Estuary Online Course https://giladjames.com Section: Coupling Watersheds, Estuaries and Regional Oceanography through Numerical Modelling in the Western Iberia: Thermohaline Flux Variability at the Ocean-Estuary Interface Lesson: Results and discussion - PART 2 Estuary. This course is brought to you by Gilad James Mystery School. Learn more at Gilad James.com. Introduction In Ref. , an estuary is defined as ‘a semi-enclosed coastal body of water, which has a free connection with the open sea, and within which sea water is measurably diluted with freshwater derived from land drainage’. On its way to the open ocean, fresh water carried by rivers is mixed with the saltier ocean water aided mainly by the action of tides and atmospheric forces. The salinity of the estuarine fluxes that are incorporated in the near ocean depend on several factors including the estuary tidal prism and magnitude of river run-off. While the size of the tidal prism is controlled by the morphodynamics of each particular area, the fresh water inputs are controlled by natural factors such as rainfall and human fresh water management upstream the river mouth, i.e. water irrigation, storage for electricity and deviation to other watersheds. The natural and man-made variability of the fresh water volume and properties is a constraint to characterise the estuarine waters that flow from estuaries to the open ocean due to the lack of permanent monitoring stations. This characterisation is critical to understand their influence in coastal hydrodynamics and ecological processes. From the European environmental legislation perspective, estuaries are typically regarded as transitional waters in the EU Water Framework Directive (WFD, Directive 2000/60/EC) and therefore may mostly fall outside the scope of EU Marine Strategy Framework Directive (MSFD, Directive 2008/56/EC). However, estuaries may have estuarine plumes which extend beyond transitional waters limit, 1 nautical mile from baseline, into coastal waters or beyond; in these cases, the estuarine habitat would fall within the scope of MSFD. For this reason, the exclusion of estuaries and other transitional waters from the MSFD has been questioned. In any case, though WFD requires the monitoring of several biological and chemical elements for the establishment of reference conditions in terms of water quality, long-term continuous monitoring with high temporal resolution are rarely deployed in estuaries and, when deployed, some sensors have limited capabilities for longstanding observations. For this reason, numerical models are able to support in situmonitoring by completing the datasets spatially and temporally and by contrasting the ambiguous observed values. Also numerical models can assist to describe the transitional waters including the extension and influence of the estuarine plumes in the near ocean. In this lecture, the tidal prism and estuarine fluxes will be characterised with the aid of numerical models and the existing monitoring stations from the main Portuguese estuaries. This work also aims to describe in more detail the estuarine component of the methodology to couple watersheds, estuaries and regional oceans through numerical modelling for Western Iberia . #estuary ... https://www.youtube.com/watch?v=SasIEM0XRwE

Recent Advances and Applications in Remote Sensing Online Course https://giladjames.com Section: A Study on Phenology Detection of Corn in Northeastern China with Fused Remote Sensing Data Lesson: Crop models for corn Recent Advances and Applications in Remote Sensing. This course is brought to you by Gilad James Mystery School. Learn more at Gilad James.com. Introduction Accurate measurements of regional-to-global-scale vegetation dynamics and phenology information improve our understanding of inter-annual vegetation change in terrestrial ecosystems, as well as climatic and other environmental variations from year to year. The phenological stages of crops provide essential information for agricultural activities such as irrigation scheduling and fertilizer management. In addition, accurate detection of key crop growth stages is a key input for crop yield estimation based on remote-sensed vegetation index (VI) data and crop model applications integrated with remote sensing data . The traditional approaches to estimating crop phenology have been through ground observations and the use of crop models (e.g., Simple and Universal CROp growth Simulator (SUCROS) , Hybrid-Corn , WOrld FOod STudies (WOFOST) ). The crop models can estimate crop growth dates with a high level of accuracy , but they require a number of detailed information inputs such as crop (e.g., cultivar used and plant population), weather (e.g., temperature, rainfall, solar radiation and wind speed) conditions and soil (e.g., initial soil moisture). On the one hand, the use of these crop models is usually limited by the availability of the required data inputs; on the other hand, the models need to be calibrated for particular species and site-specific conditions based on ground data , and the ground observations collected by observers are not cost-efficient. Accordingly, the traditional methods are site-specific and typically cannot monitor crop phenology beyond the field scale over larger areas . Satellite remote sensing observations from global imaging sensors offer considerable potential to provide