Dissertations / Theses on the topic 'Rainfall estimates'

Rain-fed agriculture is extremely important in sub-Saharan Africa, thus the ability to forecast and monitor regional crop yields throughout the growing season would be of enormous benefit to decision makers. Of equal importance to be able to assign a measure of uncertainty to the forecast, especially considering that many predictions are made in the context of a complex climate and sparse meteorological and agricultural observations. This work investigates these issues in the context of an operational updating regional crop yield forecast, concentrating in particular on a case study forecasting Ethiopian maize. Part 1 of the work presented a detailed discussion of Ethiopia' s climate and agricultural systems. As real-time ground based weather observations are sparse in Africa, Part 2 contains an investigation into remotely sensed satellite rainfall estimates. A daily TAMSAT calibration and the geostatistical process of sequential simulation were used to create a spatially correlated ensemble of Meteosat-derived rainfall estimates. The ensemble mean was evaluated as a daily deterministic rainfall product and was found to be as good as or better than other products applied in the same region. A validation of the full ensemble showed that they realistically estimated Ethiopian rainfall fields that agreed both with observed spatial correlations and input pixel level statistics. Part 3 of the work includes a discussion on regional crop simulation modelling and presents a new parameterisation of the GLAM crop simulation model for tropical maize. GLAMMAIZE was then driven using individual members of the satellite ensemble; this was shown to exhibit the correct sensitivities to climate inputs and performed reasonably against yield observations. Finally, Part 4 presented a new method of creating stochastic spatially and temporally correlated rainfall fields. This 'regional weather generator' was tested using a case study on Ethiopian April rainfall and a detailed discussion was included about future development plans.

Three-dimensional radar data for three Florida storms are used with a radiative transfer model to simulate observations at 19 GHz by a nadir pointing, satellite bourne microwave radiometer. Estimates were made of spatial sampling errors due to both horizontal and vertical variability of the precipitation. Calibrated radar data were taken as realistic representations of rainfall fields.<br>The optimal conversion between microwave brightness temperature and rainfall rate was highly sensitive to the spatial resolution of observations. Retrievals were made from the simulated microwave measurements using rainfall retrieval functions optimized for each resolution and for each storm case.<br>There is potential for microwave radiometer measurements from the planned TRMM satellite to provide better 'snapshot' estimates than area-threshold VIS/IR methods. Variability of the vertical profile of precipitation did not seriously reduce accuracy. However, it is crucial that calibration of retrieval methods be done with ground truth of the same spatial resolution.

Most of the working population in Ghana are farmers. It is of importance for them to know where and when precipitation will occur to prevent crop losses due to droughts and floodings. In order to have a sustainable agriculture, improved rainfall forecasts are needed. One way to do that is to enhance the initial conditions for the rainfall models. In the mid-latitudes, in-situ rainfall observations and radar data are used to monitor weather and measure rainfall. However, due to the lack of station data and the present absence of a radar network in West Africa, other rainfall estimates are needed as substitutes. The rainfall amount in convective systems, dominating in West Africa, is coupled to their vertical structure. Therefore, satellite measurements of cloud top temperatures and microwave scatter, as well as the number of lightning, can be used to estimate the amount of rainfall. In this report, derived rainfall estimates from satellites and the use of lightning data are analysed to see how well they estimate the actual rainfall amount. The satellite datasets used in this report are NOAA RFE2.0, NOAA ARC2, and the EUMETSAT MPE. The datasets were compared to in-situ measurements from GTS- and NGO collaborating observation stations in order to verify which satellite dataset that best estimates the rainfall or, alternatively, if a combination between two or all the datasets is a better approach. Lightning data from Vaisala GLD360 have been compared to GTS-station data and RFE2.0 to see if a relation between the number of lightning and rainfall amount could be found. It was also tested whether a combination between the satellite- and lightning data could be a better estimate than the two approaches separately. Rainfall estimates from RFE2.0 alone showed the best correlation to GTS- and the NGO collaborating station data. However, a difference in how well RFE2.0 estimated rainfall at GTS-stations compared to reference stations was seen. Comparing RFE2.0 to GTS-stations showed a better correlation, probably due to the use of these observations in the build up of RFE2.0. Even though RFE2.0 showed the best correlation compared to other datasets, satellite estimates showed in general poor skill in catching the actual rainfall amount, strongly underestimating heavy rainfall and somewhat overestimating lighter rainfall. This is probably due to the rather basic assumptions that the cloud top temperature is directly coupled to rain rate and also the poor temporal resolution of the polar orbiting satellites (carrying microwave sensors). Better instruments and algorithms need to be developed to be able to use satellite datasets as an alternative to rainfall measurements in West Africa. Furthermore, due to the lack of station data, only tentative results between GLD360 and GTS-stations could be made, showing a regime dependence. When further analysed to RFE2.0, a stronger temporal dependence, i.e. seasonal variation, rather than a spatial one was seen, especially during the build up of the monsoon. However, due to poor rainfall estimates from RFE2.0, no accurate rainfall-lightning relation could be made but trends regarding the relation were seen. The use of GLD360 showed to be an effective way to erase false precipitation from satellite estimates as well as locating the trajectory of convective cells. To be able to further analyse rainfall/lightning relation, more measurements of the true rainfall is needed from e.g. a radar.<br>Majoriteten av Ghanas befolkning arbetar inom jordbrukssektorn. Det är viktigt för jordbrukarna att veta när och var nederbörd kommer att falla för att deras skörd inte ska bli förstörd av till exempel torka eller översvämningar. Det behövs därför bättre nederbördsprognoser för ett hållbart jordbruk. Ett sätt att få mer noggranna prognoser är att förbättra initialvärden till nederbördsmodellerna. Vid de mellersta breddgraderna på norra halvklotet används nederbördsmätningar från in-situ stationer samt data från radarsystem som initialvärden, men på grund av få mätstationer och inget radarsystem i västra Afrika behövs alternativa nederbördsestimater. Nederbörden i västra Afrika domineras av konvektiva system, vars regnmängd är kopplad till dess vertikala struktur. Satellitmätningar av molntoppstemperaturen och mikrovågornas spridning och absorption, liksom antalet blixtar är också relaterat till molnets struktur och kan därför användas för att estimera nederbördsmängden. I den här rapporten analyserades nederbördsestimater från satellitdata samt användning av blixtdata för att undersöka hur bra metoderna är på att estimera den verkliga nederbördsmängden. Satellitdataseten som analyserades var NOAA RFE2.0, NOAA ARC2 och EUMETSAT MPE. Dataseten jämfördes med in-situ mätningar från GTS-stationer samt observationerfrån NGO-samarbetande jordbrukare för att verifiera vilket satellitdataset som ger det bästa nederbördsestimatet, alternativt att en kombination mellan två eller alla dataset ger det bästa estimatet. Vidare har blixtdata från Vaisala GLD360 jämförts med GTS-stationer och RFE2.0 för att se om antalet blixtar är relaterat till nederbördsmängden. Slutligen har det också undersökts om en kombination mellan satellit- och blixtdata är ett bättre än de två metoderna separat. Nederbördsestimater från RFE2.0 visade på bäst korrelation med både GTS- och NGO-stationer. En tydlig skillnad noterades dock i RFE2.0:s förmåga att estimera nederbörd vid jämförelse mellan de två stationsdataseten. En bättre korrelation mellan RFE2.0 och GTS-stationerna påvisades, troligen för att RFE2.0 använder dessa observationer i uppbyggnaden av datasetet. Även om RFE2.0 visade på bäst korrelation i jämförelse med ARC2 och MPE var samtliga satellitdataset dåliga på att estimera den verkliga nederbördsmängden. De underestimerar starkt stora mängder nederbörd samtidigt som de överestimerar små mängder. Anledningen är troligen det relativt enkla antagandet att molntoppstemperaturen är direkt kopplad till molnets regnmängd samt den dåliga tidsupplösningen på de polära satelliterna som är utrustade med mikrovågssensorer. För att satellitdataseten ska kunna användas som ett alternativt nederbördsestimat i Västafrika behövs bättre mätinstrument och algoritmer. Vid analysen mellan GLD360 och GTS-stationer kunde, på grund av för få stationsdata, endast övergripande resultat erhållas. Ett områdesberoende gick dock att urskilja som vid en ytterligare analys mellan GLD360 och RFE2.0 visade på ett större säsongsberoende, särskilt under uppbyggnaden av monsunperioden i april och maj. Eftersom RFE2.0 visade sig ha dåliga nederbördsestimat kunde ingen noggrann koppling hittas, utan resultatet visade på trender samt möjligheter att kunna använda blixtdata som ett alternativt nederbördsestimat. Till exempel visade det sig att GLD360 kunde användas som ett verktyg för att sålla bort falsk nederbörd från satellitestimat samt identifiera trajektorien för ett konvektivt system. För en djupare analys i att relatera blixtar och nederbörd i Västafrika krävs bättre tekniker för att estimera nederbörd eller fler in-situ observationer.

