Avaliações hidrológicas, hidráulicas e multicriteriais de susceptibilidade às inundações em áreas urbanas costeiras : estudo de caso da bacia do Rio Juqueriquerê no Brasil

Orientadores: Antonio Carlos Zuffo, Monzur Alam ImteazTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Civil, Arquitetura e Urbanismo e Swinburne University of Technology (Australia)Resumo: O desenvolvimento significativo de Caraguatatuba é traduzido pela sua potencialidade ao turismo, exploração de gás, proximidade do Porto de São Sebastião e ampliação do complexo viário da Tamoios, particularmente na Bacia do Rio Juqueriquerê, que é a maior planície não urbanizada do litoral norte de São Paulo, Brasil. A área é constituída por baixas declividades e lençóis freáticos rasos, cercada pelas altas escarpas da Serra do Mar. Além disso, é afetada por chuvas orográficas e variação de marés, contribuindo para a ocorrência natural de inundações. Apesar da área à jusante ser densamente urbanizada, a bacia não é propriamente monitorada, tornando a previsão de futuros cenários com a tradicional modelagem hidrológica muito desafiadora, devido à falta de dados representativos. No presente estudo, a análise multicriterial para tomada de decisão (MCDA) foi utilizada para determinar os critérios mais impactantes na susceptibilidade às inundações do local. O cenário futuro foi baseado no uso e cobertura da terra proposto pelo Plano Diretor de Caraguatatuba. A pesquisa com especialistas usando o método Delphi e o Processo de Análise Hierárquica (AHP) foram associados para a atribuição e comparação por pares dos seguintes critérios: elevação, densidade de drenagem, chuva, declividade e Curva Número (CN), do Serviço de Conservação do Solo (SCS) dos Estados Unidos. A bacia foi discretizada em 11 sub-bacias, e vários métodos estatísticos e empíricos foram empregados para a parametrização do modelo multicriterial. Após a definição dos critérios e tratamento estatístico dos julgamentos de todos os especialistas, uma faixa limitada de pesos foi gerada, variando de 8,36 a 8,88, a qual foi efetivamente convertida para uma ampla faixa de valores de prioridade pelo uso de uma abordagem extendida do método AHP. A escala de julgamento da raiz quadrada aplicada no estudo gerou resultados de boa qualidade, onde a taxa de consistência foi de 0,0218 e o índice de consistência foi de 0,0244. Além disso, a análise de sensibilidade revelou a coerência do vetor peso, por meio da variação do critério de elevação (+10 % e -5%), afetando os pesos mas não a hierarquia. Posteriormente, todos os critérios foram implementados no sistema de informações geográficas (SIG). Foi realizada uma discussão minuciosa sobre a aquisição da variável CN, levando em consideração os tipos de solo brasileiros e as condições de saturação locais. As limitações do método SCS-CN foram destacadas, especialmente no que se refere à sua aplicação em bacias não monitoradas, quando não é possível calibrar ou validar o modelo. A estimativa e a calibração dos coeficientes de rugosidade de Manning nos principais cursos d'água também foram desenvolvidas no estudo, com base nos dados observados e medidos em trabalhos de campo. Os desvios médios absolutos entre os valores de Manning variaram de 0,004 a 0,008, mostrando que a metodologia proposta pode ser aplicada em quaisquer áreas de estudo, tanto para calibrar quanto para atualizar os coeficientes de rugosidade de Manning em diferentes períodos. A distribuição da função gamma foi utilizada para o cálculo das chuvas de projeto, que posteriormente foram utilizadas para a análise de correlação entre chuvas anuais e diárias. O Sistema de Análise Fluvial do Centro de Engenharia Hidrológica em 2 dimensões (HEC-RAS 2D) e o Sistema de Modelagem Hidrológica (HEC-HMS) foram utilizados para a calibração do parâmetro CN e para a validação do modelo. Os limites de inundação gerados no processo de vadidação (pelo modelo HEC-RAS 2D) foram muito similares aos gerados pela abordagem MCDA, correspondendo a 93,92 % e 96,31 %, respectivamente. Os métodos de interpolação foram essenciais para a distribuição temporal e espacial dos dados meteorológicos no modelo de precipitação-vazão usados para validação, e também no modelo MCDA implementado no SIG. A determinação final da probabilidade de susceptibilidade às inundações nas planícies estudadas foi baseada na soma ponderada espacial dos critérios atribuídos previamente. Por fim, os mapas de susceptibilidade às inundações foram gerados para os diferentes cenários. As simulações de diferentes padrões de chuva mostraram que este critério influenciou fortemente na probabilidade de suscetibilidade às inundações. Para a simulação de maiores elevações e chuvas máximas, o índice de susceptibilidade às inundações foi 4 (do total de 5). A maior contribuição do estudo foi na aquisição de parâmetros confiáveis por meio das técnicas propostas, que também podem