Energetics of zonal waves during different phases of monsoon

Energetics of lower tropospheric zonal waves during onset, established and withdrawal phases of monsoon have been studied for 1994, 1995 and 1996. The analysis show that energetics of wave 0 over R1 (10°S-10°N), long waves (waves 1-2) over R2 (10°N - 30°S) and short waves (waves 3-10) over R3 (30° N - 50° N) influence the monsoon activity over India on intra-seasonal scale. The weekly analysis of the energetics of zonal waves indicates that the momentum transport of wave 0 over latitudinal belt L0 (12° S - 3° N), wave 1 over the belt L1(10° N - 15° N) and wave 2 over the belt L2 (33° N - 45° N) is related to all India rainfall on a weekly scale. Larger southward momentum transport of wave 0 over L0 and larger northward momentum transport of wave 1 over L1 and wave 2 over L2 enhance the monsoon activity over India.

Temporal variation in the behaviour of a cooperatively breeding bird, Jungle Babbler (Argya striata) at diel and seasonal scale

Time is an important and limited resource that can drive the trade-off between various essential activities in the lives of animals. Group-living animals need to perform different behaviour to meet their individual needs and also participate in group activities. They must, therefore, partition the available time between these activities which may vary considerably with environmental and ecological conditions. We examined time-activity budget of a cooperative passerine, Jungle Babbler (Argya striata) and how their behaviour vary across diel and seasonal scales. A repertoire of 13 behaviour was recorded of which 12 behaviour that occur throughout the year were examined further in detail. This included individual behaviour such as foraging, grooming, rest, shower and group behaviour such as allogrooming, movement, play, sentinel, mobbing and inter-group fight. Our results indicate that most of the time (about 70%) was spent performing individual behaviour and the remaining time (about 30%) was allocated to social behaviour. We also found almost all behaviour varied across diel and seasonal scale with respect to proportion of time spent performing them. This highlights the impact of environmental factors on how animals partition their time to perform various activities. Our study also lays the foundation for future studies examining the role of ecological factors such as habitat type and predation pressure in driving these patterns of behaviour in Jungle Babblers.

Analysis of Climate Change Projections for Mozambique under the Representative Concentration Pathways

Despite having contributed the least to global warming and having the lowest emissions, the African region is the most vulnerable continent to climate change impacts. To reduce the levels of risk arising from climate change, it is mandatory to combine both mitigation and adaptation. While mitigation can reduce global warming, not all impacts can be avoided. Therefore, adaptation is essential to advance strategic interventions and reduce the impacts. As part of the international effort to cope with changing climate, a set of Coordinated Regional Downscaling Experiment (CORDEX) domains have been established worldwide. The CORDEX-Africa initiative has been developed to analyze downscaled regional climate data over the African domain for climate data analysis techniques and engage users of climate information in both sector-specific and region/space-based applications. This study takes outputs of high-resolution climate multi-models from the CORDEX-Africa initiative constructed at a spatial resolution of 50 km to assess climate change projections over Mozambique. Projected spatial and temporal changes (three 30-year time periods, the present (2011-2040), mid (2041-2070), and the end (2071-2100)) in temperature and precipitation under the Representative Concentration Pathways RCP2.6, RCP4.5, and RCP8.5 are analyzed and compared relative to the baseline period (1961-1990). Results show that there is a tendency toward an increase in annual temperature as we move toward the middle and end of the century, mainly for RCP4.5 and RCP8.5 scenarios. This is evident for the Gaza Province, north of the Tete Province, and parts of Niassa Province, where variations will be Tmax (0.92 to 4.73 °C), Tmin (1.12 to 4.85 °C), and Tmean (0.99 to 4.7 °C). In contrast, the coastal region will experience less variation (values < 0.5 °C to 3 °C). At the seasonal scale, the pattern of temperature change does not differ from that of the annual scale. The JJA and SON seasons present the largest variations in temperature compared with DJF and MAM seasons. The increase in temperature may reach 4.47 °C in DJF, 4.59 °C in MAM, 5.04 °C in JJA, and 5.25 °C in SON. Precipitation shows substantial spatial and temporal variations, both in annual and seasonal scales. The northern coastal zone region shows a reduction in precipitation, while the entire southern region, with the exception of the coastal part, shows an increase up to 40% and up to 50% in some parts of the central and northern regions, in future climates for all periods under the three reference scenarios. At the seasonal scale (DJF and MAM), the precipitation in much of Mozambique shows above average precipitation with an increase up to more than 40% under the three scenarios. In contrast, during the JJA season, the three scenarios show a decrease in precipitation. Notably, the interior part will have the largest decrease, reaching a variation of -60% over most of the Gaza, Tete, and Niassa Provinces.

