4.1 Variability in Rainfall Years across Southwestern Ghana
The rainfall anomaly index (RAI) results computed for the 8 stations scattered across southwestern Ghana are presented in Fig. 5a and b and appendix 1. Positive values represent wet years whilst the negative values depict dry years within the study period of 1976 to 2018. The average maximum and minimum wet year intensity for the study area were 5.53 and 0.22 respectively. These corresponds to extremely wet and near normal intensities. Similarly, the average maximum and minimum dry year intensity were − 5.71 and − 0.14 respectively, which corresponds to an extremely dry and near normal intensities.
There was increased number of dry years within all the stations, corroborating previous observations of decreasing annual rainfall from the 1980s onwards which still persist across the entire country (Kabo-bah et al., 2016; Manzanas et al., 2014; Owusu & Waylen, 2009). For instance, with 23 wet years, the Kumasi, Saltpond and Takoradi stations experienced the highest number of wet years whereas Sefwi Bekwai experienced the least number (17). Conversely, the Sefwi Bekwai station experienced the highest number (26) of dry years while Kumasi, Saltpond and Takoradi experienced the lowest number (20) of dry years. The results match closely with the temporal trend in previous studies accounting for drying trend in the forest climatic zone such as found in Sefwi Bekwai, Akim Oda and Abetifi stations compared to the coastal zone such as found in Takoradi and Saltpond stations (Abbam et al., 2018; Atiah et al., 2019; Baidu et al., 2017; Bessah et al., 2022).
Figure 6a and b illustrates the frequencies in the intensity of wet and dry years across the landscape based on the RAI intensity class description. Following the new climatic zonation of the Ghana (Bessah et al., 2022), Saltpond, Takoradi and Axim stations are classified to fall within the coastal climatic zone whereas the remaining stations falls within the forest climatic zone. The averages across the two zones were therefore computed as shown in Fig. 6a. Over the last 40 years, southwestern Ghana has experienced 10 and 7 near normal rainfall years across the coastal and forest climatic zones respectively. The rest of the period modulated between extremes of both wet and dry years.
In addition, it was observed that changes in rainfall years corresponded keenly with the forest ecological zones in Ghana as classified by Hall and Swaine (1976, 1981) and shown in appendix 2. For instance, Sunyani, Abetifi and Takoradi are located within the Dry semideciduous forest types whilst Saltpond is located within the Marginal forest type. Kumasi, Sefwi Bekwai, Akim Oda are located within the Moist semideciduous forest types whereas Axim station is located within the Evergreen forest zones. Hence, from Fig. 6b, it can be observed that the Dry semideciduous forest as well as the Southern Marginal forest zone have experienced higher number of near normal rainfall years compared to the Moist semideciduous and Evergreen forest zones.
These results confirm the increasing rainfall variability in the forest zone of Ghana both in time and space as indicated in previous studies. Yet Manzanas et al. (2014) caution the attribution of anthropogenic activities as leading cause to climate variability in Ghana since natural variability plays a key role. To further expound on the extent of increasing variability, other extreme indices were analysed for the stations and the results presented in the ensuing section.
4.2 Trend in Extreme Rainfall Indices across Southwestern Ghana
The trend in variability for extreme rainfall frequency and intensity indices across southwestern Ghana, six extreme rainfall indices namely very heavy rainfall days (R20), simple daily intensity index (SDII), annual total wet day rainfall (PRCPTOT), consecutive wet days (CWD), consecutive dry days (CDD) and extremely wet days (R99P) were analyzed to understand variability across southwestern Ghana. After estimating the indices, Mann-Kendall (MK) test and Theil_Sen slope were conducted to detect the trend and the magnitude of change respectively. The results of the MK test and Theil_Sen slope estimation for extreme climate indices are presented in Table 3. The MK test results indicate the trend be it increasing (+) or decreasing (-), whereas the Theil-Sen Slope estimate the rate of change be it increasing (+) or decreasing (-).
Both tests revealed a gradual variation in extreme rainfall frequency indices (R20, CWD and CDD) and intensity indices (SDII, PRCPTOT and R99p) across southwestern Ghana. Based on the MK test for instance, an increasing trend in simple daily intensity index (SDII) can be observed across the entire southwestern Ghana except for the Axim station that exhibited a decreasing trend. In addition, a significant increasing trend was observed for the Kumasi, Sefwi Bekwai and Saltpond stations. Similar trend was observed for the annual total wet day rainfall (PRCPTOT) except for a decreasing trend at Takoradi and Axim stations. With respect to extremely wet days (R99p), increasing trend was observed for all the stations except for the Sefwi Bekwai, Takoradi and Axim stations, which exhibited negative trend.
