Mount Padang archeological site has been known since late nineteen century as a megalithic complex that sits on top. Our studies proves that the structure does not cover just the top but also wrap around the slopes covering about 15 ha area at least. Comprehensive geophysical surveys combining ground penetration radar (GPR) and multi-channel resistivity methods, seismic tomography augmented by bore-holes coring data and archeological excavations, show further that the structures are not only superficial but rooted into greater depth. The structures are not built at once but consisting several layers from consecutive periods. The uppermost layer on the surface consists of horizontal piles of basaltic columnar rocks forming step-structure terraces and decorated by exotic arrangements of stand-up rock columns forming walls, paths and spaces. The second layer, which had been previously misinterpreted as natural rock formation, buried 1-3 meters beneath the ground surface, is a several-meter thick fills consisting of more compact and advance arrangement of similar columnar rocks in fine-grain matrix. The third layer is also artificial arrangement of rock fragments with various kinds that extent down to about 15 meter deep. The third layer sits on fractured, massive basaltic lava tongue. The survey also reveals evidences of large underground cavities or chambers. Results of preliminary radiocarbon dating indicates that the first layer was built around Cal BP 3,000. The second layer was built around about Cal BP 7,000. The third layer was built prior to Cal.BP. 9,500, and could be as old as Cal.BP.13,000 to 28,000 years old.

We introduce a computer program for time series tuning and analysis. The well known in paleo-climatic community program AnalySeries after Paillard et al. is restricted to the Mac OS (32-bit) and, according to Apple plans to move entirely onto the 64-bit system, it will not be supported in the future upgrades of Macintosh OS. QAnalySeries is an attempt to re-implement the major functionality of AnalySeries thus providing the community with a useful tool. QAnalySeries is written using Qt SDK as a free software and can be run on Macintosh, Windows and Linux systems. Paillard, D., L. Labeyrie and P. Yiou (1996), Macintosh program performs time-series analysis, Eos Trans. AGU, 77: 379.

The latest launched Sentinel satellite mission, the Sentinel-5 Precursor (Sentinel-5P) is one of the European Space Agency’s (ESA) new mission family – Sentinels. The sole payload on Sentinel-5P is the TROPspheric Monitoring Instrument (TROPOMI), which is a nadir-viewing 108 degree Field-of-View push-broom grating hyperspectral spectrometer, covering the wavelength of ultraviolet-visible (270 nm to 495 nm), near infrared (675 nm to 775 nm), and shortwave infrared (2305 nm - 2385 nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels and is providing measurements of ozone, NO2, SO2, CH4, CO, formaldehyde, aerosols and cloud at high spatial, temporal and spectral resolutions. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is one of the 12 Distributed Active Archive Centers (DAACs) within NASA’s Earth Observing System Data and Information System (EOSDIS). The GES DISC archives and supports over a thousand data collections in the Focus Areas of Atmospheric Composition, Water & Energy Cycles, and Climate Variability. Under the End User License Agreement between NASA and ESA, GES DISC is curating TROPOMI Level-1B and Level-2 products and providing information services through enhanced tools and services that offer facile solutions for complex Earth science data and applications. This presentation will demonstrate up-to-date TROPOMI products including earthview radiance, solar irradiance, aerosol layer height, Carbon Monoxide, Nitrogen Dioxide, and cloud, as well as easy ways to access, visualize and subset TROPOMI data.

