Study focus: This review paper presents the current understanding of hydrologicalextremes in the BlueNile River basin under historic and future climate conditions, largely drawing on research outputs over the past decade. Characteristics of precipitation and streamﬂow extremes, including historic trends and future projections, are considered.
Abstract. Climatechange poses critical threats to water- related safety and sustainability in the Mekong River basin. Hydrological impact signals from earlier Coupled Model In- tercomparison Project phase 3 (CMIP3)-based assessments, however, are highly uncertain and largely ignore hydrolog- ical extremes. This paper provides one of the first hydro- logical impact assessments using the CMIP5 climate projec- tions. Furthermore, we model and analyse changes in river flow regimes and hydrologicalextremes (i.e. high-flow and low-flow conditions). In general, the Mekong’s hydrological cycle intensifies under future climatechange. The scenario’s ensemble mean shows increases in both seasonal and annual river discharges (annual change between + 5 and + 16 %, de- pending on location). Despite the overall increasing trend, the individual scenarios show differences in the magnitude of discharge changes and, to a lesser extent, contrasting di- rectional changes. The scenario’s ensemble, however, shows reduced uncertainties in climate projection and hydrologi- cal impacts compared to earlier CMIP3-based assessments. We further found that extremely high-flow events increase in both magnitude and frequency. Extremely low flows, on the other hand, are projected to occur less often under climatechange. Higher low flows can help reducing dry season wa- ter shortage and controlling salinization in the downstream Mekong Delta. However, higher and more frequent peak dis- charges will exacerbate flood risks in the basin. Climate- change-induced hydrological changes will have important implications for safety, economic development, and ecosys- tem dynamics and thus require special attention in climatechange adaptation and water management.
soil map and a land use map from the Amhara Design & Supervision Works Enterprise (ADSWE 2017). Daily weather data including rainfall and maximum and minimum temperature were available from the National Meteorological Service Agency (NMA 2016). The SWAT model with its sub-basins, hydrologic response units (HRUs), and river network shapefiles were inputs to the coupled SWATMOD-Prep model (Table 1). Moreover for the parameterization of MODFLOW, maps of initial GW head were prepared by inverse distance weighted (IDW) interpolation of water level data from hand-dug wells and boreholes that were collected from Amhara Water Works Construction (AWWCE 2016) and Tana Basin Development Authority (TBDA 2016). Saha et al. (2017) have applied the same approach in their modelling study of temporal dynamics of groundwater-surface water interactions for a watershed in Canada. Hydraulic conductivities, specific storage, and specific yield values were assigned to each soil unit based on previously published values (Morris and Jonson 1967), and river bed material K (default value of MODFLOW) were used as input data for the MODFLOW model. The horizontal and vertical anisotropy factors for all materials were set to 1, assuming that these anisotropies were not changing both horizontally and vertically. The streamflow data from gauging stations Gilgelabay near Merawi, Gumara near Bahirdar, and Ribb near Addis Zemen for the years 1980 to 2014 were provided by the Ministry of Water, Irrigation and Electricity of the Ethiopian Government (MoWIE 2016) to validate the coupled model.
As a methodology, this study adopted the livelihood vulnerability index (LVI) developed and demonstrated by Hahn et al. (2009) in Mozambique. Similarly, different authors used the same framework with some modifica- tion to major components to resemble the local context in Ethiopia (Asrat and Simane 2017; Amare and Simane 2017; Chala et al. 2017; Simane et al. 2014). The same methodology was employed in this study to assess the vulnerability of smallholder farmers to climatechange and variability in Fincha’a sub-basin. The approach de- fined eight biophysical and socioeconomic profiles re- lated to vulnerability based on review of literatures that match the conditions and constraints facing smallholder agricultural households in the sub-basin. The profiles in- clude climate, ecosystem, agriculture, wealth, technol- ogy, infrastructure, community, and social network. Accordingly, the vulnerability index was derived for four agro-ecological systems found in the sub-basin. Each profile is composed of several indicators or sub-compo- nents. The eight vulnerability profiles were then mapped onto the three IPCC contributing factors to vulnerabil- ity. Each of the profiles, with the possible exception of climate profile, can also be associated with one of the
One of the GCM outputs is actual evapotranspiration (AET) which could have been used, together with rainfall, to study the water balance over the basin. However, this was not done for several reasons. First, GCM rainfall is biased and thus AET will be biased since rainfall is parti- tioned between AET, infiltration, and runoff by the GCM land-surface scheme. While we could bias-correct the rain- fall using observations, no observations of AET are avail- able to attempt to remove the GCM bias. AET has been in- ferred from satellite images (National Oceanic Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) using energy balance methods over parts of the Nilebasin (e.g. Mohamed et al., 2004) but these es- timates suffer from poor temporal resolution as the satel- lite overpass frequency is low. Meanwhile, we could bias correct PET calculated from GCM output. Second, GCM AET will have the coarse resolution of the GCM and will need to be downscaled. As the method used for downscal- ing depends on the resolution of observed data, there is no means to downscale AET. Third, in order to apply a hydro- logical model, PET is required, rather than AET. Otherwise, we could use GCM generated runoff directly and eliminate the need for a hydrological model. It is well known that GCM runoff suffers from several drawbacks (e.g. it cannot be compared directly to streamflow due to lack of routing within most GCMs in addition to the coarse GCM resolu- tion) as stressed by several authors (e.g. Wood et al., 1991; Kite et al., 1994). Although routing may be less important when studying the annual water balance of relatively large basins, it is still important to obtain a reasonable reproduc- tion of the seasonal cycle (Kuhl and Miller, 1992; Arora and Boer, 1999; Evans, 2003).
