# Impacts of Rainfall Variability, Land Use and Land Cover Change on Stream Flow of the Black Volta Basin, West Africa

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area Presentation

^{2}. The Black Volta river basin is a trans-boundary river system that stretches from Mali, Burkina Faso, Ghana and Cote d’Ivoire (Figure 1). In the year 2000, the Black Volta was home to about 4.5 million people and the population density in the basin ranges from 8 to 123 people/km

^{2}with Lawra district in Ghana having the highest density. This population is estimated to be about 8 million by the year 2025 [17].

#### 2.2. Rainfall Data Analysis

#### 2.2.1. Aridity Index

#### 2.2.2. Standardized Precipitation Index

_{sy}is a normalized index representing the probability of occurrence of an observed rainfall amount when compared with the rainfall climatology of a given station (see Appendix A for more information).

#### 2.2.3. Mann-Kendall Trend Test

_{0}assumes that there is no trend in the rainfall time series data over a given period and it is tested against the alternative hypothesis H

_{1}which considers that there is increasing or decreasing trend [31]. The theoretical calculation of the Mann-Kendall statistic can be seen in Appendix A.

#### 2.2.4. Sen’s Slope Estimator

#### 2.3. Land Use and Land Cover Change Maps Development

#### 2.4. Brief Description of the SWAT Model

_{t}is the final soil water content (mm H

_{2}O), SW

_{o}is the initial water content on day i (mm H

_{2}O), t is the time (days), R

_{day}is the amount of precipitation on day i (mm H

_{2}O), Q

_{surf}is the amount of surface runoff on day i (mm H

_{2}O), E

_{a}is the amount of evaporation on day i (mm H

_{2}O), W

_{seep}is the amount of water entering in the vadose zone from the soil profile on day i (mm H

_{2}O), Q

_{gw}is the amount of return flow on day i (mm H

_{2}O).

#### 2.4.1. Surface Runoff

_{surf}is the accumulated runoff (rainfall excess) in (mm H

_{2}O), R

_{day}is the rainfall depth for the day (mm H

_{2}O), I

_{a}is the initial abstraction which includes surface store, interception and infiltration prior to runoff (mm H

_{2}O), S is the retention parameter (mm H

_{2}O) and it is defined as:

_{a}is commonly given as 0.2 S. Hence Equation (2) becomes:

_{day}> I

_{a}. Typical curve number for moisture conditions are classified into four hydrological groups such as: A for high infiltration, B for moderate infiltration, C for slow infiltration and D very slow infiltration. According to the U.S. National Resource Conservation Service (NRSC) Soil Survey Staff, a hydrologic group is defined as a group of soil having similar runoff potential under the similar storm and cover conditions [44]. The full description of the other components of the SWAT model can be find in the theoretical documentation of the SWAT model [41].

#### 2.4.2. Model Input

#### Digital Elevation Model

#### Soil Data

#### River Discharge

^{3}/s, 612.2 m

^{3}/s, 70.7 m

^{3}/s and 40%.

#### 2.4.3. Black Volta Basin Model Set Up

- Goodness of fit or coefficient of determination (R
^{2}) between the observation and the final best simulation:$${{\displaystyle \mathrm{R}}}^{2}=\frac{{\displaystyle {\displaystyle \sum}_{\mathrm{i}=1}^{\mathrm{n}}({{\displaystyle \mathrm{Q}}}_{\mathrm{obs},\mathrm{i}}-\hspace{0.17em}{{\displaystyle \overline{\mathrm{Q}}}}_{\mathrm{obs}})({{\displaystyle \mathrm{Q}}}_{\mathrm{sim},\mathrm{i}}-\hspace{0.17em}{{\displaystyle \overline{\mathrm{Q}}}}_{\mathrm{sim}})}}{{{\displaystyle \left[{\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{n}}{{\displaystyle ({{\displaystyle \mathrm{Q}}}_{\mathrm{obs},\mathrm{i}}-\hspace{0.17em}{{\displaystyle \overline{\mathrm{Q}}}}_{\mathrm{obs}})}}^{2}}\right]}}^{0.5}{{\displaystyle \left[{\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{n}}{{\displaystyle ({{\displaystyle \mathrm{Q}}}_{\mathrm{sim},\mathrm{i}}-\hspace{0.17em}{{\displaystyle \overline{\mathrm{Q}}}}_{\mathrm{sim}})}}^{2}}\right]}}^{0.5}}$$ - And the Nash-Sutcliffe (NS) coefficient (Nash et al., 1970) [47]:$$\mathrm{NS}=1-\frac{{\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{n}}{{\displaystyle ({{\displaystyle \mathrm{Q}}}_{\mathrm{s}\mathrm{i}\mathrm{m},\mathrm{i}}-\hspace{0.17em}{{\displaystyle \mathrm{Q}}}_{\mathrm{o}\mathrm{b}\mathrm{s},\mathrm{i}})}}^{2}}}{{\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{n}}{{\displaystyle ({{\displaystyle \mathrm{Q}}}_{\mathrm{o}\mathrm{b}\mathrm{s},\mathrm{i}}-\hspace{0.17em}{{\displaystyle \overline{\mathrm{Q}}}}_{\mathrm{o}\mathrm{b}\mathrm{s}})}}^{2}}}$$
_{obs,i}is the observed flow at time i (m^{3}/s), ${\overline{\mathrm{Q}}}_{\mathrm{obs}}$ is the mean of observed flow (m^{3}/s), Q_{sim.i}is the simulated flow at time i (m^{3}/s), ${\overline{\mathrm{Q}}}_{\mathrm{sim}}$ the mean of simulated flow (m^{3}/s). The Nash-Sutcliffe coefficient rating on monthly time step is given by (Moriasi et al., 2007) [49]. (Awotwi et al., 2015) [14] stated that for model the calibration to be accepted, R^{2}should be greater than 0.6 and the NS greater than 0.5.