ser utilizadas em outras áreas, principalmente onde os dados são escassos e há complexas limitações físicas envolvidas, visando o desenvolvimento urbano sustentável da regiãoAbstract: The significant development of Caraguatatuba Municipality is translated by its tourism potentiality, gas exploration, proximity to the Port of Sao Sebastiao and extension of the Tamoios Highway complex, particularly in the Juqueriquere River Basin, which is the major non-urbanised plains of the northern coastline of Sao Paulo, Brazil. The area is comprised of low slopes and shallow water tables, surrounded by the high elevations of the Serra do Mar mountains. Additionally, It is affected by orographic rainfalls and tide variation, contributing to the natural occurrence of floods. Even though the downstream area is densely urbanised, the watershed is not properly gauged, making it a challenging task for the prediction of future scenarios with the traditional hydrological modelling approach, due to the lack of representative data. In the current study, multicriteria decision analysis (MCDA) were used to determine the mostly impacting criteria to the local flood susceptibility. The future scenario was based on the land use and land cover proposed by the City Master Plan of Caraguatatuba. The expert-based survey using the Delphi method and the analytical hierarchical process (AHP) were associated with the attribution and pairwise comparison of the following criteria: elevation, density drainage, rainfall, slope and curve number (CN), from the US Soil Conservation Service (SCS). The watershed was discretised in 11 sub-basins, and several statistical and empirical methods were employed for the parameterisation of the multicriteria model. After the definition of the criteria and the statistical treatment of the judgements of all experts, a limited range of weights was derived, varying from 8.36 to 8.88, which was effectively converted to a larger ratio of priority values by the use of an extended approach of the AHP methodology. The root square judgement scale applied in the study generated good-quality results, where the consistency ratio was 0.0218 and the consistency index was 0.0244. Besides, the sensitivity analysis revealed the coherence of the weight vector, by the variation of the elevation criterion (+10 % and -5%), affecting the weights but not the hierarchy. Further, all the criteria were implemented in the geographical information system (GIS). There was a thorough discussion regarding the acquisition of the CN variable, taking into consideration the Brazilian soil types and the local saturated conditions. The constraints of the SCS-CN method were highlighted, especially regarding its application in ungauged basins, where it is not possible to calibrate or validate the model. The estimation and calibration of the Manning's roughness coefficients of the main watercourses were also developed in the study, based on the observed and measured data in field campaigns. The mean absolute deviations between the estimated and the calibrated Manning's values varied from 0.004 and 0.008, showing that the proposed methodology might be applied in any study areas, both to calibrate and to update the Manning's roughness coefficients in different periods. The gamma-function distribution was carried out to calculate the design rainfalls, which were later used for the correlation analysis of the annual and the daily rainfalls. The Hydrologic Engineering Center's River Analysis System 2D (HEC-RAS 2D) and the Hydrologic Modelling System (HEC-HMS) were used for the calibration of the CN variable and for the model validation. The inundation boundaries derived in the validation process (by the HEC-RAS 2D model) were very similar to the ones achieved by the MCDA approach, corresponding to 93.92 % and 96.31 %, respectively. The interpolation methods were essential for the spatial and temporal distribution of the meteorological data in the rainfall-runoff model used for validation, and also in the GIS-based MCDA model. The final determination of the likelihood of flood susceptibility in the studied plains was based on the spatially weighted summation of the previously attributed criteria. Finally, flood susceptibility maps were generated for the different scenarios. The simulations of different rainfall patterns showed that this criterion profoundly influenced the likelihood to flood susceptibility. For the simulation of higher elevations and maximum rainfalls, the achieved index of flood susceptibility was 4 (out of 5). The main contribution of the study was the achievement of reliable parameters by the proposed techniques, that may also be used in other areas, mainly where data is scarce and complex physical constraints are involved, targeting the sustainable urban development of the regionDoutoradoRecursos Hidricos, Energeticos e AmbientaisDoutora em Engenharia Civi

