Temporal changes in soil temperature at Wolverhampton, UK and Hohe Warte, Vienna, Austria 1976–2010

Soil temperature is determined by the available heat energy that the soil absorbs, with solar radiation being the primary source (Brady and Weil, 1999). Chow et al. (2011) found that, in an urban environment, soil temperature is strongly correlated (R = 0.869) with the dry-bulb air temperature, whereas its dependence on relative humidity, precipitation, global solar radiation or wind speed was weak (R < 0.250 in all cases). Snow cover, irregular episodes of cloud cover and droughts may also influence soil temperatures. Snow cover can provide an effective insulation barrier that creates an observable lag in the thermal response of a soil relative to changing air temperature (Fullen and Smith, 1983; Mackiewicz, 2012). Soil temperature fluctuates when there is a change in the ratio of heat energy absorbed by soil to energy lost from soil. This dynamic ratio changes over time and space. Soil temperature variation in different layers is a result of complex processes. The correlation with air temperature generally decreases with depth (Liu et al., 2013). Study of temperature variation in different layers of soil is useful in understanding surface energy processes and regional environmental and climatic conditions (Hu and Feng, 2003). Soil temperature has great significance for the growth and hence productivity of agricultural crops (Kaspar and Bland, 1992; Wraith and Ferguson, 1994; Bollero et al., 1996; Hu and Buyanovsky, 2003) and forest plantations (Balisky and Burton, 1995). Moreover, soil temperature affects plant diseases, soil hydrology and the over-wintering of pathogens (Marshall and Holmes, 1979; Phillips et al., 1999; Pivonia et al., 2002). Generally, the growth and development of most annual crop plants cease at temperatures <6–10°C (Subedi and Fullen, 2009). Thus, soil temperatures below this range inhibit root growth. Soil temperatures at different soil depths between 5 and 60cm at a UK research site over 35 years (1976–2010) and at a site in Austria at 10cm over the same period are reported and discussed

Involving Local Fishing Communities in Policy Making: Addressing Illegal Fishing in Indonesia

Illegal, Unreported and Unregulated (IUU) fishing has been identified by the UN as one of the seven major threats to global maritime security; it causes loss of economic revenue, severe environmental damage, and far-reaching livelihood implications for coastal communities. Indonesia, by far the biggest archipelagic state, faces enormous challenges in all aspects of IUU fishing and addressing those is one of the current Indonesian Government’s top priorities. This article addresses the under-researched dimension of how IUU fishing affects fishing communities. With the use of collage making focus groups with fishermen from different Indonesian fishing communities, the research highlights the interrelated environmental (depletion of resources), socio-economic (unbridled illegal activities at sea), cultural (favouritism) and political (weak marine governance) dimensions of IUU fishing as experienced at the local level. However, the research also indicates a strong will by fishermen to be seen as knowledge agents who can help solve the problem by better dissemination of information and cooperation between the local government(s) and the fishing communities. The article concludes by arguing for the involvement of local fishing communities in national and international policy making that addresses IUU fishing

Evapotranspiration in Northern Eurasia : impact of forcing uncertainties on terrestrial ecosystem model estimates

The ecosystems in Northern Eurasia (NE) play an important role in the global water cycle and the climate system. While evapotranspiration (ET) is a critical variable to understand this role, ET over this region remains largely unstudied. Using an improved version of the Terrestrial Ecosystem Model with five widely used forcing data sets, we examine the impact that uncertainties in climate forcing data have on the magnitude, variability, and dominant climatic drivers of ET for the period 1979-2008. Estimates of regional average ET vary in the range of 241.4-335.7mmyr(-1) depending on the choice of forcing data. This range corresponds to as much as 32% of the mean ET. Meanwhile, the spatial patterns of long-term average ET across NE are generally consistent for all forcing data sets. Our ET estimates in NE are largely affected by uncertainties in precipitation (P), air temperature (T), incoming shortwave radiation (R), and vapor pressure deficit (VPD). During the growing season, the correlations between ET and each forcing variable indicate that T is the dominant factor in the north and P in the south. Unsurprisingly, the uncertainties in climate forcing data propagate as well to estimates of the volume of water available for runoff (here defined as P-ET). While the Climate Research Unit data set is overall the best choice of forcing data in NE according to our assessment, the quality of these forcing data sets remains a major challenge to accurately quantify the regional water balance in NE

