CLIMATE CHANGE IMPACTS ON HYDRAULIC ...demonstrating probability of washing and cleaning out surfaces as exposure progresses. The spectral reflectance classification indicated the

JORIND 14(2) December, 2016. ISSN 1596-8303. www.transcampus.org/journal; www.ajol.info/journals/jorind

CLIMATE CHANGE IMPACTS ON HYDRAULIC STRUCTURES: THREAT TO THE CONTROL OF

WATER FLOW

A. I. Ayinla Department of Agricultural & Bio-Environmental Engineering, Kaduna Polytechnic, Kaduna

A. A. Jimoh Department of Civil Engineering, Kaduna Polytechnic, Kaduna

P. N. Eleke Department of Agricultural Technology, Kaduna Polytechnic, Kaduna E-mail: [email protected]. +2348023630701, +2347032182822

Abstract The study was aimed at assessing the climate change impacts on hydraulic structure as it affects the water flows. A total of 30 years period climate data (1976- 2005) was obtained from the Department of Hydro-Meteorology, Kaduna State Water Board, Kaduna, Nigeria. While the timescale of raw 1996 and 2006 Satellite Imagery of a dam dyke was sourced from AY-GEOSPATIAL CONSULTANTS with a consideration. The periods were chosen to represent the two climate data decades (1986-2005 & 1996-2012).The climatic data analysis was performed using statistical downscaling method. Minitab Computer Software was found suitable for the analysis. ERDDAS IMAGINE Computer Software was also used to process, and extract the statistical information of the structure and as well as reflective properties. The analysis techniques include ISODATA; Internal Average Relative Reflectance and measurement of climate impact by differencing. The results of climate analysis indicated that 1986-2005 data period was wetter, producing trend of 9.62mm

rainfall with increasing mean temperature trend of 0.0060C, while 1996-2012 data indicated downward

trend of 5.05mm in rainfall and trend decrease in mean temperature (0.0030C). The clustering patterns in dyke shows the pixel value of 55 - 60 for 1996 and 150 down toward 145 for the 2006 imagery, demonstrating probability of washing and cleaning out surfaces as exposure progresses. The spectral reflectance classification indicated the variability of 203% on the brightness value relative to 2006 from 1996. The study is concluded by spotting out the challenges in providing evidence of changes relative to 2006 imagery, while the use of the method is not exhaustive. Furthermore, the study provides recommendation of intensive study and concrete covers measurement so as to ascertain the strength, extent of crack on the dam dyke as to continuing providing its services for the flow of water. Keywords: Climate change, hydraulic structures, water flows, satellite imagery

Introduction Climate change has been identified to be one of the most serious global problems, which affects many sectors in the World and is considered one of the most serious threats to sustainable development, with adverse impact on environment, human health, food security, economic activities, natural resources and physical infrastructures (IPCC, 2007). The basic elements that determine the climate are rainfall and temperature, (Pidwirny, 2004). These elements vary from time to time, years or decades, and from one location to another. However, human life depends on climate dynamically. The growing awareness of t he rate o f climate change have been affirmed by the IPCC, (2007) report and few mentioned here; (Brown and Wilby, 2012; Schlosser and Strzepek, 2014; Fant

et al., 2014; Arndt and Tarp, 2015). Global temperatures on the earth surface have been found

to increase by 0.4 to 0.80C with mean of 0.740C since record started in 1860 and it was projected to

increase between 1.60C (low), 2.50C (middle) and

4.50C (high) by the year 2100 (IPCC, 2007).

Possible consequences of the climate change include increasing evapotranspiration, decreasing rainfall amount in the continental interiors, increasing rainfall in the coastal areas, increasing frequency, floods, droughts, bush fires, unpredictable rainfall patterns, sea level rise, increase desertification and land degradation, drying up of rivers and lakes and constant loss of forest cover and biodiversity (Odjugo, 2010). Also,

mailto:[email protected]


climate change influences on planning and design completion timelines Bello et al, (2012) as well as on crops and livestock production URT, (2005). In recent years, numerous researchers have extensively worked to ascertain the evidences of climate change impacts.