Studies that investigate and evaluate the quality, limitations and uncertainties of satellite rainfall estimates are fundamental to assure the correct and successful use of these products in applications, such as climate studies, hydrological modeling and natural hazard monitoring. Over regions of the globe that lack in situ observations, such studies are only possible through intensive field measurement campaigns, which provide a range of high quality ground measurements, e.g., CHUVA (Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GlobAl Precipitation Measurement) and GoAmazon (Observations and Modeling of the Green Ocean Amazon) over the Brazilian Amazon during 2014/2015. This study aims to assess the uncertainty of the Global Precipitation Measurement (GPM) satellite constellation in representing the main characteristics of precipitation over different regions of Brazil. The Integrated Multi-satellitE Retrievals for GPM (IMERG) (level-3) and the Goddard Profiling Algorithm (GPROF) (level-2) algorithms are evaluated against ground-based radar observations, specifically, the S-band weather radar from the Amazon Protection National System (SIPAM) and the X-band dual polarization weather radar (X-band CHUVA radar) as references. The space-based rainfall estimates, based on active microwave (e.g., TRMM-PR and GPM-DPR [at Ku-band] radars) are also used as references. The results for the CHUVA-Vale campaign suggest that GPROF has relatively good agreement (spatial distribution and accumulated rainfall), especially for convective rain cases, due the significant presence of ice scattering. However, the intensity and volume of light/moderate rains is overestimated and performance related to light/heavy rains (underestimated) are intrinsically linked to convectivestratiform rainfall occurrences over the study region. For the study over the Central Amazon Region (CHUVA-GoAmazon), results showed that during the wet season, IMERG, which uses the GPROF2014 rainfall retrieval from the GPM Microwave Imager (GMI) sensor, significantly overestimates the frequency of heavy rainfall volumes at around 00:0004:00 UTC and 15:0018:00 UTC. This overestimation is particularly evident over the Negro, Solimões and Amazon rivers due to the poorlycalibrated algorithm over water surfaces. On the other hand, during the dry season, the IMERG product underestimates mean precipitation in comparison to the S-band SIPAM radar, mainly due to the fact that isolated convective rain cells in the afternoon are not detected by the satellite precipitation algorithm. The study based on verification of GPM level 2 by traditional and object-based analysis shows that volume and occurrence of heavy rainfall are underestimated, a good agreement of GPROF2014 for TMI and GMI versus TRMM PR and GPM DPR (Ku band) rainfall retrievals, respectively, was noted. Such most evident good performances were found through continuous and categorical analyses, especially during the wet season, where the number of objects and larger areas were observed. The larger object area seen by GPROF2014(GMI) compared to DPR (Ku band) was directly linked to the structure of vertical profiles of the precipitanting systems and the presence of bright band was the main source of uncertainty on the estimation of precipitation area and intensity. The results via error modeling, through the Precipitation Uncertainties for Satellite Hydrology (PUSH) framework, demonstrated that the PUSH model was suitable for characterizing the error from the IMERG algorithm when applied to S-band SIPAM radar estimates. PUSH could efficiently predict the error distribution in terms of spatial and intensity distributions. However, an underestimation (overestimation) of light satellite rain rates was observed during the dry (wet) period, mainly over the river. Although the estimated error showed a lower standard deviation than the observed error, they exhibited good correlations to other, especially in capturing the systematic error along the Negro, Solimões and Amazon rivers, especially during the wet season.<br>Estudos que investigam e avaliam a qualidade, limitações e incertezas das estimativas de precipitação de satélites são fundamentais para assegurar o uso correto e bem-sucedido desses produtos em aplicações, como estudos climáticos, modelagem hidrológica e monitoramento de desastres naturais. Em regiões do globo que não possuem observações in situ, esses estudos apenas são possíveis através de campanhas intensivas de medição de campo, que oferecem uma gama de medições de superfície de alta qualidade, por exemplo, CHUVA (Cloudprocesses of tHe main precipitation systems in Brazil: A contribUtion to cloud re-solVing modeling and to the GlobAl Precipitation Measurement) e GoAmazon (Observations and Modeling of the Green Ocean Amazon) sobre a Amazônia Brasileira durante 2014/2015. Este estudo tem como objetivo avaliar as incertezas provenientes da constelação de satélites do Global Precipitation Measurement (GPM) em representar as principais características da precipitação em diferentes regiões do Brasil. Os algoritmos Integrated Multi-satellitE Retrievals for GPM (IMERG) (level-3) e Goddard Profiling Algorithm (GPROF) (level-2) são avaliados em contraste as observações de radares meteorológicos, especificamente, do Sistema Nacional de Proteção da Amazônia (SIPAM) e o radar meteorológico banda X de dupla polarização (X-band CHUVA radar) como referência. As estimativas de precipitação, baseadas em radares de microondas ativos (por exemplo, radares TRMM-PR e GPM-DPR [na banda Ku]) também são utilizadas como referência. Os resultados da campanha CHUVA-Vale sugerem que o GPROF possui uma boa concordância (distribuição espacial e precipitação acumulada), especialmente para casos de chuva convectiva, devido à presença significativa de espalhamento por gelo. No entanto, a intensidade e volume de chuvas leves/moderadas é superestimada e um desempenho (subestimado) relacionado às chuvas fracas/intensas diretamente ligado às ocorrências de chuvas convectivasestratiformes na região do estudo. Para o estudo da região da Amazônia Central (CHUVA-GoAmazon), os resultados mostraram que, durante a estação chuvosa, o IMERG, que utiliza as estimativas de precipitação do GPROF2014 a partir do sensor GPM Microwave Imager (GMI), superestima significativamente a freqüência de chuvas intensas em torno de 00:00-04:00 UTC e 15:00-18:00 UTC. Essa superestimativa é particularmente evidente nos rios Negro, Solimões e Amazonas devido ao algoritmo apresentasse erroneamente calibrado sobre as superfícies de água. Por outro lado, durante a estação seca, o produto IMERG subestima a precipitação média em comparação com o radar banda-s do SIPAM, principalmente devido ao fato de que células convectivas isoladas à tarde não são detectadas por tal algoritmo. O estudo baseado na verificação das estimativas do GPM Level 2 por abordagens tradicional e baseada em objeto mostra que, embora a subestimiativa do volume e ocorrência de chuvas intensas, foi observada uma boa concordância do GPROF2014 (TMI e GMI) versus TRMM PR e GPM DPR (Ku band), Respectivamente. Tais evidentes melhores desempenhos foram encontrados através de análises contínua e categórica, especialmente durante a estação chuvosa, onde o maior número e maiores áreas de objetos foram observados. As maiores áreas, observadas pelo GPROF2014 (GMI) comparada ao DPR (banda Ku) esteve diretamente ligada à estrutura de perfis verticais dos sistemas de precipitantes e a presença de banda brilhante foi a principal fonte de incerteza na estimativa da área e intensidade de precipitação. Os resultados referentes à modelagem do erro, através da ferramenta Precipitation Uncertainties for Satellite Hydrology (PUSH), as análises demonstraram que o modelo PUSH foi adequado para caracterizar o erro do algoritmo IMERG quando aplicado às estimativas de radar banda S do SIPAM. O modelo PUSH pôde prever eficientemente a distribuição de erro em termos espaciais e de intensidade. No entanto, observou-se uma subestimativa (superestimativa) das taxas de chuva fracas do satélite durante o período seco (chuvoso), especialmente ao longo do rio. Embora o erro estimado tenha apresentado menor desvio padrão do que o erro observado, eles apresentaram boas correlações entre si, especialmente na captura do erro sistemático ao longo dos rios Negro, Solimões e Amazonas, especialmente durante a estação chuvosa.

Weather radars have been used to quantitatively estimate precipitation since their development in the 1940s, yet these estimates are still prone to large uncertainties which dissuade the hydrological community in the UK from adopting these estimates as their primary rainfall data source. Recently dual polarisation radars have become more common, with the national networks in the USA, UK and across Europe being upgraded, and the benefits of dual polarisation radars are beginning to be realised for improving quantitative precipitation estimates (QPE). The National Centre for Atmospheric Science (NCAS) mobile Doppler X-band dual polarisation weather radar is the first radar of its kind in the UK, and since its acquisition in 2012 has been deployed on several field campaigns in both the UK and abroad. The first of these campaigns was the Convective Precipitation Experiment (COPE) where the radar was deployed in Cornwall (UK) through the summer of 2013. This thesis has used the data acquired during the COPE field campaign to develop a processing chain for the X-band radar which leverages its dual polarisation capabilities. The processing chain developed includes the removal of spurious echoes including second trip, ground clutter and insects through the use of dual polarisation texture fields, logical decision thresholds and fuzzy logic classification. The radar data is then corrected for the effects of attenuation and partial beam blockage (PBB) by using the differential phase shift to constrain the total path integrated attenuation and calibrate the radar azimuthally. A new smoothing technique has been developed to account for backscatter differential phase in the smoothing of differential phase shift which incorporates a long and a short averaging window in conjunction with weighting smoothing using the copolar correlation coefficient. During the correction process it is shown that the calculation of PBB is insensitive to the variation in the ratio between specific attenuation and specific differential phase shift provided a consistent value is used. It is also shown that the uncertainty in attenuation correction is lower when using a constrained correction such as the ZPHI approach rather than a direct linear correction using differential phase shift and is the preferred method of correction where possible. Finally the quality controlled, corrected radar moments are used to develop a rainfall estimation for the COPE field campaign. Results show that the quality control and correction process increases the agreement between radar rainfall estimates and rain gauges when using horizontal reflectivity from a regression correlation of -0.01 to 0.34, with a reduction in the mean absolute percentage difference (MAPD) from 86% to 31%. Using dual polarisation moments to directly estimate rainfall shows that rainfall estimates based on the theoretical conversion of specific attenuation to reflectivity produce the closest agreement to rain gauges for the field campaign with a MAPD of 24%. Finally it is demonstrated that merging multiple dual polarisation rainfall estimates together improves the performance of the rainfall estimates in high intensity rainfall events while maintaining the overall accuracy of the rainfall estimates when compared to rain gauges.

The role of remote sensing data for monitoring different parameters in the study of ecosystems has been increasing. Particularly the development of different indices has played a great role to study the properties of vegetation and vegetation dynamics in large countries. In addition to this, satellite rainfall estimate data has been used to study the pattern of precipitation in areas where station rain-gauge data is not available. The Normalized Difference Vegetation Index (NDVI) and rainfall estimates data from the National Oceanic and Atmospheric Administration (NOAA) satellites were used to investigate the spatio-tempotal pattern of precipitation and the response of vegetation to precipitation in Ethiopia from 1996 to 2008. The patterns were studied in different land cover classes using data from the Global Land Cover Network (GLCN). The spatial patternof NDVI and precipitation showed that vegetation responded directly to precipitation. The seasonal patterns showed that there was between 0 to 3 months lag between precipitationand vegetation. However it was not possible to draw conclusion regarding the annual trendsof precipitation and NDVI because of the nature of the NDVI data, which was produced using the 10 day maximum composite values.

Daily, quasi-global (50° N–S and 180° W–E), satellite-based estimates of actual evapotranspiration at 0.25° spatial resolution have recently become available, generated by the Global Land Evaporation Amsterdam Model (GLEAM). We investigate the use of these data to improve the performance of a simple lumped catchment-scale hydrologic model driven by satellite-based precipitation estimates to generate streamflow simulations for a poorly gauged basin in Africa. In one approach, we use GLEAM to constrain the evapotranspiration estimates generated by the model, thereby modifying daily water balance and improving model performance. In an alternative approach, we instead change the structure of the model to improve its ability to simulate actual evapotranspiration (as estimated by GLEAM). Finally, we test whether the GLEAM product is able to further improve the performance of the structurally modified model. Results indicate that while both approaches can provide improved simulations of streamflow, the second approach also improves the simulation of actual evapotranspiration significantly, which substantiates the importance of making <q>diagnostic structural improvements</q> to hydrologic models whenever possible.

