GEOSTATISTICAL ESTIMATION OF HORIZONTAL HYDRAULIC CONDUCTIVITY FOR THE KIRKWOOD‐COHANSEY AQUIFER1

ABSTRACT: The Kirkwood‐Cohansey aquifer has been identified as a critical source for meeting existing and expected water supply needs for southern New Jersey. Several contaminated sites exist in the region; their impact on the aquifer has to be evaluated using ground water flow and transport models. Ground water modeling depends on availability of measured hydrogeologic data (e.g., hydraulic conductivity, for parameterization of the modeling runs). However, field measurements of such critical data have inadequate spatial density, and their locations are often clustered. The goal of this study was to research, compile, and geocode existing data, then use geostatistics and advanced mapping methods to develop a map of horizontal hydraulic conductivity for the Kirkwood‐Cohansey aquifer. Spatial interpolation of horizontal hydraulic conductivity measurements was performed using the Bayesian Maximum Entropy (BME) Method implemented in the BMELib code library. This involved the integration of actual measurements with soft information on likely ranges of hydraulic conductivity at a given location to obtain estimate maps. The estimation error variance maps provide an insight into the uncertainty associated with the estimates, and indicate areas where more information on hydraulic conductivity is required.     

INTERACTIVE DISTRIBUTED CONSERVATION PLANNING1

ABSTRACT: In the environmental and agricultural conservation planning process, more efficient and effective tools are needed for planners to assist private landowners with making wiser land use decisions. Current methods are slow, inefficient, and costly. Scientific techniques have not been fully implemented within the planning process, yet such plans are increasingly needed to meet water quality and Total Maximum Daily Load (TMDL) requirements. The objectives of this study are to (a) utilize the web for accessing an integrated science‐based land use decision support system; (b) link decision tools, models, and databases to the user via the web; (c) link distributed models and databases for enhanced planning efficiency; and (d) integrate the above into an easily usable and readily accessible system. The procedures resulting in the initial design involved planning expertise and focus groups' input. The system was developed in partnership with the Natural Resources Conservation Service of the U.S. Department of Agriculture and several state agencies. A survey of 150 certified conservation planners, the end users, was conducted to identify the data sets and planning tools needed. Data, tools, and models then were selected and integrated into a web accessible system. Specifically, the first generation used a web interactive Geographic Information System (GIS) that overlaid onto digital orthoquads and/or soils polygons field boundaries, transportation, hydrologic features (such as drains, rivers, lakes, etc.), and high pesticide risk runoff or infiltration areas. Conservation planners found they could save time with the system. Clients could access the system quickly to help them prepare for meeting with their planner. Previously acquiring GIS maps in some cases had been a lengthy process that limited use of the information in land use decisions.     

Decision support tools for collaborative marine spatial planning: identifying potential sites for tidal energy devices around the Mull of Kintyre,Scotland

The expansion of offshore renewable energy production, such as wind, wave and tidal energy, is likely to lead to conflict between different users of the sea. Two types of spatial decision support tools were developed to support stakeholder workshops. A value mapping tool combines regional attributes with local knowledge. A negotiation support tool uses these value maps to support stakeholders in finding acceptable locations for tidal energy devices. Interactive value mapping proved useful to address deficiencies in data and to create credibility for these maps. The negotiation tool helped stakeholders in balancing objectives of the various stakeholders.     

A Hydraulic MultiModel Ensemble Framework for Visualizing Flood Inundation Uncertainty

While deterministic forecasts provide a single realization of potential inundation, the inherent uncertainty associated with forecasts also needs to be conveyed for improved decision support. The objective of this study was to develop an ensemble framework for the quantification and visualization of uncertainty associated with flood inundation forecast maps. An 11‐member ensemble streamflow forecast at lead times from 0 to 48 hr was used to force two hydraulic models to produce a multimodel ensemble. The hydraulic models used are (1) the International River Interface Cooperative along with Flow and Sediment Transport with Morphological Evolution of Channels solver and (2) the two‐dimensional Hydrologic Engineering Center‐River Analysis System. Uncertainty was quantified and augmented onto flood inundation maps by calculating statistical spread among the ensemble members. For visualization, a series of probability flood maps conveying the uncertainty in forecasted water extent, water depth, and flow velocity was disseminated through a web‐based decision support tool. The results from this study offer a framework for quantifying and visualizing model uncertainty in forecasted flood inundation maps.     

