Assessment of rainwater harvesting system through continuous simulation with sub-daily data

Santos, Valéria Carneiro dos; Neves, Marllus Gustavo Ferreira Passos das; Souza, Vladimir Caramori Borges de

doi:10.1590/2318-0331.282320230029

Valéria Carneiro dos Santos

study design

execution in all the steps

analysis in all the steps

portuguese writing

Universidade Federal de Alagoas, Maceió, AL, Brasil

http://orcid.org/0000-0003-1614-5338

Marllus Gustavo Ferreira Passos das Neves

study design

manuscript review

analysis in all the steps

portuguese writing, and english writing

Universidade Federal de Alagoas, Maceió, AL, Brasil

http://orcid.org/0000-0003-0986-0528

Vladimir Caramori Borges de Souza

manuscript review

analysis in all the steps

Universidade Federal de Alagoas, Maceió, AL, Brasil

http://orcid.org/0000-0003-2143-5081

E-mails: valeriacarneirods@gmail.com (VCS), marllus.neves@ctec.ufal.br (MGFPN), caramori@ctec.ufal.br (VCBS)

Editor-in-Chief: Adilson Pinheiro

Associated Editor: Priscilla Macedo Moura

Thumbnail

Figure 1
Flowchart with study phases.

Thumbnail

Figure 2
Sketch of RWHS and monitored variables. P is precipitation, Q_R is roof flow rate that goes through spillway and reaches the vertical conduit, Q_FD is the flow rate through the first-flush diverter, Q_I is the input flow rate into the rain barrel, Q_D is the flow rate demand in use and Q_O is overflow flow rate.

Thumbnail

Figure 2
Sketch of RWHS and monitored variables. P is precipitation, Q_R is roof flow rate that goes through spillway and reaches the vertical conduit, Q_FD is the flow rate through the first-flush diverter, Q_I is the input flow rate into the rain barrel, Q_D is the flow rate demand in use and Q_O is overflow flow rate.

Thumbnail

Figure 3
Volume and maximum flow rate attenuations owing to RWHS, for selected events at the continuous simulation at monitored period.

Thumbnail

Figure 4
Influence of total precipitation classes at attenuations of volume and maximum flow rate owing to RWHS, for selected events at the continuous simulation at monitored period.

Thumbnail

Figure 5
Influence of maximum rainfall intensity classes at the attenuations of volume and maximum flow rate owing to RWHS, for selected events after continuous simulation at monitored period.

Thumbnail

Figure 6
Comparative analysis of volume and maximum flow rate attenuations owing to RWHS, for selected events at monitored period: (a) 10 estimated events from spillway; (b) 48 selected events from continuous simulation.

Thumbnail

Figure 7
Volume and maximum flow rate attenuations, at all demand scenarios, owing to RWHS, after continuous simulation at period 2014-2020. RC is Reference Scenario.

Thumbnail

Figure 8
Total precipitation influence at volume and maximum flow rate attenuations, at reference scenario, owing to RWHS after continuous simulation at period 2014-2020.

Thumbnail

Figure 9
Maximum rainfall intensity influence at volume and maximum flow rate attenuations, at reference scenario, owing to RWHS after continuous simulation at period 2014-2020.

Thumbnail

Figure 10
Volume and maximum flow rate attenuations, per year, from the continuous simulation of the period 2014-2020.

Thumbnail

Figure 11
Classes of maximum rainfall intensity x ratio of two volumes (the one has flowed into first-flush diverter and the one has flowed into reservoir), after continuous simulation for reference scenario at period 2014-2020.

Thumbnail

Figure 12
Water saving efficiency (E) and rainwater overflow ratio (O) after continuous simulation in the period 2014-2020 at all scenarios. RC is Reference Scenario.

Thumbnail

Figure 13
Full reliability (FR) after continuous simulation in the period 2014-2020 at all scenarios. RC is Reference Scenario.

Thumbnail

Figure 14
Water saving efficiency (E) and rainwater overflow ratio (O), per year, after continuous simulation of the period 2014-2020.

Thumbnail

Table 1
Inputs and outputs of RWHS.

Thumbnail

Table 2
Selected events for calibration (C) e validation (V) at the roof and monitored period, where P is total Precipitation, D is duration, C_i initial condition in the reservoir, V_S is estimated volume from spillway and L_S is estimated runoff depth of the from the spillway.

Thumbnail

Table 3
Calibrated parameters at the roof from select events at monitored period.

Thumbnail

Table 4
Calibration and validation results of SWMM at the roof and monitored period. CV is the coefficient of variation.

