FACTORING

With changes in the climate, the frequency and magnitude of extreme hydrological events are increasing around the world (Huntington, 2006). Although it is quite evident that the impact of land use/land cover (LULC) change at plot or small catchment scale has a clear-cut effect on the flood peak (Wan and Yang, 2007) the causal relationship between the two becomes less clear as the size of the catchment and the number of LULC, increases (Andréassian, 2004). Exploring the nature of the relationship between the local LULC changes at the sub-catchment level and its impact on the hydrograph at the basin outlet is important because most of the watershed management planning is perceived and implemented at the sub-catchment level. Without a clear understanding of the scale-dependent processes responsible for propagating the effect of local LULC

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changes into the basin hydrograph it is difficult to implement remedial land management measures to reduce flood peak (Pattison et al., 2008).

Lane et al. (2007) pointed out four potential ways by which land-management practices in a rural catchment might affect a storm hydrograph as 1) the influence in determining the share of rainfall following the rapid surface route and the slower subsurface one; 2) the efficacy of the process of transferring the rainwater from the hillslope to the channel; 3) the ease of passage of the flow within the riparian zone; and 4) the effect on existing catchment storage during heavy rainfall events. Pattison and Lane (2012) envisaged that the effect of LULC might have a different impact of different scale because a particular land-management practice may have an impact over more than one of the processes mentioned above. The effect of the intensity and duration of the precipitation and the type of soil is also very important in controlling the way in which changes in LULC can lead to alteration in the downstream flood hydrograph. The processes that lead to runoff generation are sensitive to the type of precipitation, therefore the effect of LULC change and land management has to be event specific (Bronstert et al., 2002). The reduction of surface runoff due to alteration in the soil infiltration capacity is restricted only in the initial phase of the rainfall event so that for events of longer duration the soil infiltration capacity is not at all found sensitive to existing LULC (Quast et al., 2012). Pattison and Lane (2012) came to the conclusion that due to the difficulty in upscaling the straightforward relationship between LULC changes and storm runoff, the linkage between LULC and the rate of flow to the downstream outlet of a large basin is rather unique and should not be generalised. Nevertheless, there have been efforts to explore the causal relationship between LULC changes and the downstream flood hydrograph through modelling. Ewen et al. (2012) focussed on the sensitivity of the peak flow rate on the factors that control runoff generation and analysed different scenarios based on ‘impact mosaic maps’ created by an algorithmic differentiation method. Quast et al. (2012) combined the use of infiltration and erosion models in a schematised approach for achieving a similar goal and concluded that the flood-causing amount of rainfall varies with reference to the combination of soil types and land use.

In data-sparse conditions, where detailed soil maps and other measurements of soil parameters such as hydraulic conductivity is not available, the NRCS Curve Number

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(Natural Resources Conservation Service, 1986) approach can be a good proxy for capturing the combined effect of LULC and soil on the downstream flood hydrograph. Widely used hydrologic models like HEC-HMS (US Army Corps of Engineers, 2012) has been used with limited data to identify the sub-catchments that have high runoff generating potential and relative contribution to the storm hydrograph at the basin outlet (Saghafian and Khosroshahi 2005; Roughani et al., 2007; Saghafian et al. 2008). However, these investigations stopped short of evaluating the effect of changing LULC at the sub-catchment level on the downstream flow rates. Further research in this direction that make use of a modelling suite available in the public domain and inputs that can be derived from freely available satellite images and soil maps would be particularly beneficial for a wider community.

1.1.8 Summary

The scale of the fluvial system under investigation, the model output of interest (river stage vs extent of flooding) and the nature of extreme streamflow events (within bankfull level or floodplain flow) are significant considerations in the context of modelling flood without any access to the high quality model inputs and observed data for calibration and validation. Numerous studies have been successful in accurately predicting floods in a single channel fluvial environment where the extent of flooding takes place in more or less contiguous manner and the spatial extent of uncertainty is confined to a narrow strip of land at a distance from the channel. However, in an anabranching channel associated with multiple river islands and distributaries the occurrence of flooding does not take place in a spatially contiguous manner as it results from the overtopping of the levees at various points along the main channel as well as the smaller branches. The fluvial morphology of low lying areas in the tropics and sub- tropics frequently consists of multiple channel bifurcations due to frequent avulsion and they suffer major flooding such the Kosi flood in India in 2008 (Kaur and Das, 2011; Sinha, 2011). This kind of drainage pattern is also frequently observed in the flat deltaic landscape of large rivers which are inherently flood-prone. Hence, there is a need to understand to what extent inundation models of different complexities can simulate flooding in this kind of common flood-prone environment with coarse quality inputs found in the developing countries. With the development of methodologies for predicting inundation with limited data it is also required to investigate how we can

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precisely prioritise our land use planning effort to mitigate the intensity of flood waves. The success of any land management strategy depends on our understanding of the causal linkages between local LULC changes and its impact on the flood hydrograph downstream.

An assessment of the uncertainty of the model outputs is crucial in the context of data sparse modelling environment. Incorporating the element of uncertainty in flood inundation modelling will help the transition from deterministic to probabilistic modelling. A deterministic prediction of river stage or flood extents that are based on optimum choice of parameters may mask the uncertainties in the modelling process and provide spuriously precise results (Bates et al., 2004; Beven, 2006). A probabilistic approach prevents incorrect planning decisions about future development in the areas adjacent to the rivers by having an understanding of the confidence level in the modelled outcomes (Di Baldassarre et al., 2010).