Ongoing advancements in geospatial data collection has drastically increased the amount of hydrologic/environmental data available. Parallel development of Geographic Information Systems (GIS) and web-based data acquisition from big data repositories provides modelers with an unprecedented opportunity to integrate data collected from various sources (e.g., traditional hydrogeological field studies, remote sensing products, etc.) within a common spatial framework. Table 2 presents examples various types of big datasets related to hydrological modeling, including: Digital Elevation Models (DEIMS); climate forcing data (e.g., precipitation or air temperature); hydrography (lakes, streams, watersheds), and groundwater level and lithology records from massive water well datasets. 

Generally speaking, the various big datasets can be categorized as 1) remote sensing products (e.g., DEMs, hydrography, land use/land cover, or even GRACE land water storage and MODIS evapotranspiration estimates), which tend to provide seamless spatial information with excellent spatial coverage but varying degrees of quality; 2)  in-situ sensors and field instrumentation (monitoring wells, stream gages, etc.), which provide accurate historical and present-day observations needed for modeling and analysis but are scattered across distinct locations; and 3) high-density compilations of point-base information (water quality and water level measurements, borehole lithology, etc.), which often provide excellent spatial coverage but are noisy (i.e., they have significant sources of error and uncertainty that should be addressed); and 4) data layers of landscape characteristics (e.g., soil type and permeability maps, aquifer extents and landforms, etc.) derived from various sources of traditional or site-specific local data, which offer statewide/provincial, national or even global spatial coverage, but may be of provide little information for local studies because of low quality/resolution or incompleteness (spatial ‘gaps’ or clearly wrong estimates based on limited input data).

Problem: Use MAGNET to explore and experiment. For a specific watershed, or site determine what is the most important dataset that controls prevailing patterns of natural groundwater systems.

Table: Example big datasets useful for modeling watershed water systems. *MWs = monitoring wells, SW=surface water

Figure: The map shows the network of streams and rivers in the 48 contiguous states of the US. The largest, shown in pink, reveals basins for the Mississippi, Missouri, and Arkansas rivers. Other basins, including Pacific Northwest, Upper and Lower Colorado, and Great Lakes are shown. Source: https://imgur.com/gallery/N4cUA