International Soil and Water Conservation Research最新文献

Intensive farming is a primary cause of increased sediment and associated nitrogen (N) and phosphorus (P) loads in surface water systems. Determining their contributing sources, pathways and loads present major challenges in the high-intensity agricultural catchments. Herein, we quantify the sediment sources and magnitude of sediment total N and total P from different sources using a novel application of compound-specific stable isotope (CSSI) and fallout radionuclides (FRNs) of ¹³⁷Cs and ²¹⁰Pbex in an intensive agricultural catchment in North China. Sediment sources from surface and sub-surface soils were estimated from FRNs fingerprint and accounted for 62 ± 7% and 38 ± 7% respectively, while surface soil from land uses that originated from hillslope were identified by CSSI fingerprint. Using a novel application of FRNs and CSSI sediment fingerprinting techniques, the dominant sediment source was derived from maize farmland (44 ± 0.1%), followed by channel bank (38 ± 7%). The sedimentation rate (13.55 ± 0.30 t ha⁻¹ yr⁻¹) was quantified by the ¹³⁷Cs cores (0–60 cm) at the outlet of this catchment. The total N and total P in sediment were both mostly derived from maize farmland and least from channel banks. The channel banks are significant sediment sources but contribute little to the input of sediment N and P for eutrophication. It implies that chemically-applied farmlands are the main hotspots for catchment erosion control and pollution prevention. The novel application of FRNs and CSSI techniques cost-effectively quantified sediment N and P loads from different sources with a single visit to the catchment, enabling rapid assessment for optimizing soil conservation strategies and land management practices. Keywords: Sediment sources, Land use, N and P loads, Compound-specific stable isotope, Fallout radionuclides.

Estimation of the annual runoff frequency distribution is an essential basis for water resource management. This study proposes a framework for estimating the annual runoff frequency distribution across 252 catchments in China based on climatic conditions and catchment characteristics from 1956 to 2000. The Budyko land-specific parameter n, which intergrates influences other than the mean climate conditions, is firstly estimated based on easily ascertainable catchment characteristics without the requirements of having long-term runoff observations. Second, the annual runoff statistical parameters, namely, the mean value and standard deviation (STD), are derived based on the Budyko rainfall-runoff model with the central moment method. Finally, the annual runoff on any recurrence interval is obtained by the Pearson-III frequency function. Results show that the parameter n can be estimated from the catchment average slope, longitude, and climatic seasonality index. The estimated statistical parameters of annual runoff have acceptable agreement with observed values (mean value: R² ∼0.94, STD: R² ∼0.91, and both relative errors <10%). In addition, estimated annual runoff at each catchment for typical wet and dry years (25% and 75% ranked percentiles) coincides well with observed values, with R² of 0.92–0.93 and relative errors less than 10%. This result indicates the robustness of this framework for estimating the annual runoff frequency distribution, which provides a simple and effective tool for ungauged or poorly gauged catchments.

Socioeconomic development induced nonpoint source (NPS) pollution has aroused an increasing concern, however, most of the previous studies were concentrated on the impacts of environmental determinants. Here, total nitrogen (TN) and total phosphorus (TP) concentrations from 13 sampling sites were collected biweekly from January 2018 to October 2021, and 26 potential factors including environmental and socioeconomic were considered in the Wangjiaqiao watershed of the Three Gorges Reservoir Area, China. Impacts of these factors on TN and TP were evaluated by partial least squares regression (PLSR) model. It showed that average TN and TP concentrations in wet seasons (TN,14.68 mg L⁻¹; TP, 0.113 mg L⁻¹) were higher than that in dry seasons (TN, 11.73 mg L⁻¹; TP, 0.087 mg L⁻¹). Additionally, the TN concentrations were greater in downstream than upstream, however, the highest TP concentrations were found in the middle of the watershed. The optimal PLSR model explained 69.6%, 73.1% and 66.1% of the variance in TN concentration, as well as 65.7%, 79.5% and 67.4% of the variance in TP concentration during the annual, dry and wet seasons, respectively. Moreover, TN was primarily influenced by topographic wetness index, planting structure, interspersion and juxtaposition index, orchard proportion, nitrogen fertilization, per capita income, and catchment area, whereas TP was mainly controlled by slope gradient, topographic wetness index, hypsometric integral, interspersion and juxtaposition index, and population density. Collectively, environmental factors had greater impacts on the TN and TP concentrations than socioeconomic factors. Raising farmers' awareness of the hazards of NPS pollution is beneficial to watershed NPS pollution control.

