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1.
The growing concern for health‐related problems deriving from pollutants leaching is driving national and international administrations to support the development of tools for evaluating the effects of alternate management scenarios and identifying vulnerable areas. Cropping systems models are powerful tools for evaluating leachates under different environmental, social, and management conditions. As percolating water is the transport vehicle for pollutants transport in soil, a reliable evaluation of water balance models is a fundamental prerequisite for investigating pesticides and nitrate fate. As specific approaches for the evaluation of multi‐layer evolution of state variables are missing, we propose a fuzzy‐based, integrated indicator (ISWC: 0, best; 1, worst) for a comprehensive evaluation of soil water content (SWC) simulations. We aggregated error metrics with others quantifying the homogeneity of errors across different soil layers, the capability of models to reproduce complex dynamics function of both time and soil depth, and model complexity. We tested ISWC on a sample dataset where the models CropSyst and CERES‐Wheat were used to simulate SWC for winter wheat systems. ISWC revealed that, in the explored conditions, the global assessment of the two models' performances allowed identification of CropSyst as the best (average ISWC = 0·441, with a value of 0·537 obtained by CERES‐Wheat), although each model prevailed for some of the metrics. CropSyst presented the highest accuracy (average agreement module = 0·400), whereas CERES‐Wheat's accuracy was slightly worse, although achieved with a simplified modelling approach (average Akaike Information Criterion = − 230·44), thereby favouring large‐area applicability. The non‐univocal scores achieved by the models for the different metrics support the use of multi‐metric evaluation approaches for quantifying the different aspects of water balance model performances. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

2.
The aim of this work is threefold: (1) to identify the main characteristics of water‐table variations from observations in the Kervidy‐Naizin catchment, a small catchment located in western France; (2) to confront these characteristics with the assumptions of the Topmodel concepts; and (3) to analyse how relaxation of the assumptions could improve the simulation of distributed water‐table depth. A network of piezometers was installed in the Kervidy‐Naizin catchment and the water‐table depth was recorded every 15 min in each piezometer from 1997 to 2000. From these observations, the Kervidy‐Naizin groundwater appears to be characteristic of shallow groundwaters of catchments underlain by crystalline bedrock, in view of the strong relation between water distribution and topography in the bottom land of the hillslopes. However, from midslope to summit, the water table can attain a depth of many metres, it does not parallel the topographic surface and it remains very responsive to rainfall. In particular, hydraulic gradients vary with time and are not equivalent to the soil surface slope. These characteristics call into question some assumptions that are used to model shallow lateral subsurface flow in saturated conditions. We investigate the performance of three models (Topmodel, a kinematic model and a diffusive model) in simulating the hourly distributed water‐table depths along one of the hillslope transects, as well as the hourly stream discharge. For each model, two sets of parameters are identified following a Monte Carlo procedure applied to a simulation period of 2649 h. The performance of each model with each of the two parameter sets is evaluated over a test period of 2158 h. All three models, and hence their underlying assumptions, appear to reproduce adequately the stream discharge variations and water‐table depths in bottom lands at the foot of the hillslope. To simulate the groundwater depth distribution over the whole hillslope, the steady‐state assumption (Topmodel) is quite constraining and leads to unacceptable water‐table depths in midslope and summit areas. Once this assumption is relaxed (kinematic model), the water‐table simulation is improved. A subsequent relaxation of the hydraulic gradient (diffusive model) further improves water‐table simulations in the summit area, while still yielding realistic water‐table depths in the bottom land. Copyright © 2004 John Wiley & Sons, Ltd.  相似文献   

3.
Free fluid porosity and rock permeability, undoubtedly the most critical parameters of hydrocarbon reservoir, could be obtained by processing of nuclear magnetic resonance (NMR) log. Despite conventional well logs (CWLs), NMR logging is very expensive and time-consuming. Therefore, idea of synthesizing NMR log from CWLs would be of a great appeal among reservoir engineers. For this purpose, three optimization strategies are followed. Firstly, artificial neural network (ANN) is optimized by virtue of hybrid genetic algorithm-pattern search (GA-PS) technique, then fuzzy logic (FL) is optimized by means of GA-PS, and eventually an alternative condition expectation (ACE) model is constructed using the concept of committee machine to combine outputs of optimized and non-optimized FL and ANN models. Results indicated that optimization of traditional ANN and FL model using GA-PS technique significantly enhances their performances. Furthermore, the ACE committee of aforementioned models produces more accurate and reliable results compared with a singular model performing alone.  相似文献   

