Original Research
Hydrodynamic modeling of Kurang river using hydro-informatics modeling system (HIMS): an application from a scarce data region

Muhammad Waqar* , Sajid Rashid Ahmad, Shafiq Ur Rehman, Zahra Majid, Fizza Hassan

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College of Earth and Environmental Sciences, University of the Punjab, Lahore, Pakistan

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The author(s) declared that no grants were involved in supporting this work.

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Abstract

Pakistan being an underdeveloped country lacks efficient data collection setup up till today. The problem of this data shortage makes it difficult to predict the flood possibilities. Advances in methodologies such as hydro-informatics modeling system (HIMS), has put it at a precise end to forecast these extreme events over the scarce data regions in varying climate scenarios. In this study, Nash model is integrated with DEM (30 m resolution) within HIMS to compute flood peaks at river Kurang near Islamabad, Pakistan. The methodology employed inclusion of catchment parameterization, second order differential form of Nash model with its parameters (N and K), and optimization procedures, i.e., Newton-Raphson method. Results of catchment parameters mainly drainage density, bifurcation ratios, length ratios, and area ratios were calculated as 0.54, 2.94, 4.21 and 6.18, respectively. The flood peaks, on the other hand, showed a handsome alikeness with the observed values of the model, i.e., the computed peak appeared to be 50 cumecs while the observed peak was about 54 cumecs. The sensitivity analysis was also carried out to attain the accuracy level which ranged between the ± 7% of the simulated and observed results. It is concluded that the adaptation of this model may assist in floods prediction as a matter of climate change particularly having data scarcity problems.

Corresponding author: Muhammad Waqar *,

How to cite: Waqar, M., Ahmad, S.R., Rehman, S.U., Majid, Z., and Hassan, F., 2016: Hydrodynamic modeling of Kurang River using hydro-informatics modeling system (HIMS): an application from a scarce data region. Bulletin of Environmental Studies 1(4): 89-95.

open-access

Copyright © 2016 Waqar, Ahmad, Rehman, Majid, Hassan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduc-tion is permitted which does not comply with these terms.

Competing interests: The authors declare that they have no competing interests.

Edited by: Muhammad Arlan (UFZ, Germany)

Reviewed by: Muhammad Nasir Mehmood (UFZ, Germany) & Saddam Akber Abbasi (Qatar University, Doha, Qatar)

Received: 07/12/2016

Accepted: 10/29/2016

Published Online: 10/29/2016

Introduction

Seasonal availability of the water resources has been appeared to be decreasing in the near future due to climate change events (Arnell et al., 2011; Tsanis et al., 2011; Harding et al., 2011; Georgakakos et al., 2012; Alcamo et al., 2007; Barnett and Pierce, 2009 and Fujihara et al., 2008). Associated devastating effects have been observed on masses, wildlife and agriculture at global as well as local scale, for which urgent adaptations have become inevitable at the moment (Alcamo et al., 2007; Davies and Simonovic et al.,2011). Appropriate water resource planning, operation and development require a precise estimation of hydrologic response in the region susceptible to such events (Solomon et al., 2007; Kumar et al., 2007; Bhadra et al., 2008 and Akhtar et al., 2008).

Although, researchers have been able to develop informatics models which may help take adaptive measures for floods and droughts (Brigode et al., 2013). However, these models are always vulnerable to the climate change (Georgakakos et al., 2012 and García- Ruiz et al., 2011), whether they require detailed hydrological and meteorological data or not (Akerlof et al., 2012 and Fleurant et al., 2006). The categorization and flexibility of the models further depend on data requirements with their limited operational efficiency and rigidity to climatic condition (Fleurant et al., 2006).