In most regions of the world, and particularly in developing countries, the possibility and reliability of hydrologic predictions is severely limited, because conventional measurement networks (e.g. rain and stream gauges) are either nonexistent or sparsely located. This study, therefore, investigates various systems methods and newly available data acquisition techniques to evaluate their potential for improving hydrologic predictions in poorly gaged and ungaged watersheds.Part One of this study explores the utility of satellite-remote-sensing-based rainfall estimates for watershed-scale hydrologic modeling at watersheds in the Southeastern U.S. The results indicate that satellite-based rainfall estimates may contain significant bias which varies with watershed size and location. This bias, of course, then propagates into the hydrologic model simulations. However, model performance in large basins can be significantly improved if short-term streamflow observations are available for model calibration.Part Two of this study deals with the fact that hydrologic predictions in poorly gauged/ungauged watersheds rely strongly on a priori estimates of the model parameters derived from observable watershed characteristics. Two different investigations of the reliability of a priori parameter estimates for the distributed HL-DHMS model were conducted. First, a multi-criteria penalty function framework was formulated to assess the degree of agreement between the information content (about model parameters) contained in the precipitation-streamflow observational data set and that given by the a priori parameter estimates. The calibration includes a novel approach to handling spatially distributed parameters and streamflow measurement errors. The results indicated the existence of a significant trade-off between the ability to maintain reasonable model performance while maintaining the parameters close to their a priori values. The analysis indicates those parameters responsible for this discrepancy so that corrective measures can be devised. Second, a diagnostic approach to model performance assessment was developed based on a hierarchical conceptualization of the major functions of any watershed system. "Signature measures" are proposed that effectively extract the information about various watershed functions contained in the streamflow observations. Manual and automated approaches to the diagnostic model evaluation were explored and were found to be valuable in constraining the range of parameter sets while maintaining conceptual consistency of the model.

Reverse Hydrology is a term describing methods for estimating rainfall from streamflow. The method presented here is based on combining inversion of a causal rainfall-runoff model with regularisation. This novel method, termed RegDer, combines a continuous-time transfer function model with regularised derivative estimates and is compared with an alternative method for direct inversion of a discrete-time transfer function using sub-hourly data from two catchments with contrasting rainfall and catchment storage characteristics. It has been demonstrated to recover the prominent features of the observed rainfall enabling it to generate a streamflow hydrograph indistinguishable from the observed catchment outflow. The loss of temporal resolution of the resultant rainfall series is the price paid for the numerical stability of the RegDer method, however this does not affect its ability to capture the dynamics required for streamflow generation. The inferred rainfall series was initially interpreted as an estimate of catchment rainfall but was later more precisely described as the rainfall necessary for generating streamflow – Discharge Generating Rainfall (DGR). The spatial aspect of the method was investigated using data from a densely gauged catchment. Frequency domain aspects of RegDer dual interpretation as a composite spectral decomposition method are analysed and discussed in the context of catchment data. Potential applications and developments of the approach include in-filling and extending rainfall records, reducing uncertainty in both gauged and ungauged catchments by improving rainfall estimates, assessing and refining rain gauge networks and re-evaluating areal rainfall estimation.

Flood-envelope curves (FECs) are useful for constraining the upper limit of possible flood discharges within drainage basins in a particular hydroclimatic region. Their usefulness, however, is limited by their lack of a well-defined recurrence interval. In this study we use radar-derived precipitation estimates to develop an alternative to the FEC method, i.e., the frequency–magnitude–area-curve (FMAC) method that incorporates recurrence intervals. The FMAC method is demonstrated in two well-studied US drainage basins, i.e., the Upper and Lower Colorado River basins (UCRB and LCRB, respectively), using Stage III Next-Generation-Radar (NEXRAD) gridded products and the diffusion-wave flow-routing algorithm. The FMAC method can be applied worldwide using any radar-derived precipitation estimates. In the FMAC method, idealized basins of similar contributing area are grouped together for frequency–magnitude analysis of precipitation intensity. These data are then routed through the idealized drainage basins of different contributing areas, using contributing-area-specific estimates for channel slope and channel width. Our results show that FMACs of precipitation discharge are power-law functions of contributing area with an average exponent of 0.82 ± 0.06 for recurrence intervals from 10 to 500 years. We compare our FMACs to published FECs and find that for wet antecedent-moisture conditions, the 500-year FMAC of flood discharge in the UCRB is on par with the US FEC for contributing areas of  ∼ 10² to 10³ km². FMACs of flood discharge for the LCRB exceed the published FEC for the LCRB for contributing areas in the range of  ∼ 10³ to 10⁴ km². The FMAC method retains the power of the FEC method for constraining flood hazards in basins that are ungauged or have short flood records, yet it has the added advantage that it includes recurrence-interval information necessary for estimating event probabilities.

Thesis (M.S.)--University of Missouri-Columbia, 2005.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (December 19, 2006) Includes bibliographical references.

The subject of this study is rainfall-runoff forecasting and flood warning. Denote by (X(t),Y(t)) a sequence of equally spaced bivariate random variables representing rainfall and runoff, respectively. A flood is said to occur at time period (n + 1) if Y(n + 1) > T where T is a fixed number. The main task of flood warning is that of deciding whether or not to issue a flood alarm for the time period n + 1 on the basis of the past observations of rainfall and runoff up to and including time n. With each decision, warning or no warning, there is a certain probability of an error (false alarm or no alarm). Using notions from classical decision theory, the optimal solution is the decision that minimizes Bayes risk. In Chapter 1 a more precise definition of flood warning will be given. A critical review (Chapter 2) of classical methods for forecasting used in hydrology reveals that these methods are not adequate for flood warning and similar types of decision problems unless certain Gaussian assumptions are satisfied. The purpose of this study is to investigate the application of a nonparametric technique referred to as the k-nearest neighbor (k-NN) methods to flood warning and least squares forecasting. The motivation of this method stems from recent results in statistics which extends nonparametric methods for inferring regression functions in a time series setting. Assuming that the rainfall-runoff process can be cast in the framework of Markov processes then, with some additional assumptions, the k-NN technique will provide estimates that converge with an optimal rate to the correct decision function. With this in mind, and assuming that our assumptions are valid, then we can claim that this method will, as the historical record grows, provide the best possible estimate in the sense that no other method can do better. A detailed description of the k-NN estmator is provided along with a scheme for calibration. In the final chapters, the forecasts of this new method are compared with the forecasts of several other methods commonly used in hydrology, on both real and simulated data.

Thesis (M.S.)--University of Missouri-Columbia, 2008.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 13, 2009) Includes bibliographical references.

Depuis le développement des mesures satellites de nombreuses missions spatiales sont dédiées au suivi de l'atmosphère et de la surface terrestre. Ces travaux de thèse s'inscrivent dans le cadre de la mission Megha-Tropiques dédiée au cycle de l'eau et de l'énergie en zone tropicale. L'objectif est d'évaluer le potentiel des estimations de précipitation par satellite pour des applications hydrologiques en zone tropicale. Les Tropiques réunissent les plus grands fleuves du globe, mais ne bénéficient pas de réseaux d'observation in-situ denses et continus permettant une gestion intégrée efficace de la ressource et des systèmes d'alertes. Les estimations des précipitations issues des systèmes d'observation satellite offrent une alternative pour ces bassins peu ou pas instrumentés et souvent exposés aux extrêmes climatiques. C'est le cas du fleuve Niger, qui a subi une grande variabilité climatique depuis les années 1950, mais aussi d'importants changements environnementaux et hydrologiques. Depuis les années 2000, le Niger moyen connaît une recrudescence des inondations pendant la période de crue Rouge (engendrée par ses affluents sahéliens pendant la mousson). A Niamey, des niveaux record de hauteur d'eau et de période d'inondation ont été enregistrés en 2003, 2010, 2012 et 2013, engendrant de nombreuses pertes humaines et matérielles. Ces travaux analysent l'influence du forçage pluviométrique sur les inondations liées à la crue Rouge à Niamey. Une gamme de produits pluviométriques (in situ et satellite) et la modélisation hydrologique (ISBA-TRIP) sont combinés pour étudier : (i) l'apport des produits satellite pour diagnostiquer la crue Rouge récente, (ii) l'impact des caractéristiques des produits et de leurs incertitudes sur les simulations et enfin (iii) l'évaluation du rôle des précipitations, face aux changements de conditions de surface, dans l'évolution de la crue Rouge à Niamey depuis les années 1950. L'étude a mis en évidence l'impact des caractéristiques des estimations des précipitations (cumul, intensité et distribution spatio-temporelle) sur la modélisation hydrologique et le potentiel des produits satellites pour le suivi des inondations. Les caractéristiques des précipitations se propageant dans la modélisation, la détection des inondations est plus efficace avec une approche relative à chaque produit plutôt qu'avec un seuil absolu. Ainsi des produits présentant des biais peuvent être envisagés pour la simulation hydrologique et la détection des inondations. Le nouveau produit TAPEER de la mission MT présente un fort potentiel hydrologique, en 2012 et pour la zone d'étude. D'autre part, l'étude de la propagation de l'erreur associée à ces précipitations a mis en évidence, la nécessité de déterminer la structure du champ d'erreur pour l'utilisation d'une telle information en hydrologie. Enfin la modélisation a été utilisée comme levier pour décomposer les sensibilités de la crue Rouge aux variations des précipitations et des conditions de surface. Pour simuler les changements hydrologiques entre les périodes 1953-1982 et 1983-2012, les changements d'occupation du sol et d'aire de drainage doivent être pris en compte. Puis les variations des précipitations peuvent expliquer les changements majeurs décennaux et annuels entre les années 1983 et 2012<br>Since the development of satellite based remote sensing in the 1970s, many missions have been dedicated to monitoring the terrestrial atmosphere and surfaces. Some of these satellites are dedicated to the Tropics with specific orbits. Megha-Tropiques (MT) is devoted to the water and energy cycle in the tropical atmosphere and provides an enhanced sampling for rainfall estimation in the tropical region. This PhD work was initiated within MT hydro-meteorological activities, with the objective of assessing the hydrological potential of satellite rainfall products in the Tropics. The world most important rivers lay in tropical areas where the in situ observation networks are deficient. Alternative information is therefore needed for water resource management and alert systems. The present work focuses on the Niger River a basin which has undergone drastic climatic variations leading to disasters such as droughts and floods. Since 1950, the Niger has been through 3 main climatic periods: a wet period (1950-1960), a long and intense drought period (1970-1980) and since 1990 a partial recovery of the rainfall. These climatic variations and the anthropic pressure, have modified the hydrological behaviour of the basin. Since 2000, the middle Niger River has been hit by an increase of floods hazards during the so-called Red flood period. In Niamey city, the highest river levels and the longest flooded period were recorded in 2003, 2010, 2012 and 2013, leading to heavy casualties and property damage. This study combines hydrological modelling and a variety of rainfall estimation products (satellite and in-situ) to meet several objectives: (i) the simulation of the Niamey Red flood and the detection of floods (during the recent period 2000-2013) (ii) the study of the propagation of satellite rainfall errors in hydrological modelling (iii) the evaluation of the role of rainfall variability, and surface conditions, in the changes of the Red flood in Niamey since the 50s. The global model ISBA-TRIP, is run with a resolution of 0.5° and 3h, and several rainfall products were used as forcing. Products derived from gauges (KRIG, CPC), pure satellite products (TAPEER, 3B42RT, CMORPH, PERSIANN) and mixed satellite products adjusted by rain gauges (3B42v7, RFE2, PERSIANN-CDR). This work confirms the hydrological potential of satellite rainfall products and proposes an original approach to overcome their biases. It highlights the need for documenting the errors associated with the rainfall products and the error structure. Finally, the hydrological modelling results since the 1950s have given a new understanding of the relative role of rainfall and surface conditions in the drastic increase of flood risk in Niamey