4.
湖泊富营养化响应与流域优化调控决策的模型研究进展   总被引：2，自引：0，他引：2  
湖泊富营养化是全球水环境领域面临的长期挑战,富营养化响应与流域优化决策模型是制定经济和高效调控方案的关键.然而已有的模型研究综述主要集中于模型开发、案例应用、敏感性分析、不确定性分析等单一方面,而缺少针对非线性响应、生态系统长期演变等最新湖泊治理挑战的研究总结.本文对数据驱动的统计模型、因果驱动的机理模型和决策导向的优化模型进行了综述.其中,统计模型包含经典统计、贝叶斯统计和机器学习模型,常用于建立响应关系、时间序列特征分析以及预报预警;机理模型包含流域的水文与污染物输移模拟以及湖泊的水文、水动力、水质、水生态等过程的模拟,用于不同时空尺度的变化过程模拟,其中复杂机理模型的敏感性分析、参数校验、模型不确定性等需要较高的计算成本;优化模型结合机理模型形成“模拟优化”体系,在不确定性条件下衍生出随机、区间优化等多种方法,通过并行计算、简化与替代模型可一定程度上解决计算时间成本的瓶颈.本文识别了湖泊治理面临的挑战,包括:①如何定量表征外源输入的非线性叠加和湖泊氮、磷、藻变化的非均匀性?②如何提高优化调控决策和水质目标的关联与精准性?③如何揭示湖泊生态系统的长期变化轨迹与驱动因素?最后,本文针对这些挑战提出研究展望,主要包括:①基于多源数据融合与机器学习算法以提升湖泊的短期水质预测精度;②以生物量为基础的机理模型与行为驱动的个体模型的升尺度或降尺度耦合以表达多种尺度的物质交互过程;③机器学习算法与机理模型的直接耦合或数据同化以降低模拟误差;④时空尺度各异的多介质模拟模型融合以实现精准和动态的优化调控.  相似文献   

5.
Located in the northeast of the Tibetan Plateau, the headwaters of the Yellow River basin (HYRB) are very vulnerable to climate change. In this study, we used the Soil and Water Assessment Tool (SWAT) model to assess the impact of future climate change on this region's hydrological components for the near future period of 2013–2042 under three emission scenarios A1B, A2 and B1. The uncertainty in this evaluation was considered by employing Bayesian model averaging approach on global climate model (GCM) multimodel ensemble projections. First, we evaluated the capability of the SWAT model for streamflow simulation in this basin. Second, the GCMs' monthly ensemble projections were downscaled to daily climate data using the bias‐correction and spatial‐disaggregation method and then were utilized as input into the SWAT model. The results indicate the following: (1) The SWAT model exhibits a good performance for both calibration and validation periods after adjusting parameters in snowmelt module and establishing elevation bands in sub‐basins. (2) The projected precipitation suggests a general increase under all three scenarios, with a larger extent in both A1B and B1 and a slight variation for A2. With regard to temperature, all scenarios show pronounced warming trends, of which A2 displays the largest amplitude. (3) In the terms of total runoff from the whole basin, there is an increasing trend in the future streamflow at Tangnaihai gauge under A1B and B1, while the A2 scenario is characterized by a declining trend. Spatially, A1B and B1 scenarios demonstrate increasing trends across most of the region. Groundwater and surface runoffs indicate similar trends with total runoff, whereas all three scenarios exhibit an increase in actual evapotranspiration. Generally, both A1B and B1 scenarios suggest a warmer and wetter tendency over the HYRB in the forthcoming decades, while the case for A2 indicates a warmer and drier trend. Findings from this study can provide beneficial reference to water resource and eco‐environment management strategies for governmental policymakers. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

