The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes

Funk, Chris; Peterson, Peter Y.; Landsfeld, M. F.; Pedreros, Diego; Verdin, J. P.; Shukla, Shraddhanand; Husak, G. J.; Rowland, James; Harrison, L.; Hoell, Andrew; Michaelsen, Joel

doi:10.1038/sdata.2015.66

Cited by 3,962 publications

(2,618 citation statements)

References 90 publications

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“…CHIRPS is a quasi-global (50°S-50°N) rainfall dataset based on infrared Cold Cloud Duration (CCD) observations, used for seasonal drought monitoring (Funk et al, 2015). The data is temporally disaggregated from daily to 6-hourly at 0.05°spatial resolution and from 1981 to present.…”

Section: Meteorological and Precipitation Datasetsmentioning

confidence: 99%

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Upper Blue Nile basin water budget from a multi-model perspective

Jung

Getirana

Policelli

et al. 2017

Journal of Hydrology

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a b s t r a c tImproved understanding of the water balance in the Blue Nile is of critical importance because of increasingly frequent hydroclimatic extremes under a changing climate. The intercomparison and evaluation of multiple land surface models (LSMs) associated with different meteorological forcing and precipitation datasets can offer a moderate range of water budget variable estimates. In this context, two LSMs, Noah version 3.3 (Noah3.3) and Catchment LSM version Fortuna 2.5 (CLSMF2.5) coupled with the Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme are used to produce hydrological estimates over the region. The two LSMs were forced with different combinations of two reanalysis-based meteorological datasets from the Modern-Era Retrospective analysis for Research and Applications datasets (i.e., MERRA-Land and MERRA-2) and three observation-based precipitation datasets, generating a total of 16 experiments. Modeled evapotranspiration (ET), streamflow, and terrestrial water storage estimates were evaluated against the Atmosphere-Land Exchange Inverse (ALEXI) ET, insitu streamflow observations, and NASA Gravity Recovery and Climate Experiment (GRACE) products, respectively. Results show that CLSMF2.5 provided better representation of the water budget variables than Noah3.3 in terms of Nash-Sutcliffe coefficient when considering all meteorological forcing datasets and precipitation datasets. The model experiments forced with observation-based products, the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA), outperform those run with MERRA-Land and MERRA-2 precipitation. The results presented in this paper would suggest that the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System incorporate CLSMF2.5 and HyMAP routing scheme to better represent the water balance in this region.

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Section: Meteorological and Precipitation Datasetsmentioning

confidence: 99%

“…The data is temporally disaggregated from daily to 6-hourly at 0.05°spatial resolution and from 1981 to present. CHIRPS incorporates station data with about a three week latency (Funk et al, 2015).…”

Section: Meteorological and Precipitation Datasetsmentioning

confidence: 99%

Upper Blue Nile basin water budget from a multi-model perspective

Jung

Getirana

Policelli

et al. 2017

Journal of Hydrology

View full text Add to dashboard Cite

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“…Data were downloaded in Network Common Data Format (NetCDF) for each village centroid from Climate Hazards Group InfraRed Precipitation with Station data 2.0 (CHIRPS) (Funk et al, 2015), which comprises daily gridded precipitation data derived from satellite and in-situ station data at 0.05 degree spatial resolution (approximately 5.3km).…”

Section: Precipitation Datamentioning

confidence: 99%

Use, microbiological effectiveness and health impact of a household water filter intervention in rural Rwanda—A matched cohort study

Kirby

Nagel

Rosa

et al. 2017

International Journal of Hygiene and Environmental Health

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Kirby, M.A.; Nagel, C.L.; Rosa, G.; Umupfasoni, M.M.; Iyakaremye, L.; Clasen, T.F. (2017) This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Graphical abstract ABSTRACTUnsafe drinking water is a substantial health risk contributing to child diarrhoea. We investigated impacts of a program that provided a water filter to households in rural Rwandan villages. We assessed drinking water quality and reported diarrhoea 12-24 months after intervention delivery among 269 households in the poorest tertile with a child under 5 from 9 intervention villages and 9 matched control villages. We also documented filter coverage and use. In Round 1 (12-18 months after delivery), 97.4% of intervention households reported receiving the filter, 84.5%were working, and 86.0% of working filters contained water. Sensors confirmed half of households with working filters filled them at least once every other day on average. Coverage and usage was similar in Round 2 (19-24 months after delivery). The odds of detecting faecal indicator bacteria in drinking water were 78% lower in the intervention arm than the control arm (odds ratio (OR) 0.22, 95% credible interval (CrI) 0.10-0.39, p<0.001). The intervention arm also had 50% lower odds of reported diarrhoea among children <5 than the control arm (OR=0.50, 95% CrI 0.23-0.90, p=0.03). The protective effect of the filter is also suggested by reduced odds of reported diarrhoea-related visits to community health workers or clinics, although these did not reach statistical significance.

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“…The rainfall estimation dataset used in this work is the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) v1p8 by the U.S. Geological Survey and Earth Resources Observation and Science (Funk et al 2015). CHIRPS is a quasi-global daily dataset spanning 50°S-50°N, starting in 1981 to near-present.…”

Section: Methodsmentioning

confidence: 99%

Characterization of Climate Risks for Rice Crop in Casamance, Senegal

Bacci

2017

Renewing Local Planning to Face Climate Change in the Tropics

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This chapter contributes to a global reflection on climate change and its implications for agricultural production. We present a case study aiming to quantify trends of climate risks for rice crop in Casamance (Senegal). We evaluate the recurrence of drought and extreme rainfall conditions in the most sensitive phases of plant life and identify trends in the rainy season distribution. To overcome the low quality of climate records from gauge stations in the Region we use the rainfall estimation Climate Hazards group with InfraRed Precipitation Stations (CHIRPS), a daily gridded dataset with 0.05′ resolution over the period 1981-2013. The analysis is centered on the critical aspects that determine rice final yield such as: availability of water in the (i) plant germination and (ii) flowering phases, and (iii) the dynamics of the rainy season. We use the return period method to identify extreme events probability in rice crop's sensitive phases. Lastly, we identify the dynamics of the three parameters of the growing season: start, end and length by highlighting significant changes recorded in the study period . These outputs aim to support strategic agronomic choices in the Region.

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The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes

Cited by 3,962 publications

References 90 publications

Upper Blue Nile basin water budget from a multi-model perspective

Upper Blue Nile basin water budget from a multi-model perspective

Use, microbiological effectiveness and health impact of a household water filter intervention in rural Rwanda—A matched cohort study

Characterization of Climate Risks for Rice Crop in Casamance, Senegal

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