查看更多>>摘要：This study investigated the variability of intermittency with dry spells delineated using various definitions based on fixed minimum rainfall thresholds, dynamic thresholds using daily evapotranspiration rates, accumulated rainfall thresholds, or detrimental climatic conditions that coincided with consecutive dry spell days. The different spell definitions clearly effected the magnitude and timing of the average and extreme behaviour of the spell lengths for a temperate climate in Central-North Europe. Although high synchronicity for dry spells was found for some definitions, detected dry spells frequently fell into very different time periods, which was particularly pronounced for the annual maximum spells. It was concluded that there is not a 'common' or 'correct' dry spell definition but that any intermittency analysis requires a clear definition of the purpose of the study a priori. Whether it is zero rainfall or requirements for (plant) water supplies, rainwater harvesting, or pollution control, the spell definition must be set accordingly. Similarly, there can be no consistent definition for a long or rare dry spell; besides the effects of the geographic setting, the absolute differences between spell definitions are larger than differences due to various return intervals.
查看更多>>摘要：Compound extremes have increasingly become the focus of research in recent years, due to the strong impact they have upon society and ecosystems. Few studies, however, address the role of teleconnection patterns in these compound extremes, and how the former can be used to predict the latter. The present study quantifies the changes observed in the monthly frequencies of Dry-Warm, Dry-Cold, Wet-Warm and Wet-Cold concurrent extremes in the Mediterranean basin during the 1951-2020 period, and assesses the effect of different regional, continental and oceanic teleconnections upon the frequency of such concurrent extremes. Results reveal a significant increase, especially, in dry-warm months in large areas of the Mediterranean basin, mainly in summer and spring, as well as a decrease in wet-cold extremes in these seasons. On the other hand, the positive phase of the Mediterranean Oscillation (MO) has a strong capacity to drive dry-warm months in the western Mediterranean basin, as well as wet-cold extremes in the southern-east part of the Mediterranean basin. This role becomes inverted during the negative phase of this teleconnection. Furthermore, due to its subtropical linkage, the positive phase of the East Atlantic (EA) oscillation also plays an important role in accounting for the occurrence of dry-warm months in most of the Mediterranean basin, especially in the west and the north. During its negative mode, the configuration of the EA dipole favours the occurrence of wet-cold months, especially in the north and western part of the basin. The East Atlantic/Western Russia oscillation proved to be highly capable of inferring the ocurrence of dry-warm and wet-cold events in the eastern Mediterranean. The other teleconnections analysed (the North Atlantic Oscillation (NAO), the Western Mediterranean Oscillation (WeMO), and the Scandinavian (SCAND) an Polar-Eurasia (POLEUR) oscillations) played a minor role in driving these monthly concurrent extremes. The results provided by the present paper are intended to guide future research addressing the potential of teleconnection patterns to drive the temporal variability of compound extremes.
查看更多>>摘要：Recently, the use of heat as a tracer to evaluate the process of leakage in embankment dams has attracted wide attention. A more accurate flow-heat coupling model of embankment dams could help us to better understand the patterns of water flow and heat transfer in embankment dams and provide a scientific basis for the seepage prevention and repair of these dams. In this paper, combined with thermal conductivity empirical models (TCEMs), the saturated-unsaturated flow-heat coupling model of embankment dams was established. Through laboratory sand tank experiments of concentrated leakage in embankment dams, the accuracy of the flow-heat coupling model under 10 types of TCEMs were tested and compared. The results show that the performance of the flow-heat coupling model varies under different types of TCEMs, and the Chung and Horton (1987) model shows better simulation effects, with a coefficient of determination (R-2), root mean square error (RMSE) and relative error (Re) ranging from 0.916 to 0.980, 0.266-0.467. and 1.370-2.442%, respectively. Therefore, this model could better reflect the dynamic temperature variations in embankment dams. Finally, the flow-heat coupling model was improved by modifying the COMSOL built-in equation, i.e. built-in COMSOL model was replaced by the Chung and Horton (1987) model, which further improved the accuracy of the flow-heat coupling model in the numerical simulation of seepage heat monitoring. Based on the improved model, the concentrated leakage of embankment dams under dynamic water levels was simulated numerically. Under the condition of a dynamic water level, the flow velocity and pressure at the leakage passage are positively correlated with the water level change, and the temperature field also shows the same change trend.
查看更多>>摘要：Wetland plants are a key factor in ecosystems but are threatened by water extraction and water resource exploitation. Ecological water supplementation is a common solution to the water scarcity problem in wetlands. In this study, the Zhalong Wetland with a complex water regime was divided into several subareas with relatively strong hydrologic connectivity based on a hydrodynamic model and cluster analysis. The normalized difference vegetation index (NDVI) dynamics were simulated and verified with long short-term memory (LSTM) neural network models established for the various wetland subareas based on the subarea water levels and temperature and sunshine duration data. To evaluate the effects of different ecological water supplementation scenarios on the spatiotemporal NDVI variation across the wetland, the water levels of subareas under different scenarios were used to drive the LSTM model. The results indicate that (1) the spatiotemporal variation in the NDVI at most nodes within the wetland was accurately simulated, but large errors generally occurred in regions with small lakes. (2) Ecological water supplementation with continuous and low-flow discharge imposed highly positive influences on the annual maximum NDVI in the wetland against the background of limited water resources. (3) The lower reach in the largest subarea was less positively affected by wetland ecological water supplementation. (4) Water supplementation with discharge in April and September was recommended because of the benefits for the nidification and breeding of water fowl species and the limited conflict between agricultural and ecological water. This study provides important references for both wetland and water resource managers.