Quantification of ecohydrological sensitivities and their influencing factors at the seasonal scale

Ecohydrological sensitivity, defined as the response intensity of streamflow to per unit vegetation change is an integrated indicator for assessing hydrological sensitivity to vegetation change. Understanding ecohydrological sensitivity and its influencing factors is crucial for managing water supply, reducing water-related hazards and ensuring aquatic functions by vegetation management. Yet, there is still a systematic assessment on ecohydrological sensitivity and associated driving factors especially at a seasonal scale lacking. In this study, 14 large watersheds across various environmental gradients in China were selected to quantify their ecohydrological sensitivities at a seasonal scale and to examine the role of associated influencing factors such as climate, vegetation, topography, soil and landscape. Based on the variables identified by correlation analysis and factor analysis, prediction models of seasonal ecohydrological sensitivity were constructed to test their utilities for the design of watershed management and protection strategies. Our key findings were the following: (1) ecohydrological sensitivities were more sensitive under dry conditions than wet conditions – for example, 1 % LAI (leaf area index) change, on average, induced 5.05 % and 1.96 % change in the dry and wet season streamflow, respectively; (2) seasonal ecohydrological sensitivities were highly variable across the study watersheds with different climate conditions, dominant soil types and hydrological regimes; and (3) the dry season ecohydrological sensitivity was mostly determined by topography (slope, slope length, valley depth and downslope distance gradient), soil (topsoil organic carbon and topsoil bulk density) and vegetation (LAI), while the wet season ecohydrological sensitivity was mainly controlled by soil (topsoil-available water-holding capacity), landscape (edge density) and vegetation (leaf area index). Our study provided a useful and practical framework to assess and predict ecohydrological sensitivities at the seasonal scale. The established ecohydrological sensitivity prediction models can be applied to ungauged watersheds or watersheds with limited hydrological data to help decision makers and watershed managers effectively manage hydrological impacts through vegetation restoration programs. We conclude that ecohydrological sensitivities at the seasonal scale are varied by climate, vegetation and watershed property, and their understanding can greatly support the management of hydrological risks and protection of aquatic functions.

Comparison of the event and seasonal hillslope-stream hydrologic connectivity in an agricultural headwater catchment

&lt;p&gt;In agricultural catchments, subsurface runoff is an important process for streamflow generation and the transport of nutrients and pollutants within and out of the catchment. Where and when subsurface runoff occurs is linked to the hydrologic connectivity in the catchment. This study compares spatial patterns of the connectivity between the hillslope and the stream on the event and seasonal scale. We analyse streamflow and groundwater responses to 53 precipitation events and their seasonal dynamics over two years in the Hydrologic Open Air Laboratory (HOAL), a small (66 ha) agricultural headwater catchment in Lower Austria. We quantify the connectivity in terms of Spearman correlation, hysteresis index and peak-to-peak time between streamflow and groundwater dynamics. It shows a clear spatial pattern, i.e. the connectivity is greatest in the riparian zone and diminishes further away from the stream where the groundwater table is deeper. This is reflected in the significant correlation of connectivity to the topographic indices and groundwater depth. Groundwater connectivity to the stream on the seasonal scale is higher than that on the event scale, indicating that groundwater contributes more to the baseflow than event runoff.&lt;/p&gt;

Two millennia of seasonal rainfall predictability in the neotropics with repercussions for agricultural societies

&lt;p&gt;&lt;span&gt;The reconstruction and analysis of palaeoseasonality from speleothem records remains a notoriously challenging task. Although the seasonal cycle is obscured by noise, dating uncertainties and irregular sampling, its extraction can identify regime transitions and enhance the understanding of long-term climate variability. Shifts in seasonal predictability of hydroclimatic conditions have immediate and serious repercussions for agricultural societies.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;We present a highly resolved speleothem record (ca. 0.22 years temporal resolution with episodes twice as high) of palaeoseasonality from Yok Balum cave in Belize covering the Common Era (400-2006 CE) and demonstrate how seasonal-scale hydrological variability can be extracted from &amp;#948;&lt;sup&gt;13&lt;/sup&gt;C and &amp;#948;&lt;sup&gt;18&lt;/sup&gt;O isotope records. We employ a Monte-Carlo based framework in which dating uncertainties are transferred into magnitude uncertainty and propagated. Regional historical proxy data enable us to relate climate variability to agricultural disasters throughout the Little Ice Age and population size variability during the Terminal Classic Maya collapse.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Spectral analysis reveals the seasonal cycle as well as nonstationary ENSO- and multi-decadal-scale variability. Variations in both the subannual distribution of rainfall and mean average hydroclimate pose limitations on how reliably farmers can predict crop yield. A characterization of year-to-year predictability as well as the complexity of seasonal patterns unconver shifts in the seasonal-scale variability. These are discussed in the context of their implications for rainfall dependent agricultural societies.&lt;/span&gt;&lt;/p&gt;

Flash drought in Australia: deriving a long-term climatology from drought metrics based on precipitation, evapotranspiration, and evaporative demand.

