Simulated Rainfall Research Articles

USING MACHINE LEARNING TECHNIQUES TO FILTER VEGETATION IN COLORIZED SFM POINT CLOUDS OF SOIL SURFACES

Abstract. Various soil erosion processes can be quantified using digital elevation models (DEMs) of difference. In this study, cameras were used to capture images of bare soil during artificial rainfall simulations. The photos were then used to generate dense 3D point clouds with millimeter resolution using Structure-from-Motion and Multiview-Stereo (SfM+MVS) techniques. However, the point clouds also captured some vegetation, such as agricultural plants, grass, and weeds, present at the soil surface. It had to be removed to accurately measure soil erosion processes. The removal can be done manually and is hence time-consuming. In this study, several methods have been tested and compared to perform (semi-)automatic vegetation filtering from point clouds of soil surfaces. First, the point clouds were labelled into vegetation and ground data to establish a basis set for the subsequent experiments. Then, three branches of algorithms were tested. The first branch considers knowledge-based thresholding. Thereby, unique features were considered, e.g., point color, height, and roughness within a specified neighborhood. For instance, a threshold was set in the color space to separate green vegetation from brown soil considering the green band. The second branch used a machine learning (ML) algorithm to classify each point as vegetation or ground by automatically finding thresholds. Thereby, again features such as point color were used. In addition, multi-scale features were computed for each point to characterize it in the context of its neighborhood. The calculated features were used afterwards with the random forest (RF). The third branch considered an end-to-end learning approach and thus avoiding the necessity to define features. The deep learning-based architecture PointNet++ was used. For the classification, an adapted model for soil surfaces and vegetation was trained. The performance of the different methods was compared, and an assessment of each method was provided. Overall, the study aims to find a (semi-)automatic method to remove vegetation from time series of soil surface point clouds to achieve an accurate measurement of soil surface changes and thus eventually erosion processes while minimizing manual effort and time consumption.

Evaluation of CORDEX Africa regional climate models performance in simulating climatology of Zarima sub-basin northwestern Ethiopia

Climate models are basic tools to obtain reliable estimates of future climate change and its effects on the water resources and agriculture in given basin. However, all climate models are not equally valuable for all areas. Therefore, determining the most appropriate climate model for a specific study area is essential. This study examines the performance of 10 CORDEX-AFRICA-220 Regional Climate Models (RCMs), three downscaling institutional based ensembles mean (Reg ensemble, CCLM ensemble and REMOO ensemble) and the multi-model ensemble mean. The models were evaluated based on their ability in replicating the seasonal and annual rainfall, minimum and maximum temperature and inter-annual variability for the period of 1986–2005 using statistical metrics such as BIAS, Root Mean Square Error (RMSE), Pearson correlation coefficient (r), coefficient of variation (CV), Kling Gupta Efficiency (KGE) and Taylor diagram. The findings indicated that HadREMOO, MPI-Reg4-7, HadReg4-7, Reg ensemble, and multi-model ensemble mean performed relatively better in representing the mean annual observed rainfall at the Adiramets, Debarik Ketema, Niguse Maystebri, and Zarima stations, respectively. Whereas, NorESM-CCLM, MPI-CCLM, NorESM-Reg4-7, and NorESM-REMOO exhibited a weak performance in reproducing the observed mean annual rainfall at the Adiramets, Debarik Ketema Niguse, Maystebri, and Zarima stations, respectively. Similarly, RCMs generally capture the mean annual maximum temperature of climatic stationsof Zarima subbasin well. Specifically, the MPI-Reg4-7 simulation performs well in representing the mean annual observed maximum temperature at Adiramets and Maytsebri stations, while the Debarik and Ketema Niguse stations exhibit superior performance in the HadReg4-7 simulation and the Zarima station shows better representation in the CCLM ensemble simulations. The majority of the model simulations exhibit good representation of mean annual minimum temperature at Adiramets, Debarik, and Zarima stations. Specifically, CanESM-RCM, HadReg4-7, REMOOensemble, multi-model ensemble, and Regensemble simulations perform better at Adiramets, Debarik, Ketema niguse, Maystebri and Zarima stations respectively. This suggests that these models may have biases or shortcomings in capturing the temperature values in the subbasin. Furthermore, NorESM-CCLM at Adiramets, Ketema niguse, and Zarima stations, NorESM-REMOO at Debarik station, and HadReg4-7 at Maystebri station demonstrate poor performance in representing the observed mean minimum temprature. Majority of the RCMs, all institutional based ensemble means and the multi-model ensemble mean simulations overestimate the observed mean annual rainfall of the Zarima subbasin with minimum bias of 0.02 mm at Ketema niguse HadReg4-7and maximum bias of 2.81 mm at Maytsebri MPI-CCLM simulation. Similarly, HadReg4-7 simulation of Ketama Niguse MPI-CCLM showed a minimum 0.02 mm and Maytsebri simulation kiremit season mean rainfall showed a maximum bias of and 2.99 mm. Regarding mean annual and kiremit season maximum and minimum temperature of the Zarima subbasin were overestimated by majority of the simulation and the ensemble means. The correlation (r) of observed and model simulated mean annual and kiremit season rainfall was strong (0.60–0.79) and very strong (0.80–0.99) in the majority of the simulations except Ketema niguse station mean annual and kiremit season rainfall simulations of MPI-REMOO, NorESM-Reg4-7; Debarik station kiremit season rainfall of NorESM-CCLM and NorESM-REMOO, MPI-Reg4-7 and MPI-REMOO, which showed moderate correlation. The performance of the RCMs, institutional based ensemble means and multi-model ensemble mean were different in statistical metrics (BIAS, RMSE, r, CV and KGE) and Taylor diagram. Among the simulations and ensemble means, the multi-model ensemble mean was superiors in two or more of statistical metrics at each station of the Zarima subbasin except Maytsebri station kiremit season rainfall, where the CCLM ensemble was better. Consistently, the Taylor diagram showed that the multi-model ensemble was better in the replication of the areal annual and kiremit season rainfall, maximum and minimum temperature of the subbasin. This finding evidenced that selecting the best RCMs and ensemble mean is necessary for climate projection and climate change impact assessment study.