Spectral-based vegetation indices (VI) have been shown to be good proxies of grapevine stem water potential (Ψstem), potentially assisting in irrigation-decision making of commercial vineyards. However, VI-Ψstem correlations are mostly reported at the leaf or canopy scales using sensors attached to leaves or very-high-spatial resolution images derived from sensors mounted on small airplanes or drones. Here, for the first time, we take advantage of the high spatial resolution (3-m), near-daily images acquired from Planet’s nano-satellites constellation to derive VI-Ψstem correlations at the vineyard scale. Weekly Ψstem were measured along the growing season of 2017 in six vines in 81 commercial vineyards and in 60 pairs of vines in a 2.4 ha experimental vineyard in Israel. The clip application programming interface (API), provided by Planet, and Google Earth Engine platform were used to derive spatially continuous time series of four VIs: GNDVI, NDVI, EVI, and SAVI in the 82 vineyards. Results show that per-week multivariable linear models using variables extracted from VI time series successfully tracked spatial variations in Ψstem across the experimental vineyard (Pearson’s-r = 0.45–0.84: N=60). A simple linear regression model enabled monitoring seasonal changes in Ψstem along the growing season in the vineyard (r = 0.80–0.82). Planet VIs and seasonal Ψstem data from the 82 vineyards were used to derive a ‘global’ model for in-season monitoring of Ψstem at the vineyard-level (r = 0.81: RMSE = 17.5%: N=970). The ‘global’ model, which requires only a few VI variables extracted from Planet images, may be used for real-time weekly assessment of Ψstem in Mediterranean vineyards, substantially reducing expenses of conventional monitoring efforts.

The MASER (Measuring, Analysing and Simulating Radio Emissions) project provides a comprehensive infrastructure dedicated to low frequency radio emissions (typically < 50 to 100 MHz). The four main radio sources observed in this frequency are the Earth, the Sun, Jupiter and Saturn. They are observed either from ground (down to 10 MHz) or from space (down to a few kHz). Ground observatories are more sensitive than space observatories and capture high resolution data streams (up to a few TB per day for modern instruments). Conversely, space-borne instruments can observe below the ionospheric cut-off (10 MHz) and can be placed closer to the studied object. Several tools have been developed in the last decade for sharing space physcis data. Data visualization tools developed by the CDPP (http://cdpp.eu, Centre de Données de la Physique des Plasmas, in Toulouse, France) and the University of Iowa (Autoplot, http://autoplot.org) are available to display and analyse space physics time series and spectrograms. A planetary radio emission simulation software is developed in LESIA (ExPRES: Exoplanetary and Planetary Radio Emission Simulator). The VESPA (Virtual European Solar and Planetary Access) provides a search interface that allows to discover data of interest for scientific users, and is based on IVOA standards (astronomical International Virtual Observatory Alliance). The University of Iowa also develops Das2server that allows to distribute data with adjustable temporal resolution. MASER is making use of all these tools and standards to distribute datasets from space and ground radio instruments available from the Observatoire de Paris, the Station de Radioastronomie de Nançay and the CDPP deep archive. These datasets include Cassini/RPWS, STEREO/Waves, WIND/Waves, Ulysses/URAP, ISEE3/SBH, Voyager/PRA, Nançay Decameter Array (Routine, NewRoutine, JunoN), RadioJove archive, swedish Viking mission, Interball/POLRAD… MASER also includes a Python software library for reading raw data. This work is supported by CDPP, CNES, PADC and Europlanet-2020-RI. The Europlanet 2020 Research Infrastructure project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654208.

The subduction zone of the Cocos Plate beneath Southern Mexico has major variations in the megathrust geometry and behavior. The subduction segment beneath the Oaxaca state of Mexico has relatively frequent large earthquakes on the shallow part of the megathrust and within the subducting slab, and it also has large aseismic slow-slip events. The slab geometry under Oaxaca includes part of the subhorizontal “flat-slab” zone extending far from the trench beneath southern Mexico and the beginning of its transition to more regular subduction geometry to the southeast. We study the rupture of the 16 February 2018 Mw 7.2 Pinotepa earthquake near Pinotepa Nacional in Oaxaca that was a thrust event on the subduction interface. The Pinotepa earthquake was about 350 km away from the 8 September 2017 Mw 8.2 Tehuantepec earthquake in the subducting slab offshore Oaxaca and Chiapas; it was in an area of Coulomb stress decrease from the M8.2 quake, so it seems unlikely to be a regular aftershock and was not triggered by the static stress change. Geodetic measurements from interferometric analysis of synthetic aperture radar (InSAR) and time-series analysis of GPS station data constrain finite-fault slip models for the M7.2 Pinotepa earthquake. We analyzed InSAR data from Copernicus Sentinel-1A and -1B satellites and JAXA ALOS-2 satellite. Our Bayesian (AlTar) static slip model for the Pinotepa earthquake shows all of the slip confined to a very small (10-20 km diameter) rupture, similar to some early seismic waveform fits. The Pinotepa earthquake ruptured a portion of the Cocos megathrust that has been previously mapped as partially coupled and shows that at least small asperities in that zone of the subduction interface are fully coupled and fail in high-stress drop earthquakes. The previous 2012 Mw 7.4 Ometepec earthquake is another example of asperity in the partially coupled zone but was not imaged by InSAR so the rupture extent is not so well constrained. The preliminary NEIC epicenter for the Pinotepa earthquake was about 40 km away (NE) from the rupture imaged by InSAR, but the NEIC updated epicenter and Mexican SSN location are closer. Preliminary analysis of GPS data after the Pinotepa earthquake indicates rapid afterslip on the megathrust in the region of coseismic slip. Atmospheric noise masks the postseismic signal on early InSAR data.

