Adaptability of Irrigation to a Changing Monsoon in India: How far can we go?

Agriculture and the monsoon are inextricably linked in India. A large part of the steady rise in agricultural production since the onset of the Green Revolution in the 1960’s has been attributed to irrigation. Irrigation is used to supplement and buffer crops against precipitation shocks, but water availability for such use is itself sensitive to the erratic, seasonal and spatially heterogeneous nature of the monsoon. Most attention in the literature is given to crop yields (Guiteras, 2009; Fishman, 2012; Auffhammer et al, 2011) and their ability to withstand precipitation shocks, in the presence of irrigation (Fishman, 2012). However, there remains limited evidence about how natural weather variability and realized irrigation outcomes are related. We provide new evidence on the relationship between monsoon changes, irrigation variability and water availability by linking a process based hydrology model with an econometric model for one of the world’s most water stressed countries. India uses more groundwater for irrigation than any other country, and there is substantial evidence that this has led to depletion of groundwater aquifers. First, we build an econometric model of historical irrigation decisions using detailed crop-wise agriculture and weather data spanning 35 years from 1970-2004 for 311 districts across 19 major agricultural states in India. The source of agricultural data comes from the Village Dynamics in South Asia database at the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT). Weather data is sourced from the only long term continental scale daily observationally gridded precipitation and temperature dataset called APHRODITE (Asian Precipitation- Highly Resolved Observational Data Integration Towards Evaluation of the Water Resources), that captures the spatial extent of the monsoon across the Himalayas, South and South-East Asia, and the Middle East in great detail. We use panel data approaches to control for unobserved and omitted variables that can confound the true impacts of weather variability on irrigation. Exploiting the exogenous inter-annual variability in the monsoon, our multivariate regression models reveal that for crops grown in the wet season, irrigation is sensitive to distribution and total monsoon rainfall but not to ground or surface water availability. For crops grown in the dry season, total monsoon rainfall matters most, and its effect is sensitive to groundwater availability but differentially so for shallow dug wells and deep tube wells. The historical estimates from the econometric model are used to calculate future irrigated areas using three different bias-corrected climate model projections of monsoon climate for the years 2010 – 2050 under the strongest warming scenario ( business as usual scenario) RCP-8.5 from the CMIP5 (Coupled Model Intercomparison Project) models. These projections are then used as input to a physical hydrology model, such as the Water Balance Model, that tracks water use and exchange between the ground, atmosphere, runoff and stream networks. This enables us to quantify supply of water required to meet irrigation needs from sustainable sources such as rechargeable shallow groundwater, rivers and reservoirs, as well as unsustainable sources such as non- rechargeable groundwater. Preliminary results show that the significant variation in monsoon projections lead to very different results. Crops grown in the dry season show particularly divergent trends between model projections, leading to very different groundwater resource requirements. By combining the strengths of the economic and hydrology components, this work highlights potential sustainable or unsustainable water use trajectories that different regions within India will face

Invisible water, visible impact: How unsustainable groundwater use challenges sustainability of Indian agriculture under climate change

India is one of the world’s largest food producers, making the sustainability of its agricultural system of global significance. Groundwater irrigation underpins India’s agriculture, currently boosting crop production by enough to feed 170 million people. Groundwater overexploitation has led to drastic declines in groundwater levels, threatening to push this vital resource out of reach for millions of small-scale farmers who are the backbone of India’s food security. Historically, losing access to groundwater has decreased agricultural production and increased poverty. We take a multidisciplinary approach to assess climate change challenges facing India’s agricultural system, and to assess the effectiveness of large-scale water infrastructure projects designed to meet these challenges. We find that even in areas that experience climate change induced precipitation increases, expansion of irrigated agriculture will require increasing amounts of unsustainable groundwater. The large proposed national river linking project has limited capacity to alleviate groundwater stress. Thus, without intervention, poverty and food insecurity in rural India is likely to worsen

Modeling kinetic partitioning of secondary organic aerosol and size distribution dynamics: representing effects of volatility, phase state, and particle-phase reaction

This paper describes and evaluates a new framework for modeling kinetic gas-particle partitioning of secondary organic aerosol (SOA) that takes into account diffusion and chemical reaction within the particle phase. The framework uses a combination of (a) an analytical quasi-steady-state treatment for the diffusion–reaction process within the particle phase for fast-reacting organic solutes, and (b) a two-film theory approach for slow- and nonreacting solutes. The framework is amenable for use in regional and global atmospheric models, although it currently awaits specification of the various gas- and particle-phase chemistries and the related physicochemical properties that are important for SOA formation. Here, the new framework is implemented in the computationally efficient Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) to investigate the competitive growth dynamics of the Aitken and accumulation mode particles. Results show that the timescale of SOA partitioning and the associated size distribution dynamics depend on the complex interplay between organic solute volatility, particle-phase bulk diffusivity, and particle-phase reactivity (as exemplified by a pseudo-first-order reaction rate constant), each of which can vary over several orders of magnitude. In general, the timescale of SOA partitioning increases with increase in volatility and decrease in bulk diffusivity and rate constant. At the same time, the shape of the aerosol size distribution displays appreciable narrowing with decrease in volatility and bulk diffusivity and increase in rate constant. A proper representation of these physicochemical processes and parameters is needed in the next generation models to reliably predict not only the total SOA mass, but also its composition- and number-diameter distributions, all of which together determine the overall optical and cloud-nucleating properties

