If you are working on a large scale i suggest you to consider a simple conceptual pressure / impact approach (driving forces, pressures, states, impacts and responses). In this frame and because you are working on non ponctual pollutions, you will have to to put in balance the potential pressures (ex quantities of nutriments excess,...) with the vulnerability of ground water ressources (ex importance of the vadose zone, type of soil,...)

Then try to compare you indicators with stats date (water quality,....)

If you are working on a large scale you could adopt a “DPSIR” conceptual approach (Driving forces – Pressure – State – Impact – Responses). In this frame, and because you are working on non punctual pollution you will have to define indicators of potential pollution (type of agricultural activity, nutriments excess quantities,...) and indicators for ground water vulnerability (thickness of the vadose zone, type of soil,...). Then, when possible, try to compared those indicators to state data (ground water quality,...) to estimate the robustness of the indicators

Hi Naveen

I worked on this topic in the Sahel. We used a simple hydrological model, GR2M, from the IRSTEA French Institute. It only has one reservoir and 2 parameters. The maximum water level of the reservoir is usually a fixed value, that you can access from the FAO soil map of the world for instance, or local data if you have some. The problem we had was that despite the rainfall decline since the drought that started in the 70's in West Africa, the runoff were increasing.

We supposed that it was due to an increase in land use, which induced a top soil degradation. So we decided to modify the model to generate a reservoir water level varying according to the land use changes. Indeed a forst cover, a cultivated area and a bare soil have very different runoff coefficients, and changing from one LU to another obviously modifies the runoff coefficient.

In the Sahel surface flows largely predominates, so that any surface cange induces a rapid response of the runoff coefficient.

Thus we used satellite data to generate maps of land cover at different dates, and we interpolated between these dates the changes in land cover at annual time step.

We then created an annually varying reservoir water level, that was related to the land use/cover change.

The GR2M model proved better functionning after this modification.

You can find this paper in Journal of Hydrology attached paper.

I hope it is useful.

Gil Mahé

IRD Montpellier

France

Article The impact of land use change on soil water holding capacity...

 Thanks Gil for an excellent paper, I wish to include a simple component of water table contribution to crop water use as well drained water going into the aquifer if excess irrigation applied

i am a crop modeller, wish to modify the existing INFOCROP, which we developed for shallow water table conditions. Sometimes when the application for a larger region, then we need to use the hydrological balance, where your paper is quite useful

regards