Analysis of hydrometeorological variables over the transboundary Komadugu-Yobe basin, West Africa

Spatiotemporal trends in daily observed precipitation, river discharge, maximum and minimum temperature data were investigated between 1971 and 2013 in the Komadugu-Yobe basin. Signi ﬁ cant change points in time series are corrected using Adapted Caussinus-Mestre Algorithm for homogenizing Networks of Temperature series algorithm. Mann – Kendall test and Sen ’ s slope are used to estimate the trend and its magnitude at dry, wet and annual season time scales, respectively. Preliminary results show an increasing trend of the observed variables. There is a latitudinal increase (decrease) in the basin temperature (precipitation) from lower to higher latitudes. The minimum temperature (0.05 (cid:1) C/year) increases faster than the maximum temperature (0.03 (cid:1) C/ year). Overall, the percentage changes in minimum temperature range between 3 and 10% while that of maximum temperature ranges between 1 and 3%. Due to precipitation dependence on regional characteristics, the highest percentage change was recorded in precipitation with values between (cid:3) 5 and 97%. In all time scales, river discharge and precipitation have strong positive correlations while the correlation between river discharge and temperature is negative. It is imperative to advocate and support positive developmental practices as well as establishing necessary mitigation measures to cope with the effects of climate in the basin.


INTRODUCTION
The effects of global warming and climate change on the environment cannot be underemphasized. This has generated considerable interest among scientists and has resulted in various studies in climate trend detection at regional (Khatiwada et al.  in the Nepal Himalaya) and global scales (Trenberth & Shea ). Global climate warming is principally attributed to the significant rise in air temperature (IPCC ). As a result of increasing temperature, the intensity and frequency of extreme climate events such as drought and flood are likely to increase (IPCC ). This could induce extreme hydrological events such as extreme river discharge, thereby affecting the hydrological cycle. The effect of changes in a basin's precipitation and temperature can often be related to river discharge. However, uncertainties in the trends of hydrometeorological variables as a result of data's temporal limitations, gaps in spatial coverage, change points in the dataset, regional differences and forcing cannot be down-sized (Huntington    Oyebande () attributed the disruption of the Jama'are and Hadejia river flow of the KYB between 1979 and 1989 to human activities, droughts and evapotranspiration losses, while Adeyeri et al. (b) showed that approximately 50% rainfall variability and 50% human activities caused an increase in discharge between 1971 and 2013.
However, the spatiotemporal behaviour of precipitation, river discharge, minimum and maximum temperature depend on the regional and local forcing. In order to proffer solutions to the problems associated with water resources management within the KYB, there is a need to understand the relationship between these variables at the basin's scale.
Therefore, this study seeks to improve the understanding of the relationships in the observed trends of precipitation, maximum air temperature, minimum air temperature and river discharge over the KYB between 1971 and 2013. Our approaches include the detection and correction of inhomogeneity in the hydrometeorological variables, trend analyses at both temporal (monthly, seasonal and annual) and spatial scales, correlation and wavelet analyses among the variables.

STUDY AREA AND DATA USED
The KYB is situated to the south of the Sahara Desert in the Sahelian region of Africa. The elevation of the basin is between 285 and 1,750 m. It has an area cover of 150,000 km 2 (Figure 1) which is nearly 35% of the conventional Lake Chad basin. It is drained by the Hadejia and Jama'are rivers (Oyebande ). The most prominent water losses in the rivers are by infiltration, but other losses include evaporation and water abstraction (Legros ). The average rain season is between May and October.
The mean annual precipitation ranges between 300 and 1,200 mm while the mean annual temperature is 29 C.
The climate system in this basin is governed by two main wind systems; the dry season's northeasterly wind which brings sand-dust from the Sahara Desert and the rain season's southeasterly wind which brings moisture from the Atlantic Ocean (Warner ). The average annual potential evaporation in the basin ranges between 1,800 mm and 2,400 mm. The basin is characterized by the occurrences of severe drought episodes as well as high climate variability (Thompson & Polet ). The KYB is of strategic importance to both national and international communities because of its valuable wetlands. Additionally, some internationally shared waters originate from these wetlands.
Besides its contribution to the national and international economy in terms of environmental conservation and agricultural produce, proper management of the basin strengthens the diplomatic relationships among the different countries sharing the Lake Chad Basin (IUCN ).
Daily precipitation, river discharge, minimum and maximum temperature data series archived by the Nigeria Meteorological Agency (NiMet), Direction de la Météorologie Nationale (DMN) of the Niger Republic and Diffa hydrological station are used for the analysis in this study (Table 1).
The period of analysis for this study is between 1971 and 2013.

