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  • 1
    Online Resource
    Online Resource
    Land-use change may exacerbate climate change impacts on water resources in the Ganges basin
    Copernicus GmbH ; 2017
    In:  Hydrology and Earth System Sciences Discussions
    Details
    In: Hydrology and Earth System Sciences Discussions, Copernicus GmbH
    Abstract: Quantifying how land-use change and climate change affect water resources is a challenge in hydrological science. The Upper Ganges (UG) river basin in northern India experiences monsoon flooding almost every year. Studies have shown evidence of strong coupling between the land surface (soil moisture) and atmosphere (precipitation) in northern India, which means that regional climate variations and changes in land use/cover could influence the temporal dynamics of land-atmosphere interactions. 〈br〉〈br〉 This work aims to quantify how future projections of land-use and climate change are affecting the hydrological response of the UG river basin. Two different sets of modelling experiments were run using the JULES Land Surface Model and covering the period 2000–2035: In the first set, climate change is taken into account, as JULES was driven by the CMIP5 (Coupled Model Intercomparison Project Phase 5) outputs of 21 models, under two Representative Concentration Pathways (RCP4.5 & RCP8.5), whilst land use was kept constant at year 2010. In the second set, both climate change and land-use change were taken into consideration, as apart from the CMIP5 model outputs, JULES was also forced with a time-series of 15 future land-use scenarios, based on Landsat satellite imagery and Markov chain simulation. Variations in hydrological variables (stream flow, evapotranspiration and soil moisture) are calculated during the simulation period. 〈br〉〈br〉 Significant changes in the near-future (years 2030–2035) hydrologic fluxes arise under future land cover and climate change scenarios pointing towards a severe increase in high extremes of flow: the multi-model mean of the 95th percentile of streamflow [Q〈sub〉5〈/sub〉] is projected to increase by 63 % under the combined land-use and climate change high emissions scenario [RCP8.5]. The changes in all examined hydrological components are greater in the combined land-use and climate change experiment. 〈br〉〈br〉 Results are further presented in a water resources context, aiming to address potential implications of climate change from a water-demand perspective, highlighting that that demand thresholds in the UG region are projected to be exceeded in the future winter months (Dec–Feb).
    Type of Medium: Online Resource
    ISSN: 1812-2116
    URL: Article
    URL: Article
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    DOI: 10.5194/hess-2017-468
    DOI: 10.5194/hess-2017-468-RC1
    DOI: 10.5194/hess-2017-468-RC2
    DOI: 10.5194/hess-2017-468-AC3
    DOI: 10.5194/hess-2017-468-AC2
    DOI: 10.5194/hess-2017-468-AC1
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2017
    detail.hit.zdb_id: 2190493-5
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  • 2
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    Interactions between climate change, urban infrastructure and mobility are driving dengue emergence in Vietnam
    Springer Science and Business Media LLC ; 2023
    In:  Nature Communications Vol. 14, No. 1 ( 2023-12-11)
    Details
    In: Nature Communications, Springer Science and Business Media LLC, Vol. 14, No. 1 ( 2023-12-11)
    Abstract: Dengue is expanding globally, but how dengue emergence is shaped locally by interactions between climatic and socio-environmental factors is not well understood. Here, we investigate the drivers of dengue incidence and emergence in Vietnam, through analysing 23 years of district-level case data spanning a period of significant socioeconomic change (1998-2020). We show that urban infrastructure factors (sanitation, water supply, long-term urban growth) predict local spatial patterns of dengue incidence, while human mobility is a more influential driver in subtropical northern regions than the endemic south. Temperature is the dominant factor shaping dengue’s distribution and dynamics, and using long-term reanalysis temperature data we show that warming since 1950 has expanded transmission risk throughout Vietnam, and most strongly in current dengue emergence hotspots (e.g., southern central regions, Ha Noi). In contrast, effects of hydrometeorology are complex, multi-scalar and dependent on local context: risk increases under either short-term precipitation excess or long-term drought, but improvements in water supply mitigate drought-associated risks except under extreme conditions. Our findings challenge the assumption that dengue is an urban disease, instead suggesting that incidence peaks in transitional landscapes with intermediate infrastructure provision, and provide evidence that interactions between recent climate change and mobility are contributing to dengue’s expansion throughout Vietnam.