The isotopic composition of organic sulfur (δ34Sorg) is a potential recorder of past biogeochemical conditions that has, thus far, received relatively little attention in comparison to the pyrite sulfur isotope record (δ34Spyr). This study presents continuous organic and pyrite δ34S records from three basins of the organic-rich Miocene Monterey Formation, deposited over a similar time interval of c.14.5-6 Ma but under varying depositional conditions. In the San Joaquin basin, δ34Sorg and δ34Spyr average 0‰ and -4‰ respectively and maintain a relatively constant pyrite-organic sulfur isotopic offset of c. 5‰. The Santa Maria Basin exhibits δ34Sorg values that are >10‰ higher than in coeval San Joaquin basin intervals, with average δ34Sorg of c. 24‰ in the upper siliceous member the highest yet reported for marine organic sulfur and roughly 2-3‰ higher than Miocene seawater sulfate. δ34Spyr is consistently c. 12‰ depleted in comparison to organic sulfur in the lower phosphatic member of the Santa Maria Basin, but an abrupt enrichment in both δ34Spyr and δ34Sorg coincident with a sharp lithostratigraphic transition at c. 11 Ma reduces this offset to <4‰ for much of the upper siliceous shales. The Santa Barbara Basin shows a sulfur isotope record intermediate between the San Joaquin and Santa Maria Basins, with average δ34Spyr and δ34Sorg of 3‰ and 12‰ respectively, and relatively consistent c. 10‰ pyrite-organic isotope offset. Records for all three basins demonstrate a close correlation between coeval δ34Spyr and δ34Sorg values which we attribute to derivation from an equivalent, or at least similar, source of sedimentary or water column sulfide. However, marked offset in the isotopic composition of coexisting pyrite and organic sulfur, of variable magnitude within and between basins, implies some contrast in the diagenetic processes underlying sulfur incorporation into the two phases. We argue that the prominent δ34Spyr and δ34Sorg isotopic differences between broadly coeval basin sections are largely the result of differences in sedimentation regime and the associated balance of iron and sulfide supply during diagenesis. A likely factor of additional importance to this iron-sulfide balance is basin-specific sedimentary and water column redox. These findings illustrate the importance of determining independent constraints on the nature of a sedimentary system before conclusions are made relating the sulfur isotope composition of sedimentary species to paleoenvironmental conditions. Additionally, we suggest that records of δ34Spyr have a strong dependence on interaction with organic sulfur during formation, and thus that existing δ34Spyr records are more effectively interpreted in combination with δ34Sorg records.