Trend analysis has been used to investigate temperature and rainfall change in local, regional and global scales (Christy et al, 2001; Sadiq and Qureshi, 2010 and Olofintoye and Sule, 2010).

Impacts of climate change on the hydrologic component of water resources have also been demonstrated (Saminu et al, 2013 and Raje and Mujumdar, 2010); Agricultural production ranges from irrigation, crops and livestock production (Marry and Majule, 2009; Liweng et al, 2007); assessment on adaptations and vulnerability of water resources components such as hydrologic, water quality and aquatic and marine ecosystem in UNEP, (1998) has also been demonstrated.

Researchers have also dwelled on water management, which has been recognized long ago as a key to wealth and economic development of an area. Impacts of climate change on water management have come under intense economic, social and political scrutiny, with development of alternatives ranging from new off-stream surface reservoir sites, expanded on-stream reservoirs, greater conjunctive use of ground-water storage, additional water conveyance capacity, more expensive and effective forms of water treatment and waste water cycling, water transfers from inter-basins among water users, water use efficiency and demand management, as well as experimental forms of environmental restoration. The integration of such a variety of options into an already complex water management system highlights the difficulties in coping with climate change impacts.

The management system in water resources supplies is considered in two folds: surface and

groundwater. Climate changes affect these two folds via reduced flows into the storage reservoir or by increased variability in inflow, which, in turn, affects the yields from the existing storage facilities. In this context, Wegehenkel and Kersebaum, (2009) suggest that an estimation of the impacts of climate change on the discharge regime of a river or storage is required to develop adaptation strategies in water resources management. These adaptation strategies include methods of water application in agricultural field (irrigation), rainfall harvest and construction of various hydraulic structures to confined, convey, and distribute water.

Sensitivity of these hydraulic structures to climate change is a basic approach to provide an effective water resources management, since these changes directly affect the water supply and water quality (Wegehenkel and Kersebaum, 2009). Furthermore, these changes result in a feedback effect, which includes: modified albedo; surface roughness; micro-circulation processes; the heat and energy balance of the near-surface of hydraulic structures. The consequences, that result in change in runoff coefficient of a catchment; change in hydrological pattern of the catchment; water quality effect, changes account to shifts in thermal regimes (Lasco and Boer, 2006; Cocklin, 1999; Fischer et al, 2002)

Studying climate change aims to develop tools for suitable management of water resources, including risk analysis and evaluation of possible hazards related to large floods and severe droughts, development of design guidelines and assessment of environmental impacts in hydraulic structures. H e n c e , future water supply for domestic and irrigation which highly depends on the storage and conveyance m a y b e l i k e l y a f f e c t e d . T his study i s t h e n aimed to assess the impacts of climate change on water hydraulic structures using Satellite Imagery analysis on reservoir dam dyke w i t h t h e f a c t s o f i t s all year round exposures and as a major hope of control water flows.

Materials and methods

Data collection The process of physical de ter io ra t ion changes in hydraulic structures as an impact of climate change is a spatial process, which requires a long- term data set of climatic condition of the locality. Long-term, adequate and reliable time series on climatic variables, either real or

constructed, is an important pre-requisite for any reliable forecasts of extreme events in climatic data analysis. It is also important that each data series under consideration possesses a complete or near complete set of high quality s tandard value. Often time series, both in physical and behavioral dataset, tend to have contiguous missing values.


This can easily happened if the measure instrument fails to work. Apart from missing values, outliers (values greater than bulk of the range) can also occur in time series, i f i t does , the resu l t genera ted may be exaggera ted or underes t imated.

For the purposes of this work, the datasets were collected in two folds, namely, Temperature and Rainfall as climate data, and Satellite Imagery (Fig. 1a&b). For the climate data, a 30-year period (1976-2005) was sourced from Hydro-Meteorological Department, Kaduna State Water Board. While the time scale of 1996 and 2006 Satellite Imagery was sourced raw from AY-GEOSPATIAL CONSULTANTS on SPOT Earth-orbiting satellite platform with a consideration. The periods were chosen to indicate the climate data, and covered the 1986 – 2005 and 1996 – 2012 periods, respectively, since there was no available imagery for 1976 & 1986. The three multi-spectral imageries have 1048 columns and 511 rows as raster information. The unsigned 8-bit data type was compressed under Run-Length Encoding (ESRI) having Pyramid layer algorithm Imagine 2 x 2 resampling. The imagery was geo-referenced at datum. UTM zone Minna, Niger state having Clarke-1880-RGS spheroid


Fig. 1(a & b): The 1996 & 2006 raw satellite Imagery.