    Type of Medium: Online Resource
    ISSN: 2041-1723
    URL: Article
    DOI: 10.1038/s41467-023-43954-0
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2553671-0
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  • 3
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    Relaxation of anti-COVID-19 measures reveals new challenges for infectious disease outbreak forecasting
    Elsevier BV ; 2023
    In:  The Lancet Infectious Diseases Vol. 23, No. 2 ( 2023-02), p. 144-146
    Details
    In: The Lancet Infectious Diseases, Elsevier BV, Vol. 23, No. 2 ( 2023-02), p. 144-146
    Type of Medium: Online Resource
    ISSN: 1473-3099
    URL: Article
    DOI: 10.1016/S1473-3099(23)00003-8
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2061641-7
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  • 4
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    The Effects of Meteorological Factors on Dengue Cases in Malaysia
    MDPI AG ; 2022
    In:  International Journal of Environmental Research and Public Health Vol. 19, No. 11 ( 2022-05-26), p. 6449-
    Details
    In: International Journal of Environmental Research and Public Health, MDPI AG, Vol. 19, No. 11 ( 2022-05-26), p. 6449-
    Abstract: Dengue is a vector-borne disease affected by meteorological factors and is commonly recorded from ground stations. Data from ground station have limited spatial representation and accuracy, which can be overcome using satellite-based Earth Observation (EO) recordings instead. EO-based meteorological recordings can help to provide a better understanding of the correlations between meteorological variables and dengue cases. This paper aimed to first validate the satellite-based (EO) data of temperature, wind speed, and rainfall using ground station data. Subsequently, we aimed to determine if the spatially matched EO data correlated with dengue fever cases from 2011 to 2019 in Malaysia. EO data were spatially matched with the data from four ground stations located at states and districts in the central (Selangor, Petaling) and east coast (Kelantan, Kota Baharu) geographical regions of Peninsular Malaysia. Spearman’s rank-order correlation coefficient (ρ) was performed to examine the correlation between EO and ground station data. A cross-correlation analysis with an eight-week lag period was performed to examine the magnitude of correlation between EO data and dengue case across the three time periods (2011–2019, 2015–2019, 2011–2014). The highest correlation between the ground-based stations and corresponding EO data were reported for temperature (mean ρ = 0.779), followed by rainfall (mean ρ = 0.687) and wind speed (mean ρ = 0.639). Overall, positive correlations were observed between weekly dengue cases and rainfall for Selangor and Petaling across all time periods with significant correlations being observed for the period from 2011 to 2019 and 2015 to 2019. In addition, positive significant correlations were also observed between weekly dengue cases and temperature for Kelantan and Kota Baharu across all time periods, while negative significant correlations between weekly dengue cases and temperature were observed in Selangor and Petaling across all time periods. Overall negative correlations were observed between weekly dengue cases and wind speed in all areas from 2011 to 2019 and 2015 to 2019, with significant correlations being observed for the period from 2015 to 2019. EO-derived meteorological variables explained 48.2% of the variation in dengue cases in Selangor. Moderate to strong correlations were observed between meteorological variables recorded from EO data derived from satellites and ground stations, thereby justifying the use of EO data as a viable alternative to ground stations for recording meteorological variables. Both rainfall and temperature were found to be positively correlated with weekly dengue cases; however, wind speed was negatively correlated with dengue cases.
