Universiẗat, Diss., 2004--Bonn.

Techn. Hochsch., Diss., 2003--Aachen.

Zugl.: Stuttgart, Univ., Diss., 2007.

Zugleich: Diss. Naturwiss. Bern. / Literaturverz.

Thesis (doctoral)--Swedish University of Agricultural Sciences, 2000. / Includes bibliographical references.

Techn. Universiẗat, Diss., 2002--Berlin.

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 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.

Die Bemessung von Bauwerken an oder in Fließgewässern erfordert die Kenntnis des statistischen Hochwasserregimes. Beispielsweise legen Hochwasserschutzkonzeptionen häufig ein Hochwasser zu Grunde, welches in einem Jahr mit der Wahrscheinlichkeit von 1/100 auftritt. Ein extremeres Hochwasser wird für den Nachweis der Standsicherheit großer Stauanlagen nach DIN 19700-12 mit einem Hochwasser der jährlichen Eintrittswahrscheinlichkeit von 1/10000 benötigt. Ein solches Hochwasser kann bereits wegen des instationären Klimas nicht allein aus Durchflussmessdaten abgeleitet, sondern lediglich idealisiert dargestellt werden. Das resultiert nicht zuletzt daraus, dass der Mensch natürlich Zeuge eines so unwahrscheinlichen Ereignisses werden kann. Jedoch kann er die Unwahrscheinlichkeit nicht nachweisen. Jedes Berechnungsschema, mit welchem ein so unwahrscheinliches Ereignis abgeschätzt werden soll, wird nur begrenzt zuverlässig sein. Das Ziel der Arbeit ist es daher, die Schätzung etwas zuverlässiger zu gestalten. Grundsätzlich gilt, dass ein Modell umso mehr bzw. sicherere Ergebnisse liefern kann, je mehr Daten in das Modell eingehen. Direkt mit dem Durchfluss gekoppelt sind Angaben zu historischen Hochwasserereignissen bzw. qualitative Einschätzungen kleinräumiger Ereignisse. Eine wichtige Datenquelle neben den Durchflussartigen ist der mit dem Durchfluss kausal verbundene Niederschlag und dessen zu vermutendes Maximum in einem Gebiet. Wird zusätzlich regional vorgegangen, können räumliche Aspekte und Strukturen in größeren Einzugsgebieten berücksichtigt werden. Diese stärken bzw. erweitern die lokalen Berechnungsgrundlagen und gewährleisten ein räumlich konsistentes Bild. Im Umkehrschluss kann das Durchflussregime regionalisiert werden, um Informationen an nicht bemessenen Orten bereitstellen zu können. Aus den genannten erweiterten Berechnungsgrundlagen lassen sich drei Anknüpfungspunkte schließen: (i) Es muss eine sehr flexible und dennoch plausible Darstellungsmöglichkeit des statistischen Niederschlagsregimes bis zum vermutlichen Maximum formuliert werden. (ii) Das entwickelte Niederschlagsregime muss mit dem Durchflussregime gekoppelt werden, um die Informationen nutzen zu können. (iii) Die anschließende Regionalisierung muss die verschachtelte baumartige Struktur hydrologischer Einzugsgebiete berücksichtigen. Punkt (i) wird durch eine zweigeteilte Verteilungsfunktion gelöst. Damit werden die ideale Darstellung des wahrscheinlicheren Bereiches und der plausible Verlauf bis zum Maximum miteinander verbunden. Bezüglich Punkt (ii) wird ein neues Kopplungsprinzip entwickelt. Dieses basiert auf der Annahme, dass ein je nach Gebiet gültiger maximaler Scheitelabflussbeiwert existiert, welcher asymptotisch erreicht wird. Im Ergebnis erhält die Durchflussverteilung mit der Abflussbeiwertapproximation einen oberen Grenzwert in Abhängigkeit von Niederschlagsmaximum und Scheitelabflussbeiwert. Entsprechend der Vorgaben in Punkt (iii) wird die Referenzpegelmethode entwickelt. Diese basiert darauf, dass ähnliche Einzugsgebiete äquivalente Hochwasserscheitel generieren. Damit können