Variational Downscaling, Fusion and Assimilation of Hydrometeorological States via Regularized Estimation

Improved estimation of hydrometeorological states from down-sampled observations and background model forecasts in a noisy environment, has been a subject of growing research in the past decades. Here, we introduce a unified framework that ties together the problems of downscaling, data fusion and data assimilation as ill-posed inverse problems. This framework seeks solutions beyond the classic least squares estimation paradigms by imposing proper regularization, which are constraints consistent with the degree of smoothness and probabilistic structure of the underlying state. We review relevant regularization methods in derivative space and extend classic formulations of the aforementioned problems with particular emphasis on hydrologic and atmospheric applications. Informed by the statistical characteristics of the state variable of interest, the central results of the paper suggest that proper regularization can lead to a more accurate and stable recovery of the true state and hence more skillful forecasts. In particular, using the Tikhonov and Huber regularization in the derivative space, the promise of the proposed framework is demonstrated in static downscaling and fusion of synthetic multi-sensor precipitation data, while a data assimilation numerical experiment is presented using the heat equation in a variational setting

Bayesian Analysis of the Impact of Rainfall Data Product on Simulated Slope Failure for North Carolina Locations

In the past decades, many different approaches have been developed in the literature to quantify the load-carrying capacity and geotechnical stability (or the factor of safety, Fs) of variably saturated hillslopes. Much of this work has focused on a deterministic characterization of hillslope stability. Yet, simulated Fs values are subject to considerable uncertainty due to our inability to characterize accurately the soil mantles properties (hydraulic, geotechnical, and geomorphologic) and spatiotemporal variability of the moisture content of the hillslope interior. This is particularly true at larger spatial scales. Thus, uncertainty-incorporating analyses of physically based models of rain-induced landslides are rare in the literature. Such landslide modeling is typically conducted at the hillslope scale using gauge-based rainfall forcing data with rather poor spatiotemporal coverage. For regional landslide modeling, the specific advantages and/or disadvantages of gauge-only, radar-merged and satellite-based rainfall products are not clearly established. Here, we compare and evaluate the performance of the Transient Rainfall Infiltration and Grid-based Regional Slope-stability analysis (TRIGRS) model for three different rainfall products using 112 observed landslides in the period between 2004 and 2011 from the North Carolina Geological Survey database. Our study includes the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis Version 7 (TMPA V7), the North American Land Data Assimilation System Phase 2 (NLDAS-2) analysis, and the reference truth Stage IV precipitation. TRIGRS model performance was rather inferior with the use of literature values of the geotechnical parameters and soil hydraulic properties from ROSETTA using soil textural and bulk density data from SSURGO (Soil Survey Geographic database). The performance of TRIGRS improved considerably after Bayesian estimation of the parameters with the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm using Stage IV precipitation data. Hereto, we use a likelihood function that combines binary slope failure information from landslide event and null periods using multivariate frequency distribution-based metrics such as the false discovery and false omission rates. Our results demonstrate that the Stage IV-inferred TRIGRS parameter distributions generalize well to TMPA and NLDAS-2 precipitation data, particularly at sites with considerably larger TMPA and NLDAS-2 rainfall amounts during landslide events than null periods. TRIGRS model performance is then rather similar for all three rainfall products. At higher elevations, however, the TMPA and NLDAS-2 precipitation volumes are insufficient and their performance with the Stage IV-derived parameter distributions indicates their inability to accurately characterize hillslope stability