^{2}) between the observation and the final best simulation and the Nash-Sutcliffe (NS) coefficient [47]. The Nash-Sutcliffe coefficient rating on monthly time step is given by (Moriasi et al., 2007) [47]. (Awotwi et al., 2015) [14] stated that for model the calibration to be accepted, R

^{2}should be greater than 0.6 and the NS greater than 0.5.

## 3. Results and Discussions

#### 3.1. Aridity Index Profile of the Black Volta and the Standardired Precipitation Index

#### 3.2. Rainfall Trend Analysis

#### 3.3. Land Use and Land Cover Change Analysis

^{2}) and agricultural land (36,125.7 km

^{2}) representing respectively 47% and 22% (Table 4) of the total area of the basin. Forest evergreen and forest deciduous represent respectively 9% and 8%. Urban areas in 1987 occupied 6% of the Black Volta. Water bodies represent the smallest portion of the basin with an area of (101.7 km

^{2}) representing only 0.1% of the basin. In the year 2000, the Black Volta had grass land as dominant land cover type with 74,198.48 km

^{2}) representing 48% of the catchment followed by agricultural land (24%). Between 1987 and the year 2000, forest evergreen has decreased by 1% while water bodies has increased from 0.1% in 1987 to 0.6% in year 2000. Urban areas also have increased significantly. The year 2013 was characterized by the dominance of the agricultural land with an area of 47,710.41 km

^{2}representing 31% of the catchment area while grass land has decreased up to 41,151.37 km

^{2}(26%). We also notice a significant increase in urban areas (14%) and forest deciduous (15%). Table 4 summarizes the land use changes and rate of change from 1987 to 2013. Between 1987 and 2000, forest evergreen has decreased about 83.05% (5.93%/year).

#### 3.4. SWAT Modeling Results

#### 3.4.1. Sensitivity Analysis

#### 3.4.2. Calibration and Validation

^{2}is also 0.91. The strong correlation between the measured flow and the simulated (Figure 8a) showed that the physical processes implicated in the generation of stream flow in the Black Volta catchment are well captured by the model during calibration. However, the model overestimated the simulated average monthly flow (252.70 m

^{3}/s) compared to the measured (223.16 m

^{3}/s) during calibration while it is underestimated simulated standard deviation (274.3 m

^{3}/s) compared to the measured standard deviation (306.38 m

^{3}/s). Model performance during validation (Figure 7b) can be qualified as good. The NS was 0.70 while R

^{2}was 0.80 (Figure 8b).

^{3}/s) was underestimated compared to the observed one (730.94 m

^{3}/s). A close look at the Figure 7a revealed that the peak flows during calibration were correctly modeled while Figure 7b during validation showed a delay in peak flows from August 2008. The simulated peak flows are much lower than the observed peaks. One of the major reasons behind this result is the assumption that the construction of the Bui Dam disturbed the dynamism of the flows. According to the Bui Power Authority (BPA), the diversion of the Black Volta River was completed in December 2008 and the construction of the main dam began in December 2009. Similar effects have been reported by (Schuol and Abbaspour, 2006) [16] on the Niger River where construction of a reservoir is said to delay the river flows. To confirm this hypothesis in our case, a second set of calibrations and validations have been performed prior to the Bui dam construction.

^{2}is 0.82 (Figure 8c).

^{3}/s while the simulated was underestimated (161.79 m

^{3}/s). At the same time, simulated standard deviation (212.24 m

^{3}/s) was lower than the observed standard deviation (234.95 m

^{3}/s). For the validation performance of the model, the NS was 0.7 and the R

^{2}= 0.85 (Figure 7d) and can be classified as good. The mean monthly simulated flow was overestimated (254.37 m

^{3}/s) compared to the observed (229.18 m

^{3}/s). There is also difference in the standard deviation between simulated and observed flow. However, the difference is smaller compared to the first validation (Figure 7b). This can be a confirmation that the construction in the Bui dam impacts the dynamics of the river flow. The model was not able to capture the difference in flow regime. This may be explained by the fact that the reservoir was not included in the modeling process. In general, one can say that the Black Volta model has performed well despite uncertainties associated to the results.