Error Assessment for Height Above the Nearest Drainage Inundation Mapping

Real‐time flood inundation mapping is vital for emergency response to help protect life and property. Inundation mapping transforms rainfall forecasts into meaningful spatial information that can be utilized before, during, and after disasters. While inundation mapping has traditionally been conducted on a local scale, automated algorithms using topography data can be utilized to efficiently produce flood maps across the continental scale. The Height Above the Nearest Drainage method can be used in conjunction with synthetic rating curves (SRCs) to produce inundation maps, but the performance of these inundation maps needs to be assessed. Here we assess the accuracy of the SRCs and calculate statistics for comparing the SRCs to rating curves obtained from hydrodynamic models calibrated against observed stage heights. We find SRCs are accurate enough for large‐scale approximate inundation mapping while not as accurate when assessing individual reaches or cross sections. We investigate the effect of terrain and channel characteristics and observe reach length and slope predict divergence between the two types of rating curves, and SRCs perform poorly for short reaches with extreme slope values. We propose an approach to recalculate the slope in Manning’s equation as the weighted average over a minimum distance and assess accuracy for a range of moving window lengths.     

Informing the development of decision support tools for risk management: the case of electrical and magnetic fields

This paper describes tools developed through a community consultative process to help decision makers manage electrical and magnetic fields (EMF) health risk. The process involved in‐depth interviews with experts (N=12) and focus group discussions with seven different stakeholder groups. The results reveal commonly held intense public concerns about the long‐term health effects of EMF. These concerns were further reinforced by the lack of public trust in both government and industry with regards to EMF risk management. Overall, the participants wanted tools that can be used to manage EMF information, scientific uncertainty about EMF and the complex environment in which EMF issues are embedded. The findings contributed to a mapping out of response formats to address public concerns related to risk, hazard, trust, accountability and fairness across a range of stakeholder groups. These tools and their roles in the management of complex and variable risks, involving new circumstances (e.g. privatization) and information (e.g. new scientific studies) are presented. The importance of recognizing and working with uncertainty through adaptive management strategies, using qualitative approaches, is also discussed.     

Demonstrating Floodplain Uncertainty Using Flood Probability Maps1

Abstract: The U.S. Federal Emergency Management Agency (FEMA) flood maps depict the 100‐year recurrence interval floodplain boundary as a single line. However, because of natural variability and model uncertainty, no floodplain extents can be accurately defined by a single line. This article presents a new approach to floodplain mapping that takes advantage of accepted methodologies in hydrologic and hydraulic analysis while including the effects of uncertainty. In this approach, the extents of computed floodplain boundaries are defined as a continuous map of flood probabilities, rather than as a single line. Engineers and planners can use these flood probability maps for viewing the uncertainty of a floodplain boundary at any recurrence interval. Such a flood probability map is a useful tool for visualizing the uncertainty of a floodplain boundary and represents greater honesty in engineering technologies that are used for flood mapping. While institutional barriers may prevent adoption of such definitions for use in graduated flood insurance rates (as most other insurance industries use to account for relative risks), the methods open the door technically to such a reality.     

GIS‐Based Spatial Precipitation Estimation: A Comparison of Geostatistical Approaches1