查看更多>>摘要：Flood forecasting is an essential non-engineering measure for flood prevention and disaster reduction. Many models have been developed to study the complex and highly random rainfall-runoff process. In recent years, artificial intelligence methods, such as the artificial neural network (ANN), have attempted to construct rainfall-runoff models. The more advanced deep learning methods of long short-term memory (LSTM) network have been proved to better predict hydrological time series. However, the selection of LSTM hyperparameters in the past mostly relied on the experience of the staff, which often led to failure to achieve the best performance. The aim of this study is to develop a method to improve flood forecast accuracy and lead time. A deep learning neural network model based on LSTM networks and particle swarm optimization (PSO) is proposed in this paper. The PSO algorithm was used to optimize the LSTM hyperparameter to improve the ability to learn data sequence features. The model focuses on the Jingle Watershed in the Fenhe River and the Lushi Watershed in the Luohe River and was used to predict flood processes using rainfall and runoff observation data from stations in the watersheds. We evaluated the performance of the model with the Nash Sutcliffe efficiency coefficient, root mean square error, and bias. The results show that the PSO-LSTM model outperforms the M-EIES, ANN, PSO-ANN, and LSTM at all stations in the watersheds. The PSO-LSTM model improves the flood forecasting accuracy at different lead times, especially for those exceeding 6 h, and has higher prediction accuracy and stability. The PSO-LSTM model could be used to improve accuracy in short-term flood forecast applications.
查看更多>>摘要：Lake eutrophication has become a critical environmental issue due to the global effects of anthropogenic activities and climate change, and has been comprehensively studied for many years. A series of models and indicators have been proposed to assess the trophic state of lakes. The trophic state index (TSI) is a synthetic index that integrates chlorophyll-a, water clarity, and total phosphorus and is widely used to evaluate the trophic state of aquatic environments. In this study, we collected in situ lake samples (N = 431) from typical lakes to match Sentinel-2 MultiSpectral Instrument (MSI) imagery data using the Case 2 Regional Coast Color processor. Then we developed a new empirical model, TSI = -34.04 x (band 4/band 5) - 1.114 x (band 1/band 4) + 97.376). This model is valid for all of China, with good performance and few errors (RMSE = 7.36; MAE = 6.25) for the validation dataset. Recognizing that over 94% of the Chinese population located along eastern watersheds and large lakes have competing water uses, and given the TSI model on the seasonal scales, we further estimated the mean TSI and trophic state in eastern Chinese lakes (> 100 km(2)) from 2019 to 2020. The results revealed that more lakes were eutrophic in autumn (94.28%) than in spring (> 77.14%), indicating a serious eutrophication of eastern lakes. Although the eastern lakes have been studied in more detail, this study found that eutrophication still has markedly negative impacts on lake ecosystems. In addition, no significant improvement was observed in spring, most likely due to the months of curfew/lockdown from January 2020 onwards due to COVID-19. This may be due to the enrichment of nutrients deposited in sediment or watershed soil, which can be characterized as "autochthonous sources " of lake eutrophication, over decades with high rates of economic development. This study demonstrates the applicability of Sentinel-2 MSI data to monitor lake eutrophication as well as the feasibility of blue/red and red/red edge combinations. The framework and TSI model used bands available on MSI sensors to develop a novel approach for generating historical eutrophication data for large-scale evaluation of and decision-making related aquatic environmental changes, even in poorly studied areas.
查看更多>>摘要：The overexploitation of groundwater resource and its delicacy management has gained increasing attentions in recent years worldwide because of causing a series of serious environmental and geological problems. Currently, accurately predicting the groundwater level (GWL) is an important issue in effective groundwater management across scales. In the present study, three popularly-used data-driven models, which are an autoregressive inte-grated moving average (ARIMA), a back-propagation artificial neural network (BP-ANN) and long short-term memory (LSTM), were established in five zones with different hydrogeological properties to explore the model's accuracy in predicting the GWL at monthly and daily scales in a Northern Plain in China. The developed models were evaluated by both the Nash-Sutcliffe efficiency coefficient (NSE) and root mean square error (RMSE). The results indicate that the performance of the LSTM model is best at monthly time scales with the NSEs greater than 0.76 and RMSEs smaller than 1.15 m in each zone during the training period and demonstrate a good performance at daily time scales with the NSEs greater than 0.9 and the RMSEs smaller than 0.55 m at a local area. Meanwhile, the tempo-spatial distribution of the probability of drawdowns from the LSTM model was estimated by using the object-oriented spatial statistical (O2S2) method. The results show that cumulative drawdowns greater than 10 m are mainly concentrated in water source areas, with probabilities over 0.7 from 2003 to 2010 and declining to less than 0.3 from 2011 to 2014. The GWL rose generally in the study area from 2015 to 2018, but the probability of a drawdown with more than 5 m exceeded 0.8 in Zone V because of continuing groundwater exploitation. This study formulates a framework on developing effective data-driven models for predicting the GWL across scales which have the potential to aid groundwater management.
