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Article

Sensitivities of Vegetation Gross Primary Production to Precipitation Frequency in the Northern Hemisphere from 1982 to 2015

by
Shouye Xue
and
Guocan Wu
*
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(1), 21; https://doi.org/10.3390/rs16010021
Submission received: 22 November 2023 / Revised: 13 December 2023 / Accepted: 18 December 2023 / Published: 20 December 2023
(This article belongs to the Special Issue Remote Sensing of Primary Production)
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Abstract

:
Vegetation of the Northern Hemisphere plays a vital role in global ecosystems and the carbon cycle. Variations in precipitation profoundly affect vegetation productivity, plant growth, and species communities. Precipitation frequency directly controls soil moisture availability, which has an impact on the vegetation carbon sink. However, it is unclear how precipitation frequency affects the vegetation productivity of different land cover types in different seasons. In this study, the sensitivities of the gross primary production (GPP) of six vegetation types (forest, cropland, grassland, shrubland, tundra and barren land) in response to the frequency of five categories of precipitation (trace: 0.1–5 mm/day, small: 5–10 mm/day, moderate: 10–15 mm/day, heavy: 15–20 mm/day, and very heavy: >20 mm/day) were analyzed based on the XGBoost model. The results showed that, between 1982 and 2015, precipitation frequency declined in most land cover types but increased significantly in the pan-Arctic. Differences in the sensitivity to precipitation frequency were observed between seasons and precipitation categories in northern latitudes. The GPP values of forest and barren land vegetation were less sensitive to precipitation frequency than grassland, shrubland and tundra. This may be related to different vegetation community structures and underlying surfaces and gradually increasing drought resistance capability. The sensitivity to precipitation frequency declined for moderate and heavy precipitation in cropland, but it increased in winter. As the frequency of trace precipitation diminishes in winter, the sensitivity of each vegetation type reduces by an average of 0.03%/decade. Conversely, the sensitivities to small and moderate rain increase by 0.01%/decade and 0.02%/decade, respectively, for ecosystems such as cultivated land, forests, and shrubs. However, shrubs and tundra exhibit distinct behaviors, where shifts in precipitation frequency align directly with trends in sensitivity. These results show that the frequency of precipitation significantly affects vegetation productivity and has different sensitivities, and vegetation shows different feedback mechanisms in the face of environmental changes.

