ALBERT

Description: Flood risk emerges from the interaction of hazard and vulnerability. Over recent decades the notion of risk being the basis for flood management decisions has become widely accepted and operationalised through the use of models and quantified risk analysis providing the evidence for risk-informed decision making. However, it is now abundantly apparent that changes in time, at a range of scales, of pertinent variables that determine risk are not a second order consideration but, instead, fundamentally challenge the conventional approach to flood risk management. The nature of some of these changes, particularly those that operate on extended timescales, are highly uncertain, yet decisions that may have implications for several decades still have to be taken. In this paper we explore how flood risk management may be adapted to address processes of uncertain future change. We identify a range of levels at which change may be incorporated in decision making: in the representation of uncertain non-stationary quantities; in the rules that are used to identify preferred options; in the variety of options that may be contemplated for flood risk management; in the scope of problem definition, which increasingly extends to address multiple hazards and multiple functions of river basins; and in the social and organizational characteristics that promote adaptive capacity. Integrated responses to changing flood risk need to attend to each of these levels of decision making, from the technicalities of non-stationarity, to the promotion of resilient societies.

Description: In flood modelling, many one-dimensional (1-D) hydrodynamic models are too restricted in capturing the spatial differentiation of processes within a polder or system of polders and two-dimensional (2-D) models are very demanding in data requirements and computational resources. The latter is an important consideration when uncertainty analyses using the Monte Carlo techniques are to complement the modelling exercises. This paper describes the development of a quasi-2-D modeling approach, which still calculates the dynamic wave in 1-D but the discretisation of the computational units are in 2-D, allowing a better spatial representation of the flow in polders and avoiding large additional expenditure on data pre-processing and computational time. The model DYNHYD (1-D hydrodynamics) from the WASP5 modeling package was used as a basis for the simulations and extended to incorporate the quasi-2-D approach. A local sensitivity analysis shows the sensitivity of parameters and boundary conditions on the filling volume of polders and capping of the peak discharge in the main river system. Two flood events on the Elbe River, Germany were used to calibrate and test the model. The results show a good capping effect on the flood peak by the proposed systems. The effect of capping reduces as the flood wave propagates down stream from the polders (up to 0.5 cm of capping is decreased for each additional kilometer from the polder).

Description: Surface runoff generation on arable fields is an important driver of flooding, on-site and off-site damages by erosion, and of nutrient and agrochemical transport. In general, three different processes generate surface runoff (Hortonian runoff, saturation excess runoff, and return of subsurface flow). Despite the developments in our understanding of these processes it remains difficult to predict which processes govern runoff generation during the course of an event or throughout the year, when soil and vegetation on arable land are passing many states. We analysed the results from 317 rainfall simulations on 209 soils from different landscapes with a resolution of 14 286 runoff measurements to determine temporal and spatial differences in variables governing surface runoff, and to derive and test a statistical model of surface runoff generation independent from an a priori selection of modelled process types. Measured runoff was related to 20 time-invariant soil properties, three variable soil properties, four rain properties, three land use properties and many derived variables describing interactions and curvilinear behaviour. In an iterative multiple regression procedure, six of these properties/variables best described initial abstraction and the hydrograph. To estimate initial abstraction, the percentages of stone cover above 10% and of sand content in the bulk soil were needed, while the hydrograph could be predicted best from rain depth exceeding initial abstraction, rainfall intensity, soil organic carbon content, and time since last tillage. Combining the multiple regressions to estimate initial abstraction and surface runoff allowed modelling of event-specific hydrographs without an a priori assumption of the underlying process. The statistical model described the measured data well and performed equally well during validation. In both cases, the model explained 71 and 58% of variability in accumulated runoff volume and instantaneous runoff rate (RSME: 5.2 mm and 0.23 mm min−1, respectively), while RMSE of runoff volume predicted by the curve number model was 50% higher (7.7 mm). Stone cover, if it exceeded 10%, was most important for the initial abstraction, while time since tillage was most important for the hydrograph. Time since tillage is not taken into account either in typical lumped hydrological models (e.g. SCS curve number approach) or in more mechanistic models using Horton, Green and Ampt, or Philip type approaches to address infiltration although tillage affects many physical and biological soil properties that subsequently and gradually change again. This finding should foster a discussion regarding our ability to predict surface runoff from arable land, which seemed to be dominated by agricultural operations that introduce man-made seasonality in soil hydraulic properties.

