查看更多>>摘要：The model calibration is one of the essential processes in the land surface model (LSM) simulations to achieve high performance and predictability for the use in integrated water resources and disaster risk management to climate change. Since the performance criteria often used to evaluate or calibrate hydrological models have their own advantages and limitations, it is necessary to understand the nature of each performance criterion in the model performance assessment or identification to the model application purpose. For the design flow estimates from hydrological modeling data, the Liu Mean Efficiency (LME) was proposed by reformulating the three metric components (correlation, variability, and bias measures) in the Nash and Sutcliffe efficiency (NSE) or the Kling and Gupta Efficiency (KGE) to improve flow variability in the runoff simulations optimized with the NSE or KGE. However, the LME criterion can potentially pose serious challenges to comparative performance evaluation and reliable design flow estimation due to the underdetermined solutions towards the excessive flow variation. For a complementary approach to the limitations of the NSE, KGE, and LME, this study has therefore proposed a new rebalanced performance criterion based on the least-squares regression components combined from both-way regression analysis between simulations and observations. It has been illustrated that the proposed criterion can provide a rebalanced trade-off between the constituent metric components leading to the improved flow variability with taking advantages of the KGE through the theoretical comparative analysis and the case study for long-term weekly streamflow time series from the LSM simulations in the four study watersheds with natural unregulated flow observations.
查看更多>>摘要：Croplands can significantly influence the hydrologic response of catchments to climate change in a region but such influence has not been well investigated and quantified. In this study, we firstly use the conceptual hydrological model to investigate the annual water balance for croplands-altered catchments, and the "natural" state without croplands of the catchment can be traced back by setting a zero area of croplands in the calibrated model. The model results are then applied to a hydrologic sensitivity framework with the Budyko-type relationship between hydrologic ratios (i.e. evapotranspiration ratio and water storage change ratio) and the aridity index (Ep/P) to yield the hydrologic sensitivity coefficients for the croplands-altered and "natural" catchments. The method is applied to investigate three catchments that are rarely affected by inter-basin groundwater flow in Illinois. The results show that croplands significantly increase the actual evapotranspiration (E) and decrease the runoff (Q), while changes in water storage (C) are rarely affected by croplands at the annual time scale. The actual evapotranspiration at the annual timescale is much more sensitive to potential evapotranspiration when the croplands exist. Croplands in Illinois significantly increase the sensitivity of streamflow on the climate change, to at least twice of the sensitivity in the "natural" state. The effectiveness of inter-annual hydrologic sensitivity framework is also demonstrated by hydrological model. This study provides a new simple insight to quantify the inter-annual stability with varying climate and land use at the catchment scale.
查看更多>>摘要：To ensure regional grain security, the grain is transferred from grain surplus provinces to grain deficient provinces in China, then water embedded in grain (virtual water) flows accordingly, which promotes the spatial redistribution of water resources. To explore the influencing factors of virtual water flow, this study first obtained the inter-provincial virtual water flow pattern, and then constructed a virtual water flow trade gravity model based on socio-hydrology theory and estimated the model with multiple regression methods. And the impulse response function was used to analyze the response trajectory of virtual water flow under the shock of other endogenous variable fluctuation. The results show that from 1997 to 2014, the quantity of inter-provincial grain virtual water flow increased from 73 billion m3to 124.64 billion m3. The positive driving effect of the population and per capita GDP in virtual water inflow areas is significant, and the negative suppression effect of the grain output per unit in inflow-areas and the population in outflow-areas is significant. Heilongjiang is the province with the largest outflow of virtual water, and the outflow is more sensitive to the shock of local per capita water resources fluctuation. Guangdong is the province with the largest inflow, and the inflow is more sensitive to the local per capita GDP fluctuation. Chinese Mainland's grain virtual water flow pattern and trend will continue to exist in the future. In addition to the endowment of water resources, social and economic factors also need to be considered during the process of water resources management.
Araujo, Andre de SousaSilva, Adma RaiaZarate, Luis E.
16页
查看更多>>摘要：Extreme rainfall events can devastate urban and rural infrastructure, affect the economy and even lead to loss of life. In this work, we propose an approach based on Long Short Term Memory networks (LSTM) to forecast precipitation volume extreme rainfall using multivariate time series data. Our methodology combines reanalysis data from 12 isobaric pressure levels, surface data, and data from meteorological stations from Brazil's southeastern region. Our method allows handles the imbalanced data typical of time-series data used for this type of problem. In order to identify the best model, we performed several experiments with different configurations of LSTM networks. The test results showed that the best prediction model has as input previous data up to 24 h for a forecast of 6 h ahead with a mean absolute error (MAE) of 6.9 mm and root mean squared error (RMSE) of 6.94 mm. Our methodology shows the possibility to use reanalysis data from global mathematical models to obtain less computationally expensive regional models.
