Optimization of Rainwater Harvesting Sites using GIS

Ruhina Karani

1,∗

, Anant Joshi

2,†

, Miloni Joshi

2,†

, Sarmishta Velury

2,†

and Saumya Shah

2,†

Computers Department, Dwarkadas J. Sanghvi College of Engineering, University of Mumbai, Mumbai, India

Keywords:

Rainwater Harvesting, DEM, India, Drought.

Abstract:

Water scarcity is hitting new peaks every day and is exacerbated by the current rapid climatic change. Demand

for clean water in India is very high, especially for agriculture and consumption. One way to cater to these

needs is through rainwater harvesting. Through this paper, we propose a framework that optimizes the site

selection for reservoirs by intersecting various data points. Our framework uses a three-step approach to

combine stream networks, digital elevation, and soil quality to produce the most viable reservoir sites. Our

framework is easy to implement and highly scalable. For the purpose of this paper and a proof of concept, we

restrict our focus to the arid Beed district in the state of Maharashtra, India. Our approach provides consistent

results that are corroborated by the manual inferences that can be drawn from the data under consideration.

1 INTRODUCTION

India is a large and diverse country. Its exponen-

tial progress is tethered down by problems like water

scarcity and ﬂoods. Due to over-exploitation of water

resources, many of our freshwater sources are getting

depleted. Groundwater is not getting recharged. The

persistent water shortage issue and intense droughts

could be mitigated through large scale rainwater har-

vesting.

The aim of our project is to create an accurate, and

adaptable system which would provide better decision

making, and easier planning, when laying out any

plans for rainwater harvesting. Hydro-logical plan-

ning should take into account the amount of rainfall

an area receives, the groundwater and soil conditions,

land use and land cover and water requirement. Po-

tential sources for rainwater need to be identiﬁed. We

intend to use data from various sources, such as To-

pographical Maps, 3D terrain models, meteorologi-

cal data, administrative data, etc. This data will be

merged to simulate water conservation structures such

as reservoirs, canals, dams, etc.

The idealistic aim for the project is to make a sys-

tem efﬁcient enough to serve India with the objective

of careful use of water resources and optimization to

increase the water harvesting capacity. The frame-

work and methodology followed can be used univer-

sally for the purpose of demonstration. We have con-

∗

Principal Investigator.

†

Equal contribution.

centrated on the Beed and Nagpur district of Maha-

rashtra, which often suffers from droughts.

Figure 1: Map for Beed district.

Figure 2: Map for Nagpur district.

228

Karani, R., Joshi, A., Joshi, M., Velury, S. and Shah, S.

Optimization of Rainwater Harvesting Sites using GIS.

DOI: 10.5220/0007722302280233

In Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2019), pages 228-233

ISBN: 978-989-758-371-1

2 MOTIVATION

Clean, potable water is a requirement for all land-

based biological activity. However, in the past few

years, there is a decreasing amount of water avail-

able for humans to consume because of multitude of

factors such as unpredictable rainfall, droughts, over-

utilization of wells, inefﬁcient farming techniques,

etc.

Most of Maharashtra faces drought crisis during

the monsoon season. The drought in 2013 was con-

sidered as the region’s worst drought in 40 years. The

worst-hit areas in Maharashtra were Solapur, Parb-

hani, Sangli, Pune, Satara, Beed and Nashik. Beed,

a district in central Maharashtra, suffers with a seri-

ous drought crisis. In 2015, Beed (G Seetharaman,

2016) received less than 50 per cent of its average an-

nual rainfall of 670 mm. The previous year had only

been slightly better with 55.6 per cent of the average

rainfall. This has affected the groundwater level in

the district. Beed, along with Osmanabad and Latur,

is the most waterstarved in the Marathwada region

where the state government has declared droughtlike

conditions. According to a recent Press Trust of In-

dia report (Press Trust of India, 2016), there is only 5

per cent water in Marathwada’s dams. Beed collector

Ram says the government spent around Rs 80 crore in

2015-16 on water conservation projects in the district.

In 2018 (TNN, 2018), districts in the state have been

declared as drought-prone.

Hence, we introduce simulation based modelling

and optimization, to increase the efﬁciency, and water

harvesting capacity of these efforts. The system can

potentially play an important part in making appro-

priate decisions to lay out water-harvesting resources,

and thus increase the amount of water available to

people.

