This project implements a real application on the Spider II, which is a simulation of Soil Water Movement Model. The main objectives of this project were to develop a parallel and distributed algorithm for the Soil Water Model; implement the Soil Water Movement Simulation model on the Spider II distributed system and to evaluate the performance of simulating the Soil Water Movement Model on Spider II.

En stor skadegörare på de svenska granskogarna är den åttatandade granbarkborren Ips typographus (L.). Torkstress, vattentillgång och landskapets topografi är faktorer som påverkar granens vitalitet. Om granen har en låg vitalitet riskerar den i högre utsträckning att bli angripen av granbarkborren. Syftet med studien var att analysera topografi och markfuktighet för granbarkborreskadad skog. Områden med skadade träd och oskadade träd jämfördes i GIS-programmet QGIS för att se om det fanns en skillnad med avseende på markfuktighet, lutning, lutningsriktning och höjd över havet. Resultatet visade att granbarkborren angriper främst gran i syd till sydvästlig och östlig riktning. Även områden på torrare marker har en ökad risk för angrepp samt områden där terrängen inte sluttar. Troligtvis löper områden där ovan nämnda variabler sammanfaller större risk att bli angripna av granbarkborren.

Cílem tohoto projektu je vytvoření bezdrátové senzorické sítě na měření sacího potenciálu půdy. Práce obsahuje výběr vhodné měřící metody obsahu vody v půdě, jejíž součástí je i tvorba kalibrač˘ní křivky. Dále se zabývá realizací hardwaru a firmwaru pro dva typy modulů. Stanice end station, obsahující snímač˘e, a základní stanice base station, která přijímá a přeposílá data. Celá aplikace je doplněna o uživatelský program, který umožňuje zálohování dat, zobrazování průběhů dat včetně dat předchozích, změnu nastavení systému spolu s možností žádosti o potřebné informace.

Hydraulic characteristics are the most important properties of soil, i.e. retention curve of soil moisture and hydraulic conductivity. Hydraulic conductivity of soil characterizes the ability of the soil to conduct water. Retention curve expresses the relationship between moisture and moisture potential of soil. The running of retention curve is influenced by many factors, eg. grain size and mineralogical composition, content of humus, reduce bulk density and structure of soil. Measurement of retention curves takes a lot of time and money in laboratory conditions therefore pedotransfer functions seem to be an alternative solution. The thesis aims to estimate moisture retention curves of soil in a selected area of interest in South Moravia using program Rosetta (Schaap, 2003). Data of granularity (% content of clay, sand and dust), bulk density of soil and hydrolimits field water capacity and wilting point were used as predictors in individual models of program Rosetta. Data of grain were matched by FAO / USDA system. Retention curves of soil moisture were measured on a sand tank and overpressure devices. The measured retention curves were parameterized by RETC program. Estimated retention curves were graphically compared with measured to determine the quality of the estimate. The accuracy of the estimate was assessed by correlation coefficient R of determination coefficient R2 and standard error SMRE. Usability own derivatives pedotransfer functions is hard to say due to the size of the input data file. I would recommend further verification of data at the other localities in south Moravia.

Measurements of root zone soil moisture play large roles in our understanding of the water cycle, weather, climate, land-heat exchanges, drought forecasting, and agriculture. Current measurements are made using a combination of ground-based sampling and active and passive microwave remote sensing. Signals of Opportunity (SoOp) has emerged as a promising method for sensing soil moisture, using satellite communication signals to make bi-static reflectometry measurements. The current combination of ground and satellite-based measurements for soil moisture results in a gap of useful spatial and temporal resolutions, as well as limited soil penetration depth. This thesis developed and constructed an Unmanned Aerial Vehicle (UAV) mountable, I-band SoOp instrument with calibration capabilities, along with supporting specular point mapping and mission planning software. This work advances the creation of a compact, mobile, root zone soil moisture (RZSM) remote sensing system.

