International Journal of Application or Innovation in Engineering & Management... Web Site: www.ijaiem.org Email: , Volume 3, Issue 1, January 2014

advertisement
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected], [email protected]
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
Environmental Impacts of Natural and Man-Made
Hydraulic Structures-Case Study Middle Zambezi
Valley, Zimbabwe
Samson Shumba1, Hodson Makurira2, Innocent Nhapi3 and Webster Gumindoga4
1-4
Department of Civil Engineering, University of Zimbabwe, P.O Box MP167 Mount Pleasant, Harare, Zimbabwe
ABSTRACT
The Mbire District in northern Zimbabwe lies in the Lower Middle Zambezi catchment between the man- made Kariba and
Cahora Bassa dams. The district is occasionally affected by floods caused by overflowing rivers and, partly, by backwaters from
the downstream Cahora Bassa hydropower dam. This flooding affects soil properties due to rapid moisture fluxes and deposition
of fine sediments and nutrients. Despite the hazards associated with the floods, the riparian communities benefit from high
moisture levels and the nutrients deposited in the floodplains. The residual moisture after flooding events enables the cultivation
of crops just after the rainfall season with harvesting taking place around July-August. These periods are outside the normal
rainfed agricultural season elsewhere in Zimbabwe where such flooding is not experienced. This study sought to investigate the
soil moisture and nutrient dynamics in relation to natural and man-made flood occurrence in the Middle Zambezi valley of
Zimbabwe. Twelve trial pits were dug using manual methods along four transects at three different study sites across the
floodplain during the period April 2011 to May 2012. At each trial pit, three soil samples were collected at depths of 0.2 m, 0.5 m
and 1.2 m and these were analysed for moisture content, nutrient status, pH, texture and electrical conductivity. Statistical
analysis tools and GIS interpolation techniques were used to characterize the soil properties. NDVI and soil moisture distribution
at different depths were also correlated. Backwaters from the hydropower structure at Cahora Bassa Dam increased the moisture
content of the soil to above the field capacity and provided nutrients to the soil. At Manyame River there are no backwaters
observed and the research data show reduced effects of moisture and nutrient variation with distance from the floodplain. In the
floodplains, moisture content decreased with increasing distance from the river. The moisture content increased with depth and
decreased with time during the study period. There is a weak correlation between moisture content and NDVI. There were
significant differences (p < 0.05) of moisture content with time where backflows were experienced. There were no significant
differences (p > 0.05) in soil pH with the distance from the river. The soil nutrient concentration tends to decrease with an
increase in distance from the floodplain and with an increase in depth. The soils were confirmed to be saline (normal). The
findings of this study show that the temporal and spatial variation of soil properties in the Middle Zambezi Valley is affected by a
number of factors caused by the natural and man-made hydraulic structures. The research recommends use of soil moisture
probes and data loggers and installing a gauging station at Kanyemba for measuring the flows and water levels for more accurate
spatial and temporal data.
Keywords: Floods; Nutrient Dynamics; Soil Moisture Dynamics; Normalised Difference Vegetation Index
1. INTRODUCTION
1.1 Background
The Middle Zambezi Valley communities in Zimbabwe have seen the hydrological benefits that can be derived from the
floods while researchers and policy makers seem not to have understood the benefits and the processes involving soil
moisture and nutrient dynamics in the floodplains of the Middle Zambezi. [13] noted that soil moisture is critical to the
study of ecohydrology which is the interdisciplinary field studying the interactions between water and ecosystems. The
spatial and temporal distribution of soil moisture has enormous implications in hydrological, agricultural, economic and
social planning and development [29]. Soil moisture is an important parameter in the hydrological cycle since it has close
relationships with infiltration, evapotranspiration, surface runoff and groundwater recharge. Soil moisture trend analysis
has important roles in drought and flood prediction, water resources management and agricultural production [13].
Flooding events experienced in the Mbire District of Zimbabwe result in the deposition of alluvium which has an effect
on soil nutrient levels. The contribution of the four major rivers (Angwa, Mwazamutanda, Musengezi and Manyame) and
their tributaries in the Mbire District and the impact of the Cahora Bassa hydropower reservoir on soil properties and
ecohydrology can be fully understood if a study of the water, soil and fertility dynamics is done. Two types of floods affect
the Mbire District; namely, (i) flash and cyclone induced floods in January and February [14] and (ii) the backwaters from
Volume 3, Issue 1, January 2014
Page 242
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected], [email protected]
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
the Cahora Bassa hydropower dam. Most of the seasonal floods are caused by flows from upstream catchment area. Since
time immemorial, civilisations have prospered on floodplains, deriving benefits from floods. The residual moisture after
flooding is normally sufficient to support crops grown after the rainy season. The floodplain is the area from the river to
the maximum distance the water reaches in the event of flooding and this differs from one site to the other. Floods result
in damage and destruction to infrastructure and affect millions of people worldwide [32].
Water resources development projects have substantially altered the hydrological regime of the Zambezi Delta. The
Zambezi catchment has become regulated with the operation of the Kariba and Cahora Bassa dams [2]. The Zambezi
basin is a drought prone area and communities that stay in this area are likely to settle in the floodplains where better
yields are realised. The benefits of floods include deposition of nutrients and rich sediments on the land and replenishing
waters within the wetlands. The other benefits include serving as recharge areas to the aquifer and improving water
quality by filtering pollutants and sediments out of the floodwater [26]. To mitigate against drought challenges, the people
practice streambank and riverbed cultivation in search of better soil and nutrient conditions. Cultivation during flooding
seasons is affected by crops being washed away during flooding events. Persistent droughts, siltation and high
temperatures have negatively reduced yields in the floodplain fields over the years ([5]; [18]). There are limited structural
flood mitigation measures such as dams and weirs put in place in the Zambezi Valley part of Zimbabwe. Therefore
farmers are not even able to make use of the water after the flood and rainy season and most of the water flows into the
Zambezi River and Cahora Bassa Dam before it is fully utilized by the communities upstream [18]. It is necessary to study
the spatial and temporal links between vegetation, climate and soil moisture in order to understand why communities in
the Zambezi Valley widely practice recession farming with success.
