Keywords

1 Introduction

In the Developing countries, about 90% of freshwater is used for agriculture purposes (Shiklomanov 2000; Perea et al. 2016) and about 70% of people rely on agriculture (Meena and Bisht 2017). In semi-arid regions it causes extra burden on natural storage units (Krol and Bronstert 2007; Yadav and Lal 2018). As consequence of this practice further water deficit, soil moisture deficit, etc. are becoming very usual (Shao et al. 2009; Vero et al. 2014; Zandalinas et al. 2016; Mohanty et al. 2017). Some areas, especially Indian subcontinent suffer from the skewed nature of annual rainfall distribution and evapotranspiration. Thus, growing need but increasing scarcity of such precious and irreplaceable resources is, in fact, an impending concern to them (Wada et al. 2012; Rohde et al. 2015). As the largest consumer of fresh water and groundwater in the world, India withdraws 250 km3 per year (Jha 2013). This amount considers the query how to be managed such growing need for water for agriculture and other sectors. Then how could drinking water be secured for the future? To understand the principal source of water, it is highly essential to consider the rainfall regime. Spatial skewness of rainfall distribution, loss of water through evapotranspiration, runoff, etc. are also essential while dealing with water balance, surplus, or deficit of water over space. Without having any major project for rainwater harvesting, a major portion of rainfall during monsoon months (June to October) is wasted. If some parts of water flow could be arrested seasonal rainfall deficit in non-monsoon seasons to some extent could be managed (Surendran et al. 2015; Mooley and Parthasarathy 1984; Yaduvanshi and Ranade 2017). Moreover, seasonal soil moisture drought is withstanding against the multi-cropping state of many parts of the country (Thober et al. 2015; Zhang et al. 2017) and as a consequence of this either people compel to grow some inferior crops or remain seasonally fallow. In India, large semi-arid tracts including parts of Central India, Western India, and Southern India fall under this tract where this problem is very prominent (Mishra et al. 2014; Sathyanadh et al. 2016). In a word, the scientific study of water balance is highly necessary for effective decision-making of water uses in progressive sectors (Khalaf and Donoghue 2012). For the hydrological cycle and water management, the replenishment rate of groundwater resources plays a key component. Seasonal or perennial water balance deficit may enhance land surface temperature (LST). Assuming this fact the present work started an investigation on the specific relation between water balance deficit and positive temperature anomaly on spatio-seasonal scale. A good number of works highlighted change in land surface temperature in relation to shift of land use/land cover (LU/LC) (Pal and Ziaul 2017; Ziaul and Pal 2016; Jafari and Hasheminasab 2017; Metz et al. 2017) highlighted increase of urban built-up land is caused for enhancing LST. González et al. (2015) and Deo and Şahin (2017) documented that abolishing water bodies, shallowing water bodies, replacing water bodies with other land uses are responsible for rising temperatures. Pal and Ziaul (2017) reported that canopy density is negatively related to LST. But the present interest of study, i.e., role of the water balance on LST is almost unexplored.

In some recent research, remote sensing (RS) and geographical information system (GIS) has contributed a major role in providing information for water resource studies and LST. Some of the eminent scholars successfully explored water balance status, and LST pattern over space using these RS and GIS techniques. Soil moisture balance study by Crosbie et al. (2015), González‐Rojí et al. (2018), Hu et al. (2015), water balance study by Bastiaanssen et al. (2005), Hassan-Esfahani et al. (2015), Saiter et al. (2016) are very well known. Spatio-temporal temperature extraction from multi-temporal satellite images is well studied by Lee et al. (2012), Siu and Hart (2013), Jiang and Weng (2017), and Yao et al. (2018) for different urban sectors. But linkages between water balance and LST are still to be investigated. Therefore, the present work focused on calculating water balance by incorporating frequently used parameters like rainfall, evapotranspiration, and runoff on spatio-seasonal scale in the Chandrabhaga river basin of Eastern India and tried to find out the influence of the surplus or deficit water balance on LST.