6.
Shallow aquifers typically have greater hydrologic connectivity and response to recharge and changes in surface water management practices than deeper aquifers and are therefore often managed to reduce the risk of flooding. Quantification of the water table elevation response under different management scenarios provides valuable information in shallow aquifer systems to assess indirect influences of such modifications. The episodic master recession method was applied to the 15‐min water table elevation and NEXRAD rainfall data for 6 wells to identify water table response and individual rainfall events. The objectives of this study were to evaluate the effects of rainfall, water table elevation, canal stage, site‐specific characteristics, and canal structure modification/water management practice on the fluctuations in water table elevations using multiple/stepwise multiple linear regression techniques. With the modification of canal structure and operation adjustment, significant difference existed in water table response in the southern wells due to its relative downstream position regarding the general groundwater flow direction and the structural modification locations. On average, water table response height and flood risk were lower after than before the structure modification to canals. The effect of rainfall event size on the height of water table response was greater than the effect of antecedent water table elevation and canal stage on the height of water table response. Other factors including leakance of the canal bed sediment, specific yield, and rainfall on i  ? 1 day had significant effects on the height of water table response as well. Antecedent water table elevation and canal stage had greater and more linear effects on the height of water table response after the management changes to canals. Variation in water table response height/rainfall event size ratio was attributed to difference in S y , antecedent soil water content, hydraulic gradient, rainfall size, and run‐off ratio. After the structure modification, water table response height/rainfall event size ratio demonstrated more linear and proportional relationship with antecedent water table elevation and canal stage.  相似文献   

7.
This paper investigates the potential impacts of climate change on water resources in northern Tuscany, Italy. A continuous hydrological model for each of the seven river basins within the study area was calibrated using historical data. The models were then driven by downscaled and bias‐corrected climate projections of an ensemble of 13 regional climate models (RCMs), under two different scenarios of representative concentration pathway (RCP4.5 and RCP8.5). The impacts were examined at medium term (2031–2040) and long term (2051–2060) in comparison with a reference period (2003–2012); the changes in rainfall, streamflow, and groundwater recharge were investigated. A high degree of uncertainty characterized the results with a significant intermodel variability, the period being equal. For the sake of brevity, only the results for the Serchio River basin were presented in detail. According to the RCM ensemble mean and the RCP4.5, a moderate decrease in rainfall, with reference to 2003–2012, is expected at medium term (?0.6%) and long term (?2.8%). Due to the warming of the study area, the reduction in the streamflow volume is two times the precipitation decrease (?1.1% and ?6.8% at medium and long term, respectively). The groundwater recharge is mainly affected by the changes in climate with expected percolation volume variations of ?3.3% at 2031–2040 and ?8.1% at 2051–2060. The impacts on the Serchio River basin water resources are less significant under the RCP8.5 scenario. The presence of artificial structures, such as dam‐reservoir systems, can contribute to mitigate the effects of climate change on water resources through the implementation of appropriate regulation strategies.  相似文献   

8.
Urban areas in the Lake Victoria (LV) region are experiencing the highest growth rates in Africa. As efforts to meet increasing demand accelerate, integrated water resources management (IWRM) tools provide opportunities for utilities and other stakeholders to develop a planning framework comprehensive enough to include short term (e.g. landuse change), as well as longer term (e.g. climate change) scenarios. This paper presents IWRM models built using the Water Evaluation And Planning (WEAP) decision support system, for three towns in the LV region – Bukoba (Tanzania), Masaka (Uganda), and Kisii (Kenya). Each model was calibrated under current system performance based on site visits, utility reporting and interviews. Projected water supply, demand, revenues and costs were then evaluated against a combination of climate, demographic and infrastructure scenarios up to 2050. Our results show that water supply in all three towns is currently infrastructure limited; achieving existing design capacity could meet most projected demand until 2020s in Masaka beyond which new supply and conservation strategies would be needed. In Bukoba, reducing leakages would provide little performance improvement in the short-term, but doubling capacity would meet all demands until 2050. In Kisii, major infrastructure investment is urgently needed. In Masaka, streamflow simulations show that wetland sources could satisfy all demand until 2050, but at the cost of almost no water downstream of the intake. These models demonstrate the value of IWRM tools for developing water management plans that integrate hydroclimatology-driven supply to demand projections on a single platform.  相似文献   

