A systematic decision support tool for robust hydropower site selection in poorly gauged basins

Applied Energy 224 (2018) 309–321

Contents lists available at ScienceDirect

Applied Energy journal homepage: www.elsevier.com/locate/apenergy

A systematic decision support tool for robust hydropower site selection in poorly gauged basins

T

⁎

Abdul Moiza, , Akiyuki Kawasakia, Toshio Koikeb, Maheswor Shresthac a

Department of Civil Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan International Centre for Water Hazard and Risk Management, 1-6 Minamihara, Tsukuba, Ibaraki, Japan c Water and Energy Commission Secretariat, Singhdurbar Western Gate Rd, Kathmandu 44600, Nepal b

H I GH L IG H T S

site selection of small hydropower schemes in poorly gauged basins. • Systematic a distributed hydrological model with a hydropower site selection tool. • Integrating • Selection of more economic sites in a hydropower system considering basin hydrology.

A R T I C LE I N FO

A B S T R A C T

Keywords: Decision support Run-of-river Site selection Distributed hydrological model

The power output of run-of-river small hydropower (SHP) developments is very site sensitive and poses several complex challenges, such as inaccessible terrain and numerous possible hydropower scheme alternatives. We developed a geographic information system-based decision support tool that systematically evaluates all possible hydropower scheme alternatives to assist decision makers in assessing the hydropower potential of large datascarce regions more objectively. A water and energy budget – based distributed hydrological model and a preference ranking organization method for enrichment evaluations are integrated and employed in the developed tool. This approach enables the inclusion of both topographical and hydrological factors in the site selection process, allowing the use of hydropower potential as a maximizing criterion during site selection. This paper explores the consideration of topographical factors with and without hydrological factors as approaches for optimized site selection. An application of the tool in the case of Kunhar River Basin in Pakistan demonstrates its robustness. For equivalent criteria weights, site selection considering both factors identified a significantly lower number of sites with a shorter waterway length compared to when only topographic factors were considered. However, both approaches identified essentially the same hydropower potential. Notably, the integration with a distributed hydrological model and the incorporation of hydropower potential as a maximizing criterion for site selection revealed more economically attractive SHP sites. This approach enables a flexible and rigorous evaluation of the hydropower potential of large poorly gauged basins, which is particularly useful for developing countries.

1. Introduction With the continuous depletion of fossil fuel reserves, energy development trends have shifted more toward renewable energy sources. By the end of 2015, hydropower was still the primary contributor to the global renewable energy mix, with 16.6% of global renewable electricity generated by hydropower [1]. Despite being a relatively mature technology, future projections indicate continual growth in the hydropower sector. This trend is particularly true for small hydropower (SHP) developments [2]. According to the hydropower status report, global

⁎

hydropower capacity was estimated at 1246 GW in 2016, with 31.5 GW installed in the same year [3]. However, it was also estimated that in 2000, approximately 70% of the economically feasible hydropower potential had yet to be developed [4]. In this context, the importance of hydropower cannot be denied, and this importance is further highlighted by the fact that the expansion of the electricity sector in developing countries plays a significant role in enhancing their economic growth [5]. The goal of the study presented herein is therefore to devise a systematic tool for decision support during the planning phase SHP site selection process for promoting the development of untapped

Corresponding author. E-mail address: [email protected] (A. Moiz).

https://doi.org/10.1016/j.apenergy.2018.04.070 Received 16 January 2018; Received in revised form 23 March 2018; Accepted 26 April 2018 0306-2619/ © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

Applied Energy 224 (2018) 309–321

A. Moiz et al.

of the site to be known before the discharge is simulated. To tackle this issue, Soulis et al. suggested the use of distributed hydrological models to estimate streamflow values at sites that are not known during the hydrological model setup [28]. A few researchers have also developed rather complicated tools and algorithms that evaluate a number of alternative sites and suggest the best alternatives based on an overlay analysis [29,30]. However, these too rely upon the availability of regional FDCs. Recent advances in the area of distributed hydrological modeling have led to the development of a Water-and-Energy-Budget-based Distributed Hydrological Model (WEB-DHM) that is capable of giving distributed representations of the spatial variation and physical descriptions of runoff generation and routing in river channels at basin to continental scales [31,32]. The snow physics of the model were further improved by the incorporation of the three-layered snow scheme of Simplified Simple Biosphere model version 3 [33,34] with the albedo scheme of the biosphere atmosphere transfer scheme [35,36]. With this improved snow process representation, WEB-DHM is referred to as WEB-DHM-S. The strength of the improved model lies in its ability to simulate the long-term continuous spatial distribution of the snow variables in addition to other hydrological processes [37–39]. Since potential hydropower sites are often located in river basins predominantly fed by snow, accurate modeling of snow hydrology of the river basin has profound significance for hydropower potential estimation. As the use of distributed hydrological model provides distributed representations of simulated runoff, it is also possible to derive distributed environmental flows based on these physically based simulations, which are often neglected in such GIS-based tools or are simply parameterized based on the morphological characteristics of the basin [7]. Since the selection of a suitable SHP site involves several different criteria (many of which can be quite contrary to each other), it is rather difficult to achieve a solution that is truly optimal for every criterion. In such a case, Multi-Criterion Decision Making (MCDM) techniques can be used to find a compromise solution by considering many different criteria. One such family of MCDM techniques called the Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE), developed by Brans, has gained particular attention because of its flexible design and the fact that it only requires information that is easily understood by decision makers [40–42]. This approach can be contrasted with other MCDM techniques such as the ELimination Et Choix Traduisant la REalité (ELECTRE), the process and outcome of which may be difficult to understand from a decision maker’s point of view [41]. Other famous MCDM techniques, such as the Analytic Hierarchy Process (AHP), become increasingly complex as the number of alternatives and criteria increases [42]. In addition, these PROMETHEE methods have significant utility in addressing multi-criteria water resource problems [43]. Mladineo et al. suggested the application of PROMETHEE methods for ranking SHP location alternatives largely because of the flexibility they offer to the decision makers in expressing their preferences and because any number of criteria can be accommodated using these methods [44]. Considering the developments in the areas of GIS, distributed hydrological modeling, and MCDM techniques, this paper primarily focuses on the objective of developing a systematic decision support tool for robust hydropower site selection that integrates the spatial data handling capabilities of GIS, the advanced distributed snow modeling capabilities of WEB-DHM-S, and the flexible multi-criteria ranking capability of PROMETHEE methods to rigorously evaluate all the possible SHP site alternatives and suggest optimal potential sites for SHP development. Second, the paper highlights two different approaches for optimized SHP site selection, namely, (i) considering only topographical factors and (ii) considering both topographical and hydrological factors and drawing a comprehensive comparison between them. Lastly, the capability of the developed tool is demonstrated by its application to the Kunhar River Basin, a snow-fed basin in the northern

