ORCID Profile
0000-0002-1848-5080
Current Organisations
The University of Texas at Austin
,
North Carolina State University
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Publisher: Elsevier BV
Date: 11-2019
DOI: 10.1016/J.SCITOTENV.2019.07.342
Abstract: In the first two decades of the 21st century, 79 global big cities have suffered extensively from drought disaster. Meanwhile, climate change has magnified urban drought in both frequency and severity, putting tremendous pressure on a city's water supply. Therefore, tackling the challenges of urban drought is an integral part of achieving the targets set in at least 5 different Sustainable Development Goals (SDGs). Yet, the current literatures on drought have not placed sufficient emphasis on urban drought challenge in achieving the United Nations' 2030 Agenda for Sustainable Development. This review is intended to fill this knowledge gap by identifying the key concepts behind urban drought, including the definition, occurrence, characteristics, formation, and impacts. Then, four sub-categories of urban drought are proposed, including precipitation-induced, runoff-induced, pollution-induced, and demand-induced urban droughts. These sub-categories can support city stakeholders in taking drought mitigation actions and advancing the following SDGs: SDG 6 "Clean water and sanitation", SDG 11 "Sustainable cities and communities", SDG 12 "Responsible production and consumption", SDG 13 "Climate actions", and SDG 15 "Life on land". To further support cities in taking concrete actions in reaching the listed SDGs, this perspective proposes five actions that city stakeholders can undertake in enhancing drought resilience and preparedness:1) Raising public awareness on water right and water saving 2) Fostering flexible reliable, and integrated urban water supply 3) Improving efficiency of urban water management 4) Investing in sustainability science research for urban drought and 5) Strengthening resilience efforts via international cooperation. In short, this review contains a wealth of insights on urban drought and highlights the intrinsic connections between drought resilience and the 2030 SDGs. It also proposes five action steps for policymakers and city stakeholders that would support them in taking the first step to combat and mitigate the impacts of urban droughts.
Publisher: American Meteorological Society
Date: 2010
Publisher: Copernicus GmbH
Date: 08-09-2023
Publisher: Springer Science and Business Media LLC
Date: 12-06-2018
Publisher: American Geophysical Union (AGU)
Date: 10-2019
DOI: 10.1029/2019GB006276
Publisher: Springer Science and Business Media LLC
Date: 20-06-2023
Publisher: Wiley
Date: 25-02-2019
DOI: 10.1111/GCB.14537
Publisher: Springer Science and Business Media LLC
Date: 06-06-2022
Publisher: Springer Science and Business Media LLC
Date: 23-08-2017
Publisher: IEEE
Date: 06-2022
Publisher: Wiley
Date: 09-2003
DOI: 10.2134/JEQ2003.1820
Abstract: New Zealand's freshwater ecosystems are subject to microbial contamination from a predominantly agricultural landscape. This study examines the spatial and temporal distribution of the human pathogen C ylobacter in the lower Taieri River, South Island (New Zealand). Enumeration of thermophilic C ylobacter from river s les was performed using a most probable number (MPN) method. Seasonal variation in C ylobacter levels was evident, with higher median levels detected in summer, when human exposure through recreational water use is maximal. C ylobacter levels varied significantly among the 10 s ling sites, increasing below a major tributary entering the river and then showing a downstream decrease. These changes probably resulted from inputs from adjacent farms and instream C ylobacter losses (settling, death). Two main peaks in the flux of C ylobacter were observed, one in winter and one in summer. A decrease in notified cases of c ylobacteriosis in the human population was observed when levels of C ylobacter at the main recreational bathing site on the river were low. Continuing land use change and intensification in New Zealand may lead to further increases in microbial contamination of freshwaters, and an associated increase in waterborne enteric diseases such as c ylobacteriosis.
Publisher: American Meteorological Society
Date: 05-2008
Abstract: This study investigates the impact of the Flux-Adjusting Surface Data Assimilation System (FASDAS) and the four-dimensional data assimilation (FDDA) using analysis nudging on the simulation of a monsoon depression that formed over India during the 1999 Bay of Bengal Monsoon Experiment (BOBMEX) field c aign. FASDAS allows for the indirect assimilation/adjustment of soil moisture and soil temperature together with continuous direct surface data assimilation of surface temperature and surface humidity. Two additional numerical experiments [control (CTRL) and FDDA] were conducted to assess the relative improvements to the simulation by FASDAS. To improve the initial analysis for the FDDA and the surface data assimilation (SDA) runs, the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) simulation utilized the humidity and temperature profiles from the NOAA Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS), surface winds from the Quick Scatterometer (QuikSCAT), and the conventional meteorological upper-air (radiosonde/rawinsonde, pilot balloon) and surface data. The results from the three simulations are compared with each other as well as with NCEP–NCAR reanalysis, the Tropical Rainfall Measuring Mission (TRMM), and the special buoy, ship, and radiosonde observations available during BOBMEX. As compared with the CTRL, the FASDAS and the FDDA runs resulted in (i) a relatively better-developed cyclonic circulation and (ii) a larger spatial area as well as increased rainfall amounts over the coastal regions after landfall. The FASDAS run showed a consistently improved model simulation performance in terms of reduced rms errors of surface humidity and surface temperature as compared with the CTRL and the FDDA runs.