The Chandrabhaga river basin (area: 119.34 km2) is located over the Chattanagpur plateau fringe of Rarh tract of Jharkhand and West Bengal (Bagchi and Mukerjee 1983). Geologically 60% of the basin area lies in the upper catchment which is composed of granitic-gneissic rock of Pleistocene age (50 lakh years old) overlain by coarse-grain lateritic soil and 30% of the area at the lower catchment is made with newer alluvium of the Holocene period (Fig. 14.1) (GSI 1985). Coarse-grain lateritic soil with high ferrous and silica content contents insists high range of temperature. Most part of the basin area is dominated by agricultural land with poor qualities of soil fertility and soil moisture (during pre-monsoon 8–11%). The climate of this region is characterized by sub-tropical monsoon with seasonal wet and dry spells of rainfall and cold and hot spells of temperature. The entire year is subdivided into four seasons viz. (1) Winter season (January and February) with low temperature, low humidity level devoid of rain, (2) Pre-monsoon season (March to May) with little rain and high temperature and evaporation, (3) Monsoon season (June to September) with maximum (about 82% of total rain) rain and high temperature and (4) Post-monsoon season (October to November). Average annual rainfall of this basin as gauged by Suri meteorological station is 1444.432 mm. High degree of seasonality of rainfall is reflected by 82% rainfall during the months of June to September (monsoon period).

Fig. 14.1
figure 1

Chandrabhaga river basin showing the geological composition

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2 Materials and Methods

LANDSAT OLI/TIRS data (path/row: 139/43; spatial resolution 30 m) for 2016 have been obtained from the US Geological Survey (USGS) Global Visualization Viewer and used for deriving land surface temperature (LST), land use/cover (LuLc), evapotranspiration (ET). Monthly spatial rainfall data is extracted from TRMM (Tropical Rainfall Measuring Mission). Although on global scale, the spatial resolution (0.25°) of such multispectral data is considerably high, but for the study of the small-scale hydrological unit, such resolution is still to be considered as coarse (Immerzeel et al. 2009). Duan and Bastiaanssen (2013), Tong et al. (2014) suggested that for downscaling the study, close spacing point data can be created over the study area and inverse distance weighting (IDW) can be adopted for upgrading the spatial scale. Soil texture map of the basin is prepared from the map provided by National Bureau of Soil Survey and Land Use Planning department (India).

2.1 Method for Water Balance Calculation

Simple water balance equation (14.1) can be used for predicting regional and seasonal water deficit and surplus (Gokmen et al. 2013; Crosbie et al. 2015). Precipitation, runoff, evaporation, and recharge components are used for this purpose.

$$P = Q + {\text{ET}}_{\text{A}} + R_{{\text{GW}}}$$
(14.1)

where, P = precipitation; Q = runoff; ETA = Actual Evapotranspiration; RGW = Groundwater recharge.

2.2 Method for Surface Runoff Estimation

The Soil Conservation Service-Curve Number (SCS-CN) method (USDA) is used for runoff calculation (Eq. 14.2).

$$Q = \frac{(P - I_a )^2 }{{P - I_a + S}}$$
(14.2)

where, Q is actual surface runoff in mm, P is rainfall in mm, Ia is 0.4S/0.3S/0.2S/0.1S (season and climatic region-specific) initial abstraction (mm) or losses of water before runoff begins by soil and vegetation (such as infiltration, or rainfall interception by vegetation), 0.3S is usually used for wet, 0.1S is used dry seasons, S is the potential maximum retention. S can be calculated using Eq. 14.3.

$$S = \frac{25,400}{{{\text{CN}}}} - 254$$
(14.3)

Some effective parameters for estimating CN for different spatial units are Land use land cover (LULC), hydrological soil group (HSG), and antecedent moisture condition (AMC). LULC map is prepared from multispectral Landsat OLI image following supervised classification (Maximum Likelihood) technique. Accuracy assessment of the classified image is done based on 231 google reference sites and 131 ground base references. Calculated Kappa coefficient of the classified image is 0.93. Arc-CN-Runoff extension tool of ArcGIS is used for precise quantification of runoff. Land use/land cover thematic layer and polygon of soil groups have been used in ARC-CN runoff.