9.
In this study, an environmental-friendly modeling system was developed and applied to an agriculture nonpoint source (AGNPS) management in Ulansuhai Nur watershed. In this system, water environmental capacity, credibility-based chance-constrained programming (CCCP), and AGNPS optimization models were integrated into a general modeling framework. It could be used to calculate water environmental capacity of total nitrogen and total phosphorus in Ulansuhai Nur watershed, which could consequentially provide input data for the developed AGNPS optimization model. Also, the inherent uncertainties in estimating water environmental capacities that can be expressed as possibilistic distributions were reflected and addressed based on computational results of three widely used methods. Such uncertainties were consequentially transferred to the proposed CCCP model based on the adoption of multiple credibility satisfactory levels, significantly facilitating objectivity reflection of decision alternatives. The developed modeling system was then applied to Ulansuhai Nur watershed of Inner Mongolia, a semi-arid river basin in northwestern China. Optimal strategies for AGNPS management in Ulansuhai Nur watershed were generated with consideration of the maximum total agricultural income under multiple policy scenarios. The results showed that the total agricultural income would increase with point source pollution being cut down, and would decrease with rising credibility levels, representing decreasing system violation risks. It was indicated that the higher of total nitrogen/phosphorus discharge being less than water environmental capacity of Ulansuhai Nur, the lower the total agriculture incomes. The proposed methods could help decision makers establish various production patterns with cost-effective agriculture nonpoint source management schemes in the basin of Ulansuhai Nur, and gain in-depth insights into the trade-offs between total agricultural incomes and system reliabilities.  相似文献   

10.
Mapping water table depth using geophysical and environmental variables   总被引：5，自引：0，他引：5  
Despite its importance, accurate representation of the spatial distribution of water table depth remains one of the greatest deficiencies in many hydrological investigations. Historically, both inverse distance weighting (IDW) and ordinary kriging (OK) have been used to interpolate depths. These methods, however, have major limitations: namely they require large numbers of measurements to represent the spatial variability of water table depth and they do not represent the variation between measurement points. We address this issue by assessing the benefits of using stepwise multiple linear regression (MLR) with three different ancillary data sets to predict the water table depth at 100-m intervals. The ancillary data sets used are Electromagnetic (EM34 and EM38), gamma radiometric: potassium (K), uranium (eU), thorium (eTh), total count (TC), and morphometric data. Results show that MLR offers significant precision and accuracy benefits over OK and IDW. Inclusion of the morphometric data set yielded the greatest (16%) improvement in prediction accuracy compared with IDW, followed by the electromagnetic data set (5%). Use of the gamma radiometric data set showed no improvement. The greatest improvement, however, resulted when all data sets were combined (37% increase in prediction accuracy over IDW). Significantly, however, the use of MLR also allows for prediction in variations in water table depth between measurement points, which is crucial for land management.  相似文献   

11.
Stormwater management increasingly recognises the need to emulate, to the maximum extent possible, the flow regime of receiving waters in their pre‐development state. Hydrological models play a central role in assessing the catchment‐scale impacts of alternative stormwater management strategies. However, because of the complexity of physical processes involved in urban hydrology, particularly subsurface flows, the predictive performance of such models is often low. We investigated how the structure of hydrological models influenced the prediction of urbanisation and stormwater management impacts on baseflow. We calibrated three conceptual models of the same reference catchment and compared the modelled flow regime from different stormwater management scenarios, using each of the three model structures. Scenarios were assessed using six metrics, characterising the whole streamflow regime and in particular baseflow. Although the three models of the reference catchment represented the observed hydrograph well, the most complex structure developed using a thorough diagnostic of the catchment behaviour better captured the change in hydrological regime during dry years. Predictions of baseflow changes due to urbanisation varied significantly according to the model structure. Similarly, the models showed distinct responses to the stormwater management scenarios applied, especially for scenarios involving infiltration of stormwater at source. Our results confirm the importance of predicting the consequences of land use changes with conceptual models that are consistent with the hydrological behaviour of the study catchment. Future work should help to quantify the uncertainties due to model structure and thus provide practical guidance to the use of catchment models for assessing stormwater management strategies. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

