Received: 26 March 2019; Accepted: 18 April 2019; Published: 24 April 2019 Abstract: This article illustrates the impact of potential future climate scenarios on water quantity in time and space for an East African floodplain catchment surrounded by mountainous areas. In East Africa, agricultural intensification is shifting from upland cultivation into the wetlands due to year-round water availability and fertile soils. These advantageous agricultural conditions might be hampered through climatechange impacts. Additionally, water-related risks, like droughts and flooding events, are likely to increase. Hence, this study investigates future climate patterns and their impact on waterresources in one production cluster in Tanzania. To account for these changes, a regional climate model ensemble of the Coordinated Regional Downscaling Experiment (CORDEX) Africa project was analyzed to investigate changes in climatic patterns until 2060, according to the RCP4.5 (representative concentration pathways) and RCP8.5 scenarios. The semi-distributed Soil and Water Assessment Tool (SWAT) was utilized to analyze the impacts on waterresources according to all scenarios. Modeling results indicate increasing temperatures, especially in the hot dry season, intensifying the distinctive features of the dry and rainy season. This consequently aggravates hydrological extremes, such as more-pronounced flooding and decreasing low flows. Overall, annual averages of water yield and surface runoff increase up to 61.6% and 67.8%, respectively, within the bias-corrected scenario simulations, compared to the historical simulations. However, changes in precipitation among the analyzed scenarios vary between −8.3% and +22.5% of the annual averages. Hydrological modeling results also show heterogeneous spatial patterns inside the catchment. These spatio-temporal patterns indicate the possibility of an aggravation for severe floods in wet seasons, as well as an increasing drought risk in dry seasons across the scenario simulations. Apart from that, the discharge peak, which is crucial for the flood recession agriculture in the floodplain, is likely to shift from April to May from the 2020s onwards.
General circulation models (GCMs) can simulate reliably most of the important features of global climate at the large scale (Zorita and Storch, 1999) and are still the most im- portant source of generating future climate scenarios based on emission scenarios. Ranged from warmest to coolest, the emission scenarios presented in the IPCC Special Report on Emission Scenarios (SRES) are A1FI, A2, A1B, B2, A1T, and B1 (IPCC, 2000). Though climatechangeimpact studies on the hydrologic regime were relatively rare until the last decade (Dibike and Coulibaly, 2005), there have since been numerous studies carried out in a wide variety of environ- ments around the world (Kundzewicz et al., 2007; Bates et al., 2008). As hydrologists and decision makers are mostly interested in evaluating climatechangeimpact at the indi- vidual catchment and stream level, with a huge number of downscaling works from climate model output (Flower and Wilby, 2007), the number of climatechangeimpact studies at the catchment scale is increasing. It appears that all the cli- mate changeimpact studies are carried out through the down- scaling of climate model scenario(s) which are subsequently used as an input to calibrate a hydrologic model(s) for hy- drologic output. In reality, every study is unique based on the selection of climate model(s), downscaling technique(s), hydrologic model(s), environment, objective of the study, timescale and emission scenario(s). For example Cherkauer and Sinha (2010) studied the impact of projected climate (early 2010–2039, mid-century 2040–2069 and late century 2070–2099) in the Lake Michigan region using IPCC Fourth Assessment Report (AR4) data. They produced maps of surface runoff and baseflow, and presented hydrologic as- pects of the distribution of the daily flow and seasonal vari- ation of flows. Shrestha et al. (2012) investigated climatechange effects on runoff, snowmelt and discharge peaks in two representative sub-catchments of the Red and Assini- boine basins in the Lake Winnipeg watershed (dominated by spring snowmelt runoff), Canada, for a 21-year baseline (1980–2000) and future (2042–2062) climate using climate
Many studies have increased the understanding of Ks variation due to: (i) soil texture (Rawls & Brakensiek, 1989; Tietje & Hennings, 1996; Vereecken et al., 1990; Wang et al., 2013; Schulze-Makuch et al., 1999), (ii) land use and land cover change (Shabtai et al., 2014; Giertz & Diekkrüger, 2003; Bormann et al., 2005), (iii) soil type (Zeng et al., 2013; Novák et al., 2011), (iv) agricultural practices (Green et al., 2003; Moret & Arrúe, 2007) etc. Whereas the function of many Ks influencing factors is known to some degree, insights gained from agricultural practices (tillage, crop pattern, fallow duration) induced Ks variation have proved to be context specific. This is consistent with a literature review by Strudley et al., (2008) concerning the effects of agricultural practices on soil hydraulic proprieties which reached the conclusion that the results of the reviewed studies are so contradictory that hardly a general rule can be drawn when comparing the effects of agricultural practices on soil hydraulic properties. Due to its high natural spatial and temporal variability, Ks may be a poor indicator of soil hydraulic response to management practices, because this natural variability may overshadow management practices effects (Strudley et al., 2008; Green et al., 2003). Strudley et al. (2008) suggested that research to determine the impact of agricultural management options on the hydraulic conductivity, should consequently be designed in a manner to consider the effect of dominant factors affecting soil hydraulic properties such us soil type, soil local heterogeneity, topography, etc.
