Ensemble Simulation From Multiple Data Sources In A Spatially Distributed Hydrological Model Of The Rijnland Water System In The Netherlands

Data for water management is increasingly easy to access, it has finer spatial and temporal resolution, and it is available from various sources. Precipitation data can be obtained from meteorological stations, radar, satellites and weather models. Land use data is also available from different satellite products and different providers. The various sources of data may confirm each other or give very different values in space and time. However, from these various data sources, it can often not be judged beforehand that one data is correct and others are wrong. Each source has its own value for a particular purpose. The Rijnland area in the Netherlands is one of the areas for which various data sources are available. Data sources that are researched in this paper are precipitation from rain gauges and radar, and three different land use maps. Various sources of data are used as input to the hydrological model (SIMGRO) of the water system to produce different discharge model output. Each run provides a member of the ensemble simulation which are combined to improve prediction of discharge from the catchment. It is shown that even simple averaging allows for increasing the model accuracy. Acknowledgement: This research is part of the EU FP7 MyWater research project. http://www.mywater-fp7.e

Towards the development and verification of a 3D-based advanced optimized farm machinery trajectory algorithm

Efforts related to minimizing the environmental burden caused by agricultural activities and increasing economic efficiency are key contemporary drivers in the precision agriculture domain. Controlled Traffic Farming (CTF) techniques are being applied against soil compaction creation, using the on-line optimization of trajectory planning for soil-sensitive field operations. The research presented in this paper aims at a proof-of-concept solution with respect to optimizing farm machinery trajectories in order to minimize the environmental burden and increase economic efficiency. As such, it further advances existing CTF solutions by including (1) efficient plot divisions in 3D, (2) the optimization of entry and exit points of both plot and plot segments, (3) the employment of more machines in parallel and (4) obstacles in a farm machinery trajectory. The developed algorithm is expressed in terms of unified modeling language (UML) activity diagrams as well as pseudo-code. Results were visualized in 2D and 3D to demonstrate terrain impact. Verifications were conducted at a fully operational commercial farm (Rostenice, the Czech Republic) against second-by-second sensor measurements of real farm machinery trajectories

Re-structuring of marine communities exposed to environmental change: a global study on the interactive effects of species and functional richness

Species richness is the most commonly used but controversial biodiversity metric in studies on aspects of community stability such as structural composition or productivity. The apparent ambiguity of theoretical and experimental findings may in part be due to experimental shortcomings and/or heterogeneity of scales and methods in earlier studies. This has led to an urgent call for improved and more realistic experiments. In a series of experiments replicated at a global scale we translocated several hundred marine hard bottom communities to new environments simulating a rapid but moderate environmental change. Subsequently, we measured their rate of compositional change (re-structuring) which in the great majority of cases represented a compositional convergence towards local communities. Re-structuring is driven by mortality of community components (original species) and establishment of new species in the changed environmental context. The rate of this re-structuring was then related to various system properties. We show that availability of free substratum relates negatively while taxon richness relates positively to structural persistence (i.e., no or slow re-structuring). Thus, when faced with environmental change, taxon-rich communities retain their original composition longer than taxon-poor communities. The effect of taxon richness, however, interacts with another aspect of diversity, functional richness. Indeed, taxon richness relates positively to persistence in functionally depauperate communities, but not in functionally diverse communities. The interaction between taxonomic and functional diversity with regard to the behaviour of communities exposed to environmental stress may help understand some of the seemingly contrasting findings of past research.Mercator Stiftung via GAMEPostprint4,41

Integrated Methodology for Estimating Water Use in Mediterranean Agricultural Areas

Agricultural use is by far the largest consumer of fresh water worldwide, especially in the Mediterranean, where it has reached unsustainable levels, thus posing a serious threat to water resources. Having a good estimate of the water used in an agricultural area would help water managers create incentives for water savings at the farmer and basin level, and meet the demands of the European Water Framework Directive. This work presents an integrated methodology for estimating water use in Mediterranean agricultural areas. It is based on well established methods of estimating the actual evapotranspiration through surface energy fluxes, customized for better performance under the Mediterranean conditions: small parcel sizes, detailed crop pattern, and lack of necessary data. The methodology has been tested and validated on the agricultural plain of the river Strimonas (Greece) using a time series of Terra MODIS and Landsat 5 TM satellite images, and used to produce a seasonal water use map at a high spatial resolution. Finally, a tool has been designed to implement the methodology with a user-friendly interface, in order to facilitate its operational use

Monitoring of Land Degradation in Greece and Tunisia Using Trends.Earth with a Focus on Cereal Croplands

Land degradation (LD) processes are widespread in drylands worldwide and are accelerated by climate change. As a result, food security and livelihoods are at risk. Thus, there is a need to monitor LD trends, especially in agricultural areas. Mediterranean countries, including Tunisia and Greece, are concerned due to the presence of drivers and pressures causing land degradation. Through the Trends.Earth plugin, the SDG 15.3.1 indicator can be implemented to map LD status. In this study, we mapped LD in Greece and Tunisia for the recommended baseline period of 2001–2015 and the selected reporting period of 2016–2020. The land productivity was assessed within Trends.Earth using the MODIS MOD13Q1 product, while the default datasets were used for the other sub-indicators. The main findings are: (i) the percentage of degraded land decreased from the baseline to the reporting period from 4.83% to 2.62% of total area in Greece and 9.97% to 6.26% in Tunisia—degradation rates that differ from those reported to the UNCCD (United Nations Convention to Combat Desertification) by the respective national authorities; (ii) the dominant land condition in Greece was improved, while in Tunisia, it was stable; (iii) land productivity presented a similar trend through the SDG 15.3.1 indicator over both countries, including the net land productivity dynamics over croplands; (iv) based on analysis using plant functional types performed with MODIS MCD12Q1, the highest portion of degraded land in Greece was located in grasslands and in Tunisia in cereal croplands (after desert areas); and (v) with a focus on LD over cereal croplands, the portion of degraded areas appeared to decrease in both Greece and Tunisia. The percentage was higher in Tunisia, representing 16.52% of the total degraded land during the reporting period compared to 10.83% in Greece. All the above stress the need to foster the adoption of sustainable land management practices, especially in Tunisia, and speed up the implementation of measures to achieve LD neutrality