Water availability affects tree species performance and distributions in tropical forests. However, there are no studies that have measured detailed spatial variation in soil water availability within a tropical forest. This limits our understanding of how water availability shapes the demography and distributions of tree species within tropical forests. In this dissertation, I measured detailed spatial variation in soil water potential (SWP), the relevant measure of water availability for plant performance, in the seasonal tropical moist forest of the 50-ha Forest Dynamics Plot on Barro Colorado Island, Panama. In Paper 1, I mapped spatial variation in SWP across the 50-ha plot in various stages of the dry season using information on topography, soil type, dry season intensity and more. In Paper 2, I quantified the soil moisture niches of species in terms of demographic responses (growth and mortality) and species distributions. I related seedling growth and mortality responses to SWP of 62 species to their distributional centre along the SWP gradient, using data from 20 years of annual seedling censuses across 200 seedling census sites. I found that species that grew faster (slow) with increasing SWP were more common on wetter (drier) parts of the SWP gradient. Moreover, wet-distributed species grew faster on the wet side of the SWP gradient than dry-distributed species. Mortality was unrelated to species distributions but decreased strongly with seedling height. These findings indicate that species with a growth advantage with respect to SWP grow faster out of the vulnerable small size ranges, reducing their mortality in later seedling stages and thus shaping species distributions indirectly. This mechanism is a form of niche differentiation that contributes to species coexistence. In Paper 3, I related seedling growth and mortality responses to spatiotemporal variation in water availability with responses to light availability, another highly limiting resource in tropical forests. I found an interspecific trade-off in responses to shade versus inter-annual drought (dry season intensity): species that performed relatively well in the shade performed worse during more severe dry seasons and vice versa. This trade-off enables coexistence, because species are adapted to perform well under either shade or drought. In sum, water availability contributes to the maintenance of the high diversity of tropical forests through hydrological niche differentiation and a trade-off between performance in shade versus drought. Future work can use my SWP maps and species responses to SWP to identify the functional traits that underlie the species responses and improve Dynamic Global Vegetation Models. Finally, my work facilitates the prediction of future species composition, diversity and ecosystem functioning of tropical forests with shifts in rainfall patterns caused by climate change.

Hydrological modelling is a powerful tool for hydrologists and engineers involved in the planning and development of integrated approach for the management of water resources. With the recent advent of computational power and the growing availability of spatial data, RS and GIS technologies can augment to a great extent the conventional methods used in rainfall runoff studies; it is possible to accurately describe watershed characteristics in particularly when determining runoff response to rainfall input. The main objective of this study is to apply the potential of spaceborne SAR data for soil moisture retrieval in order to improve the spatial input parameters required for hydrological modelling. For the spatial database creation, high resolution 2 m aerial laser scanning Digital Terrain Model (DTM), soil map, and landuse map were used. Rainfall records were transformed into a runoff through hydrological parameterisation of the watershed and the river network using HEC-HMS software for rainfall runoff simulation. The Soil Conservation Services Curve Number (SCS-CN) and Soil Moisture Accounting (SMA) loss methods were selected to calculate the infiltration losses. In microwave remote sensing, the study of how the microwave interacts with the earth terrain has always been interesting in interpreting the satellite SAR images. In this research soil moisture was derived from two different types of Spaceborne SAR data; TerraSAR-X and ALOS PALSAR (L band). The developed integrated hydrological model was applied to the test site of the Gottleuba Catchment area which covers approximately 400 sqkm, located south of Pirna (Saxony, Germany). To validate the model historical precipitation data of the past ten years were performed. The validated model was further optimized using the extracted soil moisture from SAR data. The simulation results showed a reasonable match between the simulated and the observed hydrographs. Quantitatively the study concluded that based on SAR data, the model could be used as an expeditious tool of soil moisture mapping which required for hydrological modelling.

