The importance of incorporating rain intensity profiles in rainfall simulation studies of infiltration, runoff production, soil erosion, and related landsurface processes

Dunkerley, David

doi:10.1016/j.jhydrol.2021.126834

Cited by 29 publications

(16 citation statements)

References 116 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The infiltration process of farmland was deeply influenced by some factors such as soil type, rainfall intensity, and initial water content (Humberto, 2017;Dong et al, 2019;Dunkerley, 2021). PFM increased the farmland infiltration rate, which was similar to the research results that some porous materials (such as biochar, straw, and PAM) applied in the farmland (Abrol et al, 2016;Wang et al, 2017).…”

Section: Porous Fiber Materials Influence the Infiltration And Runoffsupporting

confidence: 70%

“…At present, most scholars have explored the impacts of porous materials on the soil structure (such as the soil porosity, bulk density, water conductivity, etc.) (Humberto, 2017;Dong et al, 2019;Dunkerley, 2021) and revealed its mechanism that the probers materials were how to influence the infiltration process and soil water-holding capacity (Li et al, 2019;Ahmadi et al, 2020;Chen et al, 2020;Yang et al, 2021). Although traditional porous materials increased the infiltration and water-retaining capacity, there were the following defects: blocking soil pores, compacting soil during the application, limiting the improvement range of water-holding capacity (Pu et al, 2019;Zhang F. B. et al, 2019), which would aggravate the soil erosion of farmland in some extreme rainfall events (Werdin et al, 2021;Li et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Experiment Study of Porous Fiber Material on Infiltration and Runoff of Winter Wheat Farmland in Huaibei Plain, China

et al. 2022

View full text Add to dashboard Cite

Drought and floods frequently occurred in the Huaibei plain, which was the main factor that restricted agricultural development. We conducted rainfall experiments, which aimed to explore the impacts of porous fiber material (PFM) on the farmland water cycle processes and soil water storage capacity. In this study, we designed two types of rainfall intensities, 4 PFM volumes, 4 growth periods of winter wheat, and a total of 8 experimental groups and 32 rainfall events to evaluate the effects. The result showed that PFM had significantly affected the soil water circulation in the grain-filling period, and the peak flow and runoff decreased maximumly compared with other periods. However, the effect of PFM on surface runoff was slighter in the fallow period, and the peak flow or runoff decreased with the PFM volumes increased (R2 = −0.92, −0.99). In the 100 and 50 mm/h rainfall intensities, PFM decreased the average values of runoff by (55.2–59.6%) and (57.2–90.2%), reduced peak flow by (62.2–68%) and (64.2–86%), and increased the stable infiltration rate by (13.4–14.3%) and (26.6–41.3%), respectively. After the rainfall experiments ended for 1 h, the surface soil water rapidly infiltrated into PFM, which made the water-storage capacity of PFM groups higher than the control groups by 0.2–11% Vol. Subsequently, PFM increased the water-holding capacity by 0.3–2.3% Vol in the 10–70 cm depth from the heading period to the fallow period. It had a positive relationship between the PFM volumes and the average values of soil water content (R2 = 0.8, 0.84). In general, PFM could increase infiltration, reduce runoff, and improve the water-storage capacity to alleviate soil water deficit and the risk of farmland drought and floods. It has an excellent application effect in long-duration rainfall.

show abstract

Section: Porous Fiber Materials Influence the Infiltration And Runoffsupporting

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Experiment Study of Porous Fiber Material on Infiltration and Runoff of Winter Wheat Farmland in Huaibei Plain, China

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Intra-event rainfall variation classification is important for accurately describing the time-varying intensities that comprise a rainfall event (Dunkerley, 2021;Liu et al, 2022). The classification refers to the overall form of a rainfall event, for example, whether the rainfall exhibits intensity peaks in the early or late stages of an event.…”

Section: Rainfall Classification Based On Intra-event Variationmentioning

confidence: 99%

Influence of intra-event rainfall variation on surface–subsurface flow generation and soil loss under different surface covers by long-term field observations