1. Introduction

Terrestrial ecosystems represent the largest carbon sink, which can offset about one-fourth of fossil fuel emissions [1] and consequently contribute to slowing down global warming [2]. At the same time, global warming has an impact on the structure and functioning of terrestrial ecosystems [3]. Recent studies have shown that climate change has promoted vegetation growth in the Northern Hemisphere [4,5]. However, as one of the climate change factors, precipitation can also modify the structure and behavior of plants, as shown in both observations [6,7] and climate model simulations [8,9].
On the one hand, increasing precipitation can promote the accumulation of plants’ ground biomass by dissolving more minerals in the soil, which are then absorbed through roots; on the other hand, decreasing precipitation will promote the growth of roots because drier soils are more porous and contain more air [10]. Precipitation tends to decrease soil respiration and soil microbial diversity [11], and, depending on both its intensity and frequency, it can further affect vegetation growth [12]. In addition, seasonal and intermittent precipitation control the status of water availability in different land cover types. It is generally believed that sufficient precipitation during the growing season supports carbon fixation in vegetation; however, for some plants, precipitation in winter plays a more important role than that in summer [13,14,15,16]. For instance, a study conducted in Switzerland showed that common tree species across the country rely on soil water from winter precipitation during summer drought [17].
The spatial–temporal distributions of precipitation and evaporation affect vegetation growth by adjusting water availability in terrestrial ecosystems [18,19]. Previous studies have suggested that only part of the total precipitation is accessed by vegetation and that precipitation provides only indirect information on the water conditions of plants [20] due to a portion of precipitation contributing to surface runoff that does not supply soil water content and light precipitation evaporating quickly at the surface [21]. Therefore, precipitation frequency can affect the intra-annual variation and allocation of soil moisture between shallow and deep soils [22,23]. Moreover, reductions in precipitation frequency accompanied by increases in the precipitation intensity of rainfall events may amplify the magnitude of soil moisture fluctuations and prolong the period of moisture stress between two consecutive rainfall events [22]. However, most studies have examined the impacts of precipitation intensity on vegetation or soil moisture rather than those of precipitation frequency. Experiments have shown that the latter is more important than the former [24,25]. Changes in precipitation frequency were shown to notably affect plant growth and productivity by regulating run-off [26], soil moisture [27,28], exposure to high radiation and temperature, and energy fluxes [22].
With global warming, extreme precipitation events have become more frequent than normal ones [29]; however, it remains unclear how precipitation frequency impacts vegetation productivity in different ecosystems within the context of climate change [30]. Furthermore, a clear seasonality has also been observed in the sensitivity of vegetation growth and development to precipitation frequency. For example, precipitation in autumn decreased while that in winter increased in recent decades [31,32]. The precipitation occurring from January to July is the primary climatic factor causing fluctuations in the biomass production of plant communities [33,34,35]. However, it is not clear how precipitation frequency affects vegetation productivity during four different seasons because of the spatial heterogeneity of different land cover types or vegetation communities. Therefore, it is imperative to further analyze the spatial pattern of the precipitation–vegetation relationship while taking into account the potential impacts of precipitation frequency and distribution [36].
In studies focused on precipitation variability, precipitation intensity can be classified based on percentiles; for instance, heavy precipitation is typically defined as amounts surpassing the 95th percentile [37,38]. However, this classification presents limitations when applied to terrestrial ecosystem studies. First, it fails to adequately address ecological requirements: the water quantity essential for vegetation growth and the soil’s effective moisture content remain constant, but relying on percentile classifications might overlook the true water demand of vegetation, leading to inaccuracies. Second, the variability aspect poses a concern: precipitation data for a consistent region can vary over time. For example, in the context of climate change, precipitation once labeled as “heavy rain” might currently be classified as moderate rain. Sole reliance on percentile-based division risks can overlook such shifts.
In recent years, a number of studies have revealed that decreasing precipitation frequency contributes to early leaf expansion of the vegetation traits in northern ecosystems [25] and to the sensitivity of different vegetation communities to precipitation intensity [39]. However, most of these studies have ignored the interactions among season, precipitation frequency, precipitation intensity, and land cover types. Changes in precipitation frequency can directly affect vegetation and surface soil moisture. Increased precipitation frequency can wash away dust on the surface of leaves multiple times, thereby promoting vegetation photosynthesis [40]. The effects of the variation of precipitation on soil water content and vegetation growth are still unclear due to spatial heterogeneity, but it can be confirmed that precipitation frequency is more important for vegetation growth than precipitation intensity [25].
The gross primary production (GPP) of vegetation is the amount of carbon fixed during photosynthesis by all plants in the ecosystem. It is the first and most important flux of the terrestrial carbon cycle and was used in this study to represent the carbon sequestration capacity of vegetation. When analyzing the sensitivity of vegetation to climate change, GPP reflects variations in photosynthesis better than indicators such as the Normalized Difference Vegetation Index. Precipitation frequency and vegetation GPP exhibit a nonlinear relationship. Traditional regression analysis falls short in accurately and appropriately capturing the nuanced response of GPP to precipitation frequency. Consequently, the machine learning approach becomes an alternative choice. The eXtreme Gradient Boosting (XGBoost) model was adopted in this study to assess the sensitivity. It excels in managing extensive datasets and intricate tasks due to its incorporation of both engineering enhancements and algorithmic improvements. XGBoost can also provide a comprehensive understanding of feature importance, shedding light on which features the model is particularly sensitive to. Additionally, the model leverages parallel processing capabilities, facilitating accelerated training processes, especially when deployed on multi-core processors [41]. Here, using machine learning, XGBoost was built to calculate the sensitivity of GPP to precipitation frequency based on data obtained from satellite observations from 1982 to 2015. The sensitivity was compared between different land cover types, precipitation intensity categories and seasons, in order to illustrate the detailed dependence of vegetation productivity on precipitation frequency.

2. Materials and Methods

2.1. GPP and Land Cover Types

This study focused on the GPP changes of the Northern Hemisphere and in particular on vegetation. To determine how GPP varied with climate change, land cover types were first obtained from annual time series of the GLASS-GLC land cover dataset. The GLASS-GLC product has a spatial resolution of 5 km, and the data examined in this study covered the period from 1982 to 2015.
The GLASS-GLC product identifies seven types of land cover [42], namely barren (barren land), snow and ice (snow/ice), all forest types (forest), forest/cropland mosaics and natural herbaceous/cropland mosaics (cropland), natural herbage and herbaceous cropland (grassland), shrubland (shrubland), and shrub/bush tundra and herbaceous tundra (tundra). GLASS-GLC data are characterized by high consistency, high reliability, and a long timespan compared to other land cover products [42]. The present study mainly focused on the changes in vegetation GPP, and therefore, the snow and ice (snow/ice) area was excluded from the analysis. Using land cover data from 1982 to 2015, this study clipped the annual GPP and precipitation data based on the specific grid locations of the vegetation for each year, which can mitigate the influence of vegetation dynamics.
Global monthly GPP data were obtained from the Oak Ridge National Laboratory Distributed Active Archive Center [43] during the 1982–2016 period. These data were generated based on the widely known Monteith light use efficiency (LUE) equation and were improved with spatially and temporally explicit optimized LUE values derived from selected FLUXNET tower data. The optimized LUE values were extrapolated to a global grid with a consistent resolution of 8 km using multiple explanatory variables representing climatic, landscape, and vegetation factors influencing both LUE and GPP. All of the GPP data are greater than 0 after quality control in the products. The monthly average values for the seasons in each year were calculated for each vegetation type.