Relative error (RE) (Eq. 14.4) and Nash–Sutcliffe efficiency (NSE) (Eq. 14.5) (Nash and Sutcliffe 1970), are widely used for evaluating the performance of runoff model (Moriasi et al. 2007). This techniqueis used here for testing the model.

$${\text{RE}} = \frac{{\left( {Q_i^{{\text{cal}}} - Q_i^{{\text{obs}}} } \right)}}{{\left( {Q_i^{{\text{obs}}} } \right)}} \times 100\%$$
(14.4)
$${\text{NSE}} = 1 - \frac{{\sum_{i = 1}^n {(Q_i^{{\text{cal}}} - Q_i^{{\text{obs}}} )^2 } }}{{\sum_{i = 1}^n {(Q_i^{{\text{cal}}} - Q_{{\text{mean}}}^{{\text{obs}}} )^2 } }}$$
(14.5)

where, \(Q_i^{{\text{obs}}}\) = runoff of the ith observation, \(Q_i^{{\text{cal}}}\) = calculated runoff of the same observation, \(Q_{{\text{mean}}}^{{\text{obs}}}\) = mean observed runoff.

RE value nearer to 0 specifies high optimality of the model performances. NSE ranges between −∞ and 1, where NSE = 1 is the optimal result. Values of NSE between 0 and1 are usually assumed as acceptable levels of performance, whereas the value of NSE < 0 points out that the mean observed value is a better predictor than the simulated value, and therefore, is treated as unacceptable (Moriasi et al. 2007). Khatun and Pal (2016) used these testing techniques for assessing the performance level of the runoff model in a similar environment. Actual monthly runoff data is measured at the confluence of Chandrabhaga river at Kaspai for validating models.

2.3 Method for Evapotranspiration Estimation

Remote sensing (RS) based Surface Energy Balance Algorithm for Land (SEBAL) is fundamentally used for estimating ETA. SEBAL utilizes multispectral RS data in corroboration with meteorological data for estimating instantaneous and daily surface energy balance (Eq. 14.6) components (Oliveira et al. 2014). This mechanism produces pixel-to-pixel energy balance information considering the latent heat flux (LE expressed as W m−2). The detail study was made by Bastiaanssen et al. (1998), Silva et al. (2015), and Mahmoud and Alazba (2016).

$${\text{LE}} = R_n - G - H$$
(14.6)

where, Rn = net radiation (W m−2), G = heat flow in the soil (W m−2), and H = sensible heat flux (W m−2).

The surface albedo (αs) is worked out from MODIS reflectance data following Trezza et al. (2013) (Eq. 14.7) and the surface emissivity (εo) (Eq. 14.8) is articulated based on the LAI (Leaf Area Index) following Allenet al. (2007).

$$\begin{aligned} \alpha_S& = 0.215_{r1} + 0.266_{r2} + 0.242_{r3} + 0.129_{r4} + 0.112_{r6}\\ &\quad + 0.036_{r7}\end{aligned}$$
(14.7)
$$\varepsilon_o = 0.95 + 0.01{\text{LAI}}$$
(14.8)

where, r1, r2, r3, r4, r6, and r7 represent the reflectance spectral bands of the MODIS data.

The LE conversion is calculated from the evaporative fraction (EF), following Bastiaanssen et al. (1998) as presented in Eqs. (14.9) and (14.10). This function supposes that the EF is steady all through the day, but always it may not be as exhibited by Van Niel et al. (2011) and Van Niel et al. (2012). Ruhoff et al. (2012) rightly portrayed that the instant ETArate can be acquired as a diurnal average with anticipated inconsistency over outsized scales.

$${\text{EF}} = \frac{{{\text{LE}}}}{R_n - G}$$
(14.9)
$${\text{ET}}_{{\text{AD}}} = 0.035{\text{EFR}}_{n24{\text{h}}}$$
(14.10)

where, Rn24h = net radiation of 24 h calculated from daily solar radiation, surface albedo, and atmospheric transmissivity data, as recommended by Bastiaanssen et al. (1998). Sequentially, the daily actual evapotranspiration is combined along with monthly proportion to the potential evapotranspiration (ETP), based on the postulation so as to the share between the ETAD and ETP remains equivalent right through the month (Morse et al. 2000) (Eq. 14.11).

$${\text{ET}}_{{\text{AM}}} = \frac{{{\text{ET}}_{{\text{AD}}} }}{{{\text{ET}}_{\text{P}} }} \times {\text{ET}}_{{\text{PM}}}$$
(14.11)

where ETAM = monthly actual evapotranspiration and ETPM = monthly cumulative potential evapotranspiration.