Variability of the Rain Drop Size Distribution:Stochastic Simulation and Application to Telecommunication Microwave Links

Precipitation is an important component of the Earth' water cycle and needs to be carefully monitored. Its large variability over a wide range of spatial and temporal scales must be taken into account. For example, hydrological models require accurate rainfall estimates at high spatial and temporal resolutions (e.g., 1 km and 5 min or higher). Obtaining accurate rainfall estimates at these scales is known to be difficult. So far, the only instruments capable of measuring rainfall over extended domains at such resolutions are weather radars. Their estimates are, however, affected by large errors and uncertainties partly due to the spatial and temporal variability of the drop size distribution (DSD). Major progress in the field is slowed down by the lack of knowledge about the spatial and temporal variability of DSD at scales that are relevant in remote sensing. This lack of reference data can be addressed through two different methods : (1) experimental investigations and (2) stochastic simulation. In this thesis, a comprehensive framework for the stochastic simulation of DSD fields at high spatial and temporal resolutions is proposed. The method is based on Geostatistics and uses variograms to describe the spatial and temporal structures of the DSD. The simulator' ability to generate large numbers of DSD fields sharing the same statistical properties provides a very useful theoretical framework that nicely complements experimental approaches based on large networks of weather sensors. To illustrate its potential, the simulator is applied to different rain events and validated using data from a network of disdrometers at EPFL. The results show that the simulator is able to reproduce realistic spatial and temporal structures that are in adequacy with ground measurements. The second part of this thesis focuses on the simulation and parametrization of intermittency (i.e., the alternating between dry/rainy periods). Simple scaling functions that can be used to downscale/upscale intermittency at different spatial and temporal resolutions are proposed and used to parametrize a new disaggregation method that includes the DSD as an output. Finally, different methods to identify dry and rainy periods and to quantify rainfall intermittency using telecommunication microwave links are proposed. The false dry/wet classification error rates of each method are estimated using data from a new and innovative experimental set-up located in Dübendorf, Switzerland. The results show that the dry/wet classification is significantly improved when data from multiple channels are used

Impacts of Climate Change on Rainfall Extremes and Urban Drainage Systems

Impacts of Climate Change on Rainfall Extremes and Urban Drainage Systems provides a state-of-the-art overview of existing methodologies and relevant results related to the assessment of the climate change impacts on urban rainfall extremes as well as on urban hydrology and hydraulics. This overview focuses mainly on several difficulties and limitations regarding the current methods and discusses various issues and challenges facing the research community in dealing with the climate change impact assessment and adaptation for urban drainage infrastructure design and managemen

A methodology to assess the combined effect of climate change and groundwater overexploitation over the Upper Guadiana basin, Spain

