Semi-arid areas are, due to their climatic setting, characterized by small water resources. An increasing water demand as a consequence of population growth and economic development as well as a decreasing water availability in the course of possible climate change may aggravate water scarcity in future, which often exists already for present-day conditions in these areas. Understanding the mechanisms and feedbacks of complex natural and human systems, together with the quantitative assessment of future changes in volume, timing and quality of water resources are a prerequisite for the development of sustainable measures of water management to enhance the adaptive capacity of these regions. For this task, dynamic integrated models, containing a hydrological model as one component, are indispensable tools.<br /> The main objective of this study is to develop a hydrological model for the quantification of water availability in view of environmental change over a large geographic domain of semi-arid environments.<br /> The study area is the Federal State of Ceará (150 000 km2) in the semi-arid north-east of Brazil. Mean annual precipitation in this area is 850 mm, falling in a rainy season with duration of about five months. Being mainly characterized by crystalline bedrock and shallow soils, surface water provides the largest part of the water supply. The area has recurrently been affected by droughts which caused serious economic losses and social impacts like migration from the rural regions. <br /> The hydrological model Wasa (Model of Water Availability in Semi-Arid Environments) developed in this study is a deterministic, spatially distributed model being composed of conceptual, process-based approaches. Water availability (river discharge, storage volumes in reservoirs, soil moisture) is determined with daily resolution. Sub-basins, grid cells or administrative units (municipalities) can be chosen as spatial target units. The administrative units enable the coupling of Wasa in the framework of an integrated model which contains modules that do not work on the basis of natural spatial units.<br /> The target units mentioned above are disaggregated in Wasa into smaller modelling units within a new multi-scale, hierarchical approach. The landscape units defined in this scheme capture in particular the effect of structured variability of terrain, soil and vegetation characteristics along toposequences on soil moisture and runoff generation. Lateral hydrological processes at the hillslope scale, as reinfiltration of surface runoff, being of particular importance in semi-arid environments, can thus be represented also within the large-scale model in a simplified form. Depending on the resolution of available data, small-scale variability is not represented explicitly with geographic reference in Wasa, but by the distribution of sub-scale units and by statistical transition frequencies for lateral fluxes between these units.<br /> Further model components of Wasa which respect specific features of semi-arid hydrology are: <br /> (1) A two-layer model for evapotranspiration comprises energy transfer at the soil surface (including soil evaporation), which is of importance in view of the mainly sparse vegetation cover. Additionally, vegetation parameters are differentiated in space and time in dependence on the occurrence of the rainy season. <br /> (2) The infiltration module represents in particular infiltration-excess surface runoff as the dominant runoff component. <br /> (3) For the aggregate description of the water balance of reservoirs that cannot be represented explicitly in the model, a storage approach respecting different reservoirs size classes and their interaction via the river network is applied. <br /> (4) A model for the quantification of water withdrawal by water use in different sectors is coupled to Wasa. <br /> (5) A cascade model for the temporal disaggregation of precipitation time series, adapted to the specific characteristics of tropical convective rainfall, is applied for the generating rainfall time series of higher temporal resolution.