Irrigation system performance assessment and diagnosis

Performance evaluation / Irrigation programs / Irrigation management / Irrigation systems / Case studies / Hydraulics / Management / Environmental effects / Asia / Africa / South America

Trend assessment: applications for hydrology and climate research

The assessment of trends in climatology and hydrology still is a matter of debate. Capturing typical properties of time series, like trends, is highly relevant for the discussion of potential impacts of global warming or flood occurrences. It provides indicators for the separation of anthropogenic signals and natural forcing factors by distinguishing between deterministic trends and stochastic variability. In this contribution river run-off data from gauges in Southern Germany are analysed regarding their trend behaviour by combining a deterministic trend component and a stochastic model part in a semi-parametric approach. In this way the trade-off between trend and autocorrelation structure can be considered explicitly. A test for a significant trend is introduced via three steps: First, a stochastic fractional ARIMA model, which is able to reproduce short-term as well as long-term correlations, is fitted to the empirical data. In a second step, wavelet analysis is used to separate the variability of small and large time-scales assuming that the trend component is part of the latter. Finally, a comparison of the overall variability to that restricted to small scales results in a test for a trend. The extraction of the large-scale behaviour by wavelet analysis provides a clue concerning the shape of the trend

Tempting long-memory - on the interpretation of DFA results

We study the inference of long-range correlations by means of Detrended Fluctuation Analysis (DFA) and argue that power-law scaling of the fluctuation function and thus long-memory may not be assumed a priori but have to be established. This requires the investigation of the local slopes. We account for the variability characteristic for stochastic processes by calculating empirical confidence regions. Comparing a long-memory with a short-memory model shows that the inference of long-range correlations from a finite amount of data by means of DFA is not specific. We remark that scaling cannot be concluded from a straight line fit to the fluctuation function in a log-log representation. Furthermore, we show that a local slope larger than α=0.5 for large scales does not necessarily imply long-memory. We also demonstrate, that it is not valid to conclude from a finite scaling region of the fluctuation function to an equivalent scaling region of the autocorrelation function. Finally, we review DFA results for the Prague temperature data set and show that long-range correlations cannot not be concluded unambiguously

Statistical characteristics of surrogate data based on geophysical measurements

In this study, the statistical properties of a range of measurements are compared with those of their surrogate time series. Seven different records are studied, amongst others, historical time series of mean daily temperature, daily rain sums and runoff from two rivers, and cloud measurements. Seven different algorithms are used to generate the surrogate time series. The best-known method is the iterative amplitude adjusted Fourier transform (IAAFT) algorithm, which is able to reproduce the measured distribution as well as the power spectrum. Using this setup, the measurements and their surrogates are compared with respect to their power spectrum, increment distribution, structure functions, annual percentiles and return values. It is found that the surrogates that reproduce the power spectrum and the distribution of the measurements are able to closely match the increment distributions and the structure functions of the measurements, but this often does not hold for surrogates that only mimic the power spectrum of the measurement. However, even the best performing surrogates do not have asymmetric increment distributions, i.e., they cannot reproduce nonlinear dynamical processes that are asymmetric in time. Furthermore, we have found deviations of the structure functions on small scales

Synoptic and meteorological drivers of extreme ozone concentrations over Europe

The present work assesses the relationship between local and synoptic meteorological conditions and surface ozone concentration over Europe in spring and summer months, during the period 1998–2012 using a new interpolated data set of observed surface ozone concentrations over the European domain. Along with local meteorological conditions, the influence of large-scale atmospheric circulation on surface ozone is addressed through a set of airflow indices computed with a novel implementation of a grid-by-grid weather type classification across Europe. Drivers of surface ozone over the full distribution of maximum daily 8 h average values are investigated, along with drivers of the extreme high percentiles and exceedances or air quality guideline thresholds. Three different regression techniques are applied: multiple linear regression to assess the drivers of maximum daily ozone, logistic regression to assess the probability of threshold exceedances and quantile regression to estimate the meteorological influence on extreme values, as represented by the 95th percentile. The relative importance of the input parameters (predictors) is assessed by a backward stepwise regression procedure that allows the identification of the most important predictors in each model. Spatial patterns of model performance exhibit distinct variations between regions. The inclusion of the ozone persistence is particularly relevant over southern Europe. In general, the best model performance is found over central Europe, where the maximum temperature plays an important role as a driver of maximum daily ozone as well as its extreme values, especially during warmer months

Wean Dairy Calves Early

Dairy calves can be weaned from milk at 1 month of age by feeding a palatable, nutritious starter. Here\u27s more about the early weaning program\u27s secret - getting the calves on starter at an early age

Florivory Shapes both Leaf and Floral Interactions

Florivory, or the consumption of flowers, is a ubiquitous interaction that can reduce plant reproduction directly by damaging reproductive tissues and indirectly by deterring pollinators. However, we know surprisingly little about how florivory alters plant traits or the larger community of species interactions. Although leaf damage is known to affect floral traits and interactions in many systems, the consequences of floral damage for leaf traits and interactions are unknown. We manipulated floral damage in Impatiens capensisand measured effects on floral attractive traits and secondary chemicals, leaf secondary chemicals, floral interactions, leaf herbivory, and plant reproduction. We also examined relationships between early season floral traits and floral interactions, to explore which traits structure floral interactions. Moderate but not high florivory significantly increased relative selfed reproduction, leading to a shift in mating system away from outcrossing. Florivory increased leaf secondary compounds and decreased leaf herbivory, although mechanisms other than leaf chemistry may be responsible for some of the reduced leaf damage. Florivory altered four of seven measured interactions, including increased subsequent florivory and reduced flower spiders, although only leaf damage effects were significant after correcting for multiple tests. Pretreatment concentrations of floral anthocyanins and condensed tannins were associated with reduced levels of many floral antagonisms, including florivory, nectar larceny, and flower spider abundance, suggesting these traits play a role in floral resistance. Overall, our results indicate a broad range of community and potential evolutionary consequences of florivory through structuring subsequent floral interactions, altering leaf secondary chemicals, and shaping leaf herbivory