Jurgen D. Garbrecht

Numerical definition of drainage network and subcatchment areas from digital elevation models

Abstract A set of ten algorithms to automate the determination of drainage network and subcatchment areas from Digital Elevation Models (DEMs) is presented. The algorithms perform such tasks as: DEM aggregation; depression identification and treatment; relief incrementation of flat areas; flow vector determination; watershed boundary delineation; drainage network and subcatchment area definition and systematic indexing; tabulation of channel and subcatchment area properties; and evaluation of drainage network composition. A computer program (written in FORTRAN 77) that integrates these algorithms for a full DEM evaluation also is discussed. The primary purpose of the algorithms and computer program is to parameterize rapidly drainage network and subcatchment properties from widely available DEMs for subsequent use in hydrologic surface runoff models, watershed discretizations, or statistical and topological evaluation of drainage networks. Selected results of a DEM evaluation are presented for illustration purposes.

The treatment of flat areas and depressions in automated drainage analysis of raster digital elevation models

Methods developed to process raster digital elevation models (DEM) automatically in order to delineate and measure the properties of drainage networks and drainage basins are being recognized as potentially valuable tools for the topographic parameterization of hydrological models. All of these methods ultimately rely on some form of overland flow simulation to define drainage courses and catchment areas and, therefore, have difficulty dealing with closed depressions and flat areas on digital land surface models. Some fundamental assumptions about the nature of these problem topographic features in DEM are implicit in the various techniques developed to deal with them in automated drainage analysis. The principal assumptions are: (1) that closed depressions and flat areas are spurious features that arise from data errors and limitations of DEM resolution; (2) that flow directions across flat areas are determined solely by adjacent cells of lower elevation; and (3) that closed depressions are caused exclusively by the underestimation of DEM elevations. It is argued that while the first of these assumptions is reasonable, given the quality of DEMs generally available for hydrological analysis, the others are not. Rather it seems more likely that depressions are caused by both under- and overestimation errors and that flow directions across flat areas are determined by the distribution of both higher and lower elevations surrounding flat areas. Two new algorithms are introduced that are based on more reasonable assumptions about the nature of flat areas and depressions, and produce more realistic results in application. These algorithms allow breaching of depression outlets and consider the distribution of both higher and lower elevations in assigning flow directions on flat areas. The results of applying these algorithms to some real and hypothetical landscapes are presented.

An outlet breaching algorithm for the treatment of closed depressions in a raster DEM

Abstract Automated drainage analysis of raster DEMs typically begins with the simulated filling of all closed depressions and the imposition of a drainage pattern on the resulting flat areas. The elimination of closed depressions by filling implicitly assumes that all depressions are caused by elevation underestimation. This assumption is difficult to support, as depressions can be produced by overestimation as well as by underestimation of DEM values.This paper presents a new algorithm that is applied in conjunction with conventional depression filling to provide a more realistic treatment of those depressions that are likely due to overestimation errors. The algorithm lowers the elevation of selected cells on the edge of closed depressions to simulate breaching of the depression outlets. Application of this breaching algorithm prior to depression filling can substantially reduce the number and size of depressions that need to be filled, especially in low relief terrain.Removing or reducing the size of a depression by breaching implicitly assumes that the depression is due to a spurious flow blockage caused by elevation overestimation. Removing a depression by filling, on the other hand, implicitly assumes that the depression is a direct artifact of elevation underestimation. Although the breaching algorithm cannot distinguish between overestimation and underestimation errors in a DEM, a constraining parameter for breaching length can be used to restrict breaching to closed depressions caused by narrow blockages along well-defined drainage courses. These are considered the depressions most likely to have arisen from overestimation errors. Applying the constrained breaching algorithm prior to a conventional depression-filling algorithm allows both positive and negative elevation adjustments to be used to remove depressions.The breaching algorithm was incorporated into the DEM pre-processing operations of the TOPAZ software system. The effect of the algorithm is illustrated by the application of TOPAZ to a DEM of a low-relief landscape. The use of the breaching algorithm during DEM pre-processing substantially reduced the number of cells that needed to be subsequently raised in elevation to remove depressions. The number and kind of depression cells that were eliminated by the breaching algorithm suggested that the algorithm effectively targeted those topographic situations for which it was intended. A detailed inspection of a portion of the DEM that was processed using breaching algorithm in conjunction with depression-filling also suggested the effects of the algorithm were as intended.The breaching algorithm provides an empirically satisfactory and robust approach to treating closed depressions in a raster DEM. It recognises that depressions in certain topographic settings are as likely to be due to elevation overestimation as to elevation underestimation errors. The algorithm allows a more realistic treatment of depressions in these situations than conventional methods that rely solely on depression-filling.

