Stefan Julich

Bio: Stefan Julich is an academic researcher from Dresden University of Technology. The author has contributed to research in topics: Soil water & Hydraulic conductivity. The author has an hindex of 14, co-authored 43 publications receiving 583 citations. Previous affiliations of Stefan Julich include University of Göttingen & University of Giessen.

Evaluating Today's Landscape Multifunctionality and Providing an Alternative Future: A Normative Scenario Approach

Abstract: Intensive agriculture has had multiple negative effects on the environment across large areas of Europe, including a decrease in the degree to which these landscapes serve multiple functions. A quantitative evaluation of the deficits in landscape multifunctionality is difficult, however, for a given landscape as long as "multifunctional reference landscapes" are lacking. We present an interdisciplinary normative scenario approach to overcome this obstacle. Given the example of the lower Wetter-catchment in the Wetterau region (Hesse, Germany), we compare the existing landscape with an expert-generated multifunctional landscape scenario that may also serve as an alternative future. This approach may inspire policy makers and land users by providing a methodology for the design of alternative multifunctional futures in five steps: (1) documentation of today's landscape structure and land use at the scale of uniformly managed land units; (2) detection of functional deficits of today's landscape considering environmental (soil contamination, groundwater production, water quality, biodiversity), economic (land rent), and societal (landscape perception by its population) attributes; (3) compilation of a catalogue of alternative land uses (including linear landscape elements) suitable to minimize the detected functional deficits; (4) rule-based modification of today's land-use pattern into a normative scenario; and (5) comparison of today's landscape and the normative scenario by applying the model network ITE2M. Results highlight a strongly unbalanced allocation of private and public goods in today's landscape with severe deficits in environmental and societal landscape features, but a significantly higher land rent. The designed multifunctional scenario, instead, may be preferred by the local population, and their willingness to pay for multifunctionality could potentially compensate calculated opportunity costs. Hence, the generated landscape scenario may be regarded as an alternative, multifunctional future.

Modelling the impact of agroforestry on hydrology of Mara River Basin in East Africa

Abstract: Land�use change is one of the main drivers of change of watershed hydrology. The effect of forestry related land�use changes (e.g. afforestation, deforestation, agroforestry) on water fluxes depends on climate, watershed characteristics and spatial scale. The Soil and Water Assessment Tool (SWAT) model was calibrated, validated and used to simulate the impact of agroforestry on the water balance in Mara River Basin (MRB) in East Africa. Model performance was assessed by Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE). The NSE (and KGE) values for calibration and validation were: 0.77 (0.88) and 0.74 (0.85) for the Nyangores sub-watershed, and 0.78 (0.89) and 0.79 (0.63) for the entire MRB. It was found that agroforestry in the watershed would generally reduce surface runoff, mainly due to enhanced infiltration. However, it would also increase evapotranspiration and consequently reduce the baseflow and the overall water yield, which was attributed to increased water use by trees. Spatial scale was found to have a significant effect on water balance; the impact of agroforestry was higher at the smaller headwater catchment (Nyangores) than for the larger watershed (entire MRB). However, the rate of change in water yield with increase in area under agroforestry was different for the two and could be attributed to the spatial variability of climate within MRB. Our results suggest that direct extrapolation of the findings from a small sub-catchment to a larger watershed may not always be accurate. These findings could guide watershed managers on the level of trade-offs to make between reduced water yields and other benefits (e.g. soil erosion control, improved soil productivity) offered by agroforestry. This article is protected by copyright. All rights reserved.

