A5073709625- Flood Risk Assessment and Management
- Hydrology and Watershed Management Studies
- Semantic Web and Ontologies
- Climate variability and models
- Hydrology and Sediment Transport Processes
- Precipitation Measurement and Analysis
- Meteorological Phenomena and Simulations
- Advanced Graph Neural Networks
- Geographic Information Systems Studies
- Hydrology and Drought Analysis
- Data Management and Algorithms
- Tropical and Extratropical Cyclones Research
- Data Quality and Management
- Transboundary Water Resource Management
- Geophysics and Gravity Measurements
- Aquatic Invertebrate Ecology and Behavior
- Soil Moisture and Remote Sensing
- Fish Ecology and Management Studies
- Maritime Navigation and Safety
- Peatlands and Wetlands Ecology
- Topic Modeling
- Computational Physics and Python Applications
- Network Traffic and Congestion Control
- Strategic Planning and Analysis
- Distributed and Parallel Computing Systems
Princeton University
2014-2021
Australian National University
2018
CSIRO Land and Water
2018
Australian Research Council
2018
Earth System Science Interdisciplinary Center
2018
University of Maryland, College Park
2018
American Meteorological Society
2018
ACT Government
2018
Ghent University Hospital
2018
Abstract We present Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2), a gridded precipitation P dataset spanning 1979–2017. MSWEP V2 is unique in several aspects: i) full global coverage (all land and oceans); ii) high spatial (0.1°) temporal (3 hourly) resolution; iii) optimal merging of estimates based on gauges [WorldClim, Global Historical Climatology Network-Daily (GHCN-D), Summary the Day (GSOD), Precipitation Centre (GPCC), others], satellites [Climate Prediction...
Abstract. Closing the terrestrial water budget is necessary to provide consistent estimates of components for understanding resources and changes over time. Given lack in situ observations at anything but local scale, merging information from multiple data sources (e.g., observation, satellite remote sensing, land surface model, reanalysis) through assimilation techniques that optimize estimation fluxes a promising approach. Conditioned on current limited availability, systematic method...
Abstract Conventional basin‐by‐basin approaches to calibrate hydrologic models are limited gauged basins and typically result in spatially discontinuous parameter fields. Moreover, the consequent low calibration density space falls seriously behind need from present‐day applications like high resolution river hydrodynamic modeling. In this study we calibrated three key parameters of Variable Infiltration Capacity (VIC) model at every 1/8° grid‐cell using machine learning‐based maps four...
Abstract Knowledge graphs (KGs) are a novel paradigm for the representation, retrieval, and integration of data from highly heterogeneous sources. Within just few years, KGs their supporting technologies have become core component modern search engines, intelligent personal assistants, business intelligence, so on. Interestingly, despite large‐scale availability, they yet to be as successful in realm environmental intelligence. In this paper, we will explain why spatial require special...
Abstract. Over the past decade, there has been appreciable progress towards modeling water, energy, and carbon cycles at field scales (10–100 m) over continental to global extents in Earth system models (ESMs). One such approach, named HydroBlocks, accomplishes this task while maintaining computational efficiency via Hydrologic Response Units (HRUs), more commonly known as “tiles” ESMs. In these HRUs are learned a hierarchical clustering approach from available high-resolution environmental...
Knowledge graphs (KGs) are a novel paradigm for the representation, retrieval, and integration of data from highly heterogeneous sources. Within just few years, KGs their supporting technologies have become core component modern search engines, intelligent personal assistants, business intelligence, so on. Interestingly, despite large-scale availability, they yet to be as successful in realm environmental intelligence. In this paper, we will explain why spatial require special treatment, how...
Global challenges such as food supply chain disruptions, public health crises, and natural hazard responses require access to integration of diverse datasets, many which are geospatial. Over the past few years, a growing number (geo)portals have been developed address this need. However, most existing stacked by separated or sparsely connected data "silos" impeding effective consolidation. A new way sharing reusing geospatial is therefore urgently needed. In work, we introduce...
The capability and synergistic use of multisource satellite observations for flood monitoring forecasts is crucial improving disaster preparedness mitigation. Here, surface fractional water cover (FW) retrievals derived from Soil Moisture Active Passive (SMAP) L-band (1.4 GHz) brightness temperatures were used assessment over southeast Africa during the Cyclone Idai event. We then focused on five subcatchments Pungwe basin developed a machine learning based approach with support Google Earth...