The Theil_Sen slope estimated similar trend in the rate of change for all the stations as MK except for the extremely wet days (R99p) where no change was detected except for the Sefwi Bekwai station, which exhibited a decreasing rate of change.
For indices estimating extreme rainfall frequency, a positive trend for very heavy rainfall days (R20) was observed for all the stations across southwestern Ghana with significant levels at Kumasi and Sefwi Bekwai stations whereas the Takoradi and Axim stations showed negative trend. Similarly, trend in consecutive dry days (CDD) is decreasing throughout southwestern Ghana except for the Sunyani and Kumasi stations. Consecutive wet days (CWD) trends are decreasing with significant levels at the Saltpond, Takoradi and Axim stations whereas an increasing trend was detected for Abetifi, Sefwi Bekwai, and Akim Oda stations. Correspondingly, the Theil-Sen slope estimated no rate of change for the upper and middle stations but negative slopes were detected for Saltpond, Takoradi and Axim stations comparable to the trend detected by the MK.
Table 3
Trend in extreme rainfall intensity and frequency for southwestern Ghana
Extreme climate indices
|
Rain gauge
|
lon
|
lat
|
Elevation
|
Mann-Kendall test
|
Theil_Sen Slope
|
Trend
|
p-value
|
SDII (mm/day)
|
Sunyani
|
-2,33
|
7,36
|
308,3
|
0,05
|
0,660
|
0,01
|
Kumasi
|
-1,6
|
6,72
|
286,3
|
0,33***
|
0,002
|
0,07
|
Abetifi
|
-0,75
|
6,67
|
594,7
|
0,09
|
0,396
|
0,01
|
Sefwi Bekwai
|
-2,32
|
6,2
|
170,8
|
0,20*
|
0,059
|
0,03
|
Akim Oda
|
-0,98
|
5,93
|
139,4
|
0,09
|
0,396
|
0,01
|
Saltpond
|
-1,06
|
5,27
|
439
|
0,23**
|
0,029
|
0,07
|
Takoradi
|
-1,75
|
4,88
|
4,6
|
0,09
|
0,408
|
0,02
|
Axim
|
-2,24
|
4,86
|
37,8
|
-0,13
|
0,229
|
-0,04
|
PRCPTOT (mm)
|
Sunyani
|
|
|
|
0,07
|
0,490
|
1,47
|
Kumasi
|
|
|
|
0,22**
|
0,040
|
5,74
|
Abetifi
|
|
|
|
0,06
|
0,572
|
1,12
|
Sefwi Bekwai
|
|
|
|
0,15
|
0,174
|
2,85
|
Akim Oda
|
|
|
|
0,06
|
0,572
|
1,12
|
Saltpond
|
|
|
|
0,09
|
0,402
|
2,97
|
Takoradi
|
|
|
|
-0,03
|
0,761
|
-0,49
|
Axim
|
|
|
|
-0,10
|
0,346
|
-6,03
|
R99p (mm)
|
Sunyani
|
|
|
|
0,05
|
0,685
|
0
|
Kumasi
|
|
|
|
0,08
|
0,449
|
0
|
Abetifi
|
|
|
|
0,10
|
0,388
|
0
|
Sefwi Bekwai
|
|
|
|
-0,14
|
0,193
|
-0,29
|
Akim Oda
|
|
|
|
0,10
|
0,388
|
0
|
Saltpond
|
|
|
|
0,05
|
0,693
|
0
|
Takoradi
|
|
|
|
-0,11
|
0,343
|
0
|
Axim
|
|
|
|
-0,02
|
0,899
|
0
|
R20 (day)
|
Sunyani
|
|
|
|
0,04
|
0,705
|
0
|
Kumasi
|
|
|
|
0,31***
|
0,004
|
0,15
|
Abetifi
|
|
|
|
0,13
|
0,231
|
0,07
|
Sefwi Bekwai
|
|
|
|
0,20*
|
0,071
|
0,1
|
Akim Oda
|
|
|
|
0,13
|
0,231
|
0,07
|
Saltpond
|
|
|
|
0,16
|
0,147
|
0,08
|
Takoradi
|
|
|
|
-0,05
|
0,629
|
-0,03
|
Axim
|
|
|
|
-0,10
|
0,344
|
-0,1
|
CDD (day)
|
Sunyani
|
|
|
|
0,04
|
0,738
|
0,1
|
Kumasi
|
|
|
|
0,07
|
0,543
|
0,12
|
Abetifi
|
|
|
|
-0,12
|
0,267
|
-0,24
|
Sefwi Bekwai
|
|
|
|
-0,10
|
0,341
|
-0,3
|
Akim Oda
|
|
|
|
-0,12
|
0,267
|
-0,24
|
Saltpond
|
|
|
|
-0,14
|
0,201
|
-0,3
|
Takoradi
|
|
|
|
-0,14
|
0,209
|
-0,24
|
Axim
|
|
|
|
-0,05
|
0,615
|
-0,09
|
CWD (day)
|
Sunyani
|
|
|
|
-0,02
|
0,897
|
0
|
Kumasi
|
|
|
|
-0,07
|
0,561
|
0
|
Abetifi
|
|
|
|
0,05
|
0,695
|
0
|
Sefwi Bekwai
|
|
|
|
0,06
|
0,580
|
0
|
Akim Oda
|
|
|
|
0,05
|
0,695
|
0
|
Saltpond
|
|
|
|
-0,34***
|