There is a growing concern about the combined effect of climate change and groundwater overexploitation on the availability of water resources in the Upper Guadiana basin (UppGb) in central Spain. General Circulation Models (GCMs) are used to evaluate the possible impact of climate change based on future scenarios of greenhouse gas emissions. However, the output of these models cannot be applied directly to hydrological models because their spatial resolution is coarse and because their simulated precipitation is highly biased. A stochastic downscaling method for generating daily spatial rainfall fields was developed. The model termed Stochastic Rainfall Generating Process (SRGP) incorporates two major non-stationarities -- changes in the frequencies of different precipitation generating mechanisms (frontal and convective), and spatial non-stationarities caused by the interactions of meso-scale atmospheric circulation patterns (ACP) with topography (orographic effects). SRGP was developed to incorporate good climate outputs simulated by GCMs (i.e. ACP), and actual observations. These capabilities enabled us to (1) use SRGP as a downscaling method for climate change impact study, and (2) generate stochastic rainfall fields conditioning to the information of rain gauges. The latter capability was used to investigate the effect of rainfall spatial variability (RSV) on the hydrological response in the UppGb. RSV exerted a major influence on the response of the system especially on the groundwater recharge and the aquifer related responses.GCMs considered in the fifth assessment report of the Intergovernmental Panel on Climate Change were used to evaluate the impact of climate change. The RCP8.5 future emission scenario (GCM-RCP8.5) and the GCM historical control (GCMH) were selected. The climate change was assumed to be the accumulated effects of increases in Temperature, changes in annual and climatological ACP frequency, and changes in probability and volume of rain. Transformations were applied to correct the bias in the temperature, probability and volume of rain, whereas the ACP sequences were used directly. The SRGP method was employed as a rainfall downscaling method for the GCMs. GCMH was used to evaluate the hydrological response obtained with GCMs as driving climate variables, introducing the concept of stochastic equivalence. This evaluation was based on the comparison of the hydrological response obtained with actual observations and transformed (bias correction and SRGP) GCMH. Although an exact stochastic equivalence response was not totally achieved, the seasonal variations were well captured and some response reported very good agreements. The combined effect of climate change and groundwater overexploitation in the UppGb was evaluated in two stages: (1) comparing the hydrological response of the system simulated under natural conditions (absence of pumping), using GCMH and GCM-RCP8.5 as climate driving variables. (2) Groundwater pumping was applied using the same GCM climate driving variables and again the responses were compared. Climate change led to reductions of 14% and 25% in the number of rainy days and volume of rain respectively and an increase of 20% in potential evapotranspiration. Under natural conditions because of climate change, soil moisture and the actual evapotranspiration were reduced by 20% and groundwater recharge, runoff generation, groundwater-river exchange and river discharge were reduced by 40%. As a result of the combined effects of pumping and climate change, all variables were reduced; soil moisture and actual evapotranspiration were reduced by 20% and recharge was reduced by 50%. Moreover, the aquifer related responses yielded annual average reductions of approximately 60%. In general, the results showed an increase in the dry season from April to October.El efecto conjunto del cambio climático y la sobreexplotación de las aguas subterráneas podría llegar a ser crítico para la disponibilidad de recursos hídricos en la cuenca del Alto Guadiana (CAG) en el centro de España. Los modelos de circulación general del clima (GCM) son utilizados para evaluar el posible impacto del cambio climático en base a futuros escenarios de emisión de gases de efecto invernadero. No obstante, la salida de estos modelos no puede aplicarse directamente en modelos hidrológicos porque: (1) la resolución espacial es demasiado grande, y (2) el gran sesgo con que simulan la precipitación. Por tanto, se desarrolló un modelo para el downscaling diario de campos espaciales de precipitaciones. El modelo denominado Stochastic Rainfall Generating Process (SRGP) incorpora dos importantes no-estacionaridades: (1) cambios en la frecuencia de los mecanismos de generación de precipitación (frontal y convectivo), y (2) no estacionaridades espaciales causadas por la interacción de patrones de circulación atmosférica (ACP) con la topografía (efecto orográfico). El SRGP se diseñó para que incorpore variables simuladas por los GCMs con sesgo reducido (ACP), así como también observaciones. Estas prestaciones permiten: (1) utilizar el SRGP como un método de downscaling para el estudio del cambio climático, (2) poder generar múltiples realizaciones de campos de precipitación condicionando a la información de estaciones meteorológicas. Esta última función fue utilizada para investigar el efecto de la variabilidad espacial de la precipitación (RSV) en la respuesta hidrogeológica en la CAG. Se constató que la RSV afecta fuertemente la respuesta hidrológica especialmente para la recarga de agua subterránea y las respuestas asociadas al acuífero. GCMs utilizados en el quinto informe de evaluación del Panel Intergubernamental del Cambio Climático fueron empleados para evaluar el efecto del cambio climático. En todos los casos se consideraron las simulaciones correspondientes al periodo histórico (GCMH) (escenario de control) y el escenario futuro de emisiones RCP8.5 (GCM-RCP8.5). El cambio climático se evaluó como el efecto acumulado en el incremento de las temperaturas, cambios en la frecuencia climatológica anual de los ACP y cambios en la probabilidad y volumen de precipitación. Se aplicaron transformaciones para