Suchergebnisse

Abstract. Drought is considered as one of the major natural hazards with a significant impact on agriculture, environment, society and economy. Droughts affect sustainability of agriculture and may result in environmental degradation of a region, which is one of the factors contributing to the vulnerability of agriculture. This paper addresses agrometeorological or agricultural drought within the risk management framework. Risk management consists of risk assessment, as well as a feedback on the adopted risk reduction measures. And risk assessment comprises three distinct steps, namely risk identification, risk estimation and risk evaluation. This paper deals with risk identification of agricultural drought, which involves drought quantification and monitoring, as well as statistical inference. For the quantitative assessment of agricultural drought, as well as the computation of spatiotemporal features, one of the most reliable and widely used indices is applied, namely the vegetation health index (VHI). The computation of VHI is based on satellite data of temperature and the normalized difference vegetation index (NDVI). The spatiotemporal features of drought, which are extracted from VHI, are areal extent, onset and end time, duration and severity. In this paper, a 20-year (1981–2001) time series of the National Oceanic and Atmospheric Administration/advanced very high resolution radiometer (NOAA/AVHRR) satellite data is used, where monthly images of VHI are extracted. Application is implemented in Thessaly, which is the major agricultural drought-prone region of Greece, characterized by vulnerable agriculture. The results show that agricultural drought appears every year during the warm season in the region. The severity of drought is increasing from mild to extreme throughout the warm season, with peaks appearing in the summer. Similarly, the areal extent of drought is also increasing during the warm season, whereas the number of extreme drought pixels is much less than those of mild to moderate drought throughout the warm season. Finally, the areas with diachronic drought persistence can be located. Drought early warning is developed using empirical functional relationships of severity and areal extent. In particular, two second-order polynomials are fitted, one for low and the other for high severity drought classes, respectively. The two fitted curves offer a forecasting tool on a monthly basis from May to October. The results of this drought risk identification effort are considered quite satisfactory offering a prognostic potential. The adopted remote-sensing data and methods have proven very effective in delineating spatial variability and features in drought quantification and monitoring.

Abstract. Extreme rainfall events are a serious concern for regional hydrology and agriculture in the Ebro River Basin. Repeated anomalous rainfall in recent decades has had a devastating impact on this region, both socially and economically. Some studies developed in Italy and USA have shown that there is a change in seasonal patterns and an increasing frequency of extreme rainfall events, whereas other studies have pointed out that no global behaviour could be observed in monthly trends due to high climatic variability. The aim of this work is to test which of these scenarios is the case for the Ebro River Basin. For this purpose, 14 meteorological stations were selected based on the length of the rainfall series and the climatic classification to obtain a representative untreated dataset from the river basin. Daily rainfall series from 1957 to 2002 were obtained from each meteorological station. First, classical climatic indexes were analysed with an autoregressive test to study possible trends in rainfall. The results can be explained following the evolution of the NAO and WeMO indexes, which indicate that the initial period should be subdivided in two periods (1957–1979 and 1980–2002) to assume stationarity and to analyse the rainfall distribution functions. The general results obtained in this study for both sub-periods, through the generalised Pareto distribution (GPD) parameters and the maximum expected return values, do not support the results previously obtained by other authors that affirm a positive trend in extreme rainfall indexes and point to a slight reduction indicated by others. Three extreme precipitation indexes show negative statistical significant trends. GPD-scale parameters decrease except for only one rain gauge, although this decrease is only statistically significant for two rain gauges. Another two locations show statistical significance decreased for maximum expected return values.

Abstract. The impact of climate change and its relation with evapotranspiration was evaluated in the Duero River Basin (Spain). The study shows possible future situations 50 yr from now from the reference evapotranspiration (ETo). The maximum temperature (Tmax), minimum temperature (Tmin), dew point (Td), wind speed (U) and net radiation (Rn) trends during the 1980–2009 period were obtained and extrapolated with the FAO-56 Penman-Montheith equation to estimate ETo. Changes in stomatal resistance in response to increases in CO2 were also considered. Four scenarios were done, taking the concentration of CO2 and the period analyzed (annual or monthly) into consideration. The scenarios studied showed the changes in ETo as a consequence of the annual and monthly trends in the variables Tmax, Tmin, Td, U and Rn with current and future CO2 concentrations (372 ppm and 550 ppm). The future ETo showed increases between 118 mm (11 %) and 55 mm (5 %) with respect to the current situation of the river basin at 1042 mm. The months most affected by climate change are May, June, July, August and September, which also coincide with the maximum water needs of the basin's crops.