There are relatively few reliable hydrologic stations in the Abay Basin, despite its size. This is mainly due to general inaccessibility, remoteness, and economic constraints on setting up and maintaining adequate monitoring networks (Kim and Kaluarachchi, 2008 ). Thus, for the present study, river discharge data were obtained from diﬀerent sources. Daily discharge data were obtained from the International Water Management Institute (IWMI) of the Hydrology Department of the Ministry of Water Resources of Ethiopia, and from the WLRC in Ethiopia. The sub-basins in the SWAT were designed to ﬁt the available hydrological stations (see Figure 1 for details). Still, the available discharge data were characterized by signiﬁcant gaps, occasional extremes for no apparent reason, and missing data. Thus, whenever possible, the apparently unreliable stations were removed and sub-basins were modelled using the most reliable data sources possible.
Climate and land cover change and associated impacts on water resources are being the hot issues in recent years (Yanhu He et al, 2013). This is due to the direct or indirect impacts brought by climate and land use change both have contributed to some water problems, such as water shortage, flooding, and water logging to different extent. According to (Dibike et al., 2012) changes in rainfall and temperature would have consequences on the evapotranspiration conditions and water balance of a given catchment. Temperature, precipitation and evapotranspiration conditions are the determining factors of watershed hydrological systems. Changes may increase or decrease runoff trends and also annual water supply.
In the past decades, much attention is given to climatechange impact studies on hy- drology of rivers in the Nilebasin    . One of the important and sensitive ba- sins towards climatechange is the BlueNile    where Ribb and Gumera sub ba- sins are located.  used two conceptual hydrological models that were calibrated and used to carry out climatechange impact assessment for two future Special Report on Emissions Scenario (SRES) A1B and B1 for 2050s using 17 GCMs for Nilebasin. The result of the study has showed that there is unclear trend (like  and ) in Lake Tana sub-basin for projected flows (mean and high/low) and this is mainly attributed to Global Climate Models (GCMs) uncertainty.
Due to the fact that discharge simulations, based on cli- mate simulations, cannot be compared to observed dis- charges on a real-time daily, monthly, or annual basis, the methods to evaluate discharge performance are limited. In this study, the annual cycle (daily time series averaged over the simulation period) was characterized by R 2 and PBIAS, where R 2 was a measure of seasonality and PBIAS a mea- sure of volumetric deviations. Flow duration curves (FDCs) were used to characterize the distribution of average flow conditions, high and low flows, as well as their extremes, by using the whole time series of daily discharge simulations. Unsurprisingly, discharge simulations show similar deficien- cies to precipitation simulations. Using bias-corrected cli- mate simulations improved the performance of non-extreme discharges (NED) significantly but, with few exceptions, the performance of high and low flows did not improve; in fact, it worsened in most of the simulations. Many BC discharge simulations tend to exaggerate high (overestimation) and low flows (underestimation). Comparing peak discharges using UC and BC climate input, for instance, showed a tremendous increase in some BC simulations, although average monthly precipitation patterns of BC models achieved a much bet- ter fit than their UC counterparts. Moreover, the multi-model means of BC simulations (both RCPs and periods) always project higher increases in average annual discharges than the UC multi-model means. However, a hydrological impact study in the Danube River basin showed in turn that relative changes in average monthly discharges projected using UC and BC climate models are overall comparable (Stagl and Hattermann, 2015).
