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Leveraging the Climate for Improved Malaria Control in Tanzania

  • Wellcome Trust

Abstract and Figures

While the potential usefulness of climate in health decision-making is increasingly recognized, few countries in Africa have the capability to routinely provide the health community with relevant, accurate and timely information that can readily be integrated into decision-support tools. This stalemate is beginning to change as new high-quality information services are being established in some African countries using a blend of quality controlled national observations and the best available remote sensing and other products. This new approach, “Enhancing National Climate Services” (ENACTS), designed to improve the availability, access and use of climate data, includes generating historical rainfall and temperature data that have the potential to transform the capacities of national meteorological agencies in partnership with stakeholders and research collaborators. This article introduces the latest tools and services piloted by the Tanzania Meteorological Agency (TMA), with technical support from the International Research Institute for Climate and Society (IRI), in service to the national health community. link:
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While the potential usefulness of climate in health decision-making is increasingly recognized, few countries in
Africa have the capability to routinely provide the health community with relevant, accurate and timely information
that can readily be integrated into decision-support tools. is stalemate is beginning to change as new high-
quality information services are being established in some African countries using a blend of quality controlled
national observations and the best available remote sensing and other products. is new approach, “Enhancing
National Climate Services” (ENACTS), designed to improve the availability, access and use of climate data, includes
generating historical rainfall and temperature data that have the potential to transform the capacities of national
meteorological agencies in partnership with stakeholders and research collaborators. is article introduces the
latest tools and services piloted by the Tanzania Meteorological Agency (TMA), with technical support from the
International Research Institute for Climate and Society (IRI), in service to the national health community.
Public health policymakers and
practitioners are increasingly
concerned about the potential
impact of climate, environmental and
social changes on the eectiveness
of current and future vector-borne
disease control and elimination
programs. Yet, while climate change
adaptation prog rams are increasing in
scope and resourcing, there remains
an identied gap in research and
professional capacity to use climate
information in decision-making. In
the health sector in particular, many
control programs of climate sensitive
diseases (such as malaria) are not
informed by grounded knowledge
and information on the climate.
is is because few public-health institutions or practitioners are equipped to understand or manage the eects
of a changing climate, despite major advances in recent years in alerting the health community to its risks. A
key challenge that has been identied is ‘market atrophy,’ a comparative lack of demand from the health sector
for climate services coupled with a lack of supply of relevant, actionable information (as there is oen no clear
Leveraging the Climate for Improved Malaria Control in Tanzania
Tufa Dinku1, Augustine Kanemba2, Barbara Platzer3 and Madeleine C. omson1,4
February 15, 2014
1 International Research Institute for Climate and Society, Earth Institute, Columbia University- Lamont Campus, Palisades, New York, USA
2 Tanzania Meteorological Agency, Dar Es Salaam, Tanzania
3 Columbia Global Center-Africa, Nairobi, Kenya
4 Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, USA
Figure 1: Possible impacts of climate suitability for malaria transmission on eorts to
reduce malaria incidences.
Malaria remains a major cause of death and illness worldwide with more than 500,000 deaths each year and
more than 200 million cases. It is widely identied and studied as the most climate sensitive vector-borne
disease, which occurs in geographic areas conducive to the malaria parasite and its mosquito vector. In the
absence of control, the spatial and seasonal risk of the disease, as well as dierences from one year to another
and in long-term trends, are oen governed by climatic factors such as rainfall, temperature and humidity.