Abstract. The evapotranspiration-based scheduling method is the most common method for irrigation programming in agriculture. There is no doubt that the estimation of the reference evapotranspiration (ETo) is a key factor in irrigated agriculture. However, the high cost and maintenance of agrometeorological stations and high number of sensors required to estimate it make it non-plausible, especially in rural areas. For this reason, the estimation of ETo using air temperature, in places where wind speed, solar radiation and air humidity data are not readily available, is particularly attractive. A daily data record of 49 stations distributed over Duero basin (Spain), for the period 2000–2018, was used for estimation of ETo based on seven models against Penman–Monteith (PM) FAO 56 (FAO – Food and Agricultural Organization of the United Nations) from a temporal (annual or seasonal) and spatial perspective. Two Hargreaves–Samani (HS) models, with and without calibration, and five Penman–Monteith temperature (PMT) models were used in this study. The results show that the models' performance changes considerably, depending on whether the scale is annual or seasonal. The performance of the seven models was acceptable from an annual perspective (R2>0.91, NSE > 0.88, MAE < 0.52 and RMSE < 0.69 mm d−1; NSE – Nash–Sutcliffe model efficiency; MAE – mean absolute error; RMSE – root-mean-square error). For winter, no model showed good performance. In the rest of the seasons, the models with the best performance were the following three models: PMTCUH (Penman–Monteith temperature with calibration of Hargreaves empirical coefficient – kRS, average monthly value of wind speed, and average monthly value of maximum and minimum relative humidity), HSC (Hargreaves–Samani with calibration of kRS) and PMTOUH (Penman–Monteith temperature without calibration of kRS, average monthly value of wind speed and average monthly value of maximum and minimum relative humidity). The HSC model presents a calibration of the Hargreaves empirical coefficient (kRS). In the PMTCUH model, kRS was calibrated and average monthly values were used for wind speed and maximum and minimum relative humidity. Finally, the PMTOUH model is like the PMTCUH model except that kRS was not calibrated. These results are very useful for adopting appropriate measures for efficient water management, especially in the intensive agriculture in semi-arid zones, under the limitation of agrometeorological data.

Abstract. The Amazon basin is the world's largest rainforest and the most biologically
diverse place on Earth. Despite the critical importance of this region,
Amazon forests continue inexorably to be degraded and deforested for various
reasons, mainly a consequence of agricultural expansion. The development of
novel policy strategies that provide balanced solutions, associating
economic growth with environmental protection, is still challenging, largely
because the perspective of those most affected – local stakeholders – is often
ignored. Participatory fuzzy cognitive mapping (FCM) was implemented to
examine stakeholder perceptions towards the sustainable development of two
agricultural-forest frontier areas in the Bolivian and Brazilian Amazon. A
series of development scenarios were explored and applied to stakeholder-derived FCM, with climate change also analysed. Stakeholders in both regions
perceived landscapes of socio-economic impoverishment and environmental
degradation driven by governmental and institutional deficiencies. Under
such abject conditions, governance and well-integrated social and
technological strategies offered socio-economic development, environmental
conservation, and resilience to climatic changes. The results suggest there are
benefits of a new type of thinking for development strategies in the Amazon
basin and that continued application of traditional development policies
reduces the resilience of the Amazon to climate change, whilst limiting
socio-economic development and environmental conservation.

Abstract. Risk assessment constitutes the first part within the risk management framework and involves evaluating the importance of a risk, either quantitatively or qualitatively. Risk assessment consists of three steps, namely risk identification, risk estimation and risk evaluation. Nevertheless, the risk management framework also includes a fourth step, i.e., the need for feedback on all the risk assessment undertakings. However, there is a lack of such feedback, which constitutes a serious deficiency in the reduction of environmental hazards at the present time. Risk identification of local or regional hazards involves hazard quantification, event monitoring including early warning systems and statistical inference. Risk identification also involves the development of a database where historical hazard information and hazard effects are included. Similarly, risk estimation involves magnitude–frequency relationships and hazard economic costs. Furthermore, risk evaluation consists of the social consequences of the derived risk and involves cost-benefit analysis and community policy. The objective of this review paper is twofold. On the one hand, it is to address meteorological hazards and extremes within the risk management framework. Analysis results and case studies over Mediterranean ecosystems with emphasis on the wider area of Greece, in the eastern Mediterranean, are presented for each of the three steps of risk assessment for several environmental hazards. The results indicate that the risk management framework constitutes an integrated approach for environmental planning and decision-making. On the other hand, it sheds light on advances and current trends in the considered meteorological and environmental hazards and extreme events, such as tornadoes, waterspouts, hailstorms, heat waves, droughts, floods, heavy convective precipitation, landslides and wildfires, using recorded datasets, model simulations and innovative methodologies.