This research seeks to explore the benefits of cultivation in the floodplain and to analyse the impacts of flooding on soil
moisture distribution and nutrient deposition and the viability of riverbank and riverbed cultivation. Understanding
hydrological processes and applications in floodplain recession farming and climate variability and maximising the water
use efficiency and increasing productivity for policy makers to make informed decisions. An understanding of the spatial
and temporal variation of soil moisture and fertility variation will facilitate better understanding of floods in the context
of Integrated Water Resources Management for the improvement of the livelihoods of people in the district. Middle
Zambezi Valley communities have seen the benefits of the hydrological benefits that can be derived from the floods,
researchers and policy makers have not understood the benefits and processes involving soil moisture and nutrient
dynamics in the floodplains of the Middle Zambezi.
2. Materials and methods
Description of the study area
The study was carried out in Mbire District (Figure 1) at Chidodo (Ward 5), Mushumbi Pools (Ward 12) and Kanyemba
(Ward 1). Mbire District lies between longitudes 30⁰ 4’E and 31⁰ 10’E and latitudes 15⁰ 37’ S and 16⁰ 15’S. The District
falls within Universal Transverse Mercator (UTM) Zone 36S. The altitude in the study area varies from 320 m to 450 m
above mean sea level, meaning the area is generally flat. Mbire District falls in Lower Manyame and Angwa-Rukomechi
Subcatchment Councils. There are 17 wards in the district. The study area was chosen because it is one of the districts
mostly affected by flooding in Zimbabwe.
The Mbire District is characterised by high temperatures, low rainfall and frequent floods due to a combination of flash
floods and operation of the Cahora Bassa Dam [2]. Historically, settlement in the valley has been discouraged due to
tsetse fly problems and that the area is a wildlife habitat [11]. Due to population pressures, some areas in the district were
opened up for settlement [11]. People have been allocated 5 hectare plots outside the floodplains by the government but
they continue to shun these upland plots in favour of the floodplains where there is greater potential to realize higher
yields. There has been continued deposition of sediments in the floodplains over time and this has altered soil properties
with some of the farmers now digging holes to depths of more than one metre in search of moisture for crop growth.
Volume 3, Issue 1, January 2014
Page 243
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected], [email protected]
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
Figure 1: Map of the study area showing service centres, road and river network
Selection of study sites and data collection methods
Chidodo and Kanyemba villages were selected because there are a number of communities whose livelihoods are affected
by flooding from upstream catchments as well as from the operation of Cahora Bassa Hydropower structure. Mushumbi
was selected because it is almost the most downstream settlement along the Manyame River on the Zimbabwean side and
experiences frequent flooding due to upstream river flows. The selection of specific sites was also influenced by
accessibility of the areas.
Having selected the study area the next step was to select the transects on which the samples were to be taken. The
transects were generated on a map in a GIS. These were then plotted on a map and the coordinates of each transect
established. Two coordinates of each transect were then entered in a Global Positioning System (GPS). Four transects
were chosen for each site as shown in Figure 2. Soil samples were collected in the field from trial pits. The spatial and
temporal variation of moisture content, electrical conductivity, soil texture and density, pH and soil nutrient (calcium,
magnesium, potassium, sodium, phosphorus and nitrogen) concentrations were measured across the floodplain.
Figure 2 shows the Location of soil sampling trial pits at different positions within the floodplain for Chidodo, Kanyemba
and Mushumbi Pools sites. Points FP1 are located at the beginning of the floodplain, FP2 at the end of the floodplain and
FP3 outside the floodplain for each of the transects. The extent of the floodplain was determined using satellite images
and local information. After each trial pit position was identified and located, its actual coordinates were then recorded
using a GPS. Four transects were chosen per site based on their accessibility and spacing. On each transect three trial pits
were excavated, the first pit at the beginning of the floodplain, the second at the end of the floodplain and the last outside
the floodplain. Samples were collected at depths of 0.2m, 0.5m and 1.2m using hand augers for moisture content and
nutrient analysis.
Volume 3, Issue 1, January 2014
Page 244
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected], [email protected]
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
Figure 2: Location of soil sampling trial pits at different positions within the floodplain for Chidodo, Kanyemba and Mushumbi Pools
sites
The moisture content was determined in the laboratory using both the gravimetric and volumetric methods as described
by [10; [25] and [31]. The mass of an empty tare was measured and wet soil added to the tare. The mass of the tare and
wet soil was determined. The soil was then dried in an oven for twenty four hours and the mass of the tare and dry soil
determined. Soil texture was determined by the sieve analysis and the hydrometer analysis methods as described by [2].
The soil was passed through a series of nested BS sieves and the mass of soil retained on each sieve determined.
The compositions of the different samples were determined from the tests and the classification was done using the USDA
textural triangle [30]. The permanent wilting point is the minimum limit of moisture in the soil in which the plants lose
their turgidity of the leaves and from this point they do not recover when they are put in a dark and saturated environment
[27]. [33] observed that the soil water retention at field capacity and permanent wilting point can be useful in the
determination of the water depth applied to a plant by irrigation and that the soil water content affects the transport
characteristics of water and solutes in the soil. For the determination of the field capacity and permanent wilting point,
tests were carried out using the pressure membrane apparatus (330kPa for field capacity and 1,500 kPa for permanent
wilting point) as described by [16]. atomic absorption spectroscopy (AAS) was used to analyse the soil for Calcium,
Volume 3, Issue 1, January 2014
Page 245
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected], [email protected]
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
Magnesium, Potassium and Sodium. The Olsen Method as described by [23] was used in the determination of phosphorus
content. The determination of the mineral nitrogen content was conducted using methods described by [1] and [25]. The
Model 370pH/mV Meter which measures both pH and temperature was used with a soil: water ratio of 1:5. The electrical
conductivity was measured by mixing 10 g of soil to 50 ml of distilled water using the Model 4071 Jenway Conductivity
Meter. The soil moisture maps for the three sites (Chidodo, Kanyemba and Mushumbi Pools) were produced using spatial
analysis tools on ArcGIS. The inverse distance weighting (IDW) method was used because it is dependent on pattern of
data locations. Quantitative data was analysed using statistical methods in Excel such as regression analysis and
Statistical Package for the Social Sciences (SPSS) using the repeated measures analysis and ANOVA as described by
[12].The repeated measures analysis with a significance level of p< 0.05 was used to analyse the relationships of soil
moisture content, electrical conductivity and pH with time, distance from the river and depth. The repeated measures
designs involve each subject being measured p times on the same dependent variable [19]. The independent sample t-Test
with a significance level of p< 0.05 was used to analyse the relationship between soil nutrient concentrations with depth.