查看更多>>摘要：Cascade hydropower development in the Lancang River (LCR) Basin has a complex and far-reaching impact on river ecosystem, which has always been the focus of international attention. In this study, diffusive gradients in thin films (DGT) technique was used to study the reactivation of arsenic (As) in the sediments of Xiaowan (XW) and Nuozhadu (NZD) cascade dams in LCR. Pearson correlation analysis and Redundancy Analysis (RDA) were used to evaluate the bioavailability and spatial distribution characteristics of As. The results showed that the vertical variation of As in DGT at different sampling points presented different activation trends. The content of DGT-labile As (C-DGT-As) in NZD Reservoir (36.21 ng/ml) was less than that in XW Reservoir (47.26 ng/ml). However, the difference of total content of As between the two reservoirs was relatively small. The results showed that the development of cascade reservoirs had no significant impact on total As, but had a great impact on DGT-labile As. The diffusion fluxes of the two reservoirs indicated that most of the sediments in the reservoir area were the source of As. Similar distribution characteristics and correlation analysis indicated that the migration mechanism of As was related to Iron (Fe) and manganese (Mn). In addition, on the basis of the results, the sources of As in sediments had certain spatial differences: cascade dam projects had significant accumulation and interception effects on As in the sediments, and at the same time strengthened the role of regional sediment, and this role reduced the risk of As release in downstream NZD Reservoir.
Hosseini-Moghari, Seyed-MohammadSun, SiaoTang, QiuhongGroisman, Pavel Yakovlevich...
14页
查看更多>>摘要：This study explores the sensitivity (termed scaling factor, SF) of daily and 30-minute precipitation extremes with several temperature variables, i.e., within-day surface air temperature (SAT) and dew point temperature (DPT), and antecedent SAT and DPT (corresponding to temperatures one day before a precipitation event, denoted as SAT-C and DPT-C) across China's mainland. To this end, we used observed daily meteorological data from CN05.1 dataset and 30-minute precipitation data from the Integrated Multisatellite Retrievals for the Global Precipitation Measurement (IMERG). Our results reveal a mix of the positive and negative SFs of extreme daily precipitation with SAT across climatic zones, with peak-like structures developing at higher temperatures (between 17 and 24 degrees C). Although almost all the SFs turn to positive when SAT-C, DPT, and DPT-C are used, a peak structure is observed over some parts of each climate zone, especially in tropical regions. A comparison between the SFs of the full temperature range and the temperature range before peak structure reveals that a single scaling rate is not valid for the entire temperature range. Moreover, the SFs calculated based on the temperature range before the peak structure (for all four types of temperatures) follow better the Clausius-Clapeyron scaling (similar to 7%/degrees C) than the SFs of the full temperature range except for the tropical region. Daily SFs based on IMERG data are mostly comparable to CN05.1 results, with discrepancies mainly in tropical and plateau climates (roughly 25% of the study area). However, IMERG' s 30-min precipitation extremes do not rise as much as expected (even decrease in some parts of the country) with increasing temperatures, contrary to common observations reported in previous studies. It suggests that another precipitation dataset is needed for scaling precipitation extremes at a 30-minute scale, at least for China's mainland.
查看更多>>摘要：Scale transformation is a problem in many fields, especially in geoscience. But there is less report on the use of scale transformation to control non-point source (NPS) pollution. This study constructs a new framework for NPS pollution management by re-downscaling pollution source area and exploring the effects of Best Management Practices (BMPs) on water quality improvement at large scale after treatment of small-scale subwatersheds. Combined with Soil and Water Assessment Tool (SWAT), the advantage and uncertainty of the framework were explored though a case study in the Three Gorges Reservoir Region, China. Based on the results, the framework improved the efficiency of priority management areas (PMAs) identification. After the re-downscaling the PMAs, the total phosphorus (TP) load intensity increased from 59.3 kg/km2 to 84.3 kg/km2 for those high-ranking PMAs, while the area of PMAs with the maximum intensity increased by 4.44%. The framework has the most obvious advantages when the TP reduction target is set as 38.00%. The NPS management area after redownscaling would reduce by 2.46% compare to primary PMAs. Water quality target, assessment points and hydrological periods are identified as the uncertain factors. The selection of proper water quality target and the assessment point would account for 19.03% and 10.61% reduction of NPS control area. From dry to wet years, the NPS control area increased by 647 km2 which accounts for 26.71% of the watershed, while the maximum intensity changed from 27.6 kg/km2 to 59.3 kg/km2. The new framework can be extended to other watersheds for the NPS management at watershed scale.
