Cada vez são mais comuns problemas relacionados a movimentos de massa nas encostas de clima tropical no Estado do Rio de Janeiro, especialmente na Serra do Mar, provocados por acumulados pluviométricos intensos. A ocupação humana desordenada de áreas sensíveis a tais processos geomorfológicos, bem como as condicionantes geológicas, geomorfológicas, pedológicas e de uso e cobertura do solo são apontadas como fatores cruciais na explicação desses processos. O maior conhecimento da dinâmica pluviométrica bem como suas interações com tais aspectos físicos ligados ao relevo parece ser a chave dessa maior compreensão desses fenômenos. Assim foram realizadas pesquisas relacionadas aos volumes e intensidades das chuvas na região do Alto Curso do Rio São João, bem como uma análise dos movimentos de massa identificados através de imagens de satélite e in loco, como forma de fornecer subsídios à melhor gestão do espaço dessas regiões montanhosa estão vulneráveis a movimentos de massa. A correlação entre os acumulados e a intensidade pluviométrica com fenômenos climáticos de escala global, como El Niño e La Niña também foi contemplada nessa pesquisa, mostrando uma relação mais alta com relação à intensidade da chuva mensal para anos de El Niño e para anos de La Niña uma reduzida ocorrência dessas intensidades pluviométricas. Os estudos revelaram que os tipos de solos e sua cobertura e uso têm uma grande influência na deflagração de movimentos de massa. Foram observados um número reduzido de movimentos de massa em áreas naturais e uma maior proporção desses movimentos em áreas utilizadas para a atividade da pecuária na região. Grande parte dos movimentos de massa ocorreram em áreas de Cambissolos (áreas mais elevadas) e Latossolos (áreas de encostas em menores altitudes). Ambos os solos são mais espessos do que os encontrados em áreas mais declivosas, apresentando maior acúmulo de materiais a serem mobilizados durante grandes acumulados pluviométricos, gerando movimentos de massa. A análise mostrou também que áreas mais chuvosas e com maior ocorrência de acumulados pluviométricos extremos, acima de 100 mm/dia e acima de 30mm/mês concentraram um número maior de movimentos de massa, como a região mais próxima da estação de Quartéis (porção leste). Por outro lado áreas bastante elevadas, com altas declividades, porém com predomínio de Mata Atlântica e áreas com solos menos espessos, como os Neossolos Litólicos, se mostraram com um número reduzido desses processos. Enfim esse estudo mostrou a necessidade de se gerir melhor os espaços dessas áreas sensíveis sob o ponto de vista geomorfológico, até por que são áreas na periferia de regiões densamente habitadas e cujas demandas tendem a se tornar cada vez mais marcantes, o que pode gerar problemas locais, atingindo sua população e economia, com sérias conseqüências para o ambiente. / Are increasingly common problems related to mass movements on the slopes of tropical climate in the State of Rio de Janeiro, especially in the Serra do Mar, caused by intense rainfall accumulated. The disorganized human occupation of sensitive areas such geomorphological processes and the geological conditions, geomorphology, soil and use and land cover are cited as crucial factors in explaining these processes. The understanding of rainfall dynamics as well as their interactions with such physical aspects related to the relief seems to be the key to this greater understanding of these phenomena. So were performed research related to volume and intensity of rainfall in the Upper St. John River Course, as well as an analysis of mass movements identified through satellite imagery and on-site as a way to provide subsidies to better management of these space mountainous regions as vulnerable to mass movements. The correlation between rainfall intensity and accumulated with global-scale climatic phenomena like El Niño and La Niña was also considered in this study, showing a higher ratio with respect to the intensity of monthly rainfall for El Niño years and La Niña years a reduced occurrence of rainfall intensities. Studies have shown that the types of soils and their cover and land use have a great influence on outbreaks of mass movements. We observed a small number of mass movements in natural areas and a higher proportion of these movements in areas used for livestock activity in the region. Most mass movements occurred in areas of Cambisols (higher areas) and Oxisols (slopes in areas of lower altitudes). Both soils are thicker than those found in hilly areas, with higher accumulation of materials to be deployed during large accumulated rainfall, generating mass movements. The analysis also showed that areas with increased wet and accumulated occurrence of precipitation extreme excess of 100 mm/day and above 30mm/month concentrated a larger number of mass movements, as the region closest to the station Quartéis (East portion).On the other hand very high areas with steep slopes, but with a predominance of Atlantic Forest and areas with thinner soils, such as Entisols, were with a small number of these processes. Finally this study showed the need to better manage these areas, sensitive areas under the geomorphological point of view, even for areas that are on the outskirts of densely populated and whose demands tend to become increasingly salient, which can cause problems locations, reaching its population and economy, with serious consequences for the environment

This diploma thesis deals with the development of methods for evaluating retention features of green roofs. For the purpose of this thesis, a rainfall simulator was designed and various types of precipitation were examined. The thesis is divided into two main parts: a theoretical part and a practical part. In the theoretical part, the importance of water retention on green roofs is discussed. In addition, a physical theory related to this topic is included and various rainfall simulators used for scientific experiments in the Czech Republic and abroad are described. The practical part deals with the measurement of droplet size and rainfall simulator design. In this part, the process of measuring the flow and the intensity of rainfall by using scales, flow meter and rain gauge is described. For this purpose, a datalogger was developed by the Faculty of Eletrical Engineering in Brno. Finally, the results are sumarized and recommendations on how to conduct the research in the future are provided.