hydropower potential, particularly in data-scarce developing countries. Over the past decade, SHP has gained substantial popularity because of its significantly lower adverse impacts compared with its larger counterparts. Being run-of-river in most cases, these SHP developments generally do not possess a storage component, and as such, they have lower socio-environmental impacts [6]. In contrast, the absence of a storage component makes hydropower potential very sensitive to the location of the project site as it is a function of the gross head and the available flow, both of which vary spatially. In a conventional site selection approach, it is rather cumbersome and time consuming to consider all the possible site alternatives in a spatial domain; hence, the site selection process may be adversely affected by the subjectivity of the decision maker, and the optimal project site may not be selected. Hydropower potential may be evaluated at four different levels depending on the constraints applied to power development [7]. The hydropower potential evaluated without the application of any constraints is known as the theoretical potential. Not all theoretical potential can be utilized to generate hydropower; the exclusion of restricted or protected areas and the consideration of environmental flows reduce the theoretical potential to yield planning potential. Planning potential is then further constrained by the consideration of turbine efficiencies and energy losses in the waterway to give technical potential [8]. Economically feasible potential can then finally be evaluated by considering the site-specific cost analysis of the hydropower project. The development of geographic information systems (GISs) has motivated several renewable energy resource potential and site selection studies in the past, such as for solar energy [9–11], biomass energy [12,13], and wind energy [14,15] site selections. However, in all these cases, the energy potential is distributed over a uniform grid, which is in contrast to the case of hydropower, which has the energy potential distributed over an intricate network of streams and in which the selection of one hydropower generation site may interfere with the selection of another site. To tackle this challenge, researchers over the years have developed tools of varied complexity through the creative use of GIS technology. However, these tools are often developed for specific locations and have limited applicability in other areas owing to their dependence upon certain databases that are unique to a particular locality [16]. Ballance et al. used the coefficient of variation and low flow index to identify areas that were less susceptible to flow variability and estimated the hydropower potential using derived slope maps and regional mean annual runoff datasets for micro- and macro-hydropower schemes [17]. The general approach to evaluating the hydropower potential of a river basin consists of (i) specifying a number of points on the stream network separated by a fixed interval, (ii) assuming these points to be potential sites, and (iii) evaluating the hydropower potential at these sites using digital elevation models (DEMs) to calculate the gross head and regional flow duration curves (FDCs) or catchment area to estimate the available flow [18–22]. Rojanamon et al. further considered the environmental and social impacts based on local datasets and field questionnaire surveys in addition to engineering and economic criteria for selecting suitable hydropower sites [23]. Palomino et al. evaluated theoretical hydropower potential under global changes, such as population growth and economic development [24]. The study by Garegnani et al. is one of the first works that explicitly calculates hydropower potential at all four levels (theoretical, planning, technical and economic) discussed earlier [7]. However, the abovementioned approaches have limited applicability in poorly gauged areas where regional FDCs and gridded mean annual discharge datasets are not available and hence are not applicable globally. A number of studies have suggested the use of hydrological models to simulate the discharge at sites screened by a GIS analysis [25–27]. However, the hydrological models employed in these studies, such as the Soil and Water Assessment Tool (SWAT) and Hydrologic Engineering Center’s Hydrologic Modeling System (HECHMS), are of a pseudo-distributed nature and require the exact location 310

Applied Energy 224 (2018) 309–321

A. Moiz et al.

Fig. 1. Kunhar River Basin, Pakistan. (a) Map shows the location of the meteorological sites and discharge gauges used in this study and the basin elevation. (b) Average precipitation (mm) over the year. (c) Average temperature (°C) over the year. (d) Average discharge (m3/s) over the year. (1971–2009).

drastically from 18.6 °C at Balakot to approximately 7.3 °C at Naran. These estimates are based on the climatological data collected from the Pakistan Meteorological Department (PMD) and the Water And Power Development Authority of Pakistan (WAPDA) for the period 1971–2010 (Fig. 1b–d). Two peaks can be observed in the average precipitation over the year, the first occurring in March as a result of western disturbances and the second in July, particularly in the southern region of the basin, driven by the summer monsoon. The precipitation caused by western disturbances mainly accumulates in the form of snow in the northern areas and contributes to a peak in the annual hydrograph during June–July as snowmelt [45].

region of Pakistan, as a case study to evaluate its hydropower potential. The scope of this study is limited to the level of planning potential. To the best of the authors’ knowledge, this is the first work that completely integrates a physically based distributed hydrological model with a GIS-based site selection and potential evaluation tool. The novel tool developed in this study can aid hydropower planners and decision makers in systematically and objectively surveying large poorly gauged river basins for potential SHP locations and in evaluating the associated hydropower potential in a short time and a robust manner. As will be demonstrated in the subsequent sections, the tool relies heavily upon datasets that are available at a global scale, thus limiting its reliance upon local datasets.

3. Methodology 2. Study area This paper discusses the development of a systematic SHP site selection tool that makes the planning phase of site selection process less time consuming, more objective, and more robust. A run-of-river-type hydropower development is generally established by the construction of a weir that diverts the water through a waterway and utilizes the natural head of the waterfall to generate electricity at the hydropower station. The gross natural head is dictated by the location of the diversion site and the hydropower station, which are connected by a waterway. The catchment area draining into the diversion site, the magnitude and availability of flow at the diversion site, the gross head between the diversion site and the hydropower station, and the length of the waterway connecting the diversion site to the hydropower station have been identified as the most significant factors that should be considered during the site selection process [47]. Fig. 2 shows the detailed analytical framework of the developed tool. At first, WEB-DHM-S is set up for the target basin to simulate the basin hydrology and to generate the FDCs in each hydrological unit.

The study area selected for this study is the Kunhar River Basin, a sub-basin of the transboundary Jhelum River Basin, located in the northern region of Pakistan. The basin lies between the longitudes of 73°17′E and 74°08′E and the latitudes of 34°11′N and 35°10′N, stretching over an area of 2632 km2 (Fig. 1a). Originating from Lulusar Lake in Kaghan Valley, the Kunhar River drains the southern slopes of the Greater Himalayas, and approximately 65% of the discharge is generated from snowmelt [45]. A number of settlements line the Kunhar River, e.g., Patrind, Balakot, Paras, Jared, Mahandri, Khannian, Kaghan, Kinari, Paludran, Naran, and Batakundi from downstream to upstream. The elevation of the basin changes precipitously from 636 to 5216 m above sea level, making it one of the most promising basins for hydropower development in Pakistan [46]. The basin exhibits a humid subtropical climatology, with the average annual precipitation ranging from 1619 mm at Naran to 1610 mm at Balakot. The average annual temperature decreases 311

Applied Energy 224 (2018) 309–321

A. Moiz et al.

Fig. 2. Analytical framework of the integrated tool.

static and dynamic datasets. Static datasets include the DEM (Fig. 1a), soil hydraulic characteristics (Fig. 3b), and static vegetation parameters (Fig. 3c). Dynamic datasets include meteorological forcings (precipitation, air temperature, wind speed, relative humidity, downward shortwave radiation, downward longwave radiation, and air pressure) and dynamic vegetation parameters (leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR)). The model was set up at a grid resolution of 300 m. The basin is discretized into seventy-three sub-basins based on the Pfafstetter codification scheme [48] (Fig. 3a) so that each sub-basin has at most one stream on which the SHPs can be located, each of which is further sub-divided into flow intervals depending on the time of concentration. Each flow interval is 1.5–2 times the model grid size. The runoff from each model grid is accumulated

