Cloud seeding is generally used to secure additional water resources, which is not an easy goal to achieve, as the spatial variability of rainfall is high. Instead, the increased rain may moisten the neighboring forest. This study focuses on this situation and estimates the possible increase in the net primary production (NPP) due to cloud seeding. This study considers the Boryeong Dam basin in Korea as a study area and uses the Carnegie–Ames–Stanford Approach (CASA) model to estimate the NPP at 8-day intervals. As a result, first, the increase of the current 8-day NPP is greater when the rainfall amount during the last 16-day period is 50 mm or more. The mean increase of the 8-day NPP is estimated at about 1.873 g/m2 of carbon. Second, the increase of the NPP with the target 16-day rainfall of 50 mm is estimated at about 3%, which is about 4% with the target 16-day rainfall of 100 mm. Simply extrapolating the derived result to the entire forest in Korea, the increased carbon accumulation can be extended to about 0.6 and 0.8% of the total carbon emission in 2018, respectively. These amounts correspond to about 1.2 and 1.5% of the target amount of carbon reduction by 2030 in Korea.

  • Cloud seeding can contribute the carbon accumulation by increasing the net primary production (NPP).

  • About 50 mm of antecedent 16-day rainfall was found effective in increasing the NPP.

  • More than 1% of the target CO2 reduction in Korea can be achieved by effective and national-scale cloud seeding.

The net primary production (NPP) is defined as the net accumulation of organic matter per unit of time and space due to the photosynthesis of green plants (Yu et al. 2009). The NPP is calculated by simply subtracting the ecosystem respiration (RE) from the gross primary production (GPP). The GPP is the total productivity of plants' photosynthesis, while the RE represents the total energy consumption during the process of respiration. That is, the NPP can also be explained as the total energy accumulated in plants by photosynthesis, subtracted by the energy used for their growth and maintenance. The NPP is also used to explain the exchange of CO2 between the atmosphere and ecosystem (Prentice et al. 2001). Traditionally, the NPP is an important indicator to find the sink in the carbon cycle of an ecosystem, and is also used as an indicator to represent food productivity (Zhang et al. 2016; Jiao et al. 2018; Li et al. 2019; Wang et al. 2019). The change in the NPP is also frequently used to evaluate the effect of climate change (Zhang et al. 2018), where the data generated based on several IPCC scenarios are used to estimate the possible change of carbon balance in the future ecosystem (Ito 2010).

The NPP is basically a part of the ecosystem's carbon balance, which has been analyzed by applying various methodologies. The biological measurement method (Son et al. 2004), carbon-flux measurement method based on the Eddy-covariance technology (Saigusa et al. 2005), a method based on bio-physical modeling (Ito & Oikawa 2002; Sasai et al. 2007), and method based on the analysis of remote sensing (RS) data, including satellite observations (Running et al. 2004), are among those methods. Each method has its own advantages and disadvantages, along with its unique characteristics, like spatial-temporal resolution, data structure, and data availability. For this reason, it is not easy to conclude the superiority of any one method over others. As the measurement of NPP is a difficult, expensive and time-consuming job, it is not surprising that the use of RS data is becoming preferable (Hadian et al. 2019).

Several methods are available for NPP estimation. Based on Pei et al. (2018), these models may be divided into three groups: the climate relative model, process model, and light use efficiency (LUE) model. The climate relative model includes the Miami Model (Lieth & Whittaker 1975), the Thornthwaite Memorial Model (Zhu 1993), and the Chikugo model (Zhou et al. 1998), etc. The process model includes the Integrated Terrestrial Ecosystem model (Raich et al. 1991), the Biosphere Simulator model (Foley et al. 1996), the Boreal Ecosystem Productivity Simulator (Liu et al. 1997), the Biome–BGC model (White et al. 2000), etc. The LUE models include the Carnegie–Ames–Stanford Approach (CASA) (Potter et al. 1993; Field et al. 1995), carbon fixation model (C − Fix model; Veroustraete et al. 1994, 2002), and MODIS NPP (Running et al. 2004). Among these, the CASA model simulates the carbon exchange processes in the terrestrial biosphere and atmosphere (Cramer et al. 1999). This model has been successfully applied to estimate the regional, continental, and global scale NPP (Jay et al. 2016). For example, Potter et al. (2006) applied the CASA model to estimate and evaluate the NPP in the US with the available RS data.

Different from the RE, photosynthesis is very sensitive to light intensity, CO2 concentration, and temperature. Generally, photosynthesis is proportional to the light intensity and CO2 concentration. However, there must be some threshold values. Above these thresholds, photosynthesis stagnates. For example, photosynthesis increases much up to the temperature of 40 °C; after that, it also decreases much (Crafts-Brandner & Salvucci 2002). It is also important to understand that each factor works as a limiting factor. For example, photosynthesis is not active in low-temperature conditions, even though the CO2 concentration is high. It is also the same in the opposite situation.

