Simultaneous multi-event universal kriging methods for spatio-temporal data analysis and mapping

A computing system configured with executable instructions to process hydrometeorological data using one or more hydrological sensors, including:

• a processing unit for receiving input from the one or more hydrological sensors associated with a geospatial area, wherein the received input comprises two or more spatio-temporal data sets, and wherein each spatio-temporal data set is collected by sets of hydrological sensors that are at least partially different;
• a memory unit for storing a trend associated with the geospatial area associated with the one or more hydrological sensors, and
• executable code with instructions for:
• estimating one or more trend coefficients based on the received spatio-temporal data sets;
• predicting data points at locations intermediate to data points within the received spatio-temporal data sets;
• producing grids of data suitable for contouring based at least in part on the spatio-temporal data sets;
• and
• constructing a geospatial map based on the spatio-temporal data sets associated with the geospatial area.

A method for analyzing a groundwater level executable by a computing device configured to process hydrometeorological data, comprising:

• receiving a first input that includes at least one of a groundwater pressure, a pumping flow-rate, and a water level, from a first set of hydrological data-collection stations at a first point in time;
• receiving at least a second input that includes at least one of a groundwater pressure, a pumping flow-rate, and a water level, from a second set of hydrological data-collection stations at least at a second point in time, wherein the first input and the at least second input comprises a series of spatio-temporal data sets;
• processing the received first input and the at least second input along with a trend associated with a geospatial area using a multi-event universal kriging method configured to;
• estimate one or more trend coefficients based at least in part on the series of spatio-temporal data sets;
• predict data points at locations intermediate to data points within the received first input and the at least second input;
• produce grids of data suitable for contouring based at least in part on the hydrometeorological data;
• and
• construct a water level map that estimates an extent of hydraulic containment in a capture zone.