Exploring water quality management with a socio-hydrological model: a case study from Burkina Faso

Figures & data

Figure 1. Map showing the Black Volta in southwest Burkina Faso and boundaries of the three Local Water Committees (CLEs)/the case study catchments.

Figure 2. Time line of relevant institutional developments in the study catchments (based on Koudougou 2018, Somda et al. 2019).

Figure 3. Graphical schematic of the socio-hydrological model. Positive/negative signs indicate the positive/negative impact of each variable on the next variable in the direction of the arrow. (Image credits: Polluted lake: iStock.com/acrylic. Sack of money: iStock.com/vladwel. Scales and books: iStock.com/UnitoneVector. Police stop: iStock.com/strelss. Stick figures with puzzle pieces: dreamstime.com/korenyugin52. Stick figure with tree: dreamstime.com/Udo Schotten. Stick figure with watering can: dreamstime.com/Udo Schotten. Sack with flour: dreamstime.com/Evgenii Naumov. Insecticide bottle: dreamstime.com/Mstjahanara903. All other images created by Carr and Barendrecht).

Table 1. Description of the variables

Variable	Description and unit
AI	Institutional awareness level [scaled between 0 (low) and 1 (high)]
$R_{PO}$	Resources and capacity of the Water Police [scaled between 0 (low) and 1 (high)]
$R_{CLE}$	Resources and capacity of the CLEs [scaled between 0 (low) and 1 (high)]
$A_F$	Farmer awareness of water quality issues [scaled between 0 (low) and 1 (high)]
$W_L$	Farmer willingness and capacity to cease cultivating in the 100-m zone [scaled between 0 (no means and capacity) and 1 (complete means and capacity)] e.g. farmer spends $50 today versus willingness to spend $100 if it was available = willingness value of 0.5
$W_T$	Farmer willingness and capacity to plant trees in the 100-m zone [Unit as with $W_L$]
$W_P$	Farmer willingness and capacity to adopt less polluting agricultural practices in 100-m zone [Unit as with $W_L$]
$I_F$	Famers’ productivity or income [($/year achieved under less polluting agricultural system)/($/year maximum that could be achieved under more polluting agricultural system)] e.g. reduced productivity due to adoption of a more stringent management approaches is $50 versus max productivity without management approaches of $100 = 0.5
$L$	Proportion of the land in the 100-m zone used for cultivation [m^2/m^2] e.g. 100-m zone is 1000 m^2 and cultivation area is 500 m^2 = 50% of 100-m zone is cultivated
T	Proportion of L planted with trees [m^2/m^2] e.g. Total cultivated area is 500 m^2 and area cultivated with trees is 250 m^2 = 50% of cultivated area is trees
P	Proportion of L on which more polluting agricultural practices versus less polluting practices are applied [m^2/m^2] e.g. Total cultivated area is 500 m^2, 250 m^2 is cultivated using high-polluting practices (fertilizer, pesticide, high tillage etc) and 250 m^2 is cultivated using low-polluting practices (organic fertilizer, no pesticide, minimal tillage) = 50% of cultivated area uses high-polluting practices
C	Water quality in the river or reservoir [mg/L] e.g. sediment concentration
$C_{inF}$	Water quality of runoff from 100-m zone [mg/L] e.g. sediment concentration
$Q_F$	Water flowing into the river/reservoir from the land in the 100-m zone [L/s]
$Q_{in}$	Water in the river stretch or reservoir [L/s]
$Q_{out}$	Water flowing out of the river stretch or reservoir [L/s]
$S$	Water storage in the river stretch or reservoir [L]

Table 2. Respondents’ perceptions of local water problems (Question: “In your view, what are the key water problems in your catchment or community?”) (after Balana et al. 2019)

Key water-related problems	Responses (% of respondents)
	Kou	Mouhoun-Tâ	Bougouiba 7	Mean
Siltation	74.6	80.0	87.6	80.7
Water shortages	17.0	8.3	8.6	11.3
Water pollution	8.5	10.0	2.5	7.0
Others	-	1.7	1.2	1.5

Figure 4. Factors influencing environmental decision making by farmers (from Mills et al. 2017). Reprinted by permission from Springer Nature: Agriculture and Human Values.

Figure 5. Percentage of farmers willing to adopt different management strategies to address (a) siltation due to river bank cultivation and (b) agricultural water pollution from agrochemicals.