Chao Phraya River is the most important and largest river flowing from Chai Nat Province to estuary at the Gulf of Thailand in SamutPrakan Province, located at central part of Thailand. The climate of the Chao Phraya River has a tropical wet and dry or savanna climate, which generates wet and dry seasons of more or less equal length. The monsoon season is usually from May until late September and/or early October. In the wet season, ave- ragely 1 - 2 tropical depressions occur over much of the area from August to October of the year. The average annual discharge is 718 m 3 /s and rainfall varied between 1122 to 1511 mm, depending on monsoon direction and elevation. However, some parts of the catchment continue to suffer from drought problems due to the un- even distribution of rainfall. Some areas experience both flooding and drought conditions in a single year, due to temporal and spatial uncertainties in the monthly rainfall and/or the poor management of the conveyance infra- structure. The common practice in Thailand is to manage the risks after considering which areas are likely to be vulnerable to either flood or drought. Failure to manage risk by addressing one aspect at a time can lead to ad- verse results. Therefore, climatechange and an association with managing flood and drought risks are new chal- lenge in Thailand and becoming increasingly important.
Isaac River catchment, which is located within Fitzroy basin in Central Queensland, Australia is mostly a semi-arid region, sparsely populated, but rife with economic activities such as mining, grazing, cropping and production forestry. Hydro-meteorological data over the past several dec- ades reveal that the catchment is experiencing increasing variability in precipitation and stream- flow contributing to more severe droughts and floods supposedly due to climatechange. The ex- posure of the economic activities in the catchment to the vagaries of nature and the possible im- pacts of climatechange on the stream flow regime are to be analyzed. For the purpose, SWAT model was adopted to capture the dynamics of the catchment. During calibration of the model 12 parameters were found to be significant which yielded a R2 value of 0.73 for calibration and 0.66 for validation. In the next stage, six GCMs from CMIP3 namely, CGCM3.1/T47, CNRM-CM3, GFDL- CM2.1, IPSLCM4, MIROC3.2 (medres) and MRI CGCM2.3.2 were selected for climatechange projec- tions in the Fitzroy basin under a very high emissions scenario (A2), a medium emissions scenario (A1B) and a low emissions scenario (B1) for two future periods (2046-2064) and (2080-2100). All GCMs showed consistent increases in temperature, and as expected, highest rate for A2 and lowest rate for B1. Precipitation predictions were mixed-reductions in A2 and increases in A1B and B1, and more variations in distant future compared to near future. When the projected temperatures and precipitation were inputted into the SWAT model, and the model outputs were compared with the baseline period (1980-2010), the picture that emerged depicted worsening waterresources variability.
 assessed the likely impact of climate changes on catchment hydrology and waterresources.  used sta- tistically downscaled out put from Global Climate Mod- els as forcing into a lumped conceptual rainfall-runoff model, to analyzed changes on ground storage, steam flow and extreme events. Future simulations using the rainfall-runoff models suggest the reductions in soil mois- ture storage throughout the summer and autumn months are likely for catchment across the globe. The decrease in storage is likely dependent on the storage potential of the individual catchments. The lower the capacity of a catch- ment to store water the greater the sensitivity of climatechange. Reductions in ground water storage during the recharge period according to  will increase the risk of severe drought because of failure of winter or spring pre- cipitation may results in prolong drought period where the ground water system is unable to recover.