Agricultural production is becoming increasingly important as the world demand increases. On the other hand, there are several factors threatening that production such as the climate change. Therefore, monitoring and management of different parameters affecting the production are important. The current study is dedicated to two key parameters, namely agricultural land cover and soil-moisture mapping using X- and L-Band Synthetic Aperture Radar (SAR) data. Land-cover mapping plays an essential role in various applications like irrigation management, yield estimation and subsidy control. A model of multi-direction/multi-distance texture analysis on SAR data and its use for agricultural land cover classification was developed. The model is built and implemented in ESRI ArcGIS software and integrated with “R Environment”. Sets of texture measures can be calculated on a plot basis and stored in an attribute table for further classification. The classification module provides various classification approaches such as support vector machine and artificial neural network, in addition to different feature-selection methods. The model has been tested for a typical Mid-European agricultural and horticultural land use pattern south to the town of Pirna (Saxony/Germany), where the high-resolution SAR data, TerraSAR-X and ALOS/PALSAR (HH/HV) imagery, were used for land-cover mapping. The results indicate that an integrated classification using textural information of SAR data has a high potential for land-cover mapping. Moreover, the multi-dimensional SAR data approach improved the overall accuracy. Soil moisture (SM) is important for various applications such as crop-water management and hydrological modelling. The above-mentioned TerraSAR-X data were utilised for soil-moisture mapping verified by synchronous field measurements. Different speckle-reduction techniques were applied and the most representative filtered image was determined. Then the soil moisture was calculated for the mapped area using the obtained linear regression equations for each corresponding land-cover type. The results proved the efficiency of SAR data in soil-moisture mapping for bare soils and at the early growing stage of fieldcrops. / Landwirtschaftliche Produktion erlangt mit weltweit steigender Nahrungsmittelnachfrage zunehmende Bedeutung. Zahlreiche Faktoren bedrohen die landwirtschaftliche Produktion wie beispielsweise die globale Klimaveränderung einschließlich ihrer indirekten Nebenwirkungen. Somit ist das Monitoring der Produktion selbst und der wesentlichen Produktionsparameter eine zweifelsfrei wichtige Aufgabe. Die vorliegende Studie widmet sich in diesem Kontext zwei Schlüsselinformationen, der Aufnahme landwirtschaftlicher Kulturen und den Bodenfeuchteverhältnissen, jeweils unter Nutzung von Satellitenbilddaten von Radarsensoren mit Synthetischer Apertur, die im X- und L-Band operieren. Landnutzungskartierung spielt eine essentielle Rolle für zahlreiche agrarische Anwendungen; genannt seien hier nur Bewässerungsmaßnahmen, Ernteschätzung und Fördermittelkontrolle. In der vorliegenden Arbeit wurde ein Modell entwickelt, welches auf Grundlage einer Texturanalyse der genannten SAR-Daten für variable Richtungen und Distanzen eine Klassifikation landwirtschaftlicher Nutzungsformen ermöglicht. Das Modell wurde als zusätzliche Funktionalität für die ArcGIS-Software implementiert. Es bindet dabei Klassifikationsverfahren ein, die aus dem Funktionsschatz der Sprache „R“ entnommen sind. Zum Konzept: Ein Bündel von Texturparametern wird durch das vorliegende Programm auf Schlagbasis berechnet und in einer Polygonattributtabelle der landwirtschaftlichen Schläge abgelegt. Auf diese Attributtabelle greift das nachfolgend einzusetzende Klassifikationsmodul zu. Die Software erlaubt nun die Suche nach „aussagekräftigen“ Teilmengen innerhalb des umfangreichen Texturmerkmalsraumes. Im Klassifikationsprozess kann aus verschiedenen Ansätzen gewählt werden. Genannt seien „Support Vector Machine“ und künstliche neuronale Netze. Das Modell wurde für einen typischen mitteleuropäischen Untersuchungsraum mit landwirtschaftlicher und gartenbaulicher Nutzung getestet. Er liegt südlich von Pirna im Freistaat Sachsen. Zum Test lagen für den Untersuchungsraum Daten von TerraSAR-X und ALOS/PALSAR (HH/HV) aus identischen Aufnahmetagen vor. Die Untersuchungen beweisen ein hohes Potenzial der Texturinformation aus hoch aufgelösten SAR-Daten für die landwirtschaftliche Nutzungserkennung. Auch die erhöhte Dimensionalität durch die Kombination von zwei Sensoren erbrachte eine Verbesserung der Klassifikationsgüte. Kenntnisse der Bodenfeuchteverteilung sind u.a. bedeutsam für Bewässerungsanwendungen und hydrologische Modellierung. Die oben genannten SAR-Datensätze wurden auch zur Bodenfeuchteermittlung genutzt. Eine Verifikation wurde durch synchrone Feldmessungen ermöglicht. Initial musste der Radar-typische „Speckle“ in den Bildern durch Filterung verringert werden. Verschiedene Filtertechniken wurden getestet und das beste Resultat genutzt. Die Bodenfeuchtebestimmung erfolgte in Abhängigkeit vom Nutzungstyp über Regressionsanalyse. Auch die Resultate für die Bodenfeuchtebestimmung bewiesen das Nutzpotenzial der genutzten SAR-Daten für offene Ackerböden und Stadien, in denen die Kulturpflanzen noch einen geringen Bedeckungsgrad aufweisen.