Along with the factors mentioned above, the soil moisture content is also an important factor in controlling photosynthesis (Jones 2004; Pereira et al. 2006). The effect of rainfall, as a source of soil moisture, on photosynthesis has also been evaluated in many studies (Hao et al. 2010; Xu & Wang 2016; Linger et al. 2020). For example, Hao et al. (2010) showed that the CO2 exchange in the temperate steppe is very dependent on the rainfall amount and timing. Xu & Wang (2016) confirmed the positive effect of rainfall on the increase of NPP. The effect of rainfall seasonality on the temporal variability of NPP was also found in this study. A similar result was also derived by Linger et al. (2020) for the estimation of NPP in tropical forests.

The relative role of temperature and rainfall has also been an interesting issue. For example, Sun et al. (2018) compared the effect of land cover change, riding CO2, temperature increase, and water condition change on the GPP. Their results show that, even though the GPP change at middle and high latitudes is dominated by the temperature rise, the NPP change in the low latitude is more governed by the water condition. A similar study in India (Bejagam & Sharma 2022) also shows that the NPP is more dependent on the increase of rainfall rather than the temperature under climate change situations. Zhang et al. (2022) compared the relative roles of temperature and rainfall on the NPP in the Dajiuhu Basin, China. Their finding indicates that the temperature was the more dominant factor on the NPP during the wet summer season, but the rainfall played a key role in the rather dry spring and fall seasons. They also mentioned that the overall contribution of rainfall on the NPP has recently become more significant than the temperature.

Water stress must have a negative impact on the NPP (Zhang et al. 2021), and thus the soil moisture content shows a strong positive correlation to the NPP (Yue et al. 2022). As a result, in the drought period, the NPP becomes significantly smaller (Arneth et al. 1998; Nanzad et al. 2021). Churchill et al. (2015) also showed that the drought has more impact on the vegetation abundance in the peatland than sustained flooding. MODIS GPP/NPP products are also generated by considering the soil water stress (Mu et al. 2007). Obviously, after the drought is over, the NPP recovers fast. Proper water supply to the plants during a drought can thus be very helpful to increase the NPP.

This study focuses on the possible increase of the NPP due to the increased rainfall by cloud seeding. Cloud seeding is generally tried to secure additional water resources (Silverman & Sukarnjanaset 2000; Maryadi et al. 2015; Pokharel et al. 2021). However, as the affecting range of cloud seeding is wide, increased rain by cloud seeding cannot easily be collected. On the other hand, it simply moistens the neighboring forest. This study focuses on the unexpected contribution of cloud seeding to the increase in the NPP. The dependency of the NPP on soil moisture must be primary information to derive the estimation of the NPP increase. The Boryeong Dam basin is considered a study area, which is a small dam known to be very vulnerable to drought. The CASA model is used in the estimation of the NPP. As a result, it may also be possible to estimate the increase of CO2 reduction, as the NPP can directly be converted into carbon accumulation.

The CASA model

The CASA is a model for estimating the NPP in a region. As the CASA model is an algorithm based on the physical theory of carbon circulation processes on the land surface soil and vegetation, it is easy to apply for the estimation of the NPP and carbon accumulation. The input data of the CASA model can also be easily prepared with some satellite information. The CASA model is composed of three sub-models: soil moisture, NPP, and soil carbon and nitrogen. In particular, the sub-model NPP is designed to use the LUE to calculate the NPP (Monteith 1972). The LUE represents the ratio of absorbed solar radiation to be used in plant photosynthesis to produce organic matter. The LUE is also a key factor in estimating the light absorption and carbon absorption of plants.

To apply the CASA model, it is required to use data, such as the total solar radiation (SOL), the water stress coefficient (WSC), and the fraction of absorbed photosynthetically active radiation (FPAR). Temperature stress factors are also important, which include the temperature at which photosynthesis is most active (temperature 1; ), the temperature at which the light use is most efficient (temperature 2; ), and the maximum possible efficiency of photosynthesis. Among those data, the FPAR and the maximum possible efficiency can be derived by RS data analysis. Tɛ1 and Tɛ2 can be derived by interpolating the temperature data on the land surface. The SOL, the most important driving force of the CASA model, can be estimated by the Angstrom–Prescott equation (Prescott 1940), or the solar radiation flux (SolarFlux) model (Rich et al. 1995). It is also possible to use the measured data on the Earth.

The WSC, another driving force of the CASA model, indicates the available moisture content. The WSC can be estimated by the ratio of the actual to potential evapotranspiration amount, factors which are included in the soil moisture sub-model of the CASA model. This sub-model requires soil information like soil type and soil depth, as well as meteorological data like temperature and precipitation amount. However, it is also possible to use the FAO Penman–Monteith equation (Allen et al. 1998) to directly estimate the potential evapotranspiration amount, and the complementary relationship of evapotranspiration of Bouchet (1963) or the Pike equation (Pike 1964) to calculate the actual evapotranspiration amount. The choice is dependent upon the available ground observation data. As the CASA model requires continuous data in time and space, it generally requires interpolation, which can be another source of error.