12.
The present study aims to develop a hybrid multi‐model using the soft computing approach. The model is a combination of a fuzzy logic, artificial neural network (ANN) and genetic algorithm (GA). While neural networks are low‐level computational structures that perform well dealing with raw data, fuzzy logic deal with reasoning on a higher level by using linguistic information acquired from domain experts. However, fuzzy systems lack the ability to learn and cannot adjust themselves to a new environment. Moreover, experts occasionally make mistakes and thus some rules used in a system may be false. A network type structure of the present hybrid model is a multi‐layer feed‐forward network, the main part is a fuzzy system based on the first‐order Sugeno fuzzy model with a fuzzification and a defuzzification processes. The consequent parameters are determined by least square method. The back‐propagation is applied to adjust weights of network. Then, the antecedent parameters of the membership function are updated accordingly by the gradient descent method. The GA was applied to select the fuzzy rule. The hybrid multi‐model was used to forecast the flood level at Chiang Mai (under the big flood 2005) and the Koriyama flood (2003) in Japan. The forecasting results are evaluated using standard global goodness of fit statistic, efficient index (EI), the root mean square error (RMSE) and the peak flood error. Moreover, the results are compared to the results of a neuro‐genetic model (NGO) and ANFIS model using the same input and output variables. It was found that the hybrid multi‐model can be used successfully with an efficiency index (EI) more than 0·95 (for Chiang Mai flood up to 12 h ahead forecasting) and more than 0·90 (for Koriyama flood up to 8 h ahead forecasting). In general, all of three models can predict the water level with satisfactory results. However, the hybrid model gave the best flood peak estimation among the three models. Therefore, the use of fuzzy rule base, which is selected by GA in the hybrid multi‐model helps to improve the accuracy of flood peak. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

13.
Interpolations of groundwater table elevation in dissected uplands   总被引：3，自引：0，他引：3  
Chung JW  Rogers JD 《Ground water》2012,50(4):598-607
The variable elevation of the groundwater table in the St. Louis area was estimated using multiple linear regression (MLR), ordinary kriging, and cokriging as part of a regional program seeking to assess liquefaction potential. Surface water features were used to determine the minimum water table for MLR and supplement the principal variables for ordinary kriging and cokriging. By evaluating the known depth to the water and the minimum water table elevation, the MLR analysis approximates the groundwater elevation for a contiguous hydrologic system. Ordinary kriging and cokriging estimate values in unsampled areas by calculating the spatial relationships between the unsampled and sampled locations. In this study, ordinary kriging did not incorporate topographic variations as an independent variable, while cokriging included topography as a supporting covariable. Cross validation suggests that cokriging provides a more reliable estimate at known data points with less uncertainty than the other methods. Profiles extending through the dissected uplands terrain suggest that: (1) the groundwater table generated by MLR mimics the ground surface and elicits a exaggerated interpolation of groundwater elevation; (2) the groundwater table estimated by ordinary kriging tends to ignore local topography and exhibits oversmoothing of the actual undulations in the water table; and (3) cokriging appears to give the realistic water surface, which rises and falls in proportion to the overlying topography. The authors concluded that cokriging provided the most realistic estimate of the groundwater surface, which is the key variable in assessing soil liquefaction potential in unconsolidated sediments.  相似文献   

14.
This paper defines a new scoring rule, namely relative model score (RMS), for evaluating ensemble simulations of environmental models. RMS implicitly incorporates the measures of ensemble mean accuracy, prediction interval precision, and prediction interval reliability for evaluating the overall model predictive performance. RMS is numerically evaluated from the probability density functions of ensemble simulations given by individual models or several models via model averaging. We demonstrate the advantages of using RMS through an example of soil respiration modeling. The example considers two alternative models with different fidelity, and for each model Bayesian inverse modeling is conducted using two different likelihood functions. This gives four single-model ensembles of model simulations. For each likelihood function, Bayesian model averaging is applied to the ensemble simulations of the two models, resulting in two multi-model prediction ensembles. Predictive performance for these ensembles is evaluated using various scoring rules. Results show that RMS outperforms the commonly used scoring rules of log-score, pseudo Bayes factor based on Bayesian model evidence (BME), and continuous ranked probability score (CRPS). RMS avoids the problem of rounding error specific to log-score. Being applicable to any likelihood functions, RMS has broader applicability than BME that is only applicable to the same likelihood function of multiple models. By directly considering the relative score of candidate models at each cross-validation datum, RMS results in more plausible model ranking than CRPS. Therefore, RMS is considered as a robust scoring rule for evaluating predictive performance of single-model and multi-model prediction ensembles.  相似文献   

