One- and Two-Dimensional Hydrological Modelling and Their Uncertainties

Earth processes, which occur in land, air and ocean in different environment and at different scales, are very complex. Flooding is also a part of the complex processes, which need to be assessed accurately to know the accurate spatial and temporal changes of flooding and their causes. Hydrological modelling has been used by several researchers in river and floodplain modelling for flood analysis. In this chapter, factors affecting flash flood, possible options of basic input parameters in one- and two-dimensional hydrological models in data sparse environment, some case studies and uncertainty in hydrological modelling were discussed. This discussion will help the readers to understand the flooding factors, selection of input parameters in data sparse environment, a brief insight of one- and two-dimensional hydrological models and uncertainties in their input and model parameters and model structures

Salvage the treasure of geographic information in Farm census data

In Germany, since several decades the RAUMIS modelling system is applied for policy impact assessments to measure the impact of agriculture on the environment. A disaggregation at the municipality level with more than 9.600 administrative units, instead of currently used 316 counties, would tremendously improve the environmental impact analysis. Two sets of data are used for this purpose. The first are geo-referenced data, that are, however, incomplete with respect its coverage of production activities in agriculture. The second set is the micro census statistic itself, that has a full coverage, but data protection rules (DPR) prohibit its straightforward use. The paper show how this bottleneck can be passed to obtain a reliable modelling data set at municipality level with a complete coverage of the agricultural sector in Germany. We successfully applied a Bayesian estimator, that uses prior information derived a cluster analysis based on the micro census and GIS information. Our test statistics of the estimation, calculated by the statistical office, comparing our estimates and the real protected data, reveals that the proposed approach adequately estimates most activities and can be used to fed the municipality layer in the RAUMIS modelling system for an extended policy analysis.Highest Posterior Density estimator (HPD), RAUMIS, Down scaling, Research Methods/ Statistical Methods, C11, C61, C81, Q15,

Multiplatform-SfM and TLS Data Fusion for Monitoring Agricultural Terraces in Complex Topographic and Landcover Conditions

Agricultural terraced landscapes, which are important historical heritage sites (e.g., UNESCO or Globally Important Agricultural Heritage Systems (GIAHS) sites) are under threat from increased soil degradation due to climate change and land abandonment. Remote sensing can assist in the assessment and monitoring of such cultural ecosystem services. However, due to the limitations imposed by rugged topography and the occurrence of vegetation, the application of a single high-resolution topography (HRT) technique is challenging in these particular agricultural environments. Therefore, data fusion of HRT techniques (terrestrial laser scanning (TLS) and aerial/terrestrial structure from motion (SfM)) was tested for the first time in this context (terraces), to the best of our knowledge, to overcome specific detection problems such as the complex topographic and landcover conditions of the terrace systems. SfM–TLS data fusion methodology was trialed in order to produce very high-resolution digital terrain models (DTMs) of two agricultural terrace areas, both characterized by the presence of vegetation that covers parts of the subvertical surfaces, complex morphology, and inaccessible areas. In the unreachable areas, it was necessary to find effective solutions to carry out HRT surveys; therefore, we tested the direct georeferencing (DG) method, exploiting onboard multifrequency GNSS receivers for unmanned aerial vehicles (UAVs) and postprocessing kinematic (PPK) data. The results showed that the fusion of data based on different methods and acquisition platforms is required to obtain accurate DTMs that reflect the real surface roughness of terrace systems without gaps in data. Moreover, in inaccessible or hazardous terrains, a combination of direct and indirect georeferencing was a useful solution to reduce the substantial inconvenience and cost of ground control point (GCP) placement. We show that in order to obtain a precise data fusion in these complex conditions, it is essential to utilize a complete and specific workflow. This workflow must incorporate all data merging issues and landcover condition problems, encompassing the survey planning step, the coregistration process, and the error analysis of the outputs. The high-resolution DTMs realized can provide a starting point for land degradation process assessment of these agriculture environments and supplies useful information to stakeholders for better management and protection of such important heritage landscapes

Mapping potential surface ponding in agriculture using UAV-SfM

Among the environmental problems that could affect agriculture, one of the most critical is ponding. This may be defined as water storage on the surface in concavities and depressions due to soil saturation. Stagnant water can seriously affect crops and the management of agricultural landscapes. It is mainly caused by prolonged rainfall events, soil type, or wrong mechanization practices, which cause soil compaction. To better understand this problem and thus provide adequate solutions to reduce the related risk, high-resolution topographic information could be strategically important because it offers an accurate representation of the surface morphology. In the last decades, new remote sensing techniques provide interesting opportunities to understand the processes on the Earth's surface based on geomorphic signatures. Among these, Uncrewed Aerial Vehicles (UAVs), combined with the structure-from-motion (SfM) photogrammetry technique, represent a solid, low-cost, rapid, and flexible solution for geomorphological analysis. This study aims to present a new approach to detect the potential areas exposed to water stagnation at the farm scale. The high-resolution digital elevation model (DEM) from UAV-SfM data is used to do this. The potential water depth was calculated in the DEM using the relative elevation attribute algorithm. The detection of more pronounced concavities and convexities allowed an estimation and mapping of the potential ponding conditions. The results were assessed by observations and field measurements and are promising, showing a Cohen's k(X) accuracy of 0.683 for the planimetric extent of the ponding phenomena and a Pearson's rxy coefficient of 0.971 for the estimation of pond water depth. The proposed workflow provides a useful indication to stakeholders for better agricultural management in lowland landscapes