Relationship between MODIS EVI and LAI across time and space

The Leaf Area Index (LAI) is used as input in hydrological and bio-chemical models for the estimation of water-cycle characteristics, agricultural primary production and other processes. Vegetation Indices (VIs) are used to monitor vegetation state and health. Considering that easily computed VIs can be used for the estimation of LAI, this study applied a regression analysis between MODIS Enhanced Vegetation Index (EVI) and LAI data in five sites around the world. A linear model was found to provide a good description of the LAI–EVI relationship across all examined vegetation types and times. Medium accuracy models were improved when variability of time and vegetation type were considered, indicating that these parameters highly affect the LAI–EVI relationship. Sensitivity of EVI to LAI was higher in periods of high biomass production. Regression analysis between LAI–EVI showed a stronger relationship for the study sites characterized by dry and warm tropical climatic conditions

Downscaling of MODIS leaf area index using landsat vegetation index

Several organizations provide satellite Leaf Area Index (LAI) data regularly, at various scales, at high frequency, but at low spatial resolution. This study attempted to enhance the spatial resolution of the MODIS LAI product to the Landsat resolution level. Four climatically diverse sites in Europe and Africa were selected as study areas. Regression analysis was applied between MODIS Enhanced Vegetation Index (EVI) and LAI data. The regression equations were used as input in a downscaling model, along with Landsat EVI images and land-cover maps. The estimated LAI values showed high correlation with field-measured LAI during the dry period. The model validation gave statistically significant results, with correlation coefficient values ranging from relatively low (0.25–0.32), to moderate (0.48–0.64) and high (0.72–0.94). Limited samples per vegetation type, the diversity of species within the same vegetation type, land-use/land-cover changes and saturated EVI values affected the accuracy of the downscaling model

Use of Modis Satellite Images for Detailed Lake Morphometry: Application to Basins with Large Water Level Fluctuations

Lake morphometry is essential for managing water resources and limnetic ecosystems. For reservoirs that receive high sediment loads, frequent morphometric mapping is necessary to define both the effective life of the reservoir and its water storage capacity for irrigation, power generation, flood control and domestic water supply. The current study presents a methodology for updating the digital depth model (DDM) of lakes and reservoirs with wide intra and interannual fluctuations of water levels using satellite remote sensing. A time series of Terra MODIS satellite images was used to map shorelines formed during the annual water level change cycle, and were validated with concurrent Landsat ETM+ satellite images. The shorelines were connected with in-situ observation of water levels and were treated as elevation contours to produce the DDM using spatial interpolation. The accuracy of the digitized shorelines is within the mapping accuracy of the satellite images, while the resulting DDM is validated using in-situ elevation measurements. Two versions of the DDM were produced to assess the influence of seasonal water fluctuation. Finally, the methodology was applied to Lake Kerkini (Greece) to produce an updated DDM, which was compared with the last available bathymetric survey (1991) and revealed changes in sediment distribution within the lake

Downscaling of MODIS leaf area index using landsat vegetation index

Several organizations provide satellite Leaf Area Index (LAI) data regularly, at various scales, at high frequency, but at low spatial resolution. This study attempted to enhance the spatial resolution of the MODIS LAI product to the Landsat resolution level. Four climatically diverse sites in Europe and Africa were selected as study areas. Regression analysis was applied between MODIS Enhanced Vegetation Index (EVI) and LAI data. The regression equations were used as input in a downscaling model, along with Landsat EVI images and land-cover maps. The estimated LAI values showed high correlation with field-measured LAI during the dry period. The model validation gave statistically significant results, with correlation coefficient values ranging from relatively low (0.25–0.32), to moderate (0.48–0.64) and high (0.72–0.94). Limited samples per vegetation type, the diversity of species within the same vegetation type, land-use/land-cover changes and saturated EVI values affected the accuracy of the downscaling model

Integrated Methodology for Estimating Water Use in Mediterranean Agricultural Areas

Agricultural use is by far the largest consumer of fresh water worldwide, especially in the Mediterranean, where it has reached unsustainable levels, thus posing a serious threat to water resources. Having a good estimate of the water used in an agricultural area would help water managers create incentives for water savings at the farmer and basin level, and meet the demands of the European Water Framework Directive. This work presents an integrated methodology for estimating water use in Mediterranean agricultural areas. It is based on well established methods of estimating the actual evapotranspiration through surface energy fluxes, customized for better performance under the Mediterranean conditions: small parcel sizes, detailed crop pattern, and lack of necessary data. The methodology has been tested and validated on the agricultural plain of the river Strimonas (Greece) using a time series of Terra MODIS and Landsat 5 TM satellite images, and used to produce a seasonal water use map at a high spatial resolution. Finally, a tool has been designed to implement the methodology with a user-friendly interface, in order to facilitate its operational use