查看更多>>摘要：Reservoirs represent a key component of the global carbon cycle. However, estimates of carbon dioxide (CO2) emissions from reservoirs remain poorly constrained due to the absence of spatially and temporally resolved measurements. We performed high-resolution monitoring of CO2 emissions (F-CO2) in a semiarid hard-water reservoir to examine its seasonal and diel variability. Our results suggest that dissolved inorganic carbon input plays a central role in sustaining the surface water CO2 partial pressure (pCO(2)), which varies from 1076 to 4587 mu atm. Although the reservoir is moderately to highly productive throughout the year, it is a net CO2 source with F-CO2 values in the range of 308-1753 mg C m(-2) d(-1). This high CO2 efflux indicates that productive waters are not necessarily CO2 sinks. Both pCO(2) and F-CO2 exhibit clear seasonal and diel patterns. Surface water pCO(2) is highest in March and presents a consistent diurnal/nocturnal pattern with the daytime pCO(2) 6-13% lower than the nighttime pCO(2). High CO2 efflux is observed during the ice-thaw period, indicating the release of CO2 that was accumulated during the winter. CO2 effluxes are typically higher during the nighttime driven by aquatic metabolism, but episodic weather events (e.g., rainfall and strong winds) can significantly enhance CO2 emissions and even reverse the diel pattern. Our study also shows that using only daytime measurements to estimate daily CO2 emissions would underestimate it by 9-25%. Hence, future global assessments should incorporate CO2 emissions from hard-water reservoirs and account for their seasonal and diel variability.
查看更多>>摘要：Significant nonstationarity of hydrological sequences, driven by the coupled influence of climate change and anthropogenic activities, has challenged traditional hydrological drought analysis under the stationarity assumption in some regions, and has questioned regional hydrological drought analysis under the changing environment. To evaluate hydrological drought characteristics, the novelty of this paper is the proposition of a GAMLSS (Generalized Additive Models for Location, Scale, and Shape)-based nonstationary standardized runoff index (NSRI), considering climate change and anthropogenic influence, for investigating the hydrological drought regime in the Wuding River basin. To that end, nonstationary test methods were performed on hydrological sequences, and data was segmented into three parts according to the results of the Pettitt test. A nonstationary probability distribution was fitted to runoff data with meteorological covariates (precipitation, temperature) and anthropogenic covariates (water consumption for social development demand, and water consumption triggered by water impounding by check dams). After selecting an optimal combination of covariates, the NSRI was calculated utilizing the GAMLSS model. A newly constructed evaluation threshold was proposed, based on the traditional SRI threshold. The performance of SRI and NSRI were compared at the Dingjiagou station in the Wuding River basin. Results of comparison between index identification and recorded drought events demonstrated that the NSRI better performed in drought event identification. Moreover, the NSRI identified more frequent severe droughts and extreme droughts. Therefore, the proposed NSRI provided a more accurate basis for the identification of drought events, which can offer valuable information for drought planning, preparedness, and mitigation.
查看更多>>摘要：The high spatiotemporal variability of rainfall in tropical regions has posed a great challenge for generating satisfactory satellite precipitation products (SPPs). Most of previous studies have found a modest performance of various SPPs in estimating daily rainfall in tropical regions such as Malaysia. In-depth research on effective ways to correct the bias of SPPs in the tropical region is urgently needed. This study aims to establish a bidirectional long short-term memory recurrent network (Bi-LSTM) framework for the bias correction of SPPs, and apply it to correct daily rainfall estimates of the early runs of Integrated Multisatellite Retrievals for the Global Precipitation Measurement (IMERG-E) from 2010 to 2016 in the Kelantan River Basin, Malaysia. After optimization and statistical comparison, Bi-LSTM with the covariates of daily maximum and minimum temperature (Bi-LSTM-T) was determined to be the best model for bias correction. Annually, Bi-LSTM-T could raise the correlation coefficient (CC) of IMERG-E by 26.7%, while reducing its root mean square error (RMSE) and mean absolute error (MAE) by 23.9% and 21.7% in the Kelantan River Basin. In the four seasons, it increased the CC of IMERG-E by 19.7-27.5%, while decreasing its RMSE and MAE by 18.4-30.0% and 20.9-23.2%. Multiple statistical tests confirmed that Bi-LSTM-T significantly outperformed two benchmark methods, namely ratio bias correction (RBC) and cumulative distribution function (CDF) matching, in correcting the bias of IMERG-E for all four seasons. This suggests that the Bi-LSTM-T model may work as a promising framework of great potentials for correcting the bias of SPPs in tropical regions, where adequate precipitation data are in great need for diverse purposes such as water-related disaster prevention and mitigation.
NSTL
Elsevier