In one hand, there are many evidences of waterresources diminution in different Spanish catchments for the last 70 years, representing in average about 0.4%. On the other hand, the main land use change observed in Spanish rural headwater areas is the afforestation of former agricultural lands. This process started in the first half of the XX century because of agricultural abandonment driven by socio-economic changes and Spanish policies. Recently, following the UE directives almost half of the surface refor- ested in Europe in the last years was in Spain (DGCONA, 2000). The finding of experi- mental hydrology obtained along the XX century clearly demonstrated that changes in land cover determine different water consumption in the catchments, and therefore changes in waterresources (Bosch and Hewlett, 1982; Sahin and Hall, 1996). Nevertheless, since now waterresources diminution has been analysed in Spain only as a result of increasing irrigation and climate variability (MIMAM 2000), but there is not discussion about the hydrological impact of land use and cover changes in headwater areas.
At the very end of the modelling chain (Fig. 2), the present and future climatological values of the hydrological indica- tors are permuted across members to increase the sample of our climatechange signals dataset (e.g., Bourdillon et al., 2011). This operation is based on the assumption that each member is considered as an independent realisation of cli- mate, both in the reference and the future periods. With per- mutation, the future of a given member is not only com- pared with the present of the same member, but also with the present of all other members. For instance, five GCM mem- bers used in a single branch of the modelling chain (i.e., used to drive only one RCM and one hydrological model) produce five present and five future hydrological outputs. With per- mutation, 25 future versus present differences are obtained for the hydrological indicators, as shown in Fig. 4. There- fore, using the permutations, 25 values of relative differences are obtained with five reference and five future hydrologi- cal indicators at the au Saumon catchment. For Schlehdorf, nine values are obtained with the three-member ECHAM5 ensemble. The median of the change values gives the climatechange signal while the variability gives an estimation of the uncertainty associated to that signal.
Climatechange is a global challenge to both sustainable livelihoods and economic development. Tanzania has been affected by climatechange due to primary dependence on rain-fed agriculture. Despite several studies being able to explore climatechange farmers’ perceptions and adaptation in Tanzania, little attention has been to humid areas specifically forest adjacent communities. This study assessed the perceptions and adaptation strategies developed by forest adjacent communities against climatechange effects in Kilombero District, Tanzania. Data collection involved use of household questionnaire, key informant interviews, focus group discussions and participant observations. Results showed that the majority of communities perceive the climate to have changed as evidenced by increase in temperature and unpredictable rainfall over the past decades. This was further evidenced by frequent occurrence of floods, increased dry spells during rainy season coupled with decreased water sources, emergence of new pests and diseases, and fluctuations in fruiting and flowering seasons for plant resources in the forests. The communities’ perceptions are in line with existing empirical climate data for Kilombero meteorological station where temperature and rainfall have indicated an increasing trend with fluctuations in some years. The perceived change in climate has impacted different sectors mostly agriculture as the main livelihood source. Local communities are responding through different coping and adaptation strategies, such as crop diversification, changing cropping calendar, adopting modern farming technologies, increasing reliance on Non-Timber Forest Products (NTFPs), animal rearing and petty trading. Household size, residence period, land ownership, and household income were the socio-economic factors that influenced coping and adaptation strategies positively and significantly. In conclusion, forest adjacent communities perceive the climate to have changed as evidenced by different climatic indicators. In actual fact the area seem to have experienced climate variability and communities have responded differently by developing both coping and adaptation strategies within the farming and non-farming context. The study recommends a need for provision of weather forecast to the area for preparedness. The need for daily recording of climatic events by meteorological stations in the study area and other places in Tanzania is crucial for future confirmation of climatechange. The observed potential coping and adaptation strategies need to be prioritized, strengthened and developed to ensure livelihood sustainability in future.
