On Latin Hypercube sampling for efficient uncertainty estimation of satellite rainfall observations in flood prediction

With the advent of the Global Precipitation Measurement (GPM) in 2009, satellite rainfall measurements are expected to become globally available at space–time scales relevant for flood prediction of un-gauged watersheds. For uncertainty assessment of such retrievals in flood prediction, error models need to be developed that can characterize the satellite's retrieval error structure. A full-scale assessment would require a large number of Monte Carlo (MC) runs of the satellite error model realizations, each passed through a hydrologic model, in order to derive the probability distribution in runoff. However, for slow running hydrologic models this can be computationally expensive and sometimes prohibitive. In this study, Latin Hypercube Sampling (LHS) was implemented in a satellite rainfall error model to explore the degree of computational efficiency that could be achieved with a complex hydrologic model. It was found that the LHS method is particularly suited for storms with moderate rainfall. For assessment of errors in time to peak, peak runoff, and runoff volume no significant computational advantage of LHS over the MC method was observed. However, the LHS was able to produce the 80% and higher confidence limits in runoff simulation with the same degree of reliability as MC, but with almost two orders of magnitude fewer simulations. Results from this study indicate that a LHS constrained sampling scheme has the potential to achieve computational efficiency for hydrologic assessment of satellite rainfall retrievals involving: (1) slow running models (such as distributed hydrologic models and land surface models); (2) large study regions; and (3) long study periods; provided the assessment is confined to analysis of the large error bounds of the runoff distribution.     

The value of information in a long–term hydro–thermal electrical planning model

This article presents the analysis, comparison, and application of two alternative models to the optimal long–term operation planning of an hydro–thermal power system under conditions of uncertainty. The electrical system considered comprises one large reservoir, with interannual regulation capacity, and several smaller ones. The analyzed models employ stochastic dynamic programming as the solution methodology. The fundamental problem is to decide, on every temporal stage, how much water to use for generating purposes and how much to store, in order to minimize the total thermal and shortage costs. The original version of the studied model, created originally to forecast fuel consumption, assumes that the decision regarding the water release from the main reservoir is taken knowing the future hydrologic conditions. This criterion is known as wait–and–see . On the contrary, the new versions of the model, proposed in this article, consider a here–and–now criterion. Specifically, it is assumed that the future hydrologic conditions are not known at the time of making the operational decisions. The difference between the optimal cost of the proposed models and the original model defines the value of having the information regarding future hydrologic conditions before taking any decision. This value is generally known as the expected value of perfect information.     

Modeling of urban growth dynamics and its impact on surface runoff characteristics

Modeling urban growth and generating scenarios are essential for studying the impact and sustainability of an urban hydrologic system. Urban systems are regarded as complex self-organizing systems, where the dynamic transitions from one form of landuse to another occur over a period of time. Therefore, a modeling framework that captures and simulates this complex behavior is essential for generating urban growth scenarios. Cellular Automata (CA)-based models have the potential to model such discrete dynamic systems. In this study, a constraint-based binary CA model was used to predict the future urban growth scenario of the city of Roorkee (India). A hydrologic model was applied on the simulated urban catchment to study its hydrologic response. The Natural Resources Conservation Service Curve Number (NRCS-CN) method, which is suitable for ungauged urban watersheds, was adopted to determine the impact of urban growth on the quantity of storm water runoff over a period of time. The results indicate that urban growth has a linear relationship with peak discharge and time to peak for the catchment under investigation.     

Monthly flow forecast for Mississippi River basin using artificial neural networks

Long-term monthly flow forecasts are essential for decision making in a river basin system. Many studies have already been reported on monthly as well as seasonal forecast using artificial neural networks (ANNs). This study demonstrates that monthly forecasts can be significantly improved if the input variables in ANN models are chosen with due consideration, even if number of training patterns are less. Monthly forecast models up to 12-month lead-time have been developed for Mississippi River in USA. It is seen that direct forecast with only antecedent flows as inputs does not improve the result. It is better to develop individual models for each month separately with information from previous years for the same month. Further, the forecast is found to significantly improve if the difference in predicted and actual flows is also included as one of the input variables (i.e. error updating), particularly where there is a clearly observed pattern in the historical information.     