Effects of slopes, rainfall intensity and grass cover on runoff loss of mercury from floodplain soil in Oak Ridge TN: A laboratory pilot study

Mercury is recognized as a global pollutant. A large amount of elemental mercury was used in the Manhattan Project in Oak Ridge, TN and Hg is still present in the water-shed surrounding the Y-12 facility and East Fork Poplar Creek. Soil erosion during rainfall-runoff events result in nonpoint source Hg transport from floodplain soils to aquatic ecosystems. This study investigated transport mechanisms of Hg through surface runoff processes from floodplain soils in Oak Ridge, TN into water with a simulated rainfall laboratory pilot scale experiment. The experiments simulated rainfall intensity (50–140 mm/h), the ground grass coverage (5 %-85 %), the soil moisture conditions and the slopes (7.5–16.32 degrees) producing soil runoff erosion processes. Results show that high grass coverage, decreased landscape slope and low rainfall resulted in the decreased Hg concentrations in sediments transported through runoff from the floodplain soil. Hg loss was strongly correlated with and controlled by soil loss. The order of environmental factors on Hg loss was the slope > rainfall intensity > grass cover. As grass cover increased from 5 % to 85 %, Hg in sediment decreased from 124 to 72.3 µg/kg and Hg in penetrating water decreased from 4.7 to 2.8 µg/kg on wet soil condition at the slope 12.560. Hg loss and soil loss significantly increased with the increase of the soil slope. As the slope increased from 7.550 to 12.560, Hg loss increased from 364 to 2905 mg/m2 in the dry soil condition with 5 % grass cover. Meanwhile Hg loss and soil loss significantly decreased with the increase of grass cover. The Hg concentration in sediment increased with increasing initial soil moisture from Dry Soil, to Wet Soil and Very Wet Soil. The roots of grasses stabilized soil Hg and the Hg content in soil rhizosphere were significantly higher than that in non-rhizosphere. This study indicates that rainfall intensity, ground grass coverages, landscape slopes and initial soil moisture content strongly controlled Hg transport from the floodplain ecosystem and its flux into stream water. Mercury associated with sediment was the major Hg input into stream waters from floodplain soils in Oak Ridge, TN. Proper onsite management, such as increasing ground coverages with local low lying grasses and decreasing landscape slopes may significantly reduce potential Hg transport into local stream water in Oak Ridge TN.