3 REVIEW OF LITERATURE

This paper (Jasrotia et al., 2009) suggests a study of

the water resources. The water balance study using

the Thornthwaite and Mather (TM) models with the

help of remote sensing and GIS found out the mois-

ture deﬁcit and moisture surplus for an entire water-

shed. It showed that the maximum annual runoff re-

sulted from the built-up areas/water body followed by

agricultural land, dense forest and minimum for the

barren land and open forest. GIS software had been

used for spatial analysis of various thematic layers

and integration to produce the ﬁnal runoff map. Af-

ter the runoff map was produced, it was found that

suitable sites for rainwater harvesting structures ac-

counted only for a fraction of the actual watershed

area whereas the rest of the area was unsuitable site

for rainwater harvesting.

This paper (Mahmoud and Alazba, 2014). says

that the very ﬁrst step in rainwater harvesting is to

trap the rainfall where it falls. To accomplish this, a

geographic information system (GIS ) based decision

support system (DSS) was implemented, which fo-

cused on developing a methodology to suggest poten-

tial sites for in-situ water harvesting (IWH) consid-

ering factors such as rainfall, slope, potential runoff

coefﬁcient (PRC), land cover/use, and soil texture.

Another research study made use of GIS and Re-

mote Sensing to delineate potential sites for rain-

water harvesting. This methodology (Kumar et al.,

2008). carried out their research in the districts of Ut-

tar Pradesh. They used thematic maps consisting of

land-use/land-cover, geomorphology as layers along

with geology and drainage integrated with the GIS

system. The maps were weighted by importance and

multiplied with the ranked features of each site. An

average score for each of the features was obtained

and integrated into the GIS system for inference.

Here, (Ziadat et al., 2012) applied a GIS approach

for identifying the suitability for rainwater harvesting

interventions in Jordan. They integrated biophysical

criteria such as slope, vegetation cover, soil texture,

and soil depth with socio-economic parameters such

as land owner and then modiﬁed the criteria. Each

criterion was assigned one of two ratings: best or sec-

ond best. These ratings provided more ﬂexibility for

determining the suitability of an intervention.

This paper (Naseef and Thomas, 2016) used the-

matic maps and soil cover to determine optimum lo-

cations for rainwater harvesting in the state of Ker-

ala near the Kechri river basin. Aster Digital Eleva-

tion Model (DEM), Rainfall data are needed for this

study. Daily rainfall data of all stations situated in and

near Kecheri river basin was obtained. The thematic

maps used in this study are Landuse map, Classiﬁed

slope map, Stream order map, Runoff potential map

and Soil permeability map.An overlay of these fea-

tures is obtained using an appropriate GIS system to

get the desired locations.

4 METHODOLOGY

The system consists of two major components, that

need to be related. The features of the terrain are pro-

cessed, along with perennial stream networks to de-

termine watershed regions, and their capacity. Data

about soil type and quality is factored in to ensure

that i) regions with less porous soil are used to reduce

Optimization of Rainwater Harvesting Sites using GIS

229

Figure 3: Overview of system architecture and processing.

seepage loss, and ii) less cropland is converted for

rainwater harvesting purposes. These features pro-

vide us a way to estimate the utility of each watershed

region as a potential site for rainwater harvesting.

Data about the terrain can be obtained in various

forms, but we chose a Raster Digital Elevation Model

(DEM), as it is universally obtainable for nearly all

regions, improving repeatability of the method. Hy-

drological conditioning of the DEM is performed to

reduce error in watershed delineation. Once the wa-

tersheds are delineated, the terrain is analyzed over

several metrics, to obtain an aggregate score for each

watershed. These scores are then used to recommend

the watersheds in which rainwater harvesting systems

should be constructed.

4.1 Hydrological Conditioning

The process of hydrological conditioning is per-

formed as follows:

1. A depressionless DEM is created by ﬁlling in

sinks.

2. Flow direction is computed for every raster cell.

3. Flow accumulation is computed for every raster

cell.

4. Points of intersection of the perennial stream net-

work are used as pour points.

4.2 Watershed Delineation

The outcomes of hydrological conditioning, namely

the depression-less DEM, the Flow accumulation

raster, and the pour points are used to delineate the

watersheds in the manner described in (Susan K. Jen-

son, 1988).