The developed tool analyzes all the possible alternative hydropower schemes and finally selects the finite set of non-conflicting alternatives that gives the highest hydropower potential for the shortest length of waterway to attain economic efficiency. To achieve this, PROMETHEE II with complete ranking was integrated into the developed tool. The tool was developed as a user-friendly Python script tool in ArcGIS with an interface similar to that of other tools in ArcGIS (see supporting information Fig. S1). The subsequent sections discuss each component in detail. 3.1. WEB-DHM-S setup To set up WEB-DHM-S, two sets of data must be prepared, namely,

Fig. 3. Static inputs for WEB-DHM-S. (a) Basin is sub-divided into seventy-three sub-basins using second-level Pfafstetter codification scheme with unique colors indicating the first level Pfafstetter codification. (b) Soil type. (c) Land cover type. 312

Applied Energy 224 (2018) 309–321

A. Moiz et al.

Table 1 Inputs for WEB-DHM-S setup and evaluation. Data

Spatial Resolution

Temporal Resolution (Extent)

Source

Static Inputs DEM Soil type Land use

30 m 5 arc minutes 1000 m

Static Static Static

SRTMa FAOb USGSc

Point 0.5625°

PMD; WAPDA JRA55d

1000 m

Daily (1977–1997; 2007–2009) 3-hourly (1977–1997; 2007–2009) 8-day composite (2007–2009)

Fraction of Photosynthetically Active Radiation (FPAR)

1000 m

8-day composite (2007–2009)

Evaluation Data Discharge Snow cover

Point 500 m

Daily (1978–1997; 2008–2009) 8-day composite (maximum snow extent)

Dynamic Inputs Precipitation Meteorological data (precipitation, air temperature, wind speed, relative humidity, downward shortwave radiation, downward longwave radiation, and air pressure) Leaf Area Index (LAI)

a b c d e

MODIS Terra (MOD15A2)e MODIS Terra (MOD15A2)e WAPDA MODIS Terra (MOD10A2)e

Shuttle Radar Topography Mission https://earthexplorer.usgs.gov/. Food and Agriculture Organization [49]. United States Geological Survey. https://lta.cr.usgs.gov/GLCC. Japanese Reanalysis 55-year. http://jra.kishou.go.jp/JRA-55/index_en.html. MODerate resolution Imaging Spectroradiometer. https://reverb.echo.nasa.gov/.

into a flow interval and routed into a virtual channel toward the basin outlet. For the purposes of this study, the simulated discharge at each flow interval is stored in a database that can be conveniently referred to using the unique sub-basin and flow interval IDs. All the necessary static and dynamic datasets (specific to Kunhar River Basin) required to set up and evaluate WEB-DHM-S are given in Table 1. Point precipitation data were spatially interpolated using inverse distance weighted interpolation. For detailed model physics, please refer to [31,32,37–39]. The model is initialized on 29 August 2007 (when the snow cover is minimal) and is allowed to spin up for 1-year multiple times until hydrological equilibrium is reached. The model is then calibrated for the year 2008 and 2009 using the observed discharge at Talhata as well as the MODIS snow cover. Since the MODIS dataset is available only after the year 2000, calibration of the model is conducted for a period following the validation period. The performance of the model was evaluated using two quantitative statistics: the Nash – Sutcliffe efficiency (NS) and percent bias (PBIAS):

3.2. SHP site selection tool The tool has been developed in such a way that it can be used with two different approaches for site selection. Site selection can be conducted based on either (i) only topographical factors or (ii) both topographical and hydrological factors. In approach (i), sites are selected by maximizing the gross head between the diversion site and the hydropower plant and by maximizing the catchment area at the diversion site, whereas the length of the waterway is minimized. The hydropower potential is then evaluated at the selected sites using the FDCs generated from the runoff simulated by distributed hydrological modeling. However, in approach (ii), the hydropower potential of each alternative is evaluated first by using the simulated runoff, and then, sites are selected by maximizing the hydropower potential and minimizing the length of the waterway. Approach (ii) should identify a better set of alternatives because in this case, a complete redistribution of the sites is achieved based on the actual hydropower potential calculated from the distributed FDCs. In addition, this approach also makes it possible to control the scale of hydropower development, which is an obvious advantage over approach (i).

n

NS = 1−

PBIAS =

∑i = 1 (Qiobs−Qisim)2 n

∑i = 1 (Qiobs−Qmean )2 n ∑i = 1

Qiobs

(Qiobs−Qisim) × 100 n ∑i = 1

Qiobs

(1) 3.2.1. Pre-processing and pre-screening search algorithm The basin under consideration is first delineated using the DEM and the location of the basin outlet through the development of the flow direction and a flow accumulation grid. Following basin delineation, the stream network grid is generated from the flow accumulation grid based on a minimum stream definition threshold that is specified as an input parameter in the tool. The stream network grid is then codified using the Strahler stream order codification scheme [50]. The algorithm then loops through each stream in a given stream order. In the case of the Kunhar River Basin, streams up to the fourth order were generated. Each stream is then converted to a polyline and densified into points using a polyline densification scheme (adopted from a custom ArcGIS tool at ianbroad.com). The stream densification interval is specified as an input parameter in the tool, and it primarily controls the computing time required by the tool since each point generated will be processed along with its alternatives. This interval also serves as the minimum distance between two consecutive hydropower schemes along the stream. The search algorithm begins by selecting the most downstream point and then moves upstream. The selected point is assumed to be a hydropower station, and then, an annular search zone is established

, (2)

Qisim

is the observed discharge, is the simulated discharge, where Qmean is the mean of the observed discharge time series over the period of simulation, and n is the total number of observations. The spatially distributed snow cover simulation is compared with the MODIS snow cover to further evaluate model accuracy. Several sensitive soil, land and snow parameters that have been calibrated in this study are listed in Tables 2 and 3. Default values have been adopted for the remaining parameters; these are given in [31,32,37–39]. However, to generate more reliable FDCs for evaluating hydropower potential, long-term data are required. Therefore, after calibration, the model is initialized on 29 August 1977 assuming minimal snow cover and is validated for 21 years (1978–1998). Owing to the unavailability of the MODIS LAI and FPAR product (MOD15A2) during this period, the average for 2001–2009 is used as a substitute assuming that the long-term changes in vegetation dynamics do not significantly affect the simulated discharge. The discharge was calibrated and validated at Talhata. 313

Applied Energy 224 (2018) 309–321

A. Moiz et al.

Table 2 Soil, land and snow calibration parameters in WEB-DHM-S. Symbol

Parameters

Unit

Soil Type

Source

Cambisols, Fine

Cambisols, Medium

Lithosols

Ksat1

Hydraulic conductivity of 1st layer of unsaturated zone

mm h−1

18.22

42.61

61.31

Optimization/FAO

Ksat2

Hydraulic conductivity of 2nd layer of unsaturated zone

mm h−1

3.64

8.52

12.26

Optimization/FAO

Kg

Groundwater hydraulic conductivity

mm h−1

0.36

0.85

1.23

Optimization/FAO

anik SSTmax

Anisotropic hydraulic conductivity ratio Maximum surface water detention

mm

1.4 35.0

Optimization Optimization

Γlapse

Air temperature lapse rate

K m−1

-8.5

Optimization/Observation

∝vis ∝nir CF

Fresh snow albedo in visible band Fresh snow albedo in near-infrared band Calibration parameter for snow correction factor