Calculation procedure of NPP by the CASA model

In the CASA model, the NPP is calculated by multiplying the absorbed photosynthetically active radiation (APAR) by the LUE (), i.e.,
formula
(1)
The APAR is also calculated with the following equation:
formula
(2)
where SOL represents the solar radiation (MJ/m2), and FPAR represents the fraction of absorbed photosynthetically active radiation with its range of 0–1. The constant 0.5 represents the ratio of effective radiation to be used for plant photosynthesis.
The LUE () is then calculated by the following equation:
formula
(3)
where represents the maximum value of LUE under the ideal situation, while is also known to be dependent on the vegetation type. The deciduous trees, the dominant plant in the study area (more than 80%), have a value of of 0.69 (Zhu et al. 2006). Also, and are the temperature stress factors, while is the water stress factor. To calculate these factors, several data are required, including the active and potential evapotranspiration amounts, precipitation amounts, and net radiation.
However, in this study, a somewhat easier equation by Xiao et al. (2005) was used to estimate the LUE (), i.e.,
formula
(4)
where , , and are effect indices representing temperature, available water, and plant growth, respectively. First, is calculated using the minimum (), maximum (), and optimal () temperature for the growth of a plant in a given soil type:
formula
(5)

The maximum, minimum, and optimal temperatures of some plant types can be found in Raich et al. (1991). In particular, for the deciduous as the representative plant in the study area, the maximum, minimum and optimal temperatures are 48, 0, and 26 °C, respectively. Also, the land surface temperature (LST) available in the MODIS data was used for the temperature T in the above equation.

The is calculated using the land surface water index (LSWI), which can be estimated using the MODIS reflection data, i.e.,
formula
(6)
where and represent the MODIS Band 2 reflectivity in the near-infrared range of (841 − 845) nm and the MODIS Band 6 reflectivity in the short-wave infrared range of (1,628 − 1,652) nm, respectively. Using the LSWI, the is then calculated as follows:
formula
(7)

The LSWI in this equation has the range of −1 to 1, while indicates the maximum during the growing period.

Finally, is dependent upon the leaf growth period. During the growing period, , but in the non-growing period, it is calculated as follows:
formula
(8)

Study area

The Boryeong Dam basin was selected as the study area in this study. The Boryeong Dam, a multi-purpose dam, is located in the western part of the Korean Peninsula. The major role of the Boryeong dam is to supply domestic and industrial water, but it still has some flood control volume (Yoon et al. 2019). The annual rainfall amount around Boryeong Dam is about 1,200 mm, and the annual mean temperature is 12.7 °C. The basin area of the Boryeong Dam is just 163.6 km2, which is small, compared to the other areas of multi-purpose dams in Korea. The surface area of the Boryeong Dam reservoir is about 5.8 km2, and the planned annual water supply is 106.6 × 106 m3 (CNI 2016). As a small dam with a small dam reservoir, the Boryeong dam is also vulnerable to drought. Several water shortage problems occurred in the 2010s (CNI 2016). Figure 1 shows the location and the shape of the basin.
Figure 1

Location of the Boryeong Dam basin and the basin shape.

Figure 1

Location of the Boryeong Dam basin and the basin shape.

Close modal

Data

This study collected the MODIS product (https://modis.gsfc.nasa.gov/data/dataprod/) and daily radiation data at the weather station near the Boryeong Dam from the Korea Meteorological Administration (KMA; https://data.kma.go.kr/). These data were all applied to the CASA model to calculate the NPP. MODIS sensors are installed in the Terra and Aqua satellites launched for the Earth Observing System (EOS). MODIS sensors include a total of 36 spectral bands, which provide detailed information about the earth's environment, including the land, ocean and atmosphere (Vermote & Vermeulen 1999; Wan 1999). The MODIS data used in this study are the surface reflectance (SR), LST, and fraction of photosynthetically active radiation (FPAR). The SR data have a resolution of 500 m, which is provided at intervals of 8 days (d) from January 1. The FPAR data are also provided with the same spatial resolution and time interval. However, the LST data have a spatial resolution of 1 km and are provided in the same way.

