Improved Understanding of the Linkages and Interactions between Vegetation, Climate, Streamflow and Drought: Case Studies in Germany

• Main Author: Liu, Zhiyong
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: 2016
• Subjects: 500; 500 Natural sciences and mathematics; 550; 550 Earth sciences; Kendall; North Atlantic
• Online Access: https://archiv.ub.uni-heidelberg.de/volltextserver/20858/; https://doi.org/10.11588/heidok.00020858; https://nbn-resolving.org/urn:nbn:de:bsz:16-heidok-208580

Global climate change has significantly impacted the terrestrial ecosystems and water cycles over the past century. This dissertation aims to further improve our knowledge of the linkages and interactions between vegetation, climate, streamflow, and drought. First, the current study investigated long-term variations in vegetation and climatic variables and their scale-dependent relationships by using Rhineland-Palatinate (Southwest Germany) as a case study area. Based upon the monthly normalized difference vegetation index (NDVI), precipitation and temperature data for six different vegetation types in two precipitation regimes (low and high precipitation regimes) of Rhineland-Palatinate, the temporal trends in the original time series of these variables and their relationships were examined. In addition, the further objectives were to evaluate which time-scale is dominantly responsible for the trend production found in the original data and find out the certain time-scales that represent the strongest correlation between NDVI and climatic variables (i.e., precipitation and temperature). A combined approach using the discrete wavelet transform (DWT), Mann-Kendall (MK) trend test and correlation analysis was implemented to achieve these goals. The trend assessment in the original data shows that the monthly NDVI time series for all vegetation types in both precipitation regimes have upward trends, most of which are significant. The precipitation and temperature data for six vegetation types in two precipitation regimes present weak downward trends and significant increasing trends, respectively. The most important time-scales contributing to the trend production in the original NDVI data are the 2-month and 8-month events. For precipitation, the most influential ones are 2-month and 4-month scales. The 4-month periodic mode predominantly affects the trends in the original temperature data. The results indicate temperature is the primary driver influencing the vegetation variability over this study area, while there is a negative correlation between NDVI and precipitation for all vegetation types and precipitation regimes. For the scale-dependent relationships between NDVI and precipitation, the 2-month and 8-month scales generally present the strongest negative correlation. The most significant positive correlation between NDVI and temperature is obtained at the 8- and 16-month scales for most vegetation types. The results might be valuable for water resources management as well as agricultural and ecological development planning in Rhineland-Palatinate, and also offer a helpful reference for other regions with similar climate condition. Then, this study presented a detailed regional investigation of the probabilistic and multi-scale relationships between streamflow and hydroclimatic variables (precipitation, temperature and soil moisture) and the potential links to large-scale atmospheric circulations over Baden-Württemberg, Southwest Germany. First, the joint dependence structure between seasonal streamflow and hydroclimatic variables was established using copulas. On the basis of the joint dependence structure, this study estimated the probability (risk) of hydrological droughts and floods conditioned upon two different scenarios of hydroclimatic variables for different seasons over the study area. Then, it was evaluated how the relationships between hydroclimatic forcings and streamflow vary among different temporal scales using wavelet coherence. The results reveal that the strong positive coupling between streamflow and both precipitation and soil moisture occurs at most temporal scales, particularly at decadal scales, while the multi-scale relationships between temperature and streamflow are significantly weak compared to precipitation and soil moisture. The connections between streamflow variability and large-scale atmospheric circulations were explored by using composite analysis. Although the atmospheric circulation patterns vary in different seasons, it can be found that the high streamflow anomalies for most seasons over Baden-Württemberg are related to strong westerly atmospheric circulations that play an important role in favoring the warm and moist air from the North Atlantic Ocean towards the study area and thus enhancing the precipitation. Moreover, the low streamflow anomalies are generally linked to the northerly circulations that induce the movement of cold air from northern Europe towards this study area and thus result in the reduced precipitation. Finally, a general probabilistic prediction network was developed in this dissertation for hydrological drought examination and environmental flow assessment. This methodology is divided into three major components. First, the joint streamflow drought indicator (JSDI) was proposed to describe the hydrological dryness/wetness conditions based on the monthly streamflow data. The JSDI relies on a high-dimensional (12-d) multivariate probabilistic model to establish a joint distribution model. In the second part, the drought-based environmental flow assessment method was introduced, which provides dynamic risk-based information about how much flow (the environmental flow target) is required for drought recovery and its likelihood under different hydrological drought initial situations. The final part involves estimating the conditional probability of achieving the required environmental flow under different precipitation scenarios according to the joint dependence structure between streamflow and precipitation. Two catchments in Germany were used to examine the usefulness of this network. The results show that the JSDI can provide an overall assessment of hydrological dryness/wetness conditions and does well in identifying both drought onset and persistence. The method also allows quantitative prediction of targeted environmental flow that is required for hydrological drought recovery and evaluates the corresponding risk. In addition, the results confirm that the general network can estimate the conditional probability associated with the required flow under different precipitation scenarios. The presented methodology offers a promising tool for water supply planning and management and for environmental flow assessment. The network has no restrictions that would prevent it from being applied to other basins worldwide.