Assessment of climate change and prospective analysis on shallow lakes. Las Encadenadas del Oeste watershed, Buenos Aires - Argentina

The earth’s ecosystem is fragile, and sometimes even small changes in the climate can have impacts on the environment and society. Changes in temperature and precipitation can cause numerous feedbacks that effect the ecosystem of the whole Earth. Many studies hold that the temperature will rise in some places, while other areas will experience a cooling in annual mean temperatures. The study area is famous for its many ponds. These ecosystems will be both physically, biologically, and chemically affected by climate change and its feedbacks. Las Encadenadas del Oeste consists of seven shallow lakes (Epecuen, La Paraguaya, Venado, Del Monte, Cochico, Alsina, and Inchauspe) of various depths and sizes is a closed river basin system aligned in an east-west direction. The objectives of this work are to demonstrate the change in shallow lake size over a period of 20 years and to relate these changes to temperature and precipitation over the basin area for the same period. It is also intended to examine future temperature and precipitation scenarios in the study area. Maximum and minimum temperature data and precipitation data was retrieved from a climate station in Carhue. A multiple regression analysis was performed and five models and the shallow lake area were compared. The water levels in the shallow lakes will continue to fluctuate in the future as precipitation and temperature varies. Temperatures will increase quickly in the area; and around a 3 ºC change is expected before 2099. Only small variations in the temperatures have previously caused the lake to change in size. Precipitation patterns show a high variation, but the change is very small. Minimum temperature, which is already the most significant factor according to the statistical analysis, will in the future be an even more important factor if changes occur.

Karst spring discharge modeling based on deep learning using spatially distributed input data

Despite many existing approaches, modeling karst water resources remains challenging and often requires solid system knowledge. Artificial Neural Network approaches offer a convenient solution by establishing a simple input-output relationship on their own. However, in this context, temporal and especially spatial data availability is often an important constraint, as usually no or few climate stations within a karst spring catchment are available. Hence spatial coverage is often unsatisfying and can introduce severe uncertainties. To avoid these problems, we use 2D-Convolutional Neural Networks (CNN) to directly process gridded meteorological data followed by a 1D-CNN to perform karst spring discharge simulation. We investigate three karst spring catchments in the Alpine and Mediterranean region with different meteorologic-hydrological characteristics and hydrodynamic system properties. We compare our 2D-models both to existing modeling studies in these regions and to 1D-models, which use climate station data, as it is common practice. Our results show that our models are excellently suited to model karst spring discharge and rival the simulation results of existing approaches in the respective areas. The 2D-models learn relevant parts of the input data and by performing a spatial input sensitivity analysis we can further show their potential for karst catchment localization and delineation.

Accuracy of Interpolated Versus In-Vineyard Sensor Climate Data for Heat Accumulation Modelling of Phenology

Background and AimsIn response to global heating, accurate climate data are required to calculate climatic indices for long-term decisions about vineyard management, vineyard site selection, varieties planted and to predict phenological development. The availability of spatially interpolated climate data has the potential to make viticultural climate analyses possible at specific sites without the expense and uncertainty of collecting climate data within vineyards. The aim of this study was to compare the accuracy and precision of climatic indices calculated using an on-site climate sensor and an interpolated climate dataset to assess whether the effect of spatial variability in climate at this fine spatial scale significantly affects phonological modelling outcomes.Methods and ResultsFour sites comprising two topographically homogenous vineyards and two topographically diverse vineyards in three wine regions in Victoria (Australia) were studied across four growing seasons. A freely available database of interpolated Australian climate data based on government climate station records (Scientific Information for Land Owners, SILO) provided temperature data for grid cells containing the sites (resolution 0.05° latitude by 0.05° longitude, approximately 5 km × 5 km). In-vineyard data loggers collected temperature data for the same time period. The results indicated that the only significant difference between the two climate data sources was the minimum temperatures in the topographically varied vineyards where night-time thermal layering is likely to occur.ConclusionThe interpolated climate data closely matched the in-vineyard recorded maximum temperatures in all cases and minimum temperatures for the topographically homogeneous vineyards. However, minimum temperatures were not as accurately predicted by the interpolated data for the topographically complex sites. Therefore, this specific interpolated dataset was a reasonable substitute for in-vineyard collected data only for vineyard sites that are unlikely to experience night-time thermal layering.Significance of the StudyAccess to accurate climate data from a free interpolation service, such as SILO provides a valuable tool tomanage blocks or sections within vineyards more precisely for vineyards that do not have a weather station on site. Care, nevertheless, is required to account for minimum temperature discrepancies in topographically varied vineyards, due to the potential for cool air pooling at night, that may not be reflected in interpolated climate data.