2.2. Precipitation Frequency

The daily precipitation data used in this study were obtained from the ERA5 product, which is the fifth generation European Centre for Medium-Range Weather Forecasts’ atmospheric reanalysis of the global climate. ERA5 replaced its predecessor, the ERA-Interim reanalysis, which is available from the Copernicus Climate Data Store [44]. The latest reanalysis combined model outputs with observations from across the world into a globally complete and consistent dataset. ERA5 has provided total precipitation data every hour at a spatial resolution of 0.25° × 0.25° from 1940 to the present day. The hourly data from 1982 to 2015 were summed up to obtain daily precipitation values, which were divided into five precipitation categories [45,46] based on the amount: trace (0.1–5 mm/day), small (5–10 mm/day), moderate (10–15 mm/day), heavy (15–20 mm/day), and very heavy (>20 mm/day). Amounts lower than 0.1 mm/day were not included. In order to match the minimum scale of gauge observations and correct bias derived from the model simulation of precipitation days, each precipitation category was used as the number of precipitation days divided by the total number of days in each season.

2.3. Analysis Methods

2.3.1. Temporal–Spatial Analysis

Using the dynamic grid analysis method, the GPP and precipitation frequency data were extracted based on annual land cover types; then, the interannual trend and p value were calculated using the Mann–Kendall trend test in the R language package “Kendall”. To eliminate anomalies from satellite sensor noise, the rate of temporal variation was calculated by robust regression at a statistical significance level of 0.05. A robust linear regression was built between time (year) and GPP values; slope and p value were calculated at a 0.05 confidence level in R language with the package “robust”.

2.3.2. Correlation between GPP Values and Precipitation Frequency

Pearson correlation coefficients were used to characterize the dependence of GPP values on precipitation frequency. We built stepwise regression between five precipitation categories and GPP values for each land cover type for different seasons. The slope and p value were calculated by stepwise regression in R language.

2.4. Sensitivity

The explainable machine learning known as SHapley Additive exPlanations (SHAP) was here used to analyze the sensitivity of GPP to precipitation frequency. SHAP is a game theoretic approach used to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions. The advantages of this system compared to other methods include: (1) the identification of a new class of additive feature importance measures and (2) a unique solution in this class with a set of desirable properties [47].
In this study, SHAP assigned each feature an optimized Shapley value, which was considered to be the value of sensitivity to precipitation frequency. The analysis procedure comprised the following steps. First, to ensure consistency with precipitation data, the spatial resolution was resampled to 0.25° × 0.25° in order to match the GPP with the precipitation grid, GPP data with a non-value, and the raster where non-vegetation growth was deleted. Second, using precipitation frequency data (trace, small, moderate, heavy, and very heavy) as the predicting variable and GPP data as the response variable, an XGBoost model was built to quantify the relationship between predicting (precipitation frequency) and response (GPP) variables. The grid values from all years for five precipitation categories for each season were utilized as input features for the XGBoost model, with the average GPP value of that season being the output target. Four models were established separately for each of the four seasons.
The model employed the “reg:squarederror” as its objective function. In experimentation, we set the training argument num_round = 1000, and printed the root mean square error one by one. We observed that the minimum occurred when num_round was about 600. Subsequently, the trained XGBoost model was used to compute the SHAP values for the frequency associated with each precipitation amount. The procedure was finished using a function that comes with packages “SHAPforxgboost” in the software R language version 4.2.2 (https://liuyanguu.github.io/post/2019/07/18/visualization-of-shap-for-xgboost/ (accessed on 19 October 2022)) and the significance level was also involved. Positive (negative) SHAP values indicated the same (opposite) trend in variation for GPP and precipitation frequency, respectively. Detailed sensitivity analysis methods are provided in Supplementary Materials A.