Why We Should Include Soil Structural Dynamics of Agricultural Soils in Hydrological Models

Abstract: Surface soil structure is sensitive to natural and anthropogenic impacts that alter soil hydraulic properties (SHP). These alterations have distinct consequences on the water cycle. In this review, we summarized published findings on the quantitative effects of different agricultural management practices on SHP and the subsequent response of the water balance components. Generally, immediately after tillage, soils show a high abundance of large pores, which are temporally unstable and collapse due to environmental factors like rainfall. Nevertheless, most hydrological modeling studies consider SHP as temporally constant when predicting the flow of water and solutes in the atmosphere-plant-soil system. There have been some developments in mathematical approaches to capture the temporal dynamics of soil pore space. We applied one such pore evolution model to two datasets to evaluate its suitability to predict soil pore space dynamics after disturbance. Lack of knowledge on how dispersion of pore size distribution behaves after tillage may have led to over-estimation of some values predicted by the model. Nevertheless, we found that the model predicted the evolution of soil pore space reasonably well (r2 > 0.80 in most cases). The limiting factor to efficiently calibrate and apply such modeling tools is not in the theoretical part but rather the lack of adequate soil structural and hydrologic data.

SWAT: Model Use, Calibration, and Validation

Abstract: SWAT (Soil and Water Assessment Tool) is a comprehensive, semi-distributed river basin model that requires a large number of input parameters, which complicates model parameterization and calibration. Several calibration techniques have been developed for SWAT, including manual calibration procedures and automated procedures using the shuffled complex evolution method and other common methods. In addition, SWAT-CUP was recently developed and provides a decision-making framework that incorporates a semi-automated approach (SUFI2) using both manual and automated calibration and incorporating sensitivity and uncertainty analysis. In SWAT-CUP, users can manually adjust parameters and ranges iteratively between autocalibration runs. Parameter sensitivity analysis helps focus the calibration and uncertainty analysis and is used to provide statistics for goodness-of-fit. The user interaction or manual component of the SWAT-CUP calibration forces the user to obtain a better understanding of the overall hydrologic processes (e.g., baseflow ratios, ET, sediment sources and sinks, crop yields, and nutrient balances) and of parameter sensitivity. It is important for future calibration developments to spatially account for hydrologic processes; improve model run time efficiency; include the impact of uncertainty in the conceptual model, model parameters, and measured variables used in calibration; and assist users in checking for model errors. When calibrating a physically based model like SWAT, it is important to remember that all model input parameters must be kept within a realistic uncertainty range and that no automatic procedure can substitute for actual physical knowledge of the watershed.

Nitrous oxide emissions from soils: how well do we understand the processes and their controls?

Abstract: Although it is well established that soils are the dominating source for atmospheric nitrous oxide (N2O), we are still struggling to fully understand the complexity of the underlying microbial production and consumption processes and the links to biotic (e.g. inter- and intraspecies competition, food webs, plant–microbe interaction) and abiotic (e.g. soil climate, physics and chemistry) factors. Recent work shows that a better understanding of the composition and diversity of the microbial community across a variety of soils in different climates and under different land use, as well as plant–microbe interactions in the rhizosphere, may provide a key to better understand the variability of N2O fluxes at the soil–atmosphere interface. Moreover, recent insights into the regulation of the reduction of N2O to dinitrogen (N2) have increased our understanding of N2O exchange. This improved process understanding, building on the increased use of isotope tracing techniques and metagenomics, needs to go along with improvements in measurement techniques for N2O (and N2) emission in order to obtain robust field and laboratory datasets for different ecosystem types. Advances in both fields are currently used to improve process descriptions in biogeochemical models, which may eventually be used not only to test our current process understanding from the microsite to the field level, but also used as tools for up-scaling emissions to landscapes and regions and to explore feedbacks of soil N2O emissions to changes in environmental conditions, land management and land use.

A Framework for Dealing With Uncertainty due to Model Structure Error

Abstract: Although uncertainty about structures of environmental models (conceptual uncertainty) is often acknowledged to be the main source of uncertainty in model predictions, it is rarely considered in environmental modelling. Rather, formal uncertainty analyses have traditionally focused on model parameters and input data as the principal source of uncertainty in model predictions. The traditional approach to model uncertainty analysis, which considers only a single conceptual model, may fail to adequately sample the relevant space of plausible conceptual models. As such, it is prone to modelling bias and underestimation of predictive uncertainty. In this paper we review a range of strategies for assessing structural uncertainties in models. The existing strategies fall into two categories depending on whether field data are available for the predicted variable of interest. To date, most research has focussed on situations where inferences on the accuracy of a model structure can be made directly on the basis of field data. This corresponds to a situation of ‘interpolation’. However, in many cases environmental models are used for ‘extrapolation’; that is, beyond the situation and the field data available for calibration. In the present paper, a framework is presented for assessing the predictive uncertainties of environmental models used for extrapolation. It involves the use of multiple conceptual models, assessment of their pedigree and reflection on the extent to which the sampled models adequately represent the space of plausible models. � 2005 Elsevier Ltd. All rights reserved.