Thesis (Ph.D.)--University of Adelaide, Dept. of Civil and Environmental Engineering, 2002.<br>"May 2002" Includes list of papers published during this study. Errata slip inserted inside back cover of v. 1. Includes bibliographical references (leaves 331-357).

Made available in DSpace on 2016-08-29T15:36:37Z (GMT). No. of bitstreams: 1 tese_7192_Franciane Louzada.pdf: 3423474 bytes, checksum: 8d995c834302f420f1480045227459b6 (MD5) Previous issue date: 2016-02-29<br>CAPES<br>A precipitação é importante para calcular a disponibilidade hídrica de uma região e a qualidade desses dados pode afetar os resultados do balanço hídrico. Portanto, dados com melhor cobertura espacial são necessários. O satélite Tropical Rainfall Measuring Mission TRMM, produto 3B43-v7, estima a precipitação com resolução espacial de 0,25° x 0,25°. O objetivo deste trabalho foi avaliar os dados de precipitação estimados pelo 3B43-v7 e seu potencial uso na elaboração do balanço hídrico climatológico para a bacia hidrográfica do rio Doce. Foi gerado o mapeamento da disponibilidade hídrica pixel-a-pixel com boa cobertura espacial. Os dados do satélite foram comparados com os dados dos postos meteorológicos. Os erros relativos na estação chuvosa apresentaram melhores resultados do que na estação seca, que apresentou maiores porcentagens de erro. O satélite estimou adequadamente a precipitação, apresentando ótima correlação com os postos meteorológicos e respondendo a sazonalidade do clima da região nas estações de seca e chuvosa. Entretanto, apresentou tendência a superestimar a precipitação. O balanço hídrico climatológico com o 3B43-v7 apresentou correlação acima de 0,82. Os maiores erros percentuais foram encontrados em postos na região sudeste e nordeste da bacia (no Baixo Doce e Médio Doce). Os dados do 3B43-v7 caracterizou o balanço hídrico climatológico de forma semelhante aos dados observados pelos postos meteorológicos. A precipitação estimada pelo 3B43-v7 proporcionou a realização do balanço hídrico climatológico dessa bacia com boa cobertura. A utilização dos dados de precipitação estimados pelo 3B43-v7 pode ser uma ferramenta importante na caracterização de disponibilidade hídrica regional, contribuindo para o planejamento e manejo agrícola, principalmente por preencher as lacunas deixadas devido à ausência de postos meteorológicos nessa bacia, disponibilizando um produto com boa cobertura espacial.<br>Rainfall is important to calculate water availability of a region and its quality may affect the results of the water balance. Therefore data source with better spatial coverage is needed. The Tropical Rainfall Measuring Mission satellite -TRMM 3B43-v7 product estimated precipitation with spatial resolution of 0.25 ° x 0.25 °. The objective of this study was to evaluate the rainfall data estimated by 3B43-v7 and its impact on climatic water balance, by Thornthwaite method and Mather (1955), to the watershed of the river Doce. Mapping was generated pixel-by-pixel water availability with good spatial coverage. Satellite data were compared with data from weather stations. The errors in the rainy season showed better results than in the dry season that had the highest error percentages. The satellite performed well, properly estimated the rainfall, showed excellent correlation with weather stations, said the seasonal climate of the area, but tended to overestimate precipitation. The climatic water balance with the 3B43-v7 showed good correlation with the observed. Errors may have had an influence of cold air masses that predominate in this period and the higher regions. The largest percentage errors were found in stations in the southeast and northeast of the basin (in the Baixo Doce and Médio Doce). The 3B43-v7 data characterized the climatic water balance in a manner similar to data collected by the meteorological stations. The rainfall estimated by 3B43-v7 provided the realization of climatic water balance of the basin with good coverage. The use of estimated rainfall data by 3B43-v7 can be an important tool in the characterization of regional water availability, contributing to the planning and agricultural management, mainly to fill the gaps left by the absence of weather stations in this basin, providing a product with good spatial coverage.

Esta tesis explora la precisión de dos métodos de estimación de precipitación, Auto-Estimator y CRR (Convective Rainfall Rate), generados a partir de imágenes infrarrojas y visibles del Meteosat. Ambos métodos junto con una serie de correcciones de la intensidad de lluvia estimada se aplican y se verifican en dos casos de inundaciones acaecidas en zonas mediterráneas. El primer caso ocurrió en Albania del 21 al 23 de septiembre de 2002 y el segundo, conocido como caso Montserrat, ocurrió en Cataluña la noche del 9 al 10 se junio de 2000. Por otro lado se investiga la posibilidad de realizar calibraciones de ambos métodos directamente con datos de estaciones pluviométricas cuando lo común es calibrar con datos de radares meteorológicos. También se propone cambios en algunas de las correcciones ya que parecen mejorar los resultados y se propone una nueva corrección muy eficiente que utiliza las descargas eléctricas para determinar la zonas más convectivas y de mayor precipitación de los sistemas nubosos.<br>This Thesis work explores the precision of two methods to estimate rainfall called Auto-Estimator and CRR (Convective Rainfall Rate). They are obtained by using infrared and visible images from Meteosat. Both Algorithms within a set of correction factors are applied and verified in two severe flood cases that took place in Mediterranean regions. The first case has occurred in Albania from 21 to 23 September 2002 and the second, known as the Montserrat case, has occurred in Catalonia the night from the 9 to 10 of June 2000. On the other hand it is explored new methods to perform calibrations to both satellite algorithms using direct rain rates from rain gauges. These kinds of adjustments are usually done using rain rates from meteorological radars. In addition it is proposed changes on some correction factors that seem to improve the results on estimations and it is defined an efficient correction factor that employ electrical discharges to detect the most convective and rainy areas in cloud systems.

Partant du constat d’une faible connaissance et d’une utilisation encore limitée des données climatiques en économie du développement, cette thèse propose une exploitation originale à la fois macroéconomique et microéconomique des données climatiques. Elle répond à un double objectif consistant d’une part, à enrichir les connaissances concernant les données climatiques en fournissant une nouvelle base de données mondiale directement exploitable par les économistes, d’autre part, à en proposer des exploitations variées et à différentes échelles de l’économie. La première partie propose une utilisation macroéconomique des données climatiques. Elle doit permettre une meilleure appréhension des données climatiques mondiales, afin d’améliorer des connaissances sur les caractéristiques du changement climatique d’un point de vue économique. Le chapitre 1 produit une base de données climatiques disponible pour près de 200 pays au niveau mensuel pour la période 1900-2008. S’appuyant sur ce premier travail, le chapitre 2 propose une utilisation originale de la base en construisant un indicateur de vulnérabilité physique au changement climatique pouvant servir d’outil pour guider l’allocation de l’aide à l’adaptation au changement climatique. La seconde partie de cette thèse est constituée de deux études microéconomiques dont le but est d’analyser les réactions des agents face à un choc climatique. Dans ces deux chapitres, les données pluviométriques pallient le manque de données concernant la production agricole. Au niveau du ménage agricole d’abord, le chapitre 3 s’intéresse aux conséquences d’un épisode de baisse de la pluviométrie sur l’état nutritionnel des enfants de moins de cinq ans et révèle que les ménages ruraux n’ont pas la capacité d’assurer ou d’absorber ces chocs climatiques. Au niveau d’un marché céréalier ensuite, le chapitre 4 s’attache à étudier la réaction des prix face à un choc pluviométrique et met en évidence la présence d’une spéculation, particulièrement sur des marchés mal intégrés et de petite taille<br>Acknowledging a limited knowledge and use of climate data in development economics, this thesis proposes a new perspective on the use of both macroeconomic and microeconomic climate data. This thesis has a two-Fold objective: to deepen knowledge on climate data through the production of a new global database directly exploitable by economists, and to propose several applications of this database at different economic scales. The first part focuses on the macroeconomic aspect of the climate data. Its aim is to empower researchers with a deeper understanding of global climate data and to increase economic knowledge on the characteristics of climate change. Chapter 1 contains a climate database available monthly over the 1900-2008 period for nearly 200 countries. Based on this initial work, Chapter 2 proposes an original use of the database with the construction of a physical indicator of vulnerability to climate change, a tool which can be used as a basis for the allocation of aid for climate change adaptation. The second part of this thesis is composed of two microeconomic studies whose aim is to analyse the behaviour of agents facing a rainfall shock. In those two chapters, rainfall data is used to compensate for the lack of data on agricultural production. At the household level, Chapter 3 focuses on the implications of an episode of reduced rainfall on the nutritional status of children under five years old. It shows that rural households do not have the capacity to provide for or absorb climate shocks. At the grain market level, Chapter 4 studies the response of prices to a shock front rainfall. It reveals the presence of speculation, especially in small and poorly integrated markets