Methods, analysis and tools

i. The method applied for climate analysis was that of statistical downscaling. The mean monthly data were transformed to mean annual data. Time series analysis was performed to generate the descriptive statistics output for the climate variables using MINITAB Software. The datasets were collected for two different decades to provide a basis for comparisons of impacts on the

dam dyke.

ii. ERDAS IMAGINE Software was used to analyze the Satellite Imagery. This software uses statistical techniques and information that extract the reflective properties of structures and develop reflective signatures. ERDAS IMAGINE enables one to process imagery and derive information (data o u t p u t ), Data Conversion, Ortho-rectified Databases, Color Balancing, Mosaic and Compression, Land-cover Mapping and Terrain


Categorization, Map Composition, Printing, Report Generation and Geo PDF Production Feature Capture and Update, and Terrain Database Creation and Analysis. The Imagery processing was first analyzed by means of stretching, filtering, and contrasting and brightness inversion, for better imagery interpretation and to remove atmospheric distortion. Brightness inversion (BI) is a radiometric enhancement of imagery using the values of individual pixels within each band. BI allows both linear and non-linear reversal of the image intensity range. It produces image that has the opposite contrast of the original image, that’s, dark detail becomes light and light detail becomes dark

The imagery analysis techniques consist of:

a. Identification (classification) of reflectance spectral into various objects and terrestrial materials. ISODATA (Iterative Self-Organizing Data Analysis) technique approach was employed. ISODATA is an alternative name for unsupervised classification or K-means. In this approach, spectral classes are determined and then

defined by their informal utility.

b. Internal Average Relative Reflectance (IAR Reflectance): This technique calculates a relative reflectance by dividing each spectrum (pixel) by the scene average spectrum (Kruse 1988). The algorithm is based on the assumption that this scene average spectrum is largely composed of the atmospheric contribution and that the atmosphere is uniformly across the scene. For this study, the average spectrum was visually inspected on the area of interest (Dam Dyke) using same coordinates on the 1996 & 2006 Imagery to check for this possibility and identify spectral characteristics.

c. Measurement of Climate Impact: The measurement of climate impacts on the structure employed here was that of differencing the descriptive statistics output (signature) of corresponding respective surface reflectance from the two sequential periods, 1996 and 2006 Satellite Imagery.

Results and discussions

Climate data analysis The descriptive statistics of climate variables (Temperature and Rainfall) using MINITAB Software for the 1986-2005 and 1992-2012 datasets are

presented in Table 1. From the result relative to

the period 1986-2005, the mean maximum temperature (Tmax), mean minimum temperature

(Tmini) and mean temperature (Tmean) values were

34.610C, 19.570C, and 27.090C respectively, while rainfall mean was 1193.40mm with a minimum and maximum of 779.90mm and 1459.40mm,

respectively. Analysis indicated that Tmax, Tmean,

and rainfall were in upward trend, with values of

0.0140C, 0.0060C and 9.62mm respectively. However, Tmini indicated a downward trend, with

value of 0.0020C. The Tmax displays average

maximum and minimum values of 35.90C and

33.080C, while Tmini exhibited 19.900C and 18.900C;

and Tmean with 27.720C and 26.480C respectively.

The 1992-2012 dataset indicated means of Tmax,

Tmini, and Tmean as 34.810C, 19.570C, and

27.190C, respectively. The maximum values of Tmax and Tmean of both periods remained

constants, while the minimum of Tmax of 1992-

2012 was 33.830C. The minimum Tmini values of both periods were the same (constant). Rainfall minimum and maximum also remain constants for the choose period, but 1992-2012 data exhibit mean value of 1169.10mm with 9.62mm upward trend, while 1986-2005 was 1193.40mm with downward trend of 5.05mm.