#### 3.4.3. Model Uncertainties

#### 3.4.4. Changes in Seasonal Stream Flow Due to LULC

^{3}/s while the dry period flow using LULC of the year 2013 was 253.42 m

^{3}/s. The results showed that there is an increase of 6% in dry period flows due to LU practices in the Black Volta basin. Comparatively, wet period was defined considering the total flow of three months such as August, September and October. The stream flow changes were also assessed due to land use changes in the catchments. The total flow for the wet period according to LULC 2000 was 2969.02 m

^{3}/s and 2995.35 m

^{3}/s for 2013 LULC. The results indicated that there was an increase by 1% in wet period stream flow. This stream flow assessment showed that it is important to monitor the land use practices in the basin since it has huge impact on river flow and by extension on the hydro-power generation at the Bui dam.

#### 3.4.5. Changes in Stream Flow Components Due to LULC

^{3}/s, 5.7 m

^{3}/s and 828 m

^{3}/s while those with the LULC 2013 are respectively 477.3 m

^{3}/s, 6.8 m

^{3}/s and 775.1 m

^{3}/s. We have noticed that surface runoff and ground water are the big contributors to stream flow in the Black Volta catchment. We noticed that the components of stream flow are not affected in the same manner. For instance, between 2000 and 2013, the SURF_Q has increased by 27% while LAT_Q has increased by 19%. In contrast to the two other components, for the same period, GW_Q has decreased by −6%. The evapotranspiration (ET) has increased at the same time by 4.59%. These results can be explained by the fact that between the year 2000 and 2013, the urbanization rate and bare lands have increased respectively by 33.22% and 67.06%. At the same time, agricultural land has increased by 29.66% as an implication for the reduction in grass land by 44.54%. These results call for paying particular attention to ground water in the basin. The change in land use in terms of increasing urbanization and bare land have resulted in increasing surface runoff and reduced groundwater. The reduction in ground water contribution to stream flow may be due to the increase in ET when grass lands have been converted in crop lands. This may be due to difference in land use that controls ET.

## 4. Conclusions

^{−1}between 2000 and 2013) has direct impact on the hydrology of the basin. The SWAT modeling has shown good performance with relatively high Nash-Sutcliffe (NS) and coefficient of determination (R

^{2}). The changes in seasonal stream flow due to land use have shown an increase by 6% and 1% respectively for wet and dry season between the year 2000 and 2013. Further analysis on the stream flow components reveled an increase in surface runoff by 27% but a decrease in groundwater contribution to stream flow by 6% between the year 2000 and 2013. On overall, there is an increase in water yield to stream flow by 4%. This could benefit the Bui hydropower plant. However, due to important land use change and high surface runoff, there is a need to properly monitor the water quality as well as sediment load in the river basin. Finally, for proper water management in the basin, an effort has to be made to increase the number of weather stations (only 13 stations in 130.000 km

^{2}were available).

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Appendix A

#### Aridity Index

#### Standardized Precipitation Index

_{sy for}each station was computed as:

_{sy}is the annual rainfall of station s for year y, ${\overline{P}}_{\mathrm{s}}$ and σ

_{s}are respectively the mean annual rainfall and the standard deviation of stations in the reference period 1976–2011. The annual index SP

_{y}was then computed as an average of a certain number n of station that are in the same climatic zone that is defined from the aridity index profile as:

#### Mann-Kendall Trend Test

_{i}and X

_{j}are the data values in time series i and j (j > i).

_{j}-x

_{i}) is the sign function defined as:

_{i}is the number of data values in the ith group. Tied group can be defined as group of sample data which have the same value. When n>10, a standardized statistic Z

_{s}is calculated as bellow:

_{0}is rejected when |Z

_{s}|> Z

_{1-}α/2 implying significant trend in the time series data (Either increase when Z

_{s}>Z

_{1-}α/2 or decrease when Z

_{s}<-Z

_{1-}α/2); H

_{0}is accepted which implies there is no trend in the rainfall time series data [30]. Z

_{1-}α/2 is given by the standard normal distribution table. For this study α = 0.05 with Z

_{1-}α/2=1.96 is considered. Another parameter considered in the Mann-Kendall test is Kendall’s tau [55]. The tau value is considered as the slope and varies between −1 and +1. A negative value of tau indicates decreasing trend and positive value indicates increasing trend. Before the application of Mann-Kendall test for trend detection in the rainfall time series data at annual level, it is important to investigate if the data is serial correlated. For this study, the serial correlation of the data was investigated using the acf (autocorrelation) and pacf (partial autocorrelation) function in R package by [35] showed no serial correlation in the dataset.

