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1.
Recently, Batabyal and Yoo (2003) have studied aspects of indivisible and divisible land development in a dynamic and stochastic framework. In this note, we complement the analysis in this paper by examining an aspect of the indivisible land development decision when a landowner has an exogenously given reservation level of revenue. In particular, we show that the magnitude of the first record (largest offer) that exceeds the landowners reservation revenue is independent of the time at which this record is received by the landowner.  相似文献   

2.
This study aims at evaluating the uncertainty in the prediction of soil moisture (1D, vertical column) from an offline land surface model (LSM) forced by hydro-meteorological and radiation data. We focus on two types of uncertainty: an input error due to satellite rainfall retrieval uncertainty, and, LSM soil-parametric error. The study is facilitated by in situ and remotely sensed data-driven (precipitation, radiation, soil moisture) simulation experiments comprising a LSM and stochastic models for error characterization. The parametric uncertainty is represented by the generalized likelihood uncertainty estimation (GLUE) technique, which models the parameter non-uniqueness against direct observations. Half-hourly infra-red (IR) sensor retrievals were used as satellite rainfall estimates. The IR rain retrieval uncertainty is characterized on the basis of a satellite rainfall error model (SREM). The combined uncertainty (i.e., SREM + GLUE) is compared with the partial assessment of uncertainty. It is found that precipitation (IR) error alone may explain moderate to low proportion of the soil moisture simulation uncertainty, depending on the level of model accuracy—50–60% for high model accuracy, and 20–30% for low model accuracy. Comparisons on the basis of two different sites also yielded an increase (50–100%) in soil moisture prediction uncertainty for the more vegetated site. This study exemplified the need for detailed investigations of the rainfall retrieval-modeling parameter error interaction within a comprehensive space-time stochastic framework for achieving optimal integration of satellite rain retrievals in land data assimilation systems.  相似文献   

3.
Gusev  E. M.  Nasonova  O. N. 《Water Resources》2004,31(2):132-147
The current state of the art in the investigation and modeling of soil–vegetation/snow cover–surface air layer system (SVAS) is briefly reviewed. This system plays a decisive role in the formation of heat and moisture exchange between the land surface and atmosphere. The SWAP model, developed by the authors of this paper, is used to illustrate the potentialities of SVAS models in reproducing the dynamics of components of water and heat balances of the land surface under different natural conditions and at different spatial and temporal scales. The challenges and perspectives of further development of SVAS models are analyzed.  相似文献   

4.
Closing the gap between theoretical reservoir operation and the real-world implementation remains a challenge in contemporary reservoir operations. Past research has focused on optimization algorithms and establishing optimal policies for reservoir operations. In this study, we attempt to understand operators’ release decisions by investigating historical release data from 79 reservoirs in California and the Great Plains, using a data-mining approach. The 79 reservoirs are classified by hydrological regions, intra-annual seasons, average annual precipitation (climate), ratio of maximum reservoir capacity to average annual inflow (size ratio), hydrologic uncertainty associated with inflows, and reservoirs’ main usage. We use information theory – specifically, mutual information – to measure the quality of inference between a set of classic indicators and observed releases at the monthly and weekly timescales. Several general trends are found to explain which sources of hydrologic information dictate reservoir release decisions under different conditions. Current inflow is the most important indicator during wet seasons, while previous releases are more relevant during dry seasons and in weekly data (as compared with monthly data). Inflow forecasting is the least important indicator in release decision making, but its importance increases linearly with hydrologic uncertainty and decreases logarithmically with reservoir size. No single hydrologic indicator is dominant across all reservoirs in either of the two regions.  相似文献   

5.
Remotely sensed land cover maps are increasingly used as inputs into environmental simulation models whose outputs inform decisions and policy-making. Risks associated with these decisions are dependent on model output uncertainty, which is in turn affected by the uncertainty of land cover inputs. This article presents a method of quantifying the uncertainty that results from potential mis-classification in remotely sensed land cover maps. In addition to quantifying uncertainty in the classification of individual pixels in the map, we also address the important case where land cover maps have been upscaled to a coarser grid to suit the users’ needs and are reported as proportions of land cover type. The approach is Bayesian and incorporates several layers of modelling but is straightforward to implement. First, we incorporate data in the confusion matrix derived from an independent field survey, and discuss the appropriate way to model such data. Second, we account for spatial correlation in the true land cover map, using the remotely sensed map as a prior. Third, spatial correlation in the mis-classification characteristics is induced by modelling their variance. The result is that we are able to simulate posterior means and variances for individual sites and the entire map using a simple Monte Carlo algorithm. The method is applied to the Land Cover Map 2000 for the region of England and Wales, a map used as an input into a current dynamic carbon flux model.  相似文献   

