The objective of this study was to: (i) check the spatial and temporal homogeneity of rainfall data in the Logonecatchment; and (ii) analyse rainfall trends data in different space and time scales. Similar analysis at sub catchment scale have been conducted across many large basins in Africa, for example in the Bani, Kariba and Upper Blue Nile catchments, located in the Niger, Zambezi and Nile basins (Louvet et al., 2016; Muchuru et al., 2015; Tabari et al., 2015). A study limitation is the absence of gauge rainfall data after the year 2000, so the trends in recent rainfallvariability after this period could not be explored. However, recent trends in rainfall within the LCB have been analysed using gridded gauge data (Okonkwo et al., 2014). Another limitation of this study is the use of rain gauge data, which are point measurements, to evaluate rainfall that is highly variable both in time and space. To be able to capture this variability a dense network of rain gauges equally distributed spatially across the catchment is needed, and this remains a major challenge in most hydro-climatic studies. Although this issue may be resolved by using satellite or reanalysis datasets, these too also need to be validated against in-situ rain gauge measurements (e.g. Nkiaka et al., in press). Accuracy, of satellite and reanalysis rainfall forecast models thus depends on the quality of gauge data used for calibration and are inevitably affected by the sparsity of gauge data or temporally incomplete gauge time series in the region under investigation.
The socio-economic consequences posed by climate change in Africa are giving increasing emphasis to the need for trend analysis and detection of changes in hydro-climatic variables in data deficient areas. This study analyses rainfall data from seventeen rain gauges unevenly distributed across the Logonecatchment in the LakeChadbasin (LCB) over a fifty-year period (1951-2000). After quality control of the rainfall data using homogeneity tests, non-parametric MannKendall (MK) and Spearman rho tests were applied to detect the presence of trends. Trend magnitude was calculated using Sen’s Slope Estimator. Results of the homogeneity test showed that rainfall was homogeneous across the catchment. Trend analysis revealed the presence of negative trends for annual rainfall at all the stations. Results of long term trend analysis at monthly time scale revealed the presence of statistically insignificant positive trends at 32% of the stations. Spatially, the analysis showed a clear distinction in rainfall magnitude between the semi-arid and Sudano zones. The slope of the trend lines for annual rainfall averaged over the respective zones was higher in the semi-arid zone (-4.37) compared to the Sudano zone (-4.02). However, the station with the greatest reduction in annual rainfall (-8.06 mm) was located in the Sudano zone.
Results from in this study are comparable to those obtained from Africa by other researchers (Maidment et al., 2013; Zhang et al., 2013; Dile, and Srinivasan, 2014; Worqlul et al., 2014; Koutsouris et al., 2015) and globally (Blacutt et al., 2015; Fu et al., 2015; Krogh et al., 2015; de Leeuw et al., 2015; Sharifi et al., 2016). From these results, the application of each reanalysis product in the catchment will depend on the purpose for which it is to be used and on the spatial scale required, given that both products have the same temporal resolution. However users may need to exclude the period during which rainfall is systematically underestimated in their analysis. The research also shows that evaluating reanalysis products in remote locations like the Logonecatchment may enable users to identify artefacts inherent in reanalysis datasets and so may enable the model developers to improve on certain aspects of the model physics and parametrisation scheme to improve the reanalysis datasets quality.
Abstract Understanding hydrological processes at catch- ment scale through the use of hydrological model param- eters is essential for enhancing water resource management. Given the difficulty of using lump parameters to calibrate distributed catchment hydrological models in spatially heterogeneous catchments, a multiple calibration technique was adopted to enhance model calibration in this study. Different calibration techniques were used to cali- brate the Soil and Water Assessment Tool (SWAT) model at different locations along the Logone river channel. These were: single-site calibration (SSC); sequential cali- bration (SC); and simultaneous multi-site calibration (SMSC). Results indicate that it is possible to reveal dif- ferences in hydrological behavior between the upstream and downstream parts of the catchment using different parameter values. Using all calibration techniques, model performance indicators were mostly above the minimum threshold of 0.60 and 0.65 for Nash Sutcliff Efficiency (NSE) and coefficient of determination (R 2 ) respectively, at both daily and monthly time-steps. Model uncertainty analysis showed that more than 60% of observed stream- flow values were bracketed within the 95% prediction uncertainty (95PPU) band after calibration and validation. Furthermore, results indicated that the SC technique out- performed the other two methods (SSC and SMSC). It was also observed that although the SMSC technique uses streamflow data from all gauging stations during calibra- tion and validation, thereby taking into account the catch- ment spatial variability, the choice of each calibration
Figure 5: Seasonal rainfallanalysis for LakeChadBasin (5.5 ◦ N-24.5 ◦ N and 5.5 ◦ E-24.5 ◦ E) using TRMM satellite data for the period 1998 − 2013. Spatial patterns (left) and temporal patterns (right) are averaged over the geographical boundary describing the spatial extent of LakeChadbasin used in this study. The inset (i.e., the solid black line) is the conventional basin boundary.