#### Sen’s Slope Estimator

_{j}and x

_{k}are data values at time j and k (j>k) respectively. The median Q

_{med}of N values of Q

_{i}is the Sen’s slope estimator. The median is calculated as the Equation (A10):

_{med}at a specific alpha level. In this study, α = 0.05 is used. The Q

_{med}value and sign represent the steepness and the trend of the data. The confidence interval for this value is computed as:

_{1−α/2}is obtained from the standard normal distribution table. Furthermore, M

_{1}= (N − α) / 2 and M

_{2}= (N + α) / 2 are computed. The lower and upper limits of the confidence interval, Q

_{min}and Q

_{max}, are the M

_{1}th largest and (M

_{2}+ 1)th largest of the N ordered slope estimates [56]. For this work, the Sen’s slope estimator was computed in R software using the Package ‘wq’ [57].

## References

- Oguntunde, P.G.; Friesen, J.; van de Giesen, N.; Savenije, H.H.G. Hydroclimatology of the Volta River Basin in West Africa: Trends and variability from 1901 to 2002. Phys. Chem. Earth
**2006**, 31, 1180–1188. [Google Scholar] [CrossRef] - Conway, D.; Persechino, A.; Ardoin-Bardin, S.; Hamandawana, H.; Dieulin, C.; Mahé, G. Rainfall and Water Resources Variability in Sub-Saharan Africa during the Twentieth Century. J. Hydrometeorol.
**2009**, 10, 41–59. [Google Scholar] [CrossRef] - Mahe, G.; L’Hote, Y.; Olivry, J.C.; Wotling, G. Trends and discontinuities in regional rainfall of West and Central Africa: 1951–1989. Hydrol. Sci. J.
**2001**, 46, 211–226. [Google Scholar] [CrossRef] - Owusu, K.; Waylen, P. Trends in spatio-temporal variability in annual rainfall in Ghana (1951–2000). Weather
**2009**, 64, 115–120. [Google Scholar] [CrossRef] - Logah, F.Y.; Obuobie, E.; Ofori, D.; Kankam-Yeboah, K. Analysis of Rainfall Variability in Ghana. Int. J. Latest Res. Engineer. Comput.
**2013**, 1, 1–8. [Google Scholar] - Lacombe, G.; McCartney, M.; Forkuor, G. Drying climate in Ghana over the period 1960–2005: Evidence from the resampling-based Mann-Kendall test at local and regional levels. Hydrol. Sci. J.
**2012**, 57, 1594–1609. [Google Scholar] [CrossRef] - Andreini, M.; van de Giesen, N.; van Edig, A.; Fosu, M.; Andah, W. Volta Basin Water Balance; ZEF: Bonn, Germany, 2000. [Google Scholar]
- Arnold, J.G.; Kiniry, J.R.; Srinivasan, R.; Williams, J.R.; Haney, E.B.; Neitsch, S.L. Soil and Water Assessment Tool, Input/output File Documentation; Technical Report 439; Texas Water Research Institute: College Station, TX, USA, 2012. [Google Scholar]
- Neitsch, S.L.; Arnold, J.G.; Kiniry, J.R.; Srinivasan, R.; Williams, J.R. Soil and Water Assessment Tool Input/Output File Documentation; Blackland Research Center: Temple, TX, USA, 2005. [Google Scholar]
- Setegn, S.G.; Srinivasan, R.; Dargahi, B. Hydrological modelling in the Lake Tana Basin, Ethiopia using SWAT model. Open Hydrol. J.
**2008**, 2, 49–62. [Google Scholar] [CrossRef] - Malunjkar, V.S.; Shinde, M.G.; Ghotekar, S.S.; Atre, A.A. Estimation of Surface Runoff using SWAT Model. Int. J. Latest Res. Engineer. Comput.
**2015**, 3, 12–15. [Google Scholar] - Easton, Z.M.; Fuka, D.R.; White, E.D.; Collick, A.S.; Ashagre, A.B.; McCartney, M.; Awulachew, S.B.; Ahmed, A.A.; Teenhuis, T.S. A multi basin SWAT model analysis of runoff and sedimentation in the Blue Nile, Ethiopia. Hydrol. Earth System Sci.
**2010**, 14, 1827–1841. [Google Scholar] [CrossRef] - Spruill, C.A.; Workman, S.R.; Taraba, J.L. Simulation of Daily and Monthly Stream Discharge from Small Watershed Using the SWAT Model. Am. Soc. Agric. Eng.
**2000**, 1, 1431–1439. [Google Scholar] [CrossRef] - Awotwi, A.; Kumi, M.; Pe, J.; Yeboah, F.; Nti, I.K. Predicting Hydrological Response to Climate Change in the White Volta. J. Earth Sci. Clim. Chang.
**2015**, 6, 1–7. [Google Scholar] [CrossRef] - Obuobie, E. Estimation of Groundwater Recharge in the Context of Future Climate Change in the White Volta River Basin, West Africa; ZEF: Bonn, Germany, 2008. [Google Scholar]
- Schuol, J.; Abbaspour, K.C. Calibration and uncertainty issues of a hydrological model (SWAT) applied to West Africa. Adv Geosci.