Description: The Tarim River basin, located in Xinjiang, NW China, is the largest endorheic river basin in China and one of the largest in all of Central Asia. Due to the extremely arid climate, with an annual precipitation of less than 100 mm, the water supply along the Aksu and Tarim rivers solely depends on river water. This is linked to anthropogenic activities (e.g., agriculture) and natural and semi-natural ecosystems as both compete for water. The ongoing increase in water consumption by agriculture and other human activities in this region has been enhancing the competition for water between human needs and nature. Against this background, 11 German and 6 Chinese universities and research institutes have formed the consortium SuMaRiO (Sustainable Management of River Oases along the Tarim River; http://www.sumario.de), which aims to create a holistic picture of the availability of water resources in the Tarim River basin and the impacts on anthropogenic activities and natural ecosystems caused by the water distribution within the Tarim River basin. On the basis of the results from field studies and modeling approaches as well as from suggestions by the relevant regional stakeholders, a decision support tool (DST) will be implemented that will then assist stakeholders in balancing the competition for water, acknowledging the major external effects of water allocation to agriculture and to natural ecosystems. This consortium was formed in 2011 and is funded by the German Federal Ministry of Education and Research. As the data collection phase was finished this year, the paper presented here brings together the results from the fields from the disciplines of climate modeling, cryology, hydrology, agricultural sciences, ecology, geoinformatics, and social sciences in order to present a comprehensive picture of the effects of different water availability schemes on anthropogenic activities and natural ecosystems along the Tarim River. The second objective is to present the project structure of the whole consortium, the current status of work (i.e., major new results and findings), explain the foundation of the decision support tool as a key product of this project, and conclude with application recommendations for the region. The discharge of the Aksu River, which is the major tributary of the Tarim, has been increasing over the past 6 decades. From 1989 to 2011, agricultural area more than doubled: cotton became the major crop and there was a shift from small-scale to large-scale intensive farming. The ongoing increase in irrigated agricultural land leads to the increased threat of salinization and soil degradation caused by increased evapotranspiration. Aside from agricultural land, the major natural and semi-natural ecosystems are riparian (Tugai) forests, shrub vegetation, reed beds, and other grassland, as well as urban and peri-urban vegetation. Within the SuMaRiO cluster, focus has been set on the Tugai forests, with Populus euphratica as the dominant tree species, because these forests belong to the most productive and species-rich natural ecosystems of the Tarim River basin. At sites close to the groundwater, the annual stem diameter increments of Populus euphratica correlated with the river runoffs of the previous year. However, the natural river dynamics cease along the downstream course and thus hamper the recruitment of Populus euphratica. A study on the willingness to pay for the conservation of the natural ecosystems was conducted to estimate the concern of the people in the region and in China's capital. These household surveys revealed that there is a considerable willingness to pay for conservation of the natural ecosystems, with mitigation of dust and sandstorms considered the most important ecosystem service. Stakeholder dialogues contributed to creating a scientific basis for a sustainable management in the future.

