ORCID Profile
0000-0003-3412-184X
Current Organisation
Australian Bureau of Meteorology
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Publisher: Inderscience Publishers
Date: 2012
Publisher: Elsevier BV
Date: 04-2018
DOI: 10.1016/J.ECOENV.2017.12.032
Abstract: Arsenic contamination of drinking water, which can occur naturally or because of human activities such as mining, is the single most important public health issue in Bangladesh. Fifty out of the 64 districts in the country have arsenic concentration of groundwater exceeding 50µgL
Publisher: MDPI AG
Date: 10-01-2023
DOI: 10.3390/HYDROLOGY10010018
Abstract: Reducing uncertainty in design flood estimates is an essential part of flood risk planning and management. This study presents results from flood frequency estimates and associated uncertainties for five commonly used probability distribution functions, extreme value type 1 (EV1), generalized extreme value (GEV), generalized pareto distribution (GPD), log normal (LN) and log Pearson type 3 (LP3). The study was conducted using Monte Carlo simulation (MCS) and bootstrapping (BS) methods for the 10 river catchments in eastern Australia. The parameters were estimated by applying the method of moments (for LP3, LN, and EV1) and L-moments (for GEV and GPD). Three-parameter distributions (e.g., LP3, GEV, and GPD) demonstrate a consistent estimation of confidence interval (CI), whereas two-parameter distributions show biased estimation. The results of this study also highlight the difficulty in flood frequency analysis, e.g., different probability distributions perform quite differently even in a smaller geographical area.
Publisher: American Meteorological Society
Date: 09-05-2014
DOI: 10.1175/JCLI-D-13-00486.1
Abstract: With the availability of hindcasts or real-time forecasts from a number of coupled climate models, multimodel ensemble forecasting systems have gained popularity in recent years. However, many models share similar physics or modeling processes, which may lead to similar (or strongly correlated) forecasts. Assigning equal weights to each model in space and time may result in a biased forecast with narrower confidence limits than is appropriate. Although methods for combining forecasts that take into consideration differences in model accuracy over space and time exist, they suffer from a lack of consideration of the intermodel dependence that may exist. This study proposes an approach that considers the dependence among models while combining multimodel ensemble forecast. The approach is evaluated by combining sea surface temperature (SST) forecasts from five climate models for the period 1960–2005. The variable of interest, the monthly global sea surface temperature anomalies (SSTA) at a 5° × 5° latitude–longitude grid, is predicted three months in advance using the proposed algorithm. Results indicate that the proposed approach offers consistent and significant improvements for all the seasons over the majority of grid points compared to the case in which the dependence among the models is ignored. Consequently, the proposed approach of combining multiple models, taking into account the interdependence that exists, provides an attractive strategy to develop improved SST forecasts.
Publisher: Springer Science and Business Media LLC
Date: 24-05-2021
Publisher: MDPI AG
Date: 05-01-2021
DOI: 10.3390/AGRICULTURE11010033
Abstract: Food security is considered as the most important global challenge. Therefore, identifying long-term drivers of food security and their connections is essential to steer policymakers determining policies for future food security and sustainable development. Given the complexity and uncertainty of multidimensional food security, quantifying the extent of uncertainty is vital. In this study, we investigated the uncertainty of a coupled hydrologic food security model to examine the impacts of climatic warming on food production (rice, cereal and wheat) in a mild temperature study site in China. In addition to varying temperature, our study also investigated the impacts of three CO2 emission scenarios—the Representative Concentration Pathway, RCP 4.5, RCP 6.0, RCP 8.5—on food production. Our ultimate objective was to quantify the uncertainty in a coupled hydrologic food security model and report the sources and timing of uncertainty under a warming climate using a coupled hydrologic food security model tested against observed food production years. Our study shows an overall increasing trend in rice, cereal and wheat production under a warming climate. Crop yield data from China are used to demonstrate the extent of uncertainty in food security modeling. An innovative and systemic approach is developed to quantify the uncertainty in food security modeling. Crop yield variability with the rising trend of temperature also demonstrates a new insight in quantifying uncertainty in food security modeling.
Publisher: American Geophysical Union (AGU)
Date: 02-05-2017
DOI: 10.1002/2016JD025953
Publisher: Wiley
Date: 23-03-2018
DOI: 10.1002/JOC.5494
Publisher: Elsevier BV
Date: 02-2020
Publisher: Elsevier BV
Date: 04-2021
Publisher: American Geophysical Union (AGU)
Date: 05-2015
DOI: 10.1002/2014WR015997
Publisher: Elsevier BV
Date: 07-2023
No related grants have been discovered for Mohammad Zaved Kaiser Khan.