It is clear that climate is only
one of many important drivers
of malaria (e.g., education,
migration, land use change,
control measures). Climate
information is dierent,
however, in that it is ideally
suited for integration into
information systems for the
health sector. is is owed
to both the nature of climate
(its climatology, seasonality,
diurnal rhythm and potential
predictability at multiple
time scales) and the fact that
it is routinely measured in a
systematic way by land observations, remote sensing and global model outputs all around the world. Climate
information therefore has the potential to inform a wide range of health decisions and improve our understanding
of the following:
Mechanisms of Disease Transmission: to help identify new opportunities for intervention
Spatial Risk: to help identify populations at risk for better targeting of interventions
Seasonal Risk: to inform the timing of routine interventions
Sub-seasonal and Year-to-Year Changes in Risk: to identify when changes in epidemic risk are
likely to occur to initiate appropriate prevention and response strategies
Trends in Risk: to identify long-term drivers of disease occurrence (including changes in the climate)
to plan for and support future prevention and response strategies
Assessment of the Impacts of Interventions: to evaluate the role of climate as it enables or limits
disease transmission.
e “Enhancing National Climate Services” (ENACTS)
approach aims at simultaneously improving the availability,
access and use of climate information [3]. It has the potential
to transform the capacities of national meteorological services
to respond to and invest in the research and operational
interests highlighted above. Now available for Tanzania,
Ethiopia, Madagascar and at the regional level in West Africa,
this approach has been designed to overcome challenges due
to the decline in the number and quality of weather stations
in many parts of Africa and the fact that available stations
are oen unevenly distributed with most of the stations
located along the main roads. ese twin challenges impose
severe limitations to the availability of climate information
and services to communities where these services are oen
Figure 2: Weighted Anomaly Standardized Precipitation (WASP) Index for Tanzania using (le)
the latest CPC Merged Analysis of Precipitation monthly precipitation data set and (right) the
gold standard ENACTS blended station and satellite data. On the right panel, b indicates baseline
years used for malaria assessment while a indicates the intervention years. e ENACTS product
improves the assessment of rainfall extremes and provides a much better estimate of the impact of
the 1997/8 El Nino [5].
Figure 3: Comparison of station measurements (le), satellite
estimates (middle) and merged products (right) for a given
10-day period. e top panel is for operational stations while
the bottom one is for all available stations.
needed most. Where observations are taken, they suer from gaps and poor quality and, because of policy and sta
constraints, are oen unavailable beyond the respective national meteorological services.
Data availability can, however, be improved by combining available local observations with satellite and other
proxies. Access and use of this information can further be improved by making information products openly and
readily available, as well as by working with stakeholders to better understand and use the information products.
is oers hope in alleviating the challenges outlined above and underpins a new approach to improved climate
resilience in Africa. is report describes how ENACTS has been implemented in Tanzania with focus on users
from the public health community.
Building Bridges between the Climate and Health Communities in Tanzania
Leveraging the recently piloted ENACTS products, the Tanzania Meteorological Agency (TMA) recently hosted
a stakeholder meeting in Dar es Salaam from Oct. 16-18, 2013, to discuss opportunities for the use of climate
information specically in malaria prevention and control. A number of dierent opportunities for using climate
information in Tanzania were discussed, including the use of the new ENACTS products in malaria impact
Figure 1 presents how change
in climate suitability for
malaria transmission might
impact intervention eorts.
Central to the evaluation of
development interventions
for malaria reduction and
prevention is to use a baseline
year or period in order to
measure changes in outcomes,
such as malaria incidence.
If the baseline year (or
period) was unusually wet
or dry (warm or cool) for
the particular outcome, then
achieving change relative to
that baseline may be confounded as a result of variability or trends in the climate. For example, if during a relatively
dry baseline year or period mosquito nets are introduced and malaria incidence declines, it may be tempting to
attribute the decline of malaria incidence to the introduction of mosquito nets alone without considering the
impact of climate. However, should the rains return and malaria incidence increase, the program managers might
be blamed. Better understanding of the underlying climate baseline is critical to evaluating the eectiveness of
dierent interventions for malaria.
