Within the frame work of the International Water Research Alliance Saxony (IWAS), project “Middle East” a complex integrated water management system is developed and tested in the project region of Middle East (Oman and Saudi-Arabia). Hence, new solutions for a sustainable management of the scarce water resources in (semi-) arid regions are explored within IWAS in the sultanate of Oman on which this study work is carried out. Rainfall runoff models are established to estimate the “water yield” of the catchments in the project region. Modeling is a very important tool that enables hydrologists to make more comprehensive use of rainfall time series. Rainfall-runoff modeling is also useful for water resources assessment as these models can generate a long representative time series of stream flow volumes from which water supply schemes can be designed (D.A. Hughes, 1995). Therefore, this study project mainly focuses on the following main tasks such as data analysis, data processing and statistical evaluation; Model selection and model setup; Model adaptation test and verification. As part of the common modeling protocol, sensitivity analysis of a Rainfall-Runoff Modeling Toolbox (RRMT) is carried out in this study with the aim to identify sensitive model parameters. RRMT has been developed in order to produce parsimonious, lumped model structures with a high level of parameter identifiability. Such identifiability is crucial if relationships between the model parameters representing the system and catchment characteristics are to be established. RRMT is a modular framework that allows its user to implement different model structures to find a suitable balance between model performance and parameter identifiability. The study is carried out in the upper catchment part of Wadi Ma’wil (gauge near to Afi’), Batinah Region of the Sultanate of Oman. Arid and semi-arid zones are characterized by rainfall which is highly variable in space, time, quantity and duration (Noy-Meir, 1973). The Sultanate of Oman is characterized by hyper-arid (<100 mm rainfall), through the arid (100–250 mm rainfall) and semi-arid (250–500 mm rainfall) environments that are experienced in different parts of the country. Furthermore, arid areas have distinctive hydrological features substantially different from those of humid areas. The high temporal and spatial distribution of the rainfall, flash floods, absence of base flow, sparsity of plant cover, high transmission losses, high amounts of evaporation and evapotranspiration and the general climatologies are examples of such differences.:Acknowledgments i Abstract ii List of Figures and Photos v List of Tables and Plots v 1. Description of Study Area 1 1.1 General characteristics of arid regions 1 1.2 Study area (Batinah Region and Ma’awil catchment of gauge ‘Afi’) 2 1.2.1 Overview of Study area 2 1.2.2 Wadi Ma’awil and Gauge near to Afi’ 3 2. Data Processing and Evaluation 6 2.1 Rainfall data 6 2.1.1 Monthly and Annual Mean Rainfall Analyses 6 2.1.2 Estimation of Missing Precipitation Data 6 2.1.3 Annual and monthly average rainfall 6 2.2 Runoff data 9 2.2.1Rainfall-Runoff events – Processing and Analysis 9 2.2.2 Wadi Ma’awil Runoff Analysis 9 2.3 Areal Precipitation 11 2.3.1 Area 11 2.3.2 Summary of Calculated Results of Mean Annual Areal Precipitation 12 2.4 Evapotranspiration 13 2.4.1 Evaporation and Potential Evapotranspiration 13 2.4.2 Calculation of Evapotranspiration by FAO Penman-Monteith Equation 13 2.4.3 Sample Calculation for Daily ET using FAO Penman-Monteith Equation 14 2.4.4 Comparisons of Evapotranspiration Calculation Results 16 3. Rainfall-Runoff Modeling 16 3.1 Modeling approach – selection of modules 16 3.1.1 Basic Principle 16 3.1.2 Classification of models 16 3.1.3 Modeling Process 17 3.2 Rainfall-Runoff Modeling Toolbox 19 3.2.1 Introduction 19 3.2.2 Data Needs and Model Structure 20 3.3 Provision of input data 20 3.4 Calibration and Validation 20 3.4.1 Model Calibration and Validation 21 3.5 Sensitivity Analysis 22 3.6 Discussions of Results 23 3.6.1 Optimization Modules 23 3.6.2 Soil Moisture Accounting (SMA) Modules 24 3.6.3 Routing (R) Modules 25 3.6.4 The objective functions 26 3.6.5 Visualization Modules Results 27 3.7 Conclusions and Recommendations 35 3.7.1 Conclusions 35 3.7.2 Limitations and Recommendations 35 References 37 Appendix 38 Appendix A: Daily extraterrestrial radiation (Ra) for different latitudes for the 15th day of the month 38 Appendix B: Mean daylight hours (N) for different latitudes for the 15th of the month 38 Annexes 39 Annex - A: Mean Rainfall for the Gauge Afi’ from 1995 – 2005 39 Annex A-1: Annual Mean Rainfall for Gauge Afi’ for the time period 1995-2005 39 Annex A-2: Monthly Mean Rainfall for Gauge Afi’ for the time period 1995-2005 39 Annex A-3: Monthly Mean Rainfall for each Rain Gauge within the Wadi Ma’awil Catchment area for the time period 1995-2005 40 Annex - B: Rainfall - Runoff events for the Gauge Afi’ 41 Annex B-1: Annual Rainfall Vs Runoff events for the Gauge Afi’ from 1995 – 2005 42 Annex B-2: Monthly Rainfall Vs Runoff events for the Gauge Afi’ from 1995 – 2005 44 Annex B-3: Daily Rainfall Vs Runoff events for the Gauge Afi’ sample graphs with the time period from 1995to 2005 46

Prairie dogs (Cynomys sp.) are considered keystone species and ecosystem engineers for their grazing and burrowing activities (summarized here as disturbances). As climate changes and its variability increases, the mechanisms underlying organisms' interactions with their habitat will likely shift. Understanding the mediating role of prairie dog disturbance on vegetation structure, and its interaction with environmental conditions through time, will increase knowledge on the risks and vulnerability of grasslands. Here, we compared how plant taxonomical diversity, functional diversity metrics, and community-weighted trait means (CWM) respond to prairie dog C. mexicanus disturbance across grassland types and seasons (dry and wet) in a priority conservation semiarid grassland of Northeast Mexico. Our findings suggest that functional metrics and CWM analyses responded to interactions between prairie dog disturbance, grassland type and season, whilst species diversity and cover measures were less sensitive to the role of prairie dog disturbance. We found weak evidence that prairie dog disturbance has a negative effect on vegetation structure, except for minimal effects on C4 and graminoid cover, but which depended mainly on season. Grassland type and season explained most of the effects on plant functional and taxonomic diversity as well as CWM traits. Furthermore, we found that leaf area as well as forb and annual cover increased during the wet season, independent of prairie dog disturbance. Our results provide evidence that grassland type and season have a stronger effect than prairie dog disturbance on the vegetation of this short-grass, water-restricted grassland ecosystem. We argue that focusing solely on disturbance and grazing effects is misleading, and attention is needed on the relationships between vegetation and environmental conditions which will be critical to understand semiarid grassland dynamics under future climate change conditions in the region.