For validating ET data derived from satellite data, the empirical formulation can be deployed. Donohue et al. (2010) established that Penman’s (1948) equation yields best-fitted result while calculating ETP using meteorological data. But based on the ground data available here Blaney–Cridle (1945) method is adopted.

$${\text{ET}}_{{\text{BC}}} = P(0.46T + 8.13)$$
(14.12)

where ETBC potential evapotranspiration is in mm/day for a period of consideration. Τ = mean daily temperature in °C for a period considered = (Tmax + Tmin)/2; Ρ = mean daily percentage of total annual daytime hours depending on month and latitude.

2.4 Method for Estimation of Recharge

Estimation of groundwater recharge is done from the water balance equation. Positive water balance deducing ET and surface runoff from rainfall can be considered as recharge. Deficit water balance indicates the excess use of soil moisture and soil moisture drought. Groundwater table fluctuation data is one of the sensitive indicators of groundwater recharge. It is assumed that commencing rise of the groundwater table or piezometric surface after rainfall signifies groundwater recharge. So, a simple comparison of monthly estimated recharge from water balance equation and monitoring of the degree and direction of groundwater level fluctuation may help to understand the accuracy level of calculation. Some empirical equations formulated by Chaturvedi (1973), Kumar, and Seetapati (2002) can also be used for calculating recharge and validating the water balance-based recharge data. Recharge volume calculated by the Central Ground Water Board (CGWB) is also taken into consideration for validating the same.

$$R = 2.0(p - 15)^{0.4}$$
(14.13)
$$R = 1.2(p - 13)^{0.5}$$
(14.14)

where R = groundwater recharge from rainfall during monsoon; p = mean rainfall in monsoon (inch).

2.5 Method for Land Surface Temperature Assessment

2.5.1 Conversion of the Digital Number (DN) to Spectral Radiance (Lλ)

Every object emits thermal electromagnetic energy as its temperature is above absolute zero (K). Following this principle, the signals received by the thermal sensors (ETM+) can be converted to at-sensor radiance. The spectral radiance (Lλ) is calculated using Eq. (14.15) (Landsat Project Science Office 2002).

$$L\lambda = {\text{``gain''}} \ast {\text{QCAL}} + {\text{``offset''}}$$
(14.15)

where “gain” is the slope of the radiance/DN conversion function; DN is the digital number of a given pixel; bias is the intercept of the radiance/DN conversion function. This is also given as:

$$\begin{aligned} L\lambda & = L_{MIN} \lambda + [\{ \left( {L_{{\text{MAX}}} \lambda --L_{{\text{MIN}}} \lambda } \right)/\left( {{\text{QCAL}}_{{\text{MAX}}} - {\text{QCAL}}_{{\text{MIN}}} } \right)\} \\ & \quad \quad *{\text{QCAL}}] \\ \end{aligned}$$
(14.16)

where, QCALMIN = 0, QCALMAX = 255 and QCAL = Digital Number (DN of each pixel).

The LMINλ and LMAXλ are the spectral radiances for band 6 at digital numbers 0 and 255, respectively. These compute to 3.2 W m−2 sr and 12.65 W m−2 sr, respectively.

Substitution of the respective values in Eq. (14.16) gives a simpler Eq. (14.17).

$$L\lambda = \left( {0.037059*{\text{DN}}} \right) + 3.2$$
(14.17)

2.5.2 Conversion of Spectral Radiance (Lλ) to At-Satellite Brightness Temperatures (TB)

Corrections for emissivity (ε) have been applied to the radiant temperatures according to the nature of the land cover. In general, vegetated areas have given a value of 0.95 and non-vegetated areas 0.92 (Nichol 1994). The emissivity corrected surface temperature has been computed following Artis and Carnahan (1982).

$$T_B = \frac{K_2 }{{\ln \left( {\frac{K_1 }{{L\lambda }} + 1} \right)}}$$
(14.18)

where, TB = At-satellite brightness temperature (K), Lλ = Spectral Radiance in W m−2 sr−1 μm−1.