The Next-Generation Ecosystem Experiment Arctic Rainfall Simulator: a tool to understand the effects of changing rainfall patterns in the Arctic

Abstract Rainfall frequency and intensity are expected to increase in the Arctic, with potential detrimental impacts on permafrost, leading to enhanced thawing and carbon release to the atmosphere. However, there have been very few studies on the effect of discrete rain events on permafrost in the Arctic and sub-Arctic. Conducting controlled rainfall experiments within permafrost landscapes can provide an improved understanding of the effect of changing intensity, duration, and timing of rain events on permafrost tundra ecosystems. Here, we describe the design and implementation of the Next-Generation Ecosystem Experiment Arctic Rainfall Simulator (NARS), a variable intensity (4–82 mm/h) rainfall simulator that can be used to study the effects of rainfall on permafrost stability. The NARS design includes a 3D-printed 4 cm H-flume and uses an eTape resistivity sensor that was calibrated (R2 = 0.9–0.96) to measure discharge from the system. NARS is designed to be lightweight, simple to construct, and can be easily deployed in remote locations. As a field validation of updated rainfall simulator design and modernized controls, NARS was tested on the Seward Peninsula, AK. Because of its portability, versatility in deployment, dimensions, and rainfall intensity, NARS represents a methodological innovation for researching the impacts of rainfall on permafrost environments.

Investigation of uniform and graded sediment wash-off in an urban drainage system: Numerical model validation from a rainfall simulator in an experimental facility

Understanding sediment wash-off in urban environments plays an essential role in sediment transport management; and is critical for accurate pluvial flood control to assist in adaptation and mitigation strategies. Sediment transport models have been researched previously, though challenges still arise due to the complicated nature of graded sediment transport. This study tested the accuracy of the van Rijn model using a sparse distribution of particle sizes using the geometric mean. As such, this study used high-resolution datasets collected in a water laboratory to investigate sediment wash-off and transport on an urban street. This included the interaction of two gully pots receiving sediment loads that were washed off from a hypothetical urban surface by three rainfall intensities. The results showed that the model was able to simulate uniform sediments entering the gully pots accurately when the sediment size was assigned to a median diameter. Using the grain diameter to represent the geometric mean can improve the model performance for simulating a graded sediment.

Historical simulations of temperature and precipitation from the CORDEX Africa model in the Wabi Shebele Basin

Rising global temperatures and shifting precipitation patterns have significant socio-economic consequences if not properly studied and predicted. Regional climate models (RCMs) are utilized to assess local-scale climate change. However, the reliability of individual models must be validated due to inherent limitations and methodological constraints. This study evaluates the performance of CORDEX Africa RCMs using observed rainfall and air temperature data from 1986 to 2005. Model performance was evaluated using statistical indicators such as bias, RMSE, r, MAE, and a concise plot of the statistical indicators which is Taylor’s diagram. In rainfall simulation, the RACMO22T performed admirably in the upper parts of the basin (region of high rainfall and cold temperature) and lower regions of the basin (region of low rainfall and hot temperature) with bias −8.64% and 6.19% respectively. HIRHAM5 and CCLM4-8 simulate well the maximum temperature in the upper parts with biases of (0.14 °C and −0.14 °C respectively), whereas RCA4 is well performed in the lower parts of the basin. CCLM4-8 is good for minimum temperature simulation in the upper parts, but HIRHAM5 and RCA4 are good in the lower parts of the basin. In rainfall simulation, all models are slightly good in dry months than in wet. All models underestimated the maximum temperature and overestimated the minimum temperature in the study area as compared to the observed.

Impact of solar panels on runoff generation process

AbstractBecause of the benefits of solar energy, solar photovoltaic (PV) technology is being deployed at an unprecedented rate and the number of photovoltaic panels is sharply increasing. Agrophotovoltaic systems (solar farms) seem to be the most sustainable tools to create renewable energy without compromising agricultural production. However, utility‐scale solar energy development is land intensive and its large‐scale installation can have negative impacts on the environment. Moreover, its impacts on soil and on relative hydrological processes have been poorly studied. This article aims to evaluate the impact of solar panels on the runoff generation process, which is directly linked to the soil erosion process. Using a rainfall simulator, runoff measurements for a rainfall intensity equal to 56 mm/h were carried out by assuming different panel arrangements with respect to the maximum slope direction of the field (cross slope and aligned slope). Results were compared to a control reference of the same plot, with no panels (bare soil). Physical models found in the literature were then applied and calibrated, to upscale the models to a much higher hillslope length. Results showed that solar panels increase the peak discharge by about 11 times compared to the reference hillslope. A moderate effect of PV panel arrangement was observed on the peak discharges (11.7 and 11.5 times higher, for cross slope and aligned slope panels, respectively), whereas the time to runoff was the lowest for aligned slope panels (0.3 h), higher for cross slope panels (0.62 h), and the highest (1.2 h), for the bare soil hillslope. As it would be expected, upscaling the models to longer hillslopes resulted in increases in outlet discharges, and in the time to runoff, with an exception for aligned slope panels.