15.
We assessed the relative hydrological impacts of climate change and urbanization using an integrated approach that links the statistical downscaling model (SDSM), the Hydrological Simulation Program—Fortran (HSPF) and the impervious cover model (ICM). A case study of the Anyangcheon watershed, a representative urban region in Korea, illustrates how the proposed framework can be used to analyse the impacts of climate change and urbanization on water quantity and quality. The evaluation criteria were measurements of low flow (99, 95, and 90 percentile flow), high flow (10, 5, and 1 percentile value), pollutant concentration (30, 10, and 1 percentile value), and the numbers of days required to satisfy the target water quantity and quality for a sensitive comparison of subtle impacts of variations in these measures. Nine scenarios, including three climate scenarios (present conditions, A1B, and A2) and three land use change scenarios, were analysed using the HSPF model. The impacts of climate change on low flow (34·1–59·8% increase) and high flow (29·1–37·1% increase) were found to be much greater than those on the biochemical oxygen demand (BOD) (3·8–10·0% decrease). On the other hand, the impacts of urbanization on water quality (19·0–44·6% increase) are more significant than those on high (1·0–4·4% increase) and low flow (11·4–25·6% decrease). Furthermore, low flows are more sensitive to urbanization than high flows. The number of days required to satisfy the target water quantity and quality can be a sensitive criterion to compare the subtle impacts of climate and urbanization on human society, especially as they are much more sensitive than low flow and pollutant concentration. Finally, urbanization has a potent impact on BOD while climate change has a high impact on flow rate. Therefore, the impacts of both climate change and urbanization must be included in watershed management and water resources planning for sustainable development. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

16.
Predicting and mapping high water table elevation in coastal landscapes is critical for both science application projects like inundation risk analysis and engineering projects like pond design and maintenance. Previous studies of water table mapping focused on the application of geostatistical methods, which cannot predict values beyond an observation spatial domain or generate an ideal pattern for regions with sparse measurements. In this study, we evaluated the multiple linear regression (MLR) and support vector machine (SVM) techniques for high water table prediction and mapping using fine spatial resolution lidar-derived Digital Elevation Model (DEM) data, and designed an application protocol of these two techniques for high water table mapping in a coastal landscape where groundwater, tide, and surface water are related. Testing results showed that SVM largely improved the high water table prediction with a mean absolute error (MAE) of 1.22 feet and root mean square error (RMSE) of 2.22 feet compared to the application of the ordinary Kriging method which could not generate a reasonable water table. MLR was also promising with a MAE of around 2 feet and RMSE of around 3 feet. The study suggests that both MLR and SVM are valuable alternatives to estimate high water table elevation in Florida. Fine resolution lidar DEMs are beneficial for high water table prediction and mapping.  相似文献   

17.
Relatively few studies have addressed water management and adaptation measures in the face of changing water balances due to climate change. The current work studies climate change impact on a multipurpose reservoir performance and derives adaptive policies for possible future scenarios. The method developed in this work is illustrated with a case study of Hirakud reservoir on the Mahanadi river in Orissa, India, which is a multipurpose reservoir serving flood control, irrigation and power generation. Climate change effects on annual hydropower generation and four performance indices (reliability with respect to three reservoir functions, viz. hydropower, irrigation and flood control, resiliency, vulnerability and deficit ratio with respect to hydropower) are studied. Outputs from three general circulation models (GCMs) for three scenarios each are downscaled to monsoon streamflow in the Mahanadi river for two future time slices, 2045–65 and 2075–95. Increased irrigation demands, rule curves dictated by increased need for flood storage and downscaled projections of streamflow from the ensemble of GCMs and scenarios are used for projecting future hydrologic scenarios. It is seen that hydropower generation and reliability with respect to hydropower and irrigation are likely to show a decrease in future in most scenarios, whereas the deficit ratio and vulnerability are likely to increase as a result of climate change if the standard operating policy (SOP) using current rule curves for flood protection is employed. An optimal monthly operating policy is then derived using stochastic dynamic programming (SDP) as an adaptive policy for mitigating impacts of climate change on reservoir operation. The objective of this policy is to maximize reliabilities with respect to multiple reservoir functions of hydropower, irrigation and flood control. In variations to this adaptive policy, increasingly more weightage is given to the purpose of maximizing reliability with respect to hydropower for two extreme scenarios. It is seen that by marginally sacrificing reliability with respect to irrigation and flood control, hydropower reliability and generation can be increased for future scenarios. This suggests that reservoir rules for flood control may have to be revised in basins where climate change projects an increasing probability of droughts. However, it is also seen that power generation is unable to be restored to current levels, due in part to the large projected increases in irrigation demand. This suggests that future water balance deficits may limit the success of adaptive policy options.  相似文献   