K1 and K2 = K2 and K1 are two pre-launch calibration constants. (For the Landsat 7 ETM + 6.2 band, these compute to 1282.71 K and 666.09 W m−2 sr−1 μm, respectively.)

2.5.3 Land Surface Temperature (LST)

The obtained temperature values above are referenced to a black body. Therefore, corrections for spectral emissivity (ε) become necessary. These can be done according to the nature of land cover (Snyder et al. 1998) or by deriving corresponding emissivity values from the NDVI values for each pixel. The emissivity corrected land surface temperatures (St) have been computed following Artis and Carnahan (1982).

$${\text{LST}} = T_B /\left[ {1 + \{ \left( {\lambda *TB/\rho } \right)*\ln \varepsilon \} } \right]$$
(14.19)

where, LST = Land Surface Temperature (LST) in Kelvin, λ = wavelength of emitted radiance in meters (for which the peak response and the average of the limiting wavelengths (λ = 11.5 μm) (Markham and Barker 1985) is used, ρ = h*c/σ (1.438 × 10−2 m K), σ = Boltzmann constant (1.38 × 10−23 J/K), h = Planck’s constant (6.626 × 10−34 J s), and c = velocity of light (2.998 × 108 m/s) and ε = emissivity (ranges between 0.97 to 0.99). For calculating emissivity of land surface Eq. (14.20) is used.

$${\text{Land surface emissivity}}\;(\varepsilon ) = 0.004*P_{\text{v}} + 0.986$$
(14.20)

where Pv is the proportion of vegetation which can be calculated following Eq. (14.21).

$$P_v = \left( {\frac{{{\text{NDVI}}_{j_r } - {\text{NDVI}}_{\min } }}{{{\text{NDVI}}_{\max } - {\text{NDVI}}_{\min } }}} \right)^2$$
(14.21)

For convenience, the above-derived LSTs’ unit is converted to degree Celsius considering the relation between Kelvin and degree Celsius (0 °C = 273.15 K). Degree Celsius data is rather conventional for interpreting the temperature intensity.

For assessing the influences of deficit or surplus water balance patterns on LST, spatial correlation and regression were made.

3 Results and Analysis

3.1 Rainfall Analysis

Figure 14.2a–l represents the spatial pattern of rainfall in different months of 2016. The annual precipitation data ranges from 1965.06 to 1991.81 mm with insignificant spatial variation in 2016. In the upper and middle catchments during monsoon times the concentration of rainfall is high. Highest rainfall is recorded in monsoon months specifically in July, August, and September. Location of low-pressure axis orients the distribution of rainfall (Fig. 14.2g–i). More than 80% of rainfall is recorded during monsoon months and other months specifically winter and summer months suffer from rainfall scarcity (Table 14.1).

Fig. 14.2
figure 2

The spatial pattern of rainfall in different months of 2016

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Table 14.1 Average, minimum, and maximum monthly rainfall of Chandrabhaga river basin with descriptive statistics
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For validating TRMM data, rain gauge data collected from Suri meteorological station is collected and compared. Result states that there is an identical pattern of average rainfall almost in all months (Fig. 14.3a). Only 0.23–21.65% departure of TRMM data from gauge data is recorded and based on this TRMM data can be accepted as representative. Yong et al. (2010), Melo et al. (2015), Coelho et al. (2017) have also applied similar approach for validating TRMM data in the reference data-scarce state.