Reliability of Runoff Hydrograph Model for Extreme Rainfall Events using HEC-RAS 2D Flow Hydrodynamics Rain-On-Grid

Hydrological modeling is a valuable tool for analyzing water resources and predicting the impacts of extreme rainfall events, particularly for flood risk mitigation. Accurate results from hydrological models rely heavily on detailed input of watershed characteristics, including morphometric factors, land cover, and slope. In the face of the increasing frequency and severity of floods due to climate change, hydrological modeling has become an essential tool for disaster risk mitigation and management. HEC-RAS is a widely used software for river flow modeling and floodplain analysis. HEC-RAS 2D with the rain-on-grid meteorological model is a powerful tool for simulating flood events in complex river systems. The rain-on-grid method enables the simulation of spatially distributed rainfall events and their impact on runoff, which is crucial for modeling extreme runoff hydrographs. Previous studies have demonstrated the accuracy and effectiveness of HEC-RAS 2D with rain-on-grid in modeling extreme runoff hydrographs. This study aims to validate the reliability of HEC-RAS 2D hydrodynamics in simulating unsteady flow with meteorological data will be examined within a laboratory-scale physical model of the watershed. Additionally, the performance of HEC-RAS 2D will be evaluated in a observed watershed, specifically the Bangga watershed in Central Sulawesi to assess the performance of the HEC-RAS 2D Hydrodynamics model. The runoff hydrograph reliability model using HEC-RAS 2D rain-on-grid yielded close results when compared to a laboratory-scale physical model with a rainfall simulator and HEC-RAS 2D simulation using meteorological input data. The error in Time of Peak (Tp) and Peak Discharge (Qp) between the physical and numerical models was 7% and 3.5%, respectively. Additionally, in the HEC-RAS 2D simulation, a Manning roughness coefficient value of 0.04 closely matched the results of the physical model, while there was some variability when comparing HEC-RAS 2D results with measured data in Bangga watershed of Central Sulawesi. However, the numerical model exhibited relatively approaching result with the HEC-HMS SCS hydrograph model.

Water-holding characteristics of litter and soil and the influencing factors after individual rainfall

To clarify the water-holding characteristics of forest litter and soil, different densities (0, 300, 600, 900 g·m-2) of Pinus tabuliformis litter were covered on the typical soil surface of loess cinnamon and calcareous cinnamon in north China. Based on an artificial simulated rainfall experiment, we observed the variation of litter and soil water content with time, and analyzed the variation process and influencing factors of litter and soil water content after rainfall. The results showed that water content of P. tabuliformis litter decreased significantly with the increases of duration after the rainfall, and that the range of litter water contents under all treatments was 22.9%-71.0%. There was a decreasing exponential function between the decline rate and time. Litter water content was only affected by litter density. The higher the litter density, the greater the decreasing rate of litter water content. Soil water content fluctuated and decreased with the increases of duration after the rainfall, with a variation of 1.5%-8.5%. Soil water-holding capacity was affected by litter density, soil type, and slope position. Litter could effectively reduce the fluctuation of soil water. The higher litter density, the smaller the coefficient of variation of soil water content, the greater the effect of litter on soil water fluctuation, and the better the soil water-holding capacity. The water-holding capacity on loess cinnamon soil was significantly higher than that of calcareous cinnamon soil. Soil water-holding capacity on the downslope was significantly higher than that on the upslope and middle slope, without any difference between upslope and middle slope.