The term trend refers to “general tendency or inclination”. In a time series of any variable, trend depicts the long smooth movement lasting over the span of observations, ignoring the short term fluctuations. It helps to determine whether the values of a series increase or decrease over the time. In statistics, trend analysis referred as an important tool and technique for extracting an underlying pattern of behaviour or trend in a time series which would otherwise be partly or nearly completely hidden by noise. Statistic and probability plays an important role in scientific and engineering community (Ayyub and McCuen, 2011) because statistical tools help to detect spatial and temporal trends for hydrological and environmental studies. Major schemes or projects are formulated based on the historical behaviour of environment under uncertain climatic conditions. Therefore, a study of trend assists to investigate the overall pattern of change over time in hydro-meteorological variables especially for water resources project on temporal and spatial scales. Trends in data can be identified by using either parametric or non-parametric methods, and both the methods are extensively used. The parametric methods are considered to be more powerful than the non-parametric methods only when the data series is normally distributed, independent and homogeneous variance (Hamed and Rao, 1998). Conversely, non- parametric methods are more advantageous as they only require the data to be independent and are also less sensitive to outliers and missing values.
interest with that of measured data. Model calibration is a means of adjusting or fine tuning model parameters to match with the observed data as much as possible, with limited range of deviation accepted (Neitsch et al., 2002). It is the procedure of adjustment of parameter values of a model to reproduce the response of reality within the range of accuracy specified in the performance criteria. Parameters for adjustment are selected from those identified by sensitivity analysis. Additional parameters other than those identified in the sensitivity analysis are used primarily for calibration due to the hydrological processes naturally occurring in the watershed. However, sometimes it is necessary to change parameters in the calibration process other than those identified during sensitivity analysis because of the type of miss match of the observed variables and predicted variables (White and Chaubey, 2005). The process of adjustment can be done manually or automatic methods. The manual method is the most common, and especially recommended for the application of more complicated models in which a good graphical representation is a prerequisite (Refsgaard and Storm, 1990). A calibration of stream flow was carried out at Upper Ribb gauging station. This site was selected due to the availability of measured flow.
The LakeChadBasin Commission (LCBC) is a sub-regional institution comprising of five (5) nations namely Cameroon, Central African Republic (CAR), Chad, Niger and Nigeria with a population of two hundred and twenty-nine million five hundred thousand (229,500,000) people (National Population Commission and ICF Macro. 2009; Ahmed and Ovie, 2009). The current population estimates of the member countries is Nigeria 180 million, Cameroon 19 million, Niger 15 million, Chad 11 million and CAR 4.5 million (Ahmed and Ovie, 2009; Liman, A. M. 2010). Out of the total population of 229,500,000 million, about twenty- seven million, nine hundred and twelve thousand, seven hundred and sixty-eight (27,912,768) people constitute the Conventional LakeChadBasin (Ahmed and Ovie, 2009; Liman, A. M. 2010). These countries whose boundaries were artificially imposed have important social, cultural and economic links. The landlocked situation of some member countries of LCBC and the natural linkage available through LakeChad provides opportunities for economic operators of member states of LCBC to do commercial
stations were obtained and plotted in Figs 1-9. Usually, when there is no intervention, the plot of rainfall values will oscillate around the horizontal axis (Parida, 2003). Based on this, Figs 1, 3 and 7 of Maiduguri, Ikeja and Benin respectively show oscillatory departures from the horizontal axis. In Fig 1, the departure occurred between 1971 and 1973 and between 1979 and 1982. In Fig 3, it occurred between 1976 and 1978 while in Fig 7 (Benin) the departure was between 2003 and 2005. These departures suggest the possibility of interventions due to the anthropogenic activities of rainfall data collectors. In some of the Figures, the rainfall values either plot above or below the horizontal axis in most of the computed years. Importantly some of the Figures show negative and positive slopes on the horizontal axis. Whitting, et al; 2003 suggest that positive slopes on the horizontal axis indicate wet periods (i.e above average values of rainfall), while negative slopes are indications of dry periods.
