Satellite-derived Rainfall Estimates Research Articles

A distributed hydrological–geotechnical model using satellite-derived rainfall estimates for shallow landslide prediction system at a catchment scale

This paper describes the potential applicability of a hydrological–geotechnical modeling system using satellite-based rainfall estimates for a shallow landslide prediction system. The physically based distributed model has been developed by integrating a grid-based distributed kinematic wave rainfall-runoff model with an infinite slope stability approach. The model was forced by the satellite-based near real-time half-hourly CMORPH global rainfall product prepared by NOAA-CPC. The method combines the following two model outputs necessary for identifying where and when shallow landslides may potentially occur in the catchment: (1) the time-invariant spatial distribution of areas susceptible to slope instability map, for which the river catchment is divided into stability classes according to the critical relative soil saturation; this output is designed to portray the effect of quasi-static land surface variables and soil strength properties on slope instability and (2) a produced map linked with spatiotemporally varying hydrologic properties to provide a time-varying estimate of susceptibility to slope movement in response to rainfall. The proposed hydrological model predicts the dynamic of soil saturation in each grid element. The stored water in each grid element is then used for updating the relative soil saturation and analyzing the slope stability. A grid of slope is defined to be unstable when the relative soil saturation becomes higher than the critical level and is the basis for issuing a shallow landslide warning. The method was applied to past landslides in the upper Citarum River catchment (2,310 km2), Indonesia; the resulting time-invariant landslide susceptibility map shows good agreement with the spatial patterns of documented historical landslides (1985–2008). Application of the model to two recent shallow landslides shows that the model can successfully predict the effect of rainfall movement and intensity on the spatiotemporal dynamic of hydrological variables that trigger shallow landslides. Several hours before the landslides, the model predicted unstable conditions in some grids over and near the grids at which the actual shallow landslides occurred. Overall, the results demonstrate the potential applicability of the modeling system for shallow landslide disaster predictions and warnings.

Improved Global Rainfall Retrieval Using the Special Sensor Microwave Imager (SSM/I)

Abstract Global monthly rainfall estimates have been produced from more than 20 years of measurements from the Defense Meteorological Satellite Program series of Special Sensor Microwave Imager (SSM/I). This is the longest passive microwave dataset available to analyze the seasonal, annual, and interannual rainfall variability on a global scale. The primary algorithm used in this study is an 85-GHz scattering-based algorithm over land, while a combined 85-GHz scattering and 19/37-GHz emission is used over ocean. The land portion of this algorithm is one of the components of the blended Global Precipitation Climatology Project rainfall climatology. Because previous SSM/I processing was performed in real time, only a basic quality control (QC) procedure had been employed to avoid unrealistic values in the input data. A more sophisticated, statistical-based QC procedure on the daily data grids (antenna temperature) was developed to remove unrealistic values not detected in the original database and was employed to reprocess the rainfall product using the current version of the algorithm for the period 1992–2007. Discrepancies associated with the SSM/I-derived monthly rainfall products are characterized through comparisons with various gauge-based and other satellite-derived rainfall estimates. A substantial reduction in biases was observed as a result of this QC scheme. This will yield vastly improved global rainfall datasets.

The TAMORA algorithm: satellite rainfall estimates over West Africa using multi-spectral SEVIRI data

Abstract. A multi-spectral rainfall estimation algorithm has been developed for the Sahel region of West Africa with the purpose of producing accumulated rainfall estimates for drought monitoring and food security. Radar data were used to calibrate multi-channel SEVIRI data from MSG, and a probability of rainfall at several different rain-rates was established for each combination of SEVIRI radiances. Radar calibrations from both Europe (the SatPrecip algorithm) and Niger (TAMORA algorithm) were used. 10 day estimates were accumulated from SatPrecip and TAMORA and compared with kriged gauge data and TAMSAT satellite rainfall estimates over West Africa. SatPrecip was found to produce large overestimates for the region, probably because of its non-local calibration. TAMORA was negatively biased for areas of West Africa with relatively high rainfall, but its skill was comparable to TAMSAT for the low-rainfall region climatologically similar to its calibration area around Niamey. These results confirm the high importance of local calibration for satellite-derived rainfall estimates. As TAMORA shows no improvement in skill over TAMSAT for dekadal estimates, the extra cloud-microphysical information provided by multi-spectral data may not be useful in determining rainfall accumulations at a ten day timescale. Work is ongoing to determine whether it shows improved accuracy at shorter timescales.

