查看更多>>摘要：Temperature stratification increases the thermal stability (TS) of the reservoir area, decreasing the vertical water exchange and going against the water environment improvement. It is affected by the reservoir operation scheme and should be considered with multiple objectives. Hence, considering the early flood season of Xiluodu Reservoir as a case, this study built a multiobjective optimization model of temperature stratification by coupling the hydro-temperature model CE-QUAL-W2. The non-dominated sorting genetic algorithm II and multiobjective particle swarm algorithm were used to solve the multiobjective optimization model, and the Pareto frontiers were evolved by reducing the peak rate of outflow (PRO), increasing total hydropower generation (THG), and decreasing TS. This paper shows that (1) the maximum-THG individual tends to release water late to increase the water level and hydraulic head, which is opposite for the minimum-TS individual. A temporally uniform outflow favors a decrease in PRO. (2) In the Pareto frontiers, THG can be increased from 47.42 x 10(8) to 52.15 x 10(8) kw.h (+9.97%), and TS can be reduced from 15448.27 to 14627.01 J/m(2) (-5.32%). (3) When the inflow rate increases, the temperature stratification is further weakened, and the minimum TS is reduced from 14627.01 to 13233.54 J/m(2). For large cascade reservoirs with low flood risk in the early or late flood season, their THG and thermal regime can be improved under a moderate outflow and should be considered for the administrations.
查看更多>>摘要：The monthly terrestrial water storage anomaly (TWSA) observations during the gap period between the Gravity Recovery and Climate Experiment (GRACE) satellite and its Follow-On (GRACE-FO) are missing, leading to discontinuity in the time series, and thus, impeding full utilization and analysis of the data. Despite previous efforts undertaken to tackle this issue, a gap-filling TWSA product with desirable accuracy at a global scale is still lacking. In this study, a straightforward and hydroclimatic data-driven Bayesian convolutional neural network (BCNN) is proposed to bridge this gap. Benefiting from the excellent capability of BCNN in handling image data and the integration of recent deep learning advances (including residual-skip connections and spatial-channel attentions), the proposed method can automatically extract informative features for TWSA predictions from multiple predictor data. The BCNN predictions are compared with reanalyzed/simulated TWSA, Swarm solution, and the TWSA prediction products generated by three recent studies, using commonly used accuracy metrics. Results demonstrate BCNN's superior performance to obtain higher-quality TWSA predictions, particularly in relatively arid regions. Additionally, a comparison with two independent datasets at the basin scale further suggests that the BCNN-infilled TWSA is reliable to bridge the gap and enhance data consistency. Our gap-filling product can ultimately contribute to correcting the bias in long-term trend estimates, maintaining the continuity of TWSA time series and thus benefiting subsequent applications desiring continuous data records.
查看更多>>摘要：Flash droughts are regional phenomena that can manifest in large areas with rapid intensification for a period of time. Few studies have considered the spatial pathways of flash droughts or their drought period. This study uses the five criteria based on the standardized evaporative stress ratio (SESR) method to identify flash droughts, and when a SESR value recovers to the 20th percentile, the flash drought is considered to terminate. To define spatially continuous flash droughts accurately, the usual order of first calculating drought patches and then identifying flash droughts is reversed to first identify flash droughts on the grid and then determine flash drought patches. In addition, this study tracks the spatial paths of flash droughts via centroid transfers of flash drought patches. Using MOD16 data, the methodology is evaluated by analyzing the regional characteristics of flash droughts in the Huaibei Plain of China from 2001 to 2019. The flash droughts in this region most frequently tracked in the northeast and west. The average flash drought duration was 31 days, of which the rapid intensification period was 18 days and drought period was 14 days. Flash drought events in this region mostly occurred in May, August and October, and east to west transition and non-transitions, which accounted for 44% and 22%, respectively, were the main spatial track paths. Only 26% of flash drought events transitioned to long term drought events. This study expands our knowledge of the evolution process of flash droughts to space-time dimensions, which is essential for flash droughts early warning and agricultural water management.
查看更多>>摘要：Accurate estimation of evapotranspiration (ET) and the partitioning of ET into transpiration (Tr), soil evaporation (Es) and interception (Ei) is critical to understand water cycle and land-atmosphere feedback. In this study, we evaluated the performances of two remote sensing-based ET models at multiple scales, and analyzed the uncertainties in ET partitioning due to the model structures. These two models were Simple Terrestrial Hydrosphere Model (SiTH) developed by our team and the Global Land Evaporation Amsterdam Model (GLEAM). As far as ET were concerned, the two models exhibited relatively good performances at different scales. However, it was found that GLEAM performed relatively poor at evergreen broadleaf forest (R2 = 0.34; RMSE = 0.87 mm day- 1; NSE = -0.28). In addition, the seasonal pattern of simulated ET by GLEAM at the tropical rainforest was not consistent with the observations. Furthermore, great discrepancies in ET partitioning were observed between the two models. Generally, GLEAM tended to underestimate Es (slope = 0.02; R2 = 0.004), and overestimate Tr (slope = 1.51; R2 = 0.78) compared to the observations. The underestimations of Es by GLEAM may partly be due to the ignorance of soil evaporation under vegetation canopy. On the contrary, SiTH displayed relatively good performances in estimations of Es (slope = 0.76; R2 = 0.62) and Tr (slope = 0.98; R2 = 0.51). However, both of the two models failed to properly simulate Ei, although GLEAM (slope = 0.55; R2 = 0.83) performed slightly better than SiTH (slope = 0.40; R2 = 0.95). Global multi-year average ratios of Tr, Es, and Ei to ET for GLEAM and SiTH were 0.76, 0.09, 0.15 and 0.67, 0.25, 0.08 respectively. In future studies, it is important to investigate direct observations on different components of ET, especially on the interception, to improve our understanding on the ET processes.