    Type of Medium: Online Resource
    ISSN: 1660-4601
    URL: Article
    DOI: 10.3390/ijerph19116449
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2175195-X
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  • 5
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    A Review of Dengue’s Historical and Future Health Risk from a Changing Climate
    Springer Science and Business Media LLC ; 2021
    In:  Current Environmental Health Reports Vol. 8, No. 3 ( 2021-09), p. 245-265
    Details
    In: Current Environmental Health Reports, Springer Science and Business Media LLC, Vol. 8, No. 3 ( 2021-09), p. 245-265
    Abstract: The purpose of this review is to summarize research articles that provide risk estimates for the historical and future impact that climate change has had upon dengue published from 2007 through 2019. Recent findings Findings from 30 studies on historical health estimates, with the majority of the studies conducted in Asia, emphasized the importance of temperature, precipitation, and relative humidity, as well as lag effects, when trying to understand how climate change can impact the risk of contracting dengue. Furthermore, 35 studies presented findings on future health risk based upon climate projection scenarios, with a third of them showcasing global level estimates and findings across the articles emphasizing the need to understand risk at a localized level as the impacts from climate change will be experienced inequitably across different geographies in the future. Summary Dengue is one of the most rapidly spreading viral diseases in the world, with ~390 million people infected worldwide annually. Several factors have contributed towards its proliferation, including climate change. Multiple studies have previously been conducted examining the relationship between dengue and climate change, both from a historical and a future risk perspective. We searched the U.S. National Institute of Environmental Health (NIEHS) Climate Change and Health Portal for literature (spanning January 2007 to September 2019) providing historical and future health risk estimates of contracting dengue infection in relation to climate variables worldwide. With an overview of the evidence of the historical and future health risk posed by dengue from climate change across different regions of the world, this review article enables the research and policy community to understand where the knowledge gaps are and what areas need to be addressed in order to implement localized adaptation measures to mitigate the health risks posed by future dengue infection.
    Type of Medium: Online Resource
    ISSN: 2196-5412
    URL: Article
    DOI: 10.1007/s40572-021-00322-8
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2760287-4
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  • 6
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    Probabilistic seasonal dengue forecasting in Vietnam: A modelling study using superensembles
    Public Library of Science (PLoS) ; 2021
    In:  PLOS Medicine Vol. 18, No. 3 ( 2021-3-4), p. e1003542-
    Details
    In: PLOS Medicine, Public Library of Science (PLoS), Vol. 18, No. 3 ( 2021-3-4), p. e1003542-
    Abstract: With enough advanced notice, dengue outbreaks can be mitigated. As a climate-sensitive disease, environmental conditions and past patterns of dengue can be used to make predictions about future outbreak risk. These predictions improve public health planning and decision-making to ultimately reduce the burden of disease. Past approaches to dengue forecasting have used seasonal climate forecasts, but the predictive ability of a system using different lead times in a year-round prediction system has been seldom explored. Moreover, the transition from theoretical to operational systems integrated with disease control activities is rare. Methods and findings We introduce an operational seasonal dengue forecasting system for Vietnam where Earth observations, seasonal climate forecasts, and lagged dengue cases are used to drive a superensemble of probabilistic dengue models to predict dengue risk up to 6 months ahead. Bayesian spatiotemporal models were fit to 19 years (2002–2020) of dengue data at the province level across Vietnam. A superensemble of these models then makes probabilistic predictions of dengue incidence at various future time points aligned with key Vietnamese decision and planning deadlines. We demonstrate that the superensemble generates more accurate predictions of dengue incidence than the individual models it incorporates across a suite of time horizons and transmission settings. Using historical data, the superensemble made slightly more accurate predictions (continuous rank probability score [CRPS] = 66.8, 95% CI 60.6–148.0) than a baseline model which forecasts the same incidence rate every month (CRPS = 79.4, 95% CI 78.5–80.5) at lead times of 1 to 3 months, albeit with larger uncertainty. The outbreak detection capability of the superensemble was considerably larger (69%) than that of the baseline model (54.5%). Predictions were most accurate in southern Vietnam, an area that experiences semi-regular seasonal dengue transmission. The system also demonstrated added value across multiple areas compared to previous practice of not using a forecast. We use the system to make a prospective prediction for dengue incidence in Vietnam for the period May to October 2020. Prospective predictions made with the superensemble were slightly more accurate (CRPS = 110, 95% CI 102–575) than those made with the baseline model (CRPS = 125, 95% CI 120–168) but had larger uncertainty. Finally, we propose a framework for the evaluation of probabilistic predictions. Despite the demonstrated value of our forecasting system, the approach is limited by the consistency of the dengue case data, as well as the lack of publicly available, continuous, and long-term data sets on mosquito control efforts a nd serotype-specific case data. Conclusions This study shows that by combining detailed Earth observation data, seasonal climate forecasts, and state-of-the-art models, dengue outbreaks can be predicted across a broad range of settings, with enough lead time to meaningfully inform dengue control. While our system omits some important variables not currently available at a subnational scale, the majority of past outbreaks could be predicted up to 3 months ahead. Over the next 2 years, the system will be prospectively evaluated and, if successful, potentially extended to other areas and other climate-sensitive disease systems.