The use of draglines to remove overburden in Queensland opencut mines, results in landscapes that consist of long parallel tertiary overburden spoil-piles that are generally highly saline, dispersive, and highly erodible. The height of these spoil-piles may exceed 50–60 m above the original landscapes and the slopes are at the angle of repose of around 75% or 37°. Legislation and public opinion require that these highly disturbed open-cut post-mining landscapes should be satisfactorily rehabilitated into an approved post-mining land use with acceptable erosion rates. Therefore, these slopes must be reduced before the landscape can be rehabilitated. The most expensive component of the rehabilitation process is the re-shaping and preparation of the overburden to create a suitable landscape for vegetation growth. As soils and overburden varies greatly in their erodibilities, the extent and cost of earthworks can be minimized, and rehabilitation failures avoided, if soil erosion from designed landscapes can be predicted using laboratory-based parameters prior to construction of these landscapes. This paper describes the development of a model for that purpose.

A catchment or landscape erosion model MINErosion 4 was developed by upscaling the existing hillslope model MINErosion 3 (So, et al., 2018) and integrate it with both ESRI ArcGIS 10.3 or QGIS 3.16 (freeware), to predict event based and mean annual erosion rate from a postmining catchment or landscape. MINErosion 3 is a model that can be used to predict event and annual erosion rates from field scale hillslopes using laboratory measured erodibility parameters or routinely measured soil physical and chemical properties, and to derive suitable landscape design parameters (slope gradient, slope length and vegetation cover) that will result in acceptable erosion rates. But it cannot be used to predict the sediment delivery from catchments or landscapes. MINErosion 4 was validated against data collected on three instrumented catchments (up to 0.91 ha in size) on the Curragh mine site in Central Queensland. The agreement between predicted (Y) and measured (X) values were very good with the regression equation of Y = 0.92X and an R² value of 0.81 for individual storm events, and Y = 1.47X and an R² value of 0.73 for the average annual soil loss. This is probably the first time that a catchment scale erosion is successfully predicted from laboratory measured erodibility parameters.

The rhizosphere is the most active soil area for material transformation and energy flow of soil, root, and microorganism, which plays an important role in soil biochemical cycling. Although the rhizospheric nitrogen (N) and phosphorous (P) were easily disturbed in the agroecosystem, the effects of rhizosphere on the dynamics of soil N and P cycling have not yet been systematically quantified globally. We summarized the magnitude, direction, and driving forces of rhizosphere effects on agroecosystem's N and P dynamics by 1063 observations and 15 variables from 122 literature. Rhizosphere effects increased available N (AN, 9%), available P (AP, 11%), and total P (TP, 5%), and decreased nitrate N (NO₃–N, 18%) and ammonia N (NH₄–N, 16%). The effect of rhizosphere on total N (TN) was not significant. These effects improved AN in tropical (12%) and subtropical (14%) regions. The effect of rhizosphere on TP was greater under subtropical conditions than in other climates. The most substantial effects of the rhizosphere on TP and AP were observed under humid conditions. Rhizosphere effects increased AN and AP in vegetables more than in other crop systems. Application of N > 300 kg ha⁻¹ had the most significant and positive rhizosphere effects on TN and AN. P application of 100–150 kg ha⁻¹ had the greatest rhizosphere effects on TP and AP. These effects also improved the microbial (biomass N and P) and enzymatic aspects (urease, acid phosphatase, and alkaline phosphatase) of soil P and N cycling. Structural equation modeling suggested that aridity indices, fertilizer application rate, soil pH, microbial biomass, and soil enzymes strongly influence the magnitude and direction of the rhizosphere's effect on the P and N cycles. Overall, these findings are critical for improving soil nutrient utilization efficiency and modeling nutrient cycling in the rhizosphere for agricultural systems.

