Md. Hossain Ali

doi:10.26599/JGSE.2025.9280062

doi: 10.26599/JGSE.2025.9280062

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出版历程
- 收稿日期: 2025-02-09
- 录用日期: 2025-08-18
- 网络出版日期: 2025-10-10
- 刊出日期: 2025-12-01

Development of a model to estimate groundwater recharge

Md. Hossain Ali^{1
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1.
Director (Research), Bangladesh Institute of Nuclear Agriculture (BINA), BAU Campus, Mymensingh-2202, Bangladesh

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Corresponding author: hossain.ali.bina@gmail.com

摘要

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Abstract: Quantifying the spatial and temporal distribution of natural groundwater recharge is essential for effective groundwater modeling and sustainable resource management. This paper presents M-RechargeCal, a user-friendly software tool developed to estimate natural groundwater recharge using two widely adopted approaches: the Water Balance (WB) method and Water Table Fluctuation (WTF) method. In the WB approach, the catchment area is divided into seven land-use categories, each representing distinct recharge characteristics. The tool includes eighteen different reference Evapotranspiration (ET₀) estimation methods, accommodating varying levels of climatic input data availability. Additional required inputs include crop coefficients for major crops and Curve Numbers (CN) for specific land-use types. The WTF approach considers up to three aquifer layers with different specific yields (for unconfined aquifer) or storage coefficient (for confined aquifer). It also takes into account groundwater withdrawal (draft) and lateral water movement within or outside the aquifer system. M-RechargeCal is process-based and does not require calibration. Its performance was evaluated using six datasets from humid-subtropical environments, demonstrating reliable results (R² = 0.867, r = 0.93, RE= 10.6%, PMARE= 9.8, E_NS = 0.93). The model can be applied to defined hydrological or hydrogeological units such as watersheds, aquifers, or catchments, and can be used to assess the impacts of land-use/land-cover changes on hydrological components. However, it has not yet been tested in arid regions. M-RechargeCal provides modelers and planners with a practical, accessible tool for recharge estimation to support groundwater modeling and water resource planning. The software is available free of charge and can be downloaded from the author's institutional website or obtained by contacting the author via email.
- Groundwater recharge /
- Modelling /
- Software /
- Water balance /
- Aquifer /
- Specific yield

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Figure 1. Schematic diagram of the model framework for WB component

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Figure 2. View of the model sketch at starting phase

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Figure 3. Operational view of the model interface during method selection

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Figure 4. View of the WB method interface during ET₀ selection phase

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Figure 5. Patterns of (a) rainfall and ET₀, and (b) average temperature throughout the year 2022 at Mymensingh

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Figure 6. Map of Bangladesh showing the study locations

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Figure 7. Geological log of the study site, Nachol

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Figure 8. Geological log of the study area, Niamatpur

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Figure 9. Observed versus model-predicted groundwater recharge

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Table 1. ET₀ methods included in M-RechargeCal and their required climatic input data

	Method	Reference	Climatic data required
1	FAO P-M (full data)	FAO 56 (Allen et al. 1998)	T_max, T_min, RH, WS, Rs
2	FAO P-M (with SH)	FAO 56 (Allen et al. 1998)	T_max, T_min, RH, WS, SH
3	FAO P-M (data-short)	FAO 56 (Allen et al. 1998)	T_max, T_min, RH
4	FAO B-C method	FAO 24 (Doorenbos and Pruitt, 1977)	T_mean
5	Makkink	Makkink (1957)	Rs
6	Hargreaves	Hargreaves and Samani (1985)	T_max, T_min
7	Hansen	Hansen (1984)	Rs
8	Turc	Turc (1961)	Rs
9	Prestley-Taylor method	Prestley & Taylor (1972)	Rs
10	Jensen-Haise	Jensen and Haise (1963)	Rs
11	Abtew	Abtew (1996)	Rs
12	de Bruin	de Bruin (1998)	Rs
13	Lobit et al.	Lobit et al. (2018)	T_max, T_min
14	Drooger and Allen	Drooger and Allen (2002)	T_max, T_min
15	Trajkovic	Trajkovic (2007)	T_max, T_min
16	Mintz and Walker	Mintz and Walker (1993)	T_mean
17	Smith and Stopp	Smith and Stopp (1978)	T_mean
18	ASCE method	Walter et al. (2000)	T_max, T_min, RH, WS, Rs

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Table 2. Tested variables, conditions, and their base values for sensitivity analysis