The daily climate data were collected from the Department of Hydrology and Meteorology (DHM), Government of Nepal. Meteorological data used in this study include daily precipitation, daily maximum temperature and daily minimum temperature of Kyanging Langtang snow and glacier hydrology station temperature. The products of scenarios generated by using CGCM3 temperature and precipitation data were used with the HBV (Hydrologiska Byråns Vattenbalansavdelning) light 3.0 hydrological model for the calculation of future discharge from Langtang Khola catchment at Syaprubesi. Seibert (2005) describes the model as follows: daily discharge is simulated by HBV light 3.0 using daily rainfall, temperature and potential evaporation as input. In this study, seasons in Nepal are classified as: winter (DJF) December of the previous year to February; spring (MAM) March to May; summer (JJAS) June to September; and autumn (ON) October to November. Four applications of computer software programs are applied in this study: (1) SDSM for generating daily climate temperature and precipitation scenarios (SDSM 4.2), developed by Wilby and Dawson (2007) in the UK, is chosen for developing daily climate scenario study, (2) hqrating model (developed by the Department of Hydrology and Meteorology, Government of Nepal) to develop the rating curve, (3) ArcGIS 9.3 for glacier area delineation, and (4) HBV Light 3.0 for discharge modelling (Seibert 2005).
available data do not offer a clear answer. The literature suggests that climatechange has had a signiﬁcant negative impact on productivity, particularly in low- income countries. Emigration can offset this negative effect of climatechange by giving households more occupational and location choice; individuals can move from low productivity agricultural areas to higher productivity urban areas and improve the economic well-being of their households at home and their commu- nities. In addition, remittance income can also be an effective tool to combat the negative impact of climatechange. Remittances increase household income and lower poverty; if remittances are used to purchase more efﬁcient durable goods and housing, then migrant households better adapt to resource scarcity and cli- mate change in their own communities. However, there are costs to long-term migration. Rural households are highly likely to invest in livestock with remittance income, and the combination of increasing livestock numbers and unsustainable pasture management can lead to pasture degradation (Schoch 2009). In addition, as the most able workers and their families leave the community, communities may be less willing and able to effect changes that would streamline community response to the pressures of climatechange. Policy initiatives can help mitigate the negative effects of migration on communities by encouraging sustainable invest- ment, education, good management, and support from a diverse diaspora.
The precipitation elasticity of total runoff is 3.3 on average and varies in the range 2.0–4.0 in the 167 catchments, which means that a +1 % change in annual precipitation will result in 2.0–4.0 % change in mean annual runoff. The mean annual precipitation and mean annual total runoff in the study catch- ments is about 903 mm and 158 mm, respectively; therefore, an increase of annual precipitation by 9 mm change will re- sult in about 3.2–6.3 mm (the average is 5.1 mm) increase of mean annual total runoff. This is mostly consistent with sim- ilar results reported in 219 locations across Australia (Jones et al., 2005; Chiew, 2006). Detailed modeling conducted in Western Australia has shown that a +1 % change of an- nual precipitation would typically result in a +2–3 % change in annual runoff (Berti et al., 2004; Kitsios et al., 2008; Smith et al., 2009). For runoff components, current year’s precipitation elasticity is a little higher for surface runoff (about 3.5) and lower for subsurface runoff (about 2.9) on average, which is consistent with results reported by Harman et al. (2011) in American MOPEX catchments. The temper- ature elasticity of total runoff is − 0.05 on average (ranges from − 0.8 to 1.1), which means that a 1 ◦ C increase of the annual temperature results in a −0.05 % change in annual runoff. The temperature elasticity of total runoff is very small. This can be explained that runoff change depends on precipitation change and is related to the change in soil moisture. The change of temperature mainly impacts on the evapotranspiration and then on the soil moisture. Strongly regulated by the soil with large water storage capacity, tem- perature elasticity of runoff is small.
To overcome these limitations, a group of indices that consider multiple variables to represent drought were developed. The drought monitor developed by Svoboda et al. (2002) considers an Objective Blend of Drought Indicators (OBDI) which is the linear weighted average of several drought indices. Aggregated Drought Index (ADI; Keyantash and Dracup, 2004) comprehensively considers all physical forms of drought through variables like precipitation, stream flow, evapotranspiration, reser- voir storage, soil moisture content and snow water content. ADI aggregates all these variables into a single time series through principal component analysis (PCA). However, the use of PCA has several limitations like linearity assumption in data transformation, and the assumption that most information is contained in those di- rections where input data variance is maximum. These assumptions however need not be always met in reality. Recently, bivariate drought indices have been derived using copulas to quantify the joint behavior of drought types. Kao and Govindaraju (2010) introduced a Joint Drought Index (JDI) using copula for obtaining the joint probabilities while considering precipitation and stream flow. Hao and Agakouchak (2013) introduced Multivariate Standardized Drought Index (MSDI) which uses cop- ula to form joint probabilities of precipitation and soil moisture content. The use of copula for multivariate analysis is, no doubt, highly effective. However, for higher dimensional cases (i.e. more than three variables), this method will not be a feasible choice due to the lack of flexibility in modeling the dependence structure.
