*2.3. Quantifying Terms of Water and Soil Balance*

#### 2.3.1. Overview

As forest and tree cover can influence various steps in the chain from rainfall to streamflow and erosion, we aimed to quantify (1) the direct effect of tree canopies on the retention of part of the rainfall (followed by direct evaporation), versus the fraction reaching the soil surface by throughfall and stemflow, (2) the partitioning of the latter into infiltration and overland flow, (3) the entrainment of soil particles into this overland flow. Within the time and resources available we did not assess (4) the seepage of groundwater beyond the root zone and access by vegetation, (5) the pathways and release of groundwater into streams, (6) the routing of overland flow into streams, or (7) the in-field (or riparian filter zone) sedimentation of entrained soil particles, beyond the scale of the measurement plots. However, we did characterize the vegetation and soil characteristics that influence the various processes.

#### 2.3.2. Rainfall and Throughfall

using Equations (1) and (2):

was calculated using Equation (3):

*2.3. Determination of Soil Properties*

for Inceptisols.

Rain gauges, outside of the direct influence of tree canopies, were installed in four observation locations (with adjacent erosion plots) upstream and four observation locations midstream of the Rejoso Watershed. In each runoff plot, throughfall was measured with five replications. The throughfall gauges below the tree canopy had a horizontally placed 30 cm diameter funnel 120 cm above the soil surface and a collector bottle with a volume of 1.5 dm<sup>3</sup> placed with bamboo as a support. Throughfall and rainfall were collected every day for two months of the rainy season, from March to May 2017. Attempts were made to also quantify stemflow, but due to technical problems with the method used, no reliable data were obtained, and the results are not shown here.

#### 2.3.3. Water Infiltration and Soil Erosion Measurement

Water infiltration was quantified in each land cover type via its complement, surface runoff, and expressed in the runoff/throughfall ratio. As the throughfall for quantified infiltration was measured below the tree canopy, the amounts were the net of canopy retention and possible stemflow. Surface runoff was measured in 6 m × 2 m plots protected from surface run-on, with two drums at the lower *Land*  end to collect surface runo **2020** ff and sediment concentrations for soil erosion measurements (Figure 3). , *9*, x FOR PEER REVIEW 7 of 30

**Figure 3.** Runoff and soil erosion plot design. **Figure 3.** Runoff and soil erosion plot design.

 = − + (13 ∗ −) (1) = 1000 ∗ ( ∗ ∗ ) (2)

10−2

) (3)

and level positions, with one connected with a second drum. The volume of water flowing from each pipe was measured to calibrate the water volume proportion entering into the second drum. The potential capacity of the runoff collector thus was (30 dm3 \* 13) + 30 dm3 = 420 dm3 for 12 m2 or 35 mm. We did nor encounter situations where the second tank overflowed. Runoff samples at each plot were collected every day and the rain that occurred during the measurement period was measured by measuring the water depth in each drum. The amount of runoff in each rain event was calculated

where Rt is total runoff (dm3), Vd is the water volume in drums I and II (dm3), L= length and W = width of drum (cm), and D is the water depth in each drum (cm). The total runoff was then divided by the area of the plot (2 m × 6 m) to convert to mm. Data could be compared to a classification developed elsewhere [40] that indicates a runoff coefficient of 0.14 as adequate for Andisols, and 0.20

Soil erosion in each rain event was determined by collecting 1 dm3 of runoff sediment in each drum. The sample was filtered with "newsprint" and dried in an oven with a temperature of 105 ℃ to get the weight of the sediment (S). In earlier studies, we found that effluent from this readily available filter material had a negligible sediment concentration [41]. Erosion (E) in each rain event

= ((−1 ∗ ) + (13 ∗ (−2 ∗ ))) ∗ (

where E is soil erosion (Mg ha-1), S is sediment (g dm-3), and A is the area of the plot (m2).

Three bulk mineral soil samples were collected from each layer of soil of 0-10 cm, 10-20 cm, 20- 30 cm, 30-40, and 40-50 cm for soil texture analysis and each layer of soil of 0–10 cm, 10–20 cm, and 20–30 cm for soil bulk density, particle density, total soil porosity, and soil organic matter content.