Duan

Zheng

Liu

et al. 2023

Preprint

View full text Add to dashboard Cite

Abstract. Rainfall is the main driver of runoff generation and soil erosion. The impacts of natural rainfall on water erosion have been extensively studied at an inter-event scale; however, very few studies have explored the intra-event influences and associated responses to different surface cover types. In this study, long-term in situ field observations of surface runoff, subsurface flow, and soil loss characteristics in three surface cover plots (bare land, litter and grass cover) under natural rainfall events were conducted from 2002 to 2012 in the red soil hilly region of southern China. According to the period of most concentrated rainfall, 262 rainfall events were classified into four types of intra-event variation: advanced, intermediate, delayed, and uniform patterns. For bare land, the advanced pattern with the shortest duration and the highest intensity was main rainfall type for surface runoff and soil loss; the contribution rates were 57.24 % and 75.17 % for surface runoff and soil loss, respectively. Sediment yields were more sensitive to intra-event rainfall variation than surface runoff. The highest subsurface flow was found in the delayed pattern with the longest duration and high depth, followed by the uniform, intermediate, and advanced patterns. For all rainfall patterns, compared to the bare land, surface cover significantly reduced surface runoff and soil erosion by 88.01 to 91.69 % and by 97.80 to 97.95 %, respectively, while subsurface flow was increased from 3.55 to 5.92 times. The reduction benefits of litter cover were comparable to those of grass cover. However, the increasing benefit of subsurface flow for litter cover for each rainfall pattern ranged from 1.38 to 2.67 times those of grass cover. Moreover, surface cover weakened the influences of intra-event rainfall variation on surface-subsurface flow and soil loss. The results demonstrated that intra-event rainfall variation had important effects on surface-subsurface flow and soil loss, and provided a basis for optimizing surface cover measures to effectively respond to extreme water erosion and drought caused by global climate change.

show abstract

“…Generally, it is a challenging task to forecast monthly runoff with reliable and applicable forecast accuracy. Hydrological models can be approximately divided into two categories: physical-driven (Liao et al, 2016;Solakian et al, 2020;Zhang et al, 2020;Dunkerley, 2021;Nonki et al, 2021;Xu et al, 2022) and data-driven models (Quilty et al, 2019;Feng et al, 2020;Liao et al, 2020;Darabi et al, 2021;Niu et al, 2021;Feng et al, 2022;Nguyen et al, 2022). Physical-driven models usually consider the physical genesis and mechanism of the runoff yield process and require a large amount of data for modeling.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model of Ensemble Empirical Mode Decomposition and Sparrow Search Algorithm-Based Long Short-Term Memory Neural Networks for Monthly Runoff Forecasting

Yang

Luo³

et al. 2022

Front. Environ. Sci.

View full text Add to dashboard Cite

Monthly runoff forecasting plays a vital role in reservoir ecological operation, which can reduce the negative impact of dam construction and operation on the river ecosystem. Numerous studies have been conducted to improve monthly runoff forecast accuracy, of which machine learning methods have been paid much attention due to their unique advantages. In this study, a conjunction model, EEMD-SSA-LSTM for short, which comprises ensemble empirical mode decomposition (EEMD) and sparrow search algorithm (SSA)–based long short-term neural networks (LSTM), has been proposed to improve monthly runoff forecasting. The EEMD-SSA-LSTM model is mainly carried out in three steps. First, the original time series data is decomposed into several sub-sequences. Second, each sub-sequence is simulated by LSTM, of which the hyperparameters are optimized by SSA. Finally, the simulated results for each sub-sequence are summarized as the final results. The data obtained from two reservoirs located in China are used to validate the proposed model performance. Meanwhile, four commonly used statistical evaluation indexes are utilized to evaluate model performance. The results demonstrate that compared to several benchmark models, the proposed model can yield satisfactory forecast results and can be conducive to improving monthly runoff forecast accuracy.

show abstract

The importance of incorporating rain intensity profiles in rainfall simulation studies of infiltration, runoff production, soil erosion, and related landsurface processes

Cited by 29 publications

References 116 publications

Experiment Study of Porous Fiber Material on Infiltration and Runoff of Winter Wheat Farmland in Huaibei Plain, China

Experiment Study of Porous Fiber Material on Infiltration and Runoff of Winter Wheat Farmland in Huaibei Plain, China

Influence of intra-event rainfall variation on surface–subsurface flow generation and soil loss under different surface covers by long-term field observations

A Hybrid Model of Ensemble Empirical Mode Decomposition and Sparrow Search Algorithm-Based Long Short-Term Memory Neural Networks for Monthly Runoff Forecasting

Contact Info

Product

Resources

About