対数正規分布(Lognormal Distribution)のあてはめについて

Abstract: Ecological data are often lognormally distributed. Nutrient concentrations, population densities and biomasses, rates of production and other flows are always positive, and generally have standard deviations that increase as the mean increases. Lognormally distributed variables have these characteristics, whereas normally distributed variables can be negative and have a standard deviation that does not change as the mean changes. Lognormal errors arise when sources of variation accumulate multiplicatively, whereas normal errors arise when sources of variation are additive. Given a normally distributed random variable Y, one can calculate a lognormally distributed random variable Z = exp(Y) where exp stands for the exponential function (exp(x) = e x). The mean Z and the standard deviation s Z of the lognormal variable are related to the mean Y and standard deviation s Y of the normal variable by) 2 / exp() exp(2 Y s Y Z = [1] 5. 0 2 ] 1) [exp(− = Y Z s Z s [2] Equation 1 can be used to correct for transformation bias in logarithmic regression. Suppose that lognormally-distributed observations Z have been log transformed as Y = log(Z) to fit a regression model such as ε + =) , (ˆ b X f Y [3] where Y is the log-transformed response variable which is predicted to be Y ˆ computed from the function f, X is a matrix of predictors, b is a vector of parameters, and the errors ε are normally distributed with mean zero and standard deviation s ε. Predictions Z ˆ in the original units are calculated using equation 1 as ] 2) ˆ exp[(ˆ 2 ε s Y Z + = [4] Note that estimates the median prediction of Z, which will be smaller than the mean for a lognormally distributed variate. Thus it makes sense to adjust the median upward, as in equation 4.) ˆ exp(Y Equation 1 is also used in drawing random numbers from a lognormal distribution. Generators for normally-distributed random variables Y are common. Suppose we draw many values of Y with mean zero and standard deviation s Y. Then from equation 1, the mean of exp(Y) will not be 1 = e 0 ; instead the mean of exp(Y) will be. Generally, however, one would prefer to have the mean of a set of lognormally distributed random numbers be 1. This can be accomplished by shifting the random numbers to Y) 2 / exp(2 Y …

Applications of the SWAT Model Special Section: Overview and Insights.

Abstract: The Soil and Water Assessment Tool (SWAT) model has emerged as one of the most widely used water quality watershed- and river basin-scale models worldwide, applied extensively for a broad range of hydrologic and/or environmental problems. The international use of SWAT can be attributed to its flexibility in addressing water resource problems, extensive networking via dozens of training workshops and the several international conferences that have been held during the past decade, comprehensive online documentation and supporting software, and an open source code that can be adapted by model users for specific application needs. The catalyst for this special collection of papers was the 2011 International SWAT Conference & Workshops held in Toledo, Spain, which featured over 160 scientific presentations representing SWAT applications in 37 countries. This special collection presents 22 specific SWAT-related studies, most of which were presented at the 2011 SWAT Conference; it represents SWAT applications on five different continents, with the majority of studies being conducted in Europe and North America. The papers cover a variety of topics, including hydrologic testing at a wide range of watershed scales, transport of pollutants in northern European lowland watersheds, data input and routing method effects on sediment transport, development and testing of potential new model algorithms, and description and testing of supporting software. In this introduction to the special section, we provide a synthesis of these studies within four main categories: (i) hydrologic foundations, (ii) sediment transport and routing analyses, (iii) nutrient and pesticide transport, and (iv) scenario analyses. We conclude with a brief summary of key SWAT research and development needs.