Explicit modeling of organic chemistry and secondary organic aerosol partitioning for Mexico City and its outflow plume

The evolution of organic aerosols (OA) in Mexico City and its outflow is investigated with the nearly explicit gas phase photochemistry model GECKO-A (Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere), wherein precursor hydrocarbons are oxidized to numerous intermediate species for which vapor pressures are computed and used to determine gas/particle partitioning in a chemical box model. Precursor emissions included observed C3-10 alkanes, alkenes, and light aromatics, as well as larger <i>n</i>-alkanes (up to C25) not directly observed but estimated by scaling to particulate emissions according to their volatility. Conditions were selected for comparison with observations made in March 2006 (MILAGRO). The model successfully reproduces the magnitude and diurnal shape for both primary (POA) and secondary (SOA) organic aerosols, with POA peaking in the early morning at 15–20 μg m<sup>−3</sup>, and SOA peaking at 10–15 μg m<sup>−3</sup> during mid-day. The majority (≥75%) of the model SOA stems from reaction products of the large <i>n</i>-alkanes, used here as surrogates for all emitted hydrocarbons of similar volatility, with the remaining SOA originating mostly from the light aromatics. Simulated OA elemental composition reproduces observed H/C and O/C ratios reasonably well, although modeled ratios develop more slowly than observations suggest. SOA chemical composition is initially dominated by δ-hydroxy ketones and nitrates from the large alkanes, with contributions from peroxy acyl nitrates and, at later times when NOx is lower, organic hydroperoxides. The simulated plume-integrated OA mass continues to increase for several days downwind despite dilution-induced particle evaporation, since oxidation chemistry leading to SOA formation remains strong. In this model, the plume SOA burden several days downwind exceeds that leaving the city by a factor of >3. These results suggest significant regional radiative impacts of SOA

Statistical analysis of variability in TnSeq data across conditions using zero-inflated negative binomial regression

BACKGROUND: Deep sequencing of transposon mutant libraries (or TnSeq) is a powerful method for probing essentiality of genomic loci under different environmental conditions. Various analytical methods have been described for identifying conditionally essential genes whose tolerance for insertions varies between two conditions. However, for large-scale experiments involving many conditions, a method is needed for identifying genes that exhibit significant variability in insertions across multiple conditions. RESULTS: In this paper, we introduce a novel statistical method for identifying genes with significant variability of insertion counts across multiple conditions based on Zero-Inflated Negative Binomial (ZINB) regression. Using likelihood ratio tests, we show that the ZINB distribution fits TnSeq data better than either ANOVA or a Negative Binomial (in a generalized linear model). We use ZINB regression to identify genes required for infection of M. tuberculosis H37Rv in C57BL/6 mice. We also use ZINB to perform a analysis of genes conditionally essential in H37Rv cultures exposed to multiple antibiotics. CONCLUSIONS: Our results show that, not only does ZINB generally identify most of the genes found by pairwise resampling (and vastly out-performs ANOVA), but it also identifies additional genes where variability is detectable only when the magnitudes of insertion counts are treated separately from local differences in saturation, as in the ZINB model

Long-range pollution transport during the MILAGRO-2006 campaign: a case study of a major Mexico City outflow event using free-floating altitude-controlled balloons

One of the major objectives of the Megacities Initiative: Local And Global Research Observations (MILAGRO-2006) campaign was to investigate the long-range transport of polluted Mexico City Metropolitan Area (MCMA) outflow and determine its downwind impacts on air quality and climate. Six research aircraft, including the National Center for Atmospheric Research (NCAR) C-130, made extensive chemical, aerosol, and radiation measurements above MCMA and more than 1000 km downwind in order to characterize the evolution of the outflow as it aged and dispersed over the Mesa Alta, Sierra Madre Oriental, Coastal Plain, and Gulf of Mexico. As part of this effort, free-floating Controlled-Meteorological (CMET) balloons, commanded to change altitude via satellite, made repeated profile measurements of winds and state variables within the advecting outflow. In this paper, we present an analysis of the data from two CMET balloons that were launched near Mexico City on the afternoon of 18 March 2006 and floated downwind with the MCMA pollution for nearly 30 h. The repeating profile measurements show the evolving structure of the outflow in considerable detail: its stability and stratification, interaction with other air masses, mixing episodes, and dispersion into the regional background. Air parcel trajectories, computed directly from the balloon wind profiles, show three transport pathways on 18–19 March: (a) high-altitude advection of the top of the MCMA mixed layer, (b) mid-level outflow over the Sierra Madre Oriental followed by decoupling and isolated transport over the Gulf of Mexico, and (c) low-level outflow with entrainment into a cleaner northwesterly jet above the Coastal Plain. The C-130 aircraft intercepted the balloon-based trajectories three times on 19 March, once along each of these pathways; in all three cases, peaks in urban tracer concentrations and LIDAR backscatter are consistent with MCMA pollution. In comparison with the transport models used in the campaign, the balloon-based trajectories appear to shear the outflow far more uniformly and decouple it from the surface, thus forming a thin but expansive polluted layer over the Gulf of Mexico that is well aligned with the aircraft observations. These results provide critical context for the extensive aircraft measurements made during the 18–19 March MCMA outflow event and may have broader implications for modelling and understanding long-range transport