<br /> All model parameters of Wasa can be derived from physiographic information of the study area. Thus, model calibration is primarily not required.<br /> Model applications of Wasa for historical time series generally results in a good model performance when comparing the simulation results of river discharge and reservoir storage volumes with observed data for river basins of various sizes. The mean water balance as well as the high interannual and intra-annual variability is reasonably represented by the model. Limitations of the modelling concept are most markedly seen for sub-basins with a runoff component from deep groundwater bodies of which the dynamics cannot be satisfactorily represented without calibration.<br /> Further results of model applications are:<br /> (1) Lateral processes of redistribution of runoff and soil moisture at the hillslope scale, in particular reinfiltration of surface runoff, lead to markedly smaller discharge volumes at the basin scale than the simple sum of runoff of the individual sub-areas. Thus, these processes are to be captured also in large-scale models. The different relevance of these processes for different conditions is demonstrated by a larger percentage decrease of discharge volumes in dry as compared to wet years.<br /> (2) Precipitation characteristics have a major impact on the hydrological response of semi-arid environments. In particular, underestimated rainfall intensities in the rainfall input due to the rough temporal resolution of the model and due to interpolation effects and, consequently, underestimated runoff volumes have to be compensated in the model. A scaling factor in the infiltration module or the use of disaggregated hourly rainfall data show good results in this respect.<br /> The simulation results of Wasa are characterized by large uncertainties. These are, on the one hand, due to uncertainties of the model structure to adequately represent the relevant hydrological processes. On the other hand, they are due to uncertainties of input data and parameters particularly in view of the low data availability. Of major importance is:<br /> (1) The uncertainty of rainfall data with regard to their spatial and temporal pattern has, due to the strong non-linear hydrological response, a large impact on the simulation results.<br /> (2) The uncertainty of soil parameters is in general of larger importance on model uncertainty than uncertainty of vegetation or topographic parameters.<br /> (3) The effect of uncertainty of individual model components or parameters is usually different for years with rainfall volumes being above or below the average, because individual hydrological processes are of different relevance in both cases. Thus, the uncertainty of individual model components or parameters is of different importance for the uncertainty of scenario simulations with increasing or decreasing precipitation trends.<br /> (4) The most important factor of uncertainty for scenarios of water availability in the study area is the uncertainty in the results of global climate models on which the regional climate scenarios are based. Both a marked increase or a decrease in precipitation can be assumed for the given data.<br /> Results of model simulations for climate scenarios until the year 2050 show that a possible future change in precipitation volumes causes a larger percentage change in runoff volumes by a factor of two to three. In the case of a decreasing precipitation trend, the efficiency of new reservoirs for securing water availability tends to decrease in the study area because of the interaction of the large number of reservoirs in retaining the overall decreasing runoff volumes. / Semiaride Gebiete sind auf Grund der klimatischen Bedingungen durch geringe Wasserressourcen gekennzeichnet. Ein zukünftig steigender Wasserbedarf in Folge von Bevölkerungswachstum und ökonomischer Entwicklung sowie eine geringere Wasserverfügbarkeit durch mögliche Klimaänderungen können dort zu einer Verschärfung der vielfach schon heute auftretenden