6.
Calibration of hydrologic models is very difficult because of measurement errors in input and response, errors in model structure, and the large number of non-identifiable parameters of distributed models. The difficulties even increase in arid regions with high seasonal variation of precipitation, where the modelled residuals often exhibit high heteroscedasticity and autocorrelation. On the other hand, support of water management by hydrologic models is important in arid regions, particularly if there is increasing water demand due to urbanization. The use and assessment of model results for this purpose require a careful calibration and uncertainty analysis. Extending earlier work in this field, we developed a procedure to overcome (i) the problem of non-identifiability of distributed parameters by introducing aggregate parameters and using Bayesian inference, (ii) the problem of heteroscedasticity of errors by combining a Box–Cox transformation of results and data with seasonally dependent error variances, (iii) the problems of autocorrelated errors, missing data and outlier omission with a continuous-time autoregressive error model, and (iv) the problem of the seasonal variation of error correlations with seasonally dependent characteristic correlation times. The technique was tested with the calibration of the hydrologic sub-model of the Soil and Water Assessment Tool (SWAT) in the Chaohe Basin in North China. The results demonstrated the good performance of this approach to uncertainty analysis, particularly with respect to the fulfilment of statistical assumptions of the error model. A comparison with an independent error model and with error models that only considered a subset of the suggested techniques clearly showed the superiority of the approach based on all the features (i)–(iv) mentioned above.  相似文献   

7.
The development, testing and application of a dynamic two-dimensional (longitudinal-vertical) mass balance model for dissolved oxygen (DO) and chlorophyll (Chl) for rivers is documented that for the first time accommodates both the oxygen demand and filtering effects of zebra mussels. The test system is a phytoplankton-rich section ( 15 km long) of the Seneca River, NY, that is believed to represent an upper bound of the impact of this exotic invader. Changes in common measures of water quality of the river brought about by the zebra mussel invasion are reviewed and related longitudinal patterns in DO, Chl, and Secchi disc transparency are documented. Model testing is supported by comprehensive measurements of DO, Chl, and various forcing conditions over a three-month period, and independent determinations of several model coefficients. Wide variations in the areal consumption rate of DO (ZOD; g·m–2·d–1) and filtering rate (m3·m–2·d–1) of zebra mussels, as determined through model calibration, occurred over the study period. Values of ZOD in areas with dense zebra mussel populations at times (e.g., > 50 g·m–2·d–1) were an order of magnitude greater than the sediment oxygen demand associated with organically enriched deposits. The value of determinations of these fluxes from model calibration procedures is evaluated within the context of the limitations of protocols presently available to support independent specification of these rates. Model analyses are conducted to evaluate the relative magnitude of source and sink processes for DO and Chl, the potential operation and implications of feedback from low DO levels on oxygen consumption by zebra mussels, and the sensitivity of model simulations to selected sources of uncertainty and variability. Model projections of oxygen resources of the river are presented in a probabilistic format in evaluating reductions in zebra mussel biomass that would be necessary to eliminate violations of standards and regain assimilative capacity.  相似文献   