The peralkaline and metaluminous rhyolitic magmas from LakeChadbasin derive likely from the same source, according to their coexistence in the same dome (Had- jer el Hamis) and their similar Zr/Nb ratios. According to the Y vs Nb diagram (Figure 6), the silicic rocks of the LakeChad are from within plate domain and likely the product of the intraplate magmatism . The values of K/Rb, Rb/Sr, Rb/Ba and Ga/Al ratios (see Table 5; Figure 6) for the LakeChad rhyolites are sim- ilar to those exhibited for the A-type granite . A-type granites (  ) are generated in association with uplift and major strike-slip faulting.
The main chemical parameters taken into ac- count in the assessment of quality of water from lakes are biogenic elements such as nitrogen and phosphorus. Taking into account the type of sur- face waters, including Lake Bialskie, in 4 cases the concentration of total phosphorus, which could contribute to the development of eutrophi- cation, was found to exceed the standard level. The highest content of total phosphorus was re- corded in autumn at three measurement points (no. 5, 6 and 7) at point no. 7, in August (Tab. 2). As regards total phosphorus the lake waters did not exceed the standard determined in the Regu- lation of the Minister of Environment  at the level 1.5 mg P∙dm -3 . The highest concentra-
The study was conducted in four Cameroon health dis- tricts (Kousseri, Goulfey, Makary and Mada) in the LakeChadbasin. These four health districts have 38 health areas, 40 health facilities (38 public and 2 private) includ- ing Kousseri Regional Hospital and a total population of about 600,000 estimated in 2015. This area borders Chad in the east, Nigeria in the West, the Lac Chad in the North and Maga and Bodo health districts in the South. The predominant tribal groups are Arabs and kotoko. This area was selected for the study as it is a hotspot for cholera outbreaks in Cameroon. Seven health facilities were selected based on high case rate during the previ- ous epidemic of cholera in 2011. These facilities treat suspect cases from Cameroon as well as from neighbour- ing countries including Chad, Niger or Nigeria. All of the study specific health facilities were public.
Urmia lakebasin located in northwestern Iran is the second largest saline lake in the world. Due to many reasons i.e. climate changes, several dam constructions, building a bridge across the Lake, extra agricultural consumption and improper management of water resources, the water level of the lake has been decreased since 1997 and thousand hectares of emerged salty land has made numerous ecological and environmental problems. Therefore, an accurate forecast of the entrance runoff to the lake is important in managing the river ﬂow and water transfer within basins. There are various methods for time-series based forecasting; in the presented study Feed-forward Neural Network and Autocorrela- tion Regressive Integrated Moving Average (ARIMA) models were applied to forecast the monthly rainfall in Urmia lakebasin. The results showed that the estimated values of monthly rainfall through Feed-forward NN were close to ARIMA model with coefﬁcient of correlation 0.62 and the root mean square error of 12.43 mm over the 6 years test period; then rainfall amount were predicted for a 6-year period starting from 2012 (2012 –2017). Using the runoff coefﬁcient regime which was calculated from Contents lists available at ScienceDirect
A stepwise discriminant analysis (DA) was run in order to confirm the groups predicted by cluster analysis and to determine the discriminant characters. The suitability of DA was determined through log determinants and Box’s M test. In DA the basic assumption is that the variance-co-variance matrices are equivalent. For this assumption to hold the log determinants should be equal and the Box’s M test should not be significant. The lat- ter tests the null hypothesis that the covariance matrices do not differ between groups formed by the dependent variable. Wilk’s lambda was used to test the discriminatory power of the discriminant functions while the signi- ficance of the distance between group centroids was tested by F-statistic.
Abstract:- For all hydrologic analyses, a watershed constitutes the spatial unit, and all hydrologic problems are solved in the context of this spatial unit. There are number of indices, which can be defined to illustrate variability of hydrologic behavior such as rainfall, runoff, evaporation, infiltration, peak discharge, unit hydrograph, groundwater table and its fluctuation, movement to name but a few. An estimate of runoff volume from a drainage basin involves precipitation, infiltration, evaporation, transpiration, interception, depression storage, each of which is complex and can interact with the other variables to either enhance or reduce runoff. These variables are variously distributed within a drainage basin. The manner in which these variables interact in time and space makes a direct determination of runoff very difficult. Therefore we estimate runoff by using methods that reflects combined effect of the variables on an individual drainage basin. Because no two drainage basins are exactly alike, no two solutions can be exactly alike. The present chapter incorporates various methods used to estimate runoff in the Shivganga drainage basin. The results obtained by analyzing basin hydrological parameters such as rainfall, evaporation and infiltration have been presented in detail on the basis of field data and the data obtained from various government agencies and institutes.