The daily radiation data and rainfall data collected at the Hongseong weather station were used in this study. The Hongseong weather station is the nearest station to the Boryeong Dam basin. Even though there are other rain gauge stations within the Boryeong Dam basin, they were excluded, as it was important to use both daily rainfall and daily radiation data observed at the same location in the derivation of their relation. Figure 2 compares the daily radiation and rainfall data, both of which have the same strong seasonality. The annual mean daily radiation at Hongseong weather station is about 15 MJ/m2, with a maximum value of about 30 MJ/m2 or more. The maximum daily radiation is observed in July. On the other hand, the minimum value is observed in January, which is generally smaller than 5 MJ/m2. Zero or near zero values could also be found, which are mostly due to the effect of rainfall. The annual rainfall at Hongseong weather station is about 1,300 mm, being (1,299.0, 922.3, and 1,609.7) mm in 2018, 2019, and 2020, respectively. The frequency of rainfall is very high during the wet summer season, and low during winter (annual rainy days is about 96). About two-thirds of the annual rainfall is concentrated in the wet summer season. As the MODIS data are available at 8-day intervals, the daily radiation and rainfall data were also averaged over the same 8-day interval, to be used for further analysis.
Figure 2

Time series plots of daily solar radiation (a) and daily rainfall (b) collected at the Hongseong weather station from 2018 to 2020.

Figure 2

Time series plots of daily solar radiation (a) and daily rainfall (b) collected at the Hongseong weather station from 2018 to 2020.

Close modal

Calculation and correction of the 8-day NPP

Both the APAR from the MODIS data and the LUE () were applied to the CASA model to calculate the 8-day NPP. Also, the 8-day MODIS GPP and annual MODIS NPP data were collected to be used to evaluate the calculated NPP. The annual MODIS NPP is calculated as the annual sum of 8-day GPPs, subtracted by the annual respiration amount. Figure 3 shows the spatial distribution of the 8-day GPPs and NPPs over the Boryeong Dam basin in several selected months. In the year 2018, the annual sum of 8-day NPPs calculated in this study showed about a 10% difference from the annual MODIS NPP. Based on this result, the calculation of the 8-day NPP was assumed acceptable.
Figure 3

Spatial distribution of 8-day GPP (a) and 8-day NPP (b) over the Boryeong Dam basin (from left, Feb. 25–Mar. 4, May 25–Jun. 1, Aug. 29–Sep. 5, and Nov. 25–Dec. 2).

Figure 3

Spatial distribution of 8-day GPP (a) and 8-day NPP (b) over the Boryeong Dam basin (from left, Feb. 25–Mar. 4, May 25–Jun. 1, Aug. 29–Sep. 5, and Nov. 25–Dec. 2).

Close modal

The calculation of the 8-day NPP was also repeated for the years 2019 and 2020. However, in these 2 years, the annual sums of the 8-day NPPs were found to be far smaller than the annual MODIS NPPs. This underestimation was mainly due to the very small daily radiation values, missing LSTs and very small FPARs during rainy days. It is true that while rainfall may decrease the daily radiation, LST and FPAR, the rainy day does not lead to extremely small radiation, LST or FPAR.

To solve this issue, this study tried to find a connection between the daily rainfall amount and these factors (Figure 4). First, the daily radiation during rainy days was investigated to derive the possible link between the decrease in daily radiation, and the daily rainfall amount. As can be seen in Figure 4(a), the increase in daily rainfall has a significant effect on the decrease in daily radiation. The daily rainfall seems to decrease the daily radiation, but their relation is not so clear. Substantial decrease of daily radiation was also found even when the rainfall amount was very small. Figure 4(b) also shows how the LST decreases, depending on the daily rainfall amount. However, their relation was also not so clear. Figure 4(c) shows the distribution of FPAR with respect to the daily rainfall. Basically, it is interesting to see the bi-modal shape of their distribution. However, in this case, their dependency is not so clear. Even the rainfall amount does not seem to have much effect on the FPAR. Rather, it is true that when the rainfall amount is large, many small values are found. All of these small values directly lead to very small NPPs.
Figure 4

Effect of daily rainfall on the change of radiation (a), LST (b), and FPAR (c).

Figure 4

Effect of daily rainfall on the change of radiation (a), LST (b), and FPAR (c).

Close modal

Based on the findings in Figure 4, the somewhat abnormal values of daily radiation, LST, and FPAR were corrected. First, the extremely small or zero daily radiation values were replaced by the neighboring normal values. The mean decrease of daily radiation in Figure 4(a) was used to correct those small or zero values. Second, the missing LST values on rainy days were filled by the normal values in neighboring cells. Finally, abnormally small FPAR values were also replaced by the normal value observed before or after the rainy day in the same cell. However, even after correcting these factors, the annual sum of the calculated 8-day NPPs was still much smaller than those annual MODIS NPP values. In the year 2020, the difference was more than 20%. However, this study used these calculated 8-day NPPs for further analysis. At this time, this calculated 8-day NPP is the maximum information the authors could obtain.

