List of Notation
2.8 Critical appraisal of literature review
Philippines. LULC change detection was carried out by using Landsat and ETM+
satellite data. Rainfall-runoff modelling was done by using SCSCN method and soil loss was calculated by modified universal soil loss equation (MUSLE).
Singh et al. (2017) used GIS-based multi-criteria decision analysis for identification and prioritization of rainwater harvesting sites in order to meet the water supply demand in upper Damodar River basin of West Bengal, India. For mapping rainwater harvesting potential, weighted thematic maps of runoff coefficient, slope and drainage density and they were combined linearly in GIS environment. On the other hand, rainwater harvesting demand areas were identified by combining the weighted thematic maps of water requirement, groundwater table fluctuations and the post-monsoon groundwater table.
Heaney 2003), this is very labour intensive and not applicable to the large and remote area. Again, cellular automata models like SLEUTH (Clarke and Gaydos 1998), CLUE and CLUE-S (Veldkamp and Fresco 1996; Verburg et al. 2002) have also been used for modelling and projecting the future urban as well as multiple land use land cover (LULC) change. Despite the extensive use of these cellular automata models for prediction of LULC change at regional scale, they are less accurate for predicting local level LULC change, e.g. prediction of urban development around National Parks (Otis 2012). Again, a few studies (Alley and Veenhuis 1983; Laenen 1983; Boyd et al. 1993;
Sutherland 1995; Han and Burian 2009; Sahoo and Sreeja 2011) have been done on the estimation of EIA. EIA is the impervious area in the catchment that is directly connected to stream channels. It means that impervious area draining water to a pervious area is not considered as effective in runoff generation. For accurate estimation of runoff generation from a catchment, it is very important to determine EIA since TIA overestimates the runoff. In some previous studies, EIA has also been estimated by relating it to rainfall- runoff ratio (Laenen 1983; Boyd et al., 1993).
However, this approach requires observed rainfall-runoff data from the watershed.
Again, the method can be sensitive to errors in rainfall-runoff measurements and also the method is not appropriate to basins with moderate to low permeable soil (Alley and Veenhuis 1983). Hence, all these past works undoubtedly show the research advances in indirect estimation and projection of urban settlement in general in a city. However, high rate of environmental degradation due to the continuous diminishment of urban forests and wetlands demands further research on modelling of urban settlement in eco- sensitive areas located within or near a city, interpreting its relationship with various factors of urbanization.
The reviewed literature gives an idea of the impact of impervious cover on hydrology. With the increase of imperviousness in an area, the groundwater recharge, stream base flow, evapotranspiration decrease and surface runoff increases. Although assessment of these impacts can be carried out by direct measurements of the rainfall and discharge at streams and the groundwater levels at wells (Burns et al. 2005), this method is quite time-consuming and labour intensive. Through the use of some assumptions, mathematical models and various empirical equations have been developed to determine the amount of runoff resulted from rainfall. Despite the fact that they are not capable of exactly replicating the complex conversion process from rainfall
to runoff, with some limitations or approximations, these models and empirical equations are capable of giving reasonably good results. It is also true that more the availability of input data to a model better the results of the model. As mentioned in the literature review, a numbers of hydrological models such as HSPF (Brun and Band 2000), SWAT (Goetz et al. 2011; Wagner et al. 2013; Li et al. 2015), MODHMS (Barron et al. 2013) etc. have been used for evaluating the impact of impervious cover on hydrology. Calibration of parameters of these models requires historical data of rainfall and runoff of the study area. As a result, these models are not directly applicable to ungauged basin having no historical observed runoff data. However, through the use of regionalization techniques, these hydrological models have been applied to a number of ungauged watersheds. Mainly two ideas are applied in regionalizing the model parameters (Merz et al. 2006). One is based on the assumption that neighbouring catchments have similar hydrologic behaviours (Mosely 1981). This is called geographical regionalization (Vandewiele and Elias 1995). In this technique, model parameters derived from a gauged basin are applied to a nearby ungauged basin.
This idea is not very useful as the neighbouring catchments can also be vastly dissimilar in hydrological characteristics (Beven 2000; Piman and Babel 2013).
Another idea of regionalization is based on the similarity of catchments attributes (catchment dimensions, vegetation type etc.) and climate variables (mean annual rainfall, temperature etc.) (Acreman and Sinclair 1986; Post et al. 1998). Yet, this concept was not too successful as most of the catchment characteristics are measured at only land surface whereas the total runoff resulted from precipitation is highly dependent on the sub-surface condition of the catchment (Merz et al. 2006).
Additionally, it is not an easy task to find a homogeneous gauged catchment which has similar hydrologic or catchment characteristics with those of the ungauged catchment.
In spite of all these complex techniques used for determination of runoff from an ungauged watershed, Rational Method (Kuichling 1889) which is one of the most widely used methods can be applied to determine the peak runoff generated from a watershed. This Rational Method is quite simple and gives the peak surface runoff without using any observed runoff data. Hence, in this study, for evaluating the impact of urbanization on runoff generation from ungauged watersheds, Rational Method has been used.
The synoptic review of the sediment yield models implies that process-based physical soil loss estimation models are a little bit complex and are not applicable in absence of observed soil loss data. Among the available empirical models, USLE/RUSLE methods are found to have ample application in analysing the sediment yield characteristics from different types of LULC (Sarma 2011). Use of remote sensing and GIS techniques in USLE/RUSLE model is another milestone in the history of soil loss estimation. Researchers from different parts of the world have extensively used GIS techniques to measure the amount of soil loss from a watershed (Fistikoglu and Harmancioglu 2002, Dabral et al. 2008, Panday et al. 2007, Biswas and Pani 2015).
The soil erosion map derived by using GIS tools helps to identify the erosion-prone areas, which basically acts as a key input into the sustainable watershed management practices. For the current research work, based on the simplicity of input data requirement, GIS-based RUSLE is found to be appropriate to apply. Presently, urban planners are giving more interest on sediment yield control by using ecological management practices (EMP) like grass, forest, gardens, detention ponds etc. (Sarma 2011). The effectiveness of these practices is well dependent on geological, topographic and climatic conditions of the watershed. The literature review revealed that with the ease of RS and GIS along with the use of optimization techniques, a few studies have determined the optimum combination of different ecological land covers for controlling runoff and sediments from hilly watersheds (Sarma et al. 2013; Sarma et al. 2015).
However, in developing countries like India, urban settlements are expanding to hilly areas in a very unplanned and unscientific way. Hence, in-depth research is required to precisely estimate the soil loss from hilly watersheds and based on those estimations, best management practices should be implemented.