Abstract: As one of the primary inputs that drive watershed dynamics, the estimation of spatial variability of precipitation has been shown to be crucial for accurate distributed hydrologic modeling. In this study, a Geographic Information System program, which incorporates Nearest Neighborhood (NN), Inverse Distance Weighted (IDW), Simple Kriging (SK), Ordinary Kriging (OK), Simple Kriging with Local Means (SKlm), and Kriging with External Drift (KED), was developed to facilitate automatic spatial precipitation estimation. Elevation and spatial coordinate information were used as auxiliary variables in SKlm and KED methods. The above spatial interpolation methods were applied in the Luohe watershed with an area of 5,239 km², which is located downstream of the Yellow River basin, for estimating 10 years’ (1991‐2000) daily spatial precipitation using 41 rain gauges. The results obtained in this study show that the spatial precipitation maps estimated by different interpolation methods have similar areal mean precipitation depth, but significantly different values of maximum precipitation, minimum precipitation, and coefficient of variation. The accuracy of the spatial precipitation estimated by different interpolation methods was evaluated using a correlation coefficient, Nash‐Sutcliffe efficiency, and relative mean absolute error. Compared with NN and IDW methods that are widely used in distributed hydrologic modeling systems, the geostatistical methods incorporated in this GIS program can provide more accurate spatial precipitation estimation. Overall, the SKlm_EL_X and KED_EL_X, which incorporate both elevation and spatial coordinate as auxiliary into SKlm and KED, respectively, obtained higher correlation coefficient and Nash‐Sutcliffe efficiency, and lower relative mean absolute error than other methods tested. The GIS program developed in this study can serve as an effective and efficient tool to implement advanced geostatistics methods that incorporate auxiliary information to improve spatial precipitation estimation for hydrologic models.     

Uncertainty Assessment for Management of Soil Contaminants with Sparse Data

In order for soil resources to be sustainably managed, it is necessary to have reliable, valid data on the spatial distribution of their environmental impact. However, in practice, one often has to cope with spatial interpolation achieved from few data that show a skewed distribution and uncertain information about soil contamination. We present a case study with 76 soil samples taken from a site of 15 square km in order to assess the usability of information gleaned from sparse data. The soil was contaminated with cadmium predominantly as a result of airborne emissions from a metal smelter. The spatial interpolation applies lognormal anisotropic kriging and conditional simulation for log-transformed data. The uncertainty of cadmium concentration acquired through data sampling, sample preparation, analytical measurement, and interpolation is factor 2 within 68.3 % confidence. Uncertainty predominantly results from the spatial interpolation necessitated by low sampling density and spatial heterogeneity. The interpolation data are shown in maps presenting likelihoods of exceeding threshold values as a result of a lognormal probability distribution. Although the results are not deterministic, this procedure yields a quantified and transparent estimation of the contamination, which can be used to delineate areas for soil improvement, remediation, or restricted area use, based on the decision-makers probability safety requirement.     

Assessing local knowledge use in agroforestry management with cognitive maps

Small-holder farmers often develop adaptable agroforestry management techniques to improve and diversify crop production. In the cocoa growing region of Ghana, local knowledge on such farm management holds a noteworthy role in the overall farm development. The documentation and analysis of such knowledge use in cocoa agroforests may afford an applicable framework to determine mechanisms driving farmer preference and indicators in farm management. This study employed 12 in-depth farmer interviews regarding variables in farm management as a unit of analysis and utilized cognitive mapping as a qualitative method of analysis. Our objectives were (1) to illustrate and describe agroforestry management variables and associated farm practices, (2) to determine the scope of decision making of individual farmers, and (3) to investigate the suitability of cognitive mapping as a tool for assessing local knowledge use. Results from the cognitive maps revealed an average of 16 ± 3 variables and 19 ± 3 links between management variables in the farmer cognitive maps. Farmer use of advantageous ecological processes was highly central to farm management (48% of all variables), particularly manipulation of organic matter, shade and food crop establishment, and maintenance of a tree stratum as the most common, highly linked variables. Over 85% of variables included bidirectional arrows, interpreted as farm management practices dominated by controllable factors, insofar as farmers indicated an ability to alter most farm characteristics. Local knowledge use on cocoa production revealed detailed indicators for site evaluation, thus affecting farm preparation and management. Our findings suggest that amid multisourced information under conditions of uncertainty, strategies for adaptable agroforestry management should integrate existing and localized management frameworks and that cognitive mapping provides a tool-based approach to advance such a management support system.     