Analysis of variance (ANOVA) or F-Test with a significance level of p< 0.05 was used to determine the differences in
moisture with the distance from the river or the position within the floodplain. ANOVA was done using SPSS 16.0.
3. RESULTS AND DISCUSSIONS
3.1 Moisture content, field capacity and permanent wilting point
The United States Department of Agriculture (USDA) soil textural classifications were Chidodo (50% of the soil was
sandy loam, 29% was loamy sand and 2% was sand); Kanyemba (46% of the soil was loamy sand, 17% was sand and 7%
was sandy loam) and Mushumbi Pools (54% of the soil was sandy loam, 25% was loamy sand and 8% was sand). The
field capacity for sandy loam is 15% and the permanent wilting point was 5%. The field capacity for loamy sand was 14%
and the permanent wilting point is 6%. The values for sandy loam (12-18% field capacity and 5% permanent wilting
point) for Chidodo were taken, for Kanyemba the values for loamy sand (14% field capacity and 6% permanent wilting
point) were taken and for Mushumbi Pools the values for sandy loam (12-18% field capacity and 5% permanent wilting
point) were taken.
Table 1 shows the moisture content as a percentage of the wilting point. The shaded cells show the locations at which
crops wilt as the water content will be lower than the wilting point. It is not practical to grow crops in these places as the
crops will quickly wilt. The work by [3] in the Field Guide to Soil and Site Description in Zimbabwe records similar
observations that soil moisture contents below the wilting point of the crop will result in drying out of the crop. At
Mushumbi Pools at the end of the floodplain, crops could only grow in the field in May 2011 and during the other months
the moisture content recorded was below the wilting point. This shows the importance of residual moisture after flood
events as cropping is only possible at the beginning of the floodplain at Mushumbi Pools. At Chidodo the crops
permanently wilted in April 2011 and May 2012 outside the floodplain. In May 2011 there were backflows from Cahora
Bassa Dam and this increased the soil moisture content to values above wilting point. At Kanyemba the soil moisture
could sustain plant growth from April to June without irrigation water required which suggests that the backwater from
Cahora Bassa Dam contributed to attaining higher yields. At Musengezi and Mwazamutanda rivers, high moisture
content values were obtained during the month of April and May 2011 near the river due to the residual moisture. [22]
established that there is a direct relationship between the operations of the Cahora Bassa Dam (reservoir) in the Lower
Middle Zambezi Valley and the occurrence of floods in the Mbire District. Moisture content decreased with distance from
the flood plain (see description of FP1, FP2 and FP3 in Figure 2). The change in field capacity across the floodplain was 32% and the change in field capacity from the start of the floodplain to a point outside the floodplain was -53.3%. Since
the moisture content of the soils in the floodplain was higher than outside the floodplain it follows that the soil within the
floodplain was better able to support crops during the period March to June and even until July of each year after the
rainy season. The effect of the floods was therefore to increase the water productivity potential within the floodplain. The
results show that the moisture content decreased with distance from the floodplain.
Table 1: Moisture content as a percentage of the wilting point for the period April 2011 to May 2012
April 2011
m
%
(%)
WP
May 2011
m
%
(%)
WP
June 2011
m
%
(%)
WP
December 2011
May 2012
m (%)
% WP
m (%)
% WP
Beginning of floodplain-FP1
Chidodo
10.60
212
8.50
170
7.50
150
5.25
105
5.75
115
Kanyemba
22.86
381
28.1
469
18.00
300
18.48
308
17.16
286
Volume 3, Issue 1, January 2014
Page 246
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected], [email protected]
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
4
Mushumbi
14.15
283
5.45
109
6.70
134
7.10
142
6.15
123
120
5.25
105
5.05
101
5.65
113
171
8.16
136
13.08
218
6.45
109
End of floodplain-FP2
Chidodo
5.45
109
Kanyemba
27.18
453
6.00
10.2
6
Mushumbi
3.85
77
7.35
147
4.40
88
3.90
78
4.65
93
Outside floodplain-FP3
Chidodo
3.05
61
5.20
104
5.15
103
4.45
89
4.05
81
Kanyemba
17.88
298
8.58
143
6.84
114
10.26
171
7.26
121
Mushumbi
4.95
99
3.90
78
4.35
87
3.65
73
4.80
96
Below permanent wilting point. Crops
permanently wilt.
At Musengezi and Mwazamutanda rivers the moisture content values of 6% to 28% were obtained during the month of
April and May 2011 near the river due to the soil moisture created during backwater flooding events in the same period.
The backwater events were experienced during the month of June 2011 in Mwazamutanda River. The results might also
be affected by different infiltration, evapotranspiration rates and soil porosity and the interaction of soil water with
shallow ground water and topography as observed by [7] and [24]. [21] concluded that evaporation and groundwater
infiltration are directly affected by land cover and use.
Table 2 shows that there is no relationship between moisture content and distance on the Manyame River at Mushumbi
Pools. The regression analysis was done according to the method by [4] at α=0.05. [37] also observed that soil moisture is
temporally and spatially variable and is affected by other factors like evapotranspiration. [15] considered measuring
moisture content at different depths within the root zone only so as to obtain a more accurate variation. [27] concluded
that the relationship between the spatial mean soil moisture and variability is not uniquely defined and changes
throughout seasons.
Table 2: Pearson’s product-moment correlation coefficients for the relationship between moisture content and distance
from the river for Mbire District for n= 461 and p<0.05
River
Correlation Coefficient
95%
Statistical
Strength
of
coefficient
of variation Confidence
significance
relationship
(r)
(R2)
level (α=0.05)
Musengezi
0.134
0.018
0.164
Not Significant
Weakly positive
Mwazamutanda
0.022
0.005
0.164
Not Significant
No association
Manyame
-0.044
0.002
0.164
Not Significant
No association
Spatial variation of soil moisture
Figure 3 shows the moisture content variation with distance from the Musengezi River at Chidodo, Mwazamutanda River
at Kanyemba and Manyame River at Mushumbi Pools respectively.
