K. G. Renard

Bio: K. G. Renard is an academic researcher. The author has contributed to research in topics: Universal Soil Loss Equation & WEPP. The author has an hindex of 8, co-authored 8 publications receiving 6451 citations.

Predicting soil erosion by water : a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE)

Abstract: Renard, K.G., G.R. Foster, G.A. Weesies, D.K. McCool, and D.C. Yoder, coordinators. Predicting Soil Erosion by Water: A Guide to Conservation Planning With the Revised Universal Soil Loss Equation (RUSLE). U.S. Department of Agriculture, Agriculture Handbook No. 703, 404 pp. The Revised Universal Soil Loss Equation (RUSLE) is an erosion model predicting longtime average annual soil loss (A) resulting from raindrop splash and runoff from specific field slopes in specified cropping and management systems and from rangeland. Widespread use has substantiated the RUSLE’s usefulness and validity. RUSLE retains the six factors of Agriculture Handbook No. 537 to calculate A from a hillslope. Technology for evaluating these factor values has been changed and new data added. The technology has been computerized to assist calculation. Thus soil-loss evaluations can be made for conditions not included in the previous handbook using fundamental information available in three data bases: CITY, which includes monthly precipitation and temperature, front-free period, annual rainfall erosivity (R) and twice monthly distributions of storm erosivity (E); CROP, including below-ground biomass, canopy cover, and canopy height at 15-day intervals as well as information on crop characteristics; and OPERATION, reflecting soil and cover disturbances that are associated with typical farming operations.

RUSLE: Revised universal soil loss equation

Abstract: THERE are many changes for estimating erosion by water in RUSLE, the revised universal soil loss equation. The changes include the following: Computerizing the algorithms to assist with the calculations. New rainfall-runoff erosivity term values (R) in the western United States, based on more than 1,200 gauge locations. Some revisions and additions for the eastern United States, including corrections for high R-factor areas with flat slopes to adjust for splash erosion associated with raindrops falling on ponded water. Development of a seasonally variable soil erodibility term (K). A subfactor approach for calculating the cover-management term (C), with the subfactors representing considerations of prior land use, crop canopy, surface cover, and surface roughness. New slope length and steepness (LS) algorithms reflecting rill to interrill erosion ratios. The capacity to calculate LS products for slopes of varying shape. New conservation practice values (P) for rangelands, stripcrop rotations, contour factor values, and subsurface drainage. History of the USLE Although the universal soil loss equation (USLE) is a powerful tool that is widely used by soil conservationists in the United States and many foreign countries, research and experience since the 1970s have provided improved technology that is incorporated in the …

Conversion of the universal soil loss equation to SI metric units

Abstract: The Universal Soil Loss Equation (USLE) is widely used to estimate erosion in the United States and foreign countries. With foreign application of the USLE and adoption of the International System of Units (SI) in the United States, conversion of the USLE to SI units is necessary. Conversion factors were derived by considering the dimensions of each variable of the USLE factors. These conversion factors may be used to convert USLE factor values given in U.S. customary units to SI units. However, when basic data for the USLE factors are already in SI units, values for the USLE factors can be computed directy in SI units without conversion from U.S. customary units.

Soil Erosion: Processes, Prediction, Measurement, and Control

Abstract: Preface. Acknowledgments. 1. Introduction. Physical and Economic Significance of Erosion. Social Significance of Erosion. Soil-Erosion Research. Terminology of Erosion. Development of Landscapes: A Context for Erosion. Summary. Suggested Readings. 2. Primary Factors Influencing Soil Erosion. Water Erosion. Wind Erosion. Integrated Site Perspective. Summary. Suggested Readings. 3. Types of Erosion. Water Erosion. Wind Erosion. Links between Wind and Water Erosion. Mechanical Movement of Soil. Summary. Suggested Readings. 4. Erosion Processes. Basic Principles Common to Water and Wind Erosion. Water Erosion. Wind Erosion. Summary. Suggested Readings. 5. Erosion-Prediction Technology. Fundamentals of Erosion-Prediction Technology. Elements of Erosion-Model Mathematics. Types of Mathematical Erosion Models. Other Types of Erosion Models. Steps in Developing an Erosion Model. Choosing a Model. Sensitivity Analysis. Summary. Suggested Readings. 6. Erosion Measurement. Reasons to Measure Erosion. Types of Erosion Measurement. Erosion-Measurement Practices. Selected Measurement Techniques. Evaluation of Erosion Measurement. Summary. Suggested Readings. 7. Erosion and Sediment Control. Principles of Erosion and Sediment Control. Examples of Water-Erosion-Control Practices. Control of Concentrated-Flow Erosion. Sediment Control. Wind-Erosion Control. Summary. Suggested Readings. 8. Land Conservation. Public Conservation Programs. Conservation Planning. Technical Tools for Conservation Planning. Local Soil Conservation Planning for On-Site Erosion and Sediment Control. Conservation Planning by Governmental Units. Lessons from the U.S. Conservation Movement. Suggested Readings. 9. Perspectives and the Future. Essential Lessons. Future for Soil Conservation. Conclusions. Appendix A: Soils. Soil Properties. Sediment Properties. Sources of Information. Suggested Readings. Appendix B: Hydrology. Precipitation Process. Water Storage. Infiltration Process. Runoff Process. Evaporation and Transpiration Processes. Sources of Information. Suggested Readings. Appendix C: Soil Erosion Web Sites. References. Index.