Using digital terrain analysis modeling techniques for the parameterization of a hydrologic model

Abstract This paper discusses the application of digital terrain analysis modeling techniques to the parameterization of a semi-distributed hydrologic model. Most current techniques for deriving physiographic parameters in watershed analyses, including those using commercial geographic information systems (GIS), are tedious, costly and time consuming. The demands of these techniques result in them usually being limited in practical application to deriving parameters at only one level of detail or for only one set of sub-basins. This paper presents a computerized interface (SLURPAZ) that was developed to combine the output of an established digital terrain analysis model (TOPAZ) with digital land cover data to derive all the necessary physiographic parameters required as input by a widely used semi-distributed hydrological model (SLURP). This interface makes it possible to derive physiographic parameters rapidly and accurately, at several different levels of detail and for varying numbers of sub-basins. This paper describes the methods by which the SLURPAZ interface integrates land cover data with the topographic parameters derived by TOPAZ from a digital elevation model (DEM). It also presents an example application of the interface to an intermediate-sized alpine basin in Yukon, Canada. Hydrological model outputs obtained using the computerized interface are compared with those obtained using manual techniques.

Environmental effects of agricultural conservation: A framework for research in two watersheds in Oklahoma's Upper Washita River Basin

Agriculture in the Upper Washita River Basin represents mixed crop-livestock systems of the Southern Plains. Research in the Little Washita River Experimental Watershed and the Fort Cobb Reservoir Experimental Watershed addresses interactive effects of variable climate, land use, and management on environmental quality. The Little Washita River watershed provides opportunities to explore impacts of flood retarding impoundments within a watershed. The Fort Cobb Reservoir watershed provides opportunities to study effects of agricultural conservation on a large eutrophic reservoir. Analysis of 1940 to 2005 data from the Fort Cobb Reservoir watershed showed that precipitation increased 33%, corresponding runoff increased 101%, and sediment yield increased 183% when comparing multi-year wet periods to multi-year dry periods. Depth to groundwater exhibited seasonal and interannual variation. A rapid geomorphic assessment indicated that unstable stream channels dominate the stream networks. Phosphorus concentration in streams was correlated to multiple attributes of the contributing areas, including contributing area, slope, stream density, and channel stability. Anticipated outcomes are improved understanding of environmental effects of conservation, new approaches to mitigation of water quality problems, and tools to support strategic placement of conservation practices on the landscape to achieve environmental goals.

Automated channel ordering and node indexing for Raster channel networks

Abstract A numerical algorithm is proposed for automated interpretation of channel networks from raster images, indexing of network nodes, and ordering of channels by the Strahler method. Channel ordering and node indexing is fundamental to the automation of flow-routing management in distributed hydrologic models and morphometric evaluation of channel-network structure. The node index numbers can also serve to link network nodes to corresponding tabulated attributes of network channels. The proposed algorithm uses a two-step approach: first, the raster image of the network is interpreted and a channel attribute table is created; second, the network nodes are indexed based only on the network connectivity information contained in the channel attribute table. The use of attribute information in the second step eliminates a repeat of the time consuming cell-by-cell interpretations of the network raster image. In addition to the general case of nodes with two upstream inflows, the algorithm also handles complex nodes (nodes with more than two upstream inflows) which are rare under natural conditions, but more frequent in raster networks. The final product of the presented algorithm is a table of channel orders and node indices derived from raster images of channel networks.

Multi-year precipitation variations and watershed sediment yield in a CEAP benchmark watershed

A case study was conducted on the Fort Cobb Reservoir watershed in central Oklahoma to investigate impacts and implications of persistent multi-year precipitation variations on watershed runoff and sediment yield. Sediment yield was calculated from a sediment-discharge relationship representing 2004 to 2005 land use, agronomic practices, and conservation measures. Several persistent multi-year precipitation variations, called wet and dry periods, occurred in central Oklahoma between 1940 and 2005. The difference in mean annual precipitation between wet and dry periods was 33% of the long-term mean. As a result of nonlinear hydrologic linkages between precipitation, runoff and sediment yield, corresponding variations in watershed runoff and calculated sediment yield were comparatively larger. The difference in mean annual runoff between wet and dry periods was 100% of the long-term mean, and for mean annual calculated sediment yield it was 183% of the long-term mean. With regard to the Conservation Effects Assessment Project (CEAP), the sensitivity of runoff and therefore of sediment yield to wet and dry periods suggests that measures of conservation program effectiveness depend on climatic conditions used in their evaluation and that great care should be taken to select a climate record representative of prevailing climate conditions. Furthermore, it was inferred that the calibration of simulation models used in the conservation effects assessment may be biased if performed with climatic data representing either just a wet or a dry period. In the presence of multi-year precipitation variations, a thorough model validation for both wet and dry periods is recommended to ensure accurate simulation results over the full range of prevailing climatic conditions.