South Asia is one of the most dynamic regions in the world that has always been prone to climate change complications. Pakistan being the second largest country of south Asia, is significantly influenced by these climate changes. Approximately 70 % of the country’s economy is based on agriculture which significantly depends on rainfall (Ghulam Rasool et al., 2012). Moreover, climate data scarcity in the region has also been a reason behind with natural calamities as forecasting methodologies largely rely on the extensive dataset. Therefore, it is necessary to simplify the rainfall and runoff processes with or without linking it to the description of physical processes (Brigode et al., 2013, Malik et al., 2011; Bhunya et al., 2008 and Bhaskar 1997). This has been achieved by applying hydro-informatics models that provide solutions closer to the natural conditions for the description of hydrological extremes, e.g., floods and droughts. Notwithstanding, its development requires a deep understanding of geomorphological, geographical, hydrological and climatological parameters towards a dignified hydrological response (Nash et al., 2015; Bhadra et al., 2008; Kumar et al., 2007; Fleurant, et al 2006 and Lee, et al 2005). In this regard, HIMS coupled with geographical information system (GIS) has been employed to develop the hydrographs in the regions with scarce climate dataset (Narayan et al., 2012, Lohani et al., 2001; Olivera et al., 1999; Horton 1945; Gupta 1980; Rinaldo 1996; Rodriguez-Iturbe 1979 1982 and Sahoo et al., 2006; Rao 1997 and Singh et al., 2002; Strahler 1957; Jain 2003 and Rai et al., 2009 and Jain et al., 2000).

The purpose of this study was to employ HIMS for flood predic-tion under changing climate patterns especially when the source data is limited. The model has been exploited previously at un-gauged catchments and therefore can be employed the aforemen-tioned purpose (Narayan et al., 2012). To best of authors knowledge, the study is pioneer in the subject which may help developing further understanding on this phenomenon at the na-tional level.

Study Area

The Korung River, as shown in Figure 1 (Survey of Pakistan Topographic Sheet No. 43G/1, 43G/2 and 43G/5), is situated in Islamabad in the direction of northeast at Ghora Gali which is part of Potwar Plateau and Lesser Himalayas (Kazmi et al., 1997). The Main Boundary Thrust (MBT) is a tectonic boundary between the lesser Himalayas and Potwar plateau (Malik 2011 and MonaLisa et al., 2004). Out cropping trend of the catchment area is NE-SW while the age of rocks is between Jurassic to Recent. The Kurang river is characterized by fluvial terraces and denudated hills. Main-ly, flow of the river is from NE to SW (Long. 73.533N to 73.3010N and Lat. 33.2045E to 33.5030E) in carved valleys by erosion and weathering of sandstone and shale. The river joins Soan river which is a tributary of Indus River. The area falls in the category of humid tropical to arid zone, including hot summers and cold winter heavily; while the variation in temperature ranges from 38 ºC to 10 ºC (average 28 ºC). The average annual rainfall is 940 mm in this area, the population is sparse and vegetal cover is main-ly consisting of herbs and shrubs.

 

Materials and Methods

The rainfall and temperature data were obtained from Pakistan Meteorological Department (PMD). Three step methodology was adopted to develop the HIMS model. At first, digital elevation model (DEM) of 30 m resolution was intercalated through geo-graphical information system along with its reconditioning (Figure 2). The intercalation was followed by flow direction algorithm accompanying flow accumulation formulation. With continuous use of prepared raster of flow direction and flow accumulation, a stream network was evaluated using threshold value (1000) grid cells of raster dataset. Finally, the catchment grid delineation along with catchment area and drainage line was developed through continuous interpolation of raster dataset (schematic diagram shown in Figure 3).

 
 

In second step, mathematical description of the geomorphologic relationship with hydrological response, considered as nonlinear, was created through geometrically interpolated data. For mathe-matical description, the Nash equation (1) is simplified using the sterling approximation given in equation (2). Subsequently, Nash equation was simplified to the first order differential from to get optimized results for the required parameter N, equation (3) and K was obtained in equation (4). To get optimized results of catch-ment response function, numerical methods like Newton-Raphson iterative method (which was more suitable keeping in view re-quired parameters) was used (5). The geomorphologic parameters given as drainage density, bifurcation ratios, length ratios, and area ratios were evaluated using raster developed in arc tools. At third step the development of flood hydrograph (unit hydrograph) was made for river Korung, using Nash model parameterization N and K. The calibration at different velocities of K was performed to get the required shapes of unit hydrographs.

Waqar2016-1e.jpg

Sterling approximation for simplification of equation (1)

Waqar2016-2e.jpg

where K is Nash constant, Ra is area ratio, Rb is bifurcation ratio, Rl is length ratio, Lr is length of longest stream and V is the velocity,

Waqar2016-3e.jpg

where N is no of linear reservoirs.