Evaluation of NPP in the Boryeong Dam basin

Figure 5 shows the annual variation of the 8-day MODIS GPP and 8-day NPP calculated by the CASA model in this study. Additionally, the 8-day rainfall data is included in this figure to show its impact on the GPP and NPP. Figure 5(a), which shows the result for the year 2018, does not contain any corrected NPPs. The overall trends of GPP and NPP are quite similar to each other. Even though the GPP is generally higher than the NPP, an exception was found at the interval of August 12 − 20. In this interval, the daily radiation and FPAR were all found to be abnormally small. Regardless of this abnormal case, the annual NPP in this study was found to be very similar to the annual MODIS NPP.
Figure 5

Time series plots of 8-day GPP, NPP, and rainfall amount in 2018 (a), 2019 (b), and 2020 (c).

Figure 5

Time series plots of 8-day GPP, NPP, and rainfall amount in 2018 (a), 2019 (b), and 2020 (c).

Close modal

It is also possible to find the effect of rainfall on the GPP and NPP. When the rainfall amount is large, both the GPP and NPP drop significantly. It is normal that the daily radiation decreases during rainfall, as it is reflected by the rain cloud. Decreased daily radiation results in decreased photosynthesis. However, it recovers fast after the rain stops.

However, in the years 2019 and 2020, there were many abnormal values, which were all corrected by the method explained in the previous section (Figures 5(b) and 5(c)). The original NPP values are marked by empty triangles, and the corrected ones by solid triangles. After correcting these abnormal values, the overall pattern of the 8-day NPPs was found to become more similar to that of the 8-day GPPs. The effect of rainfall was also found to be more reasonable, as in the case of the year 2018.

Additionally, in this study, the ratio between the annual GPP and annual NPP was derived to check if the calculated 8-day NPPs in this study were acceptable or not. Based on Zhang et al. (2009), the ratio of global NPP to global GPP fluctuated around 0.51–0.52 in the years from 2000 to 2003. The annual variation was not so large. However, as can be seen in Table 1, the ratio of the annual sum of 8-day NPPs in this study to the annual sum of 8-day GPPs was far smaller than 0.5. This ratio is near 0.5 if considering the annual MODIS NPP. The estimates in this study were smaller than the MODIS NPP. In particular, the NPP in the year 2020 was much smaller.

Table 1

Comparison of MODIS GPP, MODIS annual NPP, and annual NPP in this study and their ratios to the MODIS GPP

YearProductMODIS GPP (kg/m2 of carbon)MODIS NPP (kg/m2 of carbon)NPP (this study) (kg/m2 of carbon)
2018 Average 1.39 0.67 0.62 
NPP/GPP – 0.48 0.45 
2019 Average 1.42 0.68 0.61 
NPP/GPP – 0.48 0.43 
2020 Average 1.49 0.76 0.53 
NPP/GPP – 0.51 0.36 
YearProductMODIS GPP (kg/m2 of carbon)MODIS NPP (kg/m2 of carbon)NPP (this study) (kg/m2 of carbon)
2018 Average 1.39 0.67 0.62 
NPP/GPP – 0.48 0.45 
2019 Average 1.42 0.68 0.61 
NPP/GPP – 0.48 0.43 
2020 Average 1.49 0.76 0.53 
NPP/GPP – 0.51 0.36 

Effect of soil moisture conditions on the NPP

Soil moisture is very dependent upon rainfall. A large amount of rainfall increases the soil moisture a lot. But, after several days, the soil can be drier as the water in the soil percolates into the groundwater. When calculating the infiltration amount in a rainfall–runoff analysis, the antecedent 5-day rainfall amount is generally considered to determine if the soil condition is dry, normal or wet (Kang & Yoo 2020). The maximum antecedent 3-month rainfall is sometimes considered when evaluating agricultural drought (Kim et al. 2011). In this study, the possible dependency of 8-day NPP on the soil moisture was evaluated using three different rainfall data: the first is the rainfall amount accumulated for the current 8-day period, the second accumulated for the last 8-day period, and the third accumulated for the last two 8-day periods (i.e., the last 16 days). The use of the 8-day interval was to consider the 8-day NPP and 8-day GPP. Also, as the soil depth in Korea is very shallow, generally less than 1 m (Kwon et al. 2021), and the soil is easily dried up, the last 16 days (i.e., two 8-day intervals) was assumed to be sufficient antecedent days to estimate the soil moisture condition.