From the Proceedings of the 1971 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - April 22-23, 1971, Tempe, Arizona / Quantitative descriptions of regional differences of rainfall amounts and intensities in the southwest, such as depth-duration frequencies, generally have ignored differences in the storm system that generated the rainfall and have lumped essentially different storm systems together. Thunderstorm rainfall in southern Arizona and New Mexico were analyzed using data from both recording and standard rain gages. The results were somewhat conflicting. Possibly because of more frontal activity and less distance from the Gulf of Mexico., the thunderstorms in eastern New Mexico can be more intense than those in southeastern Arizona. Recording rain gage records suggest that air-mass thunderstorms produce a larger number of more intense short-duration (about 1 hour or less) rains in southeastern Arizona than in other parts of southern Arizona. However, standard rain gage records from southern Arizona indicate that rainfall from individual air-mass thunderstorms may be greater in south-central Arizona than in se or sw Arizona. But frequency analysis of standard gage data from air-mass storms shows that the 100-year point rainfall is about 3 inches in all 3 regions. With more data becoming available, especially from remote areas, more exact separation of thunderstorm types and a better definition of rainfall will soon be possible.

From the Proceedings of the 1977 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - April 15-16, 1977, Las Vegas, Nevada / Thunderstorms produce most of the annual rainfall and almost all runoff from arid and semiarid rangelands in the southwest U.S. A model was developed to be used for predicting runoff in river basins, flood plane zonings, estimating flood damage, erosion, and sediment transport, and estimating precipitation available for forage growth. This rainfall occurrence model has three parameters: elevation, latitude and longitude, and takes into account rainfall occurrence in 22 stations located in Arizona and New Mexico. From these variables, mathematical equations were developed in an effort to predict point rainfall occurrence. Estimates of the number of seasonal occurrences were used as a check of the equations within the model.

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.

Increasing global mean temperature or global warming has the potential to affect the hydrologic cycle. In the 21st century, according to the UN Intergovernmental Panel on Climate Change (IPCC), alterations in the frequency and magnitude of high intensity rainfall events are very likely. Increasing trend of urbanization across the globe is also noticeable, simultaneously. These changes will have a great impact on water infrastructure as well as environment in urban areas. One of the impacts may be the increase in frequency and extent of flooding. India, in the recent years, has witnessed a number of urban floods that have resulted in huge economic losses, an instance being the flooding of Mumbai in July, 2005. To prevent catastrophic damages due to floods, it has become increasingly important to understand the likely changes in extreme rainfall in future, its effect on the urban drainage system, and the measures that can be taken to prevent or reduce the damage due to floods. Reliable estimation of future design rainfall intensity accounting for uncertainties due to climate change is an important research issue. In this context, rainfall intensity-duration-frequency (IDF) relationships are one of the most extensively used hydrologic tools in planning, design and operation of various drainage related infrastructures in urban areas. There is, thus, a need for a study that investigates the potential effects of climate change on IDF relationships. The main aim of the research reported in this thesis is to investigate the effect of climate change on Intensity-Duration-Frequency relationship in an urban area. The rainfall in Bangalore City is used as a case study to demonstrate the applications of the methodologies developed in the research Ahead of studying the future changes, it is essential to investigate the signature of changes in the observed hydrological and climatological data series. Initially, the yearly mean temperature records are studied to find out the signature of global warming. It is observed that the temperature of Bangalore City shows an evidence of warming trend at a statistical confidence level of 99.9 %, and that warming effect is visible in terms of increase of minimum temperature at a rate higher than that of maximum temperature. Interdependence studies between temperature and extreme rainfall reveal that up to a certain range, increase in temperature intensifies short term rainfall intensities at a rate more than the average rainfall. From these two findings, it is clear that short duration rainfall intensities may intensify in the future due to global warming and urban heat island effect. The possible urbanization signatures in the extreme rainfall in terms of intensification in the