The calibration of this model was done by using available flood hydrograph at out let of river Korung, Rawal dam, Islamabad. The sensitivity analysis was also executed to evaluate the percentage error by comparing the computed values and observed (Figure 9).

Results and Discussion

Results illustrate that the temperature of the region is increased by 0.45-0.75 °C over the last three decades. The collected data and exploratory analysis (monthly, maximum, and minimum temperature with rainfall of 30 years) are shown in Figure 3, 4 and 5. Numerous studies are carried out in the region to analyze the temperature trend for which a constant increase is observed; this is in agreement with the climate variations as observed (Ghulam Rasool et al., 2012). Moreover, the Korung river is fed by seasonal snow, melts and rains; therefore, flow regimes might have been affected substantially by precipitation patterns in the respective region. Analysis of the catchment parameters elucidates that the changes of monthly total rainfall and increase in temperature were due to the climatic variations of this region (Akhtar et al., 2008).

 
 

Stream Order: The systematic manipulation of DEM followed by the flow direction, flow accumulation, stream definition (Figure 2), and catchment grid delineation (at 30 m resolution) of the Korung river showed maximum stream order up to the fourth level. On the whole, 91 total streams were identified among which 72 were designated as first order, 14 streams as second order, 4 streams as third order, and 1 stream was as fourth order (Figure 6).

 

Drainage Density: Drainage density represents the landscape dissection, land infiltration capacity, runoff potential, vegetation cover, and the general climate of the region (Reddy et al., 2004). In the present study, drainage density was observed to be 0.55 whose value lies within the range defined by Strahler, (1957). This low value of drainage density suggests less flow generation from the catchment along with high infiltration capacity hence indicating lesser relief. This provides a numerical measurement of its runoff potential along with the outcrop dissection and closeness of the streams (Zende and Nagrajan, 2011).

Bifurcation Ratio: The bifurcation ratio, which describes the relationship between streams order ascendingly, i.e., first and second, second and third, and so on, for the Korung river was observed to be 4.23. It has been well established that the bifurcation ratio ranging between 3.0 and 5.0 indicates least influence of geological features on drainage network (Verstappen, 1983).

Area Ratio: The streams length for each order stream was compared with the higher order streams, which revealed an aver-age value of length ratios as 2.95. Nevertheless, the fourth one was a stream area ratio which is a comparison between lower order stream areas to higher order stream areas. For the Korung river, area ratio was evaluated 6.19 (Figure 7). These values were stemmed by previously described ranges by Horton, (1952).

 

Nash Model: Embedding of Nash model was performed to achieve the final shape of the hydrograph. There were two components of Nash model, i.e., N (number of linear reservoir division for any catchment), and K. In the Korung river, N was observed to be 2.0 whereas K was evaluated at different velocities ranging from 0.5 m/s to 5 m/s. The maximum value of K at 0.5 m/s was 35.85 while at 5 m/s, it was observed to be 3.58 (Figure 8). The mod-el application revealed that the peaks of lower values of K were earlier as compare to high values of K, depicting greater time dif-ference. Moreover, results of this approach also indicate that HIMS is a good way to establish relationship among hydrology, geomor-phology and climatology. HIMS results also elucidated that geo-morphology of rivers must be dealt on priority basis to design floods especially in scarce data regions.

A comparative analysis of simulated unit flood peak and ob-served peak displays similar results, which suggests satisfactory validation to outcomes of HIMS. The observed unit peak dis-charge at Rawal Dam in the river Korung was 54 cumecs in contrary to simulated which was 50 cumecs (Figure 9 and 10). Thus, the modeling approach has reasonable justification to be applied in scarce data regions. Moreover, as sensitivity analysis shows the ± 7% variations between simulated and observed results, HIMS may be adopted in such regions where scarcity of data exists along with climate vulnerability.

 
 
 

Conclusions and Recommendations

In a nut shell, it can be concluded that the HIMS displayed suc-cessful of structure development, hydrological modeling, and flood prediction from scarce data regions. It is well established that, without catchment parameterization, it is impossible to develop information about climatic conditions, flood hydrology and rainfall-runoff modeling. Once the information is known, it would be easy to estimate flood peak in any known area.

The use of HIMS is more helpful for extraction of geomorphic characteristics at Mesoscale which may further enhance the development of new methodology with accuracy and relevant terrain information. The comparative results nevertheless show the suita-bility of this methodology and its adaptation for areas of climate variations where the conditions are far away from data availability.