1993, 1996; Strzepek and Yates, 1996; Conway 2005; Kim et al., 2008; Beyene et al., 2010; Elshamy et al., 2009a, b; Elshamy and Wheater, 2009; Soliman, et al., 2008; Githui et al., 2009). The studies used different methods for trans- lating specified changes in climatic inputs into changes in hydrological regimes. Bias correction and applying monthly changes were among some of the methods used to gener- ate the climate series. Afterwards, hydrological models were used to generate the hydrological regimes. For example, Elshamy et al. (2009a) used bias corrected statistical down- scaling method to analyze outputs of 17 GCMs and to con- struct downscaled scenarios while Kim et al. (2008) applied the change factor method using monthly totals to construct the future climate variables. Beyene et al. (2010), states that most of the previous studies were limited by the coarse spa- tial resolution of the GCMs used and the small number of GCMs that could be evaluated. In addition, the impact of us- ing different hydrological models for a given climatechange scenario is not widely investigated and reported in literature for the Nilebasin. Nevertheless, this kind of investigation is important. For instance, a study by Jiang et al. (2007) showed greater differences in impact of climatechange on water availability in the Dongjiang basin in South China, when different hydrological models were used for the same climate scenarios. It is also crucial that models are tested for their performance in describing/predicting extreme hydro- logical conditions. Projection of climatechange impacts on hydrologicalextremes (floods, droughts, or water scarcity) is however of major importance for the region. Therefore, this paper attempted to evaluate the performance of two hydro- logical models in projecting climatechange impact on the mean hydrology of selected catchments in the Nilebasin.
iv Investigation of trends in the historical hydrometeorological records formed the first objective of this study. Trend analysis results do not only provide information on the general direction of observed change but also unravel significant changes that have occurred over and above the expected natural climate variability and may link them to past consequences. Since the effects of climatechange are unleashed more through the occurrence of extremes, the presence of a significant linear trend in a long term climate record of extremes may provide evidence of a significant shift from the natural trend to that which is enhanced by, for example, anthropogenic forcing. Analysis of long term records of extremes for rainfall, temperature and streamflows from selected stations within the study area were considered in this study. The findings indicated that these extremes are generally experiencing a positive trend. Albeit positive trend was generally demonstrated in the extremes for the selected variables, the presence of significant linear trend was only manifested in the extremes of the data for the stations located in the northern and eastern parts of the Lake Victoria basin. This may suggest that the monotony in the linear trend is probably an indicator of the sensitivity of the region’s extremes to climatechange due to possible external enhancement of the natural climate agitation. The latter has implications for flood risks if the trend is maintained. Furthermore, the higher significant anomalies for the 1990s as compared to that of the 1960s may suggest a more intense enhancement of the change in the natural variability in the recent climate. Correlation between change in the extremes of rainfall and that of the minimum temperature was demonstrated to be stronger compared to other variables. This may suggest that in the absence of extreme rainfall data, minimum temperature may be a good indicator of the rainfall extremes. The credibility of the GCMs in representing the current and past climate needs to be assessed before the GCM data are employed to aid assessment of hydrological impacts of climatechange. Thus, the second objective of this study was to test the reliability of the GCMs based on their simulation runs for the 20th century (present day climate). The present day simulations were evaluated against the observed hydrometeorological records using mainly statistical metrics. The findings showed that several GCMs are quite implausible with respect to simulation of the climate of the Lake Victoria basin and more tasks still lie ahead for the climate modelers. However, the evaluation results for some of the GCMs provided motivation for their employment in the impact assessment for the region.
Despite regional downscaling to finer resolution, RCM sim- ulations often show considerable biases when compared to observed data (Addor and Seibert, 2014; Christensen et al., 2008). A review of bias correction methods (linear scaling, local intensity scaling, power transformation, and distribu- tion or quantile mapping) is provided by Teutschbein and Seibert (2012). The authors conclude that the distribution or quantile mapping method achieves the best performance for most of the selected criteria. Although quantile mapping is a successful method to improve the representation of daily rainfall characteristics, it fails to correct multi-day and inter- annual variables, such as mean maximum 4-day precipita- tion, mean minimum 14-day precipitation, and inter-annual variability (Addor and Seibert, 2014). The drawback that all approaches have in common is that they are based on the stationarity assumption, which presumes that future physi- cal processes in the atmosphere are comparable to the period used to correct the simulations. Bias correction of climate simulation data is nowadays a widely used practice in hydro- logical impact modelling, but it should be treated with cau- tion. As Maraun et al. (2010) point out, the origins of the bias in climate simulations (mathematical formulations in climate models) are not solved by the post-processing and may dis- rupt internal physical coherence between weather variables. Hence, the correction is usually based on wrong reasons (Ad- dor and Seibert, 2014). Alternatives to bias correction are so-called delta-change methods. Sophisticated approaches of this method are described by Anandhi et al. (2011), Bosshard et al. (2011), and Chiew et al. (2009).