the information of regional spatio-temporal patterns of the key crop growth stages of cash crops in a consistent, time- and cost-efficient manner. The commonly used remote-sensing based phenology detection methods can be divided into four groups: (1) threshold methods that estimate phenological stages by using either a fixed or dynamic threshold ; (2) moving window methods that determine the phenology dates by vegetation index (VI) changes of a time-series VI curve in a defined moving temporal window (e.g., 20 days) ; (3) function fitting methods that apply mathematical functions (e.g., logistic , Fourier transformation, wavelet ) to fit the time-series VI curves to a given function and extract phenological stages through the detection of defined feature points (e.g., second derivative equals 0) on the function curves; and (4) the shape model method ] that applies a novel shape-model fitting concept to times-series VI curves for date identification and by Zeng et al. who proposed a hybrid method with environmental factors taken into consideration by integrating crop models. The first three remote-sensing-based phenology detection methods summarized above are generally based on mathematical methods that directly detect the feature points as the transition dates of vegetation. Usually, these dates represent general vegetation growth stages (e.g., greenup onset, peak greenness and dormancy onset ) but have little association with the specific agronomic stages of a specific kind of crops (e.g., corn, soybeans). Some key crop growth stages (e.g., R1 (beginning bloom), R3 (beginning pod) stages of soybeans) are challenging or impossible to be detected by finding feature points of the VI time-series curve. In addition, these methods are often sensitive to observation errors and noise caused by cloud coverage, atmospheric constituents (e.g., water vapor), bi-directional reflectance distribution function (BRDF) effect and the mixed-pixel effect in time-series VI data products . The TSF approach is potential for the specific agronomic stages detection of specific crops with a high estimat #recent #advances #and #applications #in #remote #sensing ... https://www.youtube.com/watch?v=PjOAT3MZRi0

Climate Variability Online Course https://giladjames.com Section: Climate Risk Assessment for Water Resources Development in the Niger River Basin Part I: Context and Climate Projections Lesson: Introduction Climate Variability. This course is brought to you by Gilad James Mystery School. Learn more at Gilad James.com. Introduction Climate change has emerged as one of the most important challenges of the 21st century. Consequently, a plethora of studies has appeared which focus on adaptation measures for responding to the climate change threat. A cursory review of much of the adaptation literature shows, however, that most of it contains little more than generic recommendations to vaguely defined or specified threats. This situation results from the fact that generally the specific impacts of climate change in specific locations or to specific sectors remain poorly understood and under-investigated. This situation is especially acute in Africa where adaptation strategies are urgently needed. In this lecture, we present the results of a study designed to identify and quantify specific impacts of climate change to the hydro-energy, environmental, and agro-economic sectors in the Niger Basin in West Africa. The Niger River is the second largest river in Africa by discharge volume (5,600 m3/s at Onitsha; 1955-1991) and the third longest (4,100 km). Taking its source on the interior side of the Guinea Daro Massif at an altitude of 1068 meters, the Niger River runs off in a northeasterly direction towards the Niger Inland Delta. At Tossaye (Taoussa, Republic of Mali), the river turns southeast forming the Great Bend and flows on through Niger to Lokoja in Nigeria, where it receives its largest and most important tributary, the Benue River (Fig. 1). From there, the enlarged Niger runs directly south to empty into the Bight of Benin through a network of distributaries in Nigeria’s Terminal Niger Delta. The river’s unusual flow path delineates a drainage basin that is boomerang-shaped and bounded approximately by latitudes 5° N and 22° N and by longitudes 11°30΄ W and 15° E. The Niger Basin’s total drainage area is 2,170,500 km² although 35% (770,500 km2) of the basin lies within the hyper-arid Sahara and contributes no runoff to the Niger River. The Niger Basin cuts across all the major climatic zones of West Africa, including the Guinean or Equatorial forest zone, the Transitional tropical belt, the Sudan Savanna zone, the semi-arid or Sahel savanna belt, and the desert region. Rainfall, temperature, humidity and evaporation all exhibit steep gradients as one moves hinterland from the coast (Fig. 2). The Location of the Niger Basin in West Africa. Source: BRLi, 2007. The climatic characteristics of the Niger Basin. The panel on the top left shows the distribution of annual average precipitation, the panel on the top right shows the basin demarcated into active and inactive parts. Also shown is the location of river gauging sites within the basin. The bottom left panel shows the distribution of annual average temperature. Finally, the bottom right panel shows the basin divided into commonly recognized sub-basins. The Upper Niger