Abstract. Poorly monitored river flows in many regions of the world have been hindering our ability to accurately estimate global water budgets as well variability cycle. In situ gauging sites, a number satellite-based systems, make observations discharge throughout globe; however, these are often sparse due to, for example, sampling frequencies sensors or lack reporting. Recently, efforts made develop methods integrate discrete gain better understanding underlying processes. This paper...
Abstract. Over the past decade, there has been appreciable progress towards modeling water, energy, and carbon cycles at field-scales (10–100 m) over continental to global extents. One such approach, named HydroBlocks, accomplishes this task while maintaining computational efficiency via sub-grid tiles, or Hydrologic Response Units (HRUs), learned a hierarchical clustering approach from available high-resolution environmental data. However, until now, yet be macroscale river routing that is...
Knowledge graphs are a rapidly growing paradigm and technology stack for integrating large-scale, heterogeneous data in an AI-ready form, i.e., combining with the formal semantics required to understand it. However, toolchains that support synthesis knowledge discovery through information organization, search, filtering, visualization have been developed at pace lagging graph technology. In this paper, we present Explorer, open-source faceted search interface provides environmentally...
Abstract We used environmental metrics developed from multi‐source satellite observations to quantify the global influence of El Niño‐Southern Oscillation (ENSO) events on surface wetting and drying anomalies, their impact vegetation health. The included a microwave wetness index (ASWI) incorporating near‐surface atmospheric vapor pressure deficit (VPD), volumetric soil moisture (VSM), land fractional water cover (FW) derived Advanced Microwave Scanning Radiometer (AMSR) observations, health...
The capability of synergistic satellite flood monitoring and forecasts is crucial for improving disaster preparedness mitigation. In this study, the Soil Moisture Active Passive (SMAP) fractional water (FW) data sets were used mapping over southeast Africa during Cyclone Idai event. We then developed a machine-learning approach with support Google Earth Engine (GEE) 24-hour forecasting 30-m inundation using observations from SMAP Landsat coupled rainfall Global Forecast System (GFS) 384-Hour...
Abstract. Closing the terrestrial water budget is necessary to providing consistent estimates of components for understanding resources and changes over time. Given lack in-situ observations at anything but local scale, merging information from multiple data sources (e.g. observation, satellite remote sensing, land surface model reanalysis) through assimilation techniques that optimize estimation fluxes a promising approach. In this study, systematic method developed optimally combine...
Geospatial Knowledge Graphs (GeoKGs) have become integral to the growing field of Artificial Intelligence. Initiatives like U.S. National Science Foundation's Open Network program aim create an ecosystem nation-scale, cross-disciplinary GeoKGs that provide AI-ready geospatial data aligned with FAIR principles. However, building this infrastructure presents key challenges, including 1) managing large volumes data, 2) computational complexity discovering topological relations via SPARQL, and...
The integration of data along a common spatial component remains an obstacle in many problem spaces. One promising method for integrating such way is through the use common, underlying reference system, as Discrete Global Grid (e.g., S2 System), and pre-computing relations between features constituent components at resolution appropriate case. That is, by emphasizing notion cell, we can examine what predict contents its parent child cells, quickly get overview spatially co-located regions...
KnowWhereGraph is one of the largest fully publicly available geospatial knowledge graphs. It includes data from 30 layers on natural hazards (e.g., hurricanes, wildfires), climate variables air temperature, precipitation), soil properties, crop and land-cover types, demographics, human health, various place region identifiers, among other themes. These have been leveraged through graph by a variety applications to address challenges in food security agricultural supply chains;...
Abstract. Poorly monitored river flows in many regions of the world have been hindering our ability to accurately estimate global water usage as well budgets and variability cycle. In-situ gauging sites, a number satellite-based systems, make observations discharge throughout globe; however, these are often sparse due to, e.g., sampling frequencies sensors or lack reporting. Recently, efforts made develop methods integrate discrete gain better understanding underlying processes. This paper...
Disasters are often unpredictable and complex events, requiring humanitarian organizations to understand respond many different issues simultaneously immediately. Often the biggest challenge improving effectiveness of response is quickly finding right expert, with expertise concerning a specific disaster type/disaster geographic region. To assist in achieving such goal, this paper demonstrates knowledge graph-based search engine developed on top an expert graph. It accommodates three modes...