Figure 2 shows a drought index called the “Weighted Anomaly Standardized Precipitation (WASP)” index for
Tanzania. is index uses the remote sensing product Merged Analysis Product (CMAP) developed by the Climate
Prediction Center, which incorporates only those stations available to the Global Telecommunication System
(GTS) (le panel) and for the ENACTS blended product, which incorporates all suitable national meteorological
stations (right panel). e ENACTS product improves the assessment of rainfall extremes and provides a much
better estimate of the impact of the 1997/8 El Nino [6]. e improved quality of the ENACTS products over the
cruder CMAP product means that they can be used at national and sub-national (including district) levels. e
ENACTS products recently launched in Tanzania have been used to assess whether climate was likely to have
impacted malaria transmission during the pre- and post-malaria intervention period, marked “b” (1995-1999) and
Figure 4: Station measurements of maximum temperature (le), elevation map (center), and
merged station-MODIS-DEM (right).
a” respectively (2000-2010). In this example, the baseline period includes a year of very high rainfall, whereas the
malaria intervention period includes three major droughts.
Comparative Advantage of ENACTS Products for Climate Information and Analysis
e ENACTS framework (Box 1) and products are
designed to dramatically increase the availability, access
and use of climate information for decision-making.
e ENACTS products made available by TMA are
unique and leverage cutting-edge technologies to improve
availability, access and use of climate information for
Tanzania. is eort has focused on the creation of reliable
climate information that is suitable for national and local
decision-making. Data availability is improved by blending
data from the national observation network and satellite
and other proxies such as elevation. e main advantage
of the proxies is good spatial coverage: satellite data are
available over most parts of the world at increasingly
improved spatial and temporal resolutions. Satellite rainfall
estimates also now go back more than 30 years. Combining
ground-based observations with satellite and other proxies
helps to overcome the spatial and temporal gaps in station
data. Additional information on the methodology behind
these tools is outlined below.
Pioneering New Approaches to Improve Availability
e ENACTS products merge observational data with
existing and openly available products using new
techniques. For rainfall, estimates from the TAMSAT
(Tropical Applications of Meteorology using Satellite data
and ground-based observations) from the University of
Reading have been used. e TAMSAT rainfall product
[7, 8], derived from only thermal infrared satellite data,
provides temporally consistent rainfall estimates going back
to 1983. e data is free and is openly available through the
University of Reading’s website. As for temperature, there
is no reliable satellite data going back 30 years. As a result,
the Moderate Resolution Imaging Radiometer (MODIS)
land-surface temperature estimates and digital elevation
model (DEM) are used for merging with station minimum and maximum temperature measurements. MODIS
land surface temperature (LST) estimates are available at a spatial resolution of 1 kilometer, starting from 2002.
is is not a long-enough period for many climatological analyses. However, the average of the 10-year data can be
used as climatological background to interpolate station temperature measurements. us, the average of MODIS
LST from 2003 to 2011 is used for merging with station observations and elevation data.
Aer exploring dierent approaches, Regression Kriging [9, 10, 11] was selected for merging station data with
satellite and other proxies. Regression gridding is a two-step process. It models the value of a variable at a desired
location as the sum of the deterministic and stochastic components. e deterministic component is obtained
Figure 5: Tanzania’s Climate Analysis and Applications
Map Room, comprised of the Climate Analysis, Climate
Monitoring and Climate Forecast Map Rooms. Image
Credit: Map Room.
Box 1: e ENACTS Framework
through regression on an auxiliary variable and the stochastic components are interpolated residuals. e main
advantage of this method is that it could be extended to a broader range of regression techniques and allows
separate interpretation of the two components.
ese enhanced national climate time series overcome traditional barriers in data quality and availability. e
spatially and temporally continuous datasets allow for characterization of climate risks at a local scale and oer a
low-cost, high impact opportunity with major potential to support climate resilient development. ere are two
dierent products for the rainfall data.
e rst merged time series includes a few operational stations and is used mainly for monitoring purposes.