Software as a service (SaaS) is a primary working pattern and a significant application model for next generation Internet application. Web GIS services are the new generation of the Software as a service that can provide the hosted spatial data and GIS functionalities to the practical customized applications. This study focused on developing a webGIS based application, Soil Moisture-Based Irrigation Scheduling Tool (SMIST), for predicting soil moisture in the next seven days using the soil moisture diagnostic equation (SMDE) and the upcoming seven precipitation forecasts made by the National Weather Service (NWS), and ultimately producing an accurate irrigation schedule based on the predicted soil moisture. The SMIST is expected to be capable of improving the irrigation efficiency to protect groundwater resources in the Texas High Plains and reducing the cost of energy for pumping groundwater for irrigation, as an essential public concern in this area. The SMIST comprised an integration of web-based programs, a Hydrometeorological model, GIS, and geodatabase. It integrates two main web systems, the soil moisture estimating web application for irrigation scheduling based on the soil moisture diagnostic equation (SMDE), and an agricultural field delineation webGIS application to prepare input data and the model parameters. The SMIST takes advantage of the latest historical and forecasted precipitation data to predict soil moisture in the user-specified agricultural field(s). In this regard, the next seven days soil moisture versus the soil moisture threshold for normal growth would be presented in the result page of the SMIST to help users to adjust irrigation rate and sequence.