Wasserknappheit führen. Das Verständnis der Mechanismen und Wechselwirkungen des komplexen Systems von Mensch und Umwelt sowie die quantitative Bestimmung zukünftiger Veränderungen in der Menge, der zeitlichen Verteilung und der Qualität von Wasserressourcen sind eine grundlegende Voraussetzung für die Entwicklung von nachhaltigen Maßnahmen des Wassermanagements mit dem Ziel einer höheren Anpassungsfähigkeit dieser Regionen gegenüber künftigen Änderungen. Hierzu sind dynamische integrierte Modelle unerlässlich, die als eine Komponente ein hydrologisches Modell beinhalten. <br /> Vorrangiges Ziel dieser Arbeit ist daher die Erstellung eines hydrologischen Modells zur großräumigen Bestimmung der Wasserverfügbarkeit unter sich ändernden Umweltbedingungen in semiariden Gebieten.<br /> Als Untersuchungsraum dient der im semiariden tropischen Nordosten Brasiliens gelegene Bundestaat Ceará (150 000 km2). Die mittleren Jahresniederschläge in diesem Gebiet liegen bei 850 mm innerhalb einer etwa fünfmonatigen Regenzeit. Mit vorwiegend kristallinem Grundgebirge und geringmächtigen Böden stellt Oberflächenwasser den größten Teil der Wasserversorgung bereit. Die Region war wiederholt von Dürren betroffen, die zu schweren ökonomischen Schäden und sozialen Folgen wie Migration aus den ländlichen Gebieten geführt haben. <br /> Das hier entwickelte hydrologische Modell Wasa (Model of Water Availability in Semi-Arid Environments) ist ein deterministisches, flächendifferenziertes Modell, das aus konzeptionellen, prozess-basierten Ansätzen aufgebaut ist. Die Wasserverfügbarkeit (Abfluss im Gewässernetz, Speicherung in Stauseen, Bodenfeuchte) wird mit täglicher Auflösung bestimmt. Als räumliche Zieleinheiten können Teileinzugsgebiete, Rasterzellen oder administrative Einheiten (Gemeinden) gewählt werden. Letztere ermöglichen die Kopplung des Modells im Rahmen der integrierten Modellierung mit Modulen, die nicht auf der Basis natürlicher Raumeinheiten arbeiten.<br /> Im Rahmen eines neuen skalenübergreifenden, hierarchischen Ansatzes werden in Wasa die genannten Zieleinheiten in kleinere räumliche Modellierungseinheiten unterteilt. Die ausgewiesenen Landschaftseinheiten erfassen insbesondere die strukturierte Variabilität von Gelände-, Boden- und Vegetationseigenschaften entlang von Toposequenzen in ihrem Einfluss auf Bodenfeuchte und Abflussbildung. Laterale hydrologische Prozesse auf kleiner Skala, wie die für semiaride Bedingungen bedeutsame Wiederversickerung von Oberflächenabfluss, können somit auch in der erforderlichen großskaligen Modellanwendung vereinfacht wiedergegeben werden. In Abhängigkeit von der Auflösung der verfügbaren Daten wird in Wasa die kleinskalige Variabilität nicht räumlich explizit sondern über die Verteilung von Flächenanteilen subskaliger Einheiten und über statistische Übergangshäufigkeiten für laterale Flüsse zwischen den Einheiten berücksichtigt.<br /> Weitere Modellkomponenten von Wasa, die spezifische Bedingungen semiarider Gebiete berücksichtigen, sind: <br /> (1) Ein Zwei-Schichten-Modell zur Bestimmung der Evapotranspiration berücksichtigt auch den Energieumsatz an der Bodenoberfläche (inklusive Bodenverdunstung), der in Anbetracht der meist lichten Vegetationsbedeckung von Bedeutung ist. Die Vegetationsparameter werden zudem flächen- und zeitdifferenziert in Abhängigkeit vom Auftreten der Regenzeit modifiziert. <br /> (2) Das Infiltrationsmodul bildet insbesondere Oberflächenabfluss durch Infiltrationsüberschuss als dominierender Abflusskomponente ab. <br /> (3) Zur aggregierten Beschreibung der Wasserbilanz von im Modell nicht einzeln erfassbaren Stauseen wird ein Speichermodell unter Berücksichtigung verschiedener Größenklassen und ihrer Interaktion über das Gewässernetz eingesetzt. <br /> (4) Ein Modell zur Bestimmung der Entnahme durch Wassernutzung in verschiedenen Sektoren ist an Wasa gekoppelt. <br /> (5) Ein Kaskadenmodell zur zeitlichen Disaggregierung von Niederschlagszeitreihen, das in dieser Arbeit speziell für tropische konvektive Niederschlagseigenschaften angepasst wird, wird zur Erzeugung höher aufgelöster Niederschlagsdaten verwendet.