In each plot, the water flow was collected into two collection drums with a capacity of 30 dm<sup>3</sup> . The first drum had a divider system channeling into 13 channels (PVC pipes) with equal diameters and level positions, with one connected with a second drum. The volume of water flowing from each pipe was measured to calibrate the water volume proportion entering into the second drum. The potential capacity of the runoff collector thus was (30 dm<sup>3</sup> \* 13) + 30 dm<sup>3</sup> = 420 dm<sup>3</sup> for 12 m<sup>2</sup> or 35 mm. We did nor encounter situations where the second tank overflowed. Runoff samples at each plot were collected every day and the rain that occurred during the measurement period was measured by measuring the water depth in each drum. The amount of runoff in each rain event was calculated using Equations (1) and (2):

$$\mathbf{R}\_{\mathrm{l}} = \mathbf{V}\_{\mathrm{d}-\mathrm{I}} + (\mathbf{13} \ast \mathbf{V}\_{\mathrm{d}-\mathrm{II}}) \tag{1}$$

$$\mathbf{V\_d} = 1000 \ast (\mathbf{D} \ast \mathbf{L} \ast \mathbf{W}) \tag{2}$$

where R<sup>t</sup> is total runoff (dm<sup>3</sup> ), V<sup>d</sup> is the water volume in drums I and II (dm<sup>3</sup> ), L = length and W = width of drum (cm), and D is the water depth in each drum (cm). The total runoff was then divided by the area of the plot (2 m × 6 m) to convert to mm. Data could be compared to a classification developed elsewhere [40] that indicates a runoff coefficient of 0.14 as adequate for Andisols, and 0.20 for Inceptisols.

Soil erosion in each rain event was determined by collecting 1 dm<sup>3</sup> of runoff sediment in each drum. The sample was filtered with "newsprint" and dried in an oven with a temperature of 105 ◦C to get the weight of the sediment (S). In earlier studies, we found that effluent from this readily available filter material had a negligible sediment concentration [41]. Erosion (E) in each rain event was calculated using Equation (3):

$$\mathbf{E} = \left( (\mathbf{V\_{d-1}} \ast \mathbf{S}) + (13 \ast (\mathbf{V\_{d-2}} \ast \mathbf{S})) \right) \ast \left( \frac{10^{-2}}{\mathbf{A}} \right) \tag{3}$$

where E is soil erosion (Mg ha−<sup>1</sup> ), S is sediment (g dm−<sup>3</sup> ), and A is the area of the plot (m<sup>2</sup> ).

#### *2.4. Determination of Soil Properties*

Three bulk mineral soil samples were collected from each layer of soil of 0–10 cm, 10–20 cm, 20–30 cm, 30–40, and 40–50 cm for soil texture analysis and each layer of soil of 0–10 cm, 10–20 cm, and 20–30 cm for soil bulk density, particle density, total soil porosity, and soil organic matter content. Particle size distribution (particles < 2 mm) was determined with the Bouyoucos densimeter method [42] after H2O<sup>2</sup> pre-treatment and after samples had been dispersed in 5% sodium hexametaphosphate and 5% dispersing solution. Bulk density (oven dry weight per unit volume) was measured for a block-sized sample (20 cm <sup>×</sup> 20 cm <sup>×</sup> 10 cm <sup>=</sup> 4000 cm<sup>3</sup> ) collected in field moisture conditions (modified from [43]). Particle density was measured by the pycnometer method. Total soil porosity (∅), the percentage of the total soil volume that is not filled by solid (soil) particles [44], was calculated from bulk density data and particle density using Equation (4):

$$
\omega \mathcal{D} = \left( 1 - \frac{\rho\_{\rm b}}{\rho\_{\rm p}} \right) \times 100\% \tag{4}
$$

where <sup>∅</sup> is porosity (%), <sup>ρ</sup><sup>b</sup> is bulk density (g cm−<sup>3</sup> ), and ρ<sup>p</sup> is particle density (g cm <sup>−</sup><sup>3</sup> ).

Soil organic carbon (SOC) was determined by dichromate oxidation [45]. Soil infiltration was measured by the standard double-ring infiltrometer test [46]. The double-ring infiltrometer as often constructed from a thin-walled steel pipe with inner and outer cylinder diameters of 20 and 30 cm, respectively.

The soil macro-porosity was measured using the methylene blue method, by looking at the blue distribution pattern of the methylene blue solution in the soil profile. The methylene blue solution (70 g methylene blue per 200 L of water) was gradually poured into the ground, which had been bound by a metal frame measuring 100 cm × 50 cm × 30 cm (Figure 3) and left for 36 h until the methylene blue solution soaked into the soil. Methylene blue will pass through soil macropores but be absorbed by micropores and soil surfaces. After all the methylene blue solution had disappeared from the soil surface, the top 5 cm of soil was removed from a 100 cm × 100 cm sample area and infiltration patterns were recorded, before a further 10 cm of soil was removed for a second map (at 15 cm below the soil surface), and a further 10 cm of soil for a third map (25 cm below soil surface). For mapping blue patches in each horizontal plane, a transparent sheet of plastic was placed on the surface and all visible blue patches were mapped with marker pens. Blue distribution patterns, redrawn on tracing paper, were photocopied for analysis of the black-and-white pattern of the fraction of soil involved in macropore flow with the IDRISI computer program.

