Assessment of Soil Loss in a Typical Ungauged Dam Catchment using RUSLE Model (Maruba Dam, Kenya)

Soil erosion is a serious land degradation problem which nations all over the world are struggling with. It has affected many river catchments most of which are very dynamic and have become quite vulnerable due to human influence. As such, the functionality of the ecosystem has been largely compromised. Soil erosion has been reported as an expensive problem to remedy and therefore numerous of efforts have shifted to its prevention. This has called for estimation of soil loss which has been adequately achieved by use erosion models over the past. One such model is the Revised Universal Soil Loss Equation (RUSLE) which has been applied at catchment level. Maruba dam catchment has become very unhealthy due to the unsustainable modifications of the terrain. This is evident at the rate at which the dam is losing its storage capacity due to sedimentation. The current situation in the dam formed the basis for this study. Information on soil loss within the catchment is missing and as such decision makers do not have a basis for initiating soil and water conservation plans. The methodological framework for this study was the use of RUSLE model integrated in a GIS framework. The parameters of the model were derived using GIS and RS tools. The study revealed that soil loss ranged between 0 and 29 t ha-1 yr-1 and this explains why the dam if silting up at a fast rate. With this set of information on soil loss, the health of the catchment would be adequately restored and this would save the dam from unwarranted sedimentation. Keywords: Soil erosion, catchment, RUSLE, sedimentation, GIS DOI: 10.7176/JEES/11-16-06 Publication date:June 30th 202

Microbial carbon use efficiency promotes global soil carbon storage

Soils store more carbon than other terrestrial ecosystems$^{1,2}$. How soil organic carbon (SOC) forms and persists remains uncertain$^{1,3}$, which makes it challenging to understand how it will respond to climatic change$^{3,4}$. It has been suggested that soil microorganisms play an important role in SOC formation, preservation and loss$^{5–7}$. Although microorganisms affect the accumulation and loss of soil organic matter through many pathways$^{4,6,8–11}$, microbial carbon use efficiency (CUE) is an integrative metric that can capture the balance of these processes$^{12,13}$. Although CUE has the potential to act as a predictor of variation in SOC storage, the role of CUE in SOC persistence remains unresolved$^{7,14,15}$. Here we examine the relationship between CUE and the preservation of SOC, and interactions with climate, vegetation and edaphic properties, using a combination of global-scale datasets, a microbial-process explicit model, data assimilation, deep learning and meta-analysis. We find that CUE is at least four times as important as other evaluated factors, such as carbon input, decomposition or vertical transport, in determining SOC storage and its spatial variation across the globe. In addition, CUE shows a positive correlation with SOC content. Our findings point to microbial CUE as a major determinant of global SOC storage. Understanding the microbial processes underlying CUE and their environmental dependence may help the prediction of SOC feedback to a changing climate

Reply to: Contribution of carbon inputs to soil carbon accumulation cannot be neglected

In the accompanying Comment1, He et al. argue that the determinant role of microbial carbon use efficiency in global soil organic carbon (SOC) storage shown in Tao et al. (2023)2 was overestimated because carbon inputs were neglected in our data analysis while they suggest that our model-based analysis could be biased and model-dependent. Their argument is based on a different choice of independent variables in the data analysis and a sensitivity analysis of two process-based models other than that used in our study. We agree that both carbon inputs and outputs (as mediated by microbial processes) matter when predicting SOC storage – the question is their relative contributions. While we encourage further studies to examine how the evaluation of the relative importance of CUE to global SOC storage may vary with different model structures, He et al.’s claims about Tao et al. (2023) need to be taken as an alternative, unproven hypothesis until empirical data support their specific parameterization. Here we show that an additional literature assessment of global data does not support He et al.’s argument, in contrast to our study, and that further study on this topic is essential

Reply to: Beyond microbial carbon use efficiency

In their commentary, Xiao et al. cautioned that the conclusions on the critical role of microbial carbon use efficiency (CUE) in global soil organic carbon (SOC) storage in a paper by Tao et al. (2023) might be too simplistic. They claimed that Tao et al.’s study lacked mechanistic consideration of SOC formation and excluded important datasets. Xiao et al. brought up important points, which can be largely reconciled with our findings by understanding the differences in expressing processes in empirical studies and in models

Microbial carbon use efficiency promotes global soil carbon storage

Funding Information: We thank H. Yang, M. Schrumpf, T. Wutzler, R. Zheng and H. Ma for their comments and suggestions on this study. This work was supported by the National Natural Science Foundation of China (42125503) and the National Key Research and Development Program of China (2020YFA0608000, 2020YFA0607900 and 2021YFC3101600). F.T. was financially supported by China Scholarship Council during his visit at Food and Agricultural Organization of the United Nations (201906210489) and the Max-Planck Institute for Biogeochemistry (202006210289). The contributions of Y.L. were supported through US National Science Foundation DEB 1655499 and 2242034, subcontract CW39470 from Oak Ridge National Laboratory (ORNL) to Cornell University, DOE De-SC0023514, and the USDA National Institute of Food and Agriculture. S.M. has received funding from the ERC under the European Union’s H2020 Research and Innovation Programme (101001608). The contributions of U.M. were supported through a US Department of Energy grant to the Sandia National Laboratories, which is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the US Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525. We thank the WoSIS database ( https://www.isric.org/explore/wosis ) for providing the publicly available global-scale SOC database used in this study. Publisher Copyright: © 2023, The Author(s).Peer reviewedPublisher PD