Moreover, the 1986-2005 dataset demonstrated that there was more rain than the period of 1992-2012, meaning the 1986-2005 was wetter, in

average, although both of them have the same

minimum and maximum values. Tmean indicated

an increase relative to 1992-2012 but with a

decreased trend of 0.00250C, while Tmean of both

periods remain constant, though the minimum continued increasing towards the period of 1992-2012.


Table 1: Descriptive Statistics Characteristic of climate data

Statistical Parameters Climate

Variable

Periods Mean

Standard

Deviation

Coef. of

Variation Minimum Maximum Trends

Tmax 1986-2005 34.614 0.788 2.28 33.075 35.900 0.0140

1992-2012 34.809 0.603 1.73 33.834 35.900 -0.0331

Tmin 1986-2005 19.568 0.381 1.95 18.900 19.990 -0.0019

1992-2012 19.574 0.362 1.85 18.900 19.990 0.0281

Tmean 1986-2005 27.088 0.368 1.36 26.482 27.717 0.0060

1992-2012 27.191 0.323 1.19 26.505 27.717 -0.0025

Rainfall 1986-2005 1193.40 158.30 13.27 779.900 1459.40 9.621

1992-2012 1169.10 167.20 14.30 779.900 1459.40 -5.047

Imagery processing

In processing of the acquired imagery in ERDARS IMAGINE (2010), the clustering patterns were observed in the range of 0 to 255 pixels. Fig. 2a&b show the curve of the spectral reflectance which is assigned in color. The blue color indicates water, having the piecewise pixel value range of 75 to 120, which abruptly varies from 120 to 80, as indicated in 2a, whereby, in 2b, it remain constant in 250. The red color was assigned to beard-ground, having pixel value varying between 150 and 90, in 2a. In the case of 2b, it varies fromm 150 to about 160

Vegetation is known to have a distinct reflectance response and is represented here by green color. This variable shows pixel values between 50 and 55 in (Fig. 2a), while the reflectance at Fig. 2b indicated values between 225 and 230, a representation of about 82% in variability Finally, the dyke, colored in brown, has pixel values between 55 and 60 in Fig. 2a, while in Fig. 2b, it starts at the value of 150, and drops to 145, indicating probability of washing and cleaning surface as exposure to climate variability (rainfall) trends (this is an obvious).


Fig. 2a&b: Spectral Profile of 1996 & 2006 Satellite Imagery.

The available information before acquiring the imagery, dam dyke, water body, bear-ground and vegetation such as tree, grasses and algae on the dyke were identified as the features that formed the basic components and surroundings of the dam as a guide for spectral variability. Thus, reflected light as wavelength or span of wavelength can be suggested to be ranging from very low values (dark), as only few photons are received to very high values, because of high reflectance. This lead to brightness inversion processes (Kruse, 1988).

Having had the prior knowledge of the ground truth, the natural features are clearly identified in different thematic maps, as presented in Fig. 3a & b. The thematic in Fig. 3a revealed vegetation and dyke in dotted white; bare-ground in olive green accent 3; water body in red accent 2, while algae indicate patches of green-grays color. The brighter color dominates the entire water body in Fig.3b, while vegetation displays a clear line-up of black-reddish patches and algae show red accent 2 patches. Bare ground displays white and black coloration.

The consequences of these facts are that, the black-reddish patches in vegetation is indicating that weak vegetation in replacement with greenish coloration one expect from vegetation shows that the carbon dioxide absorption by this nature may likely be affected. While the white and black coloration of bare ground is a sign of low vegetation.

136

137


Figure 3(a & b): The 1996a & 2006b Imagery brightness inversion.