    Type of Medium: Online Resource
    ISSN: 1549-1676
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    DOI: 10.1371/journal.pmed.1003542
    DOI: 10.1371/journal.pmed.1003542.g001
    DOI: 10.1371/journal.pmed.1003542.g002
    DOI: 10.1371/journal.pmed.1003542.g003
    DOI: 10.1371/journal.pmed.1003542.g004
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    DOI: 10.1371/journal.pmed.1003542.g009
    DOI: 10.1371/journal.pmed.1003542.t001
    DOI: 10.1371/journal.pmed.1003542.t002
    DOI: 10.1371/journal.pmed.1003542.s001
    DOI: 10.1371/journal.pmed.1003542.s002
    DOI: 10.1371/journal.pmed.1003542.s003
    DOI: 10.1371/journal.pmed.1003542.s004
    DOI: 10.1371/journal.pmed.1003542.s005
    DOI: 10.1371/journal.pmed.1003542.s006
    DOI: 10.1371/journal.pmed.1003542.s007
    DOI: 10.1371/journal.pmed.1003542.s008
    DOI: 10.1371/journal.pmed.1003542.s009
    DOI: 10.1371/journal.pmed.1003542.s010
    DOI: 10.1371/journal.pmed.1003542.r001
    DOI: 10.1371/journal.pmed.1003542.r002
    DOI: 10.1371/journal.pmed.1003542.r003
    DOI: 10.1371/journal.pmed.1003542.r004
    DOI: 10.1371/journal.pmed.1003542.r005
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    DOI: 10.1371/journal.pmed.1003542.r007
    DOI: 10.1371/journal.pmed.1003542.r008
    Language: English
    Publisher: Public Library of Science (PLoS)
    Publication Date: 2021
    detail.hit.zdb_id: 2164823-2
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  • 7
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    Land-use change may exacerbate climate change impacts on water resources in the Ganges basin
    Copernicus GmbH ; 2018
    In:  Hydrology and Earth System Sciences Vol. 22, No. 2 ( 2018-02-27), p. 1411-1435
    Details
    In: Hydrology and Earth System Sciences, Copernicus GmbH, Vol. 22, No. 2 ( 2018-02-27), p. 1411-1435
    Abstract: Abstract. Quantifying how land-use change and climate change affect water resources is a challenge in hydrological science. This work aims to quantify how future projections of land-use and climate change might affect the hydrological response of the Upper Ganges river basin in northern India, which experiences monsoon flooding almost every year. Three different sets of modelling experiments were run using the Joint UK Land Environment Simulator (JULES) land surface model (LSM) and covering the period 2000–2035: in the first set, only climate change is taken into account, and JULES was driven by the CMIP5 (Coupled Model Intercomparison Project Phase 5) outputs of 21 models, under two representative concentration pathways (RCP4.5 and RCP8.5), whilst land use was held fixed at the year 2010. In the second set, only land-use change is taken into account, and JULES was driven by a time series of 15 future land-use pathways, based on Landsat satellite imagery and the Markov chain simulation, whilst the meteorological boundary conditions were held fixed at years 2000–2005. In the third set, both climate change and land-use change were taken into consideration, as the CMIP5 model outputs were used in conjunction with the 15 future land-use pathways to force JULES. Variations in hydrological variables (stream flow, evapotranspiration and soil moisture) are calculated during the simulation period. Significant changes in the near-future (years 2030–2035) hydrologic fluxes arise under future land-cover and climate change scenarios pointing towards a severe increase in high extremes of flow: the multi-model mean of the 95th percentile of streamflow (Q5) is projected to increase by 63 % under the combined land-use and climate change high emissions scenario (RCP8.5). The changes in all examined hydrological components are greater in the combined land-use and climate change experiment. Results are further presented in a water resources context, aiming to address potential implications of climate change and land-use change from a water demand perspective. We conclude that future water demands in the Upper Ganges region for winter months may not be met.
    Type of Medium: Online Resource
    ISSN: 1607-7938
    URL: Article
    DOI: 10.5194/hess-22-1411-2018
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2018
    detail.hit.zdb_id: 2100610-6
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