#### *2.5. Other Plot Characteristics*

#### 2.5.1. Canopy Cover

The canopy cover can be defined as the percentage of tree canopy area occupied by the vertical projection of tree crowns [47]. The percentage of canopy cover is measured by scathing the shadow of sunshine at ground level using 10 m × 10 m sheets of white paper. The canopy projection when the sun was overhead was drawn to scale on white paper in each of the four quadrants of the 20 m × 20 m plots, after which the shaded areas were cut out and weighed separately. Canopy cover was calculated according to Equation (5):

$$\% \text{Canopy Cover} = \frac{W \text{ Canopy}}{W \text{ Total}} \times 100\tag{5}$$

where %Canopy Cover is the percentage of tree canopy cover, W Canopy is the paper weight representing canopy cover and W Total is the paper weight representing the total area of observation, respectively.

#### 2.5.2. Understory and Litter

Understory vegetation and litter were measured according to the rapid carbon stock appraisal protocol [48], using 50 cm × 50 cm samples for fresh weight, with subsamples dried for dry weight determination.

#### 2.5.3. Land Surface Roughness

Surface roughness was measured in each plot as the standard deviation of elevation measured every 30 cm along a thread (thin rope) installed 30 cm from the surface vertically, horizontally, and diagonally over the erosion plot [49]. The measurement of the difference in elevation was set to a pixel size of 30 cm × 30 cm. Each plot was divided into six pixels for a 2 m plot width and 20 pixels for a 6 m plot length, so there were 120 pixels (N). Pixels were made on a flat plane 30 cm from the ground point of reference with a thin rope. In each center, the pixel was measured vertically parallel to the thin rope towards the surface of the ground with a ruler. The results of the measurements of height differences in each pixel were used to calculate Ra with the equation:

$$\text{Ra} = \frac{1}{\text{N}} \sum\_{\text{n=1}}^{\text{N}} |\text{h}\_{\text{n}}| \tag{6}$$

where N = Number of pixels in the patch and h<sup>n</sup> = difference of elevation between the nth pixel in the patch and the mean value.

#### *2.6. Data Analysis*

To answer the first research question, the null hypothesis was that within the forest to open field agriculture continuum of any observed difference in soil hydrological functions could be due to random variation. To see if that null hypothesis could be rejected, we examined differences in soil infiltration, runoff coefficient, and soil erosion between the dominant land uses in the upstream and midstream with Fisher's Least Significant Differences (LSD) test. Fisher's LSD test, which establishes differences between groups defined for independent samples, was used for hypothesis testing, given that the data met the requirements for normality and the homogeneity of variances. A probability level of 0.05 was set for rejecting the null hypothesis of no difference in tests of statistical significance. We used the GenStat 15th edition software for Fisher's LSD tests. The soil infiltration, runoff coefficient, and soil erosion were then compared with the soil infiltration category [50], existing infiltration adequacy standards, and acceptable soil erosion rates. An acceptability threshold, below which soil erosion is less than an "agriculturally permissible" rate (Eapr, Mg ha−<sup>1</sup> year−<sup>1</sup> ), was derived as:

$$\mathbf{E\_{apr}} = \left(\frac{\text{Depth of soil} \ast \text{Factor of soil depth}}{\text{Time horizon}}\right) \ast \text{Soil bulk density} \tag{7}$$

Both Andisols and Inceptisols are deep (beyond 120 cm soil depth) and have a soil depth factor of 1.0. We chose 400 years as a time horizon. Given the average soil bulk density of Andisols (0.83 g cm−<sup>3</sup> ) and Inceptisols (0.99 g cm−<sup>3</sup> ), we obtained Eapr Andisol = 24.9 Mg ha−<sup>1</sup> year−<sup>1</sup> and Eapr Inceptisol 29.7 Mg ha−<sup>1</sup> year−<sup>1</sup> .

For the second research question, we tested a number of plot scale characteristics as possible indicators of "infiltration-friendly" plot characteristics: tree canopy cover, understory vegetation, litter necromass, and land surface roughness. Linear regression relationships between the surface runoff/rainfall ratio or soil erosion and the amount of rainfall, tree canopy cover, understory, litter, and land surface roughness were determined using SigmaPlot version 10.0. While a search for "explanatory" factors might have explored multiple regression, our focus was single indicators that could be used as proxies for follow-up discussions with farmers about adjusting land use.

The third research question required the analysis of data for the first two research questions, with the expectation that any thresholds for acceptable hydrological disturbance could be zone-specific, given variations in rainfall, soil type, and the specific characteristics of land use and vegetation.