Le plateau des Guyanes est une région qui est caractérisée à 90% d’une forêt tropicale primaire et compte pour environ 20% des réserves mondiales d’eau douce. Ce territoire naturel, au vaste réseau hydrographique, montre des intensités pluviométriques annuelles atteignant 4000 mm/an ; ce qui fait de ce plateau une des régions les plus arrosées du monde. De plus les précipitations tropicales sont caractérisées par une variabilité spatiale et temporelle importante. Outre les aspects liés au climat, l’impact des précipitations dans cette région du globe est important en termes d’alimentation énergétique (barrages hydroélectriques). Il est donc important de développer des outils permettant d’estimer quantitativement et qualitativement et à haute résolution spatiale et temporelle les précipitations dans cette zone. Cependant ce vaste espace géographique est caractérisé par un réseau de stations pluviométriques peu développé et hétérogène, ce qui a pour conséquence une méconnaissance de la répartition spatio-temporelle précise des précipitations et de leurs dynamiques.Les travaux réalisées dans cette thèse visent à améliorer la connaissance des précipitations sur le plateau des Guyanes grâce à l’utilisation des données de précipitations satellites (Satellite Precipitation Product : SPP) qui offrent dans cette zone une meilleure résolution spatiale et temporelle que les mesures in situ, au prix d’une qualité moindre en terme de précision.Cette thèse se divise en 3 parties. La première partie compare les performances de quatre produits d’estimations satellitaires sur la zone d’étude et tente de répondre à la question : quelle est la qualité de ces produits au Nord de l’Amazone et sur la Guyane française dans les dimensions spatiales et temporelles ? La seconde partie propose une nouvelle technique de correction de biais des SPP qui procède en trois étapes : i) utiliser les mesures in situ de précipitations pour décomposer la zone étudiée en aires hydro-climatiques ii) paramétrer une méthode de correction de biais appelée quantile mapping sur chacune de ces aires iii) appliquer la méthode de correction aux données satellitaires relatives à chaque aire hydro-climatique. On cherche alors à répondre à la question suivante : est-ce que le paramétrage de la méthode quantile mapping sur différentes aires hydro-climatiques permet de corriger les données satellitaires de précipitations sur la zone d’étude ? Après avoir montré l’intérêt de prendre en compte les différents régimes pluviométriques pour mettre en œuvre la méthode de correction QM sur des données SPP, la troisième partie analyse l’impact de la résolution temporelle des données de précipitations utilisées sur la qualité de la correction et sur l’étendue spatiale des données SPP potentiellement corrigeables (données SPP sur lesquelles la méthode de correction peut s’appliquer avec efficacité). Concrètement l’objectif de cette partie est d’évaluer la capacité de notre méthode à corriger sur une large échelle spatiale le biais des données TRMM-TMPA 3B42V7 en vue de rendre pertinente l’exploitation de ce produit pour différentes applications hydrologiques.Ce travail a permis de corriger les séries satellites journalières à haute résolution spatiale et temporelle sur le plateau des Guyanes selon une approche nouvelle qui utilise la définition de zones hydro-climatiques. Les résultats positifs en terme de réduction du biais et du RMSE obtenus grâce à cette nouvelle approche, rendent possible la généralisation de cette nouvelle méthode dans des zones peu équipées en pluviomètres<br>The Guiana Shield is a region that is characterized by 90% of a primary rainforest and about 20% of the world’s freshwater reserves. This natural territory, with its vast hydrographic network, shows annual rainfall intensities up to 4000 mm/year; making this plateau one of the most watered regions in the world. In addition, tropical rainfall is characterized by significant spatial and temporal variability. In addition to climate-related aspects, the impact of rainfall in this region of the world is significant in terms of energy supply (hydroelectric dams). It is therefore important to develop tools to estimate quantitatively and qualitatively and at high spatial and temporal resolution the precipitation in this area. However, this vast geographical area is characterized by a network of poorly developed and heterogeneous rain gauges, which results in a lack of knowledge of the precise spatio-temporal distribution of precipitation and their dynamics.The work carried out in this thesis aims to improve the knowledge of precipitation on the Guiana Shield by using Satellite Precipitation Product (SPP) data that offer better spatial and temporal resolution in this area than the in situ measurements, at the cost of poor quality in terms of precision.This thesis is divided into 3 parts. The first part compares the performance of four products of satellite estimates on the study area and attempts to answer the question : what is the quality of these products in the Northern Amazon and French Guiana in spatial and time dimensions ? The second part proposes a new SPP bias correction technique that proceeds in three steps: i) using rain gauges measurements to decompose the studied area into hydro climatic areas ii) parameterizing a bias correction method called quantile mapping on each of these areas iii) apply the correction method to the satellite data for each hydro-climatic area. We then try to answer the following question : does the parameterization of the quantile mapping method on different hydro-climatic areas make it possible to correct the precipitation satellite data on the study area ? After showing the interest of taking into account the different rainfall regimes to implement the QM correction method on SPP data, the third part analyzes the impact of the temporal resolution of the precipitation data used on the quality of the correction and the spatial extent of potentially correctable SPP data (SPP data on which the correction method can be applied effectively). In summary, the objective of this section is to evaluate the ability of our method to correct on a large spatial scale the bias of the TRMM-TMPA 3B42V7 data in order to make the exploitation of this product relevant for different hydrological applications.This work made it possible to correct the daily satellite series with high spatial and temporal resolution on the Guiana Shield using a new approach that uses the definition of hydro-climatic areas. The positive results in terms of reduction of the bias and the RMSE obtained, thanks to this new approach, makes possible the generalization of this new method in sparselygauged areas

The Canadian Precipitation Analysis (CaPA) produces a gridded product by assimilating data from stations and the Global Environmental Multiscale (GEM) model. This project assesses the performance of the satellite based rainfall estimates for Canada, and the results of their assimilation with CaPA. The satellite based estimates considered are those from the Climate Prediction Center Morphing method (CMORPH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). Relative to the Second Generation of Daily Adjusted Precipitation for Canada (APC2), all satellite products are shown to generally underestimate rainfall, however convective events result in an overestimation. Skill scores show that the satellite products possess the most skill for eastern Canada and decreasingly so westward. When assimilated with CaPA, the satellite products express decreased skill for light rainfall and potential improvements for larger events. While central Canada experiences the greatest improvements, all regions benefit the most from June through August.

碩士<br>國立臺灣師範大學<br>地球科學系<br>104<br>Western North Pacific is an area with numberous typhoon activities. Using model to simulate typhoon is one of the important issues to research climate change. In order to verify the accuracy of models, reanalysis data or satellite observation data with high regional coverage is needed as a standard, for example: ERA-interim, áphrodite, and TRMM. This study highlights TRMM 3B42, a satellite-merge data, comparing to station observation data during typhoon affecting Taiwan. First choosing 1998-2010 for TRMM 3B42 (both v6 and v7), and 2000-2010 for GSMaP-MVK as comparison purposes. To achieve the goal of fair comparison, adjusting these three datasets to the same both time and space resolution, also space coverage is the first priority. After ranking with station observation data’s rainfall average and separate rainfall data to several groups, both TRMM 3B42 3hrly v6 and v7 are systematically weaker than station observation data, and so does GSMaP-MVK. To further understand TRMM’s contribution to TRMM 3B42 3-hrly v6, this study also uses TRMM 3G68 to separate PR (Precipitation Radar), TMI (TRMM Microwave Imager), the two variables for observing their behavior during 1998-2010 when typhoons were on Western North Pacific overseas. In contrast to PR, TMI performs more closely to reality around the eye of typhoons.