Publisher: Research Square Platform LLC
Date: 19-09-2023
Publisher: Wiley
Date: 21-06-2013
DOI: 10.1002/JOC.3736
Publisher: American Geophysical Union (AGU)
Date: 24-06-2011
DOI: 10.1029/2010JD015140
Publisher: CSIRO Publishing
Date: 2008
DOI: 10.1071/MF08146
Abstract: Discharges from historic and current coal mines frequently generate waters low in pH ( ), high in heavy metals (e.g. Fe, Al) and cover streambeds in metal precipitates. The present study investigated periphyton communities at 52 stream sites on the West Coast, South Island, New Zealand, representing a range of impacts from acid mine drainage (AMD). Taxonomic richness was negatively related to acidity and metal oxides and biomass was negatively correlated with metal oxides, but positively related to acidity. Streams with low pH ( .5) had low periphyton richness (14 taxa across all sites) and were dominated by Klebsormidium acidophilum, Navicula cincta and Euglena mutabilis. As pH increased, so did taxonomic richness while community dominance decreased and community composition became more variable. Canonical correspondence analyses of algal assemblages revealed patterns influenced by pH. These findings indicate that streams affected by AMD possess a predictable assemblage composition of algal species that can tolerate the extreme water chemistry and substrate conditions. The predictability of algal communities declines with decreasing stress, as other abiotic and biotic factors become increasingly more important.
Publisher: Elsevier BV
Date: 02-2014
Publisher: American Geophysical Union (AGU)
Date: 24-06-2011
DOI: 10.1029/2010JD015139
Publisher: Cambridge University Press
Date: 10-02-2011
Publisher: Elsevier BV
Date: 05-2009
Publisher: Springer Science and Business Media LLC
Date: 18-10-2023
Publisher: Wiley
Date: 28-10-2011
DOI: 10.1002/WCC.144
Abstract: This article summarizes the changes in landscape structure because of human land management over the last several centuries, and using observed and modeled data, documents how these changes have altered biogeophysical and biogeochemical surface fluxes on the local, mesoscale, and regional scales. Remaining research issues are presented including whether these landscape changes alter large‐scale atmospheric circulation patterns far from where the land use and land cover changes occur. We conclude that existing climate assessments have not yet adequately factored in this climate forcing. For those regions that have undergone intensive human landscape change, or would undergo intensive change in the future, we conclude that the failure to factor in this forcing risks a misalignment of investment in climate mitigation and adaptation. WIREs Clim Change 2011, 2:828–850. doi: 10.1002/wcc.144 This article is categorized under: Paleoclimates and Current Trends Climate Forcing
Publisher: Springer Science and Business Media LLC
Date: 07-07-2022
Publisher: American Geophysical Union (AGU)
Date: 31-10-2023
DOI: 10.1029/2023JD038813
Publisher: Springer Science and Business Media LLC
Date: 21-05-2018
Publisher: Springer Science and Business Media LLC
Date: 10-2009
Publisher: Springer Science and Business Media LLC
Date: 02-03-2018
DOI: 10.1038/S41598-018-22322-9
Abstract: While satellite data provides a strong robust signature of urban feedback on extreme precipitation urbanization signal is often not so prominent with station level data. To investigate this, we select the case study of Mumbai, India and perform a high resolution (1 km) numerical study with Weather Research and Forecasting (WRF) model for eight extreme rainfall days during 2014–2015. The WRF model is coupled with two different urban schemes, the Single Layer Urban Canopy Model (WRF-SUCM), Multi-Layer Urban Canopy Model (WRF-MUCM). The differences between the WRF-MUCM and WRF-SUCM indicate the importance of the structure and characteristics of urban canopy on modifications in precipitation. The WRF-MUCM simulations resemble the observed distributed rainfall. WRF-MUCM also produces intensified rainfall as compared to the WRF-SUCM and WRF-NoUCM (without UCM). The intensification in rainfall is however prominent at few pockets of urban regions, that is seen in increased spatial variability. We find that the correlation of precipitation across stations within the city falls below statistical significance at a distance greater than 10 km. Urban signature on extreme precipitation will be reflected on station rainfall only when the stations are located inside the urban pockets having intensified precipitation, which needs to be considered in future analysis.
Publisher: Springer Science and Business Media LLC
Date: 18-06-2022
DOI: 10.1007/S43762-022-00046-X
Abstract: The Local Climate Zone (LCZ) classification is already widely used in urban heat island and other climate studies. The current classification method does not incorporate crucial urban auxiliary GIS data on building height and imperviousness that could significantly improve urban-type LCZ classification utility as well as accuracy. This study utilized a hybrid GIS- and remote sensing imagery-based framework to systematically compare and evaluate different machine and deep learning methods. The Convolution Neural Network (CNN) classifier outperforms in terms of accuracy, but it requires multi-pixel input, which reduces the output’s spatial resolution and creates a tradeoff between accuracy and spatial resolution. The Random Forest (RF) classifier performs best among the single-pixel classifiers. This study also shows that incorporating building height dataset improves the accuracy of the high- and mid-rise classes in the RF classifiers, whereas an imperviousness dataset improves the low-rise classes. The single-pass forward permutation test reveals that both auxiliary datasets dominate the classification accuracy in the RF classifier, while near-infrared and thermal infrared are the dominating features in the CNN classifier. These findings show that the conventional LCZ classification framework used in the World Urban Database and Access Portal Tools (WUDAPT) can be improved by adopting building height and imperviousness information. This framework can be easily applied to different cities to generate LCZ maps for urban models.
No related grants have been discovered for Dev Niyogi.