8.
Hydrologic models are useful to understand the effects of climate and land‐use changes on dry‐season flows. In practice, there is often a trade‐off between simplicity and accuracy, especially when resources for catchment management are scarce. Here, we evaluated the performance of a monthly rainfall–runoff model (dynamic water balance model, DWBM) for dry‐season flow prediction under climate and land‐use change. Using different methods with decreasing amounts of catchment information to set the four model parameters, we predicted dry‐season flow for 89 Australian catchments and verified model performance with an independent dataset of 641 catchments in the United States. For the Australian catchments, model performance without catchment information (other than climate forcing) was fair; it increased significantly as the information to infer the four model parameters increased. Regressions to infer model parameters from catchment characteristics did not hold for catchments in the United States, meaning that a new calibration effort was needed to increase model performance there. Recognizing the interest in relative change for practical applications, we also examined how DWBM could be used to simulate a change in dry‐season flow following land‐use change. We compared results with and without calibration data and showed that predictions of changes in dry‐season flow were robust with respect to uncertainty in model parameters. Our analyses confirm that climate is a strong driver of dry‐season flow and that parsimonious models such as DWBM have useful management applications: predicting seasonal flow under various climate forcings when calibration data are available and providing estimates of the relative effect of land use on seasonal flow for ungauged catchments.  相似文献   

9.
 We illustrate a method of global sensitivity analysis and we test it on a preliminary case study in the field of environmental assessment to quantify uncertainty importance in poorly-known model parameters and spatially referenced input data. The focus of the paper is to show how the methodology provides guidance to improve the quality of environmental assessment practices and decision support systems employed in environmental policy. Global sensitivity analysis, coupled with uncertainty analysis, is a tool to assess the robustness of decisions, to understand whether the current state of knowledge on input data and parametric uncertainties is sufficient to enable a decision to be taken. The methodology is applied to a preliminary case study, which is based on a numerical model that employs GIS-based soil data and expert consultation to evaluate an index that joins environmental and economic aspects of land depletion. The index is used as a yardstick by decision-makers involved in the planning of highways to identify the route that minimises the overall impact.  相似文献   

10.
This paper proposes an approach to estimating the uncertainty related to EPA Storm Water Management Model model parameters, percentage routed (PR) and saturated hydraulic conductivity (Ksat), which are used to calculate stormwater runoff volumes. The methodology proposed in this paper addresses uncertainty through the development of probability distributions for urban hydrologic parameters through extensive calibration to observed flow data in the Philadelphia collection system. The established probability distributions are then applied to the Philadelphia Southeast district model through a Monte Carlo approach to estimate the uncertainty in prediction of combined sewer overflow volumes as related to hydrologic model parameter estimation. Understanding urban hydrology is critical to defining urban water resource problems. A variety of land use types within Philadelphia coupled with a history of cut and fill have resulted in a patchwork of urban fill and native soils. The complexity of urban hydrology can make model parameter estimation and defining model uncertainty a difficult task. The development of probability distributions for hydrologic parameters applied through Monte Carlo simulations provided a significant improvement in estimating model uncertainty over traditional model sensitivity analysis. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

11.
Stochastic analysis is commonly used to address uncertainty in the modeling of flow and transport in porous media. In the stochastic approach, the properties of porous media are treated as random functions with statistics obtained from field measurements. Several studies indicate that hydrological properties depend on the scale of measurements or support scales, but most stochastic analysis does not address the effects of support scale on stochastic predictions of subsurface processes. In this work we propose a new approach to study the scale dependence of stochastic predictions. We present a stochastic analysis of immiscible fluid–fluid displacement in randomly heterogeneous porous media. While existing solutions are applicable only to systems in which the viscosity of one phase is negligible compare with the viscosity of the other (water–air systems for example), our solutions can be applied to the immiscible displacement of fluids having arbitrarily viscosities such as NAPL–water and water–oil. Treating intrinsic permeability as a random field with statistics dependant on the permeability support scale (scale of measurements) we obtained, for one-dimensional systems, analytical solutions for the first moments characterizing unbiased predictions (estimates) of system variables, such as the pressure and fluid–fluid interface position, and we also obtained second moments, which characterize the uncertainties associated with such predictions. Next we obtained empirically scale dependent exponential correlation function of the intrinsic permeability that allowed us to study solutions of stochastic equations as a function of the support scale. We found that the first and second moments converge to asymptotic values as the support scale decreases. In our examples, the statistical moments reached asymptotic values for support scale that were approximately 1/10000 of the flow domain size. We show that analytical moment solutions compare well with the results of Monte Carlo simulations for moderately heterogeneous porous media, and that they can be used to study the effects of heterogeneity on the dynamics and stability of immiscible flow.  相似文献   