Fig. 14.3
figure 3

a Comparision of monthly average rainfall data collected from TRMM data and gauge data; b regression pattern between monthly average observed and calculated ET

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3.2 Runoff Analysis

Figure 14.4a–l represents the spatial sharing of surface runoff in different months of 2016. The maximum runoff volume is recorded at 332.37 mm and 297.11 mm in the month of August and September, respectively (Fig. 14.4h and i) and this high runoff is triggered by enhanced amount of rainfall in that period. Annual average runoff volume for this study area is 51.77 mm. which is 35.227% of average annual rainfall. Out of total annual average runoff, pre-monsoon, monsoon, post-monsoon and winter seasons, respectively carry 6.20%, 69.30%, 22.75%, and 1.75% to total runoff. This information is quite natural in parity with amount of rainfall that happens during these seasons. Spatially this is usually high in the bare lateritic parts of the basin. In few vegetated patches, high runoff is estimated and it is because of very sparse presence of vegetation (Table 14.2).

Fig. 14.4
figure 4

Monthly spatial pattern of estimated surface runoff in Chandrabhaga river basin

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Table 14.2 Average, minimum, and maximum monthly surface runoff of Chandrabhaga river basin with descriptive statistics
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For testing, the runoff models of different months’ Relative error (RE) and Nash–Sutcliffe efficiency (NSE) indices have been used. Result states that RE value ranges from 0.96 to 27% since January to December indicating very less departure of rainfall between estimated and observed runoff. This information validates the spatial runoff models. Average annual NSE value is 0.782 which is near 1 and therefore runoff model can be treated as responsive.

3.3 Evapotranspiration Analysis

Figure 14.5a–l illustrates the monthly spatial pattern of evapotranspiration (ET). Annual evapotranspiration state varies from 960.24 to 1074.15 mm/year spatially. In the lower part of the basin, ET is excessively high in monsoon months and quite less in the middle and upper part of the basin. Significant seasonal difference in ET is recorded. Highest monthly ET is found in the month of March (138.66 mm) whereas lowest ET (43.73 mm) in the month of December (Table 14.3). Degree of temperature is extremely high during pre-monsoon season, therefore, potential ET is high but actual ET as recorded is not expectedly high. It is because of scarce availability of surface water and soil moisture in this time conditioned by scanty rainfall or no rainfall. In monsoon time, the intensity of temperature is comparatively lower (25–33 °C) than in the pre-monsoon season but due to greater availability of surface water and soil moisture it registers quite greater ET.

Fig. 14.5
figure 5

Monthly spatial pattern of estimated ET in Chandrabhaga river basin

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Table 14.3 Average, minimum, and maximum monthly ET of Chandrabhaga river basin with descriptive statistics
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Estimated monthly evapotranspiration based on SEBAL method shows a distinct pixel-wise variation in the pattern of ET. The variation of ET for different land covers shows the hourly ET ranged from 0 at bareland and rock outcrops to 0.504 mm per hour in forest areas and the daily ET varies from 0.0 to 12.09 mm per day at the same land cover classes. The mainland covers controlling the ET characteristics is the vegetated land which scatteredly covers about 20.38% of the study area. The least range of variation in the hourly ET values appears in the bare land area indicating absence of vegetation cover. The results show a clear relation between land use/land cover and solar radiation parameters and impact of vegetation cover on the ET values in pixel domain.

Figure 14.3b represents the scatter plot of atmospheric ET and calculated ET for different months of 2016. This regression graph is plotted for showing validity of calculated ET models. Monthly ET data was collected from Suri gauge station for this purpose. Strong coefficient of determination (R2) value (0.8561) and correlation coefficient (0.785) establish the validity of ET models.

3.4 Groundwater Recharge/Deficit

Using water balance method, estimation of monthly groundwater recharge for the year 2016 was done. Average annual recharge is 356.386 mm with significant seasonal and intra-seasonal monthly variation. Highest estimated recharge is recorded in the months of July, August, and September (Table 14.4), and these amounts are 44.36, 41.38, and 50.89% of the rainfall, respectively, as extracted from TRMM data. These recharges are the principle source of groundwater in this region. Negative water balance is recorded in winter and summer months indicating seasonal drought. Seasonal soil moisture deficit disrupts agricultural activities in these seasons. Excessive groundwater withdrawal for agriculture and other domestic and commercial uses makes drought states more prominent. Water deficit state is very explicit in the upper and middle catchments of the basin (Fig. 14.6b–d, k, l). Coarser soils with very little moisture retaining capacity, and excess ET are responsible for such stress state of moisture deficit. Poor soil moisture (<7%) in this area also supports this state. Groundwater recharge and rise of water table are well explained by recorded groundwater level data collected from Central Ground Water Board and State Water Investigation Directorate. Groundwater level during monsoon months is 5 m below ground surface.