Preferential flow in the shallow vadose zone: Effect of rainfall intensity, soil moisture, connectivity, and agricultural management

AbstractPreferential flow is ubiquitous in soils, and it affects water infiltration, runoff, and contaminant transport. Undisturbed soil lysimeters (n = 10; 900 cm2) were collected from an agricultural field to quantify the effect of climate, soil moisture, connectivity, and agricultural practices on water transport through the shallow vadose zone. A series of 10 rainfall simulations was conducted on each lysimeter (n = 100 events) and data were analysed within a framework of five case studies where we assessed the impact of rainfall intensity (n = 30 events), soil moisture (n = 28), and tillage (n = 21). Three lysimeters that had near‐zero flow initially were modified to investigate dynamics of direct surface connectivity through an artificial macropore in which we assessed the impacts of soil moisture (n = 12) and subsequent disruption via tillage (n = 9). Stable water isotopes were used to separate leachate into event (Qe) and pre‐event water (Qpe). Results showed that event water transport in leachate was not affected by rainfall intensity (Qe/Q = 49% ± 21% to 50% ± 24%); however, event water decreased from 65% ± 5% to 23% ± 28% with increasing soil moisture. Lysimeters with artificial macropores resulted in leachate that was nearly all event water (85% ± 12% to 92% ± 4%) irrespective of soil moisture. Tillage decreased event water transport for both lysimeters with and without an artificial macropore by ~30%. Findings show how varying initial and boundary conditions produce a continuum of preferential flow. Water and tracer flux data collected in the current study are therefore essential for predicting conditions with high relevance of preferential flow and contaminant transport when assessing or modelling long‐term hydrographs where these conditions are only met during a small proportion of the flow time.

Impact of incorporated residues on runoff and soil erosion in black soil under simulated rainfall

Impact of incorporated residues on runoff and soil erosion in black soil under simulated rainfall

A storyline analysis of Hurricane Irma’s precipitation under various levels of climate warming

Understanding how extreme weather, such as tropical cyclones, will change with future climate warming is an interesting computational challenge. Here, the hindcast approach is used to create different storylines of a particular tropical cyclone, Hurricane Irma (2017). Using the community atmosphere model, we explore how Irma’s precipitation would change under various levels of climate warming. Analysis is focused on a 48 h period where the simulated hurricane tracks reasonably represent Irma’s observed track. Under future scenarios of 2 K, 3 K, and 4 K global average surface temperature increase above pre-industrial levels, the mean 3-hourly rainfall rates in the simulated storms increase by 3–7% K−1 compared to present. This change increases in magnitude for the 95th and 99th percentile 3-hourly rates, which intensify by 10–13% K−1 and 17–21% K−1, respectively. Over Florida, the simulated mean rainfall accumulations increase by 16–26% K−1, with local maxima increasing by 18–43% K−1. All percent changes increase monotonically with warming level.

Impact of rainfall intensity on soil erosion based on experimental research

Soil erosion by water is influenced by a major morphogenetic factor – precipitation. Surface runoff, initiated by rainfall, plays a key role in this process. This article addresses the effects of rainfall intensity and soil moisture on soil erosion through a series of rainfall simulations of different intensity and duration. The implementation of measurements at a research station located in the Różany Stream catchment in Poznań made it possible to study the entire water balance within the slope, including precipitation, evaporation, surface runoff and infiltration. The study included various rainfall intensities, with a focus on extreme events reflecting ongoing climate change and increasing anthropopressure. Rainfall simulations were conducted on both dry and wet ground. The results showed that increasing rainfall intensity led to greater surface runoff and soil loss. Moreover, soil moisture was identified as a critical factor affecting soil erosion, with wetter conditions reducing soil loss while increasing surface runoff.

High-level of bio-cementation by microbially induced carbonate precipitation technique with nano calcite seeds

The bio-cementation process through bacterial mineralization has emerged as an eco-friendly solution for soft or loose sandy soils, particularly in areas with ground slopes and high rainfall. This approach is aligned with sustainable development goals, promoting environmentally responsible and long-lasting solutions. In this context, ureolytic bacteria capable of decomposing urea for calcium carbonate precipitation are the primary agents for bio-cementation. This study explores the effects of nano calcite as nucleation sites on the MICP capacity to enhance the bio-cementation process. The microstructure of the precipitation was analyzed alongside its capacity for MICP. In addition, a simulated rainfall model was set up to evaluate the erosion resistance of sand samples. The findings of this study can guide the development of eco-friendly solutions for soil stabilization in environmentally vulnerable areas.

