF and FF. More attention should be payed to sensitive and highly temporal dependent parameters in a subsequent parameter transfer process. Further study into this result is required to test the applicability.
The Yarlung Zangbo River (YZR) basin on the Tibetan Plateau, China
Due to global climate change, the risk of drought disaster is increasing. Seasonal hydrological forecast can be beneficial for ...drought early warning and help reduce risks in water resources and drought management. However, the computational burden of distributed hydrological models remains a limitation for their wide use in seasonal streamflow forecast. This study designs a seasonal streamflow forecast framework based on the surrogate model (SM) for VIC. Both the impacts of pre-processing and post-processing on the seasonal streamflow forecast, and the accuracy, reliability and efficiency of the forecast framework based on SM, are carefully evaluated in the YZR basin, China.
The results show that the VIC model forced by CFSv2 has a good predictability and reliability in seasonal streamflow forecast in the YZR basin. Both pre-processing and post-processing can improve streamflow forecast accuracy, while post-processing also improves the forecast reliability more significantly. The SM-simulated streamflow is identical with that of VIC, with NSE larger than 0.95 and Pbias smaller than 5% at Nuxia station, in the YZR basin. The proposed SM-based seasonal streamflow forecast framework has been proven to be a good alternative for the VIC-based framework, with similar forecast accuracy and reliability, and higher computational efficiency, reducing up to 97% computation time.
Display omitted
•CFSv2 has a good predictability in seasonal streamflow forecast in the YZR basin.•Both pre-processing and post-processing can improve streamflow forecast accuracy.•A seasonal streamflow forecast framework based on SM for VIC is designed.•SM shows slight losses of model fidelity compared with VIC.•SM alleviates the computing burden for VIC in the seasonal streamflow forecast.
Evapotranspiration (ET) is an important element in the water and energy cycle. Potential evapotranspiration (PET) is an important measurement of ET. Its accuracy has significant influence on ...agricultural water management, irrigation planning, and hydrological modelling. However, whether current PET models are applicable under climate change or not, is still a question. In this study, five frequently used PET models were chosen, including one combination model (the FAO Penman-Monteith model, FAO-PM), two temperature-based models (the Blaney-Criddle and the Hargreaves models) and two radiation-based models (the Makkink and the Priestley-Taylor models), to estimate their appropriateness in the historical and future periods under climate change impact on the Yarlung Zangbo river basin, China. Bias correction methods were not only applied to the temperature output of Global Climate Models (GCMs), but also for radiation, humidity, and wind speed. It was demonstrated that the results from the Blaney-Criddle and Makkink models provided better agreement with the PET obtained by the FAO-PM model in the historical period. In the future period, monthly PET estimated by all five models show positive trends. The changes of PET under RCP8.5 are much higher than under RCP2.6. The radiation-based models show better appropriateness than the temperature-based models in the future, as the root mean square error (RMSE) value of the former models is almost half of the latter models. The radiation-based models are recommended for use to estimate PET under climate change in the Yarlung Zangbo river basin.
PDF