bekannte Hochwasserereignisse eines Referenzpegels auf unbeobachtete Teileinzugsgebiete übertragen werden. Bei der Wahl des Referenzpegels wird u.a. die Topologie der Einzugsgebiete berücksichtigt. Die gesamte Strategie kann auf große Untersuchungsgebiete angewandt werden. Am Beispiel sächsischer Flüsse wird die Vorgehensweise von der Datenhomogenisierung bis hin zum extremen Hochwasserdurchfluss an einem unbeobachteten Querschnitt erläutert. / The dimensioning of different constructions at and in streams respectively requires knowlegde on the flood situation at site. For instance flood protection concepts often base on a peak discharge of the annual recurrence probability of 1/100. A more severe flood of an annual recurrence probability of 1/10000 is used to confirm the stability of large dams following DIN 19700-12. Such a flood cannot be deduced from runoff data only, but rather shown in an idealised way. It results not least on the fact, that human can witness a very improbable flood event. But is it not possible to verify the improbability. Every modelling scheme that is confronted with the deduction of such an extreme flood event will be of limited reliability. The task\'s aim will therefore be to make the estimation more reliable. Generally the more data a model involves the more trustworthy the results will become. Directly coupled with runoff are historical flood data and qualitative details of small scale flood events respectively. Aside runoff information an important data source is precipitation data, which is coupled with runoff data in a causal way, and the possible maximum precipitation. If additionally whole regions are examined it is possible to consider regional facets and structures of larger catchments. These strengthen and expand local modelling basics and provide a regional consistent result. Vice versa the flood regime can be regionalised to gain information at unobserved cross sections. Out of the described expanded modelling basics follow three links: (i) It is necessary to find a flexible but still plausible formulation of the statistical precipitation regime until the probable maximum precipitation. (ii) The formulation of point i) has to be coupled with the flood regime to include these information. (iii) The adjacent regionalisation has to account for the nested and arboreal structure of hydrological catchments. Point (i) will be solved by a split distribution function. That allows the ideal display of the more probable domain as well as the characteristics until the probable maximum. Regarding point (ii) a new principle of coupling will be developed. It bases on the assumption that a regional maximum runoff coefficient exists and it will be gained asymptotically. As a result of the runoff coefficient approximation the runoff distribution function gets an upper limit depending on maximum precipitation and runoff coefficient. Respecting the guidelines in point (iii) the reference gauge method will be developed. It bases upon the fact, that likewise catchments generate equivalent peak discharges. For this reason it is possible to carry known peak discharges of a reference gauge onto unobserved subcatchments. Among other things the choice of a reference gauge accounts for the topology of the catchments. The whole strategy can be applied to large catchments what is exemplarily shown in Saxon streams. Beginning with a data homogenisation to the point of discharges of extreme low exceedance probabilities at unobserved cross sections the whole procedure is shown.

