Intelligent manipulation and calibration of parameters for hydrological models

Author name used in this publication: K. W. Chau2006-2007 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

The use of GIS in Brownfield redevelopment

In recent years, the issue of Brownfield site development - the re-use of previously used urban land - has gained a significant place in the planning agenda. However, not all Brownfield sites are derelict or contaminated land, some are significant as environmental amenities - be it part of wider ecosystem or a green area for the local population. The growing concern to include environmental aspects into the public debate have lead the Environment Agency, the Jackson Environment Institute and the Centre for Advanced Spatial Analysis to commission a short term pilot study to evaluate the contribution of a GIS for decision support and for "discussion support".In this paper, we describe how the state-of-the-art in geographic information (GI) and GI Science (GISc) can be used in a short term and limited project to achieve a practical and usable system. We are drawing on developments in information availability, as made accessible through the World Wide Web and research themes in GISc ranging from Multimedia GIS to Public Participation GIS

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

Toward improved streamflow forecasts: Value of semidistributed modeling

The focus of this study is to assess the performance improvements of semidistributed applications of the U.S. National Weather Service Sacramento Soil Moisture Accounting model on a watershed using radar-based remotely sensed precipitation data. Specifically, performance comparisons are made within an automated multicriteria calibration framework to evaluate the benefit of "spatial distribution" of the model input (precipitation), structural components (soil moisture and streamflow routing computations), and surface characteristics (parameters). A comparison of these results is made with those obtained through manual calibration. Results indicate that for the study watershed, there are performance improvements associated with semidistributed model applications when the watershed is partitioned into three subwatersheds; however, no additional benefit is gained from increasing the number of subwatersheds from three to eight. Improvements in model performance are demonstrably related to the spatial distribution of the model input and streamflow routing. Surprisingly, there is no improvement associated with the distribution of the surface characteristics (model parameters)

Assessment of coastal watershed erosion potential using geographic information systems and expert input for decision support

Sediment is a major impairment in many streams and rivers in the drainage basins along the northern Gulf of Mexico. The use of geospatial technologies improves assessment and decision making for the management of environmental resources and conditions for coastal watersheds. This research focuses on the development of a conceptual qualitative model enhanced with expert input for the assessment of soil erosion potential in coastal watersheds. The conceptual model is built upon five layers (slope, precipitation, soil brightness or exposure, Kactor, and stream density) like those in a standard numerical soil loss model such as the Revised Universal Soil Loss Equation (RUSLE). The conceptual model produced a continuous surface to index erosion potential. Pearson’s correlation coefficient was used to identify variable sensitivity. The model was most sensitive to Kactor variable, followed by soil brightness, stream density, and slope. The model was not sensitive to the precipitation variable due to the lack of variability across the watershed. Expert input was added to the conceptual model for erosion potential with the Analytical Hierarchy Process (AHP). The AHP is used to value the importance of criteria, providing a quantitative weight for the qualitative data. The expert input increased the overall importance of topographic features and this increased cell counts in the upper erosion potential classes. The AHP weights were altered in 1% increments ranging from plus to minus 20% producing 201 unique runs. A quartile analysis of the runs was used to define areas of model agreement. The quartile analysis allowed for the application of an analysis mask to identify areas of increased erosion potential for improved management related decisions. The conceptual and AHP erosion potential output data, including watershed management priority rankings, were published as web mapping services for story map development as a transition to a decision support system. The limits of the story map to allow user interactions with model output rendered an unacceptable platform for decision support. The story map does offer an alternative to static reports and could serve to improve dissemination of spatial data as well as technical reports and plans like a watershed management plan

A Data-Based Console Logger for Mission Operations Team Coordination

Concepts and prototypes1,2 are discussed for a data-based console logger (D-Logger) to meet new challenges for coordination among flight controllers arising from new exploration mission concepts. The challenges include communication delays, increased crew autonomy, multiple concurrent missions, reduced-size flight support teams that include multidisciplinary flight controllers during quiescent periods, and migrating some flight support activities to flight controller offices. A spiral development approach has been adopted, making simple, but useful functions available early and adding more extensive support later. Evaluations have guided the development of the D-Logger from the beginning and continue to provide valuable user influence about upcoming requirements. D-Logger is part of a suite of tools designed to support future operations personnel and crew. While these tools can be used independently, when used together, they provide yet another level of support by interacting with one another. Recommendations are offered for the development of similar projects

Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods

Automatic methods for model calibration seek to take advantage of the speed and power of digital computers, while being objective and relatively easy to implement. However, they do not provide parameter estimates and hydrograph simulations that are considered acceptable by the hydrologists responsible for operational forecasting and have therefore not entered into widespread use. In contrast, the manual approach which has been developed and refined over the years to result in excellent model calibrations is complicated and highly labor-intensive, and the expertise acquired by one individual with a specific model is not easily transferred to another person (or model). In this paper, we propose a hybrid approach that combines the strengths of each. A multicriteria formulation is used to "model" the evaluation techniques and strategies used in manual calibration, and the resulting optimization problem is solved by means of a computerized algorithm. The new approach provides a stronger test of model performance than methods that use a single overall statistic to aggregate model errors over a large range of hydrologic behaviors. The power of the new approach is illustrated by means of a case study using the Sacramento Soil Moisture Accounting model