Volume 3, Issue 1, January 2014
Page 247
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected], [email protected]
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
Figure 3: Moisture content variation with distance from Musengezi River at Chidodo, Mwazamutanda River at Kanyemba and
Manyame River at Mushumbi Pools respectively for the period April 2011 to May 2012
The results show that the moisture content decreased with distance from the flood plain. At Musengezi and
Mwazamutanda rivers high moisture content values were obtained during the month of April and May 2011 near the
river and this might be attributed to the residual moisture. This resulted in higher moisture content values at the
beginning of the floodplain (FP1). This also explains the reason the farmers grow crops within the floodplain where the
residual moisture is enough to support crops to maturity after the rainy season (April to July). [16] observed that soil
moisture content is an indicator of the effect of land use and cover change on hydrological processes. The results might
also be affected by different infiltration, evapotranspiration rates and soil porosity and the interaction of soil water with
shallow ground water and topography as observed by [7] and [24]. [21] concluded that evaporation and groundwater
infiltration is directly affected by land cover and use and the results are influenced by the different land covers. This can
be attributed to the flood effects which increase infiltration and evapotranspiration.
Temporal variation of soil moisture
The temporal variation in moisture content is shown in Figures 4, 5 and 6. From these results it also can be seen that the
highest value of moisture content is at the start of the floodplain and the lowest value at the end of the floodplain. Figure
5 shows that the average moisture content for each site decreased with time. For May and June 2011 the highest values
were recorded for Musengezi River at Chidodo followed by Mwazamutanda River at Kanyemba and finally Manyame
River at Mushumbi Pools. This was due to the backwaters in May 2011and June2011.
Volume 3, Issue 1, January 2014
Page 248
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected], [email protected]
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
Figure 4: Moisture content variation with time at three positions in a transect in the floodplain for Mwazamutanda River at Kanyemba
for the period April 2011 to May 2012
Figure 5: Moisture content variation with time at three positions in a transect in the floodplain for Musengezi River at Chidodo for the
period April 2011 to May 2012
Figure 6: Moisture content variation with time at three positions in a transect in the floodplain for Manyame River at Mushumbi
Pools for the period April 2011 to May 2012
Volume 3, Issue 1, January 2014
Page 249
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected], [email protected]
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
3.2 Repeated Measures Analysis with SPSS for Moisture Content
There were significant differences in moisture content with time at Musengezi River and Mwazamutanda River as shown
in Table 3.
Table 3: Summary of ‘repeated measures analysis’ method of moisture content distance factor (n= 432) at p< 0.05 for
Mbire District for the period April 2011 to May 2012
Within subject effects
Month
Sphericity
Assumed
Time distance interaction
Month*Distance Sphericity
Assumed
Between subjects effects
Distance
Sphericity
Assumed
River
F Value
p Valuesig.
Comment
Manyame, Mushumbi
Musengezi, Chidodo
Mwazamutanda,
Kanyemba
0.334
6.621
2.861
0.801
0.000
0.041
Not significant
Significant
Significant
Manyame, Mushumbi
Musengezi, Chidodo
Mwazamutanda,
Kanyemba
0.334
1.150
1.251
0.801
0.340
0.287
Not significant
Not significant
Not significant
Manyame, Mushumbi
Musengezi, Chidodo
Mwazamutanda,
Kanyemba
0.213
0.627
4.328
0.809
0.540
0.021
Not significant
Not significant
Significant
There were no significant effects of the combination of time and distance with respect to moisture content at Manyame,
Musengezi and Mwazamutanda Rivers as also confirmed by the findings of [33]. For Manyame and Musengezi rivers
there was no significant difference of moisture content with the distance from the river. This confirmed the findings by
[6] and [31] who observed that soil moisture is highly variable in the horizontal and vertical directions among other
factors
3.3 Potential flooding due to Cahora Bassa Water Levels
At the full supply level of 329 m above sea level the water level in the Cahora Bassa Dam covers 3,000 km2. At 318 m
above sea level the water in the lake, this extends 1,900 km2 and at 324 m above sea level the area of water is 2,651 km2
[22]. At the beginning of the floodplain at Chidodo, (FP1) is at an elevation of 320 m above mean sea level (amsl) while
the end of the floodplain, FP2, is at 324 m; therefore most of the areas are likely to be flooded. The mean monthly water
levels at Cahora Bassa Dam for the period January 2000 to December 2008 are shown in Figure 7. The floodplain is
measured from the river outwards.
Figure 7: Cahora Bassa Dam monthly average water levels for the period January 2000 to December 2008
Volume 3, Issue 1, January 2014
Page 250
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected], [email protected]
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
Levels above FP1 indicate the frequency of flooding at the beginning of the floodplain (near the river bank) while levels
above FP2 show the frequency of flooding outside the floodplain at Chidodo. The lowest point in the study site at Chidodo
was at an altitude of 321m and the highest was at an altitude of 327m. Figure 7 confirms that most of the areas within the
floodplain are prone to flooding almost on an annual basis. Figure 8 shows that the water level was highest at the
beginning of January 2012. The rising levels were a result of the backwaters from Cahora Bassa Dam on Musengezi
River. The peak values in January 2012 and February 2012 show the flooding due to upstream runoff generation or a
combination of natural floods and backwaters. From March 2012 the water level was below the flood level and
backwaters were not experienced in 2012.
Figure 8: Water levels at Musengezi River, Chidodo for the period May 2011to June 2012
3.4 Soil pH Analysis
Table 7 shows the summary of repeated measures analysis of soil pH distance factor (n= 432) at p< 0.05 for Mbire
District for the period April 2011 to May 2012. For tests of ‘within subjects’ on the soil pH relationship with time, the
null hypothesis Ho was that there were no significant differences in soil pH with time versus H1, that there are significant
differences in soil pH with time. The results showed that there were significant differences of soil pH with time at
Manyame, Musengezi and Mwazamutanda Rivers and the results were statistically significant (p< 0.05). There were no
significant effects of the combination of time and distance with respect to pH at Musengezi and Mwazamutanda Rivers
and the results are not statistically significant. At Manyame river there is a significant effect of the combination of time
and distance with respect to pH. There were no significant effects of the combination of time and depth with respect to pH
at Manyame, Musengezi and Mwazamutanda Rivers and the results are not statistically significant. The variations might
also be due to the movement of water and sediments with varying amounts of nutrients during flood periods.
Table 4: Summary of repeated measures analysis of soil pH distance factor (n= 432) at p< 0.05 for Mbire District for the
period April 2011 to May 2012
Source (pH)
River
F Value
p Value-sig.
