The study examines the interplay among water resources, hydropower
generation and agricultural landuse at the Shiroro hydropower station and its
environs, in north-central Nigeria. Non-parametric trend analysis, hydropower
footprint estimation, reservoir performance analysis, change detection
analysis, and inferential statistics were combined to study the water-energy
and food security nexus. Results of Mann–Kendall test and Sen's slope
estimator for the period 1960 to 2013 showed a declining rainfall trend at
Jos, around River Kaduna headwaters at
The interdependence of water, food and energy are widely recognised as important drivers of socioeconomic development. The importance of these natural resources are embedded in the United Nations (UN) 2030 Agenda for Sustainable Development Goals (SDG) 2, 6, 7 (UN SDG Report, 2016; ICSU-ISSC, 2015). With Nigeria's population expected to increase to 262, 599 000 by 2030 (UNDESA, 2015), the country faces a great challenge of meeting these goals. For example, energy demand in the country averages 7 % per year, with an estimated increase in demand for years 2000 and 2012 which is put at 14 and 37 TWh respectively, and estimates for 2020, 2030 and 2040 put at 68, 146 and 291 TWh. Similarly, this challenge may become more glaring with more than 90 million people not having access to grid electricity in Nigeria, yet the widespread use of back-up generators has further reduced the number of people without access to any form of electricity significantly (IEA, 2014).
The link between the food-water-energy nexus and climate, especially rainfall in the study area greatly influences water availability, agricultural production, and electricity generation and supply, in the long run. For instance, high inter-annual variability and marked seasonality of rainfall, coupled with episodes of streamflow drought, have often resulted in reduced power production of hydropower stations in the country owing to low gauge height level, while excessive rainfall sometimes results in downstream flooding with dire consequences especially for the cultivated lands.
Furthermore, the location of the country's hydropower stations in the savannah region suggests that a high volume of water will be lost through evaporation hence the need to know the water footprint of these reservoirs.
A number of approaches have been used to estimate the footprint of hydropower reservoirs in different parts of the world. Bueno et al. (2016) used the conventional gross water footprint method for the Camargos Hydropower Reservoir in Brazil, and reported that the average hydropower footprint in Brazil exceeded the global average. Herath et al. (2011) estimated the hydropower footprint of all major hydropower reservoirs in New Zealand using a combination of the consumptive water use method, the net consumptive water use method, and the net water balance method.
Zhao and Liu (2015) developed a method that utilises an allocation coefficient to allocate water footprint among the different ecosystem services offered by the Three Gorges Reservoir in China. The study also compared the estimates of the water footprint obtained with the values derived from estimation based on the gross water footprint method. The result of the study showed that the gross water footprint method over-estimated the hydropower footprint. Mekonen and Hoekstra (2012) employed the gross water footprint method to estimate the hydropower footprint of 35 selected hydropower reservoirs in different parts of the world. The study revealed that hydropower reservoirs located in Ghana, Mozambique, Zambia, Zimbabwe, and Kenya all had high hydropower footprint, with Akosobo-Kpong reservoirs having the highest value globally.
In evaluating the performance of water resources systems, Hashimoto et al. (1982) used three indices; reliability, resilience and vulnerability to evaluate the performance of a water resources system. The indices have also been used to assess water resources systems performance under different prevailing natural and operational conditions. For example, Longobardi et al. (2014) used the 3 indices to assess the performance of a multi-purpose dam in southern Italy under conditions of climate variability. Gohraian et al. (2016) used the 3 performance indices in combination with a hydrological model, a water system model and climate change projections in a study of the Salt Lake City's water supply system under climate change conditions. In a study by Raje and Mujumdar (2010) the reliability index was used to evaluate the performance of a reservoir in Orissa, India, with respect to hydropower generation, irrigation and flood control. The indices of resiliency and vulnerability were used to assess the system's performance in relation to hydropower generation only.