**2006**, 2, 137–143. [Google Scholar] [CrossRef] - Allwaters Consult. Diagnostic Study of The Black Volta Basin in Ghana; Final Report; ALLWATERS Consult Limited: Kumasi, Ghana, June 2012. [Google Scholar]
- Green Cross International, Burkina Faso, 2001. Trans-boundary Basin Sub-Projects: The Volta River Basin. Available online: www.greencrossitalia.it/ita/acqua/wfp/pdf/greencrosswfp_volta.pdf (accessed on 15 January 2016).
- Barry, B.; Obuobie, E.; Andreini, M.; Andah, W.; Pluquet, M. The Volta River Basin: Comparative Study of River Basin Development and Management. Comprehensive Assessment of Water Management in Agriculture; IWMI: Colombo, Sri Lanka, 2005. [Google Scholar]
- Shaibu, S.; Odai, S.N.; Adjei, K.A.; Osei, E.M.; Annor, F.O. Simulation of runoff for the Black Volta Basin using satellite observation data. Int. J. River Basin Manag.
**2012**, 10, 245–254. [Google Scholar] [CrossRef] - Peterson, T.C.; Easterling, D.R.; Karl, T.R.; Groisman, P.; Nicholls, N.; Plummer, N.; Vincent, L. Homogeneity adjustments of in situ atmospheric climate data: A review. Int. J. Climatol.
**1998**, 18, 1493–1517. [Google Scholar] [CrossRef] - Reeves, J.; Chen, J.; Wang, X.L.; Lund, R.; Lu, Q.Q. A review and comparison of changepoint detection techniques for climate data. J. Appl. Meteorol. Climatol.
**2007**, 46, 900–915. [Google Scholar] [CrossRef] - Searcy, J.K.; Hardison, C.H. Double-Mass Curves; US Government Printing Office: Washington, WA, USA, 1960.
- Westmacott, J.R.; Burn, D.H. Climate change effects on the hydrologic regime within the Churchill-Nelson River Basin. J. Hydrol.
**1997**, 202, 263–279. [Google Scholar] [CrossRef] - Cannarozzo, M.; Noto, L.V.; Viola, F. Spatial distribution of rainfall trends in Sicily (1921–2000). Phys. Chem. Earth
**2006**, 31, 1201–1211. [Google Scholar] [CrossRef] - Liu, Q.; Yang, Z.; Cui, B. Spatial and temporal variability of annual precipitation during 1961–2006 in Yellow River Basin, China. J. Hydrol.
**2008**, 361, 330–338. [Google Scholar] [CrossRef] - Zhang, Y.; Guan, D.; Jin, C.; Wang, A.; Wu, J.; Yuan, F. Analysis of impacts of climate variability and human activity on streamflow for a river basin in northeast China. J. Hydrol.
**2011**, 410, 239–247. [Google Scholar] [CrossRef] - Gocic, M.; Trajkovic, S. Analysis of changes in meteorological variables using Mann-Kendall and Sens slope estimator statistical tests in Serbia. Glob. Planet. Chang.
**2013**, 100, 172–182. [Google Scholar] [CrossRef] - Lanzante, J.R. Resistant, Robust and Non-Parametric Techniques for the Analysis of Climate Data: Theory and Examples, Including Applications to Historical Radiosonde Station Data. Int. J. Climatol.
**1996**, 16, 1197–1226. [Google Scholar] [CrossRef] - Kendall, M.G. Rank Correlation Methods; Hafner Press: New York, NY, USA, 1962. [Google Scholar]
- Ahmad, I.; Tang, D.; Wang, T.; Wang, M.; Wagan, B. Precipitation Trends over Time Using Mann-Kendall and Spearman’s Rho Tests in Swat River Basin, Pakistan. Adv. Meteorol.
**2015**, Article ID 431860. [Google Scholar] [CrossRef] - Hirsch, R.M.; Alexander, R.B.; Smith, R.A. Selection of methods for the detection and estimation of trends in water quality. Water Resour. Rese.
**1991**, 27, 803–813. [Google Scholar] [CrossRef] - Tabari, H.; Marofi, S.; Aeini, A.; Talaee, P.H.; Mohammadi, K. Trend analysis of reference evapotranspiration in the western half of Iran. Agric. For. Meteorol.
**2011**, 151, 128–136. [Google Scholar] [CrossRef] - Bayazit, M.; Önöz, B.; Yue, S.; Wang, C. Comment on “Applicability of prewhitening to eliminate the influence of serial correlation on the Mann-Kendall test” by Sheng Yue and Chun Yuan Wang. Water Resour. Res.
**2004**, 40, 1–5. [Google Scholar] [CrossRef] - Sen, P.K. Estimates of the Regression Coefficient Based on Kendall’s Tau. J. Am. Stat. Assoc.