&lt;p&gt;Until the scientific community coalesces around a consensus definition of flash drought, we might usefully distinguish them from &amp;#8220;ordinary&amp;#8221; droughts by applying a criterion of a rapid intensification from near-normal soil moisture to drought conditions over a period of a few weeks. Here, we use such a definition to generate the first spatially distributed, long-term climatology of flash droughts across Australia, which we derive using a suite of indices that capture both the supply and the demand perspectives of drought: evaporative demand describes the atmospheric demand for moisture from the surface; precipitation, the supply of moisture from the atmosphere to the surface; and evaporative stress, the supply of moisture from the surface relative to evaporative demand.&lt;/p&gt;&lt;p&gt;Regardless of metric-based definition, flash droughts are observed across all seasons. They can terminate as rapidly as they start, but in some cases can eventuate in a seasonal-scale drought. We show that flash-drought variability and its prevalence can be related to ENSO phases, which suggests an opportunity for enhanced seasonal-scale prediction. We examine a case study in the Wimmera Region of southeast Australia (around the South Australia / Victoria border), we show that monitoring precipitation is less useful for capturing the onset of flash drought. Instead, indices that capture the demand perspective of drought--such as the Evaporative Demand Drought Index (EDDI) and Evaporative Stress Index (ESI)--are more useful for monitoring flash-drought development.&lt;/p&gt;

Towards improved N2O budgets estimation from 10 site-years measurement and analysis of key drivers

&lt;p&gt;Agriculture represents 14% of global anthropogenic greenhous gases (GHG) emissions, 46% of this amount being due to N&lt;sub&gt;2&lt;/sub&gt;O emissions from soils (UNEP, 2012). N&lt;sub&gt;2&lt;/sub&gt;O is a powerful GHG (IPCC, 2013) and its emissions from agricultural soils are related to physical-chemical parameters which depend on climate (temperature, rain&amp;#8230;), soil properties (Robertson et al., 1989) and farming practices (irrigation, tillage, fertilization&amp;#8230;) (Tellez-Rio et al., 2015). The IPCC Tier 1 emission factor remains widely used to estimate annual N&lt;sub&gt;2&lt;/sub&gt;O budgets from agricultural soils by taking into account the annual amount of N input only. However, not taking into account the environmental controlling factors may introduce high uncertainty in N&lt;sub&gt;2&lt;/sub&gt;O budget estimation. Our study aims at highlighting the key drivers of N&lt;sub&gt;2&lt;/sub&gt;O emissions from two agricultural sites in the South West of France and at proposing an improved, simple and accessible methodology to estimate N&lt;sub&gt;2&lt;/sub&gt;O budget at crop plot and seasonal scale. For this purpose, we benefited from a unique long time series of daily N&lt;sub&gt;2&lt;/sub&gt;O fluxes (from 2011 to 2016) measured with 6 closed automated chambers on two ICOS sites with contrasted agricultural management (FR-Lam and FR-Aur).&lt;/p&gt;&lt;p&gt;N&lt;sub&gt;2&lt;/sub&gt;O annual budget vary from 1.04 to 7.96 kgN ha&lt;sup&gt;-1&lt;/sup&gt; yr&lt;sup&gt;-1 &lt;/sup&gt;for winter wheat and maize crop, respectively. The effects of fertilization, rain and irrigation, plant development, spring mineralization and deep tillage on N&lt;sub&gt;2&lt;/sub&gt;O emissions were investigated. Significant correlations between rain combined with fertilization and plant development, deep tillage or spring mineralisation was found with R&amp;#178; of 0.91, 0.99 and 0.85, respectively. &amp;#160;We took advantage of these results to develop an empirical model, including N input quantity, residual N, leaf area index and water input in order to estimate seasonal and annual N&lt;sub&gt;2&lt;/sub&gt;O budget. At the seasonal scale, the model output matched well with the observed budget, with a R&amp;#178; and a RMSE of 0.87 and 0.33 kgN ha&lt;sup&gt;-1&lt;/sup&gt; at FR-Lam and of 0.92 and 0.12 kgN ha&lt;sup&gt;-1&lt;/sup&gt; at FR-Aur, respectively.&amp;#160; It also gave good statistical scores at the crop year scale with a R&amp;#178; of 0.96 and a low RMSE of 0.43 kgN ha&lt;sup&gt;-1&lt;/sup&gt; when binding data from both sites. Using the IPCC Tiers 1 methodology gave lower and more scattered results with a R&amp;#178; of 0.46 and a RMSE of 1.46 kgN ha&lt;sup&gt;-1&lt;/sup&gt;. For sites where N&lt;sub&gt;2&lt;/sub&gt;O fluxes are not monitored,&amp;#160; that new methodology may be an alternative and a more precise methodology than the IPCC Tiers 1 approach. It has also the advantage to require only few and accessible input variables.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;REFERENCES&lt;/p&gt;&lt;p&gt;IPCC, 2013. Climate Change 2013: The Physical Science Basis. Cambridge University Press, Cambridge.&lt;/p&gt;&lt;p&gt;Robertson et al., 1989. Aerobic denitrification in various heterotrophic nitrifiers. Antonie van Leeuwenhock., 56, 289-299.&lt;/p&gt;&lt;p&gt;Tellez-Rio et al., 2015. N2O and CH4 Emissions from a Fallow&amp;#8211;wheat Rotation with Low N Input in Conservation and Conventional Tillage under a Mediterranean Agroecosystem. Sci. Total Environ., 508, 85&amp;#8211;94.&lt;/p&gt;&lt;p&gt;UNEP, 2012. Growing greenhouse gas emissions due to meat production.&lt;/p&gt;