Evaluation of a preliminary satellite-based landslide hazard algorithm using global landslide inventories

Abstract. Most landslide hazard assessment algorithms in common use are applied to small regions, where high-resolution, in situ, observables are available. A preliminary global landslide hazard algorithm has been developed to estimate areas of potential landslide occurrence in near real-time by combining a calculation of landslide susceptibility with satellite derived rainfall estimates to forecast areas with increased potential for landslide conditions. This paper presents a stochastic methodology to compare this new, landslide hazard algorithm for rainfall-triggered landslides with a newly available inventory of global landslide events, in order to determine the predictive skill and limitations of such a global estimation technique. Additionally, we test the sensitivity of the global algorithm to its input observables, including precipitation, topography, land cover and soil variables. Our analysis indicates that the current algorithm is limited by issues related to both the surface-based susceptibility map and the temporal resolution of rainfall information, but shows skill in determining general geographic and seasonal distributions of landslides. We find that the global susceptibility model has inadequate performance in certain locations, due to improper weighting of surface observables in the susceptibility map. This suggests that the relative contributions of topographic slope and soil conditions to landslide susceptibility must be considered regionally. The current, initial forecast system, although showing some overall skill, must be improved considerably if it is to be used for hazard warning or detailed studies. Surface and remote sensing observations at higher spatial resolution, together with improved landslide event catalogues, are required if global landslide hazard forecasts are to become an operational reality.

Daily Indian Precipitation Analysis Formed from a Merge of Rain-Gauge Data with the TRMM TMPA Satellite-Derived Rainfall Estimates

The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) have enhanced the accuracy of rainfall estimation from satellites over ocean and land. An algorithm to merge TRMM Multi-satellite Precipitation Analysis (TMPA) satellite estimates with the India Meteorological Department (IMD) rain-gauge values is tested for the Indian monsoon region. A daily merged gauge and satellite data product (NMSG) at 1° latitude-longitude resolution for the Indian monsoon region is prepared to depict the large-scale aspects of monsoon rainfall. The satellite product used as a first guess is the TRMM TMPA for daily estimates. Incorporation of IMD gauge data corrects the mean biases of the TMPA values. TMPA alone is able to depict the space-time distribution of monsoon rainfall patterns. The merging of gauge data enhances the value of the satellite information; therefore, the NMSG is more representative than TMPA. Daily, monthly, and seasonal fields are prepared and compared with the land-only gridded data of the India Meteorological Department National Climate Centre (IMDNCC) at the same resolution. This inter-comparison with another independent dataset confirms the utility of the NMSG, produced by this objective analysis algorithm. The comparison of the merged data with the TMPA data reveals the regions where the satellite estimates have mean biases. Objective statistical scores also confirm the goodness of NMSG. The NMSG data are meant for use in verification of large-scale rainfall features from numerical models for the monsoon region.

Application of Copulas to Modeling Temporal Sampling Errors in Satellite-Derived Rainfall Estimates

The dependence between temporal sampling error in satellite-derived rainfall estimates and rainfall rate is of scientific and practical interest. We explore the use of copulas to construct the needed joint distribution between the sampling error and the corresponding rainfall rate. Our approach is to first estimate the marginal distribution functions in a parametric way, and then use these with a number of copula functions in search of the one most appropriate. We use maximum likelihood to estimate the parameters of the copulas. We select the best-fitted parametric copula function as the one that gives the largest likelihood. Our findings have important implications for the interpretation and propagation studies of remote sensing precipitation uncertainties.

A Coupled Remote Sensing and Simplified Surface Energy Balance Approach to Estimate Actual Evapotranspiration from Irrigated Fields

Accurate crop performance monitoring and production estimation are critical fortimely assessment of the food balance of several countries in the world. Since 2001, theFamine Early Warning Systems Network (FEWS NET) has been monitoring cropperformance and relative production using satellite-derived data and simulation models inAfrica, Central America, and Afghanistan where ground-based monitoring is limitedbecause of a scarcity of weather stations. The commonly used crop monitoring models arebased on a crop water-balance algorithm with inputs from satellite-derived rainfallestimates. These models are useful to monitor rainfed agriculture, but they are ineffectivefor irrigated areas. This study focused on Afghanistan, where over 80 percent ofagricultural production comes from irrigated lands. We developed and implemented aSimplified Surface Energy Balance (SSEB) model to monitor and assess the performanceof irrigated agriculture in Afghanistan using a combination of 1-km thermal data and 250-m Normalized Difference Vegetation Index (NDVI) data, both from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. We estimated seasonal actual evapotranspiration (ETa) over a period of six years (2000-2005) for two major irrigated river basins in Afghanistan, the Kabul and the Helmand, by analyzing up to 19 cloud-free thermal and NDVI images from each year. These seasonal ETa estimates were used as relative indicators of year-to-year production magnitude differences. The temporal water- use pattern of the two irrigated basins was indicative of the cropping patterns specific to each region. Our results were comparable to field reports and to estimates based on watershed-wide crop water-balance model results. For example, both methods found that the 2003 seasonal ETa was the highest of all six years. The method also captured water management scenarios where a unique year-to-year variability was identified in addition to water-use differences between upstream and downstream basins. A major advantage of the energy-balance approach is that it can be used to quantify spatial extent of irrigated fields and their water-use dynamics without reference to source of water as opposed to a water- balance model which requires knowledge of both the magnitude and temporal distribution of rainfall and irrigation applied to fields.