<br /> Alle Modellparameter von Wasa können von physiographischen Gebietsinformationen abgeleitet werden, sodass eine Modellkalibrierung primär nicht erforderlich ist. <br /> Die Modellanwendung von Wasa für historische Zeitreihen ergibt im Allgemeinen eine gute Übereinstimmung der Simulationsergebnisse für Abfluss und Stauseespeichervolumen mit Beobachtungsdaten in unterschiedlich großen Einzugsgebieten. Die mittlere Wasserbilanz sowie die hohe monatliche und jährliche Variabilität wird vom Modell angemessen wiedergegeben. Die Grenzen der Anwendbarkeit des Modell-konzepts zeigen sich am deutlichsten in Teilgebieten mit Abflusskomponenten aus tieferen Grundwasserleitern, deren Dynamik ohne Kalibrierung nicht zufriedenstellend abgebildet werden kann.<br /> Die Modellanwendungen zeigen weiterhin:<br /> (1) Laterale Prozesse der Umverteilung von Bodenfeuchte und Abfluss auf der Hangskala, vor allem die Wiederversickerung von Oberflächenabfluss, führen auf der Skala von Einzugsgebieten zu deutlich kleineren Abflussvolumen als die einfache Summe der Abflüsse der Teilflächen. Diese Prozesse sollten daher auch in großskaligen Modellen abgebildet werden. Die unterschiedliche Ausprägung dieser Prozesse für unterschiedliche Bedingungen zeigt sich an Hand einer prozentual größeren Verringerung der Abflussvolumen in trockenen im Vergleich zu feuchten Jahren.<br /> (2) Die Niederschlagseigenschaften haben einen sehr großen Einfluss auf die hydrologische Reaktion in semiariden Gebieten. Insbesondere die durch die grobe zeitliche Auflösung des Modells und durch Interpolationseffekte unterschätzten Niederschlagsintensitäten in den Eingangsdaten und die daraus folgende Unterschätzung von Abflussvolumen müssen im Modell kompensiert werden. Ein Skalierungsfaktor in der Infiltrationsroutine oder die Verwendung disaggregierter stündlicher Niederschlagsdaten zeigen hier gute Ergebnisse.<br /> Die Simulationsergebnisse mit Wasa sind insgesamt durch große Unsicherheiten gekennzeichnet. Diese sind einerseits in Unsicherheiten der Modellstruktur zur adäquaten Beschreibung der relevanten hydrologischen Prozesse begründet, andererseits in Daten- und Parametersunsicherheiten in Anbetracht der geringen Datenverfügbarkeit. Von besonderer Bedeutung ist: <br /> (1) Die Unsicherheit der Niederschlagsdaten in ihrem räumlichen Muster und ihrer zeitlichen Struktur hat wegen der stark nicht-linearen hydrologischen Reaktion einen großen Einfluss auf die Simulationsergebnisse.<br /> (2) Die Unsicherheit von Bodenparametern hat im Vergleich zu Vegetationsparametern und topographischen Parametern im Allgemeinen einen größeren Einfluss auf die Modellunsicherheit.<br /> (3) Der Effekt der Unsicherheit einzelner Modellkomponenten und -parameter ist für Jahre mit unter- oder überdurchschnittlichen Niederschlagsvolumen zumeist unterschiedlich, da einzelne hydrologische Prozesse dann jeweils unterschiedlich relevant sind. Die Unsicherheit einzelner Modellkomponenten- und parameter hat somit eine unterschiedliche Bedeutung für die Unsicherheit von Szenarienrechnungen mit steigenden oder fallenden Niederschlagstrends.<br /> (4) Der bedeutendste Unsicherheitsfaktor für Szenarien der Wasserverfügbarkeit für die Untersuchungsregion ist die Unsicherheit der den regionalen Klimaszenarien zu Grunde liegenden Ergebnisse globaler Klimamodelle. Eine deutliche Zunahme oder Abnahme der Niederschläge bis 2050 kann gemäß den hier vorliegenden Daten für das Untersuchungsgebiet gleichermaßen angenommen werden.<br /> Modellsimulationen für Klimaszenarien bis zum Jahr 2050 ergeben, dass eine mögliche zukünftige Veränderung der Niederschlagsmengen zu einer prozentual zwei- bis dreifach größeren Veränderung der Abflussvolumen führt. Im Falle eines Trends von abnehmenden Niederschlagsmengen besteht in der Untersuchungsregion die Tendenz, dass auf Grund der gegenseitigen Beeinflussung der großen Zahl von Stauseen beim Rückhalt der tendenziell abnehmenden Abflussvolumen die Effizienz von neugebauten Stauseen zur Sicherung der Wasserverfügbarkeit zunehmend geringer wird.