corregir el sesgo en la temperatura, probabilidad y volumen de precipitación, mientras que se utilizó de forma directa los ACP. Se aplicó el SRGP como método de downscaling de precipitaciones. El GCMH se utilizó para evaluar la respuesta hidrológica obtenida, introduciendo el concepto de equivalencia estocástica. Esta evaluación se basó en comparar la respuesta hidrológica obtenida al aplicar como forzantes la salida transformada (corrección del sesgo y SRGP) de los GCMH, en relación a la obtenida con observaciones. Se comprobó que no se alcanza una respuesta estocástica equivalente exacta para todas las respuestas, pero sí, reproducir variaciones estacionales. El efecto conjunto del cambio climático y la sobreexplotación por bombeo en la CAG se realizó en dos etapas: (1) Se simuló en condiciones naturales (sin bombeo) comparando la respuesta hidrológica obtenida de aplicar como forzantes la salida de GCMH y GCM-RCP8.5. (2) con los mismos forzantes se incorporó los bombeos y nuevamente se compararon las respuestas. Se determinó que el efecto del cambio climático produce una reducción de 14% y 25% en el número de días de lluvia y en el volumen de precipitación respectivamente. Mientras que un incremento del 20% en la evapotranspiración potencial. En condiciones naturales esto se tradujo en una reducción relativa del 20% para la humedad de suelo y la evapotranspiración real. En tanto que, para la recarga de agua subterránea, generación de escurrimiento, intercambio río-acuífero y caudal en el río la reducción fue del 40%. Finalmente, el efecto conjunto de los bombeos y cambio climático, resultó en una reducción para todas las variables, siendo la reducción relativa de un 20% tanto para la humedad del suelo y al evapotranspiración real y del 50% para la recarga. Para las respuestas asociadas al acuífero, la reducción fue del 60 %. Los resultados mostraron un incremento de la estación seca, extendiéndose de Abril a Octubre.L'efecte conjunt del canvi climàtic i la sobreexplotació de les aigües subterrànies podria arribar a ser crític per a la disponibilitat de recursos hídrics en la conca de l'Alt Guadiana (CAG) en el centre d'Espanya. Els models de circulació general del clima (GCM) són utilitzats per avaluar el possible impacte del canvi climàtic sobre la base de futurs escenaris d'emissió de gasos d'efecte hivernacle. No obstant això, la sortida d'aquests models no pot aplicar-se directament en models hidrològics perquè: (1) la resolució espacial és massa gran, i (2) el gran biaix amb què simulen la precipitació. Per tant, es va desenvolupar un model pel downscaling diari de camps espacials de precipitacions. El model denominat Stochastic Rainfall Generating Process (SRGP) incorpora dos importants no-estacionaritats: (1) canvis en la freqüència dels diferents mecanismes de generació de precipitació (frontal i convectivo), i (2) no estacionaritats espacials causades per la interacció de patrons de circulació atmosfèrica (ACP) amb la topografia (efecte orogràfic). El SRGP es va dissenyar perquè pugui incorporar variables simulades pels GCMs amb biaix reduït (ACP), així com també observacions. Aquestes prestacions permeten: (1) utilitzar el SRGP com un mètode de downscaling per a l'estudi del canvi climàtic, (2) poder generar múltiples realitzacions de camps de precipitació condicionant a la informació d'estacions meteorològiques. Aquesta última funció va ser utilitzada per investigar l'efecte de la variabilitat espacial de la precipitació (RSV) en la resposta hidrogeologic en la CAG. Es va constatar que la RSV afecta fortament la resposta hidrològica especialment para la recarrega d'aigua subterrània i les respostes associades a l'aqüífer. GCMs utilitzats en el cinquè informe d'avaluació del Panell Intergovernamental del Canvi Climàtic van ser emprats per avaluar l'efecte del canvi climàtic. En tots els casos es van considerar les simulacions corresponents al període històric (GCMH) (escenari de control) i l'escenari futur d'emissions RCP8.5 (GCM-RCP8.5). El canvi climàtic es va avaluar com l'efecte acumulat en l'increment de les temperatures, canvis en la freqüència climatològica anual dels ACP i canvis en la probabilitat i volum de precipitació. Es van aplicar transformacions per corregir el biaix en la temperatura, probabilitat de pluja i volum de precipitació, mentre que es va utilitzar de forma directa els ACP. Es va aplicar el SRGP com a mètode de downscaling de precipitacions. El GCMH es va utilitzar per avaluar la resposta hidrològica obtinguda amb els GCMs, introduint el concepte d'equivalència estocàstica. Aquesta avaluació es va basar a comparar la resposta hidrològica obtinguda en aplicar com forçants la sortida transformada (correcció del biaix i SRGP) dels GCMH, en relació a l'obtinguda amb observacions. Es va comprovar que no s'aconsegueix una resposta estocàstica equivalent exacta per a totes les respostes, però sí, reproduir variacions estacionals. L'efecte conjunt del canvi climàtic i la sobreexplotació per bombament en la CAG es va realitzar en dues etapes: (1) Es va simular en condicions naturals (sense bombament) comparant la resposta hidrològica obtinguda d'aplicar com forçants la sortida de GCMH i GCM-RCP8.5. (2) amb els mateixos forçants es va incorporar els bombaments i novament es van comparar les respostes. Es va determinar que l'efecte del canvi climàtic produeix una reducció de 14% i 25% en el nombre de dies de pluja i en el volum de precipitació respectivament. Mentre que un increment del 20% en la evapotranspiració potencial. En condicions naturals això es tradueix en una reducció relativa del 20% per a la humitat de sòl i la evapotranspiració real. Mentre que, per la recarrega d'aigua subterrània, generació de escolament, intercanvio riu-aqüífer i cabal en el riu la reducció va ser del 40%. Finalment, l'efecte conjunt dels bombaments i canvi climàtic, va resultar en una reducció per a totes les variables, sent la reducció relativa d'un 20% tant per a la humitat del sòl i al evapotranspiració real i del 50% para la recarrega. Pel les respostes associades a l'aqüífer, la reducció arriba fins al 60 %. Els resultats van mostrar un increment de l'estació seca estenent-se d'Abril a Octubre