0,003
|
-0,07
|
Takoradi
|
|
|
|
-0,26**
|
0,018
|
-0,07
|
Axim
|
|
|
|
-0,26**
|
0,021
|
-0,07
|
(NB: ***,**&* denote significant @ 1%,5%,10% probability level), R20 = Number of very heavy precipitation days, CDD = Consecutive dry days, CWD = Consecutive wet days, SDII = Simple daily intensity index, PRCPTOT = Annual total wet-day precipitation, R99p = Extremely wet days
Similarly, the spatial pattern of the MK and Theil_Sen slope estimates are shown in Figs. 7 and 8, which align closely with the types of forest ecosystems in Ghana. A visual observation of the spatial maps in Fig. 7 shows an increasing trend in most of the extreme rainfall intensity indices (SDII, PRCPTOT, R99p) within the Moist and Dry semideciduous as well as the Marginal forest zones compared to a decreasing trend within the Evergreen forest zone based on MK test. Similar rate of change was estimated except for the very wet days (R99p) where the Theil_Slope estimate no rate of change within the entire study area except for a decreasing rate at Sefwi Bekwai, which falls within the Moist semideciduous forest zone (Fig. 8).
These results are consistent with the findings in previous studies (Abbam et al., 2018; Atiah et al., 2019; Logah et al., 2021). Atiah et al. (2018) analyzed similar indices for the four agroecological zones in Ghana and found an increasing trend in CWD, PRCPTOT and R20 for the entire forest zone. Similarly, Logah et al. (2021) found an increasing trend for PRCPTOT and SDII for the Volta basin of Ghana. Finally, Abbam et al. (2018) noted increasing temperatures and declining rainfall across the western regions of Ghana, which form a major part of the southwestern miningscapes.
4.3 Trends in Extreme Temperature Indices across Southwestern Ghana
The results of MK trend test and Theil_Sen slope estimated for extreme temperature indices, including mean monthly daily maximum (TMAXmean) and minimum (TMINmean) temperature, cool days, cool nights, warm days and warm nights are shown in Table 4 and spatially presented in Figs. 8 and 9. Generally, a significant upward trend in increasing TMAXmean and TMINmean were observed across all the stations except for the Abetifi station. The uniqueness in temperature trend at Abetifi station has been attributed to the presence of the Kwahu plateau and the close proximity of the Upland evergreen forest zone (Bessah et al., 2022; Ohmura, 2012).
Both statistical procedures reflected a positive trend in TMAXmean, TMINmean and percentage warm days compared to a negative trend in percentage cool nights across the entire landscape (Figs. 9 and 10). However, the Theil_Sen slope estimated increasing warm nights over the entire landscape whilst the Mann-Kendall test results showed an increasing trend only within the middle domain. Similarly, based on the MK we noted significantly increased trend in percentage of warm days and warm nights across the entire southwestern Ghana except for the Takoradi station. However, Theil_Sen slope estimated negative trend in rate of change in percentage warm days for Abetifi and Takoradi stations. Based on the Mann-Kendall test and the Theil_Sen slope, the percentage cool days and cool nights are significantly decreasing across the entire southwestern Ghana.