#### **3. Results**

#### *3.1. Rainfall and Throughfall*

Within the measurement period, 31 rainy days were recorded (Figure 4). Rainfall variation between the upstream and midstream observation plots was relatively high, with an average of 520 mm (range 476–556 mm among 12 rain gauge measurements), and an average of 666 mm (range 541–840 mm among 12 rain gauge measurements), respectively. In the upstream and midstream areas, 71% and 57% of the rainy days had < 20 mm day−<sup>1</sup> ("light rain"), 24% and 31% had "moderate" rainfall (21–50 mm day−<sup>1</sup> ) and 6% and 13% "heavy" rain (51–100 mm day−<sup>1</sup> ), respectively; none had "very heavy rain" (>100 mm day−<sup>1</sup> ). Such rain conditions indicate that the rain erosivity in the midstream is higher than that of the upstream.

80

is higher than that of the upstream.

a. Upstream Watershed

heavy

very heavy

mm (range 476–556 mm among 12 rain gauge measurements), and an average of 666 mm (range 541– 840 mm among 12 rain gauge measurements), respectively. In the upstream and midstream areas, 71% and 57% of the rainy days had < 20 mm day-1 ("light rain"), 24% and 31% had "moderate" rainfall (21–50 mm day−1) and 6% and 13% "heavy" rain (51–100 mm day−1), respectively; none had "very

> 80 100

*Land* **2020**, *9*, x FOR PEER REVIEW 10 of 30

mm (range 476–556 mm among 12 rain gauge measurements), and an average of 666 mm (range 541– 840 mm among 12 rain gauge measurements), respectively. In the upstream and midstream areas, 71% and 57% of the rainy days had < 20 mm day-1 ("light rain"), 24% and 31% had "moderate" rainfall (21–50 mm day−1) and 6% and 13% "heavy" rain (51–100 mm day−1), respectively; none had "very heavy rain" (> 100 mm day−1). Such rain conditions indicate that the rain erosivity in the midstream

**Figure 4.** Distribution of rainfall during observation starting on March 03, 2017 in the Rejoso **Figure 4.** Distribution of rainfall during observation starting on March 03, 2017 in the Rejoso Watershed. In the upstream area, the old production forest obtained a throughfall / rainfall ratio of 0.73 (standard deviation (SD) = 0.05), while for open-field agriculture it was 0.94 (SD = 0.5) (Figure 5a).

Watershed. In the upstream area, the old production forest obtained a throughfall / rainfall ratio of 0.73 (standard deviation (SD) = 0.05), while for open-field agriculture it was 0.94 (SD = 0.5) (Figure 5a). For young production forests of *Casuarina junghuniana*-based agroforestry, the throughfall / rainfall ratio was 0.83 (SD = 0.05). In the midstream, the throughfall / rainfall ratio in agroforestry systems with tree canopies of 87%, 75%, and 52% were 0.81 (SD = 0.07) 0.81 (SD = 0.07), and 0.1 (SD = 06), In the upstream area, the old production forest obtained a throughfall/rainfall ratio of 0.73 (standard deviation (SD) = 0.05), while for open-field agriculture it was 0.94 (SD = 0.5) (Figure 5a). For young production forests of *Casuarina junghuniana*-based agroforestry, the throughfall/rainfall ratio was 0.83 (SD = 0.05). In the midstream, the throughfall/rainfall ratio in agroforestry systems with tree canopies of 87%, 75%, and 52% were 0.81 (SD = 0.07) 0.81 (SD = 0.07), and 0.1 (SD = 06), respectively (Figure 5b). For agroforestry with low cover (26%), the throughfall/rainfall ratio was 0.96 (SD = 0.01). For young production forests of *Casuarina junghuniana*-based agroforestry, the throughfall / rainfall ratio was 0.83 (SD = 0.05). In the midstream, the throughfall / rainfall ratio in agroforestry systems with tree canopies of 87%, 75%, and 52% were 0.81 (SD = 0.07) 0.81 (SD = 0.07), and 0.1 (SD = 06), respectively (Figure 5b). For agroforestry with low cover (26%), the throughfall / rainfall ratio was 0.96 (SD = 0.01).

0.6 **Figure 5.** The throughfall / rainfall ratio variability in measured runoff plots a) upstream, b) **Figure 5.** The throughfall/rainfall ratio variability in measured runoff plots (**a**) upstream, (**b**) midstream.

UT1 UT2 UT3 UT4

Plot measurements

MT1 MT2 MT3 MT4

#### midstream. *3.2. Soil Properties*

**Figure 5.** The throughfall / rainfall ratio variability in measured runoff plots a) upstream, b) midstream. *3.2 Soil Properties 3.2 Soil Properties* The Andisols in the upstream area had a 40–60% silt fraction in all soil measured layers; the Inceptisols had a higher clay fraction (Appendix A). The upstream area had a lower bulk density and higher soil porosity, with a lower clay content than the midstream area (Table 2). The soil organic carbon content varied from 0.65 to 2.12%.



