Prediction of soil and water conservation structure impacts on runoff and erosion processes using SWAT model in the northern Ethiopian highlands

Purpose

Land degradation due to soil erosion is a serious threat to the highlands of Ethiopia. Various soil and water conservation (SWC) strategies have been in use to tackle soil erosion. However, the effectiveness of SWC measures on runoff dynamics and sediment load in terms of their medium- and short-term effects has not been sufficiently studied.

Materials and methods

A study was conducted in 2011 to 2015 in the Gumara-Maksegnit watershed to study the impacts of SWC structures on runoff and soil erosion processes using the soil and water analysis tool (SWAT) model. The study was conducted in two adjacent watersheds where in one of the watersheds, SWC structures were constructed (treated watershed (TW)) in 2011, while the other watershed was a reference watershed without SWC structures (untreated watershed (UW)). For both watersheds, separate SWAT and SWAT-CUP (SWAT calibration and uncertainty procedure) projects were set up for daily runoff and sediment yield. The SWAT-CUP program was applied to optimize the parameters of the SWAT using daily observed runoff and sediment yield data.

Results and discussion

The runoff simulations indicated that SWAT can reproduce the hydrological regime for both watersheds. The daily runoff calibration (2011–2013) results for the TW and UW showed good correlation between the predicted and the observed data (R ²?=?0.78 for the TW and R ²?=?0.77 for the UW). The validation (2014–2015) results also showed good correlation with R ² values of 0.72 and 0.70 for the TW and UW, respectively. However, sediment yield calibration and validation results showed modest correlation between the predicted and observed sediment yields with R ² values of 0.65 and 0.69 for the TW and UW for the calibration and R ² values of 0.55 and 0.65 for the TW and UW for the validation, respectively.

Conclusions

The model results indicated that SWC structures considerably reduced soil loss by as much as 25–38% in the TW. The study demonstrated that SWAT performed well for both watersheds and can be a potential instrument for upscaling and assessing the impact of SWC structures on sediment loads in the highlands of Ethiopia.

     

Evaluation of the spatial pattern of surface soil water content of a karst hillslope in Southwest China using a state-space approach

This study aims to evaluate the effects of soil physicochemical properties and environmental factors on the spatial patterns of surface soil water content (SWC) based on the state-space approach and linear regression analysis. For this purpose, based on a grid sampling scheme (10 m × 10 m) applied to a 90 m × 120 m plot located on a karst hillslope of Southwest China, the SWC at 0–16 cm depth was measured 3 times across 130 sampling points, and soil texture, bulk density (BD), saturated hydraulic conductivity (K_s), organic carbon (SOC), and rock fragment content as well as site elevation (SE) were also measured at these locations. Results showed that the distribution pattern of SWC could be more successfully predicted by the first-order state-space models (R² = 67.5–99.9% and RMSE = 0.01–0.14) than the classic linear regression models (R² = 10.8–79.3% and RMSE = 0.11–0.24). The input combination containing silt content (Silt), K_s, and SOC produced the best state-space model, explaining 99.9% of the variation in SWC. And Silt was identified as the first-order controlling factor that explained 98.7% of the variation. In contrast, the best linear regression model using all of the variables only explained 79.3% of variation.     

Using Static and Dynamic Indicators to Evaluate Soil Physical Quality in a Sicilian Area

Both capacitive indicators derived from the water retention curve and dynamic measurements of the flow‐weighted mean pore radius, R₀, were used to assess the soil physical quality of two agricultural areas (cropland and olive orchard) and two natural areas (grassland and managed woodlot plantation) potentially subject to soil degradation. The overall idea of the study was to investigate whether a dynamic indicator quantitatively derived from hydraulic conductivity measurements could be used to supplement the traditionally applied capacitive indicators retrieved from water retention measurements. According to the available criteria, only the surface layer of the cropland site showed optimal soil physical quality. In the grassland and woodlot sites, the physical quality was deteriorated also as a consequence of compaction because of grazing. Overall, the physical quality was better in tilled than nontilled soils. The optimal soil in terms of capacitive indicators had hydraulic conductivity close to saturation that was intermediate among the different land uses, and it remained 1·3–1·9 times higher than that observed in the natural sites even when the largest pores emptied. A depth effect on R₀ was observed only when larger macropores were activated. It was suggested that water transmission parameters are more affected by changes in large pore domain. The plant available water content and Dexter's S‐index showed inverse statistically significant regressions with R₀. The empirical relationships were physically convincing given that, at increasing R₀, the contribution of macropores increases, water is transmitted faster below the root zone and the soil's ability to store water is reduced. Copyright © 2013 John Wiley & Sons, Ltd.     

Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis–NIR spectral library

We need to determine the best use of soil vis–NIR spectral libraries that are being developed at regional, national and global scales to predict soil properties from new spectral readings. To reduce the complexity of a calibration dataset derived from the Chinese vis–NIR soil spectral library (CSSL), we tested a local regression method that combined geographical sub‐setting with a local partial least squares regression (local‐PLSR) that uses a limited number of similar vis–NIR spectra (k‐nearest neighbours). The central idea of the local regression, and of other local statistical approaches, is to derive a local prediction model by identifying samples in the calibration dataset that are similar, in spectral variable space, to the samples used for prediction. Here, to derive our local regressions we used Euclidean distance in spectral space between the calibration dataset and prediction samples, and we also used soil geographical zoning to account for similarities in soil‐forming conditions. We tested this approach with the CSSL, which comprised 2732 soil samples collected from 20 provinces in the People's Republic of China to predict soil organic matter (SOM). Results showed that the prediction accuracy of our spatially constrained local‐PLSR method (R² = 0.74, RPIQ = 2.6) was better than that from local‐PLSR (R² = 0.69, RPIQ = 2.3) and PLSR alone (R² = 0.50, RPIQ = 1.5). The coupling of a local‐PLSR regression with soil geographical zoning can improve the accuracy of local SOM predictions using large, complex soil spectral libraries. The approach might be embedded into vis–NIR sensors for laboratory analysis or field estimation.     

Conservation Effects on Soil Quality and Climate Change Adaptability of Ethiopian Watersheds

This study analyzes effects of soil and water conservation (SWC) on soil quality and implications to climate change adaptation and mitigation in the Upper Blue Nile River Basin of Ethiopia by using the Anjeni watershed as a case study site. Disturbed and undisturbed soil samples were collected from two sub‐watersheds of Anjeni: the Minchet sub‐watershed (with SWC measures) and the Zikrie sub‐watershed (without SWC measures). Soil samples were taken from 30‐cm depth from five representative landscape positions and analyzed following the standard soil lab analysis procedures. The results show that soils from the conserved sub‐watershed had improved quality indicators compared with those from the non‐conserved site. Significant improvement due to SWC measures was observed in the soil hydrological [total moisture content (+5·43%), field capacity (+5·35%), and available water capacity (+4·18%)] and chemical [cation exchange capacity (+4·40 cmol(+) kg⁻¹), Mg²⁺ (+1·90 cmol(+) kg⁻¹), Na⁺ (+0·10 cmol(+) kg⁻¹)] properties. SWC interventions significantly reduced soil erosion by 57–81% and surface runoff by 19–50% in the conserved sub‐watershed. Reduction in soil erosion can maintain the soil organic carbon stock, reduce the land degradation risks, and enhance the C sequestration potential of soils. Therefore, adoption of SWC measures can increase farmers' ability to offset emissions and adapt to climate change. However, SWC measures that are both protective and sufficiently productive have not yet been implemented in the conserved sub‐watershed. Therefore, it is important that SWC structures be supplemented with other biological and agronomic measures in conjunction with soil fertility amendments appropriate to site‐specific conditions. Copyright © 2015 John Wiley & Sons, Ltd.     

Pedotransfer functions for solute transport parameters of Portuguese soils

The purpose of this study is to quantify solute transport parameters of fine‐textured soils in an irrigation district in southern Portugal and to investigate their prediction from basic soil properties and unsaturated hydraulic parameters. Solute displacement experiments were carried out on 24 undisturbed soil samples by applying a 0.05 m KCl pulse during steady flow. The chloride breakthrough curves (BTCs) were asymmetric, with early breakthrough and considerable tailing characteristic of non‐equilibrium transport. The retardation factor (R), dispersion coefficient (D), partitioning coefficient (β), and mass transfer coefficient (ω) were estimated by optimizing the solution of the non‐equilibrium convection–dispersion equation (CDE) to the breakthrough data. The solution could adequately describe the observed data as proved by a median of 0.972 for the coefficient of determination (r²) and a median for the mean squared error (MSE) of 5.1 × 10^?6. The median value for R of 0.587 suggests that Cl^– was excluded from a substantial part of the liquid phase. The value for β was typically less than 0.5, but the non‐equilibrium effects were mitigated by a large mass transfer coefficient (ω > 1). Pedotransfer functions (PTFs) were developed with regression and neural network analyses to predict R, D, β and ω from basic soil properties and unsaturated hydraulic parameters. Fairly accurate predictions could be obtained for logD (r² ≈ 0.9) and β (r² ≈ 0.8). Prediction for R and logω were relatively poor (r² ≈ 0.5). The artificial neural networks were all somewhat more accurate than the regression equations. The networks are also more suitable for predicting transport parameters because they require only three input variables, whereas the regression equations contain many predictor variables.     

The use of visible and near‐infrared spectroscopy for the analysis of soil water repellency

This study investigated the potential of visible/near‐infrared reflectance spectroscopy (Vis‐NIRS) to predict soil water repellency (SWR). The top 40 mm of soils (n = 288) across 48 sites under pastoral land‐use in the North Island of New Zealand, which represented 10 soil orders and covered five classes of drought proneness, were analysed by standard laboratory methods and Vis‐NIRS. Soil WR was measured by using the molarity of ethanol droplet (MED) and the water drop penetration time (WDPT) tests. Soil organic carbon content (%C) was also measured to examine a possible relationship with SWR. A partial least squares regression (PLSR) model was developed by using Vis‐NIRS spectral data and the reference laboratory data. In addition, we explored the power of discrimination based on WDPT classes using partial least squares discriminant analysis (PLS‐DA). The PLSR of the processed spectra produced moderately accurate prediction for MED (R²_val = 0.61, RPD_val = 1.60, RMSE_val = 0.59) and good prediction for %C (R²_val = 0.82, RPD_val = 2.30, RMSE_val = 2.72). When the data from the 10 soil orders were considered separately and based on soil order rather than being grouped, the prediction of MED was further improved except for the Allophanic, Brown, Organic and Ultic soil orders. The PLS‐DA was successful in classifying 60% of soil samples into the correct WDPT classes. Our results indicate clearly that Vis‐NIRS has the potential to predict SWR. Further improvement in the prediction accuracy of SWR is envisaged by increasing the understanding of the relationship between Vis‐NIRS and the SWR of all New Zealand soil orders as a function of their physical properties and chemical constituents such as hydrophobic compounds.     

关于我国水土保持科学的内涵与研究领域问题

 根据《中国大百科全书·农业》及《中国大百科全书·水利》中水土保持条目的定义说明中国水土保持科学的内涵及范畴。指出:水土保持的内涵是山丘区及风沙区水土资源的保护、改良与合理利用;水土保持科学的研究范畴包括水土流失规律、水土保持规划、水土流失综合治理技术措施、水土保持管理、水土保持效益评价等。还提出了近期需要特别重视的研究课题。     

Evaluating a low‐cost portable NIR spectrometer for the prediction of soil organic and total carbon using different calibration models

This study aims to assess the performance of a low‐cost, micro‐electromechanical system‐based, near infrared spectrometer for soil organic carbon (OC) and total carbon (TC) estimation. TC was measured on 151 soil profiles up to the depth of 1 m in NSW, Australia, and from which a subset of 24 soil profiles were measured for OC. Two commercial spectrometers including the AgriSpec^TM (ASD) and NeoSpectra^TM (Neospectra) with spectral wavelength ranges of 350–2,500 and 1,300–2,500 nm, respectively, were used to scan the soil samples, according to the standard contact probe protocol. Savitzky–Golay smoothing filter and standard normal variate (SNV) transformation were performed on the spectral data for noise reduction and baseline correction. Three calibration models, including Cubist tree model, partial least squares regression (PLSR) and support vector machine (SVM), were assessed for the prediction of soil OC and TC using spectral data. A 10‐fold cross‐validation analysis was performed for evaluation of the models and devices accuracies. Results showed that Cubist model predicts OC and TC more accurately than PLSR and SVM. For OC prediction, Cubist showed R² = 0.89 (RMSE = 0.12%) and R² = 0.78 (RMSE = 0.16%) using ASD and NeoSpectra, respectively. For TC prediction, Cubist produced R² = 0.75 (RMSE = 0.45%) and R² = 0.70 (RMSE = 0.50%) using ASD and NeoSpectra, respectively. ASD performed better than NeoSpectra. However, the low‐cost NeoSpectra predictions were comparable to the ASD. These finding can be helpful for more efficient future spectroscopic prediction of soil OC and TC with less costly devices.     

Soil properties prediction of western Mediterranean islands with similar climatic environments by means of mid‐infrared diffuse reflectance spectroscopy

This study investigated the suitability of mid‐infrared diffuse reflectance Fourier transform (MIR‐DRIFT) spectroscopy, with partial least squares (PLS) regression, for the determination of variations in soil properties typical of Italian Mediterranean off‐shore environments. Pianosa, Elba and Sardinia are typical of islands from this environment, but developed on different geological substrates. Principal components analysis (PCA) showed that spectra could be grouped according to the soil composition of the islands. PLS full cross‐validation of soil property predictions was assessed by the coefficient of determination (R²), the root mean square error of cross‐validation and prediction (RMSECV and RMSEP), the standard error (SECV for cross‐validation and SEP for prediction), and the residual predictive deviation (RPD). Although full cross‐validation appeared to be the most accurate (R² = 0.95 for organic carbon (OC), 0.96 for inorganic carbon (IC), 0.87 for CEC, 0.72 for pH and 0.74 for clay; RPD = 4.4, 6.0, 2.7, 1.9 and 2.0, respectively), the prediction errors were considered to be optimistic and so alternative calibrations considered to be more similar to ‘true’ predictions were tested. Predictions using individual calibrations from each island were the least efficient, while predictions using calibration selection based on a Euclidian distance ranking method, using as few as 10 samples selected from each island, were almost as accurate as full cross‐validation for OC and IC (R² = 0.93 for OC and 0.96 for IC; RPD = 3.9 and 4.7, respectively). Prediction accuracy for CEC, pH and clay was less accurate than expected, especially for clay (R² = 0.73 for CEC, 0.50 for pH and 0.41 for clay; RPD = 1.8, 1.5 and 1.4, respectively). This study confirmed that the DRIFT PLS method was suitable for characterizing important properties for soils typical of islands in a Mediterranean environment and capable of discriminating between the variations in soil properties from different parent materials.