Error characteristics associated with satellite-derived precipitation products are important for atmospheric and hydrological model data assimilation, forecasting, and climate diagnostic applications. This information also aids in the refinement of physical assumptions within algorithms by identifying geographical regions and seasons where existing algorithm physics may be incorrect or incomplete. Examination of relative errors between independent estimates derived from satellite microwave data is particularly important over regions with limited surface-based equipments for measuring rain rate such as the global oceans and tropical continents. In this context, analysis of microwave based satellite datasets from the Tropical Rainfall Measuring Mission (TRMM) enables to not only provide information regarding the inherent uncertainty within the current TRMM products, but also serves as an opportunity to prototype error characterization methodologies for the TRMM follow-on program, the Global Precipitation Measurement (GPM) . Most of the TRMM uncertainty evaluation studies focus on the accuracy of rainfall accumulated over time (e.g., season/year). Evaluation of instantaneous rainfall intensities from TRMM orbital data products is relatively rare. These instantaneous products are known to potentially cause large uncertainties during real time flood forecasting studies at the watershed scale. This is more so over land regions, where the highly varying land surface emissivity offers a myriad of complications, hindering accurate rainfall estimation. The error components of orbital data products also tend to interact nonlinearly with hydrologic modeling uncertainty. Keeping these in mind, the present thesis fosters the development of uncertainty analysis using instantaneous satellite orbital data products (latest version 7 of 1B11, 2A25, 2A23, 2B31, 2A12) derived from the passive and active microwave sensors onboard TRMM satellite, namely TRMM Microwave Imager (TMI) and precipitation radar (PR). The study utilizes 11 years of orbital data from 2002 to 2012 over the Indian subcontinent and examines the influence of various error sources on the convective and stratiform precipitation types. Two approaches are taken up to examine uncertainty. While the first approach analyses independent contribution of error from these orbital data products, the second approach examines their combined effect. Based on the first approach, analysis conducted over the land regions of Mahanadi basin, India investigates three sources of uncertainty in detail. These include 1) errors due to improper delineation of rainfall signature within microwave footprint (rain/no rain classification), 2) uncertainty offered by the transfer function linking rainfall with TMI low frequency channels and 3) sampling errors owing to the narrow swath and infrequent visits of TRMM sensors. The second approach is hinged on evaluating the performance of rainfall estimates from each of these orbital data products by accumulating them within a spatial domain and using error decomposition methodologies. Microwave radiometers have taken unprecedented satellite images of earth’s weather, proving to be a valuable tool for quantitative estimation of precipitation from space. However, as mentioned earlier, with the widespread acceptance of microwave based precipitation products, it has also been recognized that they contain large uncertainties. One such source of uncertainty is contributed by improper detection of rainfall signature within radiometer footprints. To date, the most-advanced passive microwave retrieval algorithms make use of databases constructed by cloud or numerical weather model simulations that associate calculated microwave brightness temperature to physically plausible sample rain events. Delineation of rainfall signature from microwave footprints, also known as rain/norain classification (RNC) is an essential step without which the succeeding retrieval technique (using the database) gets corrupted easily. Although tremendous advances have been made to catapult RNC algorithms from simple empirical relations formulated for computational expedience to elaborate computer intensive schemes which effectively discriminate rainfall, a number of challenges remain to be addressed. Most of the algorithms that are globally developed for land, ocean and coastal regions may not perform well for regional catchments of small areal extent. Motivated by this fact, the present work develops a regional rainfall detection algorithm based on scattering index methodology for the land regions of study area. Performance evaluation of this algorithm, developed using low frequency channels (of 19 GHz, 22 GHz), are statistically tested for individual case study events during 2011 and 2012 Indian summer monsoonal months. Contingency table statistics and performance diagram show superior performance of the algorithm for land regions of the study region with accurate rain detection observed in 95% of the case studies. However, an important limitation of this approach is comparatively poor detection of low intensity stratiform rainfall. The second source of uncertainty which is addressed by the present thesis, involves prediction of overland rainfall using TMI low frequency channels. Land, being a radiometrically warm and highly variable background, offers a myriad of complications for overland rain retrieval using microwave radiometer (like TMI). Hence, land rainfall algorithms of TRMM TMI have traditionally incorporated empirical relations of microwave brightness temperature (Tb) with rain rate, rather than relying on physically based radiative transfer modeling of rainfall (as implemented in TMI ocean algorithm). In the present study, sensitivity analysis is conducted using spearman rank correlation coefficient as the indicator, to estimate the best combination of TMI low frequency channels that are highly sensitive to near surface rainfall rate (NSR) from PR. Results indicate that, the TMI channel combinations not only contain information about rainfall wherein liquid water drops are the dominant hydrometeors, but also aids in surface noise reduction over a predominantly vegetative land surface background. Further, the variations of rainfall signature in these channel combinations were seldom assessed properly due to their inherent uncertainties and highly non linear relationship with rainfall. Copula theory is a powerful tool to characterize dependency between complex hydrological variables as well as aid in uncertainty modeling by ensemble generation. Hence, this work proposes a regional model using Archimedean copulas, to study dependency of TMI channel combinations with respect to precipitation, over the land regions of Mahanadi basin, India, using version 7 orbital data from TMI and PR. Studies conducted for different rainfall regimes over the study area show suitability of Clayton and Gumbel copula for modeling convective and stratiform rainfall types for majority of the intraseasonal months. Further, large ensembles of TMI Tb (from the highly sensitive TMI channel combination) were generated conditional on various quantiles (25th, 50th, 75th, 95th) of both convective and stratiform rainfall types. Comparatively greater ambiguity was observed in modeling extreme values of convective rain type. Finally, the efficiency of the proposed model was tested by comparing the results with traditionally employed linear and quadratic models. Results reveal superior performance of the proposed copula based technique. Another persistent source of uncertainty inherent in low earth orbiting satellites like TRMM arise due to sampling errors of non negligible proportions owing to the narrow swath of satellite sensors coupled with a lack of continuous coverage due to infrequent satellite visits. This study investigates sampling uncertainty of seasonal rainfall estimates from PR, based on 11 years of PR 2A25 data product over the Indian subcontinent. A statistical bootstrap technique is employed to estimate the relative sampling errors using the PR data themselves. Results verify power law scaling characteristics of relative sampling errors with respect to space time scale of measurement. Sampling uncertainty estimates for mean seasonal rainfall was found to exhibit seasonal variations. To give a practical demonstration of the implications of bootstrap technique, PR relative sampling errors over the sub tropical river basin of Mahanadi, India were examined. Results revealed that bootstrap technique incurred relative sampling errors of <30% (for 20 grid), <35% (for 10 grid), <40% (for 0.50 grid) and <50% (for 0.250 grid). With respect to rainfall type, overall sampling uncertainty was found to be dominated by sampling uncertainty due to stratiform rainfall over the basin. In order to study the effect of sampling type on relative sampling uncertainty, the study compares the resulting error estimates with those obtained from latin hypercube sampling. Based on this study, it may be concluded that bootstrap approach can be successfully used for ascertaining relative sampling errors offered by TRMM-like satellites over gauged or ungauged basins lacking in in-situ validation data. One of the important goals of TRMM Ground Validation Program has been to estimate the random and systematic uncertainty associated with TRMM rainfall estimates. Disentangling uncertainty in seasonal rainfall offered by independent observations of TMI and PR enables to identify errors and inconsistencies in the measurements by these instruments. Motivated by this thought, the present work examines the spatial error structure of daily precipitation derived from the version 7 TRMM instantaneous orbital data products through comparison with the APHRODITE data over a subtropical region namely Mahanadi river basin of the Indian subcontinent for the seasonal rainfall of 6 years from June 2002 to September 2007. The instantaneous products examined include TMI and PR data products of 2A12, 2A25 and 2B31 (combined data from PR and TMI). The spatial distribution of uncertainty from these data products was quantified based on the performance metrics derived from the contingency table. For the seasonal daily precipitation over 10x10 grids, the data product of 2A12 showed greater skill in detecting and quantifying the volume of rainfall when compared with 2A25 and 2B31 data products. Error characterization using various error models revealed that random errors from multiplicative error models were homoscedastic and that they better represented rainfall estimates from 2A12 algorithm. Error decomposition technique, performed to disentangle systematic and random errors, testified that the multiplicative error model representing rainfall from 2A12 algorithm, successfully estimated a greater percentage of systematic error than 2A25 or 2B31 algorithms. Results indicate that even though the radiometer derived 2A12 is known to suffer from many sources of uncertainties, spatial and temporal analysis over the case study region testifies that the 2A12 rainfall estimates are in a very good agreement with the reference estimates for the data period considered. These findings clearly document that proper characterization of error structure offered by TMI and PR has wider implications in decision making, prior to incorporating the resulting orbital products for basin scale hydrologic modeling. The current missions of GPM envision a constellation of microwave sensors that can provide instantaneous products with a relatively negligible sampling error at daily or higher time scales. This study due to its simplicity and physical approach offers the ideal basis for future improvements in uncertainty modeling in precipitation.

Extreme Value Theory nds application in problems concerning low probability but high consequence events. In hydrology the study of heavy rainfall is important in regional ood risk assessment. In particular, the N-year return level is a key output of an extreme value analysis, hence care needs to be taken to ensure that the model is accurate and that the level of imprecision in the parameter estimates is made explicit. Rainfall is a process that evolves over time and space. Therefore, it is anticipated that at extreme levels the process would continue to show temporal and spatial correlation. In this study interest is in whether any trends in heavy rainfall can be detected for the Western Cape. The focus is on obtaining the 50-year daily winter rainfall return level and investigating whether this quantity is homogenous over the study area. The study is carried out in two stages. In the rst stage, the point process approach to extreme value theory is applied to arrive at the return level estimates at each of the fteen sites. Stationarity is assumed for the series at each station, thus an issue to deal with is that of short-range temporal correlation of threshold exceedances. The proportion of exceedances is found to be smaller (approximately 0.01) for stations towards the east such as Jonkersberg, Plettenbergbay and Tygerhoek. This can be attributed to rainfall values being mostly low, with few instances where large amounts of rainfall were observed. Looking at the parameters of the point process extreme value model, the location parameter estimate appears stable over the region in contrast to the scale parameter estimate which shows an increase towards in a south easterly direction. While the model is shown to t exceedances at each station adequately, the degree of uncertainty is large for stations such as Tygerhoek, where the maximum observed rainfall value is approximately twice as large as the high rainfall values. This situation was also observed at other stations and in such cases removal of these high rainfall values was avoided to minimize the risk of obtaining inaccurate return level estimates. The key result is an N-year rainfall return level estimate at each site. Interest is in mapping an estimate of the 50-year daily winter rainfall return level, however to evaluate the adequacy of the model at each site the 25-year return level is considered since a 25 year return period is well within the range of the observed data. The 25-year daily winter rainfall return level estimate for Ladismith is the smallest at 22:42 mm. This can be attributed to the station's generally low observed winter rainfall values. In contrast, the return level estimate for Tygerhoek is high, almost six times larger than that of Ladismith at 119:16 mm. Visually design values show di erences between sites, therefore it is of interest to investigate whether these di erences can be modelled. The second stage is the geostatistical analysis of the 50-year 24-hour rainfall return level The aim here is to quantify the degree of spatial variation in the 50-year 24-hour rainfall return level estimates and to use that association to predict values at unobserved sites within the study region. A tool for quantifying spatial variation is the variogram model. Estimation of the parameters of this model require a su ciently large sample, which is a challenge in this study since there is only fteen stations and therefore only fteen observations for the geostatistical analysis. To address this challenge, observations are expanded in space and time and then standardized and to create a larger pool of data from which the variogram is estimated. The obtained estimates are used in ordinary and universal kriging to derive the 50-year 24-hour winter rainfall return level maps. It is shown that 50-year daily winter design rainfall over most of the Western Cape lies between 40 mm and 80 mm, but rises sharply as one moves towards the east coast of the region. This is largely due to the in uence of large design values obtained for Tygerhoek. In ordinary kriging prediction uncertainty is lowest around observed values and is large if the distance from these points increases. Overall, prediction uncertainty maps show that ordinary kriging performs better than universal kriging where a linear regional trend in design values is included.

Thesis (M.S.)--University of Wisconsin--Madison, 1988.<br>Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 88-93).

碩士<br>國立中興大學<br>土木工程學系所<br>101<br>The global climate change leads to higher frequency of typhoon and storm events has brought tremendous amount of rain causing devastating landslides, debris flows, and flooding in Taiwan. Severe property damage and loss of lives are often inevitable. Therefore, reliable warning systems have great demand by related government agencies with vital information for early evacuation planning. Quantitative Precipitation Estimation and Segregation Using Multiple Sensor (QPESUMS) utilizes radar reflectivity measurements and integrates other weather information such as wind speed to provide past (72 hours) and forecast (0 ~ 3 hours) high-resolution precipitation data. This research extract the gridded precipitation data from the QPESUMS and apply proper adjustments to the values, then use them as inputs to a hydrologic model in hope to accurately simulate the hydrologic responses to storm events. Accurate hydrologic modeling relies on accurate rainfall inputs. Chenyulan River watershed was selected as the study area. Several geostatistically based spatial interpolation methods including Ordinary Kriging (OK), Inverse Distance Weighted (IDW), SPLINE, Co-Kriging, and Regression Kriging (RK) were tested for suitability of rainfield interpolation over the study area. OK, IDW, SPLlNE used only rain gauge data. Elevation information of the rain gauges in the watershed and surrounding area was incorporated as a secondary predictor for Co-Kriging. The result of this research shows that the percent error of the interpolated values as compared to the un-adjusted QPESUMS data was reduced by 3.6% ~ 53.4%. The islanding effect of 21-th provincial highway of Chenyulan River watershed is estimated in the research. The risk of every township of Chenyulan River watershed is also estimated. The result shows that the frequency of landslide in Xinyi, TongFu and Dongpu townships on the upstream of Chenyulan River are 3.22 times per month. And the frequency is higher than townships on the downstream of Chenyulan River which is only 0.37 times per month. All of these three townships are on the hilltop. The capacity in these three townships for refugee population is less than resident population and potential refugee population in the daytime, and the management of refuges is not completed. According to the result of this research, the alarm value of rainfall of these three dangerous townships should be reconsidered.