Precipitation is the most important water resource in semi-arid regions of China. The redistribution of precipitation among atmospheric water, soil water and groundwater are related to the land surface afforested ecological system. The study took widely replanted Pinus sylvestris var. Mongolica (PSM) in Mu Us Sandy Land (MUSL) as a research object and monitored precipitation, soil moisture, sap flow, and deep soil recharge (DSR) to find out moisture distribution in shallow soil layers. Results showed that the restoration process of PSM in MUSL changed the distribution of precipitation, with part of it infiltrating downward as DSR and part of it being stored in the shallow soil. Consequently, evapotranspiration increased and DSR significantly decreased, resulting in up to 466.9 mm of precipitation returning to the atmosphere through evapotranspiration in 2016. Vegetation increased soil water storage (SWS) capacity, with maximum SWS in PSM plot and bare sandy land (BSL) being 260 mm and 197 mm per unit horizontal area, respectively in 2016. DSR decreased from 54% of precipitation in the BSL plot to 0.2% of precipitation in the PSM plot in 2016. A great portion of infiltrated water was stored in the PSM ecosystem, resulting in a time lag of infiltration to reach the deep soil layer, and the infiltration rate in the BSL plot was 11 times of that in the PSM plot. SWS decreased 16 mm and 7.6 mm per unit horizontal area over a one-year period (from March to October, non-freezing time) in 2017 and 2019, respectively. The PSM annual sap flow was maintained at a relatively constant level of 154 mm/yr. Through in-situ measurement and comparative analysis of the precipitation redistribution of the BSL plot and the PSM plot, we find that PSM can significantly reduce the shallow soil water storage and DSR. However, substantial reduction of shallow soil water storage and DSR is detrimental for the long-term development of PSM forest. Therefore, it is necessary to reduce PSM density to cut the water consumption by PSM per unit area, thus to augment the shallow SWS and DSR, which will be beneficial for the PSM to survive under extreme drought conditions in the future. This study helps us understand the role of precipitation-induced groundwater recharge in the process of vegetation restoration in semi-arid regions and explains the possible causes of PSM forest degradation.

In this study, a soil filled Hydraulic Tilting Flume (HTF) was used as a test plot under simulated rainfall conditions. This flume was filled with mollisols soils (sandy loam in texture) collected from tarai region of Himalayas. The effects of root and shoot characteristics of Napier grass in terms of leaf area index (LAI), shoot length (SL), number of leaves (NL), number of tillers (NT), shoot biomass (SB), root density (RD), root length (RL), root biomass (RB), and total biomass (TB) were investigated on runoff and sediment outflow at 90, 120 and 150 days after planting (DAP). Four simulated rainfall intensities namely 4.0, 6.5, 8.3 and 9.4 cm/h over three land slopes of 1, 2 and 3% were selected. Runoff samples collected from whole plant plot and only root plot were analyzed for runoff and sediment outflow. Findings revealed that Napier grasses were very effective to reduce runoff and sediment outflow and its efficacy increased with the extended growth stages. The reduction in runoff and sediment outflow at 90, 120 and 150 DAP was obtained as 56% and 85%, 68% and 90%, and 74% and 96%, respectively, as compared to bare plot conditions. It was observed that the comparative contribution of shoots in runoff rate reduction was higher than the roots. On the contrary, the root part of the plant showed more contribution in sediment rate reduction as compared to the shoot part. Step wise regression was attempted for the selection of effective input parameters to establish authentic runoff and sediment outflow models. Power form of multiple non-linear regression (MNLR) showed very satisfactory results for predicting runoff and sediment outflow with coefficient of determination (R²) as 97.4% and 99.0%, respectively, root mean square error (RMSE) as 38.8 cc/m²/min and 0.126 g/m²/min, respectively, and coefficient of efficiency (CE) as 93.9% and 96.7%, respectively, during testing period.

The Water Erosion Prediction Project (WEPP) model code was modified extensively to support the simulation of nonpoint source (NPS) pollutant sourcing and transport in nonuniform hillslopes based on NPS science from the Soil and Water Assessment Tool (SWAT). This was accomplished utilizing WEPP's overland flow element (OFE) in place of SWAT's hydrologic response unit (HRU) construct which enabled more physically plausible routing within a hillslope. In addition, several improvements to the NPS code base were implemented. These include: free-source format, modern-Fortran conventions, minor enhancements to NPS model science, and code refactoring. This manuscript documents all model development activities, presents a comparison of relevant WEPP and WEPP-WQ code bases, and performs a local sensitivity analysis of the final model code for the most important input parameters and processes. Sensitivity results indicated that the model performed as expected according to its design and provided important insights for potential subsequent validation studies.

Soil erosion is mainly affected by the rainfall characteristics and land cover conditions, and soil erosion modelling is important for evaluating land degradation status. The revised Universal Soil Loss Equation (RUSLE) have been widely used to simulate soil loss rate. Previous studies usually considered the general rainfall characteristics and direct effect of runoff with the event rainfall erosivity factor (R_e) to produce event soil loss (A_e), whereas the fluctuation of rainfall intensity within the natural rainfall profile has rarely been considered. In this study, the relative amplitude of rainfall intensity (R_am) was proposed to generalize the features of rainfall intensity fluctuation under natural rainfall, and it was incorporated in a new R_e (R_e=R_amEI₃₀) to develop the RUSLE model considering the fluctuation of rainfall intensity (RUSLE-F). The simulation performance of RUSLE-F model was compared with RUSLE-M1 model (R_e=EI₃₀) and RUSLE-M2 model (R_e=Q_REI₃₀) using observations in field plots of grassland, orchard and shrubland during 2011–2016 in a loess hilly catchment of China. The results indicated that the relationship between A_e and R_amEI₃₀ was well described by a power function with higher R² values (0.82–0.96) compared to Q_REI₃₀ (0.80–0.88) and EI₃₀ (0.24–0.28). The RUSLE-F model much improved the accuracy in simulating A_e with higher NSE (0.55–0.79 vs −0.11∼0.54) and lower RMSE (0.82–1.67 vs 1.04–2.49) than RUSLE-M1 model. Furthermore, the RUSLE-F model had better simulation performance than RUSLE-M2 model under grassland and orchard, and more importantly the rainfall data in the RUSLE-F model can be easily obtained compared to the measurements or estimations of runoff data required by the RUSLE-M2 model. This study highlighted the paramount importance of rainfall intensity fluctuation in event soil loss prediction, and the RUSLE-F model contributed to the further development of USLE/RUSLE family of models.

Better soil structure promotes extension of plant roots thereby improving plant growth and yield. Differences in soil structure can be determined by changes in the three phases of soil, which in turn affect soil function and fertility levels. To compare the quality of soil structure under different conditions, we used Generalized Soil Structure Index (GSSI) as an indicator to determine the relationship between the “input” of soil three phases and the “output” of soil structure. To achieve optimum monitoring of comprehensive indicators, we used Successive Projections Algorithm (SPA) for differential processing based on 0.0–2.0 fractional orders and 3.0–10.0 integer orders and select important wavelengths to process soil spectral data. In addition, we also applied multivariate regression learning models including Gaussian Process Regression (GPR) and Artificial Neural Network (ANN), exploring potential capabilities of hyperspectral in predicting GSSI. The results showed that spectral reflection, mainly contributed by long-wave near-infrared radiation had an inverse relationship with GSSI values. The wavelengths between 404-418 nm and 2193–2400 nm were important GSSI wavelengths in fractional differential spectroscopy data, while those ranging from 543 to 999 nm were important GSSI wavelengths in integer differential spectroscopy data. Also, non-linear models were more accurate than linear models. In addition, wide neural networks were best suited for establishing fractional-order differentiation and second-order differentiation models, while fine Gaussian support vector machines were best suited for establishing first-order differentiation models. In terms of preprocessing, a differential order of 0.9 was found as the best choice. From the results, we propose that when constructing optimal prediction models, it is necessary to consider indicators, differential orders, and model adaptability. Above all, this study provided a new method for an in-depth analyses of generalized soil structure. This also fills the gap limiting the detection of soil three phases structural characteristics and their dynamic changes and provides a technical references for quantitative and rapid evaluation of soil structure, function, and quality.