Identification (classification) of reflectance spectral The thematic maps in Fig. 4a&b indicate the result of spectral reflectance classification characteristic features of the case study, using the ISODATA techniques presented on raw imagery in Fig. 1a & b. The blue theme represent water body, vegetation, grasses or trees indicated dark-green; and light-green as Algae. Dark-red and brown color indicated bare-ground in Fig.1a & b, respectively. Dyke exhibited characteristic feature of vegetative grasses and bare- ground, which may be obvious due to growing algae and nature of the materials. The descriptive statistics output was also presented in Table 2. The statistic mean of 1996 imagery shows 1.222 while that of 2006 indicated 0.404, indicating the variation of -0.818 (about 67%) in brightness values. The standard deviation is useful for comparing the distribution of features and for comparing one type of feature at different times. The imagery of 1996 exhibits a standard deviation value of 1.890, and that of 2006 is 0.670. This result indicates that the physical feature of the dyke in the period of 1996 was brighter, while it became dulled in 2006 by about 65%. This occurred probably due to continuous exposure of the part to the temperature changes as the part is not covered by water as it is believed that water to some extent prevent excessive temperature effects.


Figure 4(a & b): Thematic Map of Classified 1996a & 2006b Image

Table 2: Descriptive Statistic result of Unsupervised Classification

Statistical Parameter

Periods

1996 2006

Mean 1.222 0.404

Standard deviation 1.890 0.670

Coefficient of Variation 1.552 1.660

Internal Average Relative Reflectance (IARR) The IARR statistic results are presented in conjunction with spectral characteristics known as Digital Number (DN) or Brightness Value (Table 3). It is desired to convert the spectra recorded by the sensor into a form that can be compared to known reference spectra. In this paper we

employed the technique proposed by Krause (1988) for this purpose. Results indicate that DN values ranges between 210 to 221 and 243 to 255 for 1996 and 2006 imagery, respectively. The imagery of 1996 indicates low radiance when compare with that of 2006. This is an indicator

139


of high radiant properties on the dyke image of 2006 as exposure to climate progresses. Emittance and thermal emittance are used to describe thermal radiation measurements on complex surfaces such as insulation products (ASTM C835-06 (2013), Kruger et al, 2012). Air when enter a building

controlled, for instance, through open windows or other openings, due to pressure or temperature differences between the outdoor and indoor air negative pressure below atmospheric, variation in this pressure causes infiltration into the dike surface given room for peeling.

Table 3: The DN variability result for 1996 and 2006 Imagery

Band

1996 2006

Digital Number Digital Number

1 221 255

2 212 245

3 210 243

Climate Variability Impacts The task of any remote sensing system is to determine spectral characteristics, derive appropriate features for the surface of the earth in a given area and to inter-relate the spatial positions of the derived classes to the climate variables. Digital image analysis methods depend on tone or color, which are represented as digital numbers (i.e. brightness value) in each pixel of the image, allowing the evaluation of climate change on physical structures.

Results of statistics analysis presented in Table 4 point to some degree of climate change relative to the climate data of 1986-2005 & 1996-2012, in conjunction with classification and spectral feature analysis of the Satellite Imagery of 1996 and 2006. These results indicate a variation of 0.4%

(0.1030C) in mean temperature might have been one of factors that caused the reduction of 2% in rainfall and the increase of 0.4% in internal average relative reflectance (IAR reflectance) of the Dyke.

On the basis of CV, the decrease of 14% mean temperature might have also impacted increasing 7% in rainfall and decreasing 7% in the dyke IAR reflectance. Also, the 0.4% variability of mean temperature might have imposed 203% change in texture (classification) of the dyke relative to 2006 imagery, indicating larger roughness and dryness than 1996 imagery. This condition encourages growth of vegetation, grasses and algae on the surface of the dyke, as shown in patches on the original imagery (Fig. 1a&b), creating low resistance to thawing and wearing caused by weathering and erosion factors on the outermost parts of the structure.

The standard deviations of classes, computed from the dyke subset of 1996 and 2006 satellite imagery, are equal to 1.896 and 0.670, respectively. This shows a decrease of 183% relative to 2006 imagery, with a 14% mean temperature downward variation and a 5% increase in rainfall.