Dissertação de Mestrado em Geociências apresentada à Faculdade de Ciências e Tecnologia<br>A precipitação é um componente essencial do ciclo hidrológico e energético global. Medir este fenómeno de maneira fiável e precisa é crucial para a gestão de recursos hídricos e para a monitorização de desastres naturais.Medições de precipitação através de métodos convencionais são efetuadas com recurso a estações in situ e, quando disponíveis, radares meteorológicos. A distribuição destes sistemas raramente é adequada e a densidade das estações é altamente variável. Isto origina lacunas de dados significativas um pouco por todo o mundo, o que origina sérios problemas de representatividade.Para solucionar estes problemas, várias agências espaciais criaram e lançaram missões de observação terrestre, para coletar informação meteorológica à escala quase global. Sendo a mais recente missão a Global Precipitation Measurment Mission. Para estes dados puderem ser usados para uma dada área geográfica, quer para o desenvolvimento de aplicações críticas, quer em estudos de caracter cientifico, é imperativo que sejam validados.Neste estudo, dados gerados pelo GPM (IMERG) foram validados para a Bacia Hidrográfica do Rio Mondego. Esta é uma região com uma topografia complexa, que apresenta diversos obstáculos à obtenção de dados de precipitação via satélite. Comparou-se a Late run do IMERG com medições in situ disponibilizadas pela rede meteorológica do SNIRH.Foram estudados quatro anos e meio de dados (Março/2014 até Dezembro/2018), a várias escalas temporais, com recurso a métodos estatísticos frequentemente utilizados neste tipo de estudos, analisando-se a distribuição espacial dos resultados.Verificou-se que de um modo geral o produto IMERG é competente a quantificar a pluviosidade, no entanto os resultados não são homogéneos para a área em estudo. Existe uma sobrestimação da pluviosidade na zona costeira e uma subestimação na zona correspondente à encosta NO das Serras da Estrela e da Lousã.Concluiu-se que a qualidade das estimativas do produto IMERG e a sua alta resolução espaciotemporal potenciam o uso dos dados gerados no desenvolvimento de aplicações operacionais e em estudos futuros de carácter hidrológico.<br>Rainfall is an essential component of the energetic and hydrologic cycles. Gathering accurate rainfall measurements is a requirement for good water resource management and natural disaster monitoring. Precipitation measurements through conventional methods employ the use of in situ stations and, when available, meteorological radars. The distribution of these systems is rarely adequate and the gauge density is highly variable, this can cause serious problems due to the data being non representative.To find a solution to these issues, several space agencies designed and launched earth observation missions, with the aim to collect meteorological data at a near-global scale. The Global Precipitation Measurement Mission is the most recent of its kind.However for this data to be used, for either the development of critical applications or hydrological studies, it needs to have been previously validated.In this study, data generated by GPM (IMERG) was validated for the hydrographic basin of the Mondego River. This region is affected by complex topography which poses significant challenges to the gathering of rainfall data via satellite retrieval methods. The Late run of the IMERG product was compared with in situ measurements, made available via SNIRH’s meteorological network.Four years and a half of data were studied, from March 2014 to December 2018. Different observation lengths were studied and the data’s special distribution was analyzed.Overall the results show that the IMERG product is competent at estimating the amount of rainfall, however the results vary across the study area, with the coastal areas showing an overestimation of the rainfall amounts, and the southwest facing slopes of Serra da Estrela and Lousã, the two main topographic features present in the area showing an underestimation of the rainfall events.Lastly it was deemed that the quality and high spatiotemporal resolution of the IMERG estimates, enable its data to be used in the development of operational applications and in future hydrological studies.

碩士<br>國立臺灣大學<br>土木工程學系研究所<br>86<br>Raingauge measurements are accurate at points, but it''''''''s not adequate to repre sent area rainfall. Radar observations, on the other hand can capture the vari ation of rainfall intensity in place, but not as accurate at points. It is nat ural to project that an integration of the two observations may be able to sur pass the originals. This study use raingauge of Tanshui river basin about 2700 -km2 region and CAA radar reflective data using co-kriging method to estimate rainfall.In the aspect of actual observation, raingauge data is adopt central weather bureau and water resource agency etc. set up in Tanshui river basin ra ingauge. Radar data adopt Civil aviation agency radar, locate the west of Tans hui river basin about 20 km. CAA radar is Doppler radar wavelength is 5.31 cm. In Doppler mode, the range coverage was 120 km, and range resolution was 1 km .In Z-R relationship, using CAA radar historical data and raingauge record to contrast and make Z-R relationship by power law. It considers electromagnetic wave attenuation, heteroscedastic, and homoscedastic and unbias to make Z-R re lationship. However because of the quality of radar data is not good, using po int to point data to regress Z-R relationship was not ideal, so we using the s ub-basin average rainfall intensity and radar reflective transform rainfall in tensity. Using trial and error method to estimate best coefficient A and B. in other hand by historical CAA radar data, we also found CAA radar reflective i ntensity had decay tendency. so periodical calibrate is necessary.T.H.Lee etc. using Turning Band methods to generate the synthesized rainfall data to proof add radar information will increase observed accuracy in statistics. This stu dy was using CAA radar observe typhoon rainfall data and Tanshui river basin r aingauge record estimate the rainfall distribution in the space by radar obser ved, kriging method and co-kriging method and compare.This study using co-krig ing method to estimate rainfall found the weight coefficient unstable situatio n would occur. It is the radar and raingauge data not consistence. In actually , radar and raingauge exist lager different by their contour. This study adopt consistence variance model include two part: (1) variance and covariance mode l adopt exponential model; (2) parameter compute sequence by data reliable. In unbiased condition, to reasonable allot radar and raingauge coefficient the s tudy using correlation to objective adept. If raingauge observed rainfall and radar is not then decrease raingauge coefficient, opposite increase radar coef ficient.

碩士<br>國立中興大學<br>水土保持學系所<br>106<br>Precipitation is often regarded as a triggering factor in a rainfall-induced landslide susceptibility analysis, and maximum rainfall intensity is often used one. When most scholars apply geostatistics such as Kriging to estimate this factor, they input the maximum rainfall intensity that each rainfall station has recorded in the duration of the research event into the location of each rainfall station, which means all of these maximum rainfall intensities are considered to be recorded at the same time. However, these maximum rainfall intensities are seldom recorded in each rainfall station at the same time because of the nonuniformity of precipitation distribution in time-space. If we do a landslide susceptibility analysis by the foregoing way, it is bound to differ from the actual situation. This study estimate two types of the maximum rainfall intensity factor in the Chenyoulan watershed based on the environmental conditions caused by typhoon Morakot using kriging. The first type of the factor is to estimate it by the foregoing way; the second type of it is to estimate the rainfall intensity of each unit of analysis hourly, and we take the maximum one as the maximum rainfall intensity factor of the unit of analysis. Afterwards, we use receiver operating characteristic curve to do rainfall-induced landslide susceptibility analyses and compare the differences between the two types of maximum rainfall intensity factor. The area under the curve of receiver operating characteristic produced by the maximum rainfall intensity factor estimated by the foregoing way is 0.581 and its accuracy is 57%; the area under the curve of receiver operating characteristic produced by the maximum rainfall intensity factor estimated by the hourly-estimated way is 0.619 and its accuracy is 60%. So do rainfall-induced landslide susceptibility analyses by the maximum rainfall intensity factor estimated by the hourly-estimated way is surely better. This study also estimates accumulated rainfall factor by accumulate rainfall of the whole duration of typhoon Morakot and accumulate rainfall from the beginning to the hours of the maximum rainfall intensity factor estimated by the hourly-estimated way happened continuously. Afterwards, we use receiver operating characteristic curve to do rainfall-induced landslide susceptibility analyses and compare the differences between different types of accumulated rainfall factor, and the results show that there has none obvious difference. Finally, this study divides units of analysis into two groups by median of height of all units and do landslide susceptibility analyses individually, and the results show that overall, the accuracy can be promoted.

碩士<br>中正理工學院<br>應用物理學系研究所<br>86<br>Due to the lack of rain gauge data on ocean, satellite data are the only measurement to rainfall. Because microwave can penetrate clouds, it possesses more direct measurement to rainfall than visible and infrared data and can be used to estimate rainfall more accurate than the others. Currently, there are three microwave radiometers for rainfall estimate, i.e. Microwave Sounding Unit (MSU), Special Sensor Microwave Imager (SSM/I) and TRMM (Tropical Rainfall Measurement Mission) Microwave Imager (TMI). Because only MSU data are available on the current operational routine, the rainfall estimation is derived by MSU data only. The variation of the brightness temperatures of MSU channel 1 (Tb 1) was found to have a significant relationship with precipitation. Therefore, in this study rainfall was retrieved by MSU Tb1. But before MSU Tb1 was used to derive rainfall, it should be corrected by removing the atmospheric effect. Usually, the atmospheric effect can be calculated by the brightness temperatures of MSU channels 2(Tb 2) and 3(Tb 3). In order to improve the poor resolution of retrieval rainfall, Geostationary Meteorological Satellite 5(GMS-5) data were used at the same time. Therefore mesoscale rainfall estimation can be obtained from MSU and GMS-5 data fusion. The rain gauge measurement was used for comparison. It is shown that there is a same pattern at heavy rainfall situation. Advance Microwave Sounding Unit (AMSU) on board the NOAA-K will be the fourth microwave radiometer for rainfall estimation. Because AMSU has more channels ( 20 channels ) and fine resolutions ( 50km at nadir for AMSU-A, 15km at nadir for AMSU-B ) than MSU, So it is expected to improve the accuracy of rainfall estimate in the near future.