3. Results

3.1. GPP Trends

The variations in GPP were shown to depend largely on latitude (Figure 1). Vegetation productivity in Arctic and pan-Arctic areas increased in spring and summer but decreased in autumn. The rate of variation at high latitudes was significantly higher in summer than in other seasons. GPP values also varied greatly among seasons and land cover types (Figure 2 and Figure S1). Specifically, the values decreased in spring for all land cover types except forests. Summer GPP values were significantly different among cropland, shrubland, and tundra, varying at rates of −0.12, −0.11, and +0.08 gC/m2/decade, respectively. Positive autumn GPP trend slopes were observed for all land cover types except barren land, while winter GPP trend slope markedly varied in cropland (+0.03 gC/m2/decade) and forest (−0.08 gC/m2/decade) areas. The growth of shrubland vegetation showed a large magnitude of trend compared to that of grassland and barren land vegetation, which decreased in spring and summer and increased in autumn and winter. The GPP values of forests increased in every season except winter. In contrast, the values of vegetation in barren land declined in every season.

3.2. Precipitation Frequency Trends

Precipitation frequency was shown to increase at high latitudes and decrease at middle latitudes (Figure 3). Significant increases in this parameter were detected in more areas of the Eastern Hemisphere, especially in spring and summer, while the Western Hemisphere has more areas with significant increases in winter. In terms of precipitation categories, the greater the amount, the lower the frequency (Figure S2). Trace amounts accounted for a large proportion of precipitation events (circa 46–86%), but the trend was not significant, while the frequency of small precipitation significantly decreased. In addition, in cropland and forests, the proportion of very heavy precipitation was larger than that of heavy precipitation.
The frequency of seasonal precipitation varied in different land cover areas (Figure 4). In cropland, grassland, and barren land, this parameter showed a decrease in all precipitation intensity categories, with the largest decline detected in winter trace precipitation in shrubland (−2.3%/decade). In contrast, the frequency of trace precipitation in forests in summer and in shrubland in spring and summer increased at rates of 0.5%/decade, 0.8%/decade and 1.7%/decade, respectively. The frequency of trace precipitation in barren land declined at rates of −0.4%/decade, −1.1%/decade, −1%/decade, and −1.6%/decade in spring, summer, autumn, and winter, respectively.
It is interesting that the frequency of trace precipitation in forest and shrubland increased while other land cover types decreased. In grassland, the frequency of all five precipitation categories decreased. In tundra areas, precipitation frequency varied slightly for the trace category at an average rate of −0.5%/decade. The rates of variation increased in summer and decreased in other seasons, with no obvious trend larger than 10 mm/day precipitation, which corresponds to the precipitation frequency data recorded for barren land.

3.3. Relationship between GPP Values and Precipitation Frequency

The correlation coefficient between GPP and the frequency of seasonal precipitation mainly indicated a significantly negative relationship in most study areas, especially at northern latitudes, but a positive relationship at mid-latitudes (Figure 5). The seasonal contrasts showed that the change magnitude and areas with significance was smaller in winter than in the other seasons.
Figure 6 shows the correlation coefficients between GPP and the seasonal precipitation frequency of different categories as well as the contribution of the variation in precipitation frequency to GPP values based on stepwise regression. Overall, the annual variation in precipitation frequency improved vegetation growth for most precipitation categories except trace precipitation (“T” column in the horizontal axis). Eight cases that significantly affected vegetation growth (40% of precipitation categories across seasons) were identified. These cases mainly include moderate precipitation during the growing season and heavy/very heavy precipitation in winter. Specifically, small precipitation in spring (p < 0.05) and moderate precipitation (p < 0.01) in summer and autumn improve vegetation growth. The frequency of trace precipitation and autumn GPP value seemed linked by a separate relationship, as the former nearly half of cases (64/120) showed a negative relationship with the latter across seasons.
Precipitation amounts ranging from small to very heavy promoted cropland growth in autumn, while trace amounts inhibited it in winter. In forests, small to moderate precipitation in spring, summer, and autumn was shown to play an important role (p < 0.01), with correlation coefficients of 0.08, −0.23, and −0.09, respectively. However, Pearson correlation values were large for heavy, small, and very heavy precipitation in spring (0.19), summer (−0.23), and autumn (0.35), respectively. The variation in grassland GPP seemed to be promoted only by moderate and very heavy rain in summer (0.41–0.51), heavy precipitation during the growing season (P4–P5, p < 0.05), and moderate precipitation in winter (p < 0.01). It is notable that the variation in GPP values in grassland was less impacted by precipitation frequency in stepwise regression analysis compared to that in other land cover types. Heavy and very heavy precipitation during the growing season significantly affected (p < 0.05) shrubland vegetation throughout the year. In barren land, lots of precipitation categories could promote vegetation growth, and only trace precipitation did not contribute significantly to it. Moderate to heavy precipitation during the growing season significantly contributed to GPP (p < 0.05). The same was observed for the GPP of