In this study, only the increased cases of NPP were considered in the impact evaluation of soil moisture. In fact, if the rainfall at the present 8-day interval is large, the NPP at the present 8-day interval can be smaller. However, this rainfall can contribute much to the increase of NPP at the next 8-day interval and/or at the next two 8-day intervals. By considering only the increased cases, the pure effect of current soil moisture or antecedent rainfall could be derived. Figure 6 compares three scatter plots regarding these three cases. As can be seen in this figure, the NPP shows an increasing trend with the rainfall. This increasing trend was found in all three cases considered in this study. However, it was also found that the current 8-day NPP dropped considerably with large current 8-day rainfall. This case is easily understood, as rainfall can decrease the solar radiation to the ground. Its effect can be significant in the case where the rainfall amount and duration are large. However, it is also true that the effect of rainfall is not so consistent. This inconsistency may be due to the 8-day interval for NPP calculation. As the rainfall does not continue for the entire 8 days, or the rainfall can be during the nighttime, its effect cannot be so consistent. It is also possible for the rainfall to contribute to the NPP increase by increasing the soil moisture, which is especially true if the rainfall duration is short. If the rainfall duration is long, its negative effect on the NPP must be significant. All these cases can be found in Figure 6(a). On average, it is not possible to conclude any increase or decrease in the NPP depending on the rainfall amount. A clearer positive relation can be found in the two 8-day NPP cases. That is, with just a few exceptions, the current 8-day NPP was found to be positively related to the rainfall amount during the last two 8-day intervals, i.e., the last 16 days. The effect of rainfall during the last 8-day interval was in between the other two cases. This result indicates that the current 8-day NPP can be increased a little bit by increasing rainfall during the last two 8-day intervals.
Figure 6

Change of the NPP with respect to the rainfall amount in the current 8-day interval (a), in the last 8-day rainfall (b), and in the last two 8-day intervals (in the year of 2018).

Figure 6

Change of the NPP with respect to the rainfall amount in the current 8-day interval (a), in the last 8-day rainfall (b), and in the last two 8-day intervals (in the year of 2018).

Close modal

The above-derived results are dependent on the time scale. In cases where the monthly data were analyzed, the vegetation condition was found to be highly correlated with the 1- or 2-month-lagged rainfall. For example, García et al. (2010) investigated the lagged effect of rainfall on the vegetation greenness in the evergreen forest in central California. They confirmed that the monthly NDVI was highly correlated with the 1-month-lagged rainfall. Davenport & Nicholson (1993), Richard & Poccard (1998) reported a similar result that the monthly NDVI data were highly correlated with the 1- or 2-month-lagged monthly rainfall in the regions of Eastern and Southern Africa. Chandrasekar et al. (2006) also showed that the monthly NDVI in India was highly correlated with the 2-month-lagged rainfall.

When considering different time scales, such as the 8-day interval in this study, a few different results were also derived. For example, Li et al. (2013) showed that there was an 8- to 16-day lag time between the rainfall event and the NDVI response in the temperate desert region of northwest China. Derived by analyzing the 8-day data, this result is very close to the result in this study. Interestingly, Li et al. (2013) also suggested that the threshold rainfall for a large NDVI response could be about 30 mm. A similar analysis but with the daily data by Fan et al. (2016) showed that there was a lag of 10.2 ± 6.5 days in the desert steppe between the NPP and rainfall. Their suggestion of a rainfall threshold of 80.2 mm is also similar to that considered in this study. Regardless of the different climate conditions, the threshold rainfall seems to be around 50 mm, such as derived in this study.

Possible increase of NPP by cloud seeding

Figure 7 reinterprets Figure 6(c) by showing both the absolute and relative increases of NPP. In particular, this figure shows that the increase of the current NPP is greater when the last 16-day rainfall amount is ≥50 mm. Especially in the range between 50 and 100 mm, the mean increase of NPP is about 1.87 g/m2 of carbon. Regardless of several cases of NPP decrease, many more cases show a higher increase in NPP due to rainfall. Due to the lack of data, in the case of rainfall more than 100 mm, no significant result could be derived. However, this overall trend seems to remain in this range of 16-day rainfall amounting to 100 mm or more.
Figure 7

Effect of the antecedent 16-day rainfall on the increase of current 8-day NPP in 2018 shown in absolute amount (a) and relative change (b).

Figure 7

Effect of the antecedent 16-day rainfall on the increase of current 8-day NPP in 2018 shown in absolute amount (a) and relative change (b).

Close modal

This study estimated the increase of NPP due to the increase of the last 16-day rainfall by cloud seeding. Even though the role of cloud seeding is limited, it was assumed possible to make the last 16-day rainfall be 50 or 100 mm. In 2018, 2019, and 2020, the number of cases with the last 16-day rainfall amount <50 mm was found to be 27, 22, and 29, respectively, while those within the range between 50 and 100 mm were 11, 8, and 7, respectively. However, as the NPP increase is minimal in winter, early spring, and late fall, this study considered only the growing season from May to October. As a result, in 2018, 2019, and 2020, the cases considering the cloud seeding with the target rainfall amount of 50 mm became smaller, to 11, 4, and 8, respectively. In 2018, 2019, and 2020, additional cases to be considered for cloud seeding with the target rainfall amount of 100 mm were 3, 6, and 4, respectively. Table 2 summarizes the possible number of cases for cloud seeding for NPP increase.

