ABSTRACT

The high cost of monitoring suspended sediment concentration (SSC) in rivers calls for the development of indirect estimation methods, based on relationships with other variables, which are easier and cheaper to measure. We present an original approach to investigate the capacity of regional models to extrapolate SSC to ungauged basins, in a heterogeneous region with scarce in situ data and complex hydrography. The estimates were based on qualitative variables (drainage area, soil type, land use, land cover and mean catchment slope) to represent spatial variability, and quantitative variables (turbidity, flow, precipitation and exponentially weighted moving average of past rainfall) to represent temporal variability. We used artificial neural network (ANN)-based models, applied to the Brazilian part of the Upper Paraguay River Basin, covering an area of 362 380 km². This study demonstrates that the proposed methodology allows the regional extrapolation of SSC to ungauged basins with very good performance, even in heterogeneous regions.

摘要

监测河流中悬浮泥沙浓度 (SSC) 的高成本要求开发基于与其他变量关系的间接估计方法，这些方法更易于测量且成本更低。我们提出了一种原始方法来研究区域模型将 SSC 外推到未测量盆地的能力，在一个具有稀缺现场数据和复杂水文的异质地区。估计值基于代表空间变异性的定性变量（排水面积、土壤类型、土地利用、土地覆盖和平均集水坡度）和定量变量（浊度、流量、降水和过去降雨的指数加权移动平均值）来代表时间变化性。我们使用了基于人工神经网络 (ANN) 的模型，应用于上巴拉圭河流域的巴西部分，² . 这项研究表明，即使在非均质地区，所提出的方法也可以将 SSC 区域外推到性能非常好的未测量盆地。