Comment
Within subject effects
Month
Sphericity
Manyame, Mushumbi
10.721
0.000
Significant
Assumed
Musengezi, Chidodo
20.762
0.000
Significant
Mwazamutanda,
Kanyemba
14.637
0.000
Significant
Manyame, Mushumbi
3.180
0.007
Significant
Musengezi, Chidodo
Mwazamutanda,
Kanyemba
1.153
1.600
0.338
0.155
Not significant
Not significant
Manyame, Mushumbi
Musengezi, Chidodo
0.357
3.220
0.702
0.053
Not significant
Not significant
Time distance interaction
Month*Distanc
e
Sphericity
Assumed
Between subjects effects
Distance
Sphericity
Assumed
Volume 3, Issue 1, January 2014
Page 251
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected], [email protected]
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
Mwazamutanda,
Kanyemba
2.914
0.068
Not significant
3.5 Soil moisture content-NDVI correlation graphs
Table 6 shows the Pearson’s product-moment correlation coefficients for the relationship between NDVI and moisture
content for Mbire District at 0.2 m depth. Results show that there was a weak positive relationship between NDVI and
moisture content at 0.2 m depths for April 2011. The results are not statistically significant at the 95% confidence level.
Linear regression to justify cause-effect relationships among variables and to establish a link between NDVI and climatic
variables was used by [9] and [17]. For May 2011 there is no relationship at 0.2 m depth as shown in Table 5. The results
were not statistically significant at the 95% confidence level since the correlation coefficient was less than the critical
value. The same applies to the June values shown in Table 5. This is explained from observations by [30] who concluded
that the root and below root zone moisture content in semi arid climates (like the Mbire District) has a good correlation
with NDVI during the growing season and soil moisture has small correlation with NDVI during the non growing season.
Table 5: Pearson’s product-moment correlation coefficients for the relationship between NDVI and moisture content for Mbire District
at 0.2 m depth for n= 36 and p<0.05
Month
April 2011
May 2011
June 2011
Correlation
coefficient
(r)
0.381
-0.15
0.125
Coefficient of
variation (R2)
0.145
0.023
0.016
95%
Confidence
level (α=0.05)
0.304
0.304
0.304
Statistical
significance
Strength
relationship
of
Not Significant
Not Significant
Not Significant
Weakly positive
Weakly negative
Weakly positive
3.6 Electrical Conductivity results analysis
Table 9 shows a summary of the EC values obtained for the three sites in the Mbire District. The highest values were
observed in May 2011 which corresponds to the time period increases in water levels were recorded due to backflows
from Cahora Bassa Dam. The high values of standard deviation were due to the varying soil properties and soil nutrient
concentrations. From studies by [8] and [25] most salt affected soils occur in the arid and semi arid climates like the
Zambezi valley, having insufficient soil moisture to leach the salts which is confirmation to the high electrical
conductivity values obtained confirming the presence of high salt concentrations.
Table 6: Electrical conductivity values for Chidodo, Kanyemba and Mushumbi Pools for the period April 2011 to May 2012
SITE
Chidodo
Kanyemba
Mushumbi
Pools
EC Apr.
2011 (mS/
m)
EC May
2011 (mS/
m)
EC June
2011 (mS/
m)
EC Dec.
2011 (mS/
m)
EC May 2012
(mS/ m)
MEAN
5.05
17.25
16.33
11.10
22.93
STD. DEV
3.46
17.56
21.03
9.28
17.91
MAX
10.80
85.76
102.33
33.76
78.80
MIN
1.08
3.66
2.10
1.76
6.56
MEAN
14.48
11.88
9.76
19.16
16.38
STD. DEV
17.14
22.10
19.55
14.43
18.67
MAX
44.86
85.05
87.16
80.40
87.12
MIN
2.14
1.14
0.91
2.00
5.44
MEAN
7.68
11.25
8.03
17.51
23.33
STD. DEV
6.08
16.45
9.39
23.18
26.30
MAX
19.44
75.35
53.67
134.56
118.32
MIN
3.23
0.89
1.62
1.84
6.24
Repeated Measures Analysis: Electrical conductivity distance factor
Table 7 shows the summary of repeated measures analysis of electrical conductivity distance factor (n= 432) at p< 0.05
for Mbire District for the period April 2011 to May 2012.
Volume 3, Issue 1, January 2014
Page 252
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected], [email protected]
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
Table 7: Summary of repeated measures analysis of electrical conductivity distance factor (n= 432) at p< 0.05 for Mbire District for
the period April 2011 to May 2012
Source (EC)
Within subject effects
Month
Sphericity
Assumed
River
F Value
p Value-sig.
Comment
Manyame, Mushumbi
Musengezi, Chidodo
8.253
5.795
0.000
0.001
Significant
Significant
Mwazamutanda, Kanyemba
2.225
0.090
Not significant
Manyame, Mushumbi
2.810
0.014
Significant
Musengezi, Kanyemba
Mwazamutanda, Kanyemba
1.851
0.958
0.097
0.457
Not significant
Not significant
Manyame, Mushumbi
Musengezi, Chidodo
Mwazamutanda, Kanyemba
2.062
2.748
4.577
0.143
0.079
0.018
Not significant
Not significant
Significant
Time distance interaction
Month*Distanc
e
Sphericity
Assumed
Between subjects effects
Distance
Sphericity
Assumed
There were significant differences of electrical conductivity with time at Musengezi and Mwazamutanda Rivers and no
statistical significance for Manyame River. The electrical conductivity is a measure of the total soluble salts and this is
affected by the moisture content of the soil from the findings by [1]. There were no significant effects of the combination
of time and distance with respect to electrical conductivity at Musengezi and Mwazamutanda Rivers and the results were
not statistically significant. At Manyame River (Mushumbi Pools) there was a significant effect of the combination of
time and distance on the electrical conductivity. There were no significant effects of the combination of time and depth
with respect to electrical conductivity at Manyame, Musengezi and Mwazamutanda Rivers and the results were not
statistically significant. For Manyame and Mwazamutanda Rivers there was a significant effect of the combination of
time and depth with respect to electrical conductivity.
Repeated Measures Analysis: Electrical conductivity depth factor
For the tests of ‘between subjects effects’ on the electrical conductivity relationship with depth the null hypothesis Ho was
that there was no significant difference in electrical conductivity with depth. From the results in Table 8 it can be
concluded that the results were not statistically significant for Manyame, Musengezi and Mwazamutanda Rivers and
there was no significant difference in electrical conductivity with the depth.