Thumbnail

Table 5
Performance of RWHS for events at monitored period: P is total precipitation, I_mean is average intensity, I_max maximum intensity, IC is an initial condition in the reservoir, F_d is a pathway with flow going straight to first-flush diverter, F_o is a pathway with flow going to the reservoir after has overflowed first-flush diverter, ER_V is volume retention efficiency and ER_Q is maximum flow rate retention efficiency.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Data		Period	Time step	Method of measurement/obtainment
Rainfall		2014-2020	1min	Tipping-bucket rain gauge
Flow rates		06/2018-01/2020 (with flaws)	1min	Calibrated triangular spillway and water level sensors
The water level in the reservoir (rain barrel)		06/2018-02/2019	1min	water level sensors in the reservoir
Evaporation		1922-1978	Monthly daily average	Weather climate Station Maceió (Tabuleiro)
Water demand	Daily consumption	17/12/2017-31/12/2017	Daily	Water meter readings (Neves, 2019Neves, M. G. F. P. (2019). Aproveitamento de água de precipitação no telhado em lote urbano e sua influência na geração do escoamento superficial: relatório científico. Maceió: FAPEAL.)
	Weekly consumption	04/01/2021-23/02/2021	Weekly	Water meter readings
	Monthly consumption	2017-2020	Monthly	Consumption time series
	System uses	26/07/2018-05/05/2019	Per use	Neves (2019)Neves, M. G. F. P. (2019). Aproveitamento de água de precipitação no telhado em lote urbano e sua influência na geração do escoamento superficial: relatório científico. Maceió: FAPEAL.

Event	Date	P (mm)	D (min)	C_i (%)	V_S (L)	L_S (mm)	Phase
1	29/06/2018	9.60	132.00	63	7.59	2.21	V
2	02/07/2018	45.20	188.00	99	63.42	18.49	V
3	30/07/2018	7.00	17.00	72	5.06	1.48	V
4	31/07/2018	10.80	10.80	85	10.72	3.13	V
5	09/09/2018	5.80	83.00	40	6.74	1.97	V
6	08/11/2018	7.20	67.00	39	23.83	6.95	V
7	08/11/2018	6.60	29.00	48	23.23	6.77	C
8	27/11/2018	7.40	72.00	67	22.18	6.47	V
9	10/12/2018	5.00	9.00	69	15.43	4.50	C
10	15/12/2018	6.00	14.00	75	17.84	5.20	V
11	18/10/2019	6.60	13.00	52	4.72	1.38	C
12	21/11/2019	4.00	58.00	19	4.73	1.38	C
13	15/12/2019	3.20	9.00	29	6.81	1.99	C
14	19/12/2019	8.00	28.00	100	18.60	5.42	C
15	31/12/2019	7.40	33.00	91	6.10	1.78	C
16	20/01/2020	3.60	13.00	88	6.06	1.77	C
17	23/01/2020	3.80	28.00	97	4.50	1.31	C

Event Parameter	7	9	11	13	14	15	16	Average
W (m)	1.00	1.00	0.75	0.80	0.80	0.70	0.70	0.82
AI (%)	100.00	70.00	60.00	70.00	80.00	50.00	65.00	70.71
AS/A (%)	100.00	100.00	10.00	20.00	25.00	0.00	0.00	36.43
PAimp (mm)	1.00	3.00	2.60	1.27	1.27	3.00	1.50	1.94
nimp	0.010	0.010	0.050	0.040	0.020	0.040	0.015	0.026

Calibration
Event	NSE	│RPD_V│(%)	│RPD_Q│(%)
7	0.8	2.0	3.5
15	0.8	21.7	3.8
11	0.5	108.8	4.1
12	-0.3	123.3	11.3
13	0.7	24	19.9
14	0.95	11.2	5
15	0.7	49.3	5.4
16	0.8	25.1	14.5
17	0.1	181.9	1.6
Mean	0.56	60.8	7.7
CV (%)	71%	103%	80%
Validation
Event	NSE	│RPD_V│(%)	│RPD_Q│(%)
1	-6.91	200.88	73.11
2	-0.74	127.32	3.02
3	-3.39	182.45	90.98
4	-2.92	112.28	84.41
5	0.40	52.23	14.56
6	0.50	32.91	37.42
8	0.82	21.81	31.16
10	0.22	34.01	41.81
Mean	-1.50	95.49	47.06
CV (%)	-1.80	0.74	0.69

Event	Date	P (mm)	I_mean (mm^.h^-1)	I_max (mm^.h^-1)	IC (%)	Runoff pathway	ER_V (%)	ER_Q (%)
1	29/06/2018	9.60	4.36	24	63%	F_d	2%	83%
2	02/07/2018	45.20	14.43	48	99%	F_o	4%	3%
3	30/07/2018	7.00	24.71	60	72%	F_o	7%	88%
4	31/07/2018	10.80	8.88	48	85%	F_o	24%	84%
7	09/09/2018	5.80	4.19	60	39%	F_o	4%	84%
8	08/11/2018	7.20	6.45	36	39%	F_o	42%	88%
9	08/11/2018	6.60	8.43	72	48%	F_o	65%	96%
10	27/11/2018	7.40	6.17	60	68%	F_o	58%	96%
11	10/12/2018	5.00	33.33	72	69%	F_o	66%	96%
12	15/12/2018	6.00	25.71	120	74%	F_o	68%	98%
Mean							34%	82%
Standard deviation							29%	28%
Coefficient of variation							84%	35%

[1] E-mails: valeriacarneirods@gmail.com (VCS), marllus.neves@ctec.ufal.br (MGFPN), caramori@ctec.ufal.br (VCBS)

Brasil

Brasil

Assessment of rainwater harvesting system through continuous simulation with sub-daily data

Avaliação de sistema de captação de água de chuva através de simulação contínua com dados subdiários

ABSTRACT