18.
I. W. Jung  D. H. Bae  B. J. Lee 《水文研究》2013,27(7):1033-1045
Seasonality in hydrology is closely related to regional water management and planning. There is a strong consensus that global warming will likely increase streamflow seasonality in snow‐dominated regions due to decreasing snowfall and earlier snowmelt, resulting in wetter winters and drier summers. However, impacts to seasonality remain unclear in rain‐dominated regions with extreme seasonality in streamflow, including South Korea. This study investigated potential changes in seasonal streamflow due to climate change and associated uncertainties based on multi‐model projections. Seasonal flow changes were projected using the combination of 13 atmosphere–ocean general circulation model simulations and three semi‐distributed hydrologic models under three different future greenhouse gas emission scenarios for two future periods (2020s and 2080s). Our results show that streamflow seasonality is likely to be aggravated due to increases in wet season flow (July through September) and decreases in dry season flow (October through March). In South Korea, dry season flow supports water supply and ecosystem services, and wet season flow is related to flood risk. Therefore, these potential changes in streamflow seasonality could bring water management challenges to the Korean water resources system, especially decreases in water availability and increases in flood risk. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

19.
Several recent studies have shown the significance of representing groundwater in land surface hydrologic simulations. However, optimal methods for model parameter calibration in order to realistically simulate baseflow and groundwater depth have received little attention. Most studies still use globally constant groundwater parameters due to the lack of available datasets for calibration. Moreover, when models are calibrated, various parameter combinations are found to exhibit equifinality in simulated total runoff due to model parameter interactions. In this study, a simple lumped groundwater model is incorporated into the Community Land Model (CLM), in which the water table is interactively coupled to soil moisture through the groundwater recharge fluxes. The coupled model (CLMGW) is successfully validated in Illinois using a 22-year (1984–2005) monthly observational dataset. Baseflow estimates from the digital recursive filter technique are used to calibrate the CLMGW parameters. The advantage obtained from incorporating baseflow calibration in addition to traditional calibration based on measured streamflow alone is demonstrated by a Monte Carlo-type simulation analysis. Using the optimal parameter sets identified from baseflow calibration, flow partitioning and water table depth simulations using CLMGW are improved, and the equifinality problem is alleviated. For other regions that lack observations of water table depth, the baseflow calibration approach can be used to enhance parameter estimation and constrain water table depth simulations.  相似文献   

20.
Vulnerability maps are designed to show areas of greatest potential for groundwater contamination on the basis of hydrogeological conditions and human impacts. The objective of this research is (1) to assess the groundwater vulnerability using DRASTIC method and (2) to improve the DRASTIC method for evaluation of groundwater contamination risk using AI methods, such as ANN, SFL, MFL, NF and SCMAI approaches. This optimization method is illustrated using a case study. For this purpose, DRASTIC model is developed using seven parameters. For validating the contamination risk assessment, a total of 243 groundwater samples were collected from different aquifer types of the study area to analyze \( {\text{NO}}_{ 3}^{ - } \) concentration. To develop AI and CMAI models, 243 data points are divided in two sets; training and validation based on cross validation approach. The calculated vulnerability indices from the DRASTIC method are corrected by the \( {\text{NO}}_{3}^{ - } \) data used in the training step. The input data of the AI models include seven parameters of DRASTIC method. However, the output is the corrected vulnerability index using \( {\text{NO}}_{3}^{ - } \) concentration data from the study area, which is called groundwater contamination risk. In other words, there is some target value (known output) which is estimated by some formula from DRASTIC vulnerability and \( {\text{NO}}_{3}^{ - } \) concentration values. After model training, the AI models are verified by the second \( {\text{NO}}_{3}^{ - } \) concentration dataset. The results revealed that NF and SFL produced acceptable performance while ANN and MFL had poor prediction. A supervised committee machine artificial intelligent (SCMAI), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of vulnerability. The performance of SCMAI was also compared to those of the simple averaging and weighted averaging committee machine intelligent (CMI) methods. As a result, the SCMAI model produced reliable estimates of groundwater contamination risk.  相似文献   

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