0.75 0.65 0.00026

Optimization Optimization Optimization

Tth

Snow/rain threshold temperature

2.0

Optimization

m−1 °C

accumulation grid. Moreover, the ID of the sub-basin and flow interval grid is also assigned to the diversion site within which it falls. In approach (ii), the FDCs are constructed for every diversion site to obtain the design discharge against a specified percentage of flow exceedance. Potential candidates are then screened out based on specified thresholds, namely, minimum gross head, minimum waterway slope, and minimum catchment area. This screening is done to ensure that only sites with some significant gross head and discharge are considered in the analysis to reduce the computational costs. In the case of approach (i), the catchment area draining into the diversion site is used as an indicator of flow availability. For approach (ii), the hydropower potential for each screened alternative is evaluated using the power equation:

Table 3 Inputs for the SHP site selection tool. Input

Value/Type

Source

Elevation Raster Target Basin Outlet Stream Densification Interval Maximum Waterway Length Minimum Waterway Length Minimum Allowable Gross Head

Grid (30 m) Point 500 m 5000 m 2000 m 30 m (for medium & high head) 2%

SRTM – [26] Decision Maker Decision Maker [52]

Minimum Allowable Slope Minimum Allowable Flow Accumulation Exclude Sites in Protected Areas Protected Areas

50 km

2

[26]; Decision Maker Decision Maker – [53]

Pfafstetter Sub-basin Grid

True (Optional) Polygon Shapefile (Optional) 5000 m (Optional) (i) Topographic Factors (ii) Topographic and Hydrological Factors Grid (300 m) (Optional)

WEB-DHM Flow Interval Grid

Grid (300 m) (Optional)

System Efficiency Flow Exceedance Environmental Flow (as fraction of minimum daily historical discharge) Minimum Allowable Hydropower Maximum Allowable Hydropower

80.0% (Optional) 70.0% (Optional) 0.25

WEB-DHM-S Pre-processing WEB-DHM-S Pre-processing [25] Decision maker WAPDA

2 MW (for SHP) (Optional)

[52]

25 MW (for SHP) (Optional)

[52]

Buffer around Protected Areas Approach

Decision Maker –

P = ηρgQd H

(3)

Qd = (Qsim−Qe )FE

(4)

Qe = minQsim × e

(5)

where P is power, η is system efficiency, ρ = 1000 kg m−3 is the density of water, g = 9.81 m s−2 is the acceleration due to gravity, H is gross head, Qd is the design discharge, Qsim is the simulated discharge, and Qe is the environmental flow expressed as a fraction e of the minimum daily historical discharge. According to the recent practice of WAPDA, the value of e can range from 0.2 to 0.3. Qd is taken at a particular flow exceedance FE to take into account the seasonal variation of the river flow. FE depends on the number of days that the hydropower generation system is expected to run at full capacity in a year, which in turn is governed by the energy mix of the country’s power generation system. The scale of hydropower development is controlled by specifying the minimum and maximum target hydropower potentials. 3.2.2. PROMETHEE compromise site selection To rank the screened alternatives, a PROMETHEE II (complete ranking) approach is adopted at each search point to allow a pairwise comparison and a complete ranking of the alternatives [40]. PROMETHEE II is selected because compared with other MCDM techniques, it requires limited and easily understood inputs from the decision maker [41,42]. The only inputs required are the evaluation criteria, the preference function and the criteria weights. Moreover, it also reflects the preferences of the decision maker for each criterion, which can vary significantly from region to region. The objective is to maximize the hydropower potential and minimize the length of the waterway, as shown below.

around it, with the inner and outer radii representing the minimum and maximum allowable lengths of the waterway, which can be specified as inputs to the tool. This is done to allow the user control over the length of the waterway and to avoid the development of unrealistically long or short waterways. All the remaining points that fall within the annular search zone are assumed to be diversion site alternatives, and a number of alternative schemes are constructed, which connect each diversion site alternative with the hydropower station under consideration (Fig. 4a). Alternative schemes are only constructed for those diversion site/ hydropower station combinations that do not fall in the buffer zone created around any environmentally protected areas. Gross head is calculated as the difference between the elevation of the diversion site and hydropower station, and slope is calculated by dividing this elevation difference by the distance between the diversion site and hydropower station. The catchment area at the diversion site is evaluated based on the number of cells draining into that point using the flow

Approach (i ) max{c1 (s ),c2 (s ),−c3 (s )|s ∈ S }

(6a)

Approach (ii ) max{c4 (s ),−c3 (s )|s ∈ S },

(6b)

where c1,c2,c3, and c4 are the evaluation criteria for gross head, catchment area, waterway length, and hydropower potential (evaluated 314

Applied Energy 224 (2018) 309–321

A. Moiz et al.

Fig. 4. Functionality of the SHP site selection tool. (a) Pre-screening search algorithm. (b) PROMETHEE II site selection. (c) Cut-off tests.

adopted in this study is given by

using Eq. (3)), respectively. S is a set of all screened hydropower scheme alternatives {s1,s2,…,sn} . It should be noted that besides these four criteria, other criteria should also be included in Eq. (6) depending upon the level at which hydropower potential is to be evaluated. For example, in the present case, the only measure of cost used is the length of the waterway. However, in the case of economically feasible potential, site-specific cost factors such as the distance to the electric grid (or the closest consumer), length of access roads, turbine technology (depending upon flow characteristics), diameter of the waterway tunnel (depending upon flow characteristics), and compensation to land owners, if estimated appropriately, can be included as criteria in Eq. (6) for site selection. Each alternative is compared with every other alternative by calculating the deviation between the performance of each alternative and each criterion considered. For example, for two screened alternatives, si and sj , and for the k th criterion, the deviation is calculated as

dk (si,sj ) = ck (si )−ck (sj ).

dk (si,sj ) ⩽ q ⎧0 ⎪ dk (si,sj) − q q < dk (si,sj ) ⩽ p Pk [dk (si,sj )] = ⎨ p−q ⎪1 dk (si,sj ) > p ⎩

(8)

where P is the preference function, d is the deviation, q is the indifference threshold, and p is the strict preference threshold. The preference values for all the criteria are then aggregated into a single index as follows: N

π (si,sj ) =

∑

Pk [dk (si,sj )] wk

(9a)

k=1

N

π (sj,si ) =

∑

Pk [dk (sj,si )] wk,

(9b)

k=1

(7)

th

criterion and π (si,sj ) and where wk is the relative weight for the k π (sj,si ) indicate the degree to which si is preferred over sj and vice versa, respectively. The outranking flows are calculated by comparing each alternative si against the (n−1) other screened alternatives in S .