12.
From the data of a microseismic survey of the Lanzarote Island territory (Canary Archipelago) we obtained a microseisms amplitude distribution in the frequency range 0.3–12.5 Hz. We found a distinguished anomaly such as an amplitude depression, whose size and magnitude depend on the frequency. After studying the statistical and polarization properties of microseism signals we proposed a model explaining this depression, based on the presence of a rigid intrusive body in the center of the island. Results of our survey coincided well with independent detailed gravity survey results whose interpretation also implies the presence of intrusion. We estimated shear-wave velocity for the rocks of intrusion and for surrounding rocks using microseismic data.  相似文献   

13.
In this work, we address the mismatch in spatio-temporal resolution between individual, point-location based exposure and grid cell based air quality model predictions by disaggregating the grid model results. Variability of PM10 point measurements was modelled within each grid cell by the exponential variogram, using point support concentration measurements. Variogram parameters were estimated over the study area globally using constant estimates, and locally by multiple regression models using traffic, weather and land use data. Model predictions of spatio-temporal variability were used for geostatistical unconditional simulation, estimating the deviation of point values from grid cell averages on GPS tracks. The distribution of deviations can be used as an estimate of uncertainty for individual exposure. Results showed a relevant impact of the disaggregation uncertainties compared to other uncertainty sources, dependent of the model used for spatio-temporal variability. Depending on individual behaviour and variability of the pollutant, these uncertainties average out again over time.  相似文献   

14.
Uncertainty plagues every effort to model subsurface processes and every decision made on the basis of such models. Given this pervasive uncertainty, virtually all practical problems in hydrogeology can be formulated in terms of (ecologic, monetary, health, regulatory, etc.) risk. This review deals with hydrogeologic applications of recent advances in uncertainty quantification, probabilistic risk assessment (PRA), and decision-making under uncertainty. The subjects discussed include probabilistic analyses of exposure pathways, PRAs based on fault tree analyses and other systems-based approaches, PDF (probability density functions) methods for propagating parametric uncertainty through a modeling process, computational tools (e.g., random domain decompositions and transition probability based approaches) for quantification of geologic uncertainty, Bayesian algorithms for quantification of model (structural) uncertainty, and computational methods for decision-making under uncertainty (stochastic optimization and decision theory). The review is concluded with a brief discussion of ways to communicate results of uncertainty quantification and risk assessment.  相似文献   

15.
This study develops a novel approach for modelling and examining the impacts of time–space land‐use changes on hydrological components. The approach uses an empirical land‐use change allocation model (CLUE‐s) and a distributed hydrological model (DHSVM) to examine various land‐use change scenarios in the Wu‐Tu watershed in northern Taiwan. The study also uses a generalized likelihood uncertainty estimation approach to quantify the parameter uncertainty of the distributed hydrological model. The results indicate that various land‐use policies—such as no change, dynamic change and simultaneous change—have different levels of impact on simulating the spatial distributions of hydrological components in the watershed study. Peak flow rates under simultaneous and dynamic land‐use changes are 5·71% and 2·77%, respectively, greater than the rate under the no land‐use change scenario. Using dynamic land‐use changes to assess the effect of land‐use changes on hydrological components is more practical and feasible than using simultaneous land‐use change and no land‐use change scenarios. Furthermore, land‐use change is a spatial dynamic process that can lead to significant changes in the distributions of ground water and soil moisture. The spatial distributions of land‐use changes influence hydrological processes, such as the ground water level of whole areas, particularly in the downstream watershed. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