Table 3. Initial conditions for each time period of the baseline scenario

Variable	AI	$R_{PO}$	$R_{CLE}$	$A_F$	$W_L$	$W_T$	$W_P$	$L$	T	P	C
Initial value (1990)	0.05	0.05	0.05	0.05	0.01	0.01	0.01	0.12	0.1	0.9	70
2009	0.15	0.04	0.04	0.09	0.03	0.03	0.03	0.33	0.12	0.88	82.8
2016	0.23	0.04	0.07	0.12	0.05	0.05	0.05	0.39	0.15	0.85	85.7

Table 4. Parameters and their values used in the baseline scenario. * = value that is fixed and does not change in any scenario

Parameter	Description	1. Baseline
		1990–2008	2009–2015	2016–2020
${\alpha }_{PO}$	Level of institutional support for Water Police [1/y]	0	0	0.5
${\alpha }_{CLE}$	Level of institutional support for CLEs [1/y]	0	0.5	0.5
${\alpha }_{IFT}$	Support for planting trees [1/y]	0	0.5	0.5
${\alpha }_{IFL}$	Support (e.g. subsidies) for ceasing agriculture in the 100-m zone [1/y]	0	0.5	0.5
${\alpha }_{IFP}$	Support (e.g. technical, financial) for reducing agricultural pollution and siltation [1/y]	0	0.5	0.5
${\beta }_{IF}$	Fines as punishment for cultivating in the 100-m zone [1/y]	0	0	0
${\alpha }_{AI}$*	Influence of water quality and siltation on organizational awareness [1/(mg/L)]	2/1000	2/1000	2/1000
${\mu }_{AI}$*	Decline of organizational awareness of water quality and siltation over time [1/y]	0.05/1000	0.05/1000	0.05/1000
${\mu }_{PO}$*	Decline of Water Police capacity over time [1/y]	0.01	0.01	0.01
${\mu }_{CLE}$*	Decline of CLEs’ capacity over time [1/y]	0.01	0.01	0.01
${\alpha }_{AF}$*	Influence of water quality and siltation on farmers’ awareness [1/(mg/L)]	8/1000	8/1000	8/1000
${\beta }_{AF}$*	Influence of Water Police and CLE awareness-raising campaigns on awareness of farmers [1/($^2/$^2)]	100	100	100
${\mu }_{AF}$*	Decline of farmer awareness of water quality and siltation over time [1/y]	20/1000	20/1000	20/1000
${\alpha }_W$*	Influence of awareness on willingness to change [($/$)/y]	1	1	1
${\alpha }_{WI}$*	Influence of loss of income on willingness to change [1/y]	0.05	0.05	0.05
${\alpha }_T$*	Influence of willingness on change in area covered with trees [1/($/$)]	0.2	0.2	0.2
${\alpha }_L$*	Influence of willingness on change in area in the 100-m zone being cultivated [1/($/$)]	0.1	0.1	0.1
${\alpha }_P$*	Influence of willingness on change in polluting practices [1/($/$)]	0.2	0.2	0.2
${\mu }_T$*	Decline rate of trees in cultivated area zone [1/y]	0.015	0.015	0.015
${\mu }_L$*	Rate of increase of cultivation in 100-m zone [1/y]	0.015	0.015	0.015
${\mu }_P$*	Rate of increase of the use of polluting agricultural practices [1/y]	0.015	0.015	0.015

Table 5. Parameters and their values used in the scenarios

Parameter	Description	2. Double support for ceasing cultivation in 100-m zone	3. Double support for tree planting	4. Double support for reducing agricultural pollution	5. Additional support for all three measures	6. Additional support for all three measures and implementation of fines
${\alpha }_{PO}$	Level of institutional support for Water Police [1/y]	0	0	0	0	1
${\alpha }_{CLE}$	Level of institutional support for CLEs [1/y]	1	1	1	1	1
${\alpha }_{IFL}$	Support (e.g. subsidies) for ceasing agriculture in the 100-m zone [1/y]	3	0	0	3	1
${\alpha }_{IFT}$	Support for planting trees [1/y]	0	3	0	3	1
${\alpha }_{IFP}$	Support (e.g. technical, financial) for reducing agricultural pollution and siltation [1/y]	0	0	3	3	1
${\beta }_{IF}$	Fines as punishment for cultivating in the 100-m zone [1/y]	0	0	0	0	3

Figure 6. Modelled evolution of awareness, institutional capacity, famers’ willingness and capacity to implement measures, and change in land-use and water quality in the study regions for the six scenarios.

Koudougou, V., 2018. La Police de l’Eau : instrument innovant de protection des ressources en eau au Burkina Faso. Cas de la Région des Hauts-Bassins. MA Thesis. AGRINOVIA MA Programme. Université de Ouagadougou, 91p.

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