**1968**, 63, 1379–1389. [Google Scholar] [CrossRef] - Van Vliet, J.; Bregt, A.K.; Hagen-Zanker, A. Revisiting Kappa to account for change in the accuracy assessment of land-use change models. Ecol Modell.
**2011**, 222, 1367–1375. [Google Scholar] [CrossRef] - Stehman, S.V.; Czaplewski, R.L. Design and Analysis for Thematic Map Accuracy Assessment—An application of satellite imagery. Remote Sens. Environ.
**1998**, 64, 331–344. [Google Scholar] [CrossRef] - Foody, G.M. Status of land cover classification accuracy assessment. Remote Sens. Environ.
**2002**, 80, 185–201. [Google Scholar] [CrossRef] - Zăvoianu, F.; Caramizoiub, A.; Badeaa, D. Study and Accuracy Assessment of Remote Sensing Data for Environmental Change Detection in Romanian Coastal Zone of the Black Sea. In Proceedings International Society for Photogrammetry and Remote Sensing, Istanbul, Turkey, 12–23 July 2004.
- Carletta, J. Assessing agreement on classification tasks: The kappa statistic. Comput. Linguist.
**1996**, 22, 249–254. [Google Scholar] - Neitsch, S.L.; Williams, J.R.; Arnold, J.G.; Kiniry, J.R. Soil and Water Assessment Tool Theoretical Documentation; Version 2009; Texas Water Resources Institute: College Station, TX, USA, 2011. [Google Scholar]
- United States. Soil Conservation Service. Hydrology. In SCS National Engineering Handbook; U.S Department of Agriculture: Washington, WA, USA, 1972; Section 4. [Google Scholar]
- Green, W.H.; Ampt, G.A. Studies on Soil Phyics. J. Agric. Sci.
**1911**, 4, 1. [Google Scholar] [CrossRef] - NRCS. In Urban Hydrology for Small Watersheds TR-55; U.S Department of Agriculture: Washington, WA, USA, 1986.
- Abbaspour, K.C.; Vejdani, M.; Haghighat, S.; Yang, J. SWAT-CUP Calibration and Uncertainty Programs for SWAT. Fourth Int. SWAT Conf.
**2007**, 1596–1602. [Google Scholar] - Abbaspour, K.C.; Johnson, C.A.; van Genuchten, M.T. Estimating Uncertain Flow and Transport Parameters Using a Sequential Uncertainty Fitting Procedure. Vadose Zone J.
**2004**, 3, 1340–1352. [Google Scholar] [CrossRef] - Nash, J.E.; Sutcliffe, J.V. River flow forecasting through conceptual models part I—A discussion of principles. J. Hydrol.
**1970**, 10, 282–290. [Google Scholar] [CrossRef] - Abbaspour, K.C. SWAT-CUP 2012: SWAT calibration and uncertainty Programs-A user manual; Eawag (Swiss Federal Institute of Aquatic Science and Technology): Zurich, Switzerland, 2013. Available online: swat.tamu.edu/media/114860/usermanual_swatcup.pdf (accessed on 29 June 2016).
- Moriasi, D.; Arnold, J. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE
**2007**, 50, 885–900. [Google Scholar] [CrossRef] - L’Hôte, Y.; Mahé, G.; Somé, B.; Triboulet, J.P. Analysis of a Sahelian annual rainfall index from 1896 to 2000; the drought continues. Hydrol. Sci. J.
**2002**, 47, 563–572. [Google Scholar] [CrossRef] - Anderson, J.R. A land use and land cover classification system for use with remote sensor data. US Government Printing Office: Washington, WA, USA, 1976. [Google Scholar]
- Adjei, K.A.; Ren, L.; Appiah-adjei, E.K.; Odai, S.N. Application of satellite-derived rainfall for hydrological modelling in the data-scarce Black Volta trans-boundary basin. Hydrol. Res.
**2015**, 46, 777–791. [Google Scholar] [CrossRef] - Zomer, R.J.; Trabucco, A.; Bossio, D.A.; Verchot, L.V. Climate change mitigation: A spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agric. Ecosyst. Environ.
**2008**, 126, 67–80. [Google Scholar] [CrossRef] - Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage Paper 56; FAO: Rome, Italy, 1998. [Google Scholar]
- Mcleod, A.A.I. Kendall rank correlation and Mann-Kendall trend test. Available online: https://cran.r-project.org/web/packages/Kendall/ (accessed on 25 June 2016).
- Hollander, M.; Wolfe, D.A.; Chicken, E. Nonparametric Statistical Methods; John Wiley & Sons: Hoboken, NJ, USA, 2013. [Google Scholar]
- Jassby, A.D.; Cloern, J.E. wq: Exploring water quality monitoring data. Available online: https://www.icesi.edu.co/CRAN/web/packages/wq/ (accessed on 25 June 2016).