Quarkonium and hydrogen spectra with spin dependent relativistic wave equation

A non-linear non-perturbative relativistic atomic theory introduces spin in the dynamics of particle motion. The resulting energy levels of Hydrogen atom are exactly same as the Dirac theory. The theory accounts for the energy due to spin-orbit interaction and for the additional potential energy due to spin and spin-orbit coupling. Spin angular momentum operator is integrated into the equation of motion. This requires modification to classical Laplacian operator. Consequently the Dirac matrices and the k operator of Dirac's theory are dispensed with. The theory points out that the curvature of the orbit draws on certain amount of kinetic and potential energies affecting the momentum of electron and the spin-orbit interaction energy constitutes a part of this energy. The theory is developed for spin 1/2 bound state single electron in Coulomb potential and then further extended to quarkonium physics by introducing the linear confining potential. The unique feature of this quarkonium model is that the radial distance can be exactly determined and does not have a statistical interpretation. The established radial distance is then used to determine the wave function. The observed energy levels are used as the input parameters and the radial distance and the string tension are predicted. This ensures 100% conformance to all observed energy levels for the heavy quarkonium.Comment: 14 pages, v7: Journal reference adde

WRF-Chem model predictions of the regional impacts of N2O5 heterogeneous processes on night-time chemistry over north-western Europe

Abstract. Chemical modelling studies have been conducted over north-western Europe in summer conditions, showing that night-time dinitrogen pentoxide (N2O5) heterogeneous reactive uptake is important regionally in modulating particulate nitrate and has a~modest influence on oxidative chemistry. Results from Weather Research and Forecasting model with Chemistry (WRF-Chem) model simulations, run with a detailed volatile organic compound (VOC) gas-phase chemistry scheme and the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) sectional aerosol scheme, were compared with a series of airborne gas and particulate measurements made over the UK in July 2010. Modelled mixing ratios of key gas-phase species were reasonably accurate (correlations with measurements of 0.7–0.9 for NO2 and O3). However modelled loadings of particulate species were less accurate (correlation with measurements for particulate sulfate and ammonium were between 0.0 and 0.6). Sulfate mass loadings were particularly low (modelled means of 0.5–0.7 μg kg−1air, compared with measurements of 1.0–1.5 μg kg−1air). Two flights from the campaign were used as test cases – one with low relative humidity (RH) (60–70%), the other with high RH (80–90%). N2O5 heterogeneous chemistry was found to not be important in the low-RH test case; but in the high-RH test case it had a strong effect and significantly improved the agreement between modelled and measured NO3 and N2O5. When the model failed to capture atmospheric RH correctly, the modelled NO3 and N2O5 mixing ratios for these flights differed significantly from the measurements. This demonstrates that, for regional modelling which involves heterogeneous processes, it is essential to capture the ambient temperature and water vapour profiles. The night-time NO3 oxidation of VOCs across the whole region was found to be 100–300 times slower than the daytime OH oxidation of these compounds. The difference in contribution was less for alkenes (× 80) and comparable for dimethylsulfide (DMS). However the suppression of NO3 mixing ratios across the domain by N2O5 heterogeneous chemistry has only a very slight, negative, influence on this oxidative capacity. The influence on regional particulate nitrate mass loadings is stronger. Night-time N2O5 heterogeneous chemistry maintains the production of particulate nitrate within polluted regions: when this process is taken into consideration, the daytime peak (for the 95th percentile) of PM10 nitrate mass loadings remains around 5.6 μg kg−1air, but the night-time minimum increases from 3.5 to 4.6 μg kg−1air. The sustaining of higher particulate mass loadings through the night by this process improves model skill at matching measured aerosol nitrate diurnal cycles and will negatively impact on regional air quality, requiring this process to be included in regional models. This work was supported by the NERC RONOCO project NE/F004656/1. S. Archer-Nicholls was supported by a NERC quota studentship.This is the final version of the article. It first appeared at http://www.atmos-chem-phys.net/15/1385/2015/acp-15-1385-2015.pd