Water resources assessment in a poorly gauged mountainous catchment using a geographical information system and remote sensing

Abstract

Abstract:Water resources assessment, which is an essential task in making development plans managing water resources, is considerably difficult to do in a data-poor region. In this study, we attempted to conduct a quantitative water resources assessment in a poorly gauged mountainous catchment, i.e. the River Indrawati catchment (1233 km 2 ) in Nepal. This catchment is facing problems such as dry-season water scarcity and water use conflicts. However, the region lacks the basic data that this study needs. The data needed are supplemented from field surveys and global data (e.g. GTOPO30 DEM data, LandsatTM data and MODIS NDVI data). The global data have significantly helped us to draw out the information needed for a number of water-use scenarios. These data helped us determine that the available water quantity is enough at present to address the dry-season problems. The situation is not much worse for the immediate future; however, the threat of drought is noticed in a future scenario in which resources are consumed extensively. The study uses a geographical information system and remotely sensed data analysis tools extensively. Utilization of modern tools and global data is found effective for investigating practical problems and for detecting important features of water resources, even though the catchment is poorly gauged.

WaterWorld: a self-parameterising, physically based model for application in data-poor but problem-rich environments globally

“…Instead, many that are built for datarich environments have been subsequently applied to datapoor ones (Jayakrishnan et al 2005;Chaponnière et al 2008;Ndomba et al 2008). In ungauged, data-poor environments, other assessment approaches are usually used, for example simple GIS approaches (Shrestha et al 2004) or remote sensing water resources assessment (Liebe et al 2009). Where process models built for data-rich environments are used in data-poor ones, the following techniques are used to facilitate model application: use of remote sensing (Lakshmi 2004;Collischonn et al 2008); use of reanalysis products (Choi et al 2009) or weather generator output (Schuol & Abbaspour 2007) for model parameterisation or validation; heavy calibration of models to account for data deficiencies (Ndomba et al 2008); or use of model calibration coefficients derived from nearby and analogous, but gauged, basins.…”

“…Simple GIS and remote sensing-based national water balances (e.g. Alemaw & Chaoka 2003;Bastiaanssen & Chandrapala 2003;Shrestha et al 2004;Liebe et al 2009) do not usually permit scenario analysis nor facilitate understanding of the process-basis for hydrological state and change. If we are interested in the types of hydrological problems outlined above, this leaves us: (a) having to apply models built for data-rich environments to data-poor ones as best we can; or (b) building models specifically designed for application in ungauged and data-poor environments.…”

Impact of Climate Change on Water Resources in View of Contribution of Runoff Components in Stream Flow: A Case Study from Langtang Basin, Nepal

“…Water resource management and the evaluation of impacts of climatic change require quantification of stream flow variability and hydrologic models provide a framework to investigate these relationships (Leavesley 1994). Poor accessibility and inadequate network of hydrometeorological station in high altitude regions is a major impediment to runoff modeling and as a result only a few studies have explored snow and glacier melt runoff models in Himalayan sub basins (Akhtar et al 2008;Braun et al 1993;Shrestha et al 2004). The impact of potential climate change on snowmelt hydrology in the Himalaya, therefore, remains an active area of research ( Figure 2).…”

Remote Sensing and GIS Innovation with Hydrologic Modelling for Hydroelectric Power Plant (HPP) in Poorly Gauged Basins

“…However, most of these studies include many different parameters and variables which have been predicted comprehensively by using these geoscientific methods (Schumann et al 2000;Shrestha et al 2004;Caballero et al 2004). In water resources management, especially in stream flow prediction, more practical and less time consumption methods should be considered.…”