	Variable	Conditions	Base-value for testing sensitivity
1	ET₀	P< ET_p	ET₀ = 20 mm
1	ET₀	P> ET_p	ET₀ = 20 mm
2	P (P= 41 mm, ET_p=58 mm)	P< ET_p	P= 41 mm
	P (P= 84 mm, ET_p=58 mm)	P> ET_p	P= 84 mm
	P(P= 186 mm, ET_p= 56.65 mm)	P> ET_p	P= 186 mm
3	CN	P> ET_p	CN = 60
4	Kc	P< ET_p	Kc=0.9
4	Kc	P> ET_p	Kc=0.9

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Table 3. Characteristics of the test sites

Sl. No	Test site	Latitude deg.N	Longitude deg.E	Elevation (above m.s.l.) /m	Yearly rainfall (2022) /mm	Monthly average temperature /°C
1	Mymensingh	24.73	90.4	16	1,874	18.1–29.8
2	Nachol (ChapaiNawabgonj district)	24.73	88.42	46	1,387	18–36
3	Niamatpur (Naogaon district)	24.80	88.94	27	1,395	17.8–35.9

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Table 4. Year-wise location and method of recharge estimation

Sl. No	Test site and year	Method of recharge determination	Reference
1	Nachol, 2018	Tracer technique	Ali et al. (2022)
2	Nachol, 2019	Tracer technique	Ali et al. (2022)
3	Niamatpur, 2019	Tracer technique	Ali et al. (2022)
4	Niamatpur, 2019	Water-table fluctuation	Ali et al. (2022)
5	Mymensingh, 2015	Tracer technique	Ali (2017)
6	Mymensingh, 2016	Tracer technique	Ali (2017)
* Note: a) The data used for No.1-2 were taken from Table 2 and Table 3 of Ali et al. (2022). b) The data used for No.3 were from Table 5 of Ali et al. (2022). c) The data used for No.4 were from Table 6 of Ali et al. (2022). d) The data used for No.5-6 were taken from Table 1 of Ali et al. (2017).

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Table 5. Performance indicators of the M-RechargeCal model

Sl.	Indicators (unit)	Typical range	Obtained value (for yearly recharge, mm)
1	Mean bias (mm)	–∞ to + ∞ (perfect: 0)	19.6
2	Mean absolute bias or error (MAE) (mm)	0 to ∞ (perfect: 0)	21.3
3	RMSE (mm)	0 to ∞ (perfect: 0)	23.0
4	RE (%)	–∞ to + ∞ (perfect: 0)	10.6
5	PMARE (Percent Mean Absolute Relative Error)	0 to ∞ (perfect: 0)	9.8
6	Pearson's correlation coefficient (r)	–1 to +1 (perfect: 1)	0.93
7	R²	0 to 1 (perfect: 1)	0.867
8	Coefficient of Nash and Sutcliffe efficiency (E_NS)	–∞ to 1 (perfect: 1)	0.93

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Table 6. Summary of tested variables, conditions, and their percent variation under different changes in input parameters

Sl no.	Variable	Condition	Percent change in recharge under the percent change of input by			Incremental change in recharge (%) (for 10% change in input)
Sl no.	Variable	Condition	10%	20%	30%
1	ET₀	ET_p>P (positive change in ET₀)	0	0	0	0
	ET₀ (P=41 mm)	ET_p <P (positive change)	−14.6	−12.2	−9.6	2–3
	ET₀ (P=41 mm)	ET_p <P (negative change)	18.9	20.8	22.7	1–2
	ET₀ (P=84 mm, ET₀=52.75, ET_p=58.03)	ET_p <P (positive change)	−18	−40	−66 (ET_p close to P)	22–26
	ET₀ (P=84 mm)	ET_p <P (positive change)	16	30	42	12–14
	ET₀ (P=186 mm)	ET_p <P (positive change)	−1.6	−3.3	−5.1	1.7–1.8
2	P	P< ET_p	0	0	0	0
	P (P= 84 mm)	P> ET_p(positive change)	29.3	33.8	37.8	3–4
	P (P= 84 mm)	P> ET_p(negative change)	17.3	8.9	0 (P=ET_p)	–9
	P (P= 186 mm)	P> ET_p(positive change)	3.9	7.4	10.2	–3
	P (P= 186 mm)	P> ET_p(negative change)	−5.0	−11.1	−19.2	6–8
3	CN (a) P=84 mm, ET_p=58	P>ET_p(positive change)	−7	−16	−27	9–11
	CN (a) P=84 mm, ET_p=58	P>ET_p(negative change)	4.6	7.5	8.3	1–2
	CN (b) P=186mm, ET_p=58	P>ET_p(positive change)	−17	−33	−49	16–17
	CN (b) P=186mm, ET_p=58	P>ET_p(negative change)	18	36	55	18–19
4	K_c	P<ET_p	0	0	0	0
4	K_c	P>ET_p(P=186, ET_p=60.25)	−1.1	−2.1	−3.2	1–1.1

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