As a mountainous country, Nepal is most susceptible to precipitation extremes and related hazards, including severe floods, landslides and droughts that cause huge losses of life and property, impact ...the Himalayan environment, and hinder the socioeconomic development of the country. Given that the countrywide assessment of such extremes is still lacking, we present a comprehensive picture of prevailing precipitation extremes observed across Nepal. First, we present the spatial distribution of daily extreme precipitation indices as defined by the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI) from 210 stations over the period of 1981–2010. Then, we analyze the temporal changes in the computed extremes from 76 stations, featuring long-term continuous records for the period of 1970–2012, by applying a non-parametric Mann−Kendall test to identify the existence of a trend and Sen’s slope method to calculate the true magnitude of this trend. Further, the local trends in precipitation extremes have been tested for their field significance over the distinct physio-geographical regions of Nepal, such as the lowlands, middle mountains and hills and high mountains in the west (WL, WM and WH, respectively), and likewise, in central (CL, CM and CH) and eastern (EL, EM and EH) Nepal. Our results suggest that the spatial patterns of high-intensity precipitation extremes are quite different to that of annual or monsoonal precipitation. Lowlands (Terai and Siwaliks) that feature relatively low precipitation and less wet days (rainy days) are exposed to high-intensity precipitation extremes. Our trend analysis suggests that the pre-monsoonal precipitation is significantly increasing over the lowlands and CH, while monsoonal precipitation is increasing in WM and CH and decreasing in CM, CL and EL. On the other hand, post-monsoonal precipitation is significantly decreasing across all of Nepal while winter precipitation is decreasing only over the WM region. Both high-intensity precipitation extremes and annual precipitation trends feature east−west contrast, suggesting significant increase over the WM and CH region but decrease over the EM and CM regions. Further, a significant positive trend in the number of consecutive dry days but significant negative trend in the number of wet (rainy) days are observed over the whole of Nepal, implying the prolongation of the dry spell across the country. Overall, the intensification of different precipitation indices over distinct parts of the country indicates region-specific risks of floods, landslides and droughts. The presented findings, in combination with population and environmental pressures, can support in devising the adequate region-specific adaptation strategies for different sectors and in improving the livelihood of the rural communities in Nepal.
Precise knowledge about the soil organic carbon (SOC) content in cropland soils is one requirement to design and execute effective climate and food policies. In digital soil mapping (DSM), machine ...learning algorithms are used to predict soil properties from covariates derived from traditional soil mapping, digital elevation models, land use, and Earth observation (EO). However, such DSM models are trained for a specific dataset and region and have so far only allowed limited general statements to be made that would enable the models to be transferred to different regions. In this study, we test the transferability of SOC models for cropland soils using five different covariate groups: multispectral soil reflectance composites (satellite), soil legacy data (soil), digital elevation model derivatives (terrain), climate parameters (climate), and combined models (combined). The transferability was analyzed using data from two federal states in southern Germany: Bavaria and Baden-Wuerttemberg. First, baseline models were trained for each state with combined models performing best in both cases (R2 = 0.68/0.48). Next, the models were transferred and tested with soil samples from the other state whose data were not used during model calibration. Only satellite and combined models were transferable, but accuracy declined in both cases. In the final step, models were trained with samples from both states (mixed-data models) and applied to each state separately. This process significantly improved the accuracies of satellite, terrain, and combined models, while it showed no effect on climate models and decreased the models based on soil covariates. The experiment underlines the importance of EO for the transfer and extrapolation of DSM models.
This study tested and evaluated a suite of nine individual base learners and seven model averaging techniques for predicting the spatial distribution of soil properties in central Iran. Based on the ...nested-cross validation approach, the results showed that the artificial neural network and Random Forest base learners were the most effective in predicting soil organic matter and electrical conductivity, respectively. However, all seven model averaging techniques performed better than the base learners. For example, the Granger–Ramanathan averaging approach resulted in the highest prediction accuracy for soil organic matter, while the Bayesian model averaging approach was most effective in predicting sand content. These results indicate that the model averaging approaches could improve the predictive accuracy for soil properties. The resulting maps, produced at a 30 m spatial resolution, can be used as valuable baseline information for managing environmental resources more effectively.
•Annual soil erosion rates decreased with tree species richness.•Tree diversity reduced soil erosion by affecting tree canopy and biological soil crust development.•Restoring species-rich plantations ...may be beneficial for soil erosion control.