Climate forecast and prediction product dissemination for agriculture in the United States

A wealth of climate forecast information and related prediction products are available, but impediments to adoption of these products by ranchers and farmers in the Unites States remain to be addressed. Impediments for agricultural applications include modest forecast skill, limited climate predictability, inappropriate forecast scale for site-specific applications, difficulties in interpretation of probabilistic forecasts by farmers and integration into agricultural decision systems, uncertainty about the value and effect of forecast information in multi-variable decision system, and generally low frequency of relevant forecasts. Various research institutions have conducted case studies of climate effects on agricultural production systems, particularly effects of historical ENSO signals in the south-eastern United States. Several studies addressed risk and economic values of seasonal climate forecasts, and others bridged the gap between current forecasting software and products and agricultural applications. These studies attest to the availability and suitability of forecast and impact-prediction software, as well as derived products for agricultural applications. Yet, little attention has been given to operational and application-specific prediction products for general agricultural use, and to an effective and affordable delivery system that reaches and resonates with the agricultural end-user (a prerequisite for adoption). The two latter impediments are the focus of this paper. Two existing approaches, the top-down and the participatory end-to-end approach for development and delivery of prediction products, are reviewed. A third approach, the hybrid approach, is emphasised and uses the top-down approach for climate forecast delivery and a participatory approach for development and delivery of farm-specific prediction information for the agricultural end-user. Suitability of such prediction products for agricultural applications and constraints to successful adoption are also discussed.

Long-term environmental research: the upper washita river experimental watersheds, oklahoma, USA.

Water is central to life and earth processes, connecting physical, biological, chemical, ecological, and economic forces across the landscape. The vast scope of hydrologic sciences requires research efforts worldwide and across a wide range of disciplines. While hydrologic processes and scientific investigations related to sustainable agricultural systems are based on universal principles, research to understand processes and evaluate management practices is often site-specific to achieve a critical mass of expertise and research infrastructure to address spatially, temporally, and ecologically complex systems. In the face of dynamic climate, market, and policy environments, long-term research is required to understand and predict risks and possible outcomes of alternative scenarios. This special section describes the USDA-ARSs long-term research (1961 to present) in the Upper Washita River basin of Oklahoma. Data papers document datasets in detail (weather, hydrology, physiography, land cover, and sediment and nutrient water quality), and associated research papers present analyses based on those data. This living history of research is presented to engage collaborative scientists across institutions and disciplines to further explore complex, interactive processes and systems. Application of scientific understanding to resolve pressing challenges to agriculture while enhancing resilience of linked land and human systems will require complex research approaches. Research areas that this watershed research program continues to address include: resilience to current and future climate pressures; sources, fate, and transport of contaminants at a watershed scale; linked atmospheric-surface-subsurface hydrologic processes; high spatiotemporal resolution analyses of linked hydrologic processes; and multiple-objective decision making across linked farm to watershed scales.

Upper washita river experimental watersheds: meteorologic and soil climate measurement networks.

Hydrologic, watershed, water resources, and climate-related research conducted by the USDA-ARS Grazinglands Research Laboratory (GRL) are rooted in events dating back to the 1930s. In 1960, the 2927-km Southern Great Plains Research Watershed (SGPRW) was established to study the effectiveness of USDA flood control and soil erosion prevention programs. The size of the SGPRW was scaled back in 1978, leaving only the 610-km Little Washita River Experimental Watershed (LWREW) to be used as an outdoor hydrologic research laboratory. Since 1978, the number of measurement sites and types of instruments used to collect meteorologic and soil climate data have changed on the LWREW. Moreover, a second research watershed, the 786-km Fort Cobb Reservoir Experimental Watershed (FCREW), was added in 2004 to the GRLs outdoor research laboratories to further study the effects of agricultural conservation practices on selected environmental endpoints. We describe the SGPREW, FCREW, and LWREW and the meteorologic measurement network (historic and present) deployed on them, provide descriptions of measurements, including information on accuracy and calibration, quality assurance measures (where known), and data archiving of the present network, give examples of data products and applications, and provide information for the public and research communities regarding access and availability of both the historic and recent data from these watersheds.