4.3 Metrics

Site suitability cannot be decided without taking the

method of rainwater harvesting into account. The

chief method of rainwater harvesting in rural India,

especially rural Maharashtra is through the creation of

small-scale mud reservoirs, or embankments around

one or more sides of a natural basin. These are known

as ‘bunds’. These bunds are inexpensive to build, re-

quire materials that are locally available, and are easy

to build. Therefore, we have devised the following

metrics taking the needs of bund-based rainwater har-

vesting into account.

The metrics we have chosen to use are explained

below.

4.3.1 Watershed Capacity

We normalize the capacity of the watershed per unit

area to remove bias towards large watersheds. This

capacity is calculated by estimating a given level of

precipitation over the area, and computing the water

retained at the surface, using the slope characteristics,

and inﬁltration potential of the soil. This met-

ric is computed over several levels of precipitation,

and a weighted average is computed as the ﬁnal score.

Capacity =

∑

i=1

∑

levels

j=1

retention

×in f iltration

slope

×precipitationlevel

4.3.2 Watershed Density

For rainwater harvesting systems, it is favourable if

the storage capacity of the watershed is concentrated

over a smaller area as compared to a larger one. This

would permit the pooling of resources over a smaller

area, reduce evaporative and ingress losses, and en-

able farmers to use check-dams on natural depres-

sions. This is computed by calculating the ratio of the

total surface capacity of the watershed to the surface

area it is spread over. This is done for multiple levels

GISTAM 2019 - 5th International Conference on Geographical Information Systems Theory, Applications and Management

230

of precipitation, and a weighted average is computed

for the ﬁnal score.

Density =

Capacity

Area

4.3.3 Soil Characteristics

Soil Characteristics such as permeability, as well as

regional crop suitability are taken into account. This

allows us to ensure that fertile cropland doesn’t get

destroyed for these purposes, as well as to reduce

ingress loss at the site.

The regions consist primarily of Calcisoils, Verti-

sols, and Kastanozems. Vertisols are moderately fer-

tile, clayey, and have a low porosity. They are suit-

able for agriculture if properly maintained. The re-

gions in question both have signiﬁcant agriculture in

these soil types. Calcisoils are mainly found in arid

reigons, as the ones we see here, and are less suit-

able for agriculture. They are highly porous soils, due

to their loose composition. Kastanozems are highly

fertile, and humus rich. They are the most suitable

for agriculture. Thus, for our purposes, Calcisoils are

most suitable for rainwater harvesting, despite their

porosity, as they are used for less agriculture. Verti-

sols are less suitable and are hence weighted lower.

Areas with Kastanozems are small and used heavily

for farming, so they are weighted the least. After

analysis, we chose the weights 0.6, 0.3, and 0.1 for

Calcisoils, Vertisols, and Kastanozems respectively.

4.4 Combination

The watershed metrics are combined by taking the

weighted average of the watershed metrics above.

Each soil type is a multiplier, computed on the basis

of its permeability, and crop-suitability. This allows

us to weight our overall suitability score by the prod-

uct of the watershed score with the soil multiplier.

5 IMPLEMENTATION

The project is implemented in ArcGIS, as it is the

industry-standard for GIS applications. Each pro-

cess detailed in 4 has a corresponding ArcGIS imple-

mentation. The DEM data is the CartoDEM, created

by the Indian Space Research Organization, obtained

through its Bhuvan platform. It has a resolution of

1 arc second, which is equivalent to approximately

10m. It was published in May 2015. Stream networks

were obtained through the HydroSHEDS Asia Stream

Networks. It has a resolution of 3 arc seconds ( 90m

at the equator). All datasets use the WGS 1984 spatial

reference system.

The soil data was obtained through the ISRIC

SoilGRIDS dataset. It is a global system for auto-

mated mapping of soil classes and properties at 250m

resolution.

5.1 Hydrological Conditioning

The hydrological conditioning steps were performed

using the corresponding ArcGIS tools, i.e. The sinks

were ﬁlled in using the Fill tool, the Flow direction

was computed using the Flow Direction tool, and ﬂow

accumulation using the Flow Accumulation tool. The

pour points were obtained by intersecting the vectors

of each individual stream, to obtain a set of intersec-

tion points, using the Intersect tool.