Rift Valley Fever Potential, Arabian Peninsula

To the Editor: Rift Valley fever (RVF) virus causes severe disease, abortion, and death in domestic animals (especially young sheep, cattle, and goats) in Africa and the Arabian Peninsula. Humans are infected by mosquitoes, which maintain epizootic transmission, or through exposure to infected animal tissue. Although human disease may be mild, sometimes severe retinitis, meningoencephalitis, or hemorrhagic fever syndromes may develop in patients. In Africa, epizootics and associated human epidemics usually follow heavy rainfall (1). RVF was first confirmed outside Africa in September 2000. The outbreak in southwestern coastal Saudi Arabia and neighboring coastal areas of Yemen resulted in an epizootic with >120 human deaths and major losses in livestock populations from disease and slaughter (2,3). RVF virus isolated from the floodwater mosquito Aedes vexans arabiensis during the outbreak was closely related to strains from Madagascar (1991) and Kenya (1997), which suggests that the virus was imported through infected mosquitoes or livestock from East Africa (3). The Arabian outbreak followed increased rainfall in nearby highlands that flooded the coastal areas and created ideal environments for mosquito populations similar to those found in RVF-endemic regions of East Africa (4). Most RVF activity was associated with flooded wadi agricultural systems; no cases were reported in the mountains or in dry sandy regions, where surface water does not accumulate long enough to sustain mosquito breeding. To provide early warning of conditions favorable for RVF epidemics, the National Aeronautics and Space Administration (NASA) and the Department of Defense Global Emerging Infections Surveillance and Response System (DoD-GEIS) monitor the satellite-derived normalized difference vegetation index (NDVI), which reflects recent rainfall and other ecologic and climatic factors (5–7). NDVI anomalies in the highlands east of affected areas during the 2000 outbreak (Figure A1 panel A) showed a spatial pattern (although of lower magnitude) similar to recent anomalies in those areas (Figure A1 panel B). Greater than normal NDVIs (20%–60%) were seen in the Sarawat Mountains, from just northeast of Djeddah, Saudi Arabia, and southwestward beyond Jizan and into Hodeidah governorate in Yemen during May and June 2005. Figure A1 Figure A1. Systeme Probatoire pour l'Observation de la Terre vegetation sensor 1 km normalized difference vegetation index (NDVI) anomaly images of the Arabian Peninsula region during June 2000 (A) and June 2005 (B). Data are the percentage deviation ... Satellite-derived rainfall estimates show that widespread rainfall occurred over most of western Saudi Arabia and Yemen from mid-April to mid-June 2005 (8) and accounts for the high magnitude and spatial pattern of observed NDVI anomalies in May and June 2005. Rainfall was concentrated in the mountainous regions east of the Red Sea coast, and was heaviest in the areas east of Djeddah and Jizan, with rainfall totals as high as 120 mm and 60–80 mm, respectively, during April 2005, compared with the same period in 2000 (10–50 mm) (Figure A1 panels C and D) and in southwestern Yemen, with totals as high as 120 mm during May. In the area east of Djeddah, total rainfall in April 2005 was 150 mm above the long-term average for that month. Flooding was reported in Hodeidah Governorate, Yemen during May (9) and could be expected in other Red Sea coastal areas following such heavy rainfall. This created habitats appropriate for breeding of mosquitoes capable of transmitting RVF, as occurred in 2000. No human cases of RVF have been reported in Saudi Arabia and Yemen since the 2000 outbreak, but in September 2004 the Saudi Ministry of Agriculture reported that 5 RVF-seropositive sheep had been identified during routine surveillance in Jizan where most infected persons were exposed during the outbreak in 2000 (2). The primary infection was estimated to have occurred in April 2004 (10). The NDVIs and rainfall patterns alerted the Yemen and Saudi Arabia Ministries of Health and Ministries of Agriculture to conduct field investigations with the Food and Agriculture Organization and the World Health Organization. Since RVF virus can be maintained in mosquito eggs for extended periods and transmitted under favorable conditions (6), the high magnitude of NDVI and rainfall patterns reported should prompt heightened veterinary and human surveillance for RVF in coastal Arabia and mass vaccination of susceptible animals. The current RVF model (7) is indicative of conditions that would promote vector breeding and could result in an outbreak of mosquitoborne diseases.