Biodiversity plays a crucial role in forest ecosystem sustainability. However, it is unclear how tree diversity and especially the relationship between diversity and ecosystem functioning affect soil erosion. Based on a forest biodiversity and ecosystem functioning experiment established in subtropical China (BEF China), we measured soil erosion at four tree species richness levels (monocultures, 8 tree species, 16 tree species and 24 species stands) during the rainy seasons from 2013 to 2015. The result showed that mean annual soil erosion rates were detected to decrease with tree species richness significantly over the observed three years. Leaf area index (LAI) and biological soil crusts (BSCs) were the two main factors driving soil erosion within tree stands of different species richness. Positive effects of tree species richness on tree canopy structure and BSCs might drive the reduction of soil erosion in the earlier successional stage after afforestation of tree plantations. Therefore, we highlight the important influence of tree species richness on soil erosion control, hydrologic processes and thus sustainable ecology services.
This paper shows that memory-based learning (MBL) is a very promising approach to deal with complex soil visible and near infrared (vis–NIR) datasets. The main goal of this work was to develop a ...suitable MBL approach for soil spectroscopy. Here we introduce the spectrum-based learner (SBL) which basically is equipped with an optimized principal components distance (oPC-M) and a Gaussian process regression. Furthermore, this approach combines local distance matrices and the spectral features as predictor variables. Our SBL was tested in two soil spectral libraries: a regional soil vis–NIR library of State of São Paulo (Brazil) and a global soil vis–NIR library. We calibrated models of clay content (CC), organic carbon (OC) and exchangeable Ca (Ca++). In order to compare the predictive performance of our SBL with other approaches, the following algorithms were used: partial least squares (PLS) regression, support vector regression machines (SVM), locally weighted PLS regression (LWR) and LOCAL. In all cases our SBL algorithm outperformed the accuracy of the remaining algorithms. Here we show that the SBL presents great potential for predicting soil attributes in large and diverse vis–NIR datasets. In addition we also show that soil vis–NIR distance matrices can be used to further improve the prediction performance of spectral models.
► We developed a new local vis–NIR modeling approach. ► Distance matrices were used as source of additional predictor variables. ► The new approach outperforms other regression algorithms.
Soil organic carbon (SOC) contents and stocks provide valuable insights into soil health, nutrient cycling, greenhouse gas emissions, and overall ecosystem productivity. Given this, remote sensing ...data coupled with advanced machine learning (ML) techniques have eased SOC level estimation while revealing its patterns across different ecosystems. However, despite these advances, the intricacies of training reliable and yet certain SOC models for specific end-users remain a great challenge. To address this, we need robust SOC uncertainty quantification techniques. Here, we introduce a methodology that leverages conformal prediction to address the uncertainty in estimating SOC contents while using remote sensing data. Conformal prediction generates statistically reliable uncertainty intervals for predictions made by ML models. Our analysis, performed on the LUCAS dataset in Europe and incorporating a suite of relevant environmental covariates, underscores the efficacy of integrating conformal prediction with another ML model, specifically random forest. In addition, we conducted a comparative assessment of our results against prevalent uncertainty quantification methods for SOC prediction, employing different evaluation metrics to assess both model uncertainty and accuracy. Our methodology showcases the utility of the generated prediction sets as informative indicators of uncertainty. These sets accurately identify samples that pose prediction challenges, providing valuable insights for end-users seeking reliable predictions in the complexities of SOC estimation.
The replenishment of aquifers depends mainly on precipitation rates, which is of vital importance for determining water budgets in arid and semi-arid regions. El-Qaa Plain in the Sinai Peninsula is a ...region that experiences constant population growth. This study compares the performance of two sets of satellite-based data of precipitation and in situ rainfall measurements. The dates selected refer to rainfall events between 2015 and 2018. For this purpose, 0.1° and 0.25° spatial resolution TMPA (Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis) and IMERG (Integrated Multi-satellite Retrievals for Global Precipitation Measurement) data were retrieved and analyzed, employing appropriate statistical metrics. The best-performing data set was determined as the data source capable to most accurately bridge gaps in the limited rain gauge records, embracing both frequent light-intensity rain events and more rare heavy-intensity events. With light-intensity events, the corresponding satellite-based data sets differ the least and correlate more, while the greatest differences and weakest correlations are noted for the heavy-intensity events. The satellite-based records best match those of the rain gauges during light-intensity events, when compared to the heaviest ones. IMERG data exhibit a superior performance than TMPA in all rainfall intensities.