流域系统森林作用水文动态过程的仿真分析

该文对森林状态所引起的流域系统水文动态过程特征的变化作了计算机仿真分析。流域系统的森林状态随时间变化,包含森林的自然生长、采伐、造林等。而自然地质环境,如地形地貌等流域特征相对保持稳定。所以不同时期的流域水文动态过程特性变化主要由森林状态的变化所引起。选择长江流域湖南省境内三个集水面积在500-950KM^2的流域系统,收集连续30年的水文气象数据,以连续和5年数据作为一组,分成多个对比样本系列,辨别不同时期的水文动态过程模型。应用的模型有ARX和模糊神经网络。对模型作脉冲输入的响应仿真,得到不同时期的水文动态过程特性。比较各个时期的流域水文动态过程特性和相应时期的森林状态,得出了森林作用于水文动态过程的一些结论。     

Use of a hydrologic model as a flood control policy statement

For Southern California watersheds, as is the case for most watersheds in the United States, rainfall-runoff data are relatively sparse such that the calibration of a hydrologic model is uncertain. With the large number and types of hydrologic models currently available, the choice of the “best” hydrologic model to use is not clear. Because of the limited data, the hydrologic model must be simple in order to validate parameter values and submodel algorithms. Due to the uncertainty in stream gage data frequency analysis, a level of confidence (e.g., 85%) should be chosen to provide a level of protection against a specified flood return frequency (e.g., 100-year). Due to the calibrated model range and distribution of possible outcomes caused by uncertainty in modelling parameter values, the use of a regionally calibrated model at an ungaged catchment needs to address the probability that the hydrologic model estimate of flood quantities (e.g., peak flow rates) achieves the level of protection for a specified flood level. In this paper, a design storm unit hydrograph model is developed and calibrated with respect to model parameter values and with respect to runoff frequency tendencies (design storm) in order to address each of these issues.     

Using a parallelized MCMC algorithm in R to identify appropriate likelihood functions for SWAT

Markov Chain Monte Carlo (MCMC) algorithms allow the analysis of parameter uncertainty. This analysis can inform the choice of appropriate likelihood functions, thereby advancing hydrologic modeling with improved parameter and quantity estimates and more reliable assessment of uncertainty. For long-running models, the Differential Evolution Adaptive Metropolis (DREAM) algorithm offers spectacular reductions in time required for MCMC analysis. This is partly due to multiple parameter sets being evaluated simultaneously. The ability to use this feature is hindered in models that have a large number of input files, such as SWAT. A conceptually simple, robust method for applying DREAM to SWAT in R is provided. The general approach is transferrable to any executable that reads input files. We provide this approach to reduce barriers to the use of MCMC algorithms and to promote the development of appropriate likelihood functions.     

Mechanism-based emulation of dynamic simulation models: Concept and application in hydrology

Many model-based investigation techniques, such as sensitivity analysis, optimization, and statistical inference, require a large number of model evaluations to be performed at different input and/or parameter values. This limits the application of these techniques to models that can be implemented in computationally efficient computer codes. Emulators, by providing efficient interpolation between outputs of deterministic simulation models, can considerably extend the field of applicability of such computationally demanding techniques. So far, the dominant techniques for developing emulators have been priors in the form of Gaussian stochastic processes (GASP) that were conditioned with a design data set of inputs and corresponding model outputs. In the context of dynamic models, this approach has two essential disadvantages: (i) these emulators do not consider our knowledge of the structure of the model, and (ii) they run into numerical difficulties if there are a large number of closely spaced input points as is often the case in the time dimension of dynamic models. To address both of these problems, a new concept of developing emulators for dynamic models is proposed. This concept is based on a prior that combines a simplified linear state space model of the temporal evolution of the dynamic model with Gaussian stochastic processes for the innovation terms as functions of model parameters and/or inputs. These innovation terms are intended to correct the error of the linear model at each output step. Conditioning this prior to the design data set is done by Kalman smoothing. This leads to an efficient emulator that, due to the consideration of our knowledge about dominant mechanisms built into the simulation model, can be expected to outperform purely statistical emulators at least in cases in which the design data set is small. The feasibility and potential difficulties of the proposed approach are demonstrated by the application to a simple hydrological model.     