查看更多>>摘要：Short-term water demand forecast is one of the most important technology for urban water supply management. The accuracy and timeliness of the forecast have an important impact. Most of the reported water demand forecast models based on deep learning methods apply a manual features extraction strategy, resulting in incomplete mining of the data and weak model self-adaptability capability. To address these issues, a new framework of short-term water demand forecast is proposed, in which a data preprocessing approach, S-H-ESD (Seasonal Hybrid Extreme Student Deviate), and a forecasting model, Conv1D-GRU (one-dimensional convolution-gated recurrent unit) are mainly developed. Based on the historical monitoring data, different hyperparameter settings and training strategies were carried out with the proposed models. The results show that the data preprocessing model S-H-ESD can effectively deal with a variety of abnormal values, therefore significantly improving the accuracy of forecast (When the training dataset length is 7 days, the average accuracy of the three models is improved by1.23% when using S-H-ESD method compared with Z-Score method) and the Conv1D-GRU model shows better capability in forecast accuracy and self-adaptability of data features extraction compared with other models in literature (GRUN, ANN). With the achieving optimal parameter setting and training strategy, the developed methodology shows the best forecasted value of MAPE and NSE indicator are 1.677%, 0.983, respectively.
查看更多>>摘要：Urban surface runoff management via best management practices (BMP) and low impact development (LID) has earned significant recognition owing to positive environmental and ecological impacts. However, due to the complexity of the parameters involved, the estimation of LID efficiency in attenuating the urban surface runoff at the watershed scale is challenging. A planning analysis of employing Green Roofs and Infiltration Trenches as BMPs/LIDs practices for urban surface runoff control is presented in this study. A multi-objective optimization decision-making framework is established by coupling SWMM (Storm Water Management Model) with NSGA-II models to check the performance of BMPs/LIDs concerning the cost-benefit analysis of LID at the watershed scale. Two urbanized areas belonging to Central Delhi in India were used as case studies. The results showed that the SWMM model is useful in simulating optimization problems for managing urban surface runoff. The optimum scenarios efficiently minimized the urban runoff volume while maintaining the BMPs/LIDs implementation costs and size. With BMPs/LIDs implementation, the reduction in runoff volume increases as expenses increase initially; however, there is no noticeable reduction in flood volume after a certain threshold. Contrasted with the haphazard arrangement of BMPs/LIDs, the proposed approach demonstrates 22%-24% runoff reductions for the same expenditures in watershed 1 and 23%-26% in watershed 2. The result of the study provides insights into planning and management of the urban surface runoff control with LID practices. The proposed framework assists the hydrologists in optimum selection and placements of BMPs/LIDs practices to acquire the most extreme ecological advantages with the least expenses.
查看更多>>摘要：As an important agriculture production area in the world and a flood prone area, future hydro-climate changes in winter and spring in Northeast China could have remarkable influence on spring water resources and flood. Yet, studies on future hydro-climate variations with special considerations of snow influences remain limited so far in the region. Here, we studied future winter and spring hydro-climate changes to the end of the century with special considerations of snow variations under the RCP2.6, RCP6.0 and RCP8.5 scenarios in Northeast China. A water and energy budget-based distributed biosphere hydrological model with improved snow physics was implemented. We find that winter and spring are warming 0.08 degrees C and 0.06 degrees C annually under 30 years moving average in RCP8.5. Air temperature increasing rate in winter is approximately two times higher than that in spring in 2030-2059. In this period, snow melt contribution to spring average runoff and maximum runoff decrease by at least 39% and 23%, respectively, and spring soil moisture decreases by 7%. The spring snow melt runoff peak will move from April to March under the warmest climate condition (i.e., 2070-2099 in RCP8.5). The earlier snow melt renders the snow melt contribution to total runoff decrease to almost zero in May, which could increase drought severity. This study sheds some lights on changes in hydrological regimes under climate change with a focus on snow melt and the influences in the entire Northeast China for the first time, and is helpful for adaption to future climate change.
NSTL
Elsevier