CAJ | 학술논문

泥沙输移比是连接流域地面侵蚀与河道输沙的纽带，是反映流域侵蚀产沙及输移能力的指标，对正确评价水土保持减沙效益与生态建设宏观决策有重要意义。本文基于对国内外泥沙输移比研究的系统梳理，探讨了流域泥沙输移比的尺度依存及分形特征。分析认为：泥沙输移比在其内涵中深刻体现了对尺度的标定；国内外不同区域内流域泥沙输移比具有尺度依存性，并有严格的尺度域；泥沙输移比可以由分形维数来定量描述，即由1km2左右的典型流域单元的泥沙输移比值和流域的分形维数来建立尺度上推或下推的测算模型。本研究结果明确了泥沙输移比研究不能割裂“尺度性”这一学术认知，并为泥沙输移比尺度效应及转换研究提供了新的思路。
니사수이비시련접류역지면침식여하도수사적뉴대，시반영류역침식산사급수이능력적지표，대정학평개수토보지감사효익여생태건설굉관결책유중요의의。본문기우대국내외니사수이비연구적계통소리，탐토료류역니사수이비적척도의존급분형특정。분석인위：니사수이비재기내함중심각체현료대척도적표정；국내외불동구역내류역니사수이비구유척도의존성，병유엄격적척도역；니사수이비가이유분형유수래정량묘술，즉유1km2좌우적전형류역단원적니사수이비치화류역적분형유수래건립척도상추혹하추적측산모형。본연구결과명학료니사수이비연구불능할렬“척도성”저일학술인지，병위니사수이비척도효응급전환연구제공료신적사로。
Sediment delivery ratio (SDR) is a link to connect surface erosion and gully sediment delivery, which is also an indicator to demonstrate the capacity of sediment erosion and delivery of a certain basin. Hence, SDR is usually recognized as a helpful factor when evaluating sediment reduction effects due to soil and water conservation management and making policy-decision on ecological construction. Based on a sys-tematic review on SDR studies at home and abroad during the last 50 years, this paper analyzed and dis-cussed whether SDR has dependence characteristics on scale and its fractal characteristics or not. It was con-cluded that (1) the definition of SDR formula has embedded in the characteristic of scale;(2) SDR in differ-ent watersheds at home and abroad showed correlations with certain temporal and spatial scales, which means SDR is depended on its scale;(3) SDR could be quantitatively expressed and be scaled using fractal dimension under a certain temporal and spatial scale. The authors found that values of SDR and fractal di-mension (D) at 1km2 typical watershed unit were approximately equal to the SDR and D coefficients in corre-sponding scaling models. This paper pointed out that the calculation and study on SDR can’t be correct with-out considering its scale dependence. It is a valid and useful approach to construct SDR scaling models by using fractal dimension,which could be an interesting research topic regarding on SDR scaling in the future.