查看更多>>摘要：Assessments of the regional terrestrial water variabilities are important for improving our knowledges of the complex hydroclimate system and providing scientific information in regional water resource management under a changing environment due to climate change and intensified human activities. The Aral Sea Basin has experienced a serious ecological crisis which is majorly caused by the water resources due to the excessive water utilization. Therefore, in this study, we examine the long-term linear trend and variability of the terrestrial water cycle components based on multiple datasets over the Aral Sea Basin during 2003-2016. The terrestrial water cycle components include precipitation, evapotranspiration (ET), terrestrial water storage anomaly (TWSA), terrestrial water storage change (TWSC), runoff (R), soil moisture (SM) and groundwater. Major results show that increased trends of the annual precipitation and ET are observed during 2003-2016. For TWSA, Gravity Recovery and Climate Experiment (GRACE) derived datasets have significantly decreased trends with the values from-0.47 mm/mon to-0.29 mm/mon which reveal the serious terrestrial water depletion. Runoff, soil moisture and groundwater have the decreasing linear trends derived from multiple datasets. For the impacts of the climate factors on TWSA and TWSC, precipitation is the main climate factor with the significantly positive impacts. These results help us to have a better understanding of the complex hydrological process over the Aral Sea Basin, and provide a reliable scientific basic for police maker in the water resource management to achieve a sustainable development goal over the regions of the One Belt and One Road.
查看更多>>摘要：Multi-model ensembles enable assessment of model structural uncertainty across multiple disciplines. Bayesian Model Averaging (BMA) is one of the most popular ensemble averaging approaches in hydrology but its predictions are easily impacted by the type of ensemble members selected. Here, we propose a regression-based ensemble approach, namely a Variational Bayesian Long Short-Term Memory network (VB-LSTM) to address this issue. In this approach, a state-of-the-art variational inference (VI) algorithm that is faster and more scalable than Bayesian Markov chain Monte Carlo (MCMC) is employed to approximate the posterior distributions of thousands of parameters in the LSTM networks. To interpret the behavior of deep learning methods, the Permutation Feature Importance (PFI) algorithm is introduced to understand the degree to which VB-LSTM relies on each ensemble member. Twenty conceptual hydrological models are considered to evaluate BMA and VB-LSTM in four catchments from China. Four scenarios with different ensemble members are established to investigate the impacts of ensemble members on model results. Our results show that compared with BMA, VB-LSTM improves deterministic and probabilistic predictions by approximately 10%-30% in terms of Mean Absolute Error (MAE), Sharpness and Continous Ranked Probability Score (CRPS). In addition, the VB-LSTM predictions are more robust and less impacted by the selection of ensemble members. Furthermore, our study encourages the use of Bayesian deep learning in hydrology as an alternative to other approaches tackling model structural uncertainty.
查看更多>>摘要：The Bayesian method has been increasingly applied to the inversion of seepage parameters owing to its superiority of considering the uncertainty in the inversion process. However, most of the current Bayesian inversion studies only focus on parameter uncertainty, ignoring the model structure error. In addition, existing research has mostly adopted a single machine learning algorithm or an ensemble of surrogates based on a single prediction performance indicator as a substitute for the seepage forward model and has not considered the interactions between multiple prediction performance indicators, thereby leading to poor accuracy. To address these issues, this study proposed an efficient Bayesian inversion method for seepage parameters using a datadriven error model and an ensemble of surrogates considering the interactions between prediction performance indicators. A data-driven error model based on Gaussian process regression was integrated into the Bayesian inversion model to modify the likelihood function for dealing with the model structure error. For determining the weight coefficients of the ensemble surrogates, the improved Dempster-Shafer (D-S) evidence theory based on the Hellinger distance and Deng entropy was proposed to fuse multiple prediction performance indicators and consider their interactions. Further, an ensemble of surrogates in conjunction with support vector regression, Kriging, and multivariate adaptive regression splines was constructed through weighted summation. The validity and accuracy of the proposed method were verified by applying it to a real hydropower station in China. The results showed that the proposed method can significantly improve the accuracy and efficiency of Bayesian inversion of seepage parameters. The proposed method therefore serves as a new basis for the inversion of seepage parameters and can be applied to parameter inversion in other related fields.
