Potential of the LASH model for water resources management in data-scarce basins: a case study of the Fragata River basin, southern Brazil

Figures & data

Figure 1. Modelling steps in the LASH hydrological model for its grid-cell-based version, where P is the rainfall, ET is the actual evapotranspiration, D_CR is the capillary rise, D_S is the direct surface runoff, D_SS is the subsurface flow, and D_B is the baseflow.

Figure 2. (a) Location of the Fragata River basin; (b) drainage area upstream from the Passo dos Carros outlet and the monitoring network used.

Table 1. Parameter values associated with land-use classes identified in the FRB-PC for hydrological modelling with LASH.

Land-use classes	LAI* (m^2 m^−2)	Height (m)	Albedo	Surface resistance (s m^−1)	Root system depth (mm)
Silviculture	3.5^(a)	5^(f)	0.3–0.18^(g)	100^(h)	1500^(i)
Permanent cropping	1.82–3.62^(b)	1.13–1.62^(b)	0.15–0.2^(h)	40^(f)	500^(j)
Native grassland	0.5	0.2	0.20–0.26^(h)	70^(f)	500^(k)
Annual cropping	0.17–6.02^(c)	0–1.52^(c)	0.15–0.20^(h)	40^(f)	500^(j)
Native forest	6.25^(d)	10^(f)	0.13–0.18^(g)	100^(h)	2000^(i)
Cultivated pasture	1.86–3.99^(e)	0.5^(f)	0.20–0.26^(h)	70^(f)	600^(j)

*LAI: Leaf Area Index; ^aAlmeida and Soares (2003), ^bFavarin et al. (2002), ^cManfron et al. (2003), ^dMarques Filho et al. (2005), ^eFagundes et al. (2006), ^fCollischonn (2001), ^gMiranda et al. (1996), ^hShuttleworth (1993), ⁱLima (1996), ^jAllen et al. (1998), ^kViola (2008).

Figure 3. Land-use map for the FRB-PC.

Figure 4. Main soil types in the FRB-PC and the 15 km spatial transect established for soil sampling.

Table 2. Values of soil properties associated with land-use classes identified in the FRB-PC for hydrological modelling with LASH.

Soil classes	Saturated soil water content θS (cm^3 cm^−3)*	Soil water content retained at permanent wilting point θPWP (cm^3 cm^−3)**	Soil depth Z (mm)***
Paleudult	0.456	0.195	1500
Hapludalf + Udorthent	0.447	0.185	1000
Albaqualf	0.405	0.168	500

*Saturated soil water content; **Soil water content retained at permanent wilting point; ***Soil depth.

Table 3. Lower and upper bounds used for each parameter calibrated by the LASH model and the respective optimized values for the FRB-PC.

Parameter	Description	Lower and upper bounds	FRB-PC
λ	Initial abstraction coefficient (dimensionless)	0–0.5	0.147
KB	Shallow saturated zone reservoir hydraulic conductivity (mm d^−1)	0–6	0.192
KSS	Subsurface reservoir hydraulic conductivity (mm d^−1)	0–250	2.08
KCR	Maximum flow returning to soil by capillary rise (mm d^−1)	0–5	2.67
CS	Surface reservoir response time parameter (dimensionless)	–	3 389.2
CSS	Subsurface reservoir response time parameter (dimensionless)	–	28 216.4
CB	Baseflow recession time (d)	–	49.9

Figure 5. Hydrographs monitored in the FRB-PC and hydrographs estimated by the LASH model in the same basin for calibration (a) and validation (b) periods.

Table 4. Statistical measures to assess the performance of the LASH model in the calibration and validation steps for the FRB-PC.

Statistical measure	Calibration	Validation
Nash-Sutcliffe coefficient (CNS)	0.805	0.719
Logarithm of the Nash-Sutcliffe coefficient (log CNS)	0.693	0.830
Estimated streamflow relative error (∆Q)	−0.011	0.025

Figure 6. Values observed in the FRB-PC, and those estimated by the LASH model for minimum annual streamflow (a), mean annual streamflow (b), and maximum annual streamflow (c).

Figure 7. Observed flow–duration curve and curve estimated by the LASH model in the FRB-PC.

Figure 8. Percentage breakdown of each runoff component considering the hydrograph estimated by the LASH model from 1995 to 2008 in the FRB-PC.

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