**Figure 5.**Annual Precipitation Index. The blue line or regression line represents a smoother (mean of Precipitation index at each year). The grey band is the display of confidence interval (95% confidence interval) around the smoother. This helps to see the overall trend in the precipitation index.

Country | Gauging Station | Lat (°N) | Lon (°W) | Elevation (m) |
---|---|---|---|---|

Ghana | Sunyani | 7.3328 | −2.3281 | 296 |

Ghana | Wenchi | 7.75 | −2.1 | 322 |

Ghana | Bui | 8.2361 | −2.2772 | 176 |

Ghana | Bole | 9.0319 | −2.4762 | 295 |

Ghana | Wa | 10.0667 | −2.5 | 262 |

Burkina Faso | Batie | 9.8739 | −2.9213 | 298 |

Burkina Faso | Diebougou | 10.9667 | −3.25 | 298 |

Burkina Faso | Boura | 11.0333 | −2.5 | 306 |

Burkina Faso | Dano | 11.1490 | −3.2931 | 304 |

Burkina Faso | Boromo | 11.75 | −2.9333 | 260 |

Burkina Faso | Bondoukuy | 11.8450 | −3.7639 | 326 |

Burkina Faso | Bobo-Dioulasso | 11.1605 | −4.3298 | 374 |

Burkina Faso | Bomborokuy | 12.9972 | −3.9496 | 344 |

Gauging Station | Tau | p-Value | Zs | Qs med or Sen’s Slope Estimator | Constant B |
---|---|---|---|---|---|

Sunyani | 0.156 | 0.1864 | 1.321 | 0.3437 | 93.12 |

Wenchi | 0.073 | 0.5399 | 0.6129 | 0.1197 | 101.68 |

Bui | −0.0724 | 0.4535 | −0.7497 | −0.1283 | 94.11 |

Bole | 0.18 | 0.1271 | 1.55 | 0.3961 | 82.17 |

Wa | 0.127 | 0.2819 | 1.076 | 0.1749 | 83.29 |

Batie | −0.0635 | 0.5953 | −0.531 | −0.1559 | 88.69 |

Diebougou | 0.106 | 0.3686 | 0.899 | 0.1178 | 83.47 |

Boura | 0.279 | 0.0171 | 2.38 | 0.3218 | 70.02 |

Dano | 0.143 | 0.2254 | 1.21 | 0.222 | 72.03 |

Boromo | 0.113 | 0.3403 | 0.954 | 0.1754 | 68.17 |

Bondoukuy | −0.132 | 0.6728 | −0.422 | −0.0936 | 71.85 |

Bobo-Dioulasso | −0.0635 | 0.5952 | −0.5312 | −0.1683 | 84.75 |

Bomborokuy | −0.126 | 0.2584 | −1.13 | −0.2106 | 62.73 |

Year | Overall Accuracy | Kappa Coefficient |
---|---|---|

1987 | 90.20 | 88.57 |

2000 | 93.06 | 91.90 |

2013 | 99.18 | 99.05 |

Land Cover Type | Area Coverage (km^{2}) | Area Coverage (%) | 1987–2000 | 2000–2013 | 1987–2013 | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

1987 | 2000 | 2013 | 1987 | 2000 | 2013 | Change (%) | Change Rate (%/year) | Change (%) | Change Rate (%/year) | Change (%) | Change Rate (%/year) | |

Bare Land | 13231.9 | 11279.37 | 18842.86 | 8.5 | 7.3 | 12.15 | −14.76 | −1.05 | 67.06 | 4.79 | 42.4 | 1.57 |

Urban Areas | 9619.8 | 16537.32 | 22030.4 | 6.2 | 10.7 | 14.21 | 71.91 | 5.14 | 33.22 | 2.37 | 129.01 | 4.78 |

Water Bodies | 101.7 | 872.6904 | 939.15 | 0.1 | 0.6 | 0.61 | 758.1 | 54.15 | 7.62 | 0.54 | 823.45 | 30.5 |

Agricultural Land | 36125.7 | 36796.48 | 47710.4 | 23.3 | 23.7 | 30.77 | 1.86 | 0.13 | 29.66 | 2.12 | 32.07 | 1.19 |

Grass Land | 76253.9 | 74198.44 | 41151.4 | 49.2 | 47.9 | 26.54 | −2.7 | −0.19 | −44.54 | −3.18 | −46.03 | −1.7 |

Forest Deciduous | 14034.6 | 14398.31 | 23063.6 | 9.1 | 9.3 | 14.87 | 2.57 | 0.19 | 60.18 | 4.3 | 64.33 | 2.38 |

Forest Evergreen | 5708.9 | 967.4019 | 1338.64 | 3.7 | 0.6 | 0.86 | −83.05 | −5.93 | 38.38 | 2.74 | −76.55 | −2.84 |