Abstract. Projections of changes in the hydrological cycle from global hydrological models (GHMs) driven by global climate models (GCMs) are critical for understanding future occurrence of hydrologicalextremes. However, uncertainties remain large and need to be better assessed. In particular, recent studies have pointed to a considerable contribution of GHMs that can equal or outweigh the contribution of GCMs to uncertainty in hydrological projections. Using six GHMs and five GCMs from the ISI-MIP multi-model ensemble, this study aims: (i) to assess future changes in the frequency of both high and low flows at the global scale using control and future (RCP8.5) simulations by the 2080s, and (ii) to quantify, for both ends of the runoff spectrum, GCMs and GHMs contributions to uncertainty using a two-way ANOVA. Increases are found in high flows for northern latitudes and in low flows for several hotspots. Globally, the largest source of uncertainty is associated with GCMs, but GHMs are the greatest source in snow-dominated regions. More specifically, results vary depending on the runoff metric, the temporal (annual and seasonal) and regional scale of analysis. For instance, uncertainty contribution from GHMs is higher for low flows than it is for high flows, partly owing to the different processes driving the onset of the two phenomena (e.g. the more direct effect of the GCMs’ precipitation variability on high flows). This study provides a comprehensive synthesis of where future hydrologicalextremes are projected to increase and where the ensemble spread is owed to either GCMs or GHMs. Finally, our results underline the need for improvements in modelling snowmelt and runoff processes to project future hydrologicalextremes and the importance of using multiple GCMs and GHMs to encompass the uncertainty range provided by these two sources.
Similar conclusions can be drawn by composite analyses with large-scale atmospheric fields from the ERA reanaly- sis set. They reveal a strong influence of the geopotential height fields and wind fields over the European continent, identifying different atmospheric circulation patterns before and after the middle of 1980s for January and July, respec- tively. A link is suggested between interannual fluctuations of large-scale circulation indices. Hydrologicalextremes are negatively correlated with polar vortex related indices and positively with subtropical high related indices. Based on the maximum/minimum flow from the headwater of the Tarim River Basin, Wang et al. ( 2014 ) concluded that dif- ferent circulation indices may influence the trends of hydro- logical extremes. The area of the polar vortex in North American and the area of the Northern Hemisphere polar vortex show the most significant correlation with a 1-day maximum flow and a 1-day minimum flow in Aksu River, respectively. In Hotan River, the most significant corre- lated climate indices with the 1-day maximum and mini- mum flow were the Southern Oscillation index and the area of Northern American Subtropical High, respectively. The area of the polar vortex in the Atlantic and European Sector show significant relationships with the 1-day minimum flow in Yarkand River. Therefore, special attention should be paid on the polar vortex in the Atlantic and Europe sector.
As already elucidated in the first section, regional climatechange impact assessment studies suffer from uncertainties resulting from various sources, including limitations in scientific knowledge (for example, effect of aerosols), which can be classified as GCM uncertainty, randomness, and human actions (such as future greenhouse gas emissions) that can be classified as scenario uncertainty. It is a scientific challenge to quantify these uncertainties. We discuss some recent studies that have attempted to model the uncertainty resulting from the use of multiple GCMs and scenarios. One possible way to model these uncertainties is to treat the projected hydrological variable of interest as a random variable resulting from multiple model projections and to obtain its non-parametric probability distribution. Another straightforward method for combining information from multiple GCM-scenario combinations is to weight them equally as they are often deemed to be equally likely. Mujumdar and Ghosh (2008) developed a methodology to model GCM and scenario uncertainty using possibility theory. In this method, the performance of each GCM is first assessed in simulating signals of climate forcing in the recent past and, based on such a performance, possibility values are assigned to model simulations. These possibilistic weights are then used to arrive at the possibilistic mean cumulative distribution function (CDF) for the future scenarios, which is of use to policy makers. Monsoon streamflow in the Mahanadi River at the Hirakud Dam was chosen as the variable under consideration.