Towards integrating GIS and catchment models

Modeling the impact of non-point source pollution in catchments is a complex problem, and one that has troubled natural resource managers for many years. The development of spatially distributed hydrologic models has led to improved model forecasting at the cost of requiring more detailed spatial information. In addition, the analysis is much more sensitive to errors in the data. Incorporation of catchment models into a Geographical Information System (GIS) has improved matters by streamlining data input and providing better interpretation of model outputs. This paper reviews different strategies for linking a catchment model with GIS. It examines data issues related to the performance of models and how well they match physical landscape conditions. Integration with GIS is shown to be necessary for the efficient and proper operation of models in resource management situations. The paper concludes that tighter integration between generic sub-models for physical landscape processes and GIS is still required.     

Integration of an energy balance snowmelt model into an open source modeling framework

This paper presents a data model for organizing the inputs and outputs of an energy balance snowmelt model (the Utah Energy Balance Model, UEB) that provides a foundation for its integration into the EPA BASINS modeling framework and enables its coupling with other hydrologic models in this system. Having UEB as a BASINS component has facilitated its coupling with the Geospatial Streamflow Forecast Model (GeoSFM) to compute the melting of glaciers and subsequent streamflow in the Himalayas. The data model uses a combination of structured text and network Common Data Form (netCDF) files to represent parameters, geographical, time series, and gridded space-time data. We describe the design and structure of this data model, integration methodology of UEB and GeoSFM and illustrate the effectiveness of the resulting coupled models for the computation of surface water input and streamflow for a glaciated watershed in Nepal Himalayas.     

Evaluation of AMSR-E soil moisture results using the in-situ data over the Little River Experimental Watershed, Georgia

An operational global soil moisture data product is currently generated from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua satellite using the retrieval procedure described in Njoku and Chan [Njoku, E.G. and Chan, S.K., 2006. Vegetation and surface roughness effects on AMSR-E land observations, remote sensing environment, 100(2), 190-199]. We have generated another soil moisture dataset from the same AMSR-E observed brightness temperature data using the Land Surface Microwave Emission Model (LSMEM) adopting a different estimation method. This paper focuses on a comparison study of soil moisture estimates from the above two methods. The soil moisture data from current AMSR-E product and LSMEM are compared with the in-situ measured soil moisture datasets over the Little River Experimental Watershed (LREW), Georgia, USA for the year 2003. The comparison study was carried out separately for the AMSR-E daytime and night time overpasses. The LSMEM method performed better than the current operational AMSR-E retrieval algorithm in this study. The differences between the AMSR-E and LSMEM results are mostly due to differences in various simplifications and assumptions made for variables in the radiative transfer equations and the soil and vegetation based physical models and the accuracy of the input surface temperature datasets for the LSMEM forward model approach. This study confirms that remote sensing data have the potential to provide useful hydrologic information, but the accuracy of the geophysical parameters could vary depending on the estimation methods. It cannot be concluded from this study whether the soil moisture estimation by the LSMEM approach will perform better in other geographic, climatic or topographic conditions. Nevertheless, this study sheds light on the effects of different approaches for the estimation of geophysical parameters, which may be useful for current and future satellite missions.     

Importance of spatially distributed hydrologic variables for land use change modeling

Land use changes have a pronounced impact on hydrology. Vice versa, hydrologic changes affect land use patterns. The objective of this study is to test whether hydrologic variables can explain land use change. We employ a set of spatially distributed hydrologic variables and compare it against a set of commonly used explanatory variables for land use change. The explanatory power of these variables is assessed by using a logistic regression approach to model the spatial distribution of land use changes in a meso-scale Indian catchment. When hydrologic variables are additionally included, the accuracies of the logistic regression models improve, which is indicated by a change in the relative operating characteristic statistic (ROC) by up to 11%. This is mostly due to the complementarity of the two datasets that is reflected in the use of 44% commonly used variables and 56% hydrologic variables in the best models for land use change.     

Applying input variables selection technique on input weighted support vector machine modeling for BOF endpoint prediction

Basic oxygen furnace (BOF) steelmaking is a complex process and dynamic model is very important for endpoint control. It is usually difficult to build a precise BOF endpoint dynamic model because many input variables affect the endpoint carbon content and temperature. For this problem, two effective variables selection steps: mechanism analysis and mutual information calculation are proposed to choose appropriate input variables according to a variable selection algorithm. Then, the selected inputs are weighted on the basis of mutual information values. Finally, two input weighted support vector machine BOF endpoint dynamic models are constructed to predict endpoint carbon content and temperature. Results show that the variable selection for BOF endpoint prediction model is essential and effective. The complexity and precise of two endpoint prediction models are improved.     