evening and weekends are also inferred, although inconclusively. The IDF relationships are developed with historical data and changes in the long term daily rainfall extreme characteristics are studied. Multidecedal oscillations in the daily rainfall extreme series are also examined. Further, non-parametric trend analyses of various indices of extreme rainfall are carried out to confirm that there is a trend of increase in extreme rainfall amount and frequency, and therefore it is essential to the study the effects of climate change on the IDF relationships of the Bangalore City. Estimation of future changes in rainfall at hydrological scale generally relies on simulations of future climate provided by Global Climate Models (GCMs). Due to spatial and temporal resolution mismatch, GCM results need to be downscaled to get the information at station scale and at time resolutions necessary in the context of urban flooding. The downscaling of extreme rainfall characteristics in an urban station scale pose the following challenges: (1) downscaling methodology should be efficient enough to simulate rainfall at the tail of rainfall distribution (e.g., annual maximum rainfall), (2) downscaling at hourly or up to a few minutes temporal resolution is required, and (3) various uncertainties such as GCM uncertainties, future scenario uncertainties and uncertainties due to various statistical methodologies need to be addressed. For overcoming the first challenge, a stochastic rainfall generator is developed for spatial downscaling of GCM precipitation flux information to station scale to get the daily annual maximum rainfall series (AMRS). Although Regional Climate Models (RCMs) are meant to simulate precipitation at regional scales, they fail to simulate extreme events accurately. Transfer function based methods and weather typing techniques are also generally inefficient in simulating the extreme events. Due to its stochastic nature, rainfall generator is better suited for extreme event generation. An algorithm for stochastic simulation of rainfall, which simulates both the mean and extreme rainfall satisfactorily, is developed in the thesis and used for future projection of rainfall by perturbing the parameters of the rainfall generator for the future time periods. In this study, instead of using the customary two states (rain/dry) Markov chain, a three state hybrid Markov chain is developed. The three states used in the Markov chain are: dry day, moderate rain day and heavy rain day. The model first decides whether a day is dry or rainy, like the traditional weather generator (WGEN) using two transition probabilities, probabilities of a rain day following a dry day (P01), and a rain day following a rain day (P11). Then, the state of a rain day is further classified as a moderate rain day or a heavy rain day. For this purpose, rainfall above 90th percentile value of the non-zero precipitation distribution is termed as a heavy rain day. The state of a day is assigned based on transition probabilities (probabilities of a rain day following a dry day (P01), and a rain day following a rain day (P11)) and a uniform random number. The rainfall amount is generated by Monte Carlo method for the moderate and heavy rain days separately. Two different gamma distributions are fitted for the moderate and heavy rain days. Segregating the rain days into two different classes improves the process of generation of extreme rainfall. For overcoming the second challenge, i.e. requirement of temporal scales, the daily scale IDF ordinates are disaggregated into hourly and sub-hourly durations. Disaggregating continuous rainfall time series at sub-hourly scale requires continuous rainfall data at a fine scale (15 minute), which is not available for most of the Indian rain gauge stations. Hence, scale invariance properties of extreme rainfall time series over various rainfall durations are investigated through scaling behavior of the non-central moments (NCMs) of generalized extreme value (GEV) distribution. The scale invariance properties of extreme rainfall time series are then used to disaggregate the distributional properties of daily rainfall to hourly and sub-hourly scale. Assuming the scaling relationships as stationary, future sub-hourly and hourly IDF relationships are developed. Uncertainties associated with the climate change impacts arise due to existence of several GCMs developed by different institutes across the globe, climate simulations available for different representative concentration pathway (RCP) scenarios, and the diverse statistical techniques available for downscaling. Downscaled output from a single GCM with a single emission scenario represents only a single trajectory of all possible future climate realizations and cannot be representative of the full extent of climate change. Therefore, a comprehensive assessment of future projections should use the collective information from an ensemble of GCM simulations. In this study, 26 different GCMs and 4 RCP scenarios are taken into account to come up with a range of IDF curves at different future time