Die deterministische Modellierung des Niederschlags-Abfluss(N-A)-Prozesses mit flächendifferenzierten, prozessbasierten Modellen ist von zahlreichen Unsicherheiten beeinflusst. Diese Unsicherheiten resultieren hauptsächlich aus den genutzten Daten, die Messfehlern unterliegen sowie für eine flächendifferenzierte Modellierung entsprechend aufbereitet werden müssen, und der Abstraktion der natürlichen Prozesse im Modell selbst. Da N-A-Modelle in der hydrologischen Praxis vielfältig eingesetzt werden, sind Zuverlässigkeitsaussagen im Hinblick auf eine spezielle Anwendung nötig, um das Vertrauen in die Modellergebnisse zu festigen. Die neu entwickelte Strategie zur Analyse und Simulation der Unsicherheiten eines flächendifferenzierten, prozessbasierten N-A-Modells ermöglicht eine umfassende, globale und komponentenbasierte Unsicherheitsbestimmung. Am Beispiel des mesoskaligen Einzugsgebiets der Schwarzen Pockau/Pegel Zöblitz im mittleren Erzgebirge wird der Einfluss maßgebender Unsicherheiten im N-A-Prozess sowie deren Kombination zu einer Gesamt-Unsicherheit auf den Gebietsabfluss aufgezeigt. Zunächst werden die maßgebenden Unsicherheiten separat quantifiziert, wobei die folgenden Methoden eingesetzt werden: (i) Monte-Carlo Simulationen mit flächendifferenzierten stochastischen Bodenparametern zur Analyse des Einflusses unsicherer Bodeninformationen, (ii) Bayes’sche Inferenz und Markov-Ketten-Monte-Carlo Simulationen, die eine Unsicherheitsbestimmung der konzeptionellen Modellparameter der Abflussbildung und -konzentration ermöglichen und (iii) Monte-Carlo Simulationen mit stochastisch generierten Niederschlagsfeldern, die die raum-zeitliche Variabilität interpolierter Niederschlagsdaten beschreiben. Die Kombination der Unsicherheiten zu einer hydrologischen Unsicherheit und einer Gesamt-Unsicherheit erfolgt ebenfalls mit Monte-Carlo Methoden. Dieses Vorgehen ermöglicht die Korrelationen der Zufallsvariablen zu erfassen und die mehrdimensionale Abhängigkeitsstruktur innerhalb der Zufallsvariablen empirisch zu beschreiben. Die Ergebnisse zeigen für das Untersuchungsgebiet eine Dominanz der Unsicherheit aus der raum-zeitlichen Niederschlagsverteilung im Gebietsabfluss gefolgt von den Unsicherheiten aus den Bodeninformationen und den konzeptionellen Modellparametern. Diese Dominanz schlägt sich auch in der Gesamt-Unsicherheit nieder. Die aus Messdaten abgeleiteten Unsicherheiten weisen eine Heteroskedastizität auf, die durch den Prozessablauf geprägt ist. Weiterhin sind Indizien für eine Abhängigkeit der Unsicherheit von der Niederschlagsintensität sowie strukturelle Defizite des N-A-Modells sichtbar. Die neu entwickelte Strategie ist prinzipiell auf andere Gebiete und Modelle übertragbar. / Modelling rainfall-runoff (R-R) processes using deterministic, spatial distributed, process-based models is affected by numerous uncertainties. One major source of these uncertainties origins from measurement errors together with the errors occurring in the process of data processing. Inadequate representation of the governing processes in the model with respect to a given application is another source of uncertainty. Considering that R-R models are commonly used in the hydrologic practise a quantification of the uncertainties is essential for a realistic interpretation of the model results. The presented new framework allows for a comprehensive, total as well as component-based estimation of the uncertainties of model results from spatial distributed, process-based R-R modelling. The capabilities of the new framework to estimate the influence of the main sources of uncertainties as well as their combination to a total uncertainty is shown and analysed at the mesoscale catchment of the Schwarze Pockau of the Ore Mountains. The approach employs the following methods to quantify the uncertainties: (i) Monte Carlo simulations using spatial distributed stochastic soil parameters allow for the analysis of the impact of uncertain soil data (ii) Bayesian inference und Markov Chain Monte Carlo simulations, yield an estimate of the uncertainty of the conceptual model parameters governing the runoff formation and - concentration processes. (iii) Monte Carlo simulations using stochastically generated rainfall patterns describing the spatiotemporal variability of interpolated rainfall data. Monte Carlo methods are also employed to combine the single sources of uncertainties to a hydrologic uncertainty and a total uncertainty. This approach accounts for the correlations between the random variables as well as an empirical description of their multidimensional dependence structure. The example application shows a dominance of the uncertainty resulting from the spatio-temporal rainfall distribution followed by the uncertainties from the soil data and the conceptual model parameters with respect to runoff. This dominance is also reflected in the total uncertainty. The uncertainties derived from the data show a heteroscedasticity which is dominated by the process. Furthermore, the degree of uncertainty seems to depend on the rainfall intensity. The analysis of the uncertainties also indicates structural deficits of the R-R model. The developed framework can principally be transferred to other catchments as well as to other R-R models.