Table 4: Climate variability impact measurement

Statistical Parameters Climate

Variable

Periods Mean

Standard

Deviation

Coefficient

of Variation Minimum

Maximum

Trends

Tmean 1986-2005 27.088 0.368 1.36 26.482 27.717 0.0060 1992-2012 27.191 0.323 1.19 26.505 27.717 -0.0025 Difference 0.103 -0.045 -0.17 0.023 00 -0.0035

% Diff. 0.4 14 14 0.1 00 140

Rainfall 1986-2005 1193.40 158.30 13.27 779.900 1459.40 9.621 1992-2012 1169.10 167.20 14.30 779.900 1459.40 -5.047 Difference -24.30 8.9 1.03 00 00 -4.57

% Diff. 2 5 7 00 00 91

IARR 1996 74.413 107.233 1.44 255 2006 74.945 104.944 1.41 244


Difference 0.271 -0.711 -0.03 - -11 -

% Diff. 0.4 0.7 2 5

Classification 1996 1.222 1.896 1.552 - 2006 0.403 0.670 1.660

Difference -0.819 -1.226 1.08

% Diff. 203 183 65

The effect on hydraulic structures Climate change patterns are slow and their processes take years, the time span could range from 10 to 20years (Attah, 2010), if a project is optimally designed, while direct s ign on physical or hydraulic structures are relatively skeptical to confessional pronounced. The climate vulnerability on water resources structures is proportional to the effect of local climate to which structures have been exposed to for a long period. Zhi-min et al, (2011) categorized the vulnerability of structures to natural disasters as exposure, resistance and resilience. The exposure is the abandoning of hydraulic structures to the effects of natural climatic conditions, while the resistant has to do with ability of the structures to carry designed load without failure. The resilience has to do with potential energy carried by the structure. The exposure of the structure through climate changes combines the resultant effect on the ability of the structure to carry any load or retain the resilience energy on it. The rainfall and temperature have direct effects on the structure, rendering it to suffer from the categorized natural disaster.

The last hope and major techniques of adaptation to climate change in irrigation practices is construction of storage structure for water conservation since water is main backbone of irrigation. The system conserves water during the wet season and releases the water when the flow is low. The risk of constant maximum rainfall as indicated here is recognized as producing potential floods consequences to the storage dyke whereby putting the risk on the economy and prospect of irrigation.

Results suggest a downward trend for rainfall, at a rate of 5.05mm per year, and upwards variation in

mean temperature, at a yearly rate of 0.103oC (0.4%). The implication may be a high thermal effect on the structure leading to acute dryness,

peeling and weakening the outer part of the dyke as time progresses, leaving low concrete strength covers.

Furthermore, the yearly increase in rainfall of 9.62mm relative to 1986-2005, with its maximum of 1459.5mm, explains the 5% decrease in mean

reflectance of the structure coupled with 0.4%

Tmean increment. This probably indicated

dampness of the dyke making it susceptible to contractions and expansions.

The variability in mean total rainfall has been clearly indicated in the result relative from 1986- 2005 to 1992-2012 (24.3mm). If a dam project is to be designed at this return period of the rainfall amount or runoff produced by this rainfall, there may be over design or under design of the project leading to economy mismanagement. T h o u g h , t h i s i s n o t c o m p l e t e l y r e p r e s e n t t h e d a t a f o r d e s i g n a n a l y s i s , b u t t h i s i d e a l c o u l d b e b o r r o w t o p r o v i d e f o r t h e b a s i s o f d e s i g n .

Dyking process in a river for climate change adaptation has become a negative consequence to the dyke natural channel of the river, as a result of heavy climatic variations. This process has led to increased sediment yield into the reservoir, thereby endangering the storage capacity hence reducing water for irrigation practices.

The direct effects of alkalization and acidification on dyke are encouraged either by high variation temperatures or less rainfall or high rainfall and decrease in temperatures. The consequence is that, corrosion may develop when the gap between the concrete and the internal reinforcement bar surface are nearer e.g. 1 to 5mm (Yoon et al, 2007), and, hence, increasing the Sodic-Ferrous of the soil in an irrigation field.


Meanwhile, the consequences of economy would arise. For better performance and low damage probability of the structure, this is necessary to increase cover thickness; and higher concrete

strength grades; Provision of anti-corrosive agent will be as well as necessary, giving room for high capital for construction materials for irrigation.