Precipitation stores fresh water on the earth and therefore it is the vital segment of the water cycle. For decades Pakistan has experienced prolonged periods of rainfall fluctuation shifting between such periods in northern and Southern parts of the ., 2013). Balochistan is the largest province of Pakistan, located in the South-Western region of the country and comprising of 44% of the country’s total land mass. The province has confronted the issue of scant water In the past, Balochistan’s economy has not done well. It has experienced the worst growth record and a terrible infrastructure, and an acute water crisis has been observed in the province. The woeful economic situation of the ng standards, the highest poverty and lowest progression indicators (Khan, 2011). The Nari River of Balochistan originates from district Zhob in the North province, comprising of streams (locally named as Ruds) that connect the Loralai tributary to become Beji tributary. Subsequently, at the joining of Beji and Khost tributaries to the East of Spera Ragha (in the Toba Kakar range, 6.5km), it is named as the Nari River. It outfall into Hammal Lake in Sindh and then ultimately into Manchar Lake which further flows into the Indus River as shown in Fig.1. The province of Balochistan suffers from water scarcity with immense temporal variability in precipitation. The average yearly rainfall in Nari River Basin (NRB) is 274 mm (Riaz et al., s a crucial river basin for Balochistan and it is a tributary of the Indus river system.
As described in section 3.1 for Jetpur-pavi station, the analysis have been also made for Devhat station in the similar manner to identify 1) trend of annual rainfall and rainy days in a year; 2) trend of monthly rainfall and rainy days for each monsoon month – June to October; and 3) trend of the maximum daily rainfall of each year. The daily rainfall data series was available from year 1972 to 2016 from State Water Data Centre (SWDC) for Devhat station.The results obtained for Devhat station are as below:
In the most recent re-evaluation of the design discharge at Lobith, there was a strong feeling that the uncertainties of extrapolation could be reduced by taking the physical behaviour of the river basin into account [Delft Hydraulics and EAC-RAND, 1993]. For this purpose, it was suggested to develop a hydrological/hydraulic model for the whole basin. With such a model, it would also be possible to quantify the effects of changes in the catchment and the river geometry and to predict the potential impact of cli- mate change. The Institute for Inland Water Management and Waste Water Treatment (RIZA) adopted this idea in a research plan for a new methodology to determine the de- sign discharge [Bennekom and Parmet, 1998]. Besides the hydrological/hydraulic model, the development of a stochastic rainfall generator was also planned in order to produce long-duration rainfall series over the basin. Unprecedented extreme rainfall events are expected if the simulation run is considerably longer (300-1000 years) than the observed rainfall record. Such rainfall events in turn, may lead to more extreme river discharges at Lobith than those experienced in the past century. The use of synthetic rainfall series in combination with a hydrological/hydraulic model does not only provide the peak dis- charges but also the durations of these extreme events. This may lead to a better insight into the profile of the design flood.
29.3 ºC and 31.6 ºC. There are two main rainy seasons, the long and heavy rains occur from March to May and the short rains from November to December (Nicholson, 1998). Lake Victoria is a very important water resource in the region and supports the livelihood of millions of people in the riparian states. It is a major source of water supply for domestic and industrial purposes. However, an increasingly variable climate in the LV region, high population growth and developmental activities mainly agriculture, urbanization and industrialization contribute greatly to degradation of both land and water. This will continue to have direct and indirect effects on the Lake’s environment and ecology. Causes of the rising pollution levels in the Lake are as many as they are diverse. The Lake has for a long time been a sink to excess untreated effluent of both industrial and municipal origin and this has led to notable changes in the physical, chemical and biological regimes of the Lake (Nzomo, 2005). Increased traffic of ships has also greatly contributed to the pollution. Shipping routes continue to be expanded and to date there are a number of coastal towns such as Kisumu in Kenya; Entebbe in Uganda; Bukoba, Mwanza and Musoma in Tanzania which are connected with each other by ship routes (ILEC, 2001).