Winter wheat roots grow twice as deep as spring wheat roots, is this important for N uptake and N leaching losses?

Cropping systems comprising winter catch crops followed by spring wheat could reduce N leaching risks compared to traditional winter wheat systems in humid climates. We studied the soil mineral N (Ninorg) and root growth of winter- and spring wheat to 2.5 m depth during three years. Root depth of winter wheat (2.2 m) was twice that of spring wheat, and this was related to much lower amounts of Ninorg in the 1 to 2.5 m layer after winter wheat (81 kg Ninorg ha-1 less). When growing winter catch crops before spring wheat, N content in the 1 to 2.5 m layer after spring wheat was not different from that after winter wheat. The results suggest that by virtue of its deep rooting, winter wheat may not lead to high levels of leaching as it is often assumed in humid climates. Deep soil and root measurements (below 1 m) in this experiment were essential to answer the questions we posed

Understanding the impact of droughts in the Yarmouk Basin, Jordan: monitoring droughts through meteorological and hydrological drought indices

This article assesses drought status in the Yarmouk Basin (YB), in northern Jordan, using the Standardized Precipitation Index (SPI), the Standardized Water-Level Index (SWI), and the Percent Departure from Normal rainfall (PDNimd) during the years 1993–2014. The results showed that the YB suffers from frequent and irregular periods of drought as variations in drought intensity and frequency have been observed. The SPI results revealed that the highest drought magnitude of − 2.34 appeared at Nuaimeh rainfall station in 1991. This station has also experienced severe drought particularly in years 1995, 1999, 2005, and 2012 with SPI values ranging from − 1.51 to − 1.59. Some other rainfall stations such as Baqura, Ibbin, Khanasiri, Kharja, Mafraq police, Ramtha, Turra, and Umm Qais have also suffered several periods of drought mostly in 1993. The SWI results show the highest extreme drought events in 2001 in Souf well while other extreme drought periods were observed at Wadi Elyabis well in 1994 and at Mafraq well in 1995. As compared to SPI maps, our SWI maps reflect severe and extreme drought events in most years, negatively impacting the groundwater levels in the study area

A Monte Carlo Simulation-Based Approach to Evaluate the Performance of Three Meteorological Drought Indices in Northwest of Iran

Although meteorological drought indices are considered as important tools for drought monitoring, they are embedded with different theoretical and experimental structures. Regarding the different geographic and climatic conditions around the world, the most meteorological drought indices have been commonly applied for drought monitoring in different parts of the world. Interestingly, it is observed that such indices in the published studies on drought monitoring have usually yielded inconsistent performance. On the other hand, most studies on drought monitoring as well as the performance of drought indices has been based on short-term historical data (less than 50 years). Therefore, this study aimed to analyze and compare the performance of three common indices of SPI, RAI and PNPI to predict long-term drought events using the Monte Carlo procedure and historical data. To do this end, the 50-year recorded or historical rainfall data across 11 synoptic stations in the Northwest of Iran were employed to generate 1000 synthetic data series so that the characteristics of long-term drought might be determined and the performance of those three indices might be analyzed and compared. The results indicated a very high comparative advantage of the SPI in terms of yielding a satisfactory and detailed analysis to determine the characteristics of long-term drought. Also, the RAI indicated significant deviations from normalized natural processes. However, these results could not reasonably and sufficiently predict long-term drought. Finally, the PNPI was determined as the most uncertain and spatial index (depending on average or coefficient of variation of rainfall data) in drought monitoring

Trend and uncertainty in spatial-temporal patterns of hydrological droughts in the Amazon basin

Spatial-temporal patterns of hydrological droughts in the Amazon basin are derived from drought indices computed from existing streamflow data. Principal component analysis and Monte Carlo simulations are employed to account for the uncertainty and overcome the limitations of missing data in streamflow records. Results show that northern and southern subbasins differ in drought trends and in patterns of correlation between drought indices and climate anomalies originating from the Pacific (El Niño-Southern Oscillation) and Atlantic (differences in sea surface temperature across the equator) Oceans. A significant trend toward more intense droughts is found in the southern subbasins, which is highly correlated to tropical Atlantic Ocean sea surface temperature anomalies. That drying trend might have distinct causes in each subbasin and can lead to potential intensification of regional impacts

A new approach for developing comprehensive agricultural drought index using satellite-derived biophysical parameters and factor analysis method

The accurate assessment of drought and its monitoring is highly depending on the selection of appropriate indices. Despite the availability of countless drought indices, due to variability in environmental properties, a single universally drought index has not been presented yet. In this study, a new approach for developing comprehensive agricultural drought index from satellite-derived biophysical parameters is presented. Therefore, the potential of satellite-derived biophysical parameters for improved understanding of the water status of pistachio (Pistachio vera L.) crop grown in a semiarid area is evaluated. Exploratory factor analysis with principal component extraction method is performed to select the most in?uential parameters from seven biophysical parameters including surface temperature (Ts), surface albedo (a), leaf area index (LAI), soil heat ?ux (Go), soil-adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), and net radiation (Rn). Ts and Gowere found as the most effective parameters by this method. However, Ts, LAI, a, and SAVI that accounts for 99.6 % of the total variance of seven inputs were selected to model a new biophysical water stress index (BPWSI). The values of BPWSI were stretched independently and compared with the range of actual evapotranspiration estimated through well-known METRIC (mapping evapotranspiration at high resolution with internal calibration) energy balance model. The results showed that BPWSI can be ef?ciently used for the prediction of the pistachio water status (RMSE of 0.52, 0.31, and 0.48 mm/day on three image dates of April 28, July 17, and August 2, 2010). The study con?rmed that crop water status is accounted by several satellite-based biophysical parameters rather than single parameter