periods. Reliability ensemble averaging (REA) method is used for obtaining weighted average from the ensemble of projections. Scenario uncertainty is not addressed in this study. Two different downscaling techniques (viz., delta change and stochastic rainfall generator) are used to assess the uncertainty due to downscaling techniques. From the results, it can be concluded that the delta change method under-estimated the extreme rainfall compared to the rainfall generator approach. This study also confirms that the delta change method is not suitable for impact studies related to changes in extreme events, similar to some earlier studies. Thus, mean IDF relationships for three different future extreme events, similar to some earlier studies. Thus, mean IDF relationships for three different future periods and four RCP scenarios are simulated using rainfall generator, scaling GEV method, and REA method. The results suggest that the shorter duration rainfall will invigorate more due to climate change. The change is likely to be in the range of 20% to 80%, in the rainfall intensities across all durations. Finally, future projected rainfall intensities are used to investigate the possible impact of climate change in the existing drainage system of the Challaghatta valley in the Bangalore City by running the Storm Water Management Model (SWMM) for historical period, and the best and the worst case scenario for three future time period of 2021–2050, 2051–2080 and 2071–2100. The results indicate that the existing drainage is inadequate for current condition as well as for future scenarios. The number of nodes flooded will increase as the time period increases, and a huge change in runoff volume is projected. The modifications of the drainage system are suggested by providing storage pond for storing the excess high speed runoff in order to restrict the width of the drain The main research contribution of this thesis thus comes from an analysis of trends of extreme rainfall in an urban area followed by projecting changes in the IDF relationships under climate change scenarios and quantifying uncertainties in the projections.

MESHWR / Department of Hydrology and Water Resources / This study was aimed at investigating the long term seasonal and annual changes in rainfall duration and magnitude at Luvuvhu River Catchment (LRC). Rainfall in this catchment is highly variable and is characterised of extreme events which shift runoff process, affect the timing and magnitude of floods and drought, and alter groundwater recharge. This study was motivated by the year to year changes of rainfall which have some effects on the availability of water resources. Computed long term total seasonal, annual rainfall and total number of seasonal rainy days were used to identify trends for the period of 51 years (1965- 2015), using Mann Kendal (MK), linear regression (LR) and quantile regression methods. The MK, LR and quantile regression methods have indicated dominance of decreasing trends of the annual, seasonal rainfall and duration of seasonal rainfall although they were not statistically significant. However, statistical significant decreasing trends in duration of seasonal rainfall were identified by MK and LR at Matiwa, Palmaryville, Levubu, and Entabeni Bos stations only. Quantile regression identified the statistically significant decreasing trends on 0.2, 0.5 and 0.7 quantiles only in the Palmaryville, Levubu and Entabeni Bos, respectively. Stations with non-statistically significant decreasing trends of annual and seasonal rainfall had magnitude of change ranging from 0.12 to 12.31 and 0.54 to 6.72 mm, respectively. Stations with non-statistically increasing trends of annual and seasonal rainfall magnitude had positive magnitude of change ranging from 1.51 to 6.78 and 2.05 to 6.51 mm, respectively. The Study recommended further studies using other approaches to determine the duration of rainfall to improve, update and compare the results obtained in the current study. Continuous monitoring and installation of rain gauges are recommended on the lower reaches of the catchment for the findings to be of complete picture for the whole catchment and to also minimize the rainfall gaps in the stations. Water resources should be used in a sustainable way to avoid water crisis risk in the next generations. / NRF