The Wadi Dhuliel catchment/ North east Jordan, as any other arid area has distinctive hydrological features with limited water resources. The hydrological regime is characterized by high variability of temporal and spatial rainfall distributions, flash floods, absence of base flow, and high rates of evapotranspiration. The aim of this Ph.D. thesis was to apply lumped and distributed models to simulate stream flow in the Wadi Dhuliel arid catchment. Intensive research was done to estimate the spatial and temporal rainfall distributions using remote sensing. Because most rainfall-runoff models were undertaken for other climatic zones, an attempt was made to study limitations and challenges and improve rainfall-runoff modeling in arid areas in general and for the Wadi Dhuliel in particular. The thesis is divided into three hierarchically ordered research topics. In the first part and research paper, the metric conceptual IHACRES model was applied to daily and storm events time scales, including data from 19 runoff events during the period 1986-1992. The IHACRES model was extended for snowfall in order to cope with such extreme events. The performance of the IHACRES model on daily data was rather poor while the performance on the storm events scale shows a good agreement between observed and simulated streamflow. The modeled outputs were expected to be sensitive when the observed flood was relatively small. The optimum parameter values were influenced by the length of a time series used for calibration and event specific changes. In the second research paper, the Global Satellite Mapping of Precipitation (GSMaP_MVK+) dataset was used to evaluate the precipitation rates over the Wadi Dhuliel arid catchment for the period from January 2003 to March 2008. Due to the scarcity of the ground rain gauge network, the detailed structure of the rainfall distribution was inadequate, so an independent from interpolation techniques was used. Three meteorological stations and six rain gauges were used to adjust and compare with GSMaP_MVK+ estimates. Comparisons between GSMaP_MVK+ measurements and ground rain gauge records show distinct regions of correlation, as well as areas where GSMaP_MVK+ systematically over- and underestimated ground rain gauge records. A multiple linear regression (MLR) model was used to derive the relationship between rainfall and GSMaP_MVK+ in conjunction with temperature, relative humidity, and wind speed. The MLR equations were defined for the three meteorological stations. The ‘best’ fit of the MLR model for each station was chosen and used to interpolate a multiscale temporal and spatial distribution. Results show that the rainfall distribution over the Wadi Dhuliel is characterized by clear west-east and north-south gradients. Estimates from the monthly MLR model were more reliable than estimates obtained using daily data. The adjusted GSMaP_MVK+ dataset performed well in capturing the spatial patterns of the rainfall at monthly and annual time scales, while daily estimation showed some weakness for light and moderate storms. In the third research paper, the HEC-HMS and IHACRES rainfall runoff models were applied to simulate a single streamflow event in the Wadi Dhuliel catchment that occurred in 30-31.01.2008. Both models are considered suitable for arid conditions. The HEC-HMS model application was done in conjunction with the HEC-GeoHMS extension in ArcView 3.3. Streamflow estimation was performed on hourly data. The aim of this study was to develop a new framework of rainfall-runoff model applications in arid catchment by integrating a re-adjusted satellite derived rainfall dataset (GSMaP_MVK+) to determine the location of the rainfall storm. Each model has its own input data sets. HEC-HMS input data include soil type, land use/land cover map, and slope map. IHACRES input data sets include hourly rainfall and temperature. The model was calibrated and validated using observed stream flow data collected from Al-Za’atari discharge station. IHACRES shows some weaknesses, while the flow comparison between the calibrated streamflow results agrees well with the observed streamflow data of the HEC-HMS model. The Nash-Sutcliffe efficiency (Ef) for both models was 0.51, and 0.88 respectively. The application of HEC-HMS model in this study is considered to be satisfactory.