Conclusion Climate change and variability is undoubtedly a serious global problem, affecting many sectors in the World, among which stands out the water for sustainable agricultural development. Many places have experienced high rainfall with extreme temperature, while some others have been subjected to low rainfall and high temperature. The impacts of the change on water resources components have been demonstrated by many studies (Olmos 2001; Lasco and Boer 2006; Kandlikar and Sagar, 1999), but few studies have focused on the infrastructures (Hydraulic structures). The components of water resources are directly related to climate elements, which makes it more vulnerable to climate variations. Several researchers have provided suggestions and solutions to climate change adaptation as storage of water or harvest the water during high flow, maintain water, sequester carbon (Liu 2011, Lockaby et al 2011). These suggestions and solutions range from; the construction of channels for conveying harvested water to storage of either over-head or underground and water use system management.

Some of these structures are dam, PVC tank, concrete/earth channels, ditches lined with seepage-proof materials e.t.c collectively called Hydraulic Structures. These structures are directly affected by continuous exposure to the natural climate such as rainfall, temperature, frost, snow, sun rays, which might deteriorate them to total collapse.

The statistical downscaling provided the basis for comparisons of decadal variability of impact with the integration of satellite imagery. Changes in hydraulic structures are spatial processes which require long-term datasets of climate condition of the locality and image of previous and present condition (not less than ten years) for the same locality or nearby.

From the study it was observed that 0.4% upward variations in Tmean might have caused a downward variation of 2% in rainfall, while the dam

dyke bright intensity increased 203%, indicating

dryness. Decrease in Tmean trend of 140% might

have

induced an increase of 91% in rainfall trend, while imposing a 203% change in textural of the dyke of 2006 imagery relative to 1996 imagery. This indicates that the surface of the structure was smooth before 2006 period.

The study has demonstrated evidence of climate change, which might have induced some consequences both clear and unclear, including dampness of the structures, high thermal conditions on the dyke making it vulnerable to thawing, expansions and contractions. For better performance and low damage probability of the structure, one needs increased cover thickness, high concrete strength grades, as well as provision for anti-corrosive agent will be anticipated, hence, giving room for high capital for constructions.

The challenges in bringing out the climate change impacts from physical structures (e.g. Hydraulic structures) is a task that needs a wide range of consultations and seeking for knowledge of professionals such as Geographer, Climate experts, Space researchers and as well as

Geologist. Relating climate variables with satellite imagery (Remote Sensing) to check the impact of variation or change that occurred on surface of hydraulic structures is an recent issue. The study poses lots of challenges ranging from the recovery of the spectral reflectance, which can be complicated by environmental and sensor phenomena, sun angle relative to zenith, and the ability to extract information from the data. Interpretation will depend on the capabilities of the sensor used, how data are handled to convert the raw values into image displays that improve the products for analysis and applications, methods of image processing to obtain a good approximation of the aforementioned spectral end points and ability to assess funds to acquire high resolution satellite imagery for the analysis. These are challenges that must be overcome to get reasonable results.

However, there is need for physical measurement of the in situ concrete strength, fault or crack development and extent of gap between the reinforcement and concrete covers.

References


Arndt C, Tarp F, (2015) Climate change impacts and adaptations: Lessons learned from the greater Zambeze River Valley and beyond. Climate change. 130:1 – 8 ASTM-C835-06 (2013): Standard Test method for total Hemispherical emittance of surfaces up to 14000C. ASTM International

Avery TE, Berlin, GL (1985). Interpretation of Aerial Photographs, Burgess Publishing, Minneapolis,

Attah, DA (2010). Climate variability and its impact on the water resources of Lower Kaduna River Catchment. A PhD dissertation submitted to Department of Water Resources and Environmental Engineering, Faculty of Engineering, Ahmadu Bello University, Zaria, Nigeria.

Bello WA, Adekunle RA, Ogunsanmi OE (2012). Effect of climate change on construction project

planning in Nigeria. Procs. 4th West Africa Built Environment Research (WABER) Conference, 24-26 July, Abuja Nigeria.