RUSLE revisited: Status, questions, answers, and the future

Abstract: USLE, the Revised Universal Soil Loss Equation, is a modern erosion predicrion and conservation planning tool based in large part on the USLE (Universal Soil Loss Equation) and its supporting data, but also including major improvements and updates. Differences between RUSLE and the USLE were described in some detail in earlier articles (I I, 12). This report will describe changes in RUSLE since the time of those articles, and proposed future changes in RUSLE technology. In addition, the U.S. Department of Agriculture (USDA), Soil Conservation Service (SCS) has recently made the decision to implement RUSLE as its official erosion prediction and conservation planning tool (13). This article will answer questions concerning RUSLE's implementation and use. RUSLE description General description of RUSLE . RUSLE uses the same fundamental structure as did the USLE (15): A = predicted soil loss (tons acre−1 year−1) R = climate erosivity ([hundreds of ft-tons] inch acre−1 hr−1 year−1 K = soil erodibility measured under standard unit plot conditions (tons hour [hundreds of ft-tons]−1 in−1) LS = dimensionless factor representing the effect on erosion of slope length and steepness …

Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales

Abstract: Nature provides a wide range of benefits to people. There is increasing consensus about the importance of incorporating these “ecosystem services” into resource management decisions, but quantifying the levels and values of these services has proven difficult. We use a spatially explicit modeling tool, Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST), to predict changes in ecosystem services, biodiversity conservation, and commodity production levels. We apply InVEST to stakeholder-defined scenarios of land-use/land-cover change in the Willamette Basin, Oregon. We found that scenarios that received high scores for a variety of ecosystem services also had high scores for biodiversity, suggesting there is little tradeoff between biodiversity conservation and ecosystem services. Scenarios involving more development had higher commodity production values, but lower levels of biodiversity conservation and ecosystem services. However, including payments for carbon sequestration alleviates this tradeoff. Quantifying ecosystem services in a spatially explicit manner, and analyzing tradeoffs between them, can help to make natural resource decisions more effective, efficient, and defensible.

Global potential bioethanol production from wasted crops and crop residues

Abstract: The global annual potential bioethanol production from the major crops, corn, barley, oat, rice, wheat, sorghum, and sugar cane, is estimated. To avoid conflicts between human food use and industrial use of crops, only the wasted crop, which is defined as crop lost in distribution, is considered as feedstock. Lignocellulosic biomass such as crop residues and sugar cane bagasse are included in feedstock for producing bioethanol as well. There are about 73.9 Tg of dry wasted crops in the world that could potentially produce 49.1 GL year −1 of bioethanol. About 1.5 Pg year −1 of dry lignocellulosic biomass from these seven crops is also available for conversion to bioethanol. Lignocellulosic biomass could produce up to 442 GL year −1 of bioethanol. Thus, the total potential bioethanol production from crop residues and wasted crops is 491 GL year −1 , about 16 times higher than the current world ethanol production. The potential bioethanol production could replace 353 GL of gasoline (32% of the global gasoline consumption) when bioethanol is used in E85 fuel for a midsize passenger vehicle. Furthermore, lignin-rich fermentation residue, which is the coproduct of bioethanol made from crop residues and sugar cane bagasse, can potentially generate both 458 TWh of electricity (about 3.6% of world electricity production) and 2.6 EJ of steam. Asia is the largest potential producer of bioethanol from crop residues and wasted crops, and could produce up to 291 GL year −1 of bioethanol. Rice straw, wheat straw, and corn stover are the most favorable bioethanol feedstocks in Asia. The next highest potential region is Europe ( 69.2 GL of bioethanol), in which most bioethanol comes from wheat straw. Corn stover is the main feedstock in North America, from which about 38.4 GL year −1 of bioethanol can potentially be produced. Globally rice straw can produce 205 GL of bioethanol, which is the largest amount from single biomass feedstock. The next highest potential feedstock is wheat straw, which can produce 104 GL of bioethanol. This paper is intended to give some perspective on the size of the bioethanol feedstock resource, globally and by region, and to summarize relevant data that we believe others will find useful, for example, those who are interested in producing biobased products such as lactic acid, rather than ethanol, from crops and wastes. The paper does not attempt to indicate how much, if any, of this waste material could actually be converted to bioethanol.

