Hydrological characterization of watersheds in the Blue Nile Basin, Ethiopia

Thirty-two watersheds (31–4350 km2), in the Blue Nile Basin, Ethiopia, were hydrologically characterized with data from a study of water and land resources by the US Department of Interior, Bureau of Reclamation (USBR) published in 1964. The USBR document contains data on flow, topography, geology, soil type, and land use for the period 1959 to 1963. The aim of the study was to identify watershed variables best explaining the variation in the hydrological regime, with a special focus on low flows. Moreover, this study aimed to identify variables that may be susceptible to management policies for developing and securing water resources in dry periods. Principal Component Analysis (PCA) and Partial Least Square (PLS) were used to analyze the relationship between five hydrologic response variables (total flow, high flow, low flow, runoff coefficient, low flow index) and 30 potential explanatory watershed variables. The explanatory watershed variables were classified into three groups: land use, climate and topography as well as geology and soil type. Each of the three groups had almost equal influence on the variation in hydrologic variables (R2 values ranging from 0.3 to 0.4). Specific variables from within each of the three groups of explanatory variables were better in explaining the variation. Low flow and low flow index were positively correlated to land use types woodland, dense wet forest and savannah grassland, whereas grazing land and bush land were negatively correlated. We concluded that extra care for preserving low flow should be taken on tuffs/basalts which comprise 52% of the Blue Nile Basin. Land use management plans should recognize that woodland, dense wet forest and savannah grassland can promote higher low flows, while grazing land diminishes low flows

The combined impacts of land use change and climate change on soil organic carbon stocks in the Ethiopian highlands

Land Use Change (LUC), especially deforestation in tropical regions, significantly contributes to global anthro-pogenic greenhouse gas (GHG) emissions. Here, we address potential combined impacts of LUC and Climate Change (CC) on Soil Organic Carbon (SOC) stocks in the Ethiopian highlands. The soil model Q was employed to predict SOC stocks for various combinations of LUC and CC scenarios until the year 2100. Four reference sce-narios (cropland, bushland, natural forest, and Eucalyptus plantations under contemporary climatic conditions) were evaluated against reported measurements of SOC stocks. We studied impacts of six common LUC scenarios, including deforestation and planting Eucalyptus, on SOC stocks under contemporary and future climates. To assess the impact of CC, effects of elevated temperature (mean annual temperature + 2.6 degrees C) together with three litterfall scenarios (no change in litterfall, a 5% reduction and 22% increase, designated CC0, CCd, and CCi, respectively) were considered to test potential vegetation responses to increases in temperature and atmospheric CO2 concentrations. Most of the tested combinations of LUC and CC led to losses of SOC stocks. Losses were most severe, both relatively and absolutely, in the deforestation scenarios: up to 30% was lost if natural forest was converted to cropland and temperature increased (under the CC0 scenario). Gains in SOC stocks of 4-19% were modelled when sparse vegetation was converted to more dense vegetation like Eucalyptus plantation with sub-stantially increased litterfall (the CCi scenario). Elevated temperature accelerated decomposition rates, leading to circa 8% losses of SOC stocks.We conclude that effects of LUC and CC on SOC stocks are additive and changes in litterfall caused by LUC determine which has the largest impact. Hence, deforestation is the biggest threat to SOC stocks in the Ethiopian highlands, and stocks in sparse vegetation systems like cropland and bushland are more sensitive to CC0 than LUC. We recommend conservation of natural forests and longer rotation periods for Eucalyptus plantations to preserve SOC stocks.Finally, we suggest that use of the Q model is a viable option for national reporting changes in SOC stocks at Tier 3 within the LULUCF sector to the United Nations Framework Convention on Climate Change (UNFCCC) as it is widely applicable and robust, although it only requires input data on a few generally available variables

Interception and retention of wet-deposited radiocaesium and radiostrontium on a ley mixture of grass and clover

The aims of this study were to assess the potential radioactive contamination of fodder in the case of accidental radionuclide fallout, and to analyse the relationship between interception and retention of radionuclides as a function of biomass and Leaf Area Index (LAI). The interception and the retention of wet deposited 134Cs and 85Sr in ley (a mixture of grass and clover) were measured after artificial wet deposition in a field train in Uppsala (eastern central Sweden). The field trial had a randomised block design with three replicates. 134Cs and 85Sr were deposited at six different growth stages during two growing seasons (20101 and 2011) using a rainfall simulator. The biomass was sampled in the centre of each parcel 2 to 3 h after deposition and at later growth stages (1 to 5) during the growing season. The above ground biomass and LAI were measured as well. The interception of radionuclides by the ley was largest at the late growth stages; the spike and tassel/flowering (code 5:6) in the 1st year, and at flowering/initial flowering (code 6:5) in the 2nd year. There was a correlation between radionuclide interception and above ground biomass, as well with LAI, for both radionuclides in both years. The highest activity concentrations of both radionuclides were measured after deposition at the late growth stages and were found to be higher in the 2nd year. There weathering half-lives were shorter at the earlier growth stages than at the later growth stages for both radionuclides. For the magnitude of deposition chosen in our experiment, it can be concluded that the above ground biomass is a good predictor and the LAI a more uncertain predictor of the interception of radiocaesium and radiostrontium by ley grass and clover