These deviations are then translated into preferences using a preference function defined by the decision maker. For the sake of simplicity, in this study, a V-shaped preference function is adopted. However, depending on the way that these preferences reflect reality, the decision maker may assign a preference function other than Vshaped, such as Usual, U-shaped, Level, V-shaped with indifference criterion and Gaussian preference functions [51,40]. The preference function allows the decision maker to specify thresholds below which the deviation is insignificant and above which the deviations are so significant that a strict preference is established for one alternative over another. The mathematical representation of the preference function

Positive Outranking Flow

Negative Outranking Flow

Φ+ (si ) =

Φ− (si ) =

1 n−1

∑

1 n−1

∑

π (si,s )

π (s,si ).

(10a) (10b)

The positive outranking flow indicates how the alternative under consideration outranks the other (n−1) alternatives, whereas the 315

Applied Energy 224 (2018) 309–321

A. Moiz et al.

negative outranking flow indicates how the alternative under consideration is being outranked by (n−1) alternatives. For PROMETHEE II (complete ranking), the net outranking flow is calculated as

Φ(si ) = Φ+ (si )−Φ− (si )

assignment of a different snow correction factor for various elevation bands might reduce this discrepancy, as suggested by [37]. To further facilitate the calibration of parameters and evaluation of the model, the model-simulated snow cover was compared with the MODIS-derived snow cover and the bias between them was calculated. A pixel-by-pixel comparison suggested that the model demonstrates high accuracy in simulating the snow cover (see supporting information Fig. S2.1-S2.2). On some days, however, the model underestimates the snow cover due to the limited observation stations in high-elevations areas, resulting in a poorly interpolated precipitation grid. This effect is particularly noticeable in the case of the Kunhar River Basin as the basin is dominated by a number of small-scale valleys; as a result, most precipitation events are driven by the local orography rather than largescale processes. For the validation of long-term simulations (1978–1998), the results are reasonable during 1988–1998; however, a significant underestimation is observed in 1978–1987, particularly during the snowmelt season (Fig. 6a). Since Naran is the only high-altitude observation station, further investigation revealed that the precipitation measurement at Naran seems inconsistent as the average annual precipitation drastically changes from 1047 mm in the period 1978–1987 to 2277 mm in the period 1988–1998. This change in the average annual precipitation between these two periods was not as significant for Balakot or Muzaffarabad (see supporting information Fig. S3). To address this issue, a different snow correction factor is applied to the two periods. The value of the snow correction factor was increased from 0.00026 to 0.0008 m−1 to take into account this inconsistency between the two periods. The results show a significant improvement with an increase in the NS coefficient from 0.65 to 0.74 and a reduction in the PBIAS from −21.67% to −7.2% (Fig. 6b and supporting information Fig. S4).

(11)

All the screened alternatives within the annular search zone are ranked based on their net outranking flow; ultimately, the scheme with the highest net flow is selected (Fig. 4b). 3.2.3. Cut-off tests Following pre-screening and PROMETHEE-based compromise site selection within the annular search zone, a number of sites are selected; however, many of these sites cannot be developed simultaneously because they are cut off by some other development, as shown in Fig. 4b. A set of three cut-off tests is conducted to determine whether the selected scheme is being interfered with by other schemes and to finally generate a non-conflicting set of selected schemes, as shown in Fig. 4c. The net outranking flows for all the schemes are calculated again using the same methodology described in Section 3.2.2, but this time, each scheme is compared with all the other (n−1) schemes in the entire basin. The scheme with the highest net outranking flow is selected and tested for interferences. There are at most three possible cases of interference, namely, (i) the diversion site of another scheme is in the cutoff portion of the selected scheme, (ii) the hydropower station of another scheme is in the cut-off portion of the selected scheme, or (iii) the selected scheme is cut off by another scheme. In this way, the interfering schemes for every selected scheme are discarded until there are no more interferences. The remaining schemes are stored as the final set of selected schemes. 3.2.4. Tool application This section discusses the inputs used for the application of the tool to the Kunhar River Basin, as summarized in Table 3. It should be noted that all the spatial inputs have been re-projected to the UTM 43N WGS 1984 coordinate system. The Kunhar River Basin possesses a relatively steep topography; as such, the minimum permissible head has been set so that only mediumand high-head sites are analyzed. Although there is no generally agreed upon definition of medium- and high-head hydropower plants (as it varies from region to region), medium- and high-head hydropower plants are defined herein as having a gross head greater than 30 m. Since the target scale of the development is SHP, a threshold of 2–25 MW has been set in this study; however, this threshold also varies from region to region and should, in general, be decided by the hydropower planner or decision maker [52]. A design discharge corresponding to 70% flow exceedance is adopted based on the assumption that the hydropower plant is only used for base load generation, which is reasonable since in the case of Pakistan, during periods of low flow, hydroelectric power is supplemented by thermal power.

4.2. Distributed representation of FDCs Each sub-basin has a number of flow intervals. In this study, the simulated discharge Qsim is archived for each sub-basin – flow interval combination. The environmental flows Qe are also evaluated for each sub-basin – flow interval combination using Eq. (5) with e = 0.25 based on the minimum daily discharge simulated over 21 years. This is the portion of discharge that cannot be used for hydropower generation and must be released to sustain the basin ecology in the cut-off region of the hydropower scheme. The spatial distribution of Qe for the whole basin is shown in Fig. 7. The environmental flows are lower in the tributaries, and they increase substantially in the downstream direction along the main river, dictating the discharge available for hydropower generation. The resulting environmental flows are then subtracted from Qsim to obtain the discharge available for hydropower generation at each grid. FDCs are then plotted for each of these sub-basin – flow interval combinations, as per Eq. (12).

4. Results and discussion

FE = 100 × 4.1. Calibration and validation of WEB-DHM-S

M D+1

(12)

where M is the rank of discharge value and D is the number of events in a period. The distribution of the FDCs over the hydrological units (Fig. 8) gives a more explicit representation of the hydrological suitability of the site for hydropower generation. In addition, this result can also give us an indication of how hydrologically sensitive the location of the project site is in a particular sub-basin. For example, the water availability in sub-basins 5 and 9 varies considerably depending upon the location in the stream; however, this variation is not significant in the case of sub-basins 1, 3 and 7.

Fig. 5 shows a comparison of the simulated discharge (resampled to daily temporal resolution) with the observed discharge at Talhata for the calibration period. The simulated discharge in general agrees well with the observed one with an NS coefficient of 0.9 and 0.96 for the years 2008 and 2009, respectively. The water budget is also well simulated by the model with a PBIAS (%) of −1.86% and −6.3% for the years 2008 and 2009, respectively. Some differences can be observed in the months of May and August, which may be because the air temperature lapse rate varies considerably throughout the year in the basin under consideration, but in this simulation, only a constant air temperature lapse rate is assumed for the entire year. Second, the 316

Applied Energy 224 (2018) 309–321

A. Moiz et al.

Fig. 5. Comparing the simulated and observed discharge at Talhata for 2008–2009 (calibration period).

