School of Civil and Environmental Engineering University of the Witwatersrand Johannesburg

1. Socio-hydrological Modelling of Reservoir Operation to Assess the Effect of Human Behaviour on Yield By: N.J. Shanono, J. Ndiritu and P. Lenka-Bula School of Civil and Environmental EngineeringUniversity of the Witwatersrand Johannesburg 19th SANCIAHS Symposium, Skukuza, 19 Sept. 2018

2. Background of the paper • In SA, water demand increases at a high rate, projected demand-supply deficit of 17% by 2030 • Although, there are several measures for reducing water demand & increasing its availability • e.g. WC/WDM, WUE… • But, a problem that immensely contributes to the demand increase andoften ignored in WRS analysis is behaviour-driven human activities • e.g. unlawful water abstractions (Colvin & Muruven, 2017) (DWS, 2018) (Plummer & Cross, 2006; UNDP, 2011; Hermann-friede et al., 2014; WWAP, 2017)

3. Table 1: Reported/suspected unlawful activities relating to water abstraction in SA DWS, 2016) (Philanda, 2017) (Ndiritu et al., 2017) • Cape Town’s water crises,exacerbated due to changing users’ behaviour & • failure to impose restriction on farmers • 31.8% of total water losses in SA, 6.4% commercial losses (unlawful uses & meter inaccuracy) (Muller, 2017) (McKenzie et al., 2012 • Need, to be incorporating the realities of such behaviour in water governance such as in reservoir yield analysis • Reservoir yield analysis has been to evaluate system yield potential under hydrological and physical constraints without incorporating human behavioural impacts explicitly. • An approach that can consider the interactions, co-evolving dynamics and feedbacks btw. reservoir operation & stakeholders activities was adopted (Socio-hydrology) • Socio-hydrology, an interdisciplinary study of the co-evolutionary dynamics of human and various water systems (Sivapalan et al., 2012) (Montanari et al., 2013)

4. Norms and Values (behaviour) (of agents at different levels that shape their goals and actions) Structure and Dynamics (water systems) and biophysical, socio-economic and institutional subsystems) Outcomes (well-being and decisions) (observable at different scales and levels) Fig. 1: Generalised framework for socio-hydrological studies (Sivapalan et al., 2014) • Reservoir operation is an essential aspect of South Africa’s water management • Behaviour-driven human impacts on water systems is significant • Socio-hydrology study to relate reservoir operation and human behaviour ispertinent and essential • SH-model: simulates, couples and co-evolves reservoir operation and stakeholders' behaviour • using four-state drivers

5. Reservoir System Hydrological State Users’ Compliance and behaviour 1) Reservoir hydrological state 2) Users’ compliance and behaviour 3) Reservoir system performance 4) Management competence and decisions 4-state drivers of the coupled model Management Competence and decisions Reservoir System Performance Precipitation (streamflows) Storage state (available water) Users’ level of perceived risk and threat Users’ propensity to compliant or unlawful activities Users’ participation and engagement Interventions Decisions Monitoring and compliance enforcement Infrastructures (surveillance systems and flow measuring equipment) Managers expertise (skills, experience and training) Job satisfaction (salary, allowance and other benefits) Yield and reliability performance Other reservoir performance metrics Fig. 2: Reservoir operation-stakeholders’ behaviour interactions framework

6. Precipitation (P) Storage state (S) Climatic Conditions (CC) Concern Generated (CG) Actions: Compliant or Unlawful Estimating annual relative change of P and S Users’ risk perception Users’ behaviour and actions Reservoir Hydrological State Other Situational Factors Management Competence can decisions Reservoir Performance • Fig. 3: Conceptual framework for coupling reservoir operation and stakeholders’ behaviour • Estimating users’ annual risk perception (RP)

7. BA Unlawful abstraction not occur UB • Users’ behavioural actions (BA) – Lawful or Unlawful Unlawful abstraction may likely occur Fig.4: Sigmoidal function showing the response of users’ behavioural action (BA) due to changing RP and users’ behaviour (UB)

8. BI = Proportion of water abstracted without authorisation • Estimating the level of behavioural impacts (BI) due to changing risk perception (RP) • Using Hollings Type II Functional Response Model Fig. 5: Estimated BIfor different levels of users’ RP and proportions of water at users’ disposal

9. Management interventions as a feedbacks • Interventions, depends on the overall management competence (MC) • In field of HRM, Normal distribution is used to model MP as it accurately fit the observed data • Depending on the water managers work engagement, the distribution can skew (right or left) • MC, modelled using pdf of skew-normal random variable • Fig. 6: PDF of the 3 levels of management competence distributions MC MC MC • Behavioural impact (BI) at different levels of management competence (MC)

10. EC: Ethical Climate (overall view of ethical practices as UBX:MCY combination) UB: Users’ behaviour (lawful, moderately lawful and unlawful) MC: Management competence (effective, moderately effective and ineffective) CC: Climatic condition (favourable, moderately favourable and unfavourable) CG: Concern generated (not at all, slightly, somewhat, moderately, extremely concerned) • Scenarios:

11. Supply to irrigation users (Ri) • Reservoir simulation • Single reservoir (irrigation and municipal supply) • 76-year historical monthly dataset • Incorporate stakeholders’ propensity to unlawful abstractions, modelled as BI(Eqn. 5 and 6) Inflows (Q) BIi BIq Spills (Sp) BIm Supply to municipal users (Rm) BI = Proportion of water abstracted illegally Fig. 7: Schematic of a single reservoir when the system is adversely affected by unlawful abstractions

12. 100% 99% 100% 100% • Results • Effect of unlawful abstractions on yield 86% Fig. 8: Percentage of the actual delivered supply at different levels of EC, CC and CG

13. Effect of unlawful abstractions on yield continue….. c. b. a. 718 Mm3 Losses = 44 Mm3 (6%) • Effect of unauthorised abstractions on yield 674 Mm3 Fig. 9: Total delivered supplies at different EC: a) for five levels of CG, b) average total delivered supply, c) rearranged delivered supply. • Trade off analysis UB:MC effect on Yield - Both users’ behaviour and management competence found to affect Yield, but MC is 6 times more impact than UB • Management intervention can therefore effectively minimise adverse impact of users’ behaviour

14. b. c. a. 20% • Water loss due to unlawful abstractions 2% 0% 0% 0% Fig. 10: Percentage of system loss (including along streamflows) for different scenarios

15. Actual delivered supply (%) • Yield response to storage state Fig. 11: Trajectories of storage state and the proportion of the actual delivered to allocated supplies (Yield) Yield (actual delivered supply) increases as the storage state improves

16. Conclusions • Assess how reservoir hydrological state affect users’ risk perception and propensity to compliant or unlawful activities • Revealed how yield reduces as risk perception increases and compliant behaviour decreases due to perceived threat • Both users’ behaviour and management competence found to impact on yield, but management competence has more impact than users’ behaviour • Sensitive nature of the subject of study makes model verification on real-life WRS challenging • However,the study shows that practical reservoir system modelling that quantitatively incorporates human behaviour is a future possibility.

17. Thank you