Flood estimation is often required in hydrologic design and has important economic significance. For example, in Australia, the annual spending on infrastructure requiring flood estimation is of the order of $650 million ARR (I.E. Aust., 1998). Rainfall-based flood estimation techniques are most commonly adopted in practice. These require several inputs to convert design rainfalls to design floods. Of all the inputs, loss is an important one and defined as the amount of precipitation that does not appear as direct runoff. The concept of loss includes moisture intercepted by vegetation, infiltration into the soil, retention on the surface, evaporation and loss through the streambed and banks. As these loss components are dependent on topography, soils, vegetation and climate, the loss exhibits a high degree of temporal and spatial variability during the rainfall event. In design flood estimation, the simplified lumped conceptual loss models were used because of their simplicity and ability to approximate catchment runoff behaviour. In Australia, the most commonly adopted conceptual loss model is the initial losscontinuing loss model. For a specific part of the catchment, the initial loss occurs prior to the commencement of surface runoff, and can be considered to be composed of the interception loss, depression storage and infiltration that occur before the soil surface saturates. ARR (I. E. Aust., 1998) mentioned that the continuing loss is the average rate of loss throughout the remainder of the storm. At present, there is inadequate information on design losses in most parts of Australia and this is one of the greatest weaknesses in Australian flood hydrology. Currently recommended design losses are not compatible with design rainfall information in Australian Rainfall and Runoff. Also design losses for observed storms show a wide variability and it is always difficult to select an appropriate value of loss from this wide range for a particular application. Despite the wide variability of loss values, in the widely used Design Event Approach, a single value of initial and continuing losses is adopted. Because of the non-linearity in the rainfall-runoff process, this is likely to introduce a high degree of uncertainty and possible bias in the resulting flood estimates. In contrast, the Joint Probability Approach can consider probability-distributed losses in flood estimation. In ARR (I. E. Aust., 1998) it is recommended to use a constant continuing loss value in rainfall events. In this research it was observed that the continuing loss values in the rainfall events were not constant, rather than it decays with the duration of the rainfall event. The derived loss values from the 969 rainfall and streamflow events of Queensland catchments would provide better flood estimation than the recommended design loss values in ARR (I. E. Aust., 1998). In this research, both the initial and continuing losses were computed using IL-CL loss model and a single median loss value was used to estimate flood using Design Event Approach. Again both the initial and continuing losses were considered to be random variables and their probability distribution functions were determined. Hence, the research showed that the probability distributed loss values can be used for Queensland catchments in near future for better flood estimate. The research hypothesis tested was whether the new loss value for Queensland catchments provides significant improvement in design flood estimation. A total of 48 catchments, 82 pluviograph stations and 24 daily rainfall stations were selected from all over Queensland to test the research hypothesis. The research improved the recommended design loss values that will result in more precise design flood estimates. This will ultimately save millions of dollars in the construction of hydraulic infrastructures.

The Wadi Dhuliel catchment/ North east Jordan, as any other arid area has distinctive hydrological features with limited water resources. The hydrological regime is characterized by high variability of temporal and spatial rainfall distributions, flash floods, absence of base flow, and high rates of evapotranspiration. The aim of this Ph.D. thesis was to apply lumped and distributed models to simulate stream flow in the Wadi Dhuliel arid catchment. Intensive research was done to estimate the spatial and temporal rainfall distributions using remote sensing. Because most rainfall-runoff models were undertaken for other climatic zones, an attempt was made to study limitations and challenges and improve rainfall-runoff modeling in arid areas in general and for the Wadi Dhuliel in particular. The thesis is divided into three hierarchically ordered research topics. In the first part and research paper, the metric conceptual IHACRES model was applied to daily and storm events time scales, including data from 19 runoff events during the period 1986-1992. The IHACRES model was extended for snowfall in order to cope with such extreme events. The performance of the IHACRES model on daily data was rather poor while the performance on the storm events scale shows a good agreement between observed and simulated streamflow. The modeled outputs were expected to be sensitive when the observed flood was relatively small. The optimum parameter values were influenced by the length of a time series used for calibration and event specific changes. In the second research paper, the Global Satellite Mapping of Precipitation (GSMaP_MVK+) dataset was used to evaluate the precipitation rates over the Wadi Dhuliel arid catchment for the period from January 2003 to March 2008. Due to the scarcity of the ground rain gauge network, the detailed structure of the rainfall distribution was inadequate, so an independent from interpolation techniques was used. Three meteorological stations and six rain gauges were used