Coupling climate and hydrological models: Interoperability through Web Services

Understanding regional-scale water resource systems requires understanding coupled hydrologic and climate interactions. The traditional approach in the hydrologic sciences and engineering fields has been to either treat the atmosphere as a forcing condition on the hydrologic model, or to adopt a specific hydrologic model design in order to be interoperable with a climate model. We propose here a different approach that follows a service-oriented architecture and uses standard interfaces and tools: the Earth System Modeling Framework (ESMF) from the weather and climate community and the Open Modeling Interface (OpenMI) from the hydrologic community. A novel technical challenge of this work is that the climate model runs on a high performance computer and the hydrologic model runs on a personal computer. In order to complete a two-way coupling, issues with security and job scheduling had to be overcome. The resulting application demonstrates interoperability across disciplinary boundaries and has the potential to address emerging questions about climate impacts on local water resource systems. The approach also has the potential to be adapted for other climate impacts applications that involve different communities, multiple frameworks, and models running on different computing platforms. We present along with the results of our coupled modeling system a scaling analysis that indicates how the system will behave as geographic extents and model resolutions are changed to address regional-scale water resources management problems.     

A noninteractive procedure for land-use determination

In this paper, we report on a complete operational procedure designed for use by the U.S. Army Corps of Engineers for the deterimation of land-use information for hydrologic planning purposes. The procedure combines photo interpretation techniques and the batch-mode computer analysis of Landsat digital data. Since the operational constraints preclude the use of dedicated, interactive image processing facilities, several novel approaches to geometric correction, classification, and data input/output were developed. The procedure is summarized, and examples of the application of the procedure to urban watersheds are described. In spite of the constraints, the procedure provides results comparable in accuracy and lower in cost than those provided by commercial services using interactive techniques.     

Using a data grid to automate data preparation pipelines required for regional-scale hydrologic modeling

Modeling a regional-scale hydrologic system introduces major data challenges related to the access and transformation of heterogeneous datasets into the information needed to execute a hydrologic model. These data preparation activities are difficult to automate, making the reproducibility and extensibility of model simulations conducted by others difficult or even impossible. This study addresses this challenge by demonstrating how the integrated Rule Oriented Data Management System (iRODS) can be used to support data processing pipelines needed when using data-intensive models to simulate regional-scale hydrologic systems. Focusing on the Variable Infiltration Capacity (VIC) model as a case study, data preparation steps are sequenced using rules within iRODS. VIC and iRODS are applied to study hydrologic conditions in the Carolinas, USA during the period 1998–2007 to better understand impacts of drought within the region. The application demonstrates how iRODS can support hydrologic modelers to create more reproducible and extensible model-based analyses.     

Optimal input/output reduction in production processes

While conventional Data Envelopment Analysis (DEA) models set targets for each operational unit, this paper considers the problem of input/output reduction in a centralized decision making environment. The purpose of this paper is to develop an approach to input/output reduction problem that typically occurs in organizations with a centralized decision-making environment. This paper shows that DEA can make an important contribution to this problem and discusses how DEA-based model can be used to determine an optimal input/output reduction plan. An application in banking sector with limitation in IT investment shows the usefulness of the proposed method.     

A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation

Doubtlessly the first step in a river management is the precipitation modeling over the related watershed. However, considering high-stochastic property of the process, many models are being still developed in order to define such a complex phenomenon in the field of hydrologic engineering. Recently artificial neural network (ANN) as a non-linear inter-extrapolator is extensively used by hydrologists for rainfall modeling as well as other fields of hydrology.In the current research, the wavelet analysis was linked to the ANN concept for prediction of Ligvanchai watershed precipitation at Tabriz, Iran. For this purpose, the main time series was decomposed to some multi-frequently time series by wavelet theory, then these time series were imposed as input data to the ANN to predict the precipitation 1 month ahead. The obtained results show the proposed model can predict both short- and long-term precipitation events because of using multi-scale time series as the ANN input layer.