Table 14.4 Average, minimum, and maximum monthly water balance (surplus or deficit states) of Chandrabhaga river basin with descriptive statistics
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Fig. 14.6
figure 6

Water deficit and surplus states calculated from water balance equation in different months in 2016

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3.5 Temporal Pattern of Water Balance

Finally, from all these models of water balance components, monthly water balance equations are derived (Table 14.5). January, February, March, April, May, November, and December are appeared as water deficit (negative water balance) months and rest five months are emerged as water surplus (positive water balance) months. Annual water balance states that 15.47% of area falls under water deficit and 84.53% underwater surplus. Calculation of net water balance would result in positive water balance. But, seasonal water deficit cannot be overlooked. Seasonal water deficit not only withstands against agricultural productivity in the densely populated and demanding areas but it can aggravate the temperature condition of the deficit region. Prominent soil cracking, lowering of groundwater level, drying out of ponds and wetlands are some of the evidences for such seasonal water deficit conditions.

Table 14.5 Mean water balance component values and water balance equation; value within parenthesis indicates the ratio between the hydrological component and ppt
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3.6 Land Surface Temperature

The spatial distribution of LST in different months of 2016 is shown in Fig. 14.7. In winter season, LST ranges from 19.59 to 23.85 °C (mean 22.41 °C), pre-monsoon season temperature ranges from 23.97 to 41.43 °C (mean 35.83 °C) and in monsoon season temperature is confined within 19.74–33.23 °C (mean 26.79 °C) (Table 14.6). High temperature is registered in the upper part of the basin where lateritic bare soil predominates. Temperature regime is not only associated with incoming solar radiation but also strongly linked with land use/cover (Pal and Ziaul 2017; Ziaul and Pal 2016; Fu and Weng 2016; Feng et al. 2014). Green spaces and adjacency of water bodies lead to low-temperature state but bare soil portion of the study area recorded high temperature.

Fig. 14.7
figure 7

a–l Monthly spatial LST, 2016 in Chandrabhaga river basin

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Table 14.6 Average, minimum, and maximum monthly Surface temperature of Chandrabhaga river basin with descriptive statistics
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3.7 Spatial Association Between LST and Water Deficit

In this section, it is tried to investigate is water deficit area of the study area sensitive to high temperature and reverse in the surplus area. Pixel-based spatial correlation states that although the relation is not so strong but such relation prevails thereon. Correlation coefficient between water deficit intensity and degree of LST is −0.2366 and this value is also significant at 0.05 level of confidence. Water deficit area also experiences strong ET as indicated by significant r value (−0.6748). So, along with other factors, water deficit can also be treated as one of the causes of relatively greater LST in some parts of the basin. On the contrary, when surplus water balance pixels are related to LST of the concerned pixels, it is found that the pixels having greater surplus water balance possess lesser temperature and vice versa (Fig. 14.8).

Fig. 14.8
figure 8

a, b Correlation between LST and water deficit pixels and water surplus pixels

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4 Conclusion

The present work tried to establish the influence of seasonal and regional water deficit states on enhanced LST and water surplus states on relatively less temperatures. For doing this monthly water balance equations are derived and mapped out spatial scale on the one hand and monthly land surface temperature is estimated and mapped out on the other hand. Finally, spatial associateship is analyzed quantitatively. From the result, it is clearly found that seven months (January to May, November, and December) are identified as water deficit months (up to −150.59 mm), and five months (June to October) are recognized as water surplus months (Highest 310.81 mm). In the deficit months, upper and middle catchments are prone to water deficit. Bare coarse-grain laterite soil also triggers high temperature. LST is also correlation coefficient between water deficit and LST is −0.2366 and it is 0.1785 between water surplus and LST and these are significant at 0.05 level of significance. So, spatial and seasonal water balance state can be treated as one of the reasons for degree of temperature distribution over the study area.