Trend analysis of selected hydro-meteorological variables for the Rietspruit sub-basin, South Africa

Identifying hydro-meteorological trends is critical for assessing climate change and variability both at a basin and regional level. This study examined the long- and short-term trends from stream discharge, temperature, and rainfall data around the Rietspruit sub-basin in South Africa. The data were subjected to homogeneity testing before performing the trend tests. Inhomogeneity was widely detected in discharge data, hence no further analyses were performed on such data. Temperature and rainfall trends and their magnitudes at yearly, seasonal, and monthly time steps were identified after applying the non-parametric Mann-Kendall and Sen's slope estimator. The possible starting point of a trend was determined by performing the sequential Mann-Kendall test. This study revealed a combination of upward and downward trends in both temperature and rainfall data for the time steps under observation. For rainfall on an annual basis, there were no statistically significant monotonic trends detected, although non-significant downward trends were dominant. However, significant decreasing rainfall trends were observed in dry and low rainfall months, which were April, August, September, and November. In contrast, significant upward temperature trends were detected at the Vereeniging climate station at an annual scale and in October, November, spring, and winter. The findings are critical for climate risk management and reduction decisions for both near and long-term timescales.

Climate change adaptation in and through agroforestry: four decades of research initiated by Peter Huxley

Agroforestry (AF)-based adaptation to global climate change can consist of (1) reversal of negative trends in diverse tree cover as generic portfolio risk management strategy; (2) targeted, strategic, shift in resource capture (e.g. light, water) to adjust to changing conditions (e.g. lower or more variable rainfall, higher temperatures); (3) vegetation-based influences on rainfall patterns; or (4) adaptive, tactical, management of tree-crop interactions based on weather forecasts for the (next) growing season. Forty years ago, a tree physiological research tradition in aboveground and belowground resource capture was established with questions and methods on climate-tree-soil-crop interactions in space and time that are still relevant for today’s challenges. After summarising early research contributions, we review recent literature to assess current levels of uncertainty in climate adaptation assessments in and through AF. Quantification of microclimate within and around tree canopies showed a gap between standard climate station data (designed to avoid tree influences) and the actual climate in which crop and tree meristems or livestock operates in real-world AF. Where global scenario modelling of ‘macroclimate’ change in mean annual rainfall and temperature extrapolates from climate station conditions in past decades, it ignores microclimate effects of trees. There still is a shortage of long-term phenology records to analyse tree biological responses across a wide range of species to climate variability, especially where flowering and pollination matter. Physiological understanding can complement farmer knowledge and help guide policy decisions that allow AF solutions to emerge and tree germplasm to be adjusted for the growing conditions expected over the lifetime of a tree.

Resident hill country pasture production in response to temperature and soil moisture over 20 years in Central Hawke’s Bay

The production of resident pastures on rolling hill country was measured in three paddocks over 20 years at Poukawa in Central Hawke’s Bay. The pastures had been routinely fertilised with 250 kg/ha/yr of superphosphate but no pasture renovation, nor nitrogen fertiliser application, occurred during the measurement period. Total annual dry matter (DM) yield ranged from 4.5 to 12.8 t/ha/yr, which shows the level of variability to be expected in this summer-dry environment. The greatest proportion (60-90%) of growth occurred in winter/spring with consistent mean growth rates of 50-62 kg DM/ha/d in September and October. These rates were calculated to be 5.49±0.55 kg DM/ha/°Cd when spring moisture was non-limiting. The pastures had a mean water use efficiency of 16.9±0.34 kg DM/ha/mm of water available (R2 = 0.93). The amount of water available was calculated from a soil water budget based on a plant available water holding capacity of 124 mm (0-1.0 m depth). The results provide coefficients that can be combined with readily available climate data to predict pasture growth rates for feed budgeting purposes. Rainfall data collected on-site was highly correlated (r=0.94) with that predicted from the NIWA virtual climate station network.

Long-term recordings at the FSU Jena Geodynamic Observatory Moxa (Thuringia, central Germany)

