The tool is a database that processes information about experimental test results monitoring the infiltration capacity decrease in spreading infiltration basins due to clogging and climate related issues.
Theory
One of the main MAR branches are the surface spreading methods, where there is an added benefit of using the vadose zone as a filter and a media for organic and inorganic compounds removal, before the water reaches the aquifer (Laws etal, 2011). Since this concept is getting more relevance facing the effects of climate change, further investigation of the processes involving MAR has to performed in order to maximise its efficiency (Dillon, 2015). The hydraulic conductivity plays a key role, since is the main parameter that indicates the capacity of the site to let water flow through. A common source of water for spreading basins is the river water and its composition of suspended particles and biological content triggers the development of a layer on the top of the surface of the basin floor. This layer is mainly formed by fine colloids and bacterial growth within the pores of the soil. This generates an increase of the impedance of the recharge rate.
This platform tool sums up six different operational parameters and elaborates three decision taking instruments (table 1) to give an overview about the effect of these parameters on the infiltration capacity.
Table 1. List of operational parameters and decision taking instruments
| Operational parameters | Decision taking instruments |
| 1. Hydraulic loading rate (HLR) | 1. Infiltration capacity decrease diagram |
| 2. Hydraulic loading cycle (HLC) | 2. Infiltration capacity reduction bar |
| 3. Infiltration time | 3. Proportion of infiltration capacity phases |
| 4. Hydraulic conductivity | |
| 5. Climate conditions | |
| 6. Experimental scale |
Operational parameters
Hydraulic loading rate (m/a)
HLR defines the total volume of water that is pursued to be infiltrated. It is a function of the site-specific hydraulic capacity dependent on soil texture and bulk density mainly dependent on the availability of water and the hydraulic conductivity of the soil. It is given by the units of m/a (alternatively m³/m²), which represents the column of water that is recharged per square meter in one year. For example, a 300 m/a HLR in a basin of 100 m² will be equivalent to a daily recharge of 82.2 L per square meter.
Hydraulic loading cycle (x:y)
HLC is the ratio of infiltration period and followed drying period. A HLC of x:y represents a scenario of x unit of infiltration time against y unit of dry period time. For instance, a HLC of 1:3 is to be read as 1 unit of infiltration time and a dry period 3 times longer. When applying dry phases into the recharge basin schedule, the designed pumping rate is directly related to the length of the infiltration period. With greater dry periods the pumping rate will increase, since the given volume of water has to be infiltrated in a shorter period of time. An infiltration scenario with no dry period is categorized as “continuous” and the calculation of the pumping rate into the basin will be equivalent to the HLR multiplied by the basin area.
The HLC categories introduced in Tool 1 are as follows:
- Continuous
- 1:1
- 1:3
- 1:6
- 1:12
- 3:1
Infiltration time
Infiltration time indicates the length of each single infiltration cycle before the dry period and is expressed in hours (h). By coupling this time with the HLC, the length of the dry period can be inferred. The infiltration times for the continuous scenarios are categorized as: “Total time”.
Hydraulic conductivity (K)
The hydraulic conductivity represents the capacity of the soil media of the infiltration basin to let water go through its pores. Smaller pore space availability will increase the resistance for water to flow through. This leads consequently to a slower infiltration and a better filtration of organic and inorganic solids that might be suspended in the water source. The unit of the hydraulic conductivity is m/s.
Climate conditions
Since this database includes experiments performed in the field, they are submitted to seasonal climate variations depending on their location. Climate plays a role in the reduction of infiltration capacity of MAR basins. On one hand side solar radiation (W/m) stimulates the microorganism growth and in the other hand, viscosity of the fluid and microorganism activities varies with different temperatures (°C).
This component was categorized in cold (<10°C / <34 W/m), mild (10 to 20°C / 34 to 230 W/m) and warm (>20°C / >240 W/m). Those experiments done under laboratory conditions were set to the mild climate class.
Experimental scale
This operational parameter assesses which processes of clogging are influenced by the scale of the experiment, eg. climate or boundaries conditions. The database categorizes between (1) field, (2) lab 3D tank and (3) lab column experiments.
Decision taking instruments
Infiltration capacity decrease diagram
In the upper right section, the diagram of the relative decrease of the infiltration capacity of the selected scenarios is presented. In order to maintain all the scenarios in the same magnitude (mostly because of scale differences) the x axis does not represent infiltrated volume, but the specific volume. This is representing the relation of the amount of water infiltrated with the area of the infiltration basin.
This diagram allows the graphical evaluation on how steep is the infiltration capacity change between different scenarios.
Infiltration capacity reduction and proportion of infiltration capacity phases
In the bar charts in the lower left section the interpretation of the diagram discussed above is located.
In the upper graph the blue bar shows the maximum infiltration capacity reduction which is calculated from the minimum state of the infiltration capacity curve, whereas the yellowish bar indicates the reduction per volume of infiltrated fluid.
The graph in the bottom analyzes the steepness behavior of the infiltration capacity decrease, by identifying the volume to cause a breaking point in the inflexion of the curve. Lag phase was identified as the section of the curve where the infiltration capacity is kept above 90% from its initial condition. Lower values correspond to the deep phase section, where the infiltration capacity is decreased strongly. The deep phase volume is the one required until the curve meets a stable phase. Both, lag- and deep phase are expressed as volume of infiltrated water divided with the area of the infiltration basin.
EXAMPLE
Influence of the experiment scale in the infiltration dynamics:
Scenarios with a continuous scheme (no drying phase), a hydraulic loading rate of 300 m/a and a soil material with K = 7e-5 m/s have to be selected from the existing database filter. In order to have similar climate conditions activate the filter for mild climate. This should reduce the list to scenarios F-115, C-7 and T-7.
Activate all of them by clicking on the “+” sign in the right part. Immediately the infiltration capacity decline diagram is going to incorporate the selected scenarios. Here it can be observed already that the infiltration capacity is more affected in the field experiment, whereas the small scale units exhibit a low decrease of its infiltration capacity after the infiltration of about 12000 L/m².
A comparative analysis is done in the chart bar sector where the maximum reduction in experiment F-115 is quantified by 91% and C-7 by 7%. The 3D Tank exhibits a decrease of 73%. The relative infiltration capacity reduction to the specific infiltrated volume locates F-115 with a decrease of 18% in each m² of the basin per m³ of infiltrated water. C-7 and T-7 values are of 0.6 and 3.3 %*m²/m³, respectively.
The lag phase is only to be observed in the column experiment, where the infiltration capacity does not reduce more than 10%. The 3D Tank and the field experiment are showing both an immediate deep phase reduction, which is much faster in the field and therefore the volume it requires to get to a stable phase is much lower.
REFERENCES
- Dillon, P. (2005). Future management of aquifer recharge. Hydrogeology journal, 13(1), 313-316.
- Laws, B. V., Dickenson, E. R., Johnson, T. A., Snyder, S. A., & Drewes, J. E. (2011). Attenuation of contaminants of emerging concern during surface-spreading aquifer recharge. Science of the Total Environment, 409(6), 1087-1094.