 

Acknowledgements

The authors would like to thanks College of Earth and Environ-mental Sciences, University of the Punjab, Lahore, for providing necessary resources to accomplish the study.

Compliance with ethical standards

Conflict of Interest

The authors declare that they have no conflict of interests.

References

Ahsan, N., Waqar, M. 2015. An integrated approach for catchment parameterization through geospatial analysis: a case study of Korung river basin Pakistan. Science International.2,1309-1314

Akerlof, K., Katherine, E., Rowan, Fitzgerald, D., Andrew, Y.C. 2012. Communication of Climate Projections in US Media amid Politicization of Model Science; Nature Climate Change, Vol. 2, pp. 648-654.

Akhtar, M., Ahmad, N., Booij, M.J. 2008. The impact of climate change on the water resources of Hindukush–Karakorum–Himalaya region under different glacier coverage scenarios. Journal of Hydrology. 355, 14–163

Alcamo, J., Floerke, M., Maerker, M. 2007. Future long-term changes in global water resources driven by socio-economic and climatic changes. Hydrological Sciences Journal. 52 (2),247–275

Allan, R. 2011. Human influence on rainfall. Nature vol :470,p,344-345

Arnbjerg-Nielsen, K. 2012. Quantification of climate change effects on extreme precipitation used for high resolution hydrologic design. Urban Water Journal. 9 (2), 57–65

Arnell, N.W., van Vuuren, D.P., Isaac, M. 2011. The implications of climate policy for the impacts of climate change on global water resources. Global Environmental Change 21 (2), 592–603

Barnett, T.P., Pierce, D.W. 2009. Sustainable water deliveries from the Colorado River in a changing climate. Procedings of. National. Academy. Science letters. USA. http://dx.doi.org/ 10.1073/pnas.0812762106.

Bhadra, A., Pnigrahi, N., Singh, R., Raghuwanshi, N. S., Mal, B. C.,Tripathi, M. P. 2008. Development of geomorphological instantaneous unit hydrograph model for scantly gauged watersheds. Environmental Modelling & Software, 23, 1013-15

Bhaskar. N. R., Parida, B. P., Nayak, A. K. 1997. Flood estimation for ungauged catchments using the GIUH. Journal  of  Water Resources Planning and Management., 12(4), 228-238.

Bhunya, P. K., Berndtsson, R., Singh, P. K., Hubert, P. 2008.  Comparison between Weibull and gamma distributions to derive synthetic unit hydrograph using Horton ratios. Water Resources Research,Vol. 44, W04421.

Brigode, P., Oudin, L., Perrin, C. 2013. Hydrological model parameter instability: a source of additional uncertainty in estimating the hydrological impacts of climate change. Journal of Hydrol. 476, 410–425.

Dankers, R., Feyen, L. 2009. Flood hazard in Europe in an ensemble of regional climate scenarios. Journal of Geophysical Research. 114, D16108. http://dx.doi.org/10.1029/ 2008JD011523.

Davies, E.G.R., Simonovic, S.P. 2011. Global water resources modelling with an integrated model of the social–economic–environmental system. Advances in Water Resource 34 (6), 684–700.

De Roo, A.P.J., Wesseling, C.G., Van Deurzen, W.P.A. 2000. Physically-based river basin modeling within a GIS, the LISFLOOD model. Hydrological. Processes. 14, 1981 1992

Fleurant.C., Kartiwa, B., Roland, B. 2006. Analytical model for a geomorphological instantaneous unit hydrograph. Hydrological. Processes, 20, 3879-3895.

Fujihara, Y., Tanaka, K., Watanabe, T., Nagano, T., Kojiri, T. 2008. Assessing the impacts of climate change on the water resources of the Seyhan River Basin in Turkey, use of dynamically downscaled data for hydrologic simulations. Journal of Hydrology. 353 (1–2), 33–48.

García-Ruiz, J.M., López-Moreno, J.I., Vicente-Serrano, S.M., Lasanta-Martínez, T., Beguería, S. 2011. Mediterranean water resources in a global change scenario. Earth – Science. A. Review. 105 (3–4), 121–139.