<p>The remote location of the Geodynamic Observatory Moxa of Friedrich-Schiller University Jena, about 30 km south of Jena in the Thuringian slate mountains, results in very low ambient noise and thus very good conditions for long-term geophysical observations, which are further improved, as many sensors are installed in the subsurface in galleries or in boreholes.</p><p>So far, the focus of Moxa observatory has been on observing transients signals of deformation and fluid movements in the subsurface. This is accomplished by sensors like a superconducting gravimeter CD-034, three laser strain meters measuring nano-strain along three galleries in north-south, east-west and NW-SE directions, or borehole tiltmeters. Further, information on fluid flow is gained from downhole temperature measurements employing an optical fiber. These sensors are complemented by a climate station and two shallow drill-holes, one of which has been fully cored, which in addition to the temperature times series provide information on water level and rock physical properties. Near surface geophysical profiling using e.g. electrical resistivity tomography has led to a good knowledge of the structurally complex subsurface of the observatory.</p><p>Recently, a node for the Global Network of Optical Magnetometers for Exotic physics (GNOME) has been installed in the temperature-stabilized room at Moxa observatory close to the superconducting gravimeter. The GNOME is a world-spanning collaboration employing optically pumped magneto­meters (OPM) to search for space-time correlated transient signatures heralding exotic physics beyond the Standard Model. GNOME is sensitive to prominent classes of dark-matter scenarios, e.g., axion or axion-like particles forming macroscopic structures in the Universe. The installation in close vicinity to the superconducting gravimeter ensures well-controlled and -monitored ambient conditions such as temperature, air pressure and especially vibrations, allowing improved vetoing of false-positive detection events in the Moxa GNOME node.</p><p>Here, we focus on introducing Moxa Observatory’s sensor systems with an emphasis of actual sensor configurations and further on highlighting how various information on fluid flow coming from the specific sensors lead to an improved understanding of the direction and magnitude of subsurface fluid flow.</p>

Measurements of Evaporation in Urban area: A Comparison of two soil sealing types

<p>Paved surfaces are a necessary infrastructure of cities, traditionally they are designed to carry vehicular, pedestrian traffic and transport products, and they provide public spaces for social communication. These paved surfaces also function as channels for waste matter, sewage, gas and electrical and as transport processes of water, matter, and energy between the soil and atmosphere in urban areas. In other hand, their characteristics lead to an altered hydrological balance compared to rural counterparts.</p><p>This study aimed to gain new insights into urban hydrological balance, in particular, the evaporation from paved surfaces. Hourly data of evaporation obtained from two high-resolution weighable lysimeters, these lysimeters are covered in two pavement sealing types commonly used for sidewalks in Berlin: cobblestones and concrete slabs. Soil volumetric water content and soil temperature of sandy soil was measured in the lysimeters with capacitance soil moisture sensors at 5cm depth. Moreover, time series consisted of hourly measurements climatology observations was obtained by climate station located near to the lysimeters. The measurements started in June 2016 and have been carried out for one year.</p><p>The data could be paired to estimate the variation of evaporation and how it was affected by cobblestones and concrete slabs and environmental factors.  In this case, a generalized additive model (GAM) for each sealing type was built, where the model response was the difference between the paired samples of evaporation from cobblestones and concrete slabs and the explanatory variables were the observations from the climate station and lysimeter data according to each sealing type. The statistical model tries to explain how the explanatory variables are related to a higher or lesser difference in evaporation between the two surfaces. As the result, the modelling approach showed that the evaporation from cobblestones tends to be higher than concrete slabs when the air temperate and soil temperature at 5 cm depth increases. The evaporation from cobblestone was also higher when the relative humidity was low, while the evaporation from concrete slabs was higher than cobblestones when the relative humidity was between 50 - 75%. When the relative humidity was higher than 75% the model showed that there was no difference in evaporating between the two sealing types.  The model showed also that the evaporation from concrete slabs tends to be higher than cobblestones when the solar radiation increases. Moreover, when the cumulative precipitation data in 9-hour intervals was higher than 10mm the cobblestone evaporates more than concrete slabs.</p>

Downscaling potential evapotranspiration to the urban canyon

The future increase in urban population will lead to progressing urbanization with urban sprawl and densification. Urbanized areas show distinct changes in their hydrological behaviour, water quality and climate. In the last decades, the ability of urban hydrological models to represent the dynamic hydrological behaviour of the different surface types has been improved continuously. Dissenting from the urban surface which is mostly represented in high spatial resolution, the climatic input to these models, such as precipitation and potential evapo(transpi)ration, is usually observed at one or several reference climate stations that are representing a mesoscale urban foot print area or rural conditions. From urban climate studies it is known, that the meteorological variables that are governing potential evapotranspiration (Ep) can be highly variable even on a small spatial scale. Consequently, we expect Ep at the street level to be affected by this variability as well. We observed the urban microclimate with a mobile climate station and a rotational principle at 16 different locations in two differently oriented street canyons with vegetated and non-vegetated sections, respectively, during three seasons (spring, summer, autumn) in Freiburg, in southwestern Germany. With these observations, we simulated Ep at the street level using FAO-56 Penman-Monteith reference evapotranspiration and compared it to reference Ep derived at a rooftop station. We found that Ep on street level is negatively influenced by changes in shortwave radiation and that it is barely sensitive to changes in the other input climate variables. Significant linear relationships between the relative differences in hourly and daily short-wave radiation input and Ep at the street level have been established. The application of these relationships allows to simulate Ep at the street level for any location in a city based on simulated (or observed) short wave time series and observations at a reference climate station. Our findings can be transferred easily to existing urban hydrologic models to improve modelling results with a more precise estimate of potential evapotranspiration on street level.