Table 2

Number of cases with the rainfall amount during the last two 8-day intervals (i.e., 16 days) less than 50 mm (<50 mm), that with the rainfall amount between 50 and 100 mm (50–100 mm), and that with the rainfall amount over 100 mm (<100 mm) from May to October

YearAnnual rainfall (mm)#Rainy days#Cases (<50 mm)#Cases (50–100 mm)#Cases (<100 mm)
2018 1,457.9 93 11 14 
2019 992.0 98 10 
2020 1,624.7 114 12 
YearAnnual rainfall (mm)#Rainy days#Cases (<50 mm)#Cases (50–100 mm)#Cases (<100 mm)
2018 1,457.9 93 11 14 
2019 992.0 98 10 
2020 1,624.7 114 12 

Table 3 shows the effect of cloud seeding on the increase of NPP. As explained earlier, by cloud seeding, the rainfall amount during the last two 8-day intervals was assumed to be increased to meet the target rainfall of 50 or 100 mm. On average, in the case where the target rainfall is 50 mm, the cloud seeding should increase the rainfall by 36.27 mm. This is just 29.17 mm to satisfy the target rainfall of 100 mm for those cases with a rainfall amount between 50 and 100 mm. If considering the 16-day period for cloud seeding, the 16-day rainfall of 50 mm must be an easy target to achieve.

Table 3

Estimated amounts of the NPP increase due to cloud seeding depending on the 16-day target rainfall of 50 and 100 mm

YearTarget 16-day rainfall (mm)NPP increase
gC/m2/year%
2018 50 20.60 3.33 
100 26.22 4.24 
2019 50 8.91 1.47 
100 22.28 3.66 
2020 50 14.02 2.67 
100 21.02 4.00 
YearTarget 16-day rainfall (mm)NPP increase
gC/m2/year%
2018 50 20.60 3.33 
100 26.22 4.24 
2019 50 8.91 1.47 
100 22.28 3.66 
2020 50 14.02 2.67 
100 21.02 4.00 

In 2018, the possible increase of NPP with the target 16-day rainfall of 50 mm was estimated to be about 20.60 g/m2 of carbon. This amount corresponds to a 3.33% increase in the annual NPP. In 2019 and 2020, it was 8.91 and 14.02 g/m2 of carbon (1.47 and 2.67%), respectively. If considering the additional cases with the target rainfall of 100 mm, then the increase of NPP in 2018, 2019, and 2020 becomes 26.22, 22.28, and 21.02 g/m2 of carbon (4.24, 3.66, and 4.00%), respectively. On average, by cloud seeding with the target 16-day rainfall of 50 mm, about a 3% increase in annual NPP was found to be available, while with the target 16-day rainfall of 100 mm, about a 4% increase in annual NPP was found available.

In fact, this small increase in NPP corresponds to a large amount of carbon accumulation. In the case of considering only the Boryeong Dam basin, the amount of carbon accumulation can easily be calculated by multiplying the carbon accumulation per unit area by the basin area. In 2018, the NPP per unit area was 0.62 kg/m2/year of carbon, which if considering the basin area of 163.6 km2, corresponds to the carbon accumulation of 101,292,940 kg/year in the Boryeong Dam basin.

It is also possible to derive the increase of carbon accumulation in the Boryeong Dam basin. As the carbon accumulation is proportional to the NPP, it is easy to estimate the increase in carbon accumulation. In 2018, in the case that the target 16-day rainfall amount is 50 mm, the carbon accumulation in the Boryeong Dam basin can be increased up to 104,663,591 kg/a. In the case of considering additional cases with the target rainfall amount of 100 mm, the carbon accumulation becomes 105,582,859 kg/year. Similar to the results in Table 3, in the years 2019 and 2020, about a 4% increase in annual carbon accumulation could also be expected.

Similar examples confirm that about a 4% increase in NPP by cloud seeding seems fairly realistic. In fact, several studies have reported that additional rainfall can contribute much to increasing the NPP. Those results were derived through simulation methods or field experiments. As an example of a simulation study, Shi et al. (2014) showed, in their analysis with the terrestrial ecosystem model (TECO), that NPP was reduced significantly by extreme droughts (67% reduction of annual rainfall) at all four study sites located within the Great Plains, USA. The sensitivity of NPP to drought was also found to be directly attributable to rainfall amount. Wilcox et al. (2015) compared the above- and below-ground NPP after increasing the rainfall amount by 50%. They noticed that larger rainfall events contribute much to increasing the NPP. Heisler-White et al. (2008) also showed that the increase in rainfall amount by 15% in a semi-arid grassland increased the NPP by about 6% on average. Especially, a few larger rainfall events were found to contribute more than many small rainfall events.