Table 8: Summary of repeated measures analysis of electrical conductivity depth factor (n= 432) at p< 0.05 for Mbire District for the
period April 2011 to May 2012
Source (EC)
Within subject effects
Month
Sphericity
Assumed
River
F Value
p Value-sig.
Comment
Manyame, Mushumbi
Musengezi, Chidodo
7.423
5.327
0.000
0.002
Significant
Significant
Mwazamutanda, Kanyemba
2.140
0.100
Not significant
Manyame, Mushumbi
0.869
0.521
Not significant
Musengezi, Chidodo
Mwazamutanda, Kanyemba
0.368
0.293
0.898
0.930
Not significant
Not significant
Manyame, Mushumbi
Musengezi, Chidodo
Mwazamutanda, Kanyemba
0.096
0.968
0.049
0.909
0.390
0.952
Not significant
Not significant
Not significant
Time distance interaction
Month*Depth
Sphericity
Assumed
Between subjects effects
Depth
Sphericity
Assumed
3.7 Soil nutrient analysis
The independent t-Test or t-Statistic was used to analyse the variation of soil nutrient concentration with depth or the
difference between concentrations at the 0.2 m and 0.5 m depths. The null hypothesis Ho was that there were no
Volume 3, Issue 1, January 2014
Page 253
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected], [email protected]
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
significant differences in soil nutrient concentration with depth. This test was done only in one month so the repeated
measures analysis was not used.
Independent sample test (t-Test) for soil nutrient variation with depth
Table 9 shows the summary of the independent sample t-Test for the soil nutrient concentration for Mbire District (n= 72)
at p< 0.05 for May 2011. This test was done only in one month so the repeated measures analysis was not used. From the
results it can be concluded that there were no significant differences of soil nutrient concentrations with depth up to 0.5 m
at Manyame, Musengezi and Mwazamutanda Rivers and the results were not statistically significant. From the results it
can be concluded that there were no significant differences of soil nutrient concentrations with depth up to 0.5 m at
Manyame, Musengezi and Mwazamutanda Rivers and the results were not statistically significant.
Table 9: Summary of the independent sample t-Test for the soil nutrient concentration for Mbire District (n= 72) at p< 0.05 for May
2011
Manyame River,
Mushumbi Pools
Musengezi River,
Chidodo
Mwazamutanda
River, Kanyemba
Element
Calcium
Magnesium
Potassium
Sodium
Phosphorus
Nitrogen
Element
Calcium
Magnesium
Potassium
Sodium
Phosphorus
Nitrogen
Element
Calcium
Magnesium
Potassium
Sodium
Phosphorus
Nitrogen
t value
1.623
-0.053
-0.272
-0.440
0.524
-0.434
t value
0.619
0.182
-0.290
-0.701
-0.467
0.271
t value
0.225
0.130
0.489
0.078
-0.143
0.166
p: Sig. (2-tailed)
0.119
0.958
0.788
0.664
0.606
0.669
p: Sig. (2-tailed)
0.542
0.857
0.775
0.491
0.645
0.789
p: Sig. (2-tailed)
0.824
0.898
0.630
0.938
0.887
0.870
Comments
Not significant
Not significant
Not significant
Not significant
Not significant
Not significant
Comments
Not significant
Not significant
Not significant
Not significant
Not significant
Not significant
Comments
Not significant
Not significant
Not significant
Not significant
Not significant
Not significant
One Way Analysis of Variance (ANOVA or F-Test) for the soil nutrient variation with distance from the river
The One Way Analysis of Variance (ANOVA or F-Test) was used for the analysis of soil nutrient variation with distance
from the river. There are three variables of the test pit at start of floodplain (FP1), end of floodplain (FP2) and outside the
floodplain (FP3) so the t-test was not used. The one way analysis of variance (ANOVA) or the F-test was used for the
analysis. The null hypothesis Ho was that there were no significant differences in nutrient concentration with distance
from the river or position within the floodplain. Table 10 shows the summary of the One Way Analysis of Variance
(ANOVA) Test for the soil nutrient concentration for Mbire District (n= 72) at p< 0.05 for May 2011.
Table 10: Summary of the One Way Analysis of Variance (ANOVA) Test for the soil nutrient concentration for Mbire District (n= 72)
at p< 0.05 for May 2011
Manyame River,
Mushumbi Pools
Musengezi River,
Chidodo
Element
Calcium
Magnesium
Potassium
Sodium
Phosphorus
Nitrogen
Element
Calcium
Magnesium
Potassium
Volume 3, Issue 1, January 2014
F value
0.358
0.047
5.172
0.703
2.622
4.124
F value
0.330
0.222
3.724
p: Sig.
0.703
0.954
0.015
0.507
0.097
0.013
p: Sig.
0.722
0.802
0.041
Comments
Not significant
Not significant
Significant
Not significant
Not significant
Significant
Comments
Not significant
Not significant
Significant
Page 254
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected], [email protected]
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
Sodium
0.659
0.528
Not significant
Phosphorus
1.649
0.216
Not significant
Nitrogen
6.849
0.005
Significant
Mwazamutanda
Element
F value
p: Sig.
Comments
River, Kanyemba
Calcium
1.108
0.349
Not significant
Magnesium
0.012
0.988
Not significant
Potassium
1.454
0.256
Not significant
Sodium
2.131
0.144
Not significant
Phosphorus
0.246
0.784
Not significant
Nitrogen
5.508
0.012
Significant
There were no significant differences in calcium, magnesium, sodium and phosphorus concentrations with the distance
from the river for the Manyame, Musengezi and Mwazamutanda Rivers and the results were not statistically significant.
At Manyame River there was no significant difference of nutrient concentration with the distance from the river and the
results were not statistically significant. There was a significant difference in nitrogen concentration with distance from
the river at Chidodo, Kanyemba and Mushumbi Pools sites and the results are statistically significant. At Mushumbi
Pools and Chidodo there was a significant difference in potassium concentration with distance from the river.
4. CONCLUSIONS AND RECOMMENDATIONS
4.1 Conclusions
The findings of this study show that the temporal and spatial variation of soil properties in the Middle Zambezi
Valley is affected by a number of factors caused by the natural and man- made hydraulic structures. Floodplain
agriculture benefits farmers throughout the dry season (recession farming). Residual moisture is sufficient to
support crops to maturity. Enforcement of current cultivation laws will deprive Mbire communities of source
of food security. Temporal and spatial variation of soil moisture (soil moisture dynamics) and nutrient
dynamics are greatly influenced by flooding occurrences. Soil moisture, pH, electrical conductivity and nutrient
composition are highly variable but there is statistical significance in the variation.