to adjust and compare with GSMaP_MVK+ estimates. Comparisons between GSMaP_MVK+ measurements and ground rain gauge records show distinct regions of correlation, as well as areas where GSMaP_MVK+ systematically over- and underestimated ground rain gauge records. A multiple linear regression (MLR) model was used to derive the relationship between rainfall and GSMaP_MVK+ in conjunction with temperature, relative humidity, and wind speed. The MLR equations were defined for the three meteorological stations. The ‘best’ fit of the MLR model for each station was chosen and used to interpolate a multiscale temporal and spatial distribution. Results show that the rainfall distribution over the Wadi Dhuliel is characterized by clear west-east and north-south gradients. Estimates from the monthly MLR model were more reliable than estimates obtained using daily data. The adjusted GSMaP_MVK+ dataset performed well in capturing the spatial patterns of the rainfall at monthly and annual time scales, while daily estimation showed some weakness for light and moderate storms. In the third research paper, the HEC-HMS and IHACRES rainfall runoff models were applied to simulate a single streamflow event in the Wadi Dhuliel catchment that occurred in 30-31.01.2008. Both models are considered suitable for arid conditions. The HEC-HMS model application was done in conjunction with the HEC-GeoHMS extension in ArcView 3.3. Streamflow estimation was performed on hourly data. The aim of this study was to develop a new framework of rainfall-runoff model applications in arid catchment by integrating a re-adjusted satellite derived rainfall dataset (GSMaP_MVK+) to determine the location of the rainfall storm. Each model has its own input data sets. HEC-HMS input data include soil type, land use/land cover map, and slope map. IHACRES input data sets include hourly rainfall and temperature. The model was calibrated and validated using observed stream flow data collected from Al-Za’atari discharge station. IHACRES shows some weaknesses, while the flow comparison between the calibrated streamflow results agrees well with the observed streamflow data of the HEC-HMS model. The Nash-Sutcliffe efficiency (Ef) for both models was 0.51, and 0.88 respectively. The application of HEC-HMS model in this study is considered to be satisfactory.

The Håga river, located west of the Swedish city Uppsala, is currently without a proper gauging station. Knowing the streamflow is nonetheless important to, for example, be able to calculate the nutrient transport in the river. This project aimed to evaluate different indirect methods of streamflow estimation to investigate how they perform, in particular in relation to SMHI’s S-HYPE model. Two of the methods used were based on transferring streamflow of nearby catchments to Håga, either by using relationships between the mean and standard deviation of the streamflow time series (MOVE), or by simply scaling relative to catchment size (DAR). Furthermore, a hydrological model, HBV, was calibrated for Håga using different amounts and types of calibration data. All the methods were then compared to streamflow data from a previously active gauging station in Håga.  It was found that the overall best method to estimate the streamflow in Håga was using the MOVE method with one particular donor catchment. However, the performance of the simpler MOVE and DAR methods varied a lot from catchment to catchment. HBV was found to be able to produce better performing simulations than S-HYPE, despite being a simpler model. Even HBV-calibrations using alternative or limited data could perform rather well, although rarely at the level of a calibration utilising all available streamflow data. A big uncertainty of the study was the fact that the most recent available validation data for the Håga catchment was from two decades ago, when the old gauging station was decommissioned. Most likely the methods that worked well during the 90s would work well today as well, but this is a matter that could be studied further. / Hågaån, ett vattendrag som ligger väster om Uppsala, saknar i nuläget en mätstation för vattenföring. Att känna till flödet är dock ändå intressant, bland annat för att kunna beräkna näringstransporten i ån. Syftet med detta projekt var därmed att utvärdera och jämföra olika metoder för att uppskatta vattenflödet i Hågaån, särskilt för att undersöka hur de presterade i jämförelse med SMHI:s S-HYPE-modell. Två av metoderna som användes för detta baserades på att överföra flöden från närliggande vattendrag till Håga, antingen genom att använda förhållanden mellan medelvärde och standardavvikelse för flödes-datan (MOVE), eller genom att bara utgå från skillnader i områdenas storlek (DAR). Utöver det kalibrerades även den hydrologiska modellen HBV för Håga med olika typer och mängder av kalibreringsdata. Alla metoderna jämfördes sedan med data från en mätstation som tidigare funnits i Hågaån. Resultaten visade att den över lag bästa metoden för att uppskatta flödet i Håga var MOVE-metoden i kombination med ett av de närliggande vattendragen. Hur väl dessa simplare MOVE- och DAR-metoder presterade varierade dock mycket beroende på vilket vattendrag som användes som donator. Det visade sig även att det gick att erhålla bättre resultat med HBV än de som gavs av S-HYPE, trots att HBV är en enklare modell. Även HBV-kalibreringar baserade på alternativ eller begränsad data kunde producera välpresterande simulationer, dock sällan på samma nivå som den kalibrering som använt all tillgänglig flödesdata. En stor osäkerhet i projektet kretsar kring att den nyaste tillgängliga valideringsdatan från Hågaån var över två decennier gammal, då den mätstation som funnits stängdes ner. Med stor sannolikhet kommer metoderna som fungerade väl under 90-talet även fungera bra i modern tid, men detta är något som kräver vidare studier.