METHODS
The daily climatological time series are quality controlled using the Rclimdex package (Zhang & Yang ).    To identify the shifts in annual mean: (1) The internal distance (d) is given as: where U is the operator for generating of time averages, K is the total number of breaks, j 1 and j 2 is the starting and ending year, k is the serial number of break/step, q is the relative time series, i is the time point, j is the year, c is the To correct the inhomogeneity in data series: where HY is the adjustment term, s is the reference series serial number, x is an ensemble homogenization serial number, N 0 is the total number of usable reference series at a particular step, j is the year, g is a parameter. The details of this method are documented in Domonkos & Coll ().

Trend in data series
The trend in the homogenized data series is detected by MK where x j and x k are sequential data values for the time series data of length n. The sum of the Sgn series is defined as: The statistic S is approximately normally distributed with the mean E(S) and the variance V(S) can be computed as: where t is the extent of any given tie. Σti denotes the summation over all ties and is only used if the data series contain tied values. The standard normal variate Z is calculated by: Positive values of Z indicate a rising trend and negative values show a descending trend.
The wavelet spectral decomposes signals into signal and trends over a time period domain. This is presented as (Veleda et al. ):

Slope test
Theil-Sen's estimator estimates the slope of n pairs of data points (Khatiwada et al. ). The magnitude of the trend is calculated as: where x j and x k are values at times j and k, respectively.
Note, j > k. Q i is a Sen's estimator of the slope which is the median of these N values. If there are n values of x j present in each time period, then: where n is the number of time periods. The N values of Q i are ranked by Q 1 Q 2 ⋯ Q N-1 Q N and The percentage change is computed by approximating it with a linear trend (Yue & Hashino ): where m is the median slope, l is the length of the year and q is the mean of data series.

RESULTS
Change point detection Table 2 shows the significant change points in precipitation, maximum and minimum temperature, respectively. In

Mean climatology
The monthly analysis of temperature variables (Figure 2) shows the monthly range of maximum and minimum temperature varies from 24 to 41 C and 11 to 27 C, respectively.
The highest values of maximum temperature are seen from      Highest precipitation is seen to increase towards the south-western part of the basin (Figure 5(a)). The range of precipitation in the basin is between 400 and 750 mm in the northern part to between 750 and 1,300 mm in the southern part of the basin. There is an overall latitudinal Bold value means significant trend at 5% significant level. Positive Z means increasing trend. For more information about the location of the stations, please see Figure 1.

Temporal and spatial trends
increase (decrease) of temperature (precipitation) i.e., from lower to higher latitudes. This is in agreement with USGS ().
The results of the spatial distribution of the percentage changes in precipitation are presented in Figure 5 between 1971 and 1979, 1987 and 1995, and between 2007 and 2013.   The relationship between precipitation, temperature and river discharge In an attempt to further understand the relationship between precipitation, river discharge, minimum and maximum temperature, the correlation plots among these variables are examined. For the dry season (Figure 7(a)), there exists a positive correlation between precipitation, minimum temperature, maximum temperature and years.
Furthermore, river discharge in the dry season shows a negative correlation with the temperature variables and a decrease with years. For the wet season (Figure 7 there is a negative correlation between river discharge and the two temperature variables, i.e., À0.5 for maximum temperature and À0.1 for minimum temperature while the correlation between precipitation and the maximum temperature is also seen to be negative (À0.3). Furthermore, the atmosphere has a higher moisture-holding capacity which reduces its rate of saturation during warmer summer. In the same vein, the local mechanism of moisture and precipitation event occurrence is examined. Figure 8 shows that the recurrence interval of rainfall at 400 mm is 6 years while the recurrence interval of discharge higher than 30 mm is 2 years. At the upper bound, the return level of precipitation of almost 600 mm and discharge at above 50 mm is at 20 years' recurrence interval. This will provide useful information as regards flood event preparedness.

CONCLUSION
The study investigated the inhomogeneity and analysed the spatiotemporal trends as well as the relationship between precipitation, river discharge, maximum and minimum temperature over theKYB, Lake Chad region between 1971 and 2013. Significant change points in the time series were detected and corrected using ACMANT. This correction provides a more robust time series for climate impact studies in the basin. Initial results show a latitudinal increase (decrease) in the basin temperature (precipitation) from lower to higher latitudes. There is, overall, increasing temperature, precipitation and river discharge in the basin. On the other hand, increasing rainfall as a result of the Sahelian rainfall recovery could revive the wetlands in the basin, thereby maintaining the food chain balance and preserving the ecosystem. However, excess water from heavy precipitation intensity as a result of increasing temperature as well as increasing river discharge could lead to flooding, thus, farmlands, farm produce and properties could be affected. This could have significant impacts on water management and the socio-economic activity in the basin.