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Water Resources Assessment
Published in David Stephenson, Water resources management, 2003
The objective in developing a stream gauge network is to improve the data available for water resources evaluation and flood estimation. A practical limit should be set on the number of proposed gauges, bearing in mind the difficulty of obtaining and processing many gauges and the cost of construction of weirs or even the equipment for measuring and recording water levels or flows. Gauge stations should be spaced to obtain a reasonable indication of flows from various types of catchments, and some gauges are established specifically for selected rivers. Gauge stations must be sited bearing in mind access and the suitability of the river for either construction of a weir or for rating the cross section (which should be reasonably stable). A preference for weirs as opposed to rated natural sections is made where trained hydrologists for rating natural rivers are lacking and because weirs generally give more accurate data, especially at low flows. Access is important for the purpose of retrieving data as well as construction. The decision as to whether or not to construct a weir should be based on the importance of the site, but also on whether a weir can be practically satisfactory for rating of the natural river cross section. Owing to the flat gradient of rivers near the coast, tidal effects and backwater rule out accurate gauging. The most important areas with respect to water utilization are the interior region and the coastal escarpment region where there are possible hydroelectric sites. In general, the sections should also be straight and uniform upstream, and a pool should be created behind the weir to ensure low velocities over the weir. There should be no bends or obstacles downstream to cause backwater or non-uniform flow profiles.
Influential factors of water scarcity in Bharathapuzha basin, India
Published in Journal of Applied Water Engineering and Research, 2022
This study utilises twenty rain gauges, spatially distributed, in the Bhatrathapuzha basin, out of which four rain gauges are maintained by the India Meteorological Department (IMD), Government of India (GoI), and sixteen maintained by the Water Resources Department (WRD), Government of Kerala (GoK). The analysis used the observed discharge of nine stream gauge stations; four gauging stations, Kumbidi, Pulamanthole, Mankara and Pudur, maintained by Central Water Commission (CWC), GoI and five gauging stations, Thrithala, Cheruthuruthy, Cheerakuzhy, Vithinassery and Vandazhy by WRD, GoK. Figure 1 presents the location of the rain gauge and stream gauge stations. Climate data was collected from the meteorological station monitored by Regional Agricultural Research Station (RARS) Pattambi. The reservoir operation data from the Irrigation Department, Government of Kerala are also used for the analysis. The other data utilised are population data for the decades 1991, 2001, and 2011 (Census 2011); in India, population data is available for each decades. From the population data, the domestic water demand, including urban region, is estimated. The data on Industrial water withdrawal was obtained from Kerala Pollution Control Board (KPCB), Government of Kerala. From the quantity of industrial water withdrawal, we computed the industrial water footprint.
Investigating uniqueness and identifiability in auto-calibration of the ARNO daily rainfall-runoff model using the PSO algorithm
Published in International Journal of River Basin Management, 2021
Nima H. Ensaniyat, Nazanin Shahkarami, Reza Jafarinia, Jaleh Rezaei
Comparing the simulated and observed hydrographs reveals that the model performs almost perfect in simulating low flows but fails to predict extreme peaks and suggests relatively smaller values in comparison with the observed peak discharges. The main reason for this may lies in the instrumental errors, both in the stream gauge and the precipitation records. Since the temporal variability of the discharge is relatively high when a flash flood is taking place, the recorded peak daily discharges in a regular stream gauge are subject to uncertainty, because when there are no continuous observations available, daily discharges are probably reported close to the instantaneous peak discharges. The precipitation record is also subject to uncertainty, especially during a flash flood. The necessary adjustment which has been done to scale the precipitation input to the model and the unknown spatial distribution of precipitation in the basin while using a single meteorological station, are other sources of observational errors. Regardless of the observational errors and errors caused by the assumptions made, and more importantly the complex nature of the rainfall-runoff process, it was evident that the best calibration to the model is obtainable. The other advantage of a unique representation for different outputs of the model is that the portion of direct runoff from the total flow is assignable (Figure 10).
Alternative solutions for long missing streamflow data for sustainable water resources management
Published in International Journal of Water Resources Development, 2021
Buket Mesta, O. Burak Akgun, Elcin Kentel
A TS FRB model based on SC was applied by Akgun and Kentel (2018) for one-step-ahead monthly streamflow forecasting, by Nayak et al. (2005) and Lohani et al. (2014) for flood forecasting, by Vernieuwe et al. (2005) for rainfall-runoff modelling and by Nayak and Sudheer (2008) for reservoir inflow forecasting. To the best of our knowledge, the application of the TS FRB model based on SC is limited in hydrology, and this is its first application in the long-term forecasting of daily streamflow data by solely using neighbouring stream gauge observations. Some additional challenges are dealt with in this study. The first is the fact that there are a limited number of stream gauges within the Meric–Ergene Basin, so training of the FRB model is difficult due to the utilization of stream gauges with very different drainage areas. The procedure is further complicated because the common operation period of the stream gauges located within the basin is limited, and this restricts the length of the training period. The performance of the TS FRB model is compared to that of a hydrological model developed using HEC-HMS (Hydrologic Engineering Centre-Hydrologic Modelling System) for the study area. Data requirements, advantages, and disadvantages of the two approaches are highlighted. We believe that the utilization of the TS FRB model to fill in long data gaps in the streamflow data will provide critical input for future water resources management studies in the basin.