With respect to the performance of the Shiroro Hydropower facility as a water resources system, only a few studies exist in the literature. This include a study by Sule (1988), in which the future operations and performance of the reservoir system which was still under construction were simulated under different operating scenarios using a combination of Probabilistic Dynamic Programming Optimisation Model and the indices of reliability, resilience and vulnerability. The result of the scenario simulation showed severe and frequent failures at the 70 % plant factor recommended for Nigeria. Based on this result, it was concluded that reservoir operations should be carried out at 40 to 50 % power plant factor. Similarly, a recent study by Gbadamosi et al. (2015), which analysed the performance and efficiency of the Shiroro Hydropower Station using the plant availability factor, the capacity factor and the overall efficiency factor, showed that the capacity factor of the hydropower system varied between 23.5 and 50.7 % compared with internationally benchmark of 80 %.
With regards to the estimation of water footprint in the studies highlighted above, hydropower generation was shown as a large consumer of water, and the method, a veritable indicator of resource use. The general output from the review of studies on water resources systems performance indicated that the 3 indices are useful tools in the description of the frequency and duration of system failure and recovery. The studies also showcase how the indices can serve as a basis for the improvement of the operations and management of different water resources infrastructures and systems.
Considering the importance of the Shiroro hydropower station in the water-energy and its food security interconnections within the River Kaduna catchment, and indeed the county at large, there is a need to have a better understanding of the drivers and feedback mechanisms at play. To achieve this therefore, this study utilises a combination of statistical analyses, hydropower water footprint estimation based on the gross water footprint method, water resources systems performance indices evaluation and change detection analysis.
The Shiroro Hydropower reservoir is a storage based hydroelectric facility
(Fig. 1) located in the Shiroro Gorge approximately between latitude
9
Location of Shiroro hydropower dam.
About 70 % of inflows into the reservoir is from River Kaduna, with
lateral contributions from Rivers Dinya, Sarkin-Pawa, Erena and Muyi (Adie
et al., 2012; Eze, 2006). The reservoir is located in the River Kaduna catchment in
the Guinea Savanna zone of the country, between the semi-arid climate of the
north and the sub-humid climate of the south. The climate of the catchment
is consistent with the rest of the country. Rainfall in the area is
controlled by the tropical maritime (mT) air mass. During the dry season the
area is dominated by the tropical continental (cT) air mass (Anyadike, 1993;
Iloeje, 2009). The dry season is between the month of November and March,
while the rainy season commences in April or May and lasts till October,
(Jimoh and Ayodeji, 2003). Average annual rainfall in the catchment is
about 1204.91 mm, with an average of about 110 days per year receiving a
rainfall amount of 0.1 mm (Okafor et al., 2017). Annual temperature around the
reservoir varies between 27 and 35
Historical monthly rainfall data for locations within the catchment, Jos, Kaduna and Minna for 1960 to 2013 was obtained from the Nigerian Meteorological Agency (NIMET). Monthly data on inflow, turbine discharge, reservoir evaporation and energy generated for 1995 to 2013 was obtained from the Shiroro Hydropower Station.
To establish the presence or absence of trend in rainfall within the
catchment, a Mann–Kendall trend test was carried out in XLSTAT. The
Mann–Kendall test statistic is expressed as:
Data sources and characteristics.
The water footprint of electricity from hydropower generation was estimated
based on Mekonnen and Hoekstra (2012). This is expressed as:
The resilience of a system denoted by
Result of Mann–Kendall trend test.
Trend of annual rainfall at Jos.
The USGS Landuse Classification model was used to determine the landuse change characteristics around the Shiroro Dam in three progressive scenarios: 1986, 2000 and 2016. This is done through the remote sensing technique (Table 1) using analytical tool of ENVI. This landuse adopted a pragmatic hierarchical land use classification scheme where seven major landuse classes were identified. About 1000 pixels of training dataset were randomly sampled from the spectral signature of each of the classes to define their respective land use/land cover type. In addition, the Maximum Likelihood classifier was used to extract these classes of landuse. Analysis was based on area calculation for the three static years (1986–2000 and 2000–2016).