34. (a) Trenberth, K. E., P.D. Jones, P. Ambenje, R. Bojariu, D. Easterling, A. Klein Tank, D. Parker, F. Rahimzadeh, J.A. Renwick, M. ; Rusticucci, B. S., and P. Zhai, [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. Miller (eds.)]. , Observations: surface and atmospheric climatechange. In: ClimateChange 2007: The Physical Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on ClimateChange. Cambridge University Press, Cambridge, UK, and New York, 2007:, pp. 235-335; (b) Steffen, K., P.U. 35. Clark, J.G. Cogley, D. Holland, S. Marshall, E. Rignot, and R. Thomas,, Rapid changes in glaciers and ice sheets and their impacts on sea level. In: Abrupt ClimateChange. . 2008:. 36. Jansen, E., J. Overpeck, K.R. Briffa, J.-C. Duplessy, F. Joos, V. ; Masson-Delmotte, D. O., B. Otto-Bliesner, W.R. Peltier, S. Rahmstorf, R. Ramesh, D. Raynaud, D. Rind, O. Solomina, R. Villalba and D. Zhang, Palaeoclimate. [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. Miller (eds.)]. In: ClimateChange 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on ClimateChange Cambridge University Press, Cambridge, UK, and New York, 2007:, pp. 433-497.
The climatechange impacts at local scale are necessary and play fundamental role to downscaling the global data, by working through statistical and dynamical methods (Mearns et al., 1999 and Segui et al., 2010). Statistical downscaling model (SDSM) is the most popular and the most cited model among regression based statistical downscaling methods (Nasseri et al., 2013 and Khan et al., 2006). On the other hand, dynamic downscaling develops a regional climate model (RCM) with the course GCM data for use as boundary conditions. Wilby and Wigley (1997) stated that the major limitations or disadvantages of the dynamic modelling are that it is quite complicated as it require the computing resources, take longer simulation time, high operating cost and above all, require the downscaled data for individual sites or localities for impact studies. Statistical downscaling techniques are usually the more favourable and much easier techniques for the assessment of climatechange impacts on waterresources (Khan et al., 2006). However, the projections of future climate impacts should look at all aspects of uncertainty related to the GCM in determining the level of confidences.
Groundwater withdrawals in California in the mid-1990s are estimated to be around 14.5 million acre-feet, nearly 20 percent of all the groundwater withdrawn in the entire United States. (In typical years, groundwater accounts for around 30 percent of all urban and agricultural water use in the state ( http://www.waterplan.water.ca.gov/groundwater/DraftUpdate/Chapter1.pdf ). In some areas current levels of groundwater use are already unsustainable, with pumping rates exceeding natural recharge. Groundwater overdrafts in California in the drier years of the 1990s averaged nearly 1.5 million acre-feet per year (California Department of WaterResources 1998). Little work has been done on the impacts of climate changes for specific groundwater basins, or for general groundwater recharge characteristics or water quality. Changes in recharge will result from changes in effective rainfall as well as a change in the timing of the recharge season. Increased winter rainfall, expected for some mid-continental, mid-latitude regions could lead to increased groundwater recharge. Higher temperatures could increase the period of infiltration where soils freeze. Higher evaporation or shorter rainfall seasons, on the other hand, could mean that soil deficits persist for longer periods of time, shortening recharge seasons (Leonard et al. 1999). A significant portion of winter recharge comes from deep percolation of precipitation below the rooting zone, whether of native vegetation or farmland. Warmer winter temperatures between storms would be expected to increase ET, thereby drying out the soil between storms. A greater amount of rain in subsequent storms would then be required to wet the root zone and provide water for deep percolation.