Assessment of the Impact of Spatial Heterogeneity on Microwave Satellite Soil Moisture Periodic Error

An accurate temporal and spatial characterization of errors is required for the efficient processing, evaluation, and assimilation of remotely-sensed surface soil moisture retrievals. However, empirical evidence exists that passive microwave soil moisture retrievals are prone to periodic artifacts which may complicate their application in data assimilation systems (which commonly treat observational errors as being temporally white). In this paper, the link between such temporally-periodic errors and spatial land surface heterogeneity is examined. Both the synthetic experiment and site-specified cases reveal that, when combined with strong spatial heterogeneity, temporal periodicity in satellite sampling patterns (associated with exact repeat intervals of the polar-orbiting satellites) can lead to spurious high frequency spectral peaks in soil moisture retrievals. In addition, the global distribution of the most prominent and consistent 8-day spectral peak in the Advanced Microwave Scanning Radiometer - Earth Observing System soil moisture retrievals is revealed via a peak detection method. Three spatial heterogeneity indicators - based on microwave brightness temperature, land cover types, and long-term averaged vegetation index - are proposed to characterize the degree to which the variability of land surface is capable of inducing periodic error into satellite-based soil moisture retrievals. Regions demonstrating 8-day periodic errors are generally consistent with those exhibiting relatively higher heterogeneity indicators. This implies a causal relationship between spatial land surface heterogeneity and temporal periodic error in remotely-sensed surface soil moisture retrievals

Agricultural resources investigations in northern Italy and southern France, Agreste project. Part 1: Activity performed on the Italian test sites

The author has identified the following significant results. It was found that the standard atmospheric correction procedure cannot be successfully applied to water targets if a better correlation of MSS data with radiance input to LANDSAT sensors was not reached. It was confirmed that the six line effect must be avoided unless more sophisticated data handling techniques allow subtraction of various amounts of path radiance for the six satellite detectors. The COPTRAN program for atmospheric corrections of scan angle influence on atmospheric path was modified and completed. Six rice varieties were discriminated in proportions ranging from 65 percent to more than 80 percent. The same techniques were applied to poplar groves with a 70 percent precision

Quantification of extracellular proteases and chitinases from marine bacteria

A total of 92 marine bacteria belonging to Pseudomonas, Pseudoalteromonas, Psychrobacter, and Shewanella were first screened for their proteolytic activity. In total, four Pseudomonas strains belonging to Ps. fluorescens, Ps. fragi, Ps. gessardii, and Ps. marginalis; 14 Pseudoalteromonas strains belonging to Psa. arctica, Psa. carrageenovora, Psa. elyakovii, Psa. issachenkonii, Psa. rubra, Psa. translucida, and Psa. tunicata; and two Shewanella strains belonging to S. baltica and S. putrefaciens were identified to have a weak to high proteolytic activity (from 478 to 4445 mU/mg trypsin equivalent) against skim milk casein as protein source. Further chitinolytic activity screening based on these 20 proteolytic strains using colloidal chitin yielded five positive strains which were tested against three different chitin substrates in order to determine the various types of chitinases. Among the strains that can produce both proteases and chitinases, Psa. rubra DSM 6842T expressed not only the highest proteolytic activity (2558 mU/mg trypsin equivalent) but also the highest activity of exochitinases, specifically, β-N-acetylglucosaminidase (6.33 mU/107 cfu) as well. We anticipate that this strain can be innovatively applied to the valorization of marine crustaceans side streams

Current challenges of implementing anthropogenic land-use and land-cover change in models contributing to climate change assessments

This is the author accepted manuscript. The final version is available from European Geosciences Union (EGU) via the DOI in this record.Land-use and land-cover change (LULCC) represents one of the key drivers of global environmental change. However, the processes and drivers of anthropogenic land-use activity are still overly simplistically implemented in terrestrial biosphere models (TBMs). The published results of these models are used in major assessments of processes and impacts of global environmental change, such as the reports of the Intergovernmental Panel on Climate Change (IPCC). Fully coupled models of climate, land use and biogeochemical cycles to explore land use-climate interactions across spatial scales are currently not available. Instead, information on land use is provided as exogenous data from the land-use change modules of integrated assessment models (IAMs) to TBMs. In this article, we discuss, based on literature review and illustrative analysis of empirical and modeled LULCC data, three major challenges of this current LULCC representation and their implications for land use-climate interaction studies: (I) provision of consistent, harmonized, land-use time series spanning from historical reconstructions to future projections while accounting for uncertainties associated with different land-use modeling approaches, (II) accounting for sub-grid processes and bidirectional changes (gross changes) across spatial scales, and (III) the allocation strategy of independent land-use data at the grid cell level in TBMs. We discuss the factors that hamper the development of improved land-use representation, which sufficiently accounts for uncertainties in the land-use modeling process. We propose that LULCC data-provider and user communities should engage in the joint development and evaluation of enhanced LULCC time series, which account for the diversity of LULCC modeling and increasingly include empirically based information about sub-grid processes and land-use transition trajectories, to improve the representation of land use in TBMs. Moreover, we suggest concentrating on the development of integrated modeling frameworks that may provide further understanding of possible land-climate-society feedbacks.The research in this paper has been supported by the European Research Council under the European Union’s Seventh Framework Programme project LUC4C (Grant No. 603542), ERC grant GLOLAND (No. 311819) and BiodivERsA project TALE (No. 832.14.006) funded by the Dutch National Science Foundation (NWO). This research contributes to the Global Land Project (www.globallandproject.org). This is paper number 26 of the Birmingham Institute of Forest Research