Random walk forecast of urban water in Iran under uncertainty

There are two significant reasons for the uncertainties of water demand. On one hand, an evolving technological world is plagued with accelerated change in lifestyles and consumption patterns; and on the other hand, intensifying climate change. Therefore, with an uncertain future, what enables policymakers to define the state of water resources, which are affected by withdrawals and demands? Through a case study based on thirteen years of observation data in the Zayandeh Rud River basin in Isfahan province located in Iran, this paper forecasts a wide range of urban water demand possibilities in order to create a portfolio of plans which could be utilized by different water managers. A comparison and contrast of two existing methods are discussed, demonstrating the Random Walk Methodology, which will be referred to as the â On uncertainty pathâ , because it takes the uncertainties into account and can be recommended to managers. This On Uncertainty Path is composed of both dynamic forecasting method and system simulation. The outcomes show the advantage of such methods particularly for places that climate change will aggravate their water scarcity, such as Iran

Nested sampling and spatial analysis for reconnaissance investigations of soil: an example from agricultural land near mine tailings in Zambia

A reconnaissance survey was undertaken on soil near mine tailings to investigate variation in the content of copper, chromium and uranium. A nested sampling design was used. The data showed significant relations between the content of copper and uranium in the soil and its organic matter content, and a significant spatial trend in uranium content with distance from the tailings. Soil pH was not significantly related to any of the metals. The variance components associated with different scales of the sample design had large confidence intervals, but it was possible to show that the random variation was spatially dependent for all spatial models, whether for variation around a constant mean, or with a mean given by a linear effect of organic matter or distance to the tailings. For copper, we showed that a fractal or multifractal random model, with equal variance components for scales in a logarithmic progression, could be rejected for the model of variation around the fixed mean. The inclusion of organic matter as an explanatory factor meant that the fractal model could no longer be rejected, suggesting that the effect of organic matter results in spatial variation that is not scale invariant. It was shown, taking uranium as a case study, that further spatially nested sampling to estimate scale-dependent variance components, or to test a non-fractal model with adequate power, would require in the order of 200–250 samples in total

SPFC: a tool to improve water management and hay production in the Crau region

Correspondance: [email protected] ; UMR SYSTEM équipe CONSYSTThis article deals with the development and application of SPFC, a model used to improve water and grassland production (HC) in this region of France. This model is composed of two sub-models: an irrigation model and a crop model. As the fields are border irrigated, these two sub-models are coupled. The crop model simulates dry matter, Leaf Area Index (LAI) and soil water reserve (SWR) variations. LAI and SWR are both used for border model updating: SWR for the deficit of saturation required by the infiltration equation and LAI for the roughness coefficient n. After calibration and validation, SPFC is then used to identify realistic management strategies for the irrigation and production system at the plot level. By scheduling irrigation when SWR is 50% depleted, would result in a low Dry Matter DM production loss (around 10%), reduced labour (8 irrigation events instead of 11) and in significant water saving compared with farmers' practices, on the basis of an average climatic scenario. Furthermore, this improvement of irrigation efficiency is not incompatible with groundwater recharge used for the potable water supply of the region

Mapping the drivers of parasitic weed abundance at a national scale : a new approach applied to Striga asiatica in the mid‐west of Madagascar

The parasitic weed genus Striga causes huge losses to crop production in sub‐Saharan Africa, estimated to be in excess of $7 billion per year. There is a paucity of reliable distribution data for Striga ; however, such data are urgently needed to understand current drivers, better target control efforts, as well as to predict future risks. To address this, we developed a methodology to enable rapid, large‐scale monitoring of Striga populations. We used this approach to uncover the factors that currently drive the abundance and distribution of Striga asiatica in Madagascar. Two long‐distance transects were established across the middle‐west region of Madagascar in which S. asiatica abundance in fields adjacent to the road was estimated. Management, crop structure and soil data were also collected. Analysis of the data suggests that crop variety, companion crop and previous crop were correlated with Striga density. A positive relationship between within‐field Striga density and the density of the nearest neighbouring fields indicates that spatial configuration and connectivity of suitable habitats is also important in determining Striga spread. Our results demonstrate that we are able to capture distribution and management data for Striga density at a landscape scale and use this to understand the ecological and agronomic drivers of abundance. The importance of crop varieties and cropping patterns is significant, as these are key socio‐economic elements of Malagasy cropping practices. Therefore, they have the potential to be promoted as readily available control options, rather than novel technologies requiring introduction