@article{fdi:010062505,
  title = {{I}mpacts of satellite-based rainfall products on predicting spatial patterns of {R}ift {V}alley {F}ever vectors},
  author = {{G}uilloteau, {C}. and {G}osset, {M}arielle and {V}ignolles, {C}. and {A}lcoba, {M}. and {T}ourr{\'e}, {Y}. {M}. and {L}acaux, {J}. {P}.},
  editor = {},
  language = {{ENG}},
  abstract = {{S}patiotemporal rainfall variability is a key parameter controlling the dynamics of mosquitoes/vector-borne diseases such as malaria, {R}ift {V}alley fever ({RVF}), or dengue. {I}mpacts from rainfall heterogeneity at small scales (i.e., 1-10 km) on the risk of epidemics (i.e., host bite rate or number of bites per host and per night) must be thoroughly evaluated. {A} model with hydrological and entomological components for risk prediction of the {RVF} zoonosis is proposed. {T}he model predicts the production of two mosquito species within a 45 km {X} 45 km area in the {F}erlo region, {S}enegal. {T}he three necessary steps include 1) best rainfall estimation on a small scale, 2) adequate forcing of a simple hydrological model leading to pond dynamics (ponds are the primary larvae breeding grounds), and 3) best estimate of mosquito life cycles obtained from the coupled entomological model. {T}he sensitivity of the model to the spatiotemporal heterogeneity of rainfall is first tested using high-resolution rain fields from a weather radar. {T}he need for high-resolution rain data is thus demonstrated. {S}everal high-resolution satellite rainfall products are evaluated in the region of interest using a dense rain gauge network. {T}ropical {R}ainfall {M}easuring {M}ission ({TRMM}) {M}ultisatellite {P}recipitation {A}nalysis 3{B}42, version 6 ({TMPA}-3{B}42{V}6), and 3{B}42 in real time ({TMPA}-3{B}42{RT}); {G}lobal {S}atellite {M}apping of {P}recipitation ({GSM}a{P}) in near-real time ({GSM}a{P}-{NRT}) and {M}oving {V}ector with {K}alman version ({GSM}a{P}-{MVK}); {A}frican {R}ainfall {E}stimation {A}lgorithm, version 2.0 ({RFE} 2.0); {C}limate {P}rediction {C}enter ({CPC}) morphing technique ({CMORPH}); and {P}recipitation {E}stimation from {R}emotely {S}ensed {I}nformation {U}sing {A}rtificial {N}eural {N}etworks ({PERSIANN}) are tested and finally corrected using a probability matching method. {T}he corrected products are then used as forcing to the coupled model over the 2003-10 period. {T}he predicted number and size of ponds and their dynamics are greatly improved compared to the model forced only by a single gauge. {A} more realistic spatiotemporal distribution of the host bite rate of the {RVF} vectors is thus expected.},
  keywords = {{AFRIQUE} {DE} {L}'{OUEST} ; {ZONE} {SAHELIENNE}},
  booktitle = {},
  journal = {{J}ournal of {H}ydrometeorology},
  volume = {15},
  numero = {4},
  pages = {1624--1635},
  ISSN = {1525-755{X}},
  year = {2014},
  DOI = {10.1175/jhm-d-13-0134.1},
  URL = {https://www.documentation.ird.fr/hor/fdi:010062505},
}