Les épisodes de sécheresse et de canicule qui frappent épisodiquement les régions tempérées ont des conséquences préjudiciables sur les plans sanitaire, économique, social et écologique. Afin de pouvoir enclencher des stratégies de préparation et de prévention avec quelques semaines ou mois d'anticipation, les attentes sociétales en matière de prévision sont élevées, et ce d'autant plus que les projections climatiques font craindre la multiplication de ces épisodes au cours du 21ème siècle. Néanmoins, la saison d'été est la plus difficile à prévoir aux moyennes latitudes. Les sources connues de prévisibilité sont plus ténues qu'en hiver et les systèmes de prévision climatique actuels peinent à représenter correctement les mécanismes de téléconnexion associés. Un nombre croissant d'études a mis en évidence un lien statistique dans certaines régions entre l'humidité du sol au printemps et les températures et précipitations de l'été qui suit. Ce lien a été partiellement confirmé dans des modèles numériques de climat mais de nombreuses interrogations subsistent. L'objectif de cette thèse est donc de mieux comprendre le rôle joué par l'humidité du sol sur les caractéristiques et la prévisibilité du climat de l'été dans les régions tempérées. Grâce notamment au modèle couplé de circulation générale CNRM-CM, nous avons mis en œuvre des ensembles de simulations numériques qui nous ont permis d'évaluer le degré de persistance des anomalies d'humidité du sol printanière. En effet, une longue persistance est une condition nécessaire pour que ces anomalies influencent le climat à l'échelle de la saison, via le processus d'évapotranspiration de la surface. En imposant dans notre modèle des conditions initiales et aux limitées idéalisées d'humidité du sol, nous avons mis en évidence des régions du globe pour lesquelles l'état moyen et la variabilité des températures et des précipitations en été sont particulièrement sensibles à ces conditions. C'est notamment le cas sur une grande partie de l'Europe et de l'Amérique du nord, y compris à des latitudes élevées. Pour toutes ces régions, l'humidité du sol est une source prometteuse de prévisibilité potentielle du climat à l'horizon saisonnier, bien que de fortes incertitudes demeurent localement sur le degré de persistance de ses anomalies. Une expérience de prévisibilité effective coordonnée avec plusieurs systèmes de prévision montre qu'une initialisation réaliste de l'humidité du sol améliore la prévision de températures estivales principalement dans le sud-est de l'Europe. Dans d'autres régions, comme l'Europe du Nord, le désaccord des modèles provient de l'incertitude sur la persistance des anomalies d'humidité du sol. En revanche, sur les Grandes Plaines américaines, aucun modèle n'améliore ses prévisions qui restent donc très médiocres. La littérature ainsi que nos évaluations de sensibilité du climat à l'humidité du sol ont pourtant identifié cette région comme un "hotspot" du couplage entre l'humidité du sol et l'atmosphère. Nous supposons que l'échec de ces prévisions est une conséquence des forts biais chauds et secs présents dans tous les modèles sur cette région en été, qui conduisent à un dessèchement excessif des sols. Pour le vérifier, nous avons développé une méthode qui corrige ces biais au cours de l'intégration des prévisions avec CNRM-CM6. Les prévisions qui en résultent sont nettement améliorées sur les Grandes Plaines. La compréhension de l'origine des biais continentaux en été et leur réduction dans les prochaines générations de modèles de climat sont des étapes essentielles pour tirer le meilleur parti de l'humidité du sol comme source de prévisibilité saisonnière dans les régions tempérées. / Severe heat waves and droughts that episodically hit temperate regions have detrimental consequences on health, economy and society. The design and deployment of efficient preparedness strategies foster high expectations for the prediction of such events a few weeks or months ahead. Their likely increased frequency throughout the 21st century, as envisaged by climate projections, further emphasizes these expectations. Nevertheless, the summer season is the most difficult to predict over mid-latitudes. Well-known sources of predictability are weaker than in winter and current climate prediction systems struggle to adequately represent associated teleconnection mechanisms. An increasing number of studies have shown a statistical link over some regions between spring soil moisture and subsequent summer temperature and precipitation. This link has been partly confirmed in climate numerical models, but many questions remain. The purpose of this PhD thesis is to better understand the role played by soil moisture onthe characteristics and predictability of the summer climate in temperate regions. By means of the CNRM-CM coupled general circulation model, we have designed a range of numerical simulations which help us evaluate the persistence level of spring soil moisture anomalies. Indeed, a long persistence is a necessary condition for these anomalies to influence the climate at the seasonal scale, through the process of evapotranspiration. By imposing in our model idealized initial and boundary soil moisture conditions, we have highlighted areas of the globe for which the average state and the variability of temperatures and precipitation in summer is particularly sensitive to these conditions. This is the case in particular for Europe and North America, including over high latitudes. Soil moisture is therefore a promising source of potential seasonal climate predictability for these regions, although the persistence of soil moisture anomalies remains locally very uncertain. An effective predictability coordinated experiment, bringing together several prediction systems, shows that a realistic soil moisture initialization improves the forecast skill of summer temperatures mainly over southeast Europe. In other regions, such as Northern Europe, the disagreement between models comes from uncertainty about the persistence of soil moisture anomalies. On the other hand, over the American Great Plains, even the forecasts with improved soil moisture initialization remain unsuccessful. Yet, the literature as well as our assessment of climate sensitivity to soil moisture have identified this region as a "hotspot" of soil moisture - atmosphere coupling. We assume that the failure of these predictions relates to the strong hot and dry bias present in all models over this region in summer, which leads to excessive soil drying. To verify this assumption, we developed a method that corrects these biases during the forecast integration based on the CNRM-CM6 model. The resulting forecasts are significantly improved over the Great Plains. Understanding the origin of continental biases in the summer and reducing them in future generations of climate models are essential steps to making the most of soil moisture as a source of seasonal predictability in temperate regions