Fig. 6. Long-term validation of the discharge simulation (1978–1998). Comparison of the observed discharge with the simulated one for the (a) same value and for (b) different values of the snow correction factor.

the category of SHP are also identified as promising sites. However, in the case of approach (ii), all these sites are filtered out from the very beginning of the analysis. Second, approach (i) tended to select schemes based only on the gross head and flow accumulation, both of which are topographical factors. As a result, many sites are identified in the upper mountainous reaches of the basin. However, in the case of approach (ii), these sites are not identified since in this case, schemes are identified based on the actual hydropower potential, which is a function of the discharge in addition to the gross head. The significance of approach (ii) is quite evident because under equivalent criteria weights, this approach can select a combination of run-of-river schemes capable of generating roughly the same hydropower as approach (i) (235 MW at 70% flow exceedance) with a significantly lower number of hydropower sites (Fig. 10). A smaller number of hydropower sites could mean a lower cost for achieving the same generating capacity in the basin. Moreover, the total length of the waterway is also significantly reduced from 85 km when using approach (i) to 72 km when using approach (ii), thus achieving a higher economic efficiency for basin-scale development. The novelty of the developed tool lies in the notion that by using approach (ii), a complete redistribution of the hydropower schemes is achieved through the consideration of the spatially heterogeneous hydrology of the basin and by selecting a non-conflicting set of hydropower schemes with essentially the same hydropower potential but at a

4.3. Comparative analysis of approach (i) and approach (ii) When using only the topographic factors for site selection, a total of 40 schemes are identified with total basin hydropower potentials of 1023, 388, 239 and 149 MW at 30%, 50%, 70% and 95% flow exceedances, respectively. However, the schemes identified on a firstorder Strahler stream segment have been excluded from further analyses due to exceedingly low runoff in these reaches and because the second-level Pfafstetter delineation only covers the Strahler stream orders higher than one in the model developed, and hence, there are no flow intervals that represent first-order streams (for detailed results for each individual selected site, please see supporting information Table S1). Following this exclusion, approach (i) was able to identify 36 sites, whereas only 26 sites were identified by approach (ii), as shown in Fig. 9. Using approach (i), the total basin hydropower potential was evaluated to be 1004, 382, 235 and 147 MW compared with 1013, 388, 238 and 149 MW when using approach (ii) at 30%, 50%, 70% and 95% flow exceedances, respectively. Equivalent criteria weights were used for both approaches. There are two main reasons for the different numbers of sites identified using both approaches. One reason is that approach (ii) makes it possible to control the scale of hydropower development (in terms of generation capacity); however, this is not possible in the case of approach (i), so a number of sites that do not fall in 317

Applied Energy 224 (2018) 309–321

A. Moiz et al.

historical flows without the need to parameterize it based on catchment area. Additionally, the physical basis of WEB-DHM-S requires only a minimal effort to calibrate it in comparison to conceptual hydrological models. The capability of the tool is demonstrated in its application to the Kunhar River Basin in Pakistan. The tool is globally applicable as it relies on datasets that are available with global coverage (except for the gauge precipitation required as an input and the gauge discharge required for model validation). This feature makes the tool especially suitable for poorly gauged basins. However, it should be noted that the tool is only meant to aid the decision maker during the preliminary site selection process and is in no way intended to replace the decision maker or the detailed site feasibility studies carried out at a later stage. The scope of the tool developed in this study is limited to planning potential. The tool should be extended in the future to consider technical and economic potential. Moreover, planning potential not only includes the consideration of environmental factors but also social factors. However, due to the lack of data in the target basin to support the estimation of flows required for social needs, such factors have not been considered in the present study. The availability of high-resolution population, land use and irrigation network datasets in the future could facilitate the estimation of flows needed for water supply and irrigation. For the sake of simplicity, in this study, equivalent criteria weights were assigned to each criterion in both of the approaches discussed. However, in order to reflect regional differences and priorities, the decision maker can assign different weights for more effective planning. One of the drawbacks of using PROMETHEE is the difficulty in deciding the weights for the criteria. The combination of PROMETHEE with other MCDM techniques may be explored to overcome this issue. Another limitation of the tool algorithm developed in this study is that every alternative waterway scheme has a diversion site and hydropower station on the same stream segment. In reality, however, it is possible that a waterway may divert water from one stream segment to another. Another limitation is that the tool assumes that all waterways are tunnel type, represented by a straight line. Future research should be directed toward the development of more complex algorithms that can represent waterways between two different stream segments and open channel waterways that cannot be represented by straight lines. In real-world applications, this tool could be the key for the preparation of basin-wide hydropower development master plans in a

Fig. 7. A distributed representation of environmental flows Qe .

lower cost. The developed tool focuses on objective-oriented decision making and robustly evaluates all possible combinations of non-conflicting hydropower schemes, thus reducing the element of subjectivity in the decision-making processes. In addition, being based on a more systematic approach, the tool allows large areas to be analyzed in relatively shorter times. The strength of the developed tool primarily lies in its ability to utilize a distributed hydrological model to generate distributed FDCs, which make it possible to locate a potential site at any location in the basin irrespective of the availability of regional FDCs. This study is the first that attempts to overcome this barrier. The use of a distributed hydrological model also facilitates the generation of spatially distributed environmental flows based on

Fig. 8. A distributed representation of FDCs. (a) Level 1 group of Pfafstetter sub-basins with the even-numbered sub-basins (tributaries) grayed-out. (b) FDCs for the odd-numbered sub-basins (main river). The range shows the FDCs associated with all the flow intervals in a sub-basin. The solid line shows the average of all the flow intervals in a sub-basin. 318

Applied Energy 224 (2018) 309–321

A. Moiz et al.

Fig. 9. Comparing the spatial distribution of selected run-of-river schemes using (a) approach (i) considering topographical factors and (b) approach (ii) considering hydrological and topographical factors.

tool could make a significant contribution to studying the impact of climate change on SHP potential to facilitate long-term planning.

short time and with minimal effort. The developed plans can provide an overview of the hydropower potential in the basin to the decision maker to support the screening of sites for a detailed feasibility study. As mentioned earlier, gauge precipitation is the only local dataset used as an input for the hydrological model, with all other inputs being derived from global datasets. The use of global satellite precipitation products (after bias correction) could be used in the future to make the tool presented in this study completely independent of local datasets. Moreover, hydrological regime of many basins across the globe will likely be affected by climate change, thus causing either an increase or decrease in hydropower potential. Since the tool developed in this work is able to include the spatially distributed hydrology of the basin in site selection, as demonstrated using approach (ii) using WEB-DHM-S, the

5. Conclusions In this study, the development of a systematic GIS-based hydropower site selection tool was presented for the estimation of basin-wide planning potential. The developed tool considered all possible sets of hydropower scheme alternatives and further employed multi-criteria decision making methods to obtain a complete ranking of the alternatives. Integration of the tool with a water and energy budget – based distributed hydrological model made it possible to include hydropower potential as a criterion to be maximized in the objective function in

Fig. 10. Comparing SHP potential at 70% flow exceedance using approach (i) considering topographical factors and approach (ii) considering hydrological and topographical factors. The dashed lines represent the number of selected sites. 319