Annual water footprint of Shiroro hydropower reservoir between 1995 and 2013.
NA
Trend of annual rainfall at Kaduna.
The results of the Mann–Kendall trend test for the rainfall trend (Table 2)
shows that the computed
For Kaduna and Minna (Figs. 3 and 4) representing locations along the middle
and lower course of the River, the computed
Trend of annual rainfall at Minna.
The result of the Karl Pearson correlation coefficient (
Landuse change analysis.
Source: Author's GIS Analysis.
The water footprint of electricity from the hydropower station for the
period 1995 to 2013, (Table 3) varied between 130.4 m
As a large consumer of water, it is necessary for the decision makers to have an adequate knowledge of the water footprint of hydropower generation. This will help to forestall undesirable consequences the on downstream environmental flows and ensure sustainable management for electricity generation and other uses.
The result of the reliability and resilience of the hydropower reservoir
with respect to power generation are 31.6 and 38.5 %
respectively. This is an indication that the hydropower power system was
less than reliable more than half of the time, which is also reflected in
the low level of resilience as suggested by the low resilience value of
38.5 %. The system was at its most vulnerable and least satisfactory in 2011,
with an annual inflow of 112 000 m
From these results it is obvious that the hydropower system performance is below the optimal level. However, the low performance of the hydropower system does not signify a lack of water in the reservoir, rather other operational deficiencies also contributed to the below optimal level of performance. These operational deficiencies include breakdown of equipment, inadequate maintenance, low level of inflow into the reservoirs, poor coordination and inefficient operating guidelines (Sule, 1988). In view of the current state of affairs, there is a need for an efficient reservoir operational strategy to reduce the system's vulnerability and improve the level of reliability and resilience.
Statistics of area and yield of crops cultivated in Niger State (2011–2016).
Source: Niger State Agricultural and Mechanisation Development Authority (2017).
Landuse/landcover pattern in 1986.
Landuse/landcover pattern in 2000.
Landuse/landcover pattern in 1986.
Table 4 and Figs. 5 to 7 show the static distribution of the
landuse/landcover pattern in three periodical scenarios of landuse dynamics
around the Shiroro Dam Development Project in 1986, 2000 and 2016
respectively. Subsistence land area for crop production increased from
884.59 km
Looking at the emergence of the hydropower facility and the trend of agricultural landuse in the area, the inter-play of water, energy and agricultural nexus in Shiroro can be deemed to have had a positive impact on the livelihood of the local populace and food security in the area and the country at large.
The study showed how a combination of methods can be used to investigate feedback mechanisms operational at the food-water-energy nexus at the Shiroro hydropower station and its environs. Although the results of the reliability and resilience of the hydropower reservoir with respect to hydropower generation are below the average performance with less than 40 % for all indices. The comparative gain in agricultural sector through water utilisation by peasant farmers actually provide an additional gain derived from the interplay of food-water-energy nexus. In the face of a reducing upstream rainfall amount coupled the with high water footprint of electricity from the reservoir, it is recommended that a long term roadmap to improve the operational coordination and management has to be put in place. Also, a formal irrigation scheme should be provided to strengthen agricultural production in the area.
Links to the USGS Satellite Imagery data is available at Earth explorer
OAd conceptualized and lead the team on this research. She also wrote the manuscript and effected the corrections during the write-up. OAj gathered and processed all the spatial data used for this article. GB assisted in the gathering of the hydro-meteorological data used for the study. SO reviewed the write-up from time to time for necessarycorrections.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Water security and the food-water-energy nexus: drivers, responses and feedbacks at local to global scales”. It is a result of the IAHS Scientific Assembly 2017, Port Elizabeth, South Africa, 10–14 July 2017.
The authors wish to express their profound gratitude to the following institutions. The Nigerian Meteorological Agency for the climatic data, the Shiroro Hydropower Station for the hydrological and power generation data, and the Niger State Agricultural and Mechanisation Development Authority for the agricultural yield data. Edited by: Barry Croke Reviewed by: Mykhaylo Romashchenko and one anonymous referee