4.2 Recommendations
The study needs to be conducted over a minimum of three years to have the temporal and spatial variability for all
scenarios since flooding events change from year to year. The number of transects need to be increased so as to get spatial
variation over a wide area of the catchment. There is need to use moisture content probes in order to cover more transects.
The manual gravimetric method, although the most accurate is labour intensive and the destructive method causes affects
in situ soil moisture content. Use of data loggers (e.g. the Profile probe, Time Domain Reflectometer and Theta probe) is
recommended so as to improve data collection.
Acknowledgements
This work is part of the research activities of the Project titled “Integrating Social and Natural Systems in Enhancing
Environmental Sustainability in the Lower Middle Zambezi Valley” funded by the EchoHydro-Zambezi Project. The
authors also sincerely acknowledge the financial support received from the University of Zimbabwe Research Board and
WaterNet.
REFERENCES
[1.]
[2.]
[3.]
[4.]
[5.]
Bashour, I.I. and Sayegh, A.H., 2007. Methods of Analysis for Soils of Arid and Semi Arid Regions. Food and
Agricultural Organisation of the United Nations. Rome.
Beiffuss, R., Chilundo, A., Isaacman, A., Mulwafu, W., 2002. The Impact of Hydrological Changes on
Subsistence Production Systems and Socio-Cultural Values in the Lower Zambezi Valley. Working paper No.6
Program for the Sustainable Management of Cahora Bassa Dam and the Lower Zambezi Valley.
Bennet, J.G., 1995. Technical Handbook: A Field Guide to Soil and Site Description in Zimbabwe. Zimbabwe
Agricultural Journal No. 6. Chemistry and Soil Research Institute. Department of Research and Specialist
Services, Harare, Zimbabwe.
Burke, S., 2011. Statistics and data analysis. Laboratory of the Government Chemist, Teddington, Middlesex,
United Kingdom.
CIRAD, 2001. The Mankind and the Animal in the Mid Zambezi Valley-Zimbabwe: Boidiversity Conservation
and Sustainable Development in the Mid-Zambezi Valley. Published by Librairie du Cirad, France.
Volume 3, Issue 1, January 2014
Page 255
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected], [email protected]
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
[6.]
[7.]
[8.]
[9.]
[10.]
[11.]
[12.]
[13.]
[14.]
[15.]
[16.]
[17.]
[18.]
[19.]
[20.]
[21.]
[22.]
[23.]
[24.]
[25.]
[26.]
[27.]
[28.]
[29.]
[30.]
[31.]
Giraldo, M.A., Gale, S., 2011. Ground Penetration Radar (GPR). Analysis of soil moisture within different
landuses in an agricultural landscape in Georgia, United States of America (USA). Proceedings of the 2011
Georgia Water Resources Conference, held from 11-13 April 2011, University of Georgia, USA.
Grayson, R.B., Western, A.W., Walker, P., Kandel, D.D., Costelloe, J.F., Wilson, D.J., 2005. Controls of
Patterns of Soil Moisture in Arid and Semi-Arid Systems. Department of Civil and Environmental Engineering
and CRC for Catchment Hydrology, University of Melbourne, Victoria, Australia.
Havlin, J.L., Beaton, J.D., Tisdale, S.L., Nelson, W.L., 2004. Soil Fertility and Fertilisers. An Introduction to
Nutrient Management, 6th Edition. Pearson Education, Delhi, India.
Herrmann, S.M., Anyamba, A., Tucker, C.J., 2005. Recent trends in vegetation dynamics in the Africa Sahel
and their relationship to climate. Global Environmental Change Part A., 15(4): 394-040.
IAEA., 2008. Field Estimation of Soil Water Content, Training Course Series 30, Vienna, Austria.
Kusena, K., 2009. Landcover Change and Impact of Human-Elephant Conflict in the Zimbabwe, Mozambique
and Zambia (ZiMoZa) Transboundary Natural Resources Area. MSc. Thesis, CMB, Swedish Biodiversity
Centre, Uppsala, Sweden.
Landau, S., Everitt, B.S., 2004. A handbook of Statistical Analysis using SPSS. Chapman and Hall/ CRC, New
York, United States of America.
Liu, S., Mo, X., Zhao, W., Naemi, V., Dai, D., Shu, C., Mao, L., 2008. Temporal Variation of Soil Moisture
over the Wuding River Basin assessed with an Eco-Hydrological Model; in-situ observations and remote sensing.
Hydrology and Earth Systems Sciences Discussions. 5: 3557-3604.
Madamombe, E.K., 2004. Zimbabwe: Flood Management Practices-Selected Flood Prone Areas in the Zambezi
Basin, WMO/GWP Associated Programme on Flood Management.
Miller, G.R., Baldocchi, D.D., Law, B.E., Meyers, T., 2007. An analysis of soil moisture dynamics using multiyear data from a network of micrometreorological observation sites. Advances in Water Resources 30 1065-1081.
Elsevier, www.elsevier.com/locate/advwaters.
Modi, P.N., 2004. Irrigation Water Resources and Water Power Engineering, Sixth Edition. Standard Book
House, Delhi, India.
Musyimi, Z., 2011. Temporal Relationships between Remotely Sensed Soil Moisture and NDVI over Africa:
Potential for Drought Early Warning? MSc Thesis, University of Twente, Enschede, the Netherlands.
Mvuriye, D., 2001. Biodiversity Conservation Project. Streambank Cultivation, Ten Commandments on
riverbank and water resource management. CIRAD, Harare, Zimbabwe.
O’Brian, R.G and Kaiser, M.K., 1985. MANOVA Method for Analysing Repeated Measures Designs: An
Extensive Primer. The American Psychological Association, Vol. 97, No. 2, 316-333.
Ohio., 2005. Ohio Agronomy Guide 14th Edition, Bulletin 472. The OHIO State University Extension.
Palamuleni, L.G., Annegarn, H.J., 2011. Hydrological Response to Predicted Land Cover Change in the Upper
Shire River Catchment, Malawi. J. Hum. Ecol. 36(1): 43-52.
