The NGMS Concept

Introduction

The NGMS is a scenario analysis tool to assess and visualize the impacts of abstractions on the groundwater resources and the surface water flows.

The system contains a set of default scenarios which allow the user to understand the interaction relations between the various components in the system. To analyse a change in artifical influences (i.e. human interventions), what-if scenarios can be conducted by modifying the input of the default runs. Modifications that are supported so far are changes in abstraction rates or teh addition or removal of abstractions.

Based on the scenario definition, the NGMS executes a sequence of data processing steps to preprocess the input to the groundwater model, to run the model and to postprocess the data. The output can be presented in time series graphs, accretion profiles or spatial plots. The system automatically computes differences for comparison of two scenarios (i.e the difference between two default scenarios, or the difference between a modified scenario and its default equivalent).

Since NGMS has been implemented using DelftFEWS-software, it relies upon some of the underlying FEWS-concepts. The conceptual merge of the NGMS outline with FEWS is discussed in a seperate page. A step-by-step configuration guide has been developed to assist the uploading of nwe models on the system.

Scenario types

Within her working and legislative procedures, the Environment Agency distinguishes a few types of scenarios. These types are also used within NGMS. The so-called 'default' scenarios are the scenarios/model runs that are pre-defined and approved by the Environment Agency. The so-called 'modified scenarios' are the what-ifs where input has been manuipulated via the NGMS.

Default scenarios

• Calibrated Historic - ref(Historic)
  This default scenario/run refers to the pre-defined model run which has been calibrated for a historic period, using historic abstractions data.

• Naturalized - ref(Naturalized)
  This default scenario, alias zero flow, refers to the pre-defined model run where the Calibrated Historic run has been modified by switching off all abstractions.

• Recent Actual - ref(RecentActual)
  This default scenario refers to the pre-defined model run where the Calibrated Historic run has been modified by assigning the recent actual abstraction rates to the abstraction points over the entire (historic) simulation period.

• Fully Licenced - ref(FullyLicenced)
  This default scenario refers to the pre-defined model run where the Calibrated Historic run has been modified by assigning the fully licenced abstraction rates to the abstraction points over the entire historic period.

• Long Term Average - ref(LongTermAverage)
  This default scenario refers to the pre-defined model run which utilizes a long term average climate data sets instead of historic data. Typically, these runs start at a recent moment in time.

Modified scenarios

• Modified Recent Actual - whatif(RecentActual)
  This what-if scenario refers to a model run where the model input of the default Recent Actual scenario has been modified by the NGMS. Model states (initial heads) are derived from the default Recent Actual scenario. The following model input can be modified:
  • existing GW-abstractions
  • adding new GW-abstractions
• Modified Fully Licenced - whatif(FullyLicenced)
  This what-if scenario refers to a model run where the model input of the default Fully Licenced scenario has been modified by the NGMS. Model states (initial heads) are derived from the default Fully Licenced scenario. The following model input can be modified:
  • existing GW-abstractions
  • adding new GW-abstractions

• Modified Long Term Average - whatif(LongTermAverage)
  This what-if scenario refers to a model run where the model input of the default long Term Average scenario has been modified by the NGMS. Model states (initial heads) are similar to the initial state in the default Long Term Average scenario. The following model input can be modified:
  • selection of other recharge scenarios (fraction of Long Term Average input)
  • existing GW-abstractions
  • adding new GW-abstractions

Data processing steps

For each of the abovementioned scenarios, the following data processing steps are typically conducted:

• (adapt input files)
• run Modflow
• import Modflow results
• derived tributary inflows
• interpolate regular grids to points, sparse grids and longitudinal profiles
• compare runs