Table 4
MK and Theil_Sen slope of extreme temperature indices for southwestern Ghana
Extreme climate indices
|
Rain gauge
|
lon
|
lat
|
Elevation
|
Mann-Kendall test
|
Theil_Sen Slope
|
Trend
|
p-value
|
TXmean
(TMAXmean)
|
Sunyani
|
-2.33
|
7.36
|
308.3
|
0.215**
|
0.044
|
0.01
|
Kumasi
|
-1.6
|
6.72
|
286.3
|
0.428***
|
0.000
|
0.03
|
Abetifi
|
-0.75
|
6.67
|
594.7
|
-0.111
|
0.304
|
-0.02
|
Sefwi Bekwai
|
-2.32
|
6.2
|
170.8
|
0.309***
|
0.004
|
0.02
|
Akim Oda
|
-0.98
|
5.93
|
139.4
|
0.311***
|
0.004
|
0.03
|
Saltpond
|
-1.06
|
5.27
|
439
|
0.383***
|
0.000
|
0.02
|
Takoradi
|
-1.75
|
4.88
|
4.6
|
0.006
|
0.966
|
0
|
Axim
|
-2.24
|
4.86
|
37.8
|
0.232**
|
0.030
|
0.01
|
TNmean
(TMINmean)
|
Sunyani
|
|
|
|
-0.070
|
0.516
|
0
|
Kumasi
|
|
|
|
0.318***
|
0.003
|
0.02
|
Abetifi
|
|
|
|
0.146
|
0.173
|
0.01
|
Sefwi Bekwai
|
|
|
|
0.127
|
0.240
|
0.01
|
Akim Oda
|
|
|
|
0.320***
|
0.003
|
0.02
|
Saltpond
|
|
|
|
0.371***
|
0.001
|
0.02
|
Takoradi
|
|
|
|
0.048
|
0.666
|
0
|
Axim
|
|
|
|
0.232**
|
0.030
|
0.02
|
TN10p
(Cool nights)
|
Sunyani
|
|
|
|
-0.128
|
0.232
|
-0.09
|
Kumasi
|
|
|
|
-0.479***
|
0.000
|
-0.48
|
Abetifi
|
|
|
|
-0.428***
|
0.000
|
-0.29
|
Sefwi Bekwai
|
|
|
|
-0.305***
|
0.004
|
-0.28
|
Akim Oda
|
|
|
|
-0.625***
|
0.000
|
-0.51
|
Saltpond
|
|
|
|
-0.486***
|
0.000
|
-0.39
|
Takoradi
|
|
|
|
-0.566***
|
0.000
|
-0.49
|
Axim
|
|
|
|
-0.597***
|
0.000
|
-0.49
|
TN90p
(warm nights)
|
Sunyani
|
|
|
|
0.006
|
0.970
|
-0.1
|
Kumasi
|
|
|
|
0.340***
|
0.001
|
0.26
|
Abetifi
|
|
|
|
0.181*
|
0.103
|
0.09
|
Sefwi Bekwai
|
|
|
|
0.328***
|
0.005
|
0.15
|
Akim Oda
|
|
|
|
0.366***
|
0.001
|
0.33
|
Saltpond
|
|
|
|
0.360***
|
0.003
|
0.33
|
Takoradi
|
|
|
|
0.181*
|
0.103
|
0
|
Axim
|
|
|
|
0.514***
|
0.000
|
0.19
|
TX10p
(Cool days)
|
Sunyani
|
|
|
|
-0.466***
|
0.000
|
-0.44
|
Kumasi
|
|
|
|
-0.290**
|
0.006
|
-0.26
|
Abetifi
|
|
|
|
-0.212*
|
0.067
|
-0.35
|
Sefwi Bekwai
|
|
|
|
0.573***
|
0.000
|
-0.44
|
Akim Oda
|
|
|
|
-0.618***
|
0.000
|
-0.61
|
Saltpond
|
|
|
|
-0.336***
|
0.003
|
-0.39
|
Takoradi
|
|
|
|
-0.458***
|
0.000
|
-0.67
|
Axim
|
|
|
|
-0.238**
|
0.037
|
-0.35
|
TX90p
(Warm days)
|
Sunyani
|
|
|
|
0.158
|
0.140
|
0.09
|
Kumasi
|
|
|
|
0.560***
|
0.000
|
0.4
|
Abetifi
|
|
|
|
0.125
|
0.283
|
-0.61
|
Sefwi Bekwai
|
|
|
|
0.556***
|
0.000
|
0.25
|
Akim Oda
|
|
|
|
0.544***
|
0.000
|
0.2
|
Saltpond
|
|
|
|
0.577***
|
0.000
|
0.3
|
Takoradi
|
|
|
|
-0.150
|
0.165
|
-0.19
|
Axim
|
|
|
|
0.493***
|
0.000
|
0.13
|
NB: ***,**&* denote significant @ 1%,5%,10% probability level)
4 Implications of changing local climate for natural resource managers across miningscapes
Variability in Ghana’s climatic system is increasing and is expected to escalate just as projected for the entire West African region (Sylla et al., 2016). The study shows that extreme rainfall and temperature trend is changing throughout the forest zone as estimated by Atiah et al., (2019) based on the gridded data. Similarly, near normal intensity rainfall years have declined to make way for extreme intensity rainfall years. Invariably, increasing trend in extreme temperature indices such as warm days and warm nights as well as decreasing trend in cool days and cool nights have been observed with significant levels throughout the different forest zones.