碩士<br>國立屏東科技大學<br>水土保持系所<br>105<br>The rainfall erosivity index (R30) based on 30minutes of rainfall data is commonly used in the Universal Soil Loss Equation for predicting soil loss from agricultural hillslopes. R30 values are calculated from breakpoint rainfall information obtained from continuous recording rain gauge charts; however, in many places in Taiwan and other parts of the world, detailed chart-recorded rain gauge data relative to storm intensities are not readily available, whereas hourly rainfall is readily available. This paper proposes a method to estimate the rainfall erosivity index using hourly rainfall dada. A simple method for estimating the rainfall erosivity index R30 by using the value of R60 calculated from the rainfall kinetic energy (E60) and maximum intensity (I60max) measured at multiple rainfall stations was established. The data set consists of 31,383 storm events, monitored by 73 rainfall stations in eastern Taiwan. It is shown that the average conversion factors (αE, αI and αRy) of the rainfall kinetic energy (E), the rainfall intensity (I) and annual rainfall erosivity (Ry) (i.e., the ratios of the 30- to 60-min interval values of the corresponding factors) are 1.04, 1.46 and 1.46, respectively, over the considered rainfall stations. Furthermore, the correlation coefficients of the above average conversion factors are found to have the value of 0.99, 0.95 and 0.96, respectively.

碩士<br>國立屏東科技大學<br>水土保持系所<br>104<br>The 30-min rainfall erosivity index (R30) is commonly used in the Universal Soil Loss Equation for predicting soil loss from agricultural hillslopes. R30 values are calculated from breakpoint rainfall information obtained from continuous recording rain gauge charts; however, in many places in Taiwan and other parts of the world, detailed chart-recorded rain gauge data relative to storm intensities are not readily available, whereas hourly rainfall is readily available. A simple method for estimating the rainfall erosivity index R30 by using the value of R60 calculated from the rainfall kinetic energy (E60) and maximum intensity (I60max) measured at multiple rainfall stations was established. This study involved calculating R30 and R60 by using 10- and 60-min data obtained from 51 rainfall stations in Southern Taiwan. The results show that the kinetic energy values derived using the two sets of data are related as E10 = 1.04E60 (r2= 0.99). In addition, the maximum rainfall intensity values of 30- and 60-min intervals are related as I30max = 1.56I60 max (r2= 0.90). The R30j associated with a rainfall event and R60j associated with a rainfall event are related as R30j = 1.31R60j (r2= 0.95). Finally, the annual average R30y and annual average R60y are related as R30y = 1.44R60y (r2= 0.93). The 60-min rainfall data can be successfully mused to estimate rainfall erosivity where no finer time resolution data are available and there was a marked improvement in predictions between the 60-min data and the 30-min data. The method is expected to be of significant benefit in future studies concerning the effects of climate change.

碩士<br>國立臺北科技大學<br>土木與防災研究所<br>101<br>Precisely estimating precipitation is very important for hydrological planning. However, lack detailed data on spatial or temporal precipitation distributions frequently cause design errors. Although many researches attempted to reduce the errors, a combination of several rainfall networks is a feasible way to increase the accuracy of estimation. This study applied universal cokriging (UCK) combining two precipitation data observed by Central Weather Bureau (CWB) and Water Resources Agency (WRA) to estimate the precipitation distributions in Gau-Pin region. To reduce the non-stationary effect on spatial variance of rainfall data due to terrain, residual values are first determined between observed rainfall data and spatial trends estimated by stepwise regression analysis. Then, UK was used to estimate spatial variability of precipitation. Finally, a cross-validation procedure was adopted to characterize the estimated errors of individual and combining rainfall networks. A T-test was used to quantify the difference of estimated errors between two rainfall data. If the difference is significant, UCK is more suitable than universal kriging (UK). The analyzed result reveals that about 53% of data do not pass the T-test. Thus, applying UCK to estimate spatial precipitation variability can increase the accuracy of estimation for combining several rainfall networks.

碩士<br>國立中央大學<br>水文所<br>97<br>The heavy rainfalls associated with typhoons often induce sereve flooding, and cause a great lose of human lives in Taiwan. The hydrological model usually takes the observational rainfall data as an input, but the spatial distribution of rainfall is not the same as the distribution of atmospheric circulation, and lower time resolution of observed rainfall data may cause errors. In this thesis research, we use the rainfall data estimates by the radar-rainfall relationship to drive the hydrological model (CASC-2D) to simulate the river-runoff. We have used the radar data of Typhoon Nari (2001) observed by the Wu-fen Shan WSR-88D weather radar. Results show that the rainfall estimated by radar-rainfall relationship is lower than the observed rainfall. A correction factor of 3.77 for the radar-estimated rainfall is suggested to use to improve rainfall estimation. The simulated river runoff using the radar-estimated rainfall is in better agreement with observed runoff in the upstream region of the Shinmen watershed area. For Typhoons Longwong (2005) and Bilis (2006), the now dual-polarmetric radar data is used, and several radar-observed parameters are used to estimate rainfall and the corresponding runoff. For Typhoon Longwong, the KDP-estimated rainfall is in better agreement with the observed rainfall, compared to estimated rainfalls by other radar parameters, although it is still under-estimated by 40%. As a result, the predicted river runoff by the KDP-estimated rainfall is in better agreement with the observed runoff. For Typhoon Bilis, the ZDR-estimated rainfall is closed to the observed rainfall, and the estimated rainfalls by other radar parameters (KDP, ZH, and DSD) are all significantly lower than the observation. The predicted peak runoff by the ZDR-estimated rainfall is higher than the observed peak runoff by 30-40% in Typhoon Bilis.

碩士<br>國立臺灣科技大學<br>營建工程系<br>88<br>This study suggests a concept based on the hourly precipitation to determine the workable time each day, which could predict more precisely then daily precipitation. We used fuzzy inference model to estimate the workable hour each day of both earthmoving and pavement activities on road construction project. A case study is shown to explain how the inference model output works. We also compared the output with both the duration estimated by the “coefficient method” and the actual duration. The result revealed that the inference model is suitable to be applied in the planning stage.

碩士<br>國立臺灣海洋大學<br>河海工程學系<br>102<br>Under the influence of climate change, extreme rainfall events with high intensity and long duration frequently occurred in Taiwan area. It oftenthreatens the safety of people living in mountain area. This study focuses on the Gaoping river where landslide occur frequently. Firstly, considering the geological characteristics of Gaoping River is required. According to the past landslide events, the study area can be divided by different zones. The TRIGRS model developed by USGS and the landslide analytical methods the deterministic is chosen to analyze. Then, this study adopted Morakot Typhoon occurred in 2009 to simulate on Gaoping river area. Therefore, therelated geophysical parameters of Gaoping river area are established and the factor of safety in the various situations can be assessed. Considering the required time for typhoon events, the study proposes the factor of safety for township. The design rainfall pattern for Gaoping river is chosen. Finally, this study simulates the accumulated rainfall when the factor of safety drops to the drawing warning levels of landslide occurrence. 　　The result of this study estimates that the red and yellow warn monitoring value was proposed byfactor of safety are 1.12 and 1.15, respectively. Due to the simulation of design rainfall pattern, accumulated rainfall occurring in different areas can be calculated. In order to identify this method, the model verification is alsoperformed. The warm accumulated rainfall which SWCB, NCDR and this method develop are roughly the similarly tendency. Therefore, the result of this study can be proposed for the practical application of prevention disaster. 　　Finally, Liugui area is chosen and by use of the typhoon Fanapi as situation to simulate and to assess the threshold whether can apply in this study. The results showed that the Liugui its factor of safety drop down to the yellow warm factor of safety, it has 10 hours to the landslide. And the factor of safety drop down to the red warm factor of safety, it has 9 hours to the landslide. This time can provide the government agencies of hazardmitigation to apply. From the study simulation, it can find the distribution of the factor of safety on the study area. The evaluation results of Ligui relatively unstable area at typhoon Fanapi including the northwest side in Laonong, the east side in Xinfa, the east side in Xinlong, the west side in Wenwu, the east side in Zhongxing and the east side in Dajin total of six. From the SWCB disaster report, Liugui area total occurred 5 landslide during the typhoon Fanapi, were Laonong once, Wenwu once and Dajin three times, all belong to the result of simulation. The result shows we can use it to judge the unstable area and improve the efficiency of hazard mitigation, provided the reference of government agencies of hazard mitigation.

Magister Scientiae (Biodiversity and Conservation Biology)<br>Since the late 1970 there has been increased concern of amphibian decline and extinction. Several causes for the worldwide declines have been suggested and include ultraviolet radiation, predation, pollution, climate change, diseases and habitat modification. To counter this, more research on the subject has been encouraged of which long term monitoring has been suggested as a research method. The study was conducted in Kakamega Forest in Kenya, which is the country's remnant of the once vast Guineo-Congolian forest. A rectangular transect whose sides measured 600 m in total was established and transect walks were carried out every two weeks for two consecutive days between 2002 and 2006. 24 species were targeted in the study and were sampled through VES and AES and data recorded in a GPS and later downloaded. In this study I examined the influence of rainfall, temperature, habitat and moon phases on the activity of frogs in Kakamega Forest. I also determined under which weather conditions sampling was more efficient. When monitoring was carried out by two observers I tested whether their data were similar. Data were analysed using non-parametric methods (Kruskal-wallis and Tukey test), species abundances analysed using EstimateS..Out of the 24 targeted species only 14 were recorded, with a total of 535 specimens being counted mostly at night. Most frogs in Kakamega Forest were more active in temperatures between 20 and 25oC. There was not much variation and there was no frog activity when the temperature was extremely high. There was rainfall throughout the year and there was no significant differences in the number of frogs counted in rainfall above 200 mm or below 200 mm. There was no significant difference in the number of specimens found in the different vegetation segments in the forest. More amphibians were caught under cloudy, rainy and clear conditions at night than under any weather condition during the day. During the day, more amphibians were caught during cloudy conditions than when it rained or when there was no cloud cover. There was no difference in catch among night conditions and there was no difference between clear and rainy days In Kakamega Forest, night is the best time to sample amphibians. In terms of weather it is best to sample when it is cloudy both during the day and at night. There were no differences in sampling abilities between two observers tested under similar weather conditions.

"May 2002" Includes list of papers published during this study Errata slip inserted inside back cover of v. 1 Includes bibliographical references (leaves 331-357) V. 1. [Text} -- v. 2. Appendices Develops an alternative design flood estimation methodology. Establishing a relationship between catchment characteristics and the rainfall excess frequency duration proportions enables the definition of these proportions for generic catchment types, increasing the potential for translation to catchments with limited data but similar hydrographic properties, thereby improving design process.