5.2 Watershed Boundaries

The watershed boundaries are computed using the

Watershed tool.

5.3 Watershed Capacity

This was computed by estimating basin volume, mi-

nus the percolation amount, obtained through the

runoff curve values. This gives an estimate of the true

capacity of a given basin to retain water. This pro-

cess is repeated multiple times to obtain a weighted

average score.

5.4 Wateshed Density

This is computed by obtaining the resultant surface

area of the waterlogged region, at varying water vol-

umes. This process was repeated at several levels, and

a weighted mean was taken.

5.5 Soil Characteristics

The soil data was obtained through SoilGRIDS, and

each type of soil was manually analysed. Each coefﬁ-

cient was calculated, and assigned to a particular type

of soil. Each watershed’s total coefﬁcient was calcu-

lated by weighting the areas of each soil type, with

their respective coefﬁcients, and dividing it by the to-

tal area of the watershed.

5.6 Final Score

The ﬁnal score is computed as mentioned in 4.4. The

suitability of the entire watershed is then visualized

based on the score by assigning them colors on a spec-

trum of blue to red. Blue means high suitability, while

red means low.

Optimization of Rainwater Harvesting Sites using GIS

231

Figure 4: Soil Coverage in the Nagpur District.

Figure 5: Soil Coverage in the Beed District.

Watershed density was given the highest weigh-

tage, as a watershed with large capacity over a large

area is not as useful for harvesting. However, too

small a capacity is also detrimental, hence this term

is also included in the score.

Since the region contains just a few soils, we

performed a manual study of the soils and assigned

weights according to their properties.

6 RESULTS AND DISCUSSION

Results generated for the Nagpur area using the de-

scribed framework, along with the legend have been

depicted in 6, 8 and 9. The results for Beed are de-

picted in 7. The generated results have been assigned

a colour scale to make it easy to understand the de-

gree of usability of the watersheds delineated. Here,

we see that the gradation from dark blue to red indi-

cate suitability from best to worst.

The suitability score has values in the possible

range of:

Figure 6: Site Legend.

Figure 7: Results obtained for the Beed district.

Figure 8: Best site for Rainwater Harvesting in Nagpur.

Figure 9: Worst site for Rainwater Harvesting in Nagpur.

The Marathwada region of India is a highly arid

region with vast undulating planes. As such, the wa-

tersheds generated are much larger than average. Ver-

tisols cover a vast majority of the area and have high

water retention. Rainwater received in this area is sig-

niﬁcantly lesser than the required amount, especially

for the largely agrarian occupations in the region.

By using permanent stream networks, we have

provided a way to leverage perennial stream water and

stream networks to ﬁnd suitable watersheds. By re-

lying entirely on satellite data, we have reduced the

GISTAM 2019 - 5th International Conference on Geographical Information Systems Theory, Applications and Management

232

reliance on human data collection that is usually re-

quired for such a vast undertaking. Moreover, the lo-

cation independent nature of the framework make it

applicable to any area of the world, as long as there is

satellite data available.

The results produced by our model have been able

to make fairly consistent inferences pertaining to the

construction of dams, canals at the requisite loca-

tions. Based on the satellite data found, the methodol-

ogy has provided decent visualizations of the possible

construction locations.

7 CONCLUSION

Our methodology can be used for any location and

will provide results irrespective of the district of in-

vestigation. The algorithm requires three layers for

reaching its conclusion - the DEM, hydrosheds and

soil grids for a location speciﬁed by its latitude and

longitude. Since the data is available for the whole

country, the implementation can be reproduced for

any location.

In terms of future scope, we could use this

methodology on different districts in different parts

of the country where the climate and soil conditions

would be considerably different from those in the

state of Maharashtra. Another improvement to the im-

plementation would be to use topography data avail-

able from the Government of India which would in-

crease the accuracy of the model, if used above the

Digital Elevation Model.

ACKNOWLEDGEMENTS

We would like to thank Dr. Narendra M. Shekokar,

Head of Department, Computers, and Dr. Hari Va-

sudevan, Principal, Dwarkadas J. Sanghvi College

of Engineering, for their steady support and guid-

ance. We would also like to thank the University

Grants Commission, for providing the “Minor Re-

search Grant”, without which this project would not

have been possible.

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