Effects of sewage sludge application methods on the transport of heavy metals with runoff and their mechanisms

Woodland utilization is a promising disposal method for sewage sludge (SS). However, the potential risk of heavy metals (HMs) transport with runoff must be considered. Among the various factors influencing HMs loss, SS application methods (Holing application, HA; Broadcasting and mixing application, BM; Broadcasting application, BA) are likely to cause significant effects by altering soil erosion and soil aggregates. This study aimed to determine how SS application methods affect HMs loss, soil aggregates erosion, and how they are related. Accordingly, the losses of HMs in surface runoff, interflow, and sediment were quantified during six simulated rainfalls. The results demonstrated that all methods reduced surface runoff, but BA was the most effective. Additionally, BA significantly reduced the total sediment yield and the total proportion of the <0.05 mm fraction aggregates. Moreover, BA had the smallest cumulative losses of Pb and Cd through surface runoff and Cu, Pb, and Cd through sediment. Sediment was the most important pathway for HMs loss, through which over 76.56 % of HMs were lost. In BA, the <0.05 mm fraction aggregates had the lowest HMs load, whereas in other treatments had the highest (54.33 %–80.33 %). The potential ecological risk coefficient of Cd was beyond “moderate” in all the pathways of BM and “high” in the interflow of each SS treatment. Nonetheless, when the multi-elements were evaluated collectively, the potential ecological risk index for each SS treatment was categorized as “low”. Overall, BA not only reduced soil erosion but also posed no risk of HMs pollution. It should be noted that the loss of Cd in the interflow had a great impact, while the <0.05 mm fraction aggregates played a significant role in the HMs load. Thus, the current study not only provides an effective approach for the environmentally safe disposal of SS but also proposes a scientific method for the application of SS in woodlands.

Explicit Representation of Orographic Anisotropy for All Directions Improves Nanling Mountain Rainfall Simulation

AbstractClimate models exhibit significant rainfall bias in mountainous regions. One reason is the insufficient representation of orographic anisotropy in these models. In this study, we implement the orographic drag scheme with 3‐D orographic anisotropy (all flow directions (AFD)) into a general circulation model and investigate its impact on Nanling rainfall simulation where models have systematic dry bias in the summer. It is shown that the AFD alleviated the Nanling mountain rainfall bias by over 60%. This is through an anomalous “lower‐convergence‐higher‐divergence” deceleration pattern of the flow windward of the Nanling Mountains that enhanced vertical motion and low‐level moisture convergence. The results suggest the importance of explicit orographic anisotropy representation in rainfall simulation in mountainous regions.

The Rainwater Interception Process and Capacity of Urban Tree Organs in Shanghai

The process of rainwater interception by tree organs is crucial in mitigating the impact of intense rainfall on urban drainage systems, particularly in the context of climate change. For this study, we selected ten commonly found tree species in Shanghai, and the main parts of trees, including their leaves, branches, and bark, were collected to analyze their ability to intercept rainwater. The optimized Artificial Rainfall Simulation System (ARSS) was applied to simulate rainfall. The time-changing process of rainwater interception in three organs was measured during a 180 min rainfall event under four different rainfall intensities (4, 8, 12, and 16 mm/h, respectively). Process models of rainwater interception in different organs were fitted with adsorption kinetic equations. The rainwater interception process of tree organs complied with the quasi second-order adsorption kinetic equation. The rainwater interception capacity values of the leaves, branches, and bark of the ten urban tree species ranged from 0.05 to 0.34 mm, 0.13 to 0.24 mm, and 0.29 to 1.22 mm, respectively. The rainwater interception capacity values of the three organs significantly differ (p &lt; 0.05). The results of this study reveal that bark exhibits the greatest rainwater interception ability. Coniferous tree species have a greater ability to intercept rainwater than broad-leaved tree species. There are also differences in the rainwater interception ability of trees in urban and natural areas.

Effects of different vegetation components on soil erosion and response to rainfall intensity under simulated rainfall