The need for integration of drought monitoring tools for proactive food security management in sub‐Saharan Africa

Reducing the impact of drought and famine remains a challenge in sub‐Saharan Africa despite ongoing drought relief assistance in recent decades. This is because drought and famine are primarily addressed through a crisis management approach when a disaster occurs, rather than stressing preparedness and risk management. Moreover, drought planning and food security efforts have been hampered by a lack of integrated drought monitoring tools, inadequate early warning systems (EWS), and insufficient information flow within and between levels of government in many sub‐Saharan countries. The integration of existing drought monitoring tools for sub‐Saharan Africa is essential for improving food security systems to reduce the impacts of drought and famine on society in this region. A proactive approach emphasizing integration requires the collective use of multiple tools, which can be used to detect trends in food availability and provide early indicators at local, national, and regional scales on the likely occurrence of food crises. In addition, improving the ability to monitor and disseminate critical drought‐related information using available modern technologies (e.g., satellites, computers, and modern communication techniques) may help trigger timely and appropriate preventive responses and, ultimately, contribute to food security and sustainable development in sub‐Saharan Africa.     

Evaluation of Methods for Delineating Riparian Zones in a Semi‐Arid Montane Watershed

Riparian zones in semi‐arid, mountainous regions provide a disproportionate amount of the available wildlife habitat and ecosystem services. Despite their importance, there is little guidance on the best way to map riparian zones for broad spatial extents (e.g., large watersheds) when detailed maps from field data or high‐resolution imagery and terrain data are not available. Using well‐established accuracy metrics (e.g., kappa, precision, computational complexity), we evaluated eight methods commonly used to map riparian zones. Focusing on a semi‐arid, mountainous watershed, we found that the most accurate and robust method for mapping riparian zones combines data on upstream drainage area and valley topography. That method performed best regardless of stream order, and was most effective when implemented with fine resolution topographic and stream line data. Other commonly used methods to model riparian zones, such as those based on fixed‐width buffers, yielded inaccurate results. We recommend that until very‐high resolution (<1 m) elevation data are available at broad extents, models of riparian zones for semi‐arid mountainous regions should incorporate drainage area, valley topography, and quantify uncertainty.     

Use of ecosystem information to improve soil organic carbon mapping of a Mediterranean island

Detailed maps of soil C are needed to guide sustainable soil uses and management decisions. The quality of soil C maps of Italian Mediterranean areas may be improved and the sampling density reduced using secondary data related to the nature of the ecosystem. The current study was conducted to determine: (i) the improvements obtainable in mapping soil C over a Mediterranean island by using ecosystem features and (ii) the effect of different sampling densities on the map accuracy. This work relied on field sampling (n=164) of soil properties measured over the island of Pianosa (Central Italy). Statistical analysis assessing the relationship between soil properties and ecosystem features revealed that the conceptual model of ecosystems defined on the basis of environmental features such as vegetation cover, land use, and soil type was mainly related to the variation of soil organic carbon (OC) content and to the type of Mediterranean environment. The distribution of ecosystems was used to improve the accuracy of soil OC maps obtainable by a simple interpolation approach (ordinary kriging). Substantial improvement was obtained by: (i) stratification into ecosystem types and (ii) applying locally calibrated regressions to satellite imagery that introduced both inter-ecosystem and intra-ecosystem information linked to vegetation features. This study showed that interpolation methods using information on ecosystem distribution can produce accurate maps of soil OC in Mediterranean environments, mostly because of the linkage between soil OC and vegetation types, which are spatially fragmented and heterogeneous.     

A Comprehensive Python Toolkit for Accessing High‐Throughput Computing to Support Large Hydrologic Modeling Tasks

The National Flood Interoperability Experiment (NFIE) was an undertaking that initiated a transformation in national hydrologic forecasting by providing streamflow forecasts at high spatial resolution over the whole country. This type of large‐scale, high‐resolution hydrologic modeling requires flexible and scalable tools to handle the resulting computational loads. While high‐throughput computing (HTC) and cloud computing provide an ideal resource for large‐scale modeling because they are cost‐effective and highly scalable, nevertheless, using these tools requires specialized training that is not always common for hydrologists and engineers. In an effort to facilitate the use of HTC resources the National Science Foundation (NSF) funded project, CI‐WATER, has developed a set of Python tools that can automate the tasks of provisioning and configuring an HTC environment in the cloud, and creating and submitting jobs to that environment. These tools are packaged into two Python libraries: CondorPy and TethysCluster. Together these libraries provide a comprehensive toolkit for accessing HTC to support hydrologic modeling. Two use cases are described to demonstrate the use of the toolkit, including a web app that was used to support the NFIE national‐scale modeling.     