These changing trend present opportunities and threats for natural resources development and management within southwestern Ghana. Apart from the impacts on climate-sensitive livelihood resources such as artisanal fishing, collection of non-timber forest products and rain-fed agriculture, that dominant the rural landscapes, natural resource extraction activities within southwestern Ghana have to be adapted to the local effects of the changing climate. Resource extraction within southwestern Ghana have high relevance to the nation Ghana as well as the wellbeing of its local inhabitants. For instance, minerals extraction, petroleum, cash crop production (cocoa, rubber, oil palm, coconut) as well as food crops contributes immensely to the economic growth of the country (Amisigo et al., 2015; Ayee et al., 2011; Kumah, 2006; MoFa, 2011). Hence, adapting to the local effects of changing climatic pattern is necessary. The current discussion centers on the implications on the mining industry and the hydrologic regime of basins within southwestern Ghana where the industry dominant although the effects of increasing variability in local climate equally affects other natural resource sectors.
Each phase of the minerals extraction process has some form of connection with the prevailing climatic condition within the mining territory. The operational phase is mainly the commercial phase whereby minerals are produced in commercial quantity and is perceived to be the phase most susceptible to climate extremes (Gonzalez et al., 2019) although literature affirms the impact of changing climate on each phase of the mining cycle be it feasibility, exploration, development, operation or closure (J.. Hodgkinson et al., 2014; Pearce et al., 2010; Rüttinger et al., 2020; Rüttinger & Sharma, 2016).
Odell et al., (2018) has illustrated the double exposure of water resources to mining and climate change effects whereas Philips (2016) reviewed the linkages between surface mining and water resources amidst the impact of changing climate. However, both surface and underground mining operational activities navigate such that the profiles of the host basins’ hydrology and hydrogeology within a mining concession are modified. Key sources of these modification include the direct effects on effective rainfall and base flow contributions due to rapid landuse changes to make way for mining infrastructure and settlement developments, diversion of streams and rivers away from operational areas, surface and underground pit dewatering, tailings and impoundments facilities among others. Some of these modifications have led to increased risk of rainstorm and flooding within surrounding communities. Flood-led incidence and disasters within miningscapes would be intensified by increasing extreme events within southwestern Ghana. Unfortunately, the rural households within miningscapes are most susceptible to such rainstorm and flood risk (Osei et al., 2021) such as the inundation of their farmlands and human settlements.
Sustainability best practices such as mine water management activities within the mining industry including raw water abstraction, storm water and sediment control, water discharges and impoundments, acid mine drainages among others can be affected by the changing trend in extreme climate variability. For instance, rainfall has been identified as main formation factor of acid mine drainage, hence increasing rainfall will increase the cost of treatment (Rochyani, 2017) as well as increased risk of contaminating surrounding water bodies. Already, managing acid mine drainage issue is a persisting challenge for most mines within southwestern Ghana (Asamoah et al., 2007) and can be compounded by increasing climatic variability. The consequences can be dire for sustainable mining as well as the overall development and allocation of water resources within the southwestern basin system where water resources are already stressed in quality (Armah et al., 2012) and quantity from competing users (WRC, 2009, 2012b, 2012a). Other best practices such as environmental quality monitoring (water quality, air quality, blast vibration and noise), erosion and sediment transport control, land reclamation and post closure activities will not be spared by the effects of increasing climatic variability.