Applied Energy 224 (2018) 309–321

A. Moiz et al.

addition to the generation of distributed flow duration. This study is the first to attempt to tackle this issue. Moreover, the tool was developed as a user-friendly and flexible ArcGIS script tool to allow users to tailor the selection criteria per the preferences of the decision maker and the local regulations. Finally, the integrated tool was applied to the Kunhar River Basin in Pakistan as a case study to demonstrate its capabilities. Two approaches were explored for site selection, namely, (i) considering only topographic factors and (ii) considering both topographic and hydrological factors. For equivalent factor criteria weights, the site selection using approach (i) revealed 36 optimal SHP sites with a waterway length of 85 km in the basin compared with only 26 sites with a waterway length of 72 km identified when considering both factors but essentially the same hydropower potential of 235 MW at 70% flow exceedance. Evidently, the generation of distributed flow duration curves using distributed hydrological modeling and the incorporation of hydropower potential as a maximizing criterion for site selection revealed more economically attractive small hydropower sites in basin-scale planning. In addition, this method also allowed the decision maker to limit the analysis to the target scale of hydropower development. The developed tool made the preliminary hydropower site selection process less time consuming, more robust, and more systematic, consequently making it less susceptible to the possible subjectiveness or bias introduced by the decision maker, both of which are due to the complexity of the problem when using a conventional approach. The new approach also reduced the number of suitable hydropower sites to a finite set that can be investigated in detail during feasibility studies. Although the tool’s capability is demonstrated for the Kunhar River Basin, it is not limited in application to a specific basin as it exhibits reduced reliance upon local datasets and is thus especially well-suited for applications in poorly gauged basins.

[7] Garegnani G, Sacchelli S, Balest J, Zambelli P. GIS-based approach for assessing the energy potential and the financial feasibility of run-off-river hydro-power in Alpine valleys. Appl Energy 2018;216:709–23. http://dx.doi.org/10.1016/j.apenergy. 2018.02.043. [8] Price T, Probert D. Harnessing hydropower: a practical guide. Appl Energy 1997;57:175–251. http://dx.doi.org/10.1016/S0306-2619(97)00033-0. [9] Wu Y, Geng S, Zhang H, Gao M. Decision framework of solar thermal power plant site selection based on linguistic Choquet operator. Appl Energy 2014;136:303–11. http://dx.doi.org/10.1016/j.apenergy.2014.09.032. [10] Al Garni HZ, Awasthi A. Solar PV power plant site selection using a GIS-AHP based approach with application in Saudi Arabia. Appl Energy 2017;206:1225–40. http:// dx.doi.org/10.1016/j.apenergy.2017.10.024. [11] Assouline D, Mohajeri N, Scartezzini JL. Large-scale rooftop solar photovoltaic technical potential estimation using Random Forests. Appl Energy 2018;217:189–211. http://dx.doi.org/10.1016/j.apenergy.2018.02.118. [12] Gonzalez-Salazar MA, Morini M, Pinelli M, Spina PR, Venturini M, Finkenrath M, et al. Methodology for estimating biomass energy potential and its application to Colombia. Appl Energy 2014;136:781–96. http://dx.doi.org/10.1016/j.apenergy. 2014.07.004. [13] Chen X. Economic potential of biomass supply from crop residues in China. Appl Energy 2016;166:141–9. http://dx.doi.org/10.1016/j.apenergy.2016.01.034. [14] Sánchez-Lozano JM, García-Cascales MS, Lamata MT. GIS-based onshore wind farm site selection using Fuzzy Multi-Criteria Decision Making methods. Evaluating the case of Southeastern Spain. Appl Energy 2016;171:86–102. http://dx.doi.org/10. 1016/j.apenergy.2016.03.030. [15] Ritter M, Deckert L. Site assessment, turbine selection, and local feed-in tariffs through the wind energy index. Appl Energy 2017;185:1087–99. http://dx.doi.org/ 10.1016/j.apenergy.2015.11.081. [16] Punys P, Dumbrauskas A, Kvaraciejus A, Vyciene G. Tools for small hydropower plant resource planning and development: a review of technology and applications. Energies 2011;4:1258–77. [17] Ballance A, Stephenson D, Chapman RA, Muller J. A geographic information systems analysis of hydropower potential in South Africa. J Hydroinformatics 2000;2:247–54. [18] Arefiev N, Nikonova O, Badenko N, Ivanov T, Oleshko V. Development of automated approaches for hydropowerpotential estimations and prospective hydropower plants siting 2015; 2:41–50. doi:http://doi.org/10.17770/etr2015vol2.260. [19] Feizizadeh B, Haslauer EM. GIS-based procedures of hydropower potential for tabriz basin, Iran. Int J 2012:495–502. [20] Gergeľová M, Kuzevičová Ž, Kuzevič Š. A GIS based assessment of hydropower potential in Hornád basin. Acta Montan Slovaca 2013;18:91–100. [21] Khan M, Zaidi AZ. Run-of-river hydropower potential of Kunhar River, Pakistan. Pakistan J Meteorol 2015. [accessed 10 March 2016]. [22] Müller MF, Thompson SE, Kelly MN. Bridging the information gap: a webGIS tool for rural electrification in data-scarce regions. Appl Energy 2016;171:277–86. http://dx.doi.org/10.1016/j.apenergy.2016.03.052. [23] Rojanamon P, Chaisomphob T, Bureekul T. Application of geographical information system to site selection of small run-of-river hydropower project by considering engineering/economic/environmental criteria and social impact. Renew Sustain Energy Rev 2009;13:2336–48. http://dx.doi.org/10.1016/j.rser.2009.07.003. [24] Palomino Cuya DG, Brandimarte L, Popescu I, Alterach J, Peviani M. A GIS-based assessment of maximum potential hydropower production in La Plata basin under global changes. Renew Energy 2013;50:103–14. http://dx.doi.org/10.1016/j. renene.2012.06.019. [25] Al-Juboori AM, Guven A. Hydropower plant site assessment by integrated hydrological modeling, gene expression programming and visual basic programming. Water Resour Manag 2016;30:2517–30. http://dx.doi.org/10.1007/s11269-0161300-3. [26] Kusre BC, Baruah DC, Bordoloi PK, Patra SC. Assessment of hydropower potential using GIS and hydrological modeling technique in Kopili River basin in Assam (India). Appl Energy 2010;87:298–309. http://dx.doi.org/10.1016/j.apenergy. 2009.07.019. [27] Rospriandana N, Fujii M. Assessment of small hydropower potential in the Ciwidey subwatershed, Indonesia: a GIS and hydrological modeling approach. Hydrol Res Lett 2017;11:6–11. http://dx.doi.org/10.3178/hrl.11.6. [28] Soulis KX, Manolakos D, Anagnostopoulos J. Development of a geo-information system embedding a spatially distributed hydrological model for the preliminary assessment of the hydropower potential of historical hydro sites in poorly gauged areas. Renew Energy 2016;92:222–32. [29] Larentis DG, Collischonn W, Olivera F, Tucci CEM. Gis-based procedures for hydropower potential spotting. Energy 2010;35:4237–43. [30] Yi CS, Lee JH, Shim MP. Site location analysis for small hydropower using geospatial information system. Renew Energy 2010;35:852–61. http://dx.doi.org/10. 1016/j.renene.2009.08.003. [31] Wang L, Koike T, Yang K, Jackson TJ, Bindlish R, Yang D. Development of a distributed biosphere hydrological model and its evaluation with the Southern Great Plains experiments (SGP97 and SGP99). J Geophys Res Atmos 2009;114:1–15. http://dx.doi.org/10.1029/2008JD010800. [32] Wang L, Koike T, Yong K, Yeh PJF. Assessment of a distributed biosphere hydrological model against streamflow and MODIS land surface temperature in the upper Tone River Basin. J Hydrol 2009; 377:21–34. [33] Shufen S, Yongkang X. Implementing a new snow scheme in Simplified Simple Biosphere Model. Adv Atmos Sci 2001;18:335–54. http://dx.doi.org/10.1007/ BF02919314.