Georgakakos, A.P., Yao, H., Kistenmacher, M., Georgakakos, K.P., Graham, N.E., Cheng, F.-Y., Spencer, C., Shamir, E. 2012. Value of adaptive water resources management in Northern California under climatic variability and change: reservoir management. Journal of Hydrol. 412–413, 34–46.

Grimaldi, A., Petroselli, F., Nardi 2012. A parsimonious geomorphological unit hydrograph for rainfall–runoff modelling in small ungauged basins. Hydrological Science Journal. 57 (1) pp. 73–83

Haase, D., and Frotscher, K. 2005.Topography data harmonization and uncertainties applying SRTM, laser scanner and cartographic elevation models. Advances in Geosciences. 5, 65-73.

Harding, R., Best, M., Blyth, E., Hagemann, S., Kabat, P., Tallaksen, L.M., Warnaars, T.,Wiberg, D., Weedon, G.P., van Lanen, H., Ludwig, F., Haddeland, I. 2011. Preface to the Water and Global Change (WATCH) special collection: current knowledge of the terrestrial global water cycle. Journal of Hydrometeorology. 12 (6), 1149–1156.

Horton. R. E. 1945. Erosional development of streams and their drainage basins: Hydro physical approach to quantitative morphology. Bulletin of Geological Society of Amer.,56, 275–370.

Jain, V., and Sinha, R. 2003. Derivation of unit hydrograph from GIUH analysis for a Himalayan river. Water Resources Management., 17, 355-375.

Kazmi, A.H., Jan, M.Q. 1997. Geology and Tectonics of Pakistan. Graphic Publishers, Karachi.,1-554.

Kumar, R., Chatterjee, C., Singh, R. D., Lohani, A. K. 2007. Runoff estimation for an ungauged catchment using geomorphological instantaneous unit hydrograph (GIUH) model. Hydrological Processes. 21, 1829-1840.

Lee, K.T., Chang, C. 2005. Incorporating subsurface-flow mechanism into geomorphology-based IUH modeling. Journal of Hydrology. 91-105.

Malik, M. I., Bhat, S. M. Kuchay, N. A. 2011. Watershed based drainage morphometric analysis of Lidder catchment in Kashmir valley using geographical information system. Recent Research Science and Technology. 3(4), 118-126

MonaLisa. Khwaja, A. A. 2004. Structural trends and focal mechanism studies in the Potwar area with special emphasis on hydrocarbon exploration.Pakistan Journal Hydrocarbon Research. 14, 49-60.

Narayan, K., P.K.S., Dikshit, S.B., Dwivedi. 2012. GIS supported Geomorphologic Instantaneous Unit Hydrograph (GIUH) of Varuna River basin using Geomorphological Characteristics International Journal of Advances in Earth Sciences, 1 (2) pp. 68–76

Naoum, S., Tsanis, I.K. 2004. Orographic precipitation modelling with multiple linear regressions. Journal of Hydrological Research Engneering. 9 (2), 79–102.

Nash, J. E. 1957. The forms of instantaneous unit hydrograph. International Association of  Science and Hydrology. Pub. 1, 45(3).

Nazari, S.A., Ahmadi, H., Mohammadi, A., Ghoddousi, J., Salajeghe, A., Bogg, G. 2009.  Factors Controling Gully Advancement and Models Evaluation (Hable Rood Basin, Iran). Water Resources. Managment. Published Online, DOI 10.1007/s 11269-009-9512-4.

Rai. R.K., Upadhyay, A., Sankar, S., Upadhyay. A.M., Singh, V.P. 2009. GIUH based transfer function for Gomti river basin of India. Journal of Spatial Hydrology. 9(2), 29-50.

Sahoo, B., Chatterjee, C., Raghuwanshi, N.S., Singh, R., Kumar, R. 2006. Flood estimation by GIUH-based Clark and Nash models. Journal of Hydrologic Engneering. 11(6), 515-525.

Solomon, S. 2007. Climate Change 2007: The Physical Science Basis. Cambridge University Press, 996 pp.

Strahler, A. N. 1957. Quantitative analysis of watershed geomorphology. Trans. Am. Geophys. Uni., 38, 913-920.

Tsanis, I.K., Koutroulis, A.G., Daliakopoulos, I.N., Jacob, D. 2011. Severe climate induced water shortage and extremes in Crete. Climate. Change 106 (4), 667–677.

 


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