Developing Subseasonal to Seasonal Climate Forecast Products for Hydrology and Water Management

We describe a new effort to enhance climate forecast relevance and usability through the development of a system for evaluating and displaying real‐time subseasonal to seasonal (S2S) climate forecasts on a watershed scale. Water managers may not use climate forecasts to their full potential due to perceived low skill, mismatched spatial and temporal resolutions, or lack of knowledge or tools to ingest data. Most forecasts are disseminated as large‐domain maps or gridded datasets and may be systematically biased relative to watershed climatologies. Forecasts presented on a watershed scale allow water managers to view forecasts for their specific basins, thereby increasing the usability and relevance of climate forecasts. This paper describes the formulation of S2S climate forecast products based on the Climate Forecast System version 2 (CFSv2) and the North American Multi‐Model Ensemble (NMME). Forecast products include bi‐weekly CFSv2 forecasts, and monthly and seasonal NMME forecasts. Precipitation and temperature forecasts are aggregated spatially to a United States Geological Survey (USGS) hydrologic unit code 4 (HUC‐4) watershed scale. Forecast verification reveals appreciable skill in the first two bi‐weekly periods (Weeks 1–2 and 2–3) from CFSv2, and usable skill in NMME Month 1 forecast with varying skills at longer lead times dependent on the season. Application of a bias‐correction technique (quantile mapping) eliminates forecast bias in the CFSv2 reforecasts, without adding significantly to correlation skill.     

EFFECTS OF IMPROVED PRECIPITATION ESTIMATES ON AUTOMATED RUNOFF MAPPING: EASTERN UNITED STATES1

ABSTRACT: We evaluated maps of runoff created by means of two automated procedures. We implemented each procedure using precipitation estimates of both 5-km and 10-km resolution from PRISM (Parameter-elevation Regressions on Independent Slopes Model). Our goal was to determine if using the 5-km PRISM estimates would improve map accuracy. Visual inspection showed good general agreement among our runoff maps, as well as between our maps and one produced using a manual method. A quantitative uncertainty analysis comparing runoff interpolated from our maps with gage data that had been withheld showed slightly smaller actual and percentage interpolation errors for the 5-km PRISM-based maps. Our analyses suggest a modest region-wide improvement in runoff map accuracy with the use of PRISM-based precipitation estimates of 5-km (compared to 10-km) resolution.     

A Faster and Economical Approach to Floodplain Mapping Using Soil Information

Flood inundation maps play a key role in assessment and mitigation of potential flood hazards. However, owing to high costs associated with the conventional flood mapping methods, many communities in the United States lack flood inundation maps. The objective of this study is to develop and examine an economical alternative approach to floodplain mapping using widely available soil survey geographic (SSURGO) database. In this study, floodplain maps are developed for the entire state of Indiana, and some counties in Minnesota, Wisconsin, and Washington states by identifying flood‐prone soil map units based on their attributes. For validation, the flood extents obtained from SSURGO database are compared with the extents from other floodplain maps such as the Federal Emergency Management Agency issued flood insurance rate maps (FIRMs), flood extents observed during past floods, and flood maps derived using digital elevation models. In general, SSURGO‐based floodplain maps (SFMs) are largely in agreement with other flood inundation maps. Specifically, the floodplain extents from SFMs cover 78‐95% area compared to FIRMs and observed flood extents. Thus, albeit with a slight loss in accuracy, the SSURGO approach offers an economical and fast alternative for floodplain mapping. In particular, it has potentially high utility in areas where no detailed flood studies have been conducted.     

Site search and multicriteria evaluation

Geographic information systems (GIS) are potentially powerful collections of tools which planners can use in the manipulation and analysis of spatial information. One traditional area of interest in this context is site search. Here, the functionality of GIS is, however, limited to certain deterministic analyses which serve merely as a computerised automation of the manual sieve mapping approach. The integration of multicriteria evaluation (MCE) techniques with GIS, is forwarded as a means of providing the user with the means to evaluate sites identified by sieve mapping techniques on the basis of multiple and conflicting criteria and objectives. An example application based on the search for suitable radioactive waste disposal sites in Britain using the Arc/Info GIS is included. The potential use of a combined GIS/MCE approach in the development of spatial decision support systems (SDSS) is considered.