Another aspect of mining likely to be affected by the increasing variability in southwestern Ghana include occupational health and safety. Frameworks advancing climate change impacts on occupational health and safety incidences have been highlighted in literature (Applebaum et al., 2016; Bi et al., 2011; Kjellstrom et al., 2014; P. A. Schulte et al., 2016; Paul A. Schulte & Chun, 2009) and the situation of miningscapes within southwestern Ghana would not be an exception. Recent empirical studies on mining workers located within the middle miningscapes (Evergreen forest zone) of southwestern Ghana confirmed increasing incidence in heat stress and related discomforts among mineworkers (Nunfam et al., 2019). In addition, an informant interviewed within the Sefwi miningscapes indicated the fatality of a colleague driver whose vehicle drown during the peak of the unexpectedly prolonged rain season in 2014. The Community and Public Affairs department confirmed the incidence. Secondly, the rate of incidence risk on equipment safety is likely to be ascending with increasing temperature extremes including wear and tear, increased water demand for cooling systems including fuel storage and power generation systems. Prolonged dry season increase pollen agents in the atmosphere which couple with the excess dust generation within mining sites to affect workers. These have consequences on workers productivity owing to increased rate of absenteeism. Similarly, flooding and inundation pose safety risk for mobile equipment as well as tailings storage facilities and water storage impoundments. For instance, the risk of dam failure and toppling effects become high with extreme rainfall as slope stability of the engineered berms are compromised (Chioti & Lavender, 2008).
Be it surface or underground mining, infrastructural systems within operational sites are susceptible to intensified climate events within southwestern Ghana. Underground mines can be overwhelmed by increasing recharge rate for which water discharge practices become ineffective. Similarly, extreme temperature can render an underground operation unsafe place to operate especially in situations whereby underground ventilation systems are inadequate. Surface mining operations are equally susceptible to extreme rainfall and temperature frequencies and intensities. The load and haul activities as well as the general transportation and logistics activities of the mine can be curtailed by extreme rainfall given the dilapidated nature of road networks within rural landscapes of Ghana. While prolonged dry periods may provide a lean way for surface operational activities, which are mostly disrupted by increased rainfall frequency, a mine with negative mine water balance may experience water scarcity. Apart from the potential of a mine operating with a negative water balance due prolonged dry periods, higher temperature and excessive dust have numerous effects as identified by Damigos (2012) and Gonzalez et al. (2021). These consequences can undermine the capacity of the mine to comply with the safety, health and environment regulations of the industry as well as other requirements such as in the global advocacy for clean energy. Whilst energy demand of the industry will increase from the effects of increasing rainfall or otherwise, the use of genset and other non-renewable energy sources as commonly practiced within southwestern miningscapes is becoming unpopular. Energy regulations requirements are likely to be stringent within international markets for mining firms (Nelson et al., 2011) even if energy regulation is porous within the Ghanaian context. Thus, there is an increased risk of regulatory non-compliance can increase with changing local climate variability.
In addition, the double exposure of the surrounding socio-ecological systems including the riparian forest ecosystem and the hydrologic regime of river basins within southwestern Ghana to impacts of mining activities and increasing climatic variability will enhance water resources competition with the basin systems. The southwestern basin system including the transboundary rivers (Bia and Tano) provide portable water and hydro-electricity within several regions of Ghana and Ivory Coast. For example, the Pra sub-basin host nine constructed water supply reservoirs in Ghana coupled with the construction of small hydroelectric dam in addition to the two Ayame hydroelectric dams located in Ivory Coast ((Konan et al., 2013). The Ankobra basin though considered as fully mine-take sub-basin (Obodai et al., 2019) still provides portable water for several municipalities and districts (WRC, 2009) just as Tano and Bia sub-basins (WRC, 2012b). Hence, the need for collaborative efforts toward adaptation to climate change impacts within miningscapes in particular cannot be overemphasized.