TY - JOUR
T1 - Process-constrained statistical modeling of sediment yield
AU - Shojaeezadeh, Shahab Aldin
AU - Nikoo, Mohammad Reza
AU - Talebbeydokhti, Nasser
AU - Sadegh, Mojtaba
AU - Franklin Adamowski, Jan
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2022/2
Y1 - 2022/2
N2 - Sediment transport is a major contributor to a non-point source of pollution impacted by various factors that are modulated by climatic changes and anthropogenic influences. Quantifying and disentangling the contribution of these factors to sediment yield at large scales and across different flow regimes has not been fully explored. Here we present a framework to fine-tune a stochastic sediment yield model by classifying discharge and Suspended Sediment Load (SSL) observations based on the underlying governing processes in unregulated streams with various hydrological regimes. This stochastic model, rooted in copula theory, constructs a joint distribution between discharge and SSL storm events using historical time series of observations, classified based on seasonality, hysteresis patterns, and hydrograph components of the sediment transport processes. We include hydrological, land use, and geological properties of the watersheds to describe and discuss the effects of different factors on applying the underlying dynamics to enhance sediment yield estimation/prediction accuracy. We evaluated the proposed method on 67 streams across the United States. Our results show significant improvements in sediment yield modeling performance.
AB - Sediment transport is a major contributor to a non-point source of pollution impacted by various factors that are modulated by climatic changes and anthropogenic influences. Quantifying and disentangling the contribution of these factors to sediment yield at large scales and across different flow regimes has not been fully explored. Here we present a framework to fine-tune a stochastic sediment yield model by classifying discharge and Suspended Sediment Load (SSL) observations based on the underlying governing processes in unregulated streams with various hydrological regimes. This stochastic model, rooted in copula theory, constructs a joint distribution between discharge and SSL storm events using historical time series of observations, classified based on seasonality, hysteresis patterns, and hydrograph components of the sediment transport processes. We include hydrological, land use, and geological properties of the watersheds to describe and discuss the effects of different factors on applying the underlying dynamics to enhance sediment yield estimation/prediction accuracy. We evaluated the proposed method on 67 streams across the United States. Our results show significant improvements in sediment yield modeling performance.
KW - Copula
KW - Hysteresis loops
KW - Physics-constrained Statistics
KW - Sediment yield
KW - Stochastic model
UR - http://www.scopus.com/inward/record.url?scp=85117703143&partnerID=8YFLogxK
U2 - 10.1016/j.catena.2021.105794
DO - 10.1016/j.catena.2021.105794
M3 - Article
AN - SCOPUS:85117703143
SN - 0341-8162
VL - 209
JO - Catena
JF - Catena
M1 - 105794
ER -