Brown W, Wilby RL, (2012). An alternate approach to assessing climate. EOS, v. 41(9), pp. 401 – 402.

Cockling C (1999): Inslands in the midst: Environmental change, vulnerability and security in the Pacific. In: Lonergan, S.C.(ed), Environmental Change, Adoptation, and Security. Dordrecht: NATO/KLUWER.

Christy JR, Parker DE, Stendel M, Norris WB (2001). Differential trends in tropical sea surface temperature and atmospheric temperature since 1979. Geophysical Res. Letters, 28:183-186.

ERDAS Field Guide (2010). Fifth Edition, Revised and Expanded. Atlanta, Georgia.

Fisher G, Shah M, Van Velthuizen H (2002): Climate change and agriculture vulnerability. A special report, report, prepared by the int. Inst. For Applied Systems Analysis under Unted Nations Inst. Contract Agreement No. 1113 on Climate change and Agric vulnerability as a contribution to the World Summit in sustainable development, Johannesburg.

Fant C, Gebretsadik, McCluskey, Strzapek K (2015). An uncertainty approach to assessment of climate changes impacts on the Zambezi River Basin, Climate Change. 130:35-48

Frank J, Goldberg V, Eichelman U, Freydank E, Bernhofer C (2004). Statistical analysis of regional climate trends in Saxony, Germany. Climate Res., 27(2):145-150.

IPCC, (2007). Climate Change 2007: The Physical Science Basis. Cambridge University Press, New York.

Kandlikar, M and Sagar A (1999): Climate change research and analysis in Indiai: an integrated assessment of a South-North Divide. Global Environmental Change, 9:119-138.

Kruger, Abe, Seville and Carl (2012): Green building: Principles and practices in residential construction. Cengage Learning

Kruse, FA (1988). “Use of Airborne Imaging Spectrometer Data to Map Minerals Associated with Hydrothermally Altered Rocks in the Northern Grapevine Mountains, Nevada.” Remote Sensing of the Environment, 24: 31-51.

Lasco RD and Boer R (2006): An integrated assessment of climate change impacts, adaptations and vulnerability in watershed areas and communities in Southeast Asia. A final report submitted to Assessments of Impacts and adaptations to climate change (AIACC), Project No. AS 21. International START Secretariat edition.

Liwenga, ET, Kangalawe RYM, Lyimo JG, Majule AE, Ngana JO (2007). Research Protocols for Assessing the Impact of CC & V in Rural Tanzania: Water, Food Systems, Vulnerability and Adaptation. START/PACOM, African Global Change Research.

Mary AL, Majule AE (2009). Impacts of climate change, variability and adaptation strategies on agriculture in semi arid areas of Tanzania: The case of Manyoni District in Singida Region, Tanzania. African Journal of Environmental Science and Technology. Vol. 3 (8).

Odjugo, PAO (2010). Regional evidence of climate change in Nigeria. J. of Geography and Regional Planning 3(6):142-150

Pidwirny, M (2004). Fundamentals of physical geography. Okonagan: University of British, Columbia.

Olofintoye, OO (2010). Impact of global warming on the rainfall and temperature in the Niger Delta of Nigeria. J. of Research information in Civil Engineering, 7(2):33-48.

Raje, D, Mujumdar PP (2010). Reservoir performance under uncertainty in hydrologic


impacts of climate change. J. of Advances in water resources 33:312-326. Elsevier.

Sadiq N, Qureshi MS (2010). Climate variability and linear trend models for the five major cities in Pakistan. J. of Geo and Geology, 2(1):83-92.

URT (2005). Singida Region Socio-economic Profile. Second eds, Printed with Cooperation of

National Bureau of Statistics and Singida Regional Commissioner’s office, Tanzania.

Wegehenkel M, Kersebaum KC (2009). An assessment of the impact of climate change on evapotranspiration, groundwater recharge and low-flow conditions in a mesoscale catchment in Northeast Germany. J. Plant Nutrient Soil Science 17

2:737-744.

Documents

CLIMATE CHANGE IMPACTS ON HYDRAULIC ...demonstrating probability of washing and cleaning out surfaces as exposure progresses. The spectral reflectance classification indicated the