Gully erosion and environmental change: importance and research needs

Abstract: Assessing the impacts of climatic and, in particular, land use changes on rates of soil erosion by water is the objective of many national and international research projects. However, over the last decades, most research dealing with soil erosion by water has concentrated on sheet (interrill) and rill erosion processes operating at the (runoff) plot scale. Relatively few studies have been conducted on gully erosion operating at larger spatial scales. Recent studies indicate that (1) gully erosion represents an important sediment source in a range of environments and (2) gullies are effective links for transferring runoff and sediment from uplands to valley bottoms and permanent channels where they aggravate off site effects of water erosion. In other words, once gullies develop, they increase the connectivity in the landscape. Many cases of damage (sediment and chemical) to watercourses and properties by runoff from agricultural land relate to (ephemeral) gullying. Consequently, there is a need for monitoring, experimental and modelling studies of gully erosion as a basis for predicting the effects of environmental change (climatic and land use changes) on gully erosion rates. In this respect, various research questions can be identified. The most important ones are: (1) What is the contribution of gully erosion to overall soil loss and sediment production at various temporal and spatial scales and under different climatic and land use conditions? (2) What are appropriate measuring techniques for monitoring and experimental studies of the initiation and development of various gully types at various temporal and spatial scales? (3) Can we identify critical thresholds for the initiation, development and infilling of gullies in different environments in terms of flow hydraulics, rain, topography, soils and land use? (4) How does gully erosion interact with hydrological processes as well as with other soil degradation processes? (5) What are appropriate models of gully erosion, capable of predicting (a) erosion rates at various temporal and spatial scales and (b) the impact of gully development on hydrology, sediment yield and landscape evolution? (6) What are efficient gully prevention and gully control measures? What can be learned from failures and successes of gully erosion control programmes? These questions need to be answered first if we want to improve our insights into the impacts of environmental change on gully erosion. This paper highlights some of these issues by reviewing recent examples taken from various environments.

RUSLE: Revised universal soil loss equation

Abstract: THERE are many changes for estimating erosion by water in RUSLE, the revised universal soil loss equation. The changes include the following: Computerizing the algorithms to assist with the calculations. New rainfall-runoff erosivity term values (R) in the western United States, based on more than 1,200 gauge locations. Some revisions and additions for the eastern United States, including corrections for high R-factor areas with flat slopes to adjust for splash erosion associated with raindrops falling on ponded water. Development of a seasonally variable soil erodibility term (K). A subfactor approach for calculating the cover-management term (C), with the subfactors representing considerations of prior land use, crop canopy, surface cover, and surface roughness. New slope length and steepness (LS) algorithms reflecting rill to interrill erosion ratios. The capacity to calculate LS products for slopes of varying shape. New conservation practice values (P) for rangelands, stripcrop rotations, contour factor values, and subsurface drainage. History of the USLE Although the universal soil loss equation (USLE) is a powerful tool that is widely used by soil conservationists in the United States and many foreign countries, research and experience since the 1970s have provided improved technology that is incorporated in the …

An assessment of the global impact of 21st century land use change on soil erosion.

Abstract: Human activity and related land use change are the primary cause of accelerated soil erosion, which has substantial implications for nutrient and carbon cycling, land productivity and in turn, worldwide socio-economic conditions. Here we present an unprecedentedly high resolution (250 × 250 m) global potential soil erosion model, using a combination of remote sensing, GIS modelling and census data. We challenge the previous annual soil erosion reference values as our estimate, of 35.9 Pg yr−1 of soil eroded in 2012, is at least two times lower. Moreover, we estimate the spatial and temporal effects of land use change between 2001 and 2012 and the potential offset of the global application of conservation practices. Our findings indicate a potential overall increase in global soil erosion driven by cropland expansion. The greatest increases are predicted to occur in Sub-Saharan Africa, South America and Southeast Asia. The least developed economies have been found to experience the highest estimates of soil erosion rates.