DEVELOPING INDEX MAPS OF WATER-HARVEST POTENTIAL IN AFRICA

The food security problem in Africa is tied to the small farmer, whose subsistence farming relies heavily on rain-fed agriculture. A dry spell lasting two to three weeks can cause a significant yield reduction. A small-scale irrigation scheme from small-capacity ponds can alleviate this problem. This solution would require a water harvest mechanism at a farm level. In this study, we looked at the feasibility of implementing such a water harvest mechanism in drought prone parts of Africa. A water balance study was conducted at different watershed levels. Runoff (watershed yield) was estimated using the SCS curve number technique and satellite derived rainfall estimates (RFE). Watersheds were delineated from the Africa-wide HYDRO-1K digital elevation model (DEM) data set in a GIS environment. Annual runoff volumes that can potentially be stored in a pond during storm events were estimated as the product of the watershed area and runoff excess estimated from the SCS Curve Number method. Estimates were made for seepage and net evaporation losses. A series of water harvest index maps were developed based on a combination of factors that took into account the availability of runoff, evaporation losses, population density, and the required watershed size needed to fill a small storage reservoir that can be used to alleviate water stress during a crop growing season. This study presents Africa-wide water-harvest index maps that could be used for conducting feasibility studies at a regional scale in assessing the relative differences in runoff potential between regions for the possibility of using ponds as a water management tool.

Tropical-temperate links in southern African and Southwest Indian Ocean satellite-derived daily rainfall

An objective analysis of daily austral summer rainfall variability in the southern Africa and Southwest Indian Ocean region, using satellite-derived rainfall estimates, is conducted for the first time. A new method to reconstruct the GOES Precipitation Index (GPI), at the daily scale on a 2.5° grid, from ISCCP D1 satellite products is introduced. Estimates using this method are validated with GPI estimates on a monthly scale over global and regional domains. The reconstructed GPI (RGPI) estimates correlate very strongly with those from the GPI. Empirical Orthogonal Function (EOF) analysis of RGPI daily rainfall estimates for November–March (15–40°S, 10–70°E) shows that the leading mode of variability is characterised by tropical–temperate links in all months. These rain bands are orientated from north-west to south-east across the study region. Events characteristic of the leading mode of variability are relatively infrequent, occurring on average only twice per month, and can be stationary or propagating systems. A rainfall dipole exists, with parallel centres of activity producing enhanced (suppressed) activity over the Southwest Indian Ocean, associated with suppressed (enhanced) activity over southern Africa. Previous studies have shown that this dipole exists at monthly and interannual scales. This rainfall dipole has tropical–temperate links, whereas previous land-based rainfall analyses have emphasised only the tropical and subtropical components. Copyright © 1999 Royal Meteorological Society

A decision support system for optimum use of multisensor information in flood forecasting

The hydrometeorological and conceptual bases and guidelines for the implementation of a Decision Support System (DSS) able to provide policy makers and local civil protection authorities with suitable procedures in order to issue flash flood warnings in the southern European regions are presented in this paper. The research was undertaken within the EU Environment project STORM'93, aiming at the optimum use of multisensor monitoring tools in supporting flood forecasting applications. The proposed DSS is designed to improve cross‐fertilizing interactions and proper interpretation of data coming from different monitoring tools, addressing in particular radar and satellite derived rainfall estimates together with the traditional use of rain‐gauge networks and weather map forecasts. The structure of the system is oriented to a suitable non‐traditional approach, based on distributed warnings, to the assessment of flood hazard at the regional scale.

Tropical Rainfall: A Comparison of Satellite-derived Rainfall Estimates with Model Precipitation Forecasts, Climatologies, and Observations

Abstract Quantitative estimates of rainfall, as derived from satellite observations of cloud-top temperature, have been produced for each 2.5° lat-long location from 30°N to 30°S for the period 1986–89. These remotely sensed estimates are a preliminary product of the Global Precipitation Climatology Project (GPCP), which is charged with the development of a global rainfall dataset during the period 1986–95, using all available in situ data supplemented with satellite-derived estimates of precipitation. Four-year means (1986–89) of monthly satellite-derived rainfall estimates were compared to the climatological rainfall datasets compiled by Jaeger and Legates and Willmott in the tropics, to assess the performance of the satellite estimates in the context of existing state-of-the-art global precipitation datasets. It is shown that the satellite estimates and the climatologies are in reasonable agreement both in temporal and spatial variation, although some large regional differences were found. A more quant...