Vegetation effectively prevents soil erosion, however, the reductions in runoff and erosion caused by the different components of vegetation are unclear. Under simulated rainfall intensities (60, 90, and 120 mm h−1) and litter amount (5, 15, and 25 g m−2), five different treatments were established: intact grass (IG), litter and roots (LR), canopy and roots (CR), only litter (OL), only roots (OR), and a control bare plot (CK). The effects of the canopy, roots, and litter on erosion were quantitatively analysed. (1) The runoff rate and erosion rate were reduced by different treatments, and the IG treatment most effectively reduced runoff and sediment, followed by the LR, OL, CR, and OR treatments. (2) The velocity, runoff, and sediment reductions by litter were optimal. The velocity and runoff reduction effects of the roots were stronger than those of the canopy, reducing the sediment reduction more than the litter at 5 g m−2. The velocity, runoff, and sediment reductions produced by the canopy were weak, providing average contributions of 11.40%, 8.01%, and 7.34%, respectively. The vegetation more strongly affected sediment than runoff reduction. (3) Different treatments most effectively reduced runoff and sediment under a 60 mm h−1 rainfall. The contributions of the canopy, roots, and litter to reducing runoff and sediment decreased with increasing rainfall intensity. (4) Erosion rate and shear stress showed a linear relationship. The soil erodibility Kr and critical runoff shear stress τc of the IG plot were lowest and highest, respectively. Litter and roots decreased Kr and increased τc, respectively. Compared with the bare plot, Kr decreased by 44.44%–61.54% and 15.38%–40.74% and τc increased by 3.85%–7.44% and -1.65% to 7.69% in litter and roots, respectively. This study provides insights regarding the mechanism through which vegetation controls erosion and effective scientific basis for rational vegetation allocation.

Effect of soil properties on adsorption, degradation and leaching potential of four herbicides

ABSTRACT Once a herbicide is introduced into the environment, some risks are inevitable as it can trigger accumulation, leaching and runoff, thus contaminating soil and water resources. For a proper risk assessment, the present study investigates the adsorption, degradation and leaching behaviour of pretilachlor, butachlor, anilofos and pendimethalin. Among the studied herbicides, pendimethalin (KFads 1.466 ± 0.087 to 2.569 ± 0.072 µg1–1/n g−1 ml1/n; DT50 65.34–120.29 days) has the highest adsorption and persistence in soil, followed by anilofos (KFads 0.967 ± 0.063 to 1.637 ± 0.077 µg1–1/n g−1 ml1/n; DT50 37.12–77.63 days), butachlor (KFads 0.722 ± 0.077 to 1.273 ± 0.067 µg1–1/n g−1 ml1/n; DT50 31.23–65.13 days) and pretilachlor (KFads 0.834 ± 0.066 to 1.422 ± 0.071 µg1–1/n g−1 ml1/n; DT50 26.32–61.36 days). Pretilachlor, butachlor, anilofos and pendimethalin leached up to 40–60, 30–40, 20–30 and 10–20 cm soil layers, respectively, indicating that pendimethalin was least mobile in the studied soils. Increase in simulated rainfall (300 mm) increased the downward mobility of herbicides. Among different soils, herbicide degradation and leaching were highest in loamy sand and decreased with an increase in organic matter (OM) content, while adsorption was lowest in loamy sand and increased with an increase in OM content. The addition of farmyard manure amendment reduced the mobility of pretilachlor, butachlor, pendimethalin and anilofos, and these leached up to 20–30, 20–30, 0–10 and 10–20 cm soil layers, respectively. The results indicated that organic amendments can be used as an effective management practice for controlling groundwater contamination.

Deep learning precipitation prediction models combined with feature analysis.

Precise rainfall forecasting modeling assumes a pivotal role in water resource planning and management. Conducting a comprehensive analysis of the rainfall time series and making appropriate adjustments to the forecast model settings based on the characterization results of the rainfall series significantly enhance the accuracy of the forecast model. This paper employed the Mann-Kendall and sliding T mutation tests to identify the mutational components in rainfall between 1961 and 2013 at four meteorological stations located in Henan Province. Wavelet analysis was utilized to determine the periodicity of the precipitation series. The model parameters were adjusted based on the mutation and periodicity findings, and appropriate training and test sets were selected accordingly. Rainfall simulation in Henan Province, China, was conducted using a combination of complementary ensemble empirical mode decomposition (CEEMD) and bi-directional long short-term memory (BiLSTM) networks. The integrated approach aimed at predicting rainfall in the region. The findings of this study demonstrate that the CEEMD-BiLSTM model, coupled with feature analysis, yielded favorable results in terms of prediction accuracy. The model achieved a mean MAE (mean absolute error) of 131.210, a mean MRE (mean relative error) of 0.637, a mean RMSE (root mean square error) of 187.776, and an NSE (Nash-Sutcliffe efficiency) above 0.910. The data processed according to the feature analysis results and then predicted; Zhengzhou, Anyang, Lushi, and Xinyang stations improved by 39.548%, 14.478%, 11.548%, and 19.037% respectively compared with the original prediction model.