Tillage-induced microtopography alters time-dependent intrinsic correlation of runoff and sediment yield

Luo, Jian; Wang, Nana; Zheng, Zicheng; Li, Tingxuan; He, Shuqin; Tarolli, Paolo

doi:10.1016/j.still.2022.105423

Cited by 19 publications

(6 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to other studies based on microtopography in recent years [36][37][38], this study clarified the influence mechanism of the geometric shape and physical characteristics of microtopography on runoff and revealed the root cause of the many impacts of microtopography on runoff and soil moisture. Different from the composite microtopography that both mounds and depressions existed simultaneously [32,37,38], separately studying of these two patterns of microrelief can reveal the essential impact of different topographies on runoff, which is the biggest innovation of this work. By stripping the interaction between mounds and depressions, their separate action mechanisms can be studied and a comparative analysis with the interaction mechanism can be made as well.…”

Section: Discussionmentioning

confidence: 77%

The Effect of Soil Surface Mounds and Depressions on Runoff

Liang

Feng

et al. 2022

Sustainability

Self Cite

View full text Add to dashboard Cite

Surface mounds and depressions are the basic patterns of microtopography. Their geometric forms and physical properties affect rainfall infiltration, runoff generation and runoff confluence process. In this study, soil beds were set up with seven different types of microtopography to study the effects of surface mounds and depressions on runoff. They were the control check (CK), alternate mounds (AM), continuous mounds (CM), alternate depressions (AD), continuous depressions (CD), alternate mounds and depressions (AMD) and continuous mounds and depressions (CMD). There was only one microtopography type for monomorphic surface relief (MSR) while two for compound surface relief (CSR). All soil beds were exposed under 60, 90 or 120 mm/h rainfall intensity for 90 min. The main results are as follows: surface mounds could promote surface runoff, triggering and shortening runoff generation time, while surface depressions showed contrary results. Whether there was an interval between mounds or depressions also affected the characteristics of runoff. The runoff generation time was 3.8–5.0 times higher for continuous slope than for interval slope, while the runoff yield and runoff coefficient both decreased by approximately 40%. CSR can significantly neutralize the flow-promoting effects of the mounds and the flow-inhibiting effects of the depressions, making the runoff yield and runoff process present a neutral state between the mounds and depressions. CSR prolongs runoff generation time from 1–10 min of MSR to 5–16 min. The runoff yield of CSR presented as 0.12, between 0.17 for mounds and 0.10 for depressions, and so did the runoff coefficient and hydrodynamic parameters. In addition, with rainfall intensity increased, the runoff pattern of CSR and MSR became more similar to each other, and the retarding effects of topography on overland flow were more effective.

show abstract

Section: Discussionmentioning

confidence: 77%

The Effect of Soil Surface Mounds and Depressions on Runoff

Liang

Feng

et al. 2022

Sustainability

Self Cite

View full text Add to dashboard Cite

show abstract

“…Another virtuous use of 3D acquisition on extensive terrain surfaces is to detect possible preferential path for overland water flow by rill erosion, which, depending on slopes and rainfall intensity, can significantly increase soil loss [45]. Rapid and effective assessment of soil surface roughness would be useful to assess the susceptibility of a soil to produce runoff and sediment yield, which are affected by the adopted tillage practices with a relevant multi-scale temporal, and spatial, variation [46]. To identify possible critical situation in time, means to prevent by adopting good practices in soil management.…”

Section: Discussionmentioning

confidence: 99%

Novel Measurements and Features for the Characterization of Soil Surface Roughness

et al. 2022

View full text Add to dashboard Cite

Agricultural soils provide a variety of ecological services, including nutrient cycling, water purification and storage, carbon sequestration, and flood protection. Soil Surface Roughness (SSR) represents a key parameter for evaluating the terrain quality structure, especially in the layers beneath the usual primary tillage depth, and therefore its estimation has been widely investigated over the years in many fields of science and engineering. This paper proposes the adoption of an innovative sensing approach that relies on contactless measurements provided by a depth camera in contrast to traditional contact measurements such as pin meter and roller chain. In addition, novel features are investigated to achieve a complete statistical description of the SSR with the least number of parameters. The proposed methods are validated in an experimental campaign performed on a vineyard plot. This research could be useful for many applications, including soil erosion prediction models, autonomous vehicle navigation in rural and agricultural settings, and controlled traffic farming.

show abstract

“…Empirical mode decomposition is an adaptive nonlinear signal decomposition method, which has been extensively adopted in wave height prediction (Hao et al., 2022), water quality prediction (Y. Zhang et al., 2022), and other domains. However, when applying EMD for hydrological analysis, the IMF components generated by hydrological processes at different time scales may overlap in the same frequency range, resulting in the phenomenon of mode confusion, thus limiting its practicality (Q. J. Liu et al., 2019; Luo et al., 2022). Although VMD and other methods can effectively solve the problem of mode confusion, the number of decomposition layers needs to be adjusted manually (G. Li, Tang, & Yang, 2022; Q. Liu et al., 2022; Z. Wang et al., 2023), which is inconvenient in practical application.…”

Section: Methodsmentioning

confidence: 99%

“…However, when applying EMD for hydrological analysis, the IMF components generated by hydrological processes at different time scales may overlap in the same frequency range, resulting in the phenomenon of mode confusion, thus limiting its practicality (Q. J. Liu et al, 2019;Luo et al, 2022). Although VMD and other methods can effectively solve the problem of mode confusion, the number of decomposition layers needs to be adjusted manually (G. Li, Tang, & Yang, 2022;, which is inconvenient in practical application.…”

Section: Time-varying Filtered Based Empirical Mode Decomposition (Tv...mentioning

confidence: 99%

Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model

Wu,

Wang,

Dong

et al. 2023

Water Resources Research

View full text Add to dashboard Cite

Accurate runoff forecasting plays a vital role in issuing timely flood warnings. Whereas, previous research has primarily focused on historical runoff and precipitation variability while disregarding other factors’ influence. Additionally, the prediction process of most machine learning models is opaque, resulting in low interpretability of model predictions. Hence, this study develops an ensemble deep learning model to forecast runoff from three hydrological stations. Initially, time‐varying filtered based empirical mode decomposition (TVFEMD) is employed to decompose the runoff series into several internal mode functions (IMFs). Subsequently, the complexity of each IMF component is evaluated by the multi‐scale permutation entropy (MPE), and the IMFs are classified into high‐ and low‐frequency portions based on entropy values. Considering the high‐frequency portions still exhibit great volatility, robust local mean decomposition (RLMD) is adopted to perform secondary decomposition of the high‐frequency portions. Then, the meteorological variables processed by the Relief algorithm and variance inflation factor (VIF) features are employed as inputs, the individual subsequences of secondary and preliminary decomposition as outputs to the bidirectional gated recurrent unit (BiGRU) and extreme learning machine (ELM) models. Random forests (RF) are introduced to nonlinear ensemble the individual predicted sub‐models to obtain the final prediction results. The proposed model outperforms other models in various evaluation metrics. Meanwhile, due to the opaque nature of machine learning models, shapley is employed to assess the contribution of each selected meteorological variable to the long‐term trend of runoff. The proposed model could serve as an essential reference for precise flood prediction and timely warning.

show abstract

Tillage-induced microtopography alters time-dependent intrinsic correlation of runoff and sediment yield

Cited by 19 publications

References 46 publications

The Effect of Soil Surface Mounds and Depressions on Runoff

The Effect of Soil Surface Mounds and Depressions on Runoff

Novel Measurements and Features for the Characterization of Soil Surface Roughness

Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model

Contact Info

Product

Resources

About