Vi lever i ett samhälle med pågående urbanisering. Nyexploateringar och förtätningar istadsmiljöer bidrar till minskad infiltration och evapotranspiration samt ökad ytavrinning avregn- och smältvatten; så kallat dagvatten. Det är viktigt att kvantifiera denna diffusaföroreningskälla i urbana miljöer då bland annat näringsämnen och tungmetaller transporterasmed dagvattnet, vilket bidrar till försämrad status i recipienter. Enföroreningsbelastningsmodell för näringsämnen i dagvatten på nationell nivå används avSvenska miljöemissionsdata (SMED). Beräkningsmetoden använder empiriskt framtagnaavrinningskoefficienter och belastningsschabloner som varierar beroende påmarkanvändningsslag. Flödesproportionell provtagning av totalhalter kväve, fosfor, bly, koppar, zink, kadmium ochsuspenderad substans genomfördes under nio veckor i ett av Uppsalas störstadagvattenavrinningsområden. Syftet var att kvantifiera områdets föroreningsbelastning för attutvärdera och föreslå förbättringar till SMED:s beräkningsmetod som enligt tidigare studierhar stora osäkerheter, men även för att ge underlag till placering av eventuella reningsåtgärderi området. Provtagningen kompletterades med en modellutvärdering där beräkningsmetodenskänslighet med avseende på markanvändning och inkludering av basflöde testades. Även enmer fysikaliskt förankrad modell för näringsämnestransport i naturliga avrinningsområdenanvändes i modellutvärderingen. Resultaten visade att bly, koppar och zink transporteras till Fyrisån i koncentrationer somöverskrider föreslagna regionala riktvärden för dagvatten. För koppar och zink är även dentotala belastningen på recipient högre än tidigare modellerade värden. Då tungmetaller, menäven fosfor, till stor del transporteras i partikulär form bör en eventuell reningsåtgärd iavrinningsområdet fokusera på att avskilja partikulärt material. Åtgärden bör även placerasuppströms industrin GE Healthcare Bio-Sciences AB där föroreningskoncentrationerna spädsut genom att stora volymer kyl- och regenereringsvatten tillförs dagvattennätet. Vidare visade modellutvärderingen att när SMED:s beräkningsmetod applicerades på detstuderade avrinningsområdet överskattades volymavrinningen från dagvattnet jämfört medprovtagningsresultaten medan medelkoncentrationen för kväve underskattades. För fosfor gavprovtagningsresultaten och beräkningsmetoden samstämmiga svar. Sammantagetöverskattade modellen fosforbelastningen något men underskattade kvävebelastningen. Förkväve har basflödet visat sig stå för en betydande del av belastningen och bör därför iframtiden inkluderas i SMED:s beräkningsmetod. Även den markanvändningskarta somanvänds i metoden bör bytas ut på grund av dess inaktualitet samt att modellutvärderingenvisade relativt stora känsligheter i resultaten för ändring i markanvändning. / We live in a society with an ongoing urbanization. New development projects anddensifications in urban areas contribute to reduced infiltration and evapotranspiration and anincreased surface runoff from rain and melt water, i.e. stormwater. It is essential to quantifythis diffuse source of pollution in urban environments since nutrients, heavy metals and otherpollutants, are transported by the stormwater and contribute to recipient degradation. Anutrient pollution load model in stormwater is used by Swedish environmental emission data(SMED) on a national level. The SMED computational method utilizes empirical runoffcoefficients and standard concentrations, which vary depending on the catchment land-use. Flow proportional sampling of total concentrations of nitrogen, phosphorus, lead, copper,zinc, cadmium and suspended solids was conducted during nine weeks in one of the largeststormwater catchments in Uppsala city, Sweden. The study aimed at quantifying the pollutionload of the catchment in order to evaluate and suggest improvements to the SMEDcomputational method, which contains large uncertainties according to previous studies.Furthermore, the study aimed at providing a basis for potential treatment measures in thecatchment. The sampling was complemented with a model evaluation where the sensitivity ofthe computational method was tested with respect to land-use input and the inclusion ofbaseflow. The model evaluation also included a comparison with a more physically basedmodel for nutrient transport in natural catchments. The results revealed that lead, copper and zinc are discharged into the Fyris River inconcentrations exceeding proposed regional guideline values. For copper and zinc the totalpollution loads on the recipient are higher than previously modeled values. As heavy metalsand phosphorus are transported largely in particulate form the potential treatment measureshould have the ability to effectively separate particulate matter from the stormwater matrix.Due to emissions of large volumes of cooling and regeneration water from the industry GEHealthcare Bio-Sciences AB, the stormwater pollutants are diluted. The treatment measureshould therefore be placed upstream from the industry. When applying the SMED computational method on the studied catchment, the modeloverestimated the runoff volume from stormwater compared to the sampling results, while theaverage nitrogen concentration was underestimated. Regarding phosphorus concentrations,the model and the sampling results concurred relatively well. This resulted in a higherphosphorus, but lower nitrogen, pollution load predicted by the model. It can partly beattributed the fact that baseflow transport of nitrogen is a significant part of the total pollutionload, and should thus be included in the SMED computational method in future calculations.Another model improvement would be to replace the outdated land-use map currently beingused in the method as the model evaluation indicated a relatively large sensitivity in theresults with regards to alterations in the land-use type input.