Toward a reliable decomposition of predictive uncertainty in hydrological modeling: Characterizing rainfall errors using conditional simulation

This study explores the decomposition of predictive uncertainty in hydrological modeling into its contributing sources. This is pursued by developing data-based probability models describing uncertainties in rainfall and runoff data and incorporating them into the Bayesian total error analysis methodology (BATEA). A case study based on the Yzeron catchment (France) and the conceptual rainfall-runoff model GR4J is presented. It exploits a calibration period where dense rain gauge data are available to characterize the uncertainty in the catchment average rainfall using geostatistical conditional simulation. The inclusion of information about rainfall and runoff data uncertainties overcomes ill-posedness problems and enables simultaneous estimation of forcing and structural errors as part of the Bayesian inference. This yields more reliable predictions than approaches that ignore or lump different sources of uncertainty in a simplistic way (e.g., standard least squares). It is shown that independently derived data quality estimates are needed to decompose the total uncertainty in the runoff predictions into the individual contributions of rainfall, runoff, and structural errors. In this case study, the total predictive uncertainty appears dominated by structural errors. Although further research is needed to interpret and verify this decomposition, it can provide strategic guidance for investments in environmental data collection and/or modeling improvement. More generally, this study demonstrates the power of the Bayesian paradigm to improve the reliability of environmental modeling using independent estimates of sampling and instrumental data uncertainties.Benjamin Renard, Dmitri Kavetski, Etienne Leblois, Mark Thyer, George Kuczera, Stewart W. Frank

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne