scholarly journals Comparison of the spatial patterns of schistosomiasis in Zimbabwe at two points in time, spaced twenty-nine years apart: is climate variability of importance?

2017 ◽  
Vol 12 (1) ◽  
Author(s):  
Ulrik B. Pedersen ◽  
Dimitrios-Alexios Karagiannis-Voules ◽  
Nicholas Midzi ◽  
Tkafira Mduluza ◽  
Samson Mukaratirwa ◽  
...  
Keyword(s):  
Climate Change ◽  
Geographical Distribution ◽  
Predictive Accuracy ◽  
Weather Data ◽  
Geostatistical Model ◽  
Prevalence Data ◽  
Time Period ◽  
Long Time ◽  
Potential Risk Factors ◽  
The Impact

Temperature, precipitation and humidity are known to be important factors for the development of schistosome parasites as well as their intermediate snail hosts. Climate therefore plays an important role in determining the geographical distribution of schistosomiasis and it is expected that climate change will alter distribution and transmission patterns. Reliable predictions of distribution changes and likely transmission scenarios are key to efficient schistosomiasis intervention-planning. However, it is often difficult to assess the direction and magnitude of the impact on schistosomiasis induced by climate change, as well as the temporal transferability and predictive accuracy of the models, as prevalence data is often only available from one point in time. We evaluated potential climate-induced changes on the geographical distribution of schistosomiasis in Zimbabwe using prevalence data from two points in time, 29 years apart; to our knowledge, this is the first study investigating this over such a long time period. We applied historical weather data and matched prevalence data of two schistosome species (<em>Schistosoma haematobium</em> and <em>S. mansoni</em>). For each time period studied, a Bayesian geostatistical model was fitted to a range of climatic, environmental and other potential risk factors to identify significant predictors that could help us to obtain spatially explicit schistosomiasis risk estimates for Zimbabwe. The observed general downward trend in schistosomiasis prevalence for Zimbabwe from 1981 and the period preceding a survey and control campaign in 2010 parallels a shift towards a drier and warmer climate. However, a statistically significant relationship between climate change and the change in prevalence could not be established.

scholarly journals Integrated assessment of impact of water resources of important river basins in Eastern India under projected climate conditions

2015 ◽  
Vol 17 (3) ◽  
pp. 594-606 ◽  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Catchment Area ◽  
River Basins ◽  
Weather Data ◽  
Distributed Hydrological Model ◽  
Eastern India ◽  
Impact Of Climate Change ◽  
Daily Weather Data ◽  
The Impact

<div> <p>The impact of climate change on water resources through increased evaporation combined with regional changes in precipitation characteristics has the potential to affect mean runoff, frequency and intensity of floods and droughts, soil moisture and water supply for irrigation and hydroelectric power generation. The Ganga-Brahmaputra-Meghna (GBM) system is the largest in India with a catchment area of about 110Mha, which is more than 43% of the cumulative catchment area of all the major rivers in the country. The river Damodar is an important sub catchment of GBM basin and its three tributaries- the Bokaro, the Konar and the Barakar form one important tributary of the Bhagirathi-Hughli (a tributary of Ganga) in its lower reaches. The present study is an attempt to assess the impacts of climate change on water resources of the four important Eastern River Basins namely Damodar, Subarnarekha, Mahanadi and Ajoy, which have immense importance in industrial and agricultural scenarios in eastern India. A distributed hydrological model (HEC-HMS) has been used on the four river basins using HadRM2 daily weather data for the period from 2041 to 2060 to predict the impact of climate change on water resources of these river systems.&nbsp;</p> </div> <p>&nbsp;</p>

scholarly journals An evaluation of the effect of future climate on runoff in the Dongjiang River basin, South China

2015 ◽  
Vol 368 ◽  
pp. 257-262
Author(s):  
K. Lin ◽  
W. Zhai ◽  
S. Huang ◽  
Z. Liu
Keyword(s):  
Climate Change ◽  
River Basin ◽  
South China ◽  
Annual Runoff ◽  
Future Climate ◽  
Weather Data ◽  
Future Climate Change ◽  
Dongjiang River ◽  
The Future ◽  
The Impact

Abstract. The impact of future climate change on the runoff for the Dongjiang River basin, South China, has been investigated with the Soil and Water Assessment Tool (SWAT). First, the SWAT model was applied in the three sub-basins of the Dongjiang River basin, and calibrated for the period of 1970–1975, and validated for the period of 1976–1985. Then the hydrological response under climate change and land use scenario in the next 40 years (2011–2050) was studied. The future weather data was generated by using the weather generators of SWAT, based on the trend of the observed data series (1966–2005). The results showed that under the future climate change and LUCC scenario, the annual runoff of the three sub-basins all decreased. Its impacts on annual runoff were –6.87%, –6.54%, and –18.16% for the Shuntian, Lantang, and Yuecheng sub-basins respectively, compared with the baseline period 1966–2005. The results of this study could be a reference for regional water resources management since Dongjiang River provides crucial water supplies to Guangdong Province and the District of Hong Kong in China.

Assessing the impact of climate change on crop management in winter wheat – a case study for Eastern Austria

2016 ◽  
Vol 154 (7) ◽  
pp. 1153-1170 ◽  
Author(s):  
E. EBRAHIMI ◽  
A. M. MANSCHADI ◽  
R. W. NEUGSCHWANDTNER ◽  
J EITZINGER ◽  
S. THALER ◽  
...  
Keyword(s):  
Climate Change ◽  
Management Practices ◽  
Crop Management ◽  
Weather Data ◽  
Climate Change Scenarios ◽  
Management Scenarios ◽  
Global Circulation Models ◽  
Daily Weather Data ◽  
Eastern Austria ◽  
The Impact

SUMMARYClimate change is expected to affect optimum agricultural management practices for autumn-sown wheat, especially those related to sowing date and nitrogen (N) fertilization. To assess the direction and quantity of these changes for an important production region in eastern Austria, the agricultural production systems simulator was parameterized, evaluated and subsequently used to predict yield production and grain protein content under current and future conditions. Besides a baseline climate (BL, 1981–2010), climate change scenarios for the period 2035–65 were derived from three Global Circulation Models (GCMs), namely CGMR, IPCM4 and MPEH5, with two emission scenarios, A1B and B1. Crop management scenarios included a combination of three sowing dates (20 September, 20 October, 20 November) with four N fertilizer application rates (60, 120, 160, 200 kg/ha). Each management scenario was run for 100 years of stochastically generated daily weather data. The model satisfactorily simulated productivity as well as water and N use of autumn- and spring-sown wheat crops grown under different N supply levels in the 2010/11 and 2011/12 experimental seasons. Simulated wheat yields under climate change scenarios varied substantially among the three GCMs. While wheat yields for the CGMR model increased slightly above the BL scenario, under IPCM4 projections they were reduced by 29 and 32% with low or high emissions, respectively. Wheat protein appears to increase with highest increments in the climate scenarios causing the largest reductions in grain yield (IPCM4 and MPEH-A1B). Under future climatic conditions, maximum wheat yields were predicted for early sowing (September 20) with 160 kg N/ha applied at earlier dates than the current practice.

Impacts of the climate change on runoff and diffuse phosphorus load to Lake Balaton (Hungary)

2009 ◽  
Vol 59 (3) ◽  
pp. 417-423 ◽  
Author(s):  
Á. Kovács ◽  
A. Clement
Keyword(s):  
Climate Change ◽  
Hydrological Cycle ◽  
Empirical Relationship ◽  
Urban Runoff ◽  
Weather Data ◽  
Phosphorus Concentration ◽  
Climate Change Scenarios ◽  
Lake Balaton ◽  
Phosphorus Load ◽  
The Impact

The paper outlines a multi-component assessment of the impacts of the climate change on runoff and total phosphorus loads to the large shallow Lake Balaton in Hungary. Present hydrological cycle of the lake catchment has been examined using the rainfall-runoff model WetSpa. Particular phosphorus concentration in runoff was estimated on the basis of the simulated streamflow using an empirical power equation. Dissolved phosphorus concentrations were determined as a function of landuse and soil type of the corresponding sub-catchment. The model was calibrated and validated against daily observations manually at monitoring sites of sixteen inflowing streams around the lake. Runoff stemming from shoreline urban developments was calculated by the urban runoff simulation model SWMM. Phosphorus concentrations in urban runoff were calculated by an empirical relationship derived from field measurements. The model was henceforward run for climate change scenario analysis. Present weather data were modified by the climate change scenarios imported from the results of the CLIME project. The results indicate that the impact of the climate change on runoff and phosphorus load appears in the change of the distribution within a time period rather than in the total volume. However, due to the high uncertainties in climate models, the presented calculations are possible assumptions rather than established statements.

scholarly journals How might climate change affect river flows across West Africa?

2021 ◽  
Vol 169 (3-4) ◽  
Author(s):  
Ponnambalam Rameshwaran ◽  
Victoria A. Bell ◽  
Helen N. Davies ◽  
Alison L. Kay
Keyword(s):  
Climate Change ◽  
West Africa ◽  
Regional Scale ◽  
Weather Data ◽  
Eastern Region ◽  
Water Shortages ◽  
River Flows ◽  
Considerable Uncertainty ◽  
Modelling Framework ◽  
The Impact

AbstractWest Africa and its semi-arid Sahelian region are one of the world’s most vulnerable regions to climate change with a history of extreme climate variability. There is still considerable uncertainty as to how projected climate change will affect precipitation at local and regional scales and the consequent impact on river flows and water resources across West Africa. Here, we aim to address this uncertainty by configuring a regional-scale hydrological model to West Africa. The model (hydrological modelling framework for West Africa—HMF-WA) simulates spatially consistent river flows on a 0.1° × 0.1° grid (approximately 10 km × 10 km) continuously across the whole domain and includes estimates of anthropogenic water use, wetland inundation, and local hydrological features such as endorheic regions. Regional-scale hydrological simulations driven by observed weather data are assessed against observed flows before undertaking an analysis of the impact of projected future climate scenarios from the CMIP5 on river flows up to the end of the twenty-first century. The results indicate that projected future changes in river flows are highly spatially variable across West Africa, particularly across the Sahelian region where the predicted changes are more pronounced. The study shows that median peak flows are projected to decrease by 23% in the west (e.g. Senegal) and increase by 80% in the eastern region (e.g. Chad) by the 2050s. The projected reductions in river flows in western Sahel lead to future droughts and water shortages more likely, while in the eastern Sahel, projected increases lead to future frequent floods.

scholarly journals Long time frames to detect the impact of changing COVID-19 measures, Canada, March to July 2020

2021 ◽  
Vol 26 (40) ◽  
Author(s):  
Jessica E Stockdale ◽  
Renny Doig ◽  
Joosung Min ◽  
Nicola Mulberry ◽  
Liangliang Wang ◽  
...  
Keyword(s):  
Population Level ◽  
Time Frame ◽  
Control Measures ◽  
Type Model ◽  
Time Period ◽  
Long Time ◽  
The Mean ◽  
Reported Data ◽  
Time Frames ◽  
The Impact

Background Many countries have implemented population-wide interventions to control COVID-19, with varying extent and success. Many jurisdictions have moved to relax measures, while others have intensified efforts to reduce transmission. Aim We aimed to determine the time frame between a population-level change in COVID-19 measures and its impact on the number of cases. Methods We examined how long it takes for there to be a substantial difference between the number of cases that occur following a change in COVID-19 physical distancing measures and those that would have occurred at baseline. We then examined how long it takes to observe this difference, given delays and noise in reported cases. We used a susceptible-exposed-infectious-removed (SEIR)-type model and publicly available data from British Columbia, Canada, collected between March and July 2020. Results It takes 10 days or more before we expect a substantial difference in the number of cases following a change in COVID-19 control measures, but 20–26 days to detect the impact of the change in reported data. The time frames are longer for smaller changes in control measures and are impacted by testing and reporting processes, with delays reaching ≥ 30 days. Conclusion The time until a change in control measures has an observed impact is longer than the mean incubation period of COVID-19 and the commonly used 14-day time period. Policymakers and practitioners should consider this when assessing the impact of policy changes. Rapid, consistent and real-time COVID-19 surveillance is important to minimise these time frames.

scholarly journals Utilization of Climate Files Predicting Future Weather in Dynamic Building Performance Simulation – A review

2021 ◽  
Vol 2069 (1) ◽  
pp. 012070
Author(s):  
C N Nielsen ◽  
J Kolarik
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Models ◽  
Weather Data ◽  
Building Performance ◽  
Comprehensive Overview ◽  
Local Climate ◽  
The Impact

Abstract As the climate is changing and buildings are designed with a life expectancy of 50+ years, it is sensible to take climate change into account during the design phase. Data representing future weather are needed so that building performance simulations can predict the impact of climate change. Currently, this usually requires one year of weather data with a temporal resolution of one hour, which represents local climate conditions. However, both the temporal and spatial resolution of global climate models is generally too coarse. Two general approaches to increase the resolution of climate models - statistical and dynamical downscaling have been developed. They exist in many variants and modifications. The present paper aims to provide a comprehensive overview of future weather application as well as critical insights in the model and method selection. The results indicate a general trend to select the simplest methods, which often involves a compromise on selecting climate models.

scholarly journals Long time frames to detect the impact of changing COVID-19 control measures

2020 ◽  
Author(s):  
Jessica E Stockdale ◽  
Renny Doig ◽  
Joosung Min ◽  
Nicola Mulberry ◽  
Liangliang Wang ◽  
...  
Keyword(s):  
Population Level ◽  
Time Frame ◽  
Control Measures ◽  
Policy Makers ◽  
Time Period ◽  
Reporting Process ◽  
Long Time ◽  
The Mean ◽  
Time Frames ◽  
The Impact

AbstractBackgroundMany countries have implemented population-wide interventions such as physical distancing measures, in efforts to control COVID-19. The extent and success of such measures has varied. Many jurisdictions with declines in reported COVID-19 cases are moving to relax measures, while others are continuing to intensify efforts to reduce transmission.AimWe aim to determine the time frame between a change in COVID-19 measures at the population level and the observable impact of such a change on cases.MethodsWe examine how long it takes for there to be a substantial difference between the cases that occur following a change in control measures and those that would have occurred at baseline. We then examine how long it takes to detect a difference, given delays and noise in reported cases. We use changes in population-level (e.g., distancing) control measures informed by data and estimates from British Columbia, Canada.ResultsWe find that the time frames are long: it takes three weeks or more before we might expect a substantial difference in cases given a change in population-level COVID-19 control, and it takes slightly longer to detect the impacts of the change. The time frames are shorter (11-15 days) for dramatic changes in control, and they are impacted by noise and delays in the testing and reporting process, with delays reaching up to 25-40 days.ConclusionThe time until a change in broad control measures has an observed impact is longer than is typically understood, and is longer than the mean incubation period (time between exposure than onset) and the often used 14 day time period. Policy makers and public health planners should consider this when assessing the impact of policy change, and efforts should be made to develop rapid, consistent real-time COVID-19 surveillance.

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