Even though just a few cases are available, field experiments also derived similar results. For example, Chou et al. (2008) reported a 4-year experiment result at the Sierra Foothill Research and Extension Center in California. Their result shows that there was a significant positive relationship between the NPP and annual rainfall that about a 5% increase of NPP was expected with the annual rainfall increase by 10%. Also, in a long-term rainfall manipulating experiment in the northern Chihuahuan Desert, USA, Báez et al. (2013) showed that chronic drought had a higher impact than water addition. During the drought, the vegetation cover was significantly decreased, but water addition slightly decreased the vegetation coverage.

About a 4% increase in NPP by cloud seeding can have another important meaning in Korea. The national target for carbon reduction in 2030 of Korea is 40% of the total carbon emission in 2018. Based on the National Greenhouse Gas Statistics in Korea (MOE 2022), the total amount of greenhouse gas emissions in 2018 was 727,000,000 ton-eq/year of CO2, which, if considering only the carbon, also corresponds to 198,272,727 ton/year. That is, the target amount of carbon reduction becomes 107,067,273 tons/year.

It was shown that by applying cloud seeding, the amount of carbon accumulation can be increased. If considering the target rainfall of 50 and 100 mm during the last 16 days, the increased carbon accumulation could be 3,373 and 4,292 tons/year, respectively. These increased amounts are just 0.0017 and 0.0022% of the total carbon emissions in 2018. However, if simply extrapolating this result to the entire forest in Korea (i.e., 62,900 km2), the increased carbon accumulation can be extended up to 1,295,740 and 1,649,238 ton/a, respectively, depending on the target rainfall amount. These amounts correspond to 0.65 and 0.83% of the total carbon emissions in 2018. Or, these amounts correspond to 1.21 and 1.54% of the target amount of carbon reduction in 2030.

Cloud seeding is generally tried to secure additional water resources, but, due to its high spatial variability, it is not easy to collect the increased rain. On the other hand, the increased rain moistens the neighboring forest. This study focused on this situation and estimated the possible increase of NPP due to cloud seeding. The Boryeong Dam basin in Korea was considered a study area, which is a small dam basin that is highly vulnerable to drought. The CASA model was considered in the estimation of the NPP. The results may be summarized as follows:

First, the 8-day NPP was calculated by the CASA model. The annual sum of 8-day NPPs calculated in this study was about 10% smaller than the annual MODIS NPP in the year 2018. However, the calculated annual NPPs in this study for years 2019 and 2020 were found to be much smaller than the annual MODIS NPPs. This was mainly due to some abnormal factors observed on rainy days. These abnormal factors were corrected by considering the effect of the rainfall amount and the corrected factors were then used to re-estimate the 8-day NPPs.

Second, the current 8-day NPP was found to be increased with the rainfall amount during the last two 8-day intervals (i.e., the last 16 days). It was especially clear that the increase of the current 8-day NPP was greater when the rainfall amount during the last two 8-day intervals was ≥50 mm. The mean increase of the 8-day NPP was estimated to be about 1.873 g/m2 of carbon.

Third, the possible increase of NPP with the target 16-day rainfall of 50 mm was estimated to be about 20.60 g/m2 of carbon in 2018. This amount corresponds to a 3.33% increase in annual NPP. In 2019, it was rather small at 1.47%, and, in 2020, it was 2.67%. If considering additional cases with a target rainfall of 100 mm, then the increase of NPP became 26.22 g/m2 (4.24%) of carbon in 2018, and 3.66 and 4.00% in 2019 and 2020, respectively. On average, about a 3% increase in annual NPP was found available by cloud seeding with the target 16-day rainfall of 50 mm, and about a 4% increase in annual NPP was found available with the target 16-day rainfall of 100 mm.

The above-mentioned result can be directly converted into the increase in carbon accumulation due to cloud seeding. If simply extrapolating the derived result to the entire forest in Korea (i.e., 62,900 km2), the increased carbon accumulation could be extended to 0.65 and 0.83% of the total carbon emission in 2018. These amounts also correspond to 1.2102 and 1.54% of the target amount of carbon reduction in 2030 in Korea. It is obvious these last estimates could not be achieved easily, mainly due to the large coverage. However, when economically evaluating cloud seeding, it is, at least, necessary to consider this unexpected benefit. It is also worth considering that if the spatial coverage is rather small, the 16-day rainfall of 50 mm is an easy target to achieve.

C.Y. designed the research, interpreted the results and prepared the draft manuscript. M.L. analyzed the data and performed the computational work. K.C. revised the draft manuscript and added comments on the major findings of this study.

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2021R1A5A1032433) and the National Institute of Meteorological Sciences (NIMS) through ‘Development of Application Technology on Atmospheric Research Aircraft (KMA2018-00222)’.

All relevant data are available from an online repository or repositories. (Satellite data: https://modis.gsfc.nasa.gov/data/dataprod/ (MODIS product); Meteorological data: https://data.kma.go.kr/ (KMA)).

The authors declare there is no conflict.

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