Soil Loss Assessment Using the Revised Universal Soil Loss Equation (RUSLE) Model

Many catchment areas have suffered from exhaustive changes because of various land use activities over the recent past. These land use changes are associated with intensified environmental degradation witnessed in catchment areas. Such environmental problems include extreme soil erosion. Soil erosion is one of the most critical problems responsible for the degradation of land worldwide. This phenomenon occurs as a result of the complex interactions that exist between natural and human-induced factors. Most factors experience spatiotemporal variations, hence complicating the soil erosion phenomenon. This complexity in the erosion process makes it difficult to quantify soil loss. Without proper information on soil loss, it becomes quite hard for decision-makers and managers to manage catchment areas. However, the availability of soil erosion models has made it easy to estimate soil loss. Many models have been developed to consider these complexities in soil erosion studies. Empirical models such as RUSLE provide a simple and broad methodology through which soil erosion is assessed. The RUSLE model integrates well geographic information system (GIS) and above all remote sensing. This paper presents an overview of the developmental milestones in estimating soil loss using the RUSLE model. The parameterization of the RUSLE model has been adequately reviewed with much emphasis on challenges and successes in derivation of each individual factor. From the review, it was established that different equations have been developed by researchers for modeling the five factors for the RUSLE model. The development of such equations was found to take into account the different variations that depict the soil erosion process

The Physicochemical Properties of Deposited Sediments at the Maruba Dam Reservoir Inlet, Machakos County, Kenya

Energy and water are the two most important natural resources in the globe. In this regard, dams and reservoirs are the critical hydraulic structures that store water and, above all, provide energy required by humanity. However, water storage and the provision of energy by reservoirs and dams have been disrupted by significant environmental changes taking place in the catchment areas and the reservoir environment. These disruptions are brought about by climatic parameters and sediment transport by different eroding agents. One such environmental problem is soil erosion, whose effect is reservoir sedimentation. Consequently, a part of the transported sediment is deposited at the catchment outlet, which serves as the reservoir inlet. This study was carried out to establish the physicochemical characteristics of the deposited sediment at the reservoir inlet. The following parameters were analyzed: particle size distribution, organic matter content, bulk density, porosity, electrical conductivity, penetration resistance, hydraulic conductivity, pH, and nutrients (nitrogen, phosphorous, and potassium) using standard laboratory procedures. The study established that the deposited sediments were predominantly sand particles with mean values of 50.60% and 58.60% for the surface (0–10 cm) or sub-surface horizons (10–20 cm), respectively. The average values for sediment pH, organic matter, porosity, bulk density, electrical conductivity, penetration resistance, hydraulic conductivity, and nutrients were 6.30 and 6.61; 1.91 and 1.80%; 54.10 and 57.10%; 1.22 and 1.14 g·cm−3 for the surface and sub-surface horizons, respectively. The most variable parameters were silt content (sub-surface horizon), hydraulic conductivity, penetration resistance, electrical conductivity, nitrogen content (surface horizon), and phosphorous (surface horizon) content with CV >0.35. Based on the present study results, the deposited sediments at the reservoir inlet were found to have low concentrations of nutrients and high sand proportions. Therefore, the deposited sediments appear to have great potential to reclaim the immediate barren dam environment upon enrichment and to promote sand harvesting programs for economic benefits

Physicochemical Properties of Bottom Sediments in Maruba Dam Reservoir, Machakos, Kenya

Bottom sediments form an integral part of the aquatic ecosystem, where they serve as important sinks for contaminants. However, management options for bottom sediments require an analysis of the physical and chemical properties. Therefore, the aim of this study was to assess the physicochemical properties of bottom sediments in the Maruba dam reservoir in order to inform their potential use. The bottom sediments were obtained from three sampling points using a vibe-coring device. The samples were analyzed for grain size, sediment bulk density, pH, electrical conductivity, organic matter content, and nutrient content (nitrogen, phosphorous, and potassium) using standard laboratory procedures. The results of the study revealed that the bottom sediments were predominantly clay (56%). The mean pH value of the sediments was 6.63, which was found to be slightly acidic. The concentration of cations and anions in the bottom sediments was found to be quite high, with a mean value of 0.225 dS⋅m−1. The bottom sediments in the reservoir were found to be quite rich in the organic matter content (2.10%) and had a mean bulk density of 0.620 g·cm−3. The macronutrients (nitrogen, potassium, and phosphorous) had mean values of 0.12%, 0.46%, and 12.81 mg·kg−1, respectively. The study established that finely grained particles together with organic matter had a potential effect on the availability of macronutrients in bottom sediments. The concentration of the macronutrients of the bottom sediments evaluated in this study points to their potential use in agricultural activities or even in land reclamation