A better understanding of the linkages between aboveground and belowground biotic communities is needed for more accurate predictions about how ecosystems may be altered by climate change, land ...management, or biodiversity loss. Soil microbes are strongly affected by multiple factors including local abiotic environmental conditions and plant characteristics. To find out how soil microbial communities respond to multiple facets of the local soil and plant environment, we analysed soil lipid profiles associated with three-year-old monocultures of 29 tree species. These species are native of the diverse subtropical forests of southeast China and greatly vary in functional traits, growth or biomass characteristics, and phylogenetic relatedness. Along with the traits of each tree species, we also determined the soil and plot characteristics in each monoculture plot and tested for phylogenetic signals in tree species-specific microbial indicators. Microbial community structure and biomass were influenced by both soil properties and plant functional traits, but were not related to the phylogenetic distances of tree species. Specifically, total microbial biomass, indicators for fungi, bacteria, and actinomycetes were positively correlated with soil pH, soil organic nitrogen, and soil moisture. Our results also indicate that leaf dry matter content and the leaf carbon to nitrogen ratio influence multivariate soil microbial community structure, and that these factors and tree growth traits (height, crown or basal diameter) positively promote the abundances of specific microbial functional groups. At the same time, a negative correlation between leaf nitrogen content and Gram positive bacterial abundance was detected, indicating plant–microbial competition for nitrogen in our system. In conclusion, even at early stages of tree growth, soil microbial community abundance and structure can be significantly influenced by plant traits, in combination with local soil characteristics.
•Species identity, with soil properties, shape microbial communities even at early tree growth.•Tree species traits explain less variation in microbial communities than soil characteristics.•Tree species phylogeny itself does not drive microbial community development.
Predicting the spatio-temporal distribution of absorbable heavy metals in soil is needed to identify the potential contaminant sources and develop appropriate management plans to control these ...hazardous pollutants. Therefore, our aim was to develop a model to predict soil adsorbable heavy metals in arid regions of Iran from 1986 to 2016. Soil adsorbable heavy metals were measured in 201 samples from locations selected using the Latin hypercube sampling method in 2016. A random forest (RF) model was used to determine the relationship between a suite of geospatial predictors derived from remote sensing and digital elevation model data with georeferenced measurements of soil absorbable heavy metals. The trained RF model from 2016 was used to reconstruct the spatial distribution of soil absorbable heavy metals at three historical timesteps (1986, 1999, and 2010). Results indicated that the RF model was effective at predicting the distribution of heavy metals with coefficients of determination of 0.53, 0.59, 0.41, 0.45, and 0.60 for Fe, Mn, Ni, Pb, and Zn, respectively. The predicted maps showed high spatio-temporal variability; for example, there were substantial increases in Pb (the 1.5–2 mg/kg−1 class) where its distribution increased by ~25% from 1988 to 2016—similar trends were observed for the other heavy metals. This study provides insights into the spatio-temporal trends and the potential causes of soil heavy metal contamination to facilitate appropriate planning and management strategies to prevent, control, and reduce the impact of heavy metal contamination in soils.
Multi-scale contextual modelling is an important toolset for environmental mapping. It accounts for spatial dependence by using covariates on multiple spatial scales and incorporates spatial context ...and structural dependence to environmental properties into machine learning models. For spatial soil modelling, three relevant scales or ranges of scale exist: quasi-local soil formation processes that are independent of the spatial context, short-range catenary processes, and long-range processes related to climate and large-scale terrain settings. Recent studies investigated the spatial dependence of topsoil properties only. We hypothesize that soil properties within a soil profile were formed due to specific interactions between different features and scales of the spatial context, and that there are depth gradients in spatial and structural dependencies. The results showed that for topsoil, features at small to intermediate scales do not increase model accuracy, whereas large scales increase model accuracy. In contrast, subsoil models benefit from all scales-small, intermediate, and large. Based on the differences in relevance, we conclude that the relevant ranges of scales do not only differ in the horizontal domain, but also in the vertical domain across the soil profile. This clearly demonstrates the impact of contextual spatial modelling on 3D soil mapping.
PDF