Acknowledgment The authors would also like to extend their appreciation to the Water And Power Development Authority (WAPDA) of Pakistan and Pakistan Meteorological Department (PMD) for sharing the necessary meteorological and hydrometric datasets. Funding This research is partially supported by the DIAS project funded by the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT). Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.apenergy.2018.04.070. References [1] REN21. Renewables 2016 Global Status Report. Paris; 2016. doi:ISBN 978-39818107-0-7. [2] REN21. Renewables 2013 Global Futures Report. Paris; 2013. [accessed 10 March 2016]. [3] International Hydropower Association. Hydropower Status Report; 2017. p. 1–83. [accessed 10 January 2018]. [4] International Hydropower Association, International Comission on Large Dams, International Energy Agency, Canadian Hydropower Association. Hydropower and the World’s Energy Future; 2000. [accessed 10 March 2016]. [5] Chen ST, Kuo HI, Chen CC. The relationship between GDP and electricity consumption in 10 Asian countries. Energy Policy 2007;35:2611–21. http://dx.doi.org/ 10.1016/j.enpol.2006.10.001. [6] Paish O. Small hydro power: Technology and current status. Renew Sustain Energy Rev 2002;6:537–56. http://dx.doi.org/10.1016/S1364-0321(02)00006-0.

320

Applied Energy 224 (2018) 309–321

A. Moiz et al.

[34] Xue Y, Sun S, Kaha DS, Jiao Y. Impact of parameterizations in snow physics and interface processes on the simulation of snow cover and runoff at several cold region sites. J Geophys Res 2003;108:8859. http://dx.doi.org/10.1029/ 2002JD003174. [35] Dickinson E, Henderson-Sellers A, Kennedy J. Biosphere-atmosphere Transfer Scheme (BATS) Version 1e as Coupled to the NCAR Community Climate Model. NCAR Tech Rep NCAR/TN-3871STR 1993; 72:77. doi:http://doi.org/10.5065/ D67W6959. [36] Yang ZL, Dickinson RE, Robock A, Vinnikov KY. Validation of the snow submodel of the biosphere-atmosphere transfer scheme with Russian snow cover and meteorological observational data. J Clim 1997;10:353–73. http://dx.doi.org/10.1175/ 1520-0442(1997) 010<0353:VOTSSO>2.0.CO;2. [37] Shrestha M, Koike T, Hirabayashi Y, Xue Y, Wang L, Rasul G, et al. Integrated simulation of snow and glacier melt in water and energy balance-based, distributed hydrological modeling framework at Hunza River Basin of Pakistan Karakoram region. J Geophys Res Atmos 2015; 120(10). doi:http://dx.doi.org/10.1002/ 2014JD022666. [38] Shrestha M, Wang L, Koike T, Xue Y, Hirabayashi Y. Improving the snow physics of WEB-DHM and its point evaluation at the SnowMIP sites. Hydrol Earth Syst Sci 2010;14:2577–94. http://dx.doi.org/10.5194/hess-14-2577-2010. [39] Shrestha M, Wang L, Koike T, Xue Y, Hirabayashi Y. Modeling the Spatial Distribution of Snow Cover in the Dudhkoshi Region of the Nepal Himalayas. J Hydrometeorol 2012;13:204–22. http://dx.doi.org/10.1175/JHM-D-10-05027.1. [40] Brans JP, Mareschal B. PROMETHEE methods. In: Figueira J, Greco S, Ehrgott M, editors. Multiple criteria decision analysis: state of the art surveys, Boston: Springer Science + Business Media Inc; 2005, p. 163–95. doi:https://doi.org/10.1007/ b100605. [41] Velasquez M, Hester PT. An analysis of multi-criteria decision making methods. Int J Oper Res 2013;10:56–66. [42] Balali V, Zahraie B, Roozbahani A. A comparison of AHP and PROMETHEE family

[43]

[44]

[45]

[46] [47]

[48]

[49] [50] [51]

[52] [53]

321

decision making methods for selection of building structural system. Am J Civ Eng Archit 2014;2:149–59. http://dx.doi.org/10.12691/ajcea-2-5-1. Hajkowicz S, Collins K. A review of multiple criteria analysis for water resource planning and management. Water Resour Manag 2007;21:1553–66. http://dx.doi. org/10.1007/s11269-006-9112-5. Mladineo N, Margeta J, Brans JP, Mareschal B. Multicriteria ranking of alternative locations for small scale hydro plants. Eur J Oper Res 1987;31:215–22. http://dx. doi.org/10.1016/0377-2217(87)90025-7. Mahmood R, Jia S, Babel MS. Potential impacts of climate change on water resources in Kunhar River Basin, Pakistan. Water 2016:1–24. http://dx.doi.org/10. 3390/w8010023. Private Power and Infrastructure Board. Hydropower Resources of Pakistan; 2011. [accessed 15 March 2017]. Japan International Cooperation Agency. Guideline and Manual for Hydropower Development Vol. 2 Small Scale Hydropower; 2011. [accessed 15 March 2017]. Verdin KL, Verdin JP. A topological system for delineation and codification of the Earth’s river basins. J Hydrol 1999;218:1–12. http://dx.doi.org/10.1016/S00221694(99)00011-6. Food and Agriculture Organization. Digital soil map of the world and derived soil properties. L Water Digit Media Ser Rev 1 2003. Available in CD-ROM. Strahler AN. Hypsometric (area-altitude) analysis of erosional topology. Geol Soc Am Bull 1952;63:1117–42. Brans JP. L’ingénièrie de la décision; Elaboration d’instruments d’aide à la décision. La méthode PROMETHEE. In: Nadeau R, Landry M, editors. L’aide à la décision Nature. Quebec, Canada: Instruments Perspect. d’Avenir; 1982. p. 183–213. Gatte MT, Kadhim RA. Hydro Power, Energy Conservation, Dr. Azni Zain Ahmed (Ed.), ISBN: 978-953-51-0829-0, InTech, doi: 10.5772/52269. IUCN, UNEP-WCMC. The World Database on Protected Areas (WDPA). Cambridge, UK UNEP-WCMC 2017. .