The model takes into account the important hydrological processes such as interception, evapotranspiration, snow and glacier melt, soil water, groundwater, and routing. For details of model input data, modeling application, and calibration param- eters for the J2000 model, see Nepal (2012). Briefly, in the model, the precipitation is first distributed between rain and snow depending upon the threshold air temperature. The inter- ception modules take into account the rain and snow stored in vegetation by considering the leaf area index of the vegetation types. The snowmelt is calculated by considering the external energy, supplied in the form of temperature, and rain and soil, which are provided as calibration parameters. The meltwater is stored in a snow pack as liquid water which is released only when the snow density is higher than a threshold provided by the user. The snowmelt is then supplied to the soil water module. The input to the soil water module is distributed in soil storage, which is divided into large pore storage (LPS) and middle pore storage (MPS) depending on the soil texture of a particular soil type. When the soil storage is filled (i.e. soil is saturated), the saturation excess runoff dominates. The model also considers the infiltration excess runoff (summer and winter seasons and snowmelt runoff), which is controlled by a thresh- old in the form of calibration parameters. These are taken together as surface runoff (RD1). The water stored in the MPS is reduced by plant transpiration and is determined by actual evapotranspiration (actET). The model first calculates the potential evapotranspiration (potET) which is the maximum amount of water evaporated under the given climatic conditions using the Penman–Monteith approach as described in Allen et al. (1998). Later, the actET is estimated based on the avail- able water storage in the snowpack, interception, and MPS. The soil moisture zone provides sub-surface runoff (RD2), which is the excess
In the BlueNile State, the current major climatic hazards are consisted of drought and extreme flooding events. (Figure 5) indicated that average temperature degrees were expected to rise above normal mean. Moreover, there is an increase of the total rainfall intensity concentrated in mid-season month of August with a slight decrease in July and September (Figure 6). During El Nino, rainfall behavior changed with season peak shift from July to September. That was most often followed by an opposite behavior with rainfall duration and intensity decrease leading to a short wet season as illustrated by (Figure 7).
In order to measure the impact of adaptation options (soil and water conservation, agronomic practices, liveli- hood diversification, and small-scale irrigation) in re- sponse to climatechange and variability, this research used Propensity score matching technique. The first step in estimating the treatment effect is to estimate the pro- pensity score. To get this propensity scores, any stand- ard probability model can be used (for example, logit, probit or multinomial logit) (Rajeev et al. 2007). Since the propensity to adoption is unknown, the first task in matching is to estimate this propensity. Any resulting estimates of program effect rest on the quality of the adoption estimate. This can be routinely carried out using a choice model. Which choice model is appropri- ate depends on the nature of the program being evalu- ated. If the program offers a single treatment, the propensity score can be estimated in a standard way using, for example, a probit or logit model, where the dependent variable is “adaptation” and the independent variables are the factors thought to influence adaptation and outcome (Getachew et al. 2011b).
The Nile River is the longest river in the world (UNEP, 2010) and the NileBasin (Fig. 1) is one of the most critical and most important shared basins in Africa, hosting 25 % of Africa’s population (SEDAC, 2010) while accounting for only 10 % of its landmass. Within the basin, agriculture, energy pro- duction and livelihoods all depend strongly on the flows. The region is facing rising levels of water scarcity, high popula- tion growth, watershed degradation and loss of environmen- tal services (UNEP, 2010). In addition, the water resources are highly sensitive to climatechange (Conway et al., 2007). The Nile basin’s climate varies significantly from ex- treme aridity in the north, including Egypt and Sudan, to the tropical rainforests in Central and East Africa and parts of Ethiopia. The distribution of precipitation can be categorised into two distinct regions; the Equatorial (or East African) lakes and the Ethiopian highlands (Fig. 1). There are signif- icant differences in the wet and dry period distribution with
The ArcSWAT2009 version has been used for simulations in the present study. The spatial inputs, DEM, SRTM data at 90m x 90m resolution regridded was obtained from USGS site http://srtm.csi.cgiar.org has been used for delineation of watershed and to create drainage pattern. The land use land cover digital layers data has been obtained from National Remote Sensing Centre (NRSC), GOI. The soils data has been obtained from National Bureau of Soil Survey and Land Use Planning (NBSSLUP), Indian Council of Agricultural Research (ICAR), GOI. Weather data has been obtained from IMD for a period of 35 years (1961 to 2005) and incorporated in the model. Future weather data generated by the Hadley Centre for Climate prediction at U.K. using PRECIS RCM for IPCC SRES A1B scenarios with a resolution of 0.44°×0.44°, latitude by longitude grid points were obtained from IITM, Pune has been utilized for Baseline (1961–1990), Mid Century (2021–2050) and End Century (2071–2098) in present study. The observed stream flow data of the watershed has been obtained from Central Water Commission, MoWR, GOI.