Description: The Tarim River Basin, located in Xinjiang, NW China, is the largest endorheic river basin of China and one of the largest in whole Central Asia. Due to the extremely arid climate with an annual precipitation of less than 100 mm, the water supply along the Aksu and Tarim River solely depends on river water. This applies for anthropogenic activities (e.g. agriculture) as well as for the natural ecosystems so that both compete for water. The on-going increase of water consumption by agriculture and other human activities in this region has been enhancing the competition for water between human needs and nature. Against this background, 11 German and 6 Chinese universities and research institutes formed the consortium SuMaRiO (www.sumario.de), which aims at gaining a holistic picture of the availability of water resources in the Tarim River Basin and the impacts on anthropogenic activities and natural ecosystems caused by the water distribution within the Tarim River Basin. The discharge of the Aksu River, which is the major tributary to the Tarim, has been increasing over the past 6 decades due to enhanced glacier melt. Alone from 1989 to 2011, the area under agriculture more than doubled. Thereby, cotton became the major crop and there was a shift from small-scale farming to large-scale intensive farming. The major natural ecosystems along the Aksu and Tarim River are riparian ecosystems: Riparian (Tugai) forests, shrub vegetation, reed beds, and other grassland. Within the SuMaRiO Cluster the focus was laid on the Tugai forests, with Populus euphratica as dominant tree, because the most productive and species-rich natural ecosystems can be found among those forests. On sites with groundwater distance of less than 7.5 m the annual increments correlated with river runoffs of the previous year. But, the further downstream along the Tarim River, the more the natural river dynamics ceased, which impacts on the recruitment of Populus euphratica. Household surveys revealed that there is a considerable willingness to pay for conservation of those riparian forests with the mitigation of dust and sandstorms considered as the most important ecosystem service. This interdisciplinary project will result in a decision support tool (DST), build on the participation of regional stakeholders and models based on results and field experiments. This DST finally shall assist stakeholders in balancing the water competition acknowledging the major external effects of any water allocation.

Description: Surface runoff generation on arable fields is an important driver of (local) flooding, on-site and off-site damages by erosion, and of nutrient and agrochemical transport. In general, three different processes generate surface runoff (Hortonian runoff, saturation excess runoff, and return of subsurface flow). Despite the developments in our understanding of these processes it remains difficult to predict, which processes govern runoff generation during the course of an event or throughout the year, when soil and vegetation on arable land are passing many states. We analysed the results from 317 rainfall simulations with a resolution of 14286 runoff measurements to determine temporal and spatial differences in parameters governing surface runoff, and to derive and test a statistical model of surface runoff generation independent from an a priori selection of modelled processes types. Measured runoff was related to 20 time-invariant soil properties, three variable soil properties, four rain properties, three land use properties and many derived variables describing interactions and curvilinear behaviour. In an iterative multiple regression procedure, six of these properties/variables best described initial abstraction and the hydrograph. To estimate initial abstraction, a percentage of stone cover above 10% and of sand content in the bulk soil were needed, while the hydrograph could be predicted best from rain depth exceeding initial abstraction, rainfall intensity, soil organic carbon content, and time since last tillage. Combining the multiple regressions to estimate initial abstraction and surface runoff allowed modelling of event-specific hydrographs without an a priori assumption of the underlying process. The statistical model described the measured data well and performed equally well during validation. In both cases, the model explained 71 and 58% of variability in runoff volume and runoff rate (RSME: 5.2 mm and 0.23 mm min−1, respectively). Stone cover was most important for the initial abstraction while time since tillage was most important for the hydrograph. The latter variable is neither taken into account in typical lumped hydrological models (e.g. SCS CN approach) nor in more mechanistic models using Horton, Green and Ampt or Philips type approaches to address infiltration. This finding should foster a discussion regarding our ability to predict surface runoff from arable land, which seemed to be dominated by agricultural operations that introduce man-made seasonality in soil hydraulic properties.

Description: Distributed flood control measures such as land-use changes or differing soil tillage practices which affect the runoff generation process, are hard to simulate physically based due to a high degree of uncertainty with regard to soil parameterisation. In this study the physically based rainfall runoff model WaSiM-ETH (Version 8.4.2) was used with a multi-layered vegetation and soil parameterisation. The modelling area was the meso-scaled and rurally characterised Windach catchment. In addition, soil measurement datasets were compared to demonstrate the uncertainties in soil parameterisation of physically based models. The datasets were gained from the hillslope scale at the Scheyern research farm with similar soil conditions to the Windach catchment. While parameterising and calibrating the model, seven different pedotransfer functions were used with strong influence on the simulated hydrographs. The differing bulk densities of soils depending on land-use and soil tillage must be taken into consideration due to their high impact on modelling results, and they also offer a comprehensive way to model distributed flood control measures. These measures have noticeable effects on flood events under HQ10, especially if the land-use type which is affected by the distributed flood control measure is the dominating land-use form in the catchment area. To account for the variability of soils in the investigation area of Scheyern, different approaches were applied to estimate soil hydraulic properties and saturated hydraulic conductivity, and were compared to field measurements.