Parameter Name | Definition | Absolute SWAT Values | Fitted Value | Minimum Value | Maximum Value | Sensitivity Rank |
---|---|---|---|---|---|---|

R__SOL_K | Saturated hydraulic conductivity | 0–2000 | −0.999737 | −1.009404 | −0.966056 | 1 |

V__MSK_CO2 | Calibration coefficient used to control impact of the storage time constant for low flow | 0–10 | 3.117479 | 2.870465 | 5.470611 | 2 |

V__SURLAG | Surface runoff lag time | 0.05–24 | 22.728235 | 22.493849 | 22.75284 | 3 |

V__MSK_CO1 | Calibration coefficient used to control impact of the storage time constant for normal flow | 0–10 | 10.726233 | 9.479877 | 14.147874 | 4 |

R__SOL_AWC | Available water capacity of the soil layer | 0–1 | 0.201121 | 0.145896 | 0.2093 | 5 |

R__CN2 | SCS runoff curve number f | −0.2–0.2 | −0.537993 | −0.552411 | −0.505139 | 6 |

A__GWQMN | Threshold depth of water in the shallow aquifer required for return flow to occur (mm) | 0–5000 | 73.4655 | 73.35611 | 74.378479 | 7 |

V__ALPHA_BF | Base flow alpha factor (days) | 0–1 | −0.155831 | −0.160484 | −0.153358 | 8 |

V__GW DELAY | Groundwater delay (days) | 0–500 | 1.087874 | 0.994085 | 1.122037 | 9 |

V__RCHRG_DP | Deep aquifer percolation fraction | 0–1 | 0.159342 | 0.148055 | 0.176487 | 10 |

V__ESCO | Soil evaporation compensation factor | 0–1 | 0.599675 | 0.595305 | 0.646715 | 11 |

V__CH_N2 | Manning’s “n” value for the main channel | −0.01–0.3 | 0.267461 | 0.242134 | 0.271966 | 12 |

Period | Average Monthly Flow (m^{3}/s) | Standard Deviation (m^{3}/s) | Model Performance | |||||
---|---|---|---|---|---|---|---|---|

Measured | Simulated | Measured | Simulated | P-Factor | R-Factor | R^{2} | NS | |

Calibration (2000–2005) | 223.16 | 252.70 | 306.38 | 274.3 | 0.64 | 0.66 | 0.91 | 0.9 |

Validation (2006–2010) | 447.99 | 371.23 | 730.94 | 433.84 | 0.2 | 0.33 | 0.80 | 0.7 |

Period | Average Monthly Flow (m^{3}/s) | Standard Deviation (m^{3}/s) | Model Performance | |||||
---|---|---|---|---|---|---|---|---|

Measured | Simulated | Measured | Simulated | P-Factor | R-Factor | R^{2} | NS | |

Calibration (1990–1995) | 172.28 | 161.79 | 234.95 | 212.24 | 0.97 | 0.83 | 0.82 | 0.9 |

Validation (1996–2000) | 229.18 | 254.37 | 338.39 | 394.68 | 0.64 | 0.9 | 0.85 | 0.7 |

LULC 2000 | LULC 2013 | Mean Monthly Flow Change | |||
---|---|---|---|---|---|

Dry Months (Jan, Feb, Mar) | Wet Months (Aug, Sep, Oct) | Dry Months (Jan, Feb, Mar) | Wet Months (Aug, Sep, Oct) | Dry | Wet |

238.62 | 2969.02 | 253.42 | 2995.35 | +6% | +1% |

Stream Flow Components (m^{3}/s) and ET (mm) | LULC 2000 | LULC 2013 | Changes (%) |
---|---|---|---|

SURF_Q | 376.7 | 477.3 | 27% |

LAT_Q | 5.7 | 6.8 | 19% |

GW_Q | 828 | 775.1 | −6% |

WATER_YIELD | 1210.4 | 1259.2 | 4% |

ET | 521.6 | 546.7 | 4.59% |

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Akpoti, K.; Antwi, E.O.; Kabo-bah, A.T.
Impacts of Rainfall Variability, Land Use and Land Cover Change on Stream Flow of the Black Volta Basin, West Africa. *Hydrology* **2016**, *3*, 26.
https://doi.org/10.3390/hydrology3030026

**AMA Style**

Akpoti K, Antwi EO, Kabo-bah AT.
Impacts of Rainfall Variability, Land Use and Land Cover Change on Stream Flow of the Black Volta Basin, West Africa. *Hydrology*. 2016; 3(3):26.
https://doi.org/10.3390/hydrology3030026

**Chicago/Turabian Style**

Akpoti, Komlavi, Eric Ofosu Antwi, and Amos T. Kabo-bah.
2016. "Impacts of Rainfall Variability, Land Use and Land Cover Change on Stream Flow of the Black Volta Basin, West Africa" *Hydrology* 3, no. 3: 26.
https://doi.org/10.3390/hydrology3030026