Research outputs
13364
Projects
6
People
268
Recent Additions
- PublicationImplications of 1.5 and 2.0 ºC additional warming for wheat yield using a gridded modeling approach(2020)The goal of limiting the increasing global mean temperature below 2.0 and possibly 1.5 ºC, was decided in the Paris Agreement of 2015. It is therefore important to understand the climate risk and impacts associated with 1.5 and 2.0 ºC additional warming scenarios. The current study investigates the impacts of 1.5 and 2.0 ºC additional warming on wheat yield in Pakistan using a gridded modeling approach. The generated climate data by four GCMs under 1.5 and 2.0 ºC were acquired from the Half a Degree Additional Warming, Prognosis and Projected Impacts (HAPPI) scenarios group. The CERES-Wheat model was calibrated and evaluated using field data and then applied to the entire region of Pakistan. Model calibration results showed a close association between observed and simulated wheat yield with an error ranging from 0.52 to 1.36%. Climate change projections indicated that the mean temperature is expected to rise by 0.46 and 1.44 ºC in the 1.5 and 2.0 ºC additional warming scenarios in the GCMs, respectively. The spatial variations of precipitation range from –22.4 to 42.6% and 4.6 to 34.1% under the 1.5 and 2.0 ºC HAPPI scenarios, respectively. Higher precipitation was recorded in northern Pakistan as compared to central and southern Pakistan. The projected changes in temperature and precipitation will decrease the wheat yield by 3.2 and 4.7% in Punjab, 17.8% and 13.8% in Sindh province under 1.5 and 2.0 ºC additional warming, respectively. However, the wheat yield will increase by 4.7 and 13% in Khyber Pakhtunkhwa and 9.4 and 15.3% in Baluchistan under 1.5 and 2.0 ºC additional warming, respectively.
- PublicationInvestigating the performance of SWAT and IHACRES in simulation streamflow under different climatic regions in Iran(2020)It is often reported that simpler models, due to their low parameter requirements, perform better than more complex models. To test this, the current study compared a simple rainfall-runoff model (IHACRES) with a complex watershed model (SWAT). Based on these two approaches, six models were developed for three climatically distinct (arid, semi-arid and semi-humid) watersheds in Iran. The coefficient of determination (R2) and the Nash-Sutcliffe model efficiency coefficient (NS) were calculated in each case. In arid, semi-arid, and semi-humid watersheds the SWAT model (R2 = 0.52, 0.68, 0.66; NS = 0.54, 0.63, 0.64, respectively) outperformed the IHACRES model (R2 = 0.37, 0.70, 0.57; NS = 0.22, 0.57, 0.56, respectively) for the same respective climate zones. Overall, SWAT performed better than IHACRES, although both models had acceptable performances in the semi-arid and semi-humid watersheds. In the arid watershed, the IHACRES model performed poorly compared to SWAT.
- Publicationgate.io app(2020)Photosynthetically active radiation (PAR) is important in applications related to plant physiology or the carbon cycle. However, despite its importance, a global network for its measurement has not yet been established. This work consists of the revision of a series of works related to the development of empirical models for the estimation of PAR in places where it is not regularly measured, using for this purpose measurements of meteorological and radiation parameters available in weather stations. A list of the models developed, the study site, the results obtained, and the nomenclature used in each of them is made. The most common way to develop empirical estimation models is by studying spatio-temporal changes in the relationship between PAR and global solar radiation. Other estimation methods include the use of satellite-derived products such as MODIS-derived products and the use of artificial neural networks. Despite being more efficient for estimating PAR, the use of artificial neural networks is not as widespread because its use is more complex than the development of empirical models. The PAR to global solar radiation ratio reached its maximum in the summer months and the minimum in the winter months; in addition, the daily values per hour reached their maximum at sunrise and sunset, and their minimum around noon.
- PublicationIs the energy balance in a tropical lowland rice paddy perfectly closed?(2020)A two-year (2015 and 2016) field experiment was carried out to study the surface energy budget and energy balance closure (EBC) in a tropical lowland rice paddy in Cuttack, India. Maintenance of a standing water layer in lowland irrigated rice ecosystem makes it unique and this strongly influences the surface energy balance which may alter the surface runoff, ground water storage, water cycle, surface energy budget, and possibly microclimate of the region. To study this, an experiment was conducted using eddy covariance system to measure the surface energy balance components during two cropping seasons (dry season, DS and wet season, WS) and two consecutive fallow periods (dry fallow, DF and wet fallow, WF). The rice was grown in puddled wet lands in DS and WS and the ground was left fallow (DF and WF) during the rest of the year. Results displayed that daily average latent heat flux at surface (LE) and at canopy height (LEc) dominated over sensible heat flux at surface (H) and canopy height (Hc), respectively due to the presence of water source coming from the standing water in the rice field. The EBC was evaluated by ordinary least square (OLS), energy balance ratio (EBR) and residual heat flux (RHF). In OLS, the slope ranged 0.38-0.89 (2015) and 0.28-0.99 (2016) during the study period. Average RHF was 10.3-12.0% higher in WS as compared to DS. It was concluded that the EBC estimated using RHF is the most suitable way to calculate closure for lowland rice paddy since it can distinguish different seasons distinctively, followed by OLS. Much variation was not observed in EBR after inclusion of storage terms (water, soil, photosynthesis, canopy) to the classical EBR.
- Publicationgate io app(2020)This study examines the impact of fossil fuels consumption, renewable energy use and industrial growth on carbon emissions in the developing economies of Latin America and the Caribbean. An industrial growth index is developed using competitive industrial indicators, and a two-step system generalized method of moments robust estimator is employed, involving a panel of 16 middle- and lower-middle-income economies for the period 1990 to 2015. The empirical results show an Inverted-U shaped relationship between economic growth and carbon emissions and confirm the existence of the environmental Kuznets curve for the region. The results indicate that industrial growth and consumption of fossil fuels are significantly contributing to carbon emissions in the region. The results highlight that, based on competitiveness in manufacturing and the transition from simple to sophisticated technologies, advance technology-based industrial growth increases the potential to produce goods competitively with lower carbon emissions. The findings suggest that such advanced industrial growth is unavoidable to attain sustainable economic growth. Thus, technological advancement and consumption of renewable energies have the potential to both meet the rising demand for goods and energy and to control carbon emissions in the developing countries of Latin America and the Caribbean.
Most viewed
- PersonSkiba, Yuri NickolaevichInvestigador Titular C
- PersonGarduño López, Manuel René de JesúsInvestigador Titular A
- PersonGay García, CarlosInvestigador Titular C
- PersonPeralta Rosales, Oscar AugustoInvestigador Titular A
- PersonRosas Pérez, IrmaInvestigador Titular C