E. Geomorphology
The identification of the geological characteristics of the branches can be classified in the frozen PEDI plain, and the dissolution zone, the alluvial plains and valleys fill shallow water that is deemed to determine the potential section according to the reaction to groundwater. The interpretation methods of geomorphic units / relief (CGWB) and verification and resale air shooting made of CGWB annual report (CGWB) have been confirmed and verified in on-site conditions.
The following molding units were defined using an overview of cards, Lucknow, IRS1A images and Indian parent sheets, prepared by the center of the remote sensing application, and the following molding units were defined and the following formative units are defined and the following formative units are defined and the following typical plains (figure 5)
(a) Sand Bar
(b) Flood Plain
(c) Alluvium Plain
(d) Ravines
(e) Older – Meander Plain
(f) Ox-Bow-Lakes
Ground water prospects in this geomorphic units are tabulated below:
Geomorphic Units
|
Ground Water Prospects
|
Sand bars
|
Good to excellent
|
Flood plain
|
Good to very good
|
Ravines
|
Poor
|
Alluvium plain
|
Good to very good
|
Older meander Plain
|
Good to excellent
|
Oxbow lakes
|
Good to excellent
|
Table. 2 geomorphic results
F. Floodplain Management
Rapti flows under the monsoon or in the moist area of the central Ganges plain. It is the largest tributary of the Ghaghara River and an important part of the Ganges. The Rapti River basin is at latitude 26 ° 18'00 "N Longitude 28 ° 33'06 "N 81 ° 33'00" E83 ° 45'06 "and covers an area of 25,793 square kilometers. 44% (11,380 km2) is in Nepal, 58% (14,413 km2)) Is located at Uttar Pradesh.
The Rapti River flows through the Rukum, Salyan, Rolpa, Gurumi, Argahanchi, Dan and Bank regions of Nepal. Uttar Pradesh. It rises at Dregaunla, 3048 meters above sea level. Nepal's Sivalik Hills total km, 782 Participated in Gagar in Barhaj, Deoria region, eastern Uttar Pradesh, including 331 km in Nepal. The Rapti River has many tributaries. Those on the north or left bank are from the Wrinkle and Babal areas. To the south is the old riverbed.
Rapti The important branches of the left eye of are Bulkira Petit, Gongi, Cain and Rokin. Bakura, Ami, and Taraina are known as tributaries of 's right eye,
Geologically, the basin consists of two separate parts. Structurally, it is part of India's large Ganges Valley, with some contours of the Himalayan hills in the Siwarik region. .. Part of the Ganges valley in India is entirely composed of alluvial deposits composed of different proportions of sand, silt and clay.
In the geological age, these sediments correspond to two major divisions: Quaternary, Pleistocene, and present. Alluvium can be divided into two large groups. The old alluvium, known as the "Banger", is believed to be of mid-Pleistocene origin, while the new Hadder alluvium is younger and formed by river sedimentation. Recent dating results in the Gandakumegafan region show that the ancient Burkhi Rapti alluvium is 5000 years old and the Rapti is more than 500 years old.
The Rapti River basin can be divided into two basins:
(Central Groundwater Authority, 1984). (Table 3). As a result, the two sub watersheds can be divided into several watersheds. The catchment area of this watershed ranges from 42,000 ha (LB Ghaghara) to 145,000 ha (Ami).
Sub-catchment
|
Watershed number
|
Name of stream
|
Area (thousands of hectares, ha)
|
District covered
|
Lower Rapti (C) (682)
|
C1
|
LB-Ghaghara
|
42
|
Deoria, Saharsa
|
C2
|
Taraina
|
55
|
Gorakhpur
|
C3
|
Gaura
|
144
|
Gorakhpur, Deoria
|
C4
|
Lower Rapti
|
123
|
Gorakhpur, Basti
|
C5
|
Rohin
|
112
|
Gorakhpur
|
C6
|
Chillua
|
91
|
Gorakhpur
|
C7
|
Ami
|
145
|
Gorakhpur
|
Upper Rapti (D) (732)
|
D1
|
Main
|
63
|
Basti, Gorakhpur
|
D2
|
Rapti
|
84
|
Gonad, Basti, Bahraich
|
D3
|
Bhakla
|
93
|
Bahraich
|
D4
|
Ghonghi
|
103
|
Basti, Gorakhpur
|
D5
|
Burhi Rapti
|
88
|
Basti
|
D6
|
Bhainbar
|
92
|
Gonda, Basti
|
D7
|
Pera, Bahuwa
|
120
|
Gonad
|
D8
|
Kain, Gholia
|
90
|
Bahraich, Gonda
|
Table 3. Watershed divisions of Rapti River Basin
G. Hydrogelogoy
The district Gorakhpur is underlain with the aid of using quaternary alluvium introduced with the aid of using Ghaghra and Rapti river system. At contains especially sand of diverse grades, sandy clay, silt, clay with various quantity of kankar and gravels. The alluvial deposits are widely categorized below classes
(a) older
(b) more youthful alluvium.
The older alluvium deposits recognized as `Banger' or excessive land soils are because of denudation. The Banger may be in addition sub divided into 3 sub classes on the idea of percent of the sand content material viz Balua containing greater than 70% silica, Loam containing silica approximately 50% and Matiar containing much less than 40% silica.
The more youthful alluvium deposits recognized as 'Kachhar' occupy the marginal tract of Rapti and Ghaghra and different 0.33 order streams and includes sandy clay and sand alongside the river tract and quality silt withinside the mild sloping plains. Occurrence of floor water withinside the vicinity is managed with the aid of using Ghaghra and Rapti and their important tributaries. Fine to coarse grained sand, combined with gravel and kankar shape the foremost aquifer withinside the district. Ground water withinside the vicinity happens each below limited and water desk conditions. It happens withinside the area of saturation in the granular zones encountered under land surface. South and East of Rapti the formation are sandy and appropriate for production of shallow and deep tube wells.
Evapotranspiration data used in this study were calculated using the Hargreaves temperature model. This model was used in this research area due to the lack of sundial data. The Hargreaves temperature equation is one of the simplest and most accurate empirical equations used to estimate evapotranspiration (ETo) in mm/day. which is expressed as
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)
H. Geology
The lithology of the study area is considered to be one of the factors that control groundwater flow and existence. The arrangement of different rocks or lithological units and their interaction determines the total infiltration capacity of the area. Porosity and permeability of lithological units relate to the storage and transport capacity that supports the occurrence of groundwater and the occurrence of an area. Limestone and shale within the Uttar Pradesh Rock Group (figure 6)
I. Drainage Density
The length of the creek on a unit area of the region is defined as the drainage density (Horton radiator). It is a suitable tool for relief analysis in terms of groundwater potential. The order of the tributaries was carried out after the stroller stream. The development of the drainage network within an area is controlled by the draining rock formation and indirectly provides information about the infiltration rate. The drainage density (Dd) [L] is determined using Equation (2):
Dd =Li/A ………………………………………(2)
where Dd denotes the Drainage density and Li is the total length of drainage.Dd is the drainage density, which correlates significantly with groundwater recharge. In fact, a high Dd zone indicates a probable groundwater recharge zone.
The resulting map is the drainage density map, which is classified into five zones, viz. very high, high, medium, low, very low (Figure 7).
J. Slope Map
The slope is an important criterion for delimiting the potential ground water zone Directly affects infiltration and surface runoff. The low/near-level gradient exhibits high infiltration and low runoff, resulting in good groundwater recharge, while a moderate to steep gradient improves surface runoff. A slope map was created from the SRTM elevation data using the software ArcGIS
, which is divided into three classes: almost steep (0-1%), very gently sloping (1-3%) and gently sloping (3-5%) (figure 8)
K. Soil texture.
The soil is an important parameter for the identity of the area of possible groundwater occurrence. The study area consists of two soil types,
(Bk ,Be) i.e namely sandy, slit and clay soil.
The texture of the soil in the area. It is one of the main factors controlling surface runoff and stormwater intrusion. The sandy group soils have low runoff and high groundwater potential, while the clay soil group has high runoff and very low groundwater values. Sandy soil has a high leaching rate and Clay soil has the lowest leaching capacity. Most of the
study area (52.52%) was covered by Sandy soil with low to moderate infiltration rate. (figure 9)
L. Land Uses Land Cover
(LULC) Earth consumption for other processes affects the penetration drawings in this field. Like a research area, ERDAS imaginative visual interpolation methods are captured and controlled by the Satellite Image, and checks the fields. Mulk was determined as shown in Figure 10.
comprehensive classes, crushed, crushed, calculating, plants, open districts, industrial, drainage, lakes and roads. Most research areas are applied as grown (43.78%), agreement (24.04%), outdoor land (22.18%), road (2.21%) and lake (5.37%). Each subclass on earth uses the ground coating class assigned to various scales as to participate in groundwater infiltration. Within the subclass, lakes and watersheds are weighted high because they have less runoff. In comparison, settlements and roads have low weight due to high flow, and moderately good groundwater potential can be found in areas that cover plains and croplands with medium weight.
In the Saaty AHP, an elaborate criteria comparison of 'n' numbers (in this case LULC, Geomorphology, Geology, Slope, Lineament, Drainage Density, Groundwater Depth, Soil and Precipitation)
The forest and water are classified as excellent because the runoff water seeps away slowly and heavily due to the presence of trees and water. be available in sufficient quantity to cover the study area. Open scrub is classified as moderate as the surface is wavy. Sand stone is classified as Very Poor as there is no ability to retain water. (Table 4)
Table 4. LULC Details
Landuse/ Landcover
|
Rank
|
Area(Km2 )
|
Forest
|
Excellent
|
8.6
|
Vegetation (Agricultural)
|
Very Good
|
60.33
|
Water
|
Excellent
|
3.82
|
Settlement
|
Good
|
1.64
|
Scrub
|
Moderate
|
32.79
|
Loose Sand
|
Very Poor
|
39.4
|
Stony Wasteland |
Poor
|
8.80
|
Table 5. drainage density areas details
ZONES
|
AREAS(M)
|
Very High
|
0.000043-0.001482
|
High
|
0.000004-0.000154
|
Medium
|
0.000008-0.00528
|
Low,
|
0.000016-0.018907
|
Very Low
|
0.000069-0.000536
|