Detecting anomalies in multivariate time series from automotive systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In the automotive industry test drives are conducted during the development of new vehicle models or as a part of quality assurance for series vehicles. During the test drives, data is recorded for the use of fault analysis resulting in millions of data points. Since multiple vehicles are tested in parallel, the amount of data that is to be analysed is tremendous. Hence, manually analysing each recording is not feasible. Furthermore the complexity of vehicles is ever-increasing leading to an increase of the data volume and complexity of the recordings. Only by effective means of analysing the recordings, one can make sure that the effort put in the conducting of test drives pays off. Consequently, effective means of test drive analysis can become a competitive advantage. This Thesis researches ways to detect unknown or unmodelled faults in recordings from test drives with the following two aims: (1) in a data base of recordings, the expert shall be pointed to potential errors by reporting anomalies, and (2) the time required for the manual analysis of one recording shall be shortened. The idea to achieve the first aim is to learn the normal behaviour from a training set of recordings and then to autonomously detect anomalies. The one-class classifier “support vector data description” (SVDD) is identified to be most suitable, though it suffers from the need to specify parameters beforehand. One main contribution of this Thesis is a new autonomous parameter tuning approach, making SVDD applicable to the problem at hand. Another vital contribution is a novel approach enhancing SVDD to work with multivariate time series. The outcome is the classifier “SVDDsubseq” that is directly applicable to test drive data, without the need for expert knowledge to configure or tune the classifier. The second aim is achieved by adapting visual data mining techniques to make the manual analysis of test drives more efficient. The methods of “parallel coordinates” and “scatter plot matrices” are enhanced by sophisticated filter and query operations, combined with a query tool that allows to graphically formulate search patterns. As a combination of the autonomous classifier “SVDDsubseq” and user-driven visual data mining techniques, a novel, data-driven, semi-autonomous approach to detect unmodelled faults in recordings from test drives is proposed and successfully validated on recordings from test drives. The methodologies in this Thesis can be used as a guideline when setting up an anomaly detection system for own vehicle data

Understanding nitrogen transfer dynamics in a small agricultural catchment: Comparison of a distributed (TNT2) and a semi distributed (SWAT) modeling approaches

The coupling of an hydrological and a crop model is an efficient approach to study the impact of the interactions between agricultural practices and catchment physical characteristics on stream water quality. We analyzed the consequences of using different modeling approaches of the processes controlling the nitrogen (N) dynamics in a small agricultural catchment monitored for 15 years. Two agro-hydrological models were applied: the fully distributed model TNT2 and the semi-distributed SWAT model. Using the same input dataset, the calibration process aimed at reproducing the same annual water and N balance in both models, to compare the spatial and temporal variability of the main N processes. The models simulated different seasonal cycles for soil N. The main processes involved were N mineralization and denitrification. TNT2 simulated marked seasonal variations with a net increase of mineralization in autumn, after a transient immobilization phase due to the burying of the straw with low C:N ratio. SWAT predicted a steady humus mineralization with an increase when straws are buried and a decrease afterwards. Denitrification was mainly occuring in autumn in TNT2 because of the dynamics of N availability in soil and of the climatic and hydrological conditions. SWAT predicts denitrification in winter, when mineral N is available in soil layers. The spatial distribution of these two processes was different as well: less denitrification in bottom land and close to ditches in TNT2, as a result of N transfer dynamics. Both models simulate correctly global trend and inter-annual variability of N losses in small agricultural catchment when a sufficient amount data is available for calibration. However, N processes and their spatial interactions are simulated very differently, in particular soil mineralization and denitrification. The use of such tools for prediction must be considered with care, unless a proper calibration and validation of the different N processes is carried out

A national-scale high-resolution runoff risk and channel network mapping workflow for diffuse pollution management

Managing diffuse pollution from agricultural land requires a spatially explicit risk assessment that can be applied over large areas. Major components of such assessments are the precise definition of both channel networks that often originate as small channels and streams, and Hydrologically Sensitive Areas (HSAs) of storm runoff that occur on land surfaces. Challenges relate to regions of complex topography and land use patterns, particularly those which have been heavily modified by arterial drainage. In this study, a national scale, transferrable workflow and analysis were developed using a specifically commissioned LiDAR survey. Research on the first half of Northern Ireland (6927 km2) is reported where field-edge drain to major river channels were mapped from 1 m (16 points per metre) digital terrain models, and in-field HSAs were defined across over 400,000 fields with a median field size of 0.86 ha. Manual drainage mapping supplemented with a novel automated drainage channel correction process resulted in an unparalleled high-resolution national drainage network with 37,320 km of channels, increasing mapped channel density from 0.9 km km−2 to 5.5 km km−2. The HSAs were based on a Soil Topographic Index (STI) system using hillslope and contributing area models combined with soil hydraulic characteristics. In all, 249 km2 of runoff risk HSAs were identified by extracting the top 95th percentile of the modelled STI as the areas with the highest propensity to generate in-field runoff. At field and individual farm scale these targeted risk maps of diffuse pollution were delivered to over 13,000 farmers and form part of the nationwide Soil Nutrient Health Scheme programme