Operational stations are those stations that report daily. is product is updated every 10 days. e data from the
other stations are received at TMA headquarters much later aer observation. e second version of the merged
product uses all available stations. is is the standard product and is updated about once a year. e merged
temperature uses all stations that report temperature, but these are much fewer than the rainfall stations.
Figure 3 and Figure 4 present sample products for rainfall and temperature, respectively. e le panel in
Figure 3 represents station measurements for a 10-day period. e middle and right panels are satellite estimate
and combined gauge-satellite products, respectively. e station data is assumed to represent the “true” rainfall,
but there are no stations over many parts of the country, particularly for the operational product. e satellite
product covers the entire country, but tends to underestimate rainfall amounts over most parts of the country. e
merged products overcome the lack of station coverage as well as the underestimation by the satellite product. is
is accomplished by combining the spatial information from the satellite estimates with the point measurements
at station locations. ere is a signicant dierence between the two versions of the merged rainfall products.
However, even the product with operational stations is a signicant improvement over the satellite estimate.
e same is true for temperature shown in Figure 4. Here, elevation is shown instead of satellite data. A comparison
of the elevation map with station measurements and the merged product shows the inuence of elevation on the
spatial variation of temperature in Tanzania.
Investing in Access and Use
Improved data availability, however, may not necessarily lead to improved data access and use. Dedicated eorts
also need to be made to improve access to the data and its operational utility. Access to information through the
ENACTS products are provided through virtual “map rooms”. TMA’s online tool currently includes three map
rooms for: Climate Analysis, Climate Monitoring, and Climate Forecast (Figure 5). e Climate Analysis Map
Room provides information on the mean climate (in terms of rainfall and temperatures) at any point and at national
and sub-national levels dened by administrative boundaries. It can also be used to explore the performance of
the rainfall for a specic season over the years as compared to the mean. e Climate Monitoring Map Room
enables monitoring of the current season in terms of rainfall. Dierent maps and graphs compare the latest 10-
day period with the mean or values for recent years. is information also can be extracted at any point or for any
administrative boundary. Data is updated every 10 days, thus enabling close monitoring of the season. Extracting
and presenting information at any administrative level enables focusing on specic areas of interest. e Climate
Forecast Map Room translates TMAs seasonal rainfall forecasts to values that can easily be understood by users. It
presents the forecasts in the context of historical rainfall data.
Improving the use of climate information involves an iterative process of engaging with a wide range of stakeholders
and users. is includes involving users directly in the development of information products and training
policymakers, researchers and practitioners on how to best use the derived information products and tools. e
workshop organized by TMA in 2013 was the beginning of the engagement process with the health community in
Tanzania. e primary objectives of the workshop were to showcase TMA’s new data and information products,
demonstrate examples of how they can be used for disease stratication, improved early warning systems and
impact assessments, as well as to solicit critical feedback from the health community on their needs for climate,
environmental and epidemiological information, in particular for use in malaria decision-making. e workshop
participants oered useful recommendations on how to improve existing information products and develop new
Summary and Next Steps
TMA, in collaboration with partners including the IRI, has made signicant progress in enhancing its national
climate services. ese enhancements include a more-than-30 year time series of spatially and temporally complete
climate data, online facilities to make information products available to users and dedicated engagements with
stakeholders. e rst outreach to users included the public health community. Further investments are still
needed to respond to needs articulated by public health practitioners and researchers invited to initial stakeholder
consultations and to build capacity to meet demands across additional sectors moving forward. TMA will need
to continue to demonstrate its commitment to iteratively improving its service in the provision of climate data
and products by pioneering and ensuring uptake of relevant, accurate and timely information that can readily be
integrated into decision-support tools for improved resilience.
is research was funded through grant support from NASA SERVIR and the World Meteorological Organization
(WMO), as well as support from the U.S. Agency for International Development (USAID) through the President’s
Malaria Initiative (PMI). Support to the ENACTS development has also been provided by the CGIAR Research
Program on Climate Change, Agriculture and Food Security (CCAFS). e authors would like to acknowledge
the contribution of Dr. Pietro Ceccato as principal investigator of the NASA funded project. e authors also
acknowledge Rene Salgado, Achuyt Bhattarai, Christie Hershey and Carrie Nielsen of PMI for helpful discussions
on the needs and uses of climate and malaria data in conducting evaluations of the impact of malaria control
interventions. e authors also thank the countless partners that have contributed to this work through partnership
and leveraged resources.
