Frankfurt Hydrology Paper
Anthropogenic river flow alterations and their impacts on freshwater ecosystems in China
- In the past sixty years, excessive water consumption and dam construction have significantly influenced natural flow regimes and surface freshwater ecosystems throughout China, and thus resulted in serious environmental problems. In order to balance the competing water demands between human and environment and provide knowledge on sustainable water management, assessments on anthropogenic flow alterations and their impacts on aquatic and riparian ecosystems in China are needed.
In this study, the first evaluation on quantitative relationships between anthropogenic flow alterations and ecological responses in eleven river basins and watersheds in China was performed based on the data that could be obtained from published case studies. Quantitative relationships between changes in average annual discharge, seasonal low flow and seasonal high flow and changes in ecological indicators (fish diversity, fish catch and vegetation cover, etc.) were analyzed. The results showed that changes in riparian vegetation cover as well as changes in fish diversity and fish catch were strongly correlated with the changes in flow magnitude (r = 0.77, 0.66), especially with changes in average annual river discharge. In addition, more than half of the variations in vegetation cover could be explained by changes in average annual river discharge (r² = 0.63) and roughly 50 % changes in fish catch in arid and semi-arid region and 60% changes of fish catch in humid region could be related to alterations in average annual river discharge (r² = 0.53, 0.58).
In a supplementary analysis of this study, the first estimation on quantitative relationships between decreases in native fish species richness and anthropogenic flow alterations in 34 river basins and sub-basins in China was conducted. Linear relationships between losses of native fish species and five ecologically relevant flow indicators were analyzed by single and multiple regression models. For the single regression analysis, significant linear relationships were detected for the indicators of long-term average annual discharge (ILTA) and statistical low flow Q90 (IQ90). For the multiple regressions, no indicator other than ILTA has significant relationships with changes in number of fish species mainly due to collinearity. Two conclusions emerged from the analysis: 1) losses of fish species were positively correlated with changes in ILTA in China and 2) indicator of ILTA was dominant over other flow indicators included in this research for the given dataset. These results provide a guideline for the sustainable water resources management in rivers with high risk of fish extinction in China.
Expert-based Bayesian Network modeling for environmental management
Sina Kai Frank
- Bayesian Networks are computer-based environmental models that are frequently used to support decision-making under uncertainty. Under data scarce conditions, Bayesian Networks can be developed, parameterized, and run based on expert knowledge only. However, the efficiency of expert-based Bayesian Network modeling is limited by the difficulty in deriving model inputs in the time available during expert workshops. This thesis therefore aimed at developing a simple and robust method for deriving conditional probability tables from expert estimates in a time-efficient way. The design and application of this new elicitation and conversion method is demonstrated using a case study in Xinjiang, Northwest China. The key characteristics of this method are its time-efficiency and the approach to use different conversion tables based on varying levels of confidence. Although the method has its limitations, e.g. it can only be applied for variables with one conditioning variable; it provides the opportunity to support the parameterization of Bayesian Networks which would otherwise remain half-finished due to time constraints. In addition, a case study in the Murray-Darling Basin, Australia, is used to compare Bayesian Network types and software to improve the presentation clarity of large Bayesian Networks. Both case studies aimed at gaining insights on how to improve the applicability of Bayesian Networks to support environmental management.
Global estimation of monthly irrigated and rainfed crop areas on a 5 arc-minute grid
Felix Theodor Portmann
- Agriculture of crops provides more than 85% of the energy in human diet, while also securing income of more than 2.6 billion people. To investigate past, present and future changes in the domain of food security, water resources and water use, nutrient cycles, and land management it is required to know the agricultural land use, in particular which crop grows where and when. The current global land use or land cover data sets are based on remote sensing and agricultural census statistics. In general, these only contain one or very few classes of agricultural land use. When crop-specific areas are given, no distinction of irrigated and rainfed areas is made, whereas it is necessary to distinguish rainfed and irrigated crops, because crop productivity and water use differ significantly between them.
To support global-scale assessments that are sensitive to agricultural land use, the global data set of Monthly Irrigated and Rainfed Crop Areas around the year 2000 (MIRCA2000) was developed by the author. With a spatial resolution of 5 arc-minutes (approximately 9.2 km at the equator), MIRCA2000 provides for the first time, spatially explicit irrigated and rainfed crop areas separately for each of the 26 crop classes for each month of the year, and includes multi-cropping. The data set covers all major food crops as well as cotton, while the remaining crops are grouped into three categories (perennial, annual and fodder grasses). Also for the first time, crop calendars on national or sub-national level were consistently linked to annual values of harvested area at the 5 arc-minutes grid cell level, such that monthly growing areas could be computed that are representative for the time period 1998 to 2002.
The downscaling algorithm maximizes the consistency to the grid-based input data of cropland extent [Ramankutty et al., 2008], crop-specific total annual harvested area [Monfreda et al., 2008], and area equipped for irrigation [Siebert et al., 2007]. In addition to the methodology, this dissertation describes differences to other datasets and standard scaling methods, as well as some applications. For quality assessment independent datasets and newly developed quality parameters are used, and scale effects are discussed.
Supplementary Appendices document crop calendars for irrigated and rainfed crops for each of the 402 spatial units (Appendix I), data sources of harvested area and of cropping periods for irrigated crops, country by country (Appendix K), as well as data quality parameters (Appendix L, including spreadsheet files).