Introducing the 2-DROPS model for two-dimensional simulation of crop roots and pesticide within the soil-root zone

Mathematical models of pesticide fate and behaviour in soils have been developed over the last 30 years. Most models simulate fate of pesticides in a 1-dimensional system successfully, supporting a range of applications where the prediction target is either bulk residues in soil or receiving compartments outside of the soil zone. Nevertheless, it has been argued that the 1-dimensional approach is limiting the application of knowledge on pesticide fate under specific pesticide placement strategies, such as seed, furrow and band applications to control pests and weeds. We report a new model (2-DROPS; 2-Dimensional ROots and Pesticide Simulation) parameterised for maize and we present simulations investigating the impact of pesticide properties (thiamethoxam, chlorpyrifos, clothianidin and tefluthrin), pesticide placement strategies (seed treatment, furrow, band and broadcast applications), and soil properties (two silty clay loam and two loam top soils with either silty clay loam, silt loam, sandy loam or unconsolidated bedrock in the lower horizons) on microscale pesticide distribution in the soil profile. 2-DROPS is to our knowledge the first model that simulates temporally- and spatially-explicit water and pesticide transport in the soil profile under the influence of explicit and stochastic development of root segments. This allows the model to describe microscale movement of pesticide in relation to root segments, and constitutes an important addition relative to existing models. The example runs demonstrate that the pesticide moves locally towards root segments due to water extraction for plant transpiration, that the water holding capacity of the top soil determines pesticide transport towards the soil surface in response to soil evaporation, and that the soil type influences the pesticide distribution zone in all directions. 2-DROPS offers more detailed information on microscale root and pesticide appearance compared to existing models and provides the possibility to investigate strategies targeting control of pests at the root/soil interface

Soil health cluster analysis based on national monitoring of soil indicators

A major challenge in soil science is to monitor and understand the state and change of soils at a national scale to inform decision making and policy. To address this, there is a need to identify key parameters for soil health and function and determine how they relate to other parameters, including traditional soil surveys. Here we present a national‐scale dataset of topsoil sampled as part of a wider agri‐environment monitoring scheme in Wales, UK. Over 1,350 topsoils (0–15 cm) were sampled across a very wide range of habitats and a range of physical, chemical and biological soil quality indicators were measured. We show consistent differences in soil physicochemical properties across habitat types, with carbon decreasing and pH increasing across the habitat productivity gradient from bogs through woodlands and grasslands to arable systems. The soils within our dataset are largely within the limits identified as important for supporting habitat function, with the exception of excessive phosphate levels in mesotrophic grassland. Cluster detection methods identified four soil functional classes based on measured topsoil properties, which were more related to habitat type than the genesis‐based soil classification from soil maps. These soil functional classes can be interpreted as phenoforms within the soil genoforms found by traditional soil classification. This shows the importance of land‐use management in determining the soil health and functional capacity of soils. Our work provides an account of the current state of soil health in Wales, its relationship to soil function and a baseline for future monitoring to track changes against agri‐environment and other policy targets

Rhizosphere priming effects on soil carbon and nitrogen mineralization

Living roots and their rhizodeposits affect microbial activity and soil carbon (C) and nitrogen (N) mineralization. This so-called rhizosphere priming effect (RPE) has been increasingly recognized recently. However, the magnitude of the RPE and its driving mechanisms remain elusive. Here we investigated the RPE of two plant species (soybean and sunflower) grown in two soil types (a farm or a prairie soil) and sampled at two phenological stages (vegetative and mature stages) over an 88-day period in a greenhouse experiment. We measured soil C mineralization using a continuous 13C-labeling method, and quantified gross N mineralization with a 15N-pool dilution technique. We found that living roots significantly enhanced soil C mineralization, by 27-245%. This positive RPE on soil C mineralization did not vary between the two soils or the two phenological stages, but was significantly greater in sunflower compared to soybean. The magnitude of the RPE was positively correlated with rhizosphere respiration rate across all treatments, suggesting the variation of RPE among treatments was likely caused by variations in root activity and rhizodeposit quantity. Moreover, living roots stimulated gross N mineralization rate by 36-62% in five treatments, while they had no significant impact in the other three treatments. We also quantified soil microbial biomass and extracellular enzyme activity when plants were at the vegetative stage. Generally, living roots increased microbial biomass carbon by 0-28%, β-glucosidase activity by 19-56%, and oxidative enzyme activity by 0-46%. These results are consistent with the positive rhizosphere effect on soil C (45-79%) and N (10-52%) mineralization measured at the same period. We also found significant positive relationships between β-glucosidase activity and soil C mineralization rates and between oxidative enzyme activity and gross N mineralization rates across treatments. These relationships provide clear evidence for the microbial activation hypothesis of RPE. Our results demonstrate that root-soil-microbial interactions can stimulate soil C and N mineralization through rhizosphere effects. The relationships between the RPE and rhizosphere respiration rate and soil enzyme activity can be used for explicit representations of RPE in soil organic matter models. © 2014