Phiri, M., 2011. An analysis of the Cahora Bassa Dam water balance and reservoir operations and their flooding
impact on upstream settlements. MSc Thesis. University of Zimbabwe, Zimbabwe.
Pierzynsky, G.M., 2000. Methods of phosphorous analysis for soils, sediments, residuals and waters. Southern
Cooperative Series Bulletin 396. North Carolina University, United States of America.
Rodriguez-Iturbe, I., D’Odorico, P., Porporato, A., Rudolph, L., 1999. On the Spatial and Temporal Link
between Vegetation, Climate and Soil Moisture. Water Resour. Res., 35(12): 3709-3722.
Sandor, F., 2008. Soil Testing: Perrenial Crops Support Series Jalalabad, Afghanistan. Roots of Peace.
Publication No. 001-AFG.
Southwest Florida Water Management District, 2012. Understanding Flooding and Floodplains.
Watermatters.org. 1-800-423-1476. Accessed on 2/7/12.
Su, Z., Van der Velde, R., Ma, Y., 2008. Impact of Soil Moisture Dynamics on Advanced Synthetic Aperture
Radar (ASAR) backscatter (σo) Signatures and Its Spatial Variability Observed over the Tibetan Plateau.
Sensors., 8: 5479-549.
USDA., 2008. United States Department of Agriculture (USDA). Natural Resources Conservation Service: Soil
Quality Indicators Manual.
Vicent, S., Pons, F.N., Cuadrat, P., 2004. Mapping soil moisture in the central Ebro river valley (Northeast
Spain) with Landsat and NOAA satellite imagery: a comparison with meteorological data. International Journal
of Remote Sensing., 25: 4325-4350.
Wang, X., 2005. Relationship between Ground Based Soil Moisture and Satellite Image Based NDVI. Earth and
Environmental Science Department, University of Texas, San Antonio, United States of America.
Western, A.W., Grayson, R.B., Bloschl, G., 2002. Scaling of Moisture: A hydrologic Perspective. Annu. Rev.
Earth Planet. Sci., 30:149–80.
Volume 3, Issue 1, January 2014
Page 256
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: [email protected], [email protected]
Volume 3, Issue 1, January 2014
ISSN 2319 - 4847
[32.]
[33.]
WMO., 2006. Social Aspects and Stakeholder Involvement in Integrated Flood Management. World
Meteorological Organisation. Associated Programme on Flood Management Technical Document No. 5, Flood
Management Policy Series. Geneva. Switzerland.
Zhou, X., Lin, H.S., White, E.A., 2008. Surface soil hydraulic properties in four soil series under different land
uses and their temporal changes. Catena. 73, 180-188.
Authors’ Profiles
Mr Samson Shumba is currently employed as a lecturer in the Department of Civil Engineering, University of
Zimbabwe where he teaches geotechnical engineering and construction materials technology related courses. Mr
Shumba graduated with a BSc Civil Enginering Honours and MSc Integrated Water Resources Management from the
University of Zimbabwe. He has previously worked for LBK Consulting Engineers and the Harare Polytechnic as a
Principal Lecturer and Head of Division for Civil Engineering and Construction before joining the University of Zimbabwe in 2007.
Mr Shumba has worked as a part time capacity development consultant for the Institute of Water and Sanitation Development (IWSD)
and German Development Cooperation (GIZ) Water Quality Capacity Development Training for Norton, Gweru, Kadoma, Kariba and
Chinhoyi towns in Zimbabwe. Mr Shumba is a member of various national councils which include the Standards Association of
Zimbabwe Building and Civil Engineering Standards Council. His research interests include geotechnical engineering, concrete
technology, water resources management, solid waste management, environmental engineering, materials technology and low cost
infrastructure design.
Dr Hodson Makurira is a Senior Lecturer in the Civil Engineering Department and is currently the Chairman of the
Department. Dr Makurira has over 20 years experience working in the water sector having worked for the Ministry of
Water and Resources and Development and ZINWA prior to joining the Civil Engineering Department in 2003. He
specialises in integrated water management, hydrology, water allocation, water governance and climate change. He
teaches water engineering courses at BSc and postgraduate level. In the process, he has supervised more than 30 BSc
projects and at least 20 Masters Dissertations in the water engineering field. Dr Makurira is a Corporate Member of the Zimbabwe
Institution of Engineers and participates in various capacity building programmes including lecturing and examining at various
universities in the region. Dr Makurira holds a PhD in Water Management from UNESCO-IHE/TU Delft and he is involved in several
research initiatives locally and on international river basins. He has published several articles in international journals and has
presented scientific research at various conferences and workshops. He is a reviewer for several international scientific journals.
Prof. Innocent Nhapi is an expert in sanitary and environmental engineering. He has previously worked for Redcliff
Municipality in Zimbabwe for nearly 10 years before joining the University of Zimbabwe in January 1999. At the
University of Zimbabwe he was involved in water quality and wastewater management projects in the cities of Harare,
Kwekwe, Gweru, and Masvingo towns. He was also involved in the running of two masters programmes in water at the
University of Zimbabwe and was attached to the WaterNet Project (regional capacity building project in Southern
Africa) at various times in 2004 to 2005. Prof. Nhapi was appointed as the SADC-WaterNet Professorial Chair in Integrated Water
Resources Management in the Department of Civil Engineering, University of Zimbabwe in 2009. He has supervised more than 25
MSc students and involved in the start up of 6 PhD students in Rwanda. Innocent has written many journal articles and has also
reviewed several papers and research reports and is an editor/co-editor of a regular special issue of the Elsevier Physics and Chemistry
of the Earth journal and an editorial board member of the Reviews in Environmental Science and Bio/Technology journal.
Mr. Gumindoga is currently employed in the Department of Civil Engineering, University of Zimbabwe where he
teaches water related courses including GIS and remote sensing at both BSc and MSc level. Mr. Gumindoga’s
research interests are in the areas of: Multicriteria GIS Analysis for Environmental Impact Assessments, Spatial and
quantitative hydrology, Parameterization of hydrological, vegetation and climate models using hyper-temporal remote
sensing data for improved Water Resources Management, Feedbacks between climate, vegetation and landuses,
water cycles and other societal influences, Data assimilation of ground data and Earth Observation data in hydrological models to
create improved variable or model estimates and Quantification of Water Cycle Components.
Volume 3, Issue 1, January 2014
Page 257
Download