Another climate related disease is Leptospirosis. It is a zoonotic disease found mostly in tropical and subtropical countries favoured by extreme weather events (Biggs et al. 2011). It spreads via the urine of infected animals which gets into water or soil (Mboera et al. 2011). Areas experiencing repeated floods and typhoons particularly in urban slums and areas with poor sanitation are homes of leptospirosis infection. Biggs et al. (2011) reported cases of leptospirosis prevalence in two hospitals in Moshi region. This reveals that the disease affect health of people but also health sector in the country. Humans and animals are affected in a similar manner with this disease (Mgonde et al. 2006). They get infected through contact with contaminated soils or water, ingestion or inhalation of contaminated soils (Mgonde et al. 2006). Although the disease is climatic change related and affect people and animals, its epidemiological status to humans in the country is less considered probably due to its diagnostic complications and little awareness. Plague is also important climatic related disease caused by the bacteria known as bacillus Yersinia pestis (Stenseth et al. 2008). Distribution of the disease is regular with climatechange (Drancourt et al. (2006; Stenseth et al. 2008; Nakazawa et al., 2008). For example, Pham et al. (2009) indicated that plague incidences usually tend to increase during hot and dry season and then followed by a period of seasonal rainfall. Drancourt et al. (2006) indicated that this bacteria lives in rodent hosts and transmitted to human and other animals through animal fleas. It can also be transmitted via predation, cannibalism or contaminated soils. In Tanzania, the disease is endemic in Lushoto district and causes human health problems, nevertheless, the plague cases are seasonal (Mboera et al. 2011).
dominated by the volcanic terrain of the Borrowdale volcanic groups formed by an Ordovician volcanic island arc. Their hydraulic properties are affected by the mode of extrusion, where permeability is greatest in the direction of basalt movement, and geologic controls on fracture development (Smith and Wheatcraft, 1993). The latter affects the formation of tuffs and agglomerates and whether or not these are welded. The porosity of a welded tuff is 15% and 30% for a non-welded tuff (Smith and Wheatcraft, 1993). These controls also affect the amount of fracturing which occur due to shear stress. Fractures can increase permeability and hydraulic conductivity since water will flow through them much faster than through the crystal lattice of the rocks. An unfractured volcanic bedrock has a hydraulic conductivity four orders of magnitude slower than fractured volcanic bedrock (Smith and Wheatcraft, 1993). The Rhyolitic lava flow may represent a volcanic eruption late in the Ordovician period which was not incorporated into Borrowdale volcanic group. Nevertheless its hydraulic properties and those of the metamorphosed sand- and silt-stones of the Skiddaw group are very similar due to the important role of shear stress fracturing in determining hydraulic conductivity and permeability. The carboniferous limestone in the north-west corner of the catchment was formed from calcite deposits in a warm tropical sea during the Carboniferous period. It has a relatively large porosity of 20% for coarse, blocky limestone (Freeze and Cherry, 1979) but its brittle nature meant it fractured easily under shear stress resulting in secondary permeability which increased the hydraulic conductivity by a few orders of magnitude.
Currently almost a third of the earth population stays at the nations which are living at a certain stress because of lack of water, and this makes the domestic, industrial and farms exceeds 22% of the total water surface. And a billion persons are now in dire need of drinking water, 260% million suffering from the health sickness because of the lack of clean water access and poor water treatment. Every year floods takes thousands lives and displace half million (Hans Joachim Schellnhuber, 2006).thus, it said that climatechange can increase the intensity of floods and drought which may be more severely than that of the previous years due to increase in industrialization and economic widens done by the advanced countries (Charles J. Vo¬ro¬smarty, 2000). And the fast developed countries in Asia like China, India and South East Asia countries, U.S and Western countries already did negative impacts on the environment and created a vacuum in the climatechange recovery. Sensitivity of the pacific countries to climatechange is due to the present of Deltas, Low coral reefs areas so it worries people that their response to this may increase because they keeps exploiting due to their economic expansion (Japan International Cooperation Agency, 2010). addition, some human interactions like exploitation of land, change in cover, deforestation agriculture activities urban planning, use of water, mining activities ,ecological production ,rain water management all these can collectively cause change in evaporation, runoff surface water, precipitation, concentration of the hydrological cycle, and absolute influence (Xia, 2017).