查看更多>>摘要：Different indicators such as precipitation, surface and groundwater availability, vegetation, and soil types are indispensable in developing land management plans for detecting, monitoring, and evaluating drought impacts at the watershed scale. Because of the complex interactions among these indicators, it is well known that one single hydrometeorological variable would be unable to capture all the aspects of drought characteristics. In this study, a methodology based on Principal Component Analysis (PCA)-Copula was proposed to assess the fre-quency of different drought events and improve mitigation in watersheds. A comprehensive approach for an Integrated Drought Indicator (IDI) was proposed using a combination of three hydrometeorological variables (precipitation, runoff, and soil moisture) based on PCA and tested for the upstream Nanpan river in China. IDI confirmed the cumulative contribution rate of the first and second principal components (85%), and its design included simulation of monthly average soil moisture content (from 0 to 0.4-meter soil layer) using Soil and Water Assessment Tool (SWAT) model. The parameters were calibrated using the SUFI-2 optimization algorithm in SWAT-CUP. Then, the IDI relative anomaly characterization was used to identify drought processes based on the Run theory. Two major drought characteristics, duration, and severity were abstracted from the observed drought events. Finally, two-dimensional copulas were applied for analyzing comprehensive drought charac-teristics in the region. Our results showed that significant differences in drought duration periods could be identified. Drought duration, predicted by runoff and soil moisture, were found longer than those assessed using the precipitation index. IDI suggest our proposed model was suitable to identify comprehensive drought events and describe the overall drought characteristics of the region. In the same way, PCA-Copula methodology was found with high potential for drought analysis in areas where no previous studies have been performed.
Van Huizen, BrandonSutton, Owen F.Price, Jonathan S.Petrone, Richard M....
12页
查看更多>>摘要：The modeling of seasonal ground ice (SGI) freeze/thaw a common feature in boreal peatlands, has often been completed using a unidirectional approach, where melting is driven by energy inputs from the surface. However, bi-directional melt is known to occur, and can potentially increase the spring melt rate. Accurate modelling of the timing of ice-free conditions in peatlands is important because SGI can impede spring infiltration and lead to substantial spring snowmelt runoff from peatlands. However, when modelling melt only from above, erroneous results in the model estimation of ice-free conditions can occur, which can lead to knock on-effects for modelling peatland hydrological function. Furthermore, as the climate warms, it is unclear how this role of SGI may change in the future. This study used the Stefan Equation to model unidirectional and bi-directional melt to assess which performed better in modelling the timing of ice-free conditions compared to observed values (BI: 3.9 +/- 2.1 days, UNI: 9.0 +/- 4.7). Including bi-directional melt improved model performance by reducing this difference by approximately 5 days. Model performance for SGI freeze/thaw cycles were similar, with BI being slightly more accurate in freezing (RMSE:2.7 cm versus 3.3 cm) and melting (RMSE: 2.6 cm vs 3.7 cm) compared to the unidirectional approach. While the model improvement in the timing of ice-free conditions was substantial, careful consideration is needed in determining when a peatland is functionally ice free in future modelling studies. The Stefan Equation was found to be most sensitive to changes in soil moisture, compared to ground surface temperature and peat porosity, likely due to the relationship between thermal conductivity and frozen and liquid water content. Comparisons with future climate change projections suggest that the timing of ice-free conditions 'could shift by as much as 2 weeks earlier in the 2050's and by almost a month earlier in the 2080's. However, the timing of snowfall, and rain on snow events continues to be a source of model uncertainty. Future studies should work to investigate the potential positive feedbacks this could create. In conclusion, the Stefan Equation presents a relatively easy path for incorporating bi-directional melt into peatland models. This process should be included in peatland ecohydrological models in order to properly model the timing of melt and ice-free conditions.
查看更多>>摘要：Hydrologic regionalization consists of regrouping stations and catchments in pools based on a similarity measure. Regionalization is commonly used to extract a robust signal that can be used to describe the hydrology of the region or extrapolated to a location without measured information. Obviously, the similarity measure used af-fects the type of hydrological behavior one would expect from stations within a region. Most regionalization methods assume a stable and/or linear relationship between parameters of interests while it is well known that the physical processes driving the behavior of hydrometeorological variables are inherently non-linear and non-stationary. In this paper, we propose a similarity measure that is based on the location of changepoints in hy-drological time series. The proposed method has the unique advantage over other hydrological region delin-eation methods to detect regions where hydrological member stations are non-linearly correlated, and where the strength of the relation varies with time. It therefore has the potential to uncover similarities that would not have been detected by existing regionalization techniques. The proposed method is applied to the Tensift watershed located in Morocco, North Africa. The coherence of the detected regions is checked using wavelet coherence.
NSTL
Elsevier