16.
The intersection of the developing topic of rating curve and discharge series uncertainty with the topic of hydrological change detection (e.g., in response to land cover or climatic change) has not yet been well studied. The work herein explores this intersection, with consideration of a long‐term discharge response (1964–2007) for a ~650‐km2 headwater basin of the Mara River in west Kenya, starting with stream rating and daily gauge height data. A rating model was calibrated using Bayesian methods to quantify uncertainty intervals in model parameters and predictions. There was an unknown balance of random and systemic error in rating data scatter (a scenario not likely unique to this basin), which led to an unknown balance of noise and information in the calibrated statistical error model. This had implications on testing for hydrological change. Overall, indications were that shifts in basin's discharge response were rather subtle over the 44‐year period. A null hypothesis for change using flow duration curves (FDCs) from four different 8‐year data intervals could be either accepted or rejected over much of the net flow domain depending on different applications of the statistical error model (each with precedence in the literature). The only unambiguous indication of change in FDC comparisons appeared to be a reduction in lowest baseflow in recent years (flows with >98% exceedance probability). We defined a subjective uncertainty interval based on an intermediate balance of random and systematic error in the rating model that suggested a possibility of more prevalent impacts. These results have relevance to management in the Mara basin and to future studies that might establish linkages to historic land use and climatic factors. The concern about uncertain uncertainty intervals (uncertainty2) extends beyond the Mara and is relevant to testing change where non‐random rating errors may be important and subtle responses are investigated. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

17.
The potential for flooding and sediment transport is greatly affected by river channel form and changes in land use. Therefore the modelling of channel morphology prior to canalization and of land‐use change is important with respect to the prediction of floods and sediment yield and their consequences. A combination of land‐use transformation maps and soil properties shows certain decision rules for the conversion of forest into arable or vice versa. The model proposed, from this study, was used to simulate possible past and/or future channel and land‐use patterns. Subsequently, the outcome of this simulation was used to assess the risk of flooding, sediment transport and soil‐erosion under different conditions. In this study, channel morphology prior to canalization and land‐use change in the Ishikari basin, Hokkaido, Japan, were analysed by comparing three scenarios using a physical based channel and slope model. The results indicate that pre‐canalization channel morphology has a significant impact on flood peak, but no significant effect on sediment yield. In contrast, land‐use change has a significant effect on soil eroded from hillslopes, but no significant effect on flooding for Ishikari basin. This study also illustrates the challenges that a simple model, such as a physical based channel and slope model, can simulate large‐scale river basin processes using fewer hydrological data resources. Copyright © 2004 John Wiley & Sons, Ltd.  相似文献   

18.
Decision-making in traffic regulations is challenging with uncertainty in the environment. In this research we extend probabilistic engineering design concepts to policy decision-making for urban traffic with variability from field data. City traffic is simulated using user equilibrium and cellular automata. A cellular automata (CA) model is developed by combing existing CA models with tailored rules for local traffic behaviors in Tainan, Taiwan. Both passenger sedans and motorcycles are considered with the possibility of passing between different types of vehicles. The tailpipe emissions from all mobile sources are modeled as Gaussian dispersion with finite line sources. Speed limits of all roads are selected as independent policy design variables, resulting in a problem with 50 dimensions. We first study the impacts of a particular policy-setting on traffic behaviors and on the environment under various sources of uncertainties. The genetic algorithm, combined with probabilistic analysis, is then used to obtain the optimal regulations with the minimal cost to the environment in compliance to the current ambient air quality standards.  相似文献   

19.
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20.
We analyzed gravity data obtained in Juneau and global positioning system (GPS) data obtained from three PBO sites in southeastern Alaska (SE-AK), which are part of a US research facility called ‘EarthScope’, and we compared the obtained tidal amplitudes and phases with those estimated from the predicted tides including both effects of the body tide and ocean tide. Global tide models predict the ocean tides in this region of complex coastline and bathymetry. To improve the accuracy of prediction, we developed a regional ocean tide model in SE-AK.Our comparison results suggest: (1) by taking into account the ocean tide effect, the amplitude differences between the observation and the predicted body tide is remarkably reduced for both the gravity and displacement (e.g. for the M2 constituent, 8.5–0.3 μGal, and 2.4–0.1 cm at the AB50 GPS site in Juneau in terms of the vector sum of three components of the north–south, east–west and up–down), even though the ocean tide loading is large in SE-AK. (2) We have confirmed the precise point positioning (PPP) method, which was used to extract the tidal signals from the original GPS time series, works well to recover the tidal signals. Although the GPS analysis results still contain noise due to the atmosphere and multipath, we may conclude that the GPS observation surely detects the tidal signals with the sub-centimeter accuracy or better for some of the tidal constituents. (3) In order to increase the accuracy of the tidal prediction in SE-AK, it is indispensable to improve the regional ocean tide model developed in this study, especially for the phase.  相似文献   

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