Chapter 25 of the Fourth National Climate Assessment (NCA4) is an assessment of climate change and variability, climate-related risks, impacts and adaptation in the U.S. Southwest. The chapter builds on assessments of climate change in the Southwest from the three previous U.S. National Climate Assessments. Each assessment has consistently identified drought, water resource reliability, and loss of ecosystem integrity as climate change challenges for the Southwest region. Chapter 25 further examines interconnections among water, ecosystems, coastal and marine systems, food, and human health and adds new key messages concerning energy and Indigenous peoples. The Southwest chapter is one of 29 chapters in Volume II of the Fourth National Climate Assessment - Impacts, Risks, and Adaptation in the United States. The National Climate Assessment fulfills the mandate of the Global Change Research Act (GCRA) of 1990 to provide the nation with a timely assessment and analysis of scientific findings of the effects of global change on multiple economic and natural resource sectors in the United States, and an analysis of observed and projected trends in global change. Chapter 25, Southwest, was written by a team of scientists and practitioners with expertise spanning areas specified in the GCRA, after extensive stakeholder engagement that involved the collection of input on key climate-related challenges, impacts, and opportunities in the Southwest region. The chapter went through multiple rounds of public and governmental review, during 2017 and 2018. This poster will focus on the findings from Chapter 25.

One third of all coastlines worldwide consist of permafrost. Many of these permafrost coasts are presently exposed to greater environmental forcing as a consequence of climate change, such as a lengthening of the open water season, intensified storms, and higher water and air temperatures. As a result, increasing erosion rates are currently reported from various sites across the Arctic. It is crucial to synthetize these data on Arctic shoreline dynamics in order to improve our understanding on present coastal dynamics on the pan-Arctic scale. A first synthesis product was released in form of the Arctic Coastal Dynamics databse in 2012, which included data published until 2009 (Lantuit et al., 2012). Since then, numerous publications and data products were published on short and long term changes of Arctic coasts across a wide range of study sites. We made an extensive literature review of publications released within the last 10 years and updated the shoreline change data section in the Arctic Coastal Dynamics database. While in 2009 for one percent of the Arctic shoreline data on coastal dynamics was available, the addition of new data leads to a broader data coverage, which is mainly the effect of the greater availability of remotely sensed products for analyses conducted in these remote regions. Further, the additional data allow us to update the current mean rate of Arctic shoreline change.

Melt ponds play an important role in the seasonal evolution of Arctic sea ice. During the melt season, snow atop the sea ice begins to metamorphose and melt, forming ponds on the ice. These ponds reduce the albedo of the surface, allowing for increased solar energy absorption and thus further melting of snow and ice. Analyzing the spatial distribution and temporal evolution of melt ponds helps us understand the sea ice processes that occur during the summer melt season. It has been shown that the inclusion of melt pond parameters in sea ice models increases the skill of predicting the summer sea ice minimum extent. Previous studies have used remote sensing imagery to characterize surface features and calculate melt pond statistics. Here we use new observations of melt ponds obtained by the Digital Mapping System (DMS) flown onboard NASA Operation IceBridge (OIB) during two Arctic summer melt campaigns which surveyed thousands of kilometers of sea ice and resulted in more than 45,000 images. One campaign was conducted in the Beaufort Sea (July 2016), and one in the Lincoln Sea and the Arctic Ocean north of Greenland (July 2017). Using these data we expect to advance our understanding of the differences and similarities between melt pond features on young, thin sea ice seen in the Beaufort Sea versus those on multi-year ice. We have developed a pixel-based classification scheme by considering the different RGB spectral values associated with each surface type. We identify four sea ice surface types (level ice, rubbled ice, open water, and melt ponds). The classification scheme enables the calculation of parameters including melt pond fraction, ice concentration, melt pond area, and melt pond dimensions. We compare results with data from the Airborne Topographic Mapper (ATM), a laser altimeter also operated during these OIB missions. Given the extent over which the OIB data are available, regional information may be derived. Leveraging existing satellite data products, we examine whether the high-resolution airborne statistics are representative of the region and can be scaled up for comparison against satellite-derived parameters such as ice concentration and extent.

River corridors, the spatial domains around rivers in which river water interacts with surrounding sediment and rock, are important components of watersheds. They comprise extremely complex ecosystems: heterogeneous at all spatial scales with strong temporal dynamics, coupled biological, geochemical, and hydrologic processes, and ubiquitous human impacts. We present several ways that our project, focused around the 75 km Hanford Reach of the Columbia River but with multiple connections to other systems, is addressing this challenge. These include 1) deployment of intensive, automated sensor networks supplemented by data from the Hanford Environmental Information System (HEIS) for hyporheic zone monitoring 2) data assimilation of these and other data into models using joint hydrologic and geophysical inversion, 3) integrating MASS2 model outputs and bathymetry data using machine learning to classify hydromorphologic features, 4) a community-based effort to develop broad understanding of organic carbon biogeochemistry and microbiomes in diverse river systems, and 5) use of multi-‘omics data to develop new biogeochemical reaction networks. These underpin the incorporation of process understanding and diverse data into high-resolution mechanistic models, and employment of those models to develop reduced-order models that can be applied at large scales while retaining the effects of local features and processes. In so doing we are contributing to reduction of uncertainties associated with major Earth system biogeochemical fluxes, thus improving predictions of environmental and human impacts on water quality and riverine ecosystems and supporting environmentally responsible management of linked energy-water systems.

Coastal areas are highly vulnerable to flooding, due to hydrological extreme events such heavy rainfalls and/or storm surges which are supposed to be increasing in the next future due to the emission in atmosphere of anthropogenic greenhouse gases. In this study, in order to assess the future hydraulic risk in coastal regions, as well as, to identify optimal defense/adaptation policies, a risk analysis model is developed to calculate the present day and future flood risk, accounting for climate change uncertainties and mitigation measures. Such model juxtaposes a number of coupled/nested models as: a) a stacking daily rainfall downscaling model which combines simulations from multiple predictive models, as Random Forest, extreme gradient boosting and Non-homogeneous Hidden Markov Model (NHMM) (Cioffi et al. 2018); b) a Bivariate Point Process model (BPPM) (Zheng et al., 2014) that calculates Joint probability density function between extreme daily rainfall amount and daily extreme storm tide depth; c) a simulation-optimization model - in which multi-objective GA optimization model (Deb et al., 2002) and 2D hydraulic model are combined (Cioffi et al. 2018) - calculates sets of Pareto optimal solutions which are obtained by defining two optimality criteria consisting in: minimizing both the cost of the flood defense infrastructure system and the flooding hydraulic risk. ; d) a mathematical decision model which is aimed to identify the best policies of mitigation of hydraulic risk and the timing, taking into account the uncertainties in hydrological extreme event predictions. The risk analysis model is applied to the study case of Mazzocchio area which is the most depressed area (about 10000 ha) within the Pontinia Plain, a large reclamation region in the south of Lazio (Italy), particularly vulnerable to extreme events - as extreme rainfall amount and sea level rise due to storm surge at the sea outfall of the river- which in the past caused the crisis of hydraulic network system with flooding of large areas and collapse of levees. XXI Century projections of daily rainfall amount and sea level for the RCP 8.5-IPCC scenarios were performed using ensemble of 35 GCM simulations (CESM1 CAM5 BGC 20C + RCP8.5 Large Ensemble) (Kay et al., 2015).

Coastal risks are increasing due to the warming of the climate, resulting in rising mean sea levels and changes in storminess. Projections of future coastal flooding rely on global climate models based on greenhouse gas scenarios with inherent large uncertainties. The past warm climate of the Last Interglacial (LIG, ~127,000 years ago) is considered a partial analogue of a future warmer world. Therefore, understanding how coastal systems were affected by changes in atmospheric and relative sea levels during the LIG can inform us about possible future changes. In this contribution we will analyze extreme sea levels and coastal flooding during the LIG. The analysis is based on the hydrodynamic Global Tide and Surge Model (GTSM; Muis et al., 2016, doi: 10.1038/ncomms11969). To simulate storm surges during the LIG GTSM will be forced by 6-hourly wind and surface pressure fields from LIG simulations of IPCC-type climate models. Due to non-linear effects, tides and surge levels will be influenced by changes in mean sea level. Therefore, a key input variable is map of regional mean sea levels during LIG. However, there is still considerable uncertainty on sea level high-stands and regional patterns during the LIG. Using output from a Glacial Isostatic Adjustment model (GIA), we will model tides and surges for a set of plausible scenarios of relative sea levels and assess sensitivities.

HydroShare is a domain specific data and model repository operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) to advance hydrologic science by enabling individual researchers to more easily share products resulting from their research. The community platform supports, not just the scientific publication summarizing a study, but also the data, models and workflow scripts used to create the scientific publication and reproduce the results therein. HydroShare accepts data from anybody, and supports Findable, Accessible, Interoperable and Reusable (FAIR) principles. HydroShare is comprised of two sets of functionality: (1) a repository for users to share and publish data and models, collectively referred to as resources, in a variety of formats, and (2) tools (web apps) that can act on content in HydroShare and support web based access to compute capability. Together these serve as a platform for collaboration and computation that integrates data storage, organization, discovery, and analysis through web applications (web apps) and that allows researchers to employ services beyond the desktop to make data storage and manipulation more reliable and scalable, while improving their ability to collaborate and reproduce results. This presentation will describe the capabilities developed for HydroShare to support the full research data management life cycle. Data can be entered into HydroShare as soon as it is collected, and initially shared only with the team directly working on the data. As analysis proceeds, tools, scripts and models that act on the data to produce research results may be stored in HydroShare resources alongside the data. At the time of publication these resources may be permanently published and receive digital object identifiers and cited in research papers. Resources may themselves include citations to the research papers, thereby linking the publications to the supporting data, scripts and models. HydroShare design choices and capabilities for establishing relationships and versioning, based on simplicity, and ease of use, and some of the challenges encountered, will be discussed.

Wireless sensor networks are increasingly important in monitoring water quality changes. High frequency monitoring can be used to gather water quality information, identify existing problems and improve water quality management activities. However, missing data are unavoidable because of network communication issues, sensor maintenance or failure. Data interpolation is a process for constructing missing values based on known data points. Though traditional methods like polynomial or linear interpolation are widely used in sensor data pre-processing, there are still many limitations. Firstly, current interpolation methods give poor estimations when a continuous number of data within a period of time are missing. Secondly, many interpolation methods reconstruct missing data based on other parameters available at the same time step. When all the data are missing, these methods cannot be used. In our work, we are developing a sequence-to-sequence interpolation model (SIM) for recovering missing data sequences in wireless sensor networks. SIM uses the state-of-the-art sequence-to-sequence architecture. It consists of two parts: an encoder that reads from the source water quality time series data and a decoder that generates the missing data sequences. In our design, Bi-directional Long Short Term Memory Network is used as the encoder and decoder due to its capability in using both past and future information for a given time. The attention mechanism is applied to make the SIM focus on different parts of the input time series when interpolating missing values at different time steps. We evaluated the SIM by using time series data from Queensland government’s water quality monitoring network. Compared to Seasonal-ARIMA, the SIM reduced 23.2% MAE and 40.3% RMSE when recovering missing data in 2 adjacent time steps. The reason for the superior performance is that SIM interpolates missing data based on both the inner relationships between water quality parameters and the accumulated information through time.

Recent research in real-time tsunami early warning can be broadly classified into two approaches. The first involves the use of seismic and regional geodetic data to calculate the tsunami wavefield indirectly through the estimation of earthquake source parameters. The second directly reconstructs the tsunami wavefield using data assimilation of ocean-bottom pressure sensor data such as those from DONET and S-NET (Maeda et al. 2015, Gusman et al. 2016). Data assimilation interpolates between the numerical solution and the observations to make the forecast more consistent with real data. Currently, the most popular method for forecasting the waveform is optimal interpolation, which uses a Kalman filter (KF) like approach, but holds the Kalman gain matrix fixed to reduce the runtime. This approach, coupled with tsunami Green’s functions, is very efficient and generates useful predictions. Here, we demonstrate that more accurate and stable forecasts can be obtained using the ensemble KF (enKF), a more computationally efficient variant of KF, in which the gain matrix is updated according to the physical model and the evolution of the error covariance matrix. The ensemble representation is a form of dimensionality reduction, in that only a small ensemble is propagated, instead of the joint distribution including the full covariance matrix. This method also provides a means to obtain the probability distribution of the forecast at each grid point location. We use a scenario tsunami in the Cascadia subduction zone, generated from a 2D fully-coupled dynamic rupture simulation (Lotto et al., submitted 2018). Randomly perturbed tsunami wave height data is used in the assimilation process, as we propagate the wave using a 1D linear shallow water code on a staggered grid. Better waveform agreement is achieved even in the early stages of assimilation, with much less fluctuation compared to optimal interpolation. We also explore spatial and temporal aliasing effects, in terms of the relation between observation station spacing and wavelength, as well as between assimilation and forecast time intervals. Although enKF is computationally more expensive, we are working on a fast, parallelized GPU implementation, which will significantly reduce the runtime, taking us a step closer to reliable real-time tsunami early warning.

The Lower Mekong is facing an increasing impact of droughts and at the regional level, the Mekong River Commission (MRC) is mandated to work with government agencies on creating and distributing flood, drought, water resource governance and use to improve policy and practice. The MRC is striving to provide regional, locally calibrated and downscaled information on drought forecasts and real-time monitoring through a portal. The Regional Drought and Crop Yield Information System (RDCYIS) is built on regionally and locally calibrated Regional Hydrologic Extreme Assessment System (RHEAS) framework that integrates the Variable Infiltration Capacity (VIC) and Decision Support System for Agro-technology Transfer (DSSAT) models, allowing both nowcast and forecast of drought. This model is co-developed by NASA Jet Propulsion Laboratory (JPL) and the SERVIR-Mekong teams. In this work, we outline how the MRC Drought Team’s requirements were met via RHEAS. Driven with earth observation data, the main aim of this service is to improve present regional and national drought monitoring and forecasting services to Lower Mekong countries for their water allocation and drought mitigation information needs. We provide an overview of the model calibration and validation methodology, and we find reasonable reliability of the soil moisture model results with the satellite based observations from the SMAP and SMOS retrievals. Through this support to MRC in integrating new drought assessment, monitoring and warning methodologies using RHEAS, more data and analyses will be available to support them to develop improved advice on drought early warning to the National Mekong Committees across the Mekong countries. MRC’s assistance is envisaged to enable comprehensive, accurate and useful warnings for the decision-makers at local and provincial level to take effective action. Ultimately this service is expected to assist farmers to make preemptive decisions about their water use, cropping and planting patterns and market decisions which should reduce crop loss and support livelihoods from farming, including from appropriate compensation to farmers from the governments, wherever this is in effect.

Unmanned aerial vehicles (UAV) have been increasingly used for field data collection and remote sensing purposes. Their ease of use, ability to carry sensors and cameras, low cost, and precise maneuverability and navigation makes them a versatile tool for a field researcher. Procedures and instrumentation to use UAVs in the field are largely undefined, especially for atmospheric and hydrologic applications. A field study was conducted to test the UAV’s ability to collect atmospheric data in order to locate and analyze the canopy boundary layer (CBL) above a Costa Rican tropical montane rainforest. This study aims to give further insight on the changes of the CBL throughout the day and for atmospheric comparison to land development. Through the study, it was found that there was little previously defined procedures and preferred instrumentation that directly applied to the UAV field study. Because of this, the methodology of using an UAV for atmospheric and CBL remote sensing and data collection was developed and refined by testing and comparing precision and performance of sensors and executing systematic flight patterns throughout the day. The UAV allowed for quick and specific access to sampling locations and for all of the variables to be measured by sensors over vertical profiles. Flights were scheduled at different locations throughout the day over the Texas A&M Soltis Center and surrounding forest in San Isidro, Costa Rica. Vertical profiles were also measured over the Soltis Center grounds to determine how development has affected the presence of the CBL. The UAV was successful in gathering data above the forest canopy and the Soltis Center at varying elevations during clear and cloudy conditions. The procedure produced reliably consistent vertical profiles over small domains in space and time, validating the general approach. The technique also identified unique profiles at spatially and temporally distinct sample sites including response to meteorological events. These findings suggest a healthy ability to diagnose CBL characteristics of interest. It was also found that there is a distinct increase in temperature and dew point over land development when compared to over forest. Future studies include flights at other locations and determining preferred instrumentation for UAV atmospheric data collection.