Basin includes all of the sub basins above the Inner Delta (red area) i.e. Siguiri, Koulikoro (or Sélingué) and Douna (or Bani) watersheds. The Niger Basin is experiencing considerable pressures on ecosystem resources and with good reason: all 9 countries in the active basin rank among the bottom 40 countries on the Human Development Index (UNDP, 2010). Over 100 million people inhabit the Niger basin distributed in nine countries, namely Benin, Burkina Faso, Cameroon, Chad, Cote D’Ivoire, Guinea, Mali, Niger, and Nigeria (Fig. 1). Average population growth rate is estimated at 2.7% and the rate of urbanization at 4.3%, both numbers among the highest rates in the world (UNFPA, 2010). Approximately 70% of the population depends on agriculture for their livelihood and overwhelmingly on rain-fed agriculture, and 60% lack access to a developed source of water. Somewhat paradoxically, the basins’ environmental and water resources are currently grossly underutilized. The irrigation potential is es #climate #variability ... https://www.youtube.com/watch?v=z0b5yfcQ75c

Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure Online Course https://giladjames.com Section: L-Band SAR Disaster Monitoring for Harbor Facilities Using Interferometric Analysis Lesson: Synthetic aperture radar Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure. This course is brought to you by Gilad James Mystery School. Learn more at Gilad James.com. Introduction Satellite microwave data, such as synthetic aperture radar (SAR), have the great potential for retrieving ocean dynamic parameters, for instance, ocean surface current and ocean wave dynamic. One of the attention-grabbing topics is current flow that is needed for short go back satellite cycle and high resolution. These will provide precisely data concerning current dynamic flow. In fact, current is very important for ship navigation, fishing, waste matter substances transport, and sediment transport. Respectively, optical and microwave sensors are enforced to monitor the current flows. Indeed, the ocean surface dynamic options of sea surface current are vital parameters for atmospheric-sea surface interactions. In this regard, the global climate change, marine pollution, and coastal risky are preponderantly dominated by current speed and direction. The measurements of ocean current from space rely on the electromagnetic signal. Truly, associate degree of an electromagnetic signal of optical and microwave reflects from the ocean carrying records concerning one among the first discernible quantities that are the color, the beamy temperature, the roughness, and also the height of the ocean . Recently, the high resolution of SAR sensors such as TerraSar-X, RADARSAT-2, ALOS PALSAR, and the foremost three of the Italian satellite of COSMO-SkyMed have been commenced. Once the four satellites in the COSMO-SkyMed constellation are developed, they are conceivable functioning with a tiny resume time of a little hours. Nevertheless, the initial three of the COSMO-SkyMed, ALOS PALSAR, and RADARSAT-2, satellite data are the cross-track interferometry, which do not allow determining neither coastal water flow nor coastal water level changing. In this regard, the TerraSAR-X satellite data use an along-track interferometric proficiency which simply permits the quantity of sea surface speed. Additionally, phase alterations between the coregistered pixels of an image pair are consistent to Doppler frequency shifts of the signal backscattered and according to line-of-sight velocities of the scatterers. In this view, phase alterations include influences of surface flows and of the dynamic of wave movement. Consequently, the retrieving of tidal current flow can be accurately achieved by both of TerraSAR-X and TanDEM-X. These can be depleted to regulate precisely coastal water height fluctuations. The TerraSAR-X can regulate perfectly the digital surface model (DSM), where depiction of surface-containing topographies exceeds the terrain height, for example, plants and constructions through precision of 2 m. Moreover, TanDEM-X involves dual high-resolution imaging SAR data. In this understanding, both TerraSAR-X and TanDEM-X are hovering in tandem and establishing an enormous radar interferometer with an anticipated competence of creating a comprehensive DSM through a perpendicular resolution of 2 m, exceeding whatever obtainable currently from space. Consistent with Romeiser et al. , with the usual helical revolution configuration, the dual satellites ensure an along-track gap between 0 at the northern and southern utmost locations of the orbit and approximately 550 m over the equator, restrictive of the district of convenient baselines for intersatellite interferometry above the sea surface to restricted space crews far-off the north and south. In districts of elongated along-track baselines, the data characteristic undergoes since sequential decorrelation of the signal backscattered. Nonetheless, the TanDEM-X geometry constructions acquire adjusted from period to period to enhance the cross-track interferometry performing in coastal water height fluctuations and surface stream flow attentions . On the word of Yoon et al. , t #advanced #remote #sensing #technology #for #synthetic #aperture #radar #applications, #tsunami #disasters, #and #infrastructure ... https://www.youtube.com/watch?v=fqzbJgM9QH4