The climate adaptation of our cities and the optimization of our technical systems with regards to weather sets high demands on the availability and the processing of weather data. The possibility to forecast disturbances of influent flow rate to wastewater treatment plants allow control systems counteract these disturbances before they have a harmful effect on the treatment processes. These forecasts can be made by different models A neural network models complex patterns between different data sets through a multi-layered structure containing a large amount of transformation functions. The aim of this project was to examine how the complex neural network performed compared with a simpler linear regression model when forecasting wastewater flow using high resolution X-band rain radar data. The study also investigated to what extent X-band rain radar data contributes to the performance of the model. The performance was evaluated at rain flow periods only. Wastewater flow data were provided by Avedøre wastewater treatment plant in Copenhagen operated by BIOFOS. The X-band rain radar data was provided by HOFOR. The neural network was developed by Informetics on the TensorFlow platform. This project concluded that the neural network and the linear regression model performed equally well at predicting when a rain flow period began. The neural network was more accurate at predicting the flow rate while the linear regression was better at approximating the accumulated flow over an entire rain flow period. Using additional rain data up to 30 km within the radar station location in comparison with using data only from within the catchment indicated a 20 to 30-minutes improvement of possible lead time. A conceivable lead time when forecasting the sewer flow to Avedøre wastewater treatment plant was estimated to be around 4 hours. / Det föreligger höga krav på tillgänglighet och bearbetning av väderdata för att kunna optimera tekniska system i förhållande till väder och klimat. Att kunna förutsäga ändrat inkommande flöde till avloppsreningsverk möjliggör för kontrollsystem att kunna motverka negativa konsekvenser på reningsprocesserna på grund av det ändrade flödet. X-band radardata kan användas för att prognoser av flöden med hjälp av olika modeller.Ett neuralt nätverk, reproducerar komplexa mönster mellan olika dataset genom en struktur med flera lager och en mängd överföringsfunktioner.  Målsättningen med det här projektet var att utvärdera hur ett komplext neuralt nätverk presterar jämfört med en enklare regressionsmodell i att förutsäga avloppsflöde med hjälp av högupplöst X-band radardata. I projektet undersöktes också hur tillgång av olika radardata kunde bidra till modellens prestanda. Modellerna utvärderades endast under regnflödesperioder. Data över avloppsflödet som användes i projektet kom från Avedøre avloppsreningsverk i Köpenhamn. Reningsverket drivs av BIOFOS. Radardata kom från HOFOR. Det neurala nätverket som användes har utvecklats av Informetics på plattformen Tensorflow. Slutsatser som kunde dras i projektet var att det neurala nätverket och den linjär regressionsmodellen var lika bra på att förutsäga när en regnflödesperiod startade. Det neurala nätverket kunde förutsäga det momentana flödet bättre än regressionsmodellen, medan det omvända gällde för att uppskatta den totala flödesvolymen under en hel regnflödesperiod. Genom att använda ytterligare regndata, upp till 30 kilometer från radarstationen, jämfört med att endast använda data från avrinningsområdet kunde en 20–30 minuters förbättring av den möjliga prognostiden påvisas. En tänkbar prognostiden för att förutsäga avloppsflödet till Avedøre avloppsreningsverk visades ligga omkring 4 timmar.

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