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Climate data are essential in an array of climate research and applications, that include analyses of climate variability and trends and modelling the impact of climate variability and change on different socioeconomic activities. However, the use of climate data for research and applications in Africa has been scanty because availability of and access to climate data is very limited. In many parts of Africa, weather stations are sparse and their number has been declining. Besides, the distribution of existing stations is uneven, with most located along major roads. Where data exist, they are often of poor quality with many gaps. There are different efforts underway to overcome these challenges. One of these efforts is the ENACTS (Enhancing National Climate Services) initiative. This initiative works with National Meteorological Services in Africa to improve the availability and quality of climate data by combining quality-controlled station observation with satellite and reanalysis proxies.
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In 2005, the International Research Institute for Climate and Society published its assessment of key gaps in the use of climate information for health, agriculture, water and other sectors in countries across Africa. The results from the report were less than stellar. After an extensive review of use of climate information in the development sectors of Africa, the authors concluded that the continent suffered from “market atrophy” – the reinforcing effect of inadequate effective supply of climate information and weak effective demand. Twelve years later, organizations such as the IRI, CSRD program, CCAFS, ICPAC, and UKMO have made enormous strides at increasing both climate information supply and effective demand through the implementation of climate data platforms and the organizing of capacity-building seminars. In order to capitalize on the presence of the many climate and sector experts from across the IGAD region, the organizations above held a joint event, the CSRD Technical Exchange: ICPAC and National Climate Maprooms – Existing and New Tools for Drought Monitoring and Forecasting in Eastern Africa, in Zanzibar on August 23-25, 2017, immediately after the 47th Greater Horn of Africa Climate Outlook Forum (GHACOF47). The workshop was designed to offer potential and existing users a platform to voice their needs for the development and better use of historical, monitored and forecast information for the management of drought across climate-sensitive sectors.
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Natural climate variability and long-term changes in rainfall and temperature are expected to have major impact in Africa, where most of the population depends on rain-fed agriculture for their food and livelihood. Reliable climate information will be crucial in efforts to build resilience against the negative impact of climate change and to maximize the benefits of favorable conditions.
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Earth’s environment has direct and dramatic effects on its inhabitants in the realms of health and air quality. The climate, even in an unaltered state, poses great challenges but also presents great opportunity for the mankind to survive and flourish. Anthropogenic factors lead to even greater stress on the global ecosystem and to mankind, particularly with respect to air quality and the concomitant health issues. While the use of remote sensing technology to address issues is in its infancy, there is tremendous potential for using remote sensing as part of systems that monitor and forecast conditions that directly or indirectly affect health and air quality. This chapter discusses current status and future prospects in this field and presents three case studies showing the great value of remote sensing assets in distinct disciplines.
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During El Niño events, several spatially coherent, nearly synchronous droughts typically develop in teleconnected tropical land areas. These droughts, reflected in below-average tropical mean land area precipitation, are frequently accompanied by multiple and wide ranging impacts. Here it is shown, based on precipitation observations for the past half-century, that there is a remarkably robust relationship between El Niño strength and the spatial extent of drought in the global tropics. Not reported previously, drought covers more than twice the land area in strong versus weak El Niños and in many areas severe drought is shown to be more likely during El Niño than for all other times. The results provide insight into large-scale tropical rainfall variability and have implications for future droughts under global warming scenarios.
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This paper discusses the characteristics of regression-kriging (RK), its strengths and limitations, and illustrates these with a simple example and three case studies. RK is a spatial interpolation technique that combines a regression of the dependent variable on auxiliary variables (such as land surface parameters, remote sensing imagery and thematic maps) with simple kriging of the regression residuals. It is mathematically equivalent to the interpolation method variously called “Universal Kriging” (UK) and “Kriging with External Drift” (KED), where auxiliary predictors are used directly to solve the kriging weights. The advantage of RK is the ability to extend the method to a broader range of regression techniques and to allow separate interpretation of the two interpolated components. Data processing and interpretation of results are illustrated with three case studies covering the national territory of Croatia. The case studies use land surface parameters derived from combined Shuttle Radar Topography Mission and contour-based digital elevation models and multitemporal-enhanced vegetation indices derived from the MODIS imagery as auxiliary predictors. These are used to improve mapping of two continuous variables (soil organic matter content and mean annual land surface temperature) and one binary variable (presence of yew). In the case of mapping temperature, a physical model is used to estimate values of temperature at unvisited locations and RK is then used to calibrate the model with ground observations. The discussion addresses pragmatic issues: implementation of RK in existing software packages, comparison of RK with alternative interpolation techniques, and practical limitations to using RK. The most serious constraint to wider use of RK is that the analyst must carry out various steps in different software environments, both statistical and GIS.
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A methodological framework for spatial prediction based on regression-kriging is described and compared with ordinary kriging and plain regression. The data are first transformed using logit transformation for target variables and factor analysis for continuous predictors (auxiliary maps). The target variables are then fitted using step-wise regression and residuals interpolated using kriging. A generic visualisation method is used to simultaneously display predictions and associated uncertainty. The framework was tested using 135 profile observations from the national survey in Croatia, divided into interpolation (100) and validation sets (35). Three target variables: organic matter, pH in topsoil and topsoil thickness were predicted from six relief parameters and nine soil mapping units. Prediction efficiency was evaluated using the mean error and root mean square error (RMSE) of prediction at validation points. The results show that the proposed framework improves efficiency of predictions. Moreover, it ensured normality of residuals and enforced prediction values to be within the physical range of a variable. For organic matter, it achieved lower relative RMSE than ordinary kriging (53.3% versus 66.5%). For topsoil thickness, it achieved a lower relative RMSE (66.5% versus 83.3%) and a lower bias than ordinary kriging (0.15 versus 0.69 cm). The prediction of pH in topsoil was difficult with all three methods. This framework can adopt both continuous and categorical soil variables in a semi-automated or automated manner. It opens a possibility to develop a bundle algorithm that can be implemented in a GIS to interpolate soil profile data from existing datasets.
Climate data are used in a number of applications including climate risk management and adaptation to climate change. However, the availability of climate data, particularly throughout rural Africa, is very limited. Available weather stations are unevenly distributed and mainly located along main roads in cities and towns. This imposes severe limitations to the availability of climate information and services for the rural community where, arguably, these services are needed most. Weather station data also suffer from gaps in the time series. Satellite proxies, particularly satellite rainfall estimate, have been used as alternatives because of their availability even over remote parts of the world. However, satellite rainfall estimates also suffer from a number of critical shortcomings that include heterogeneous time series, short time period of observation, and poor accuracy particularly at higher temporal and spatial resolutions. An attempt is made here to alleviate these problems by combining station measurements with the complete spatial coverage of satellite rainfall estimates. Rain gauge observations are merged with a locally calibrated version of the TAMSAT satellite rainfall estimates to produce over 30-years (1983-todate) of rainfall estimates over Ethiopia at a spatial resolution of 10 km and a ten-daily time scale. This involves quality control of rain gauge data, generating locally calibrated version of the TAMSAT rainfall estimates, and combining these with rain gauge observations from national station network. The infrared-only satellite rainfall estimates produced using a relatively simple TAMSAT algorithm performed as good as or even better than other satellite rainfall products that use passive microwave inputs and more sophisticated algorithms. There is no substantial difference between the gridded-gauge and combined gauge-satellite products over the test area in Ethiopia having a dense station network; however, the combined product exhibits better quality over parts of the country where stations are sparsely distributed.
Two different TAMSAT methods of Rainfall Estimation were developed respectively for northern and southern Africa, based on Meteosat TIR images; northern Africa since 1987 and southern Africa since 1990. These rainfall estimates are used operationally for agricultural purposes and for predicting famines and floods. The two different methods have both been used to make rainfall estimates for the southern rainy season October 1995 to April 1996, and then compared with estimates produced by the CPC method. The latter are made more simply from TIR, but have the addition of GTS rainfall data and orographic rain. All these estimates were then compared with kriged data from over 800 raingauges in southern Africa. The detailed results were then compared for the whole season across the whole SADC region, and then two detailed cross- sections were studied, with different orography. The results show that operational TAMSAT estimates are better over plateau regions, with 59% estimates within 1 Std of the rainfall, but over the whole region the CPC estimates perform best. Over mountainous regions all methods under-estimate and give only 40% within 1Std. The two TAMSAT methods show little difference across a whole season, but when looked at in detail the northern method gives unsatisfactory calibrations. The CPC method does have significant overall improvements by building in real-time raingauge data, but only where sufficient raingauges are available.
Several methods involving spatial prediction of soil properties from landform attributes are compared using carefully designed validation procedures, The methods, tested against ordinary kriging and universal kriging of the target variables, include multi-linear regression, isotopic cokriging, heterotopic cokriging and regression-kriging models A, B and C. Prediction performance by ordinary kriging and universal kriging was comparatively poor as the methods do not use covariation of the predictor variable with terrain attributes. Heterotopic cokriging outperformed isotopic cokriging because the former utilised more of the local information from the covariables. The combined regression-kriging methods generally performed well. Both the regression-kriging model C and heterotopic cokriging performed well when soil variables were predicted into a relatively finer gridded digital elevation model (DEM) and when all the local information was utilised. Regression-kriging model C generally performed best and is, perhaps, more flexible than heterotopic cokriging. Potential for further research and developments rests in improving the regression part of model C.
The main aim of this paper is to present a new method of areal rainfall estimation using satellite and ground-based data. This method involves optimal merging of the estimates provided by satellite information and estimates obtained from raingauges. In the merging procedure, each estimate is weighted according to its uncertainty given by its estimation variance. The uncertainty attributed to the raingauge estimates is obtained using block kriging, while for the satellite uncertainties, a novel regression approach is developed. A standard error is also attached to the new merged estimates. In order to test the algorithm, a case study has been undertaken using the EPSAT dense raingauge network in Niger. The complete EPSAT raingauge network (94 gauges distributed over a 1×1° square) has been used to obtain a detailed picture of the rainfall pattern which is then used as a reference for comparing the estimation schemes. The schemes compared are: (1) estimates based on satellite data only; (2) kriged estimates from a randomly selected subset of four gauges; (3) kriging with external drift using both satellite data and the subset of gauges; and (4) the new merging algorithm. The merging process gives more reliable results both for the mean areal rainfall and its spatial distribution.
Development of Climate Analysis Section for the President's Malaria Initiative Impact Evaluation: Reports for Ethiopia and Tanzania
  • M C Thomson
  • F Zadravecz
  • B Lyon
  • G Mantilla
  • D Willis
  • P Ceccato
  • T Dinku
M. C. Thomson, F. Zadravecz, B. Lyon, G. Mantilla, D. Willis, P. Ceccato and T. Dinku, "Development of Climate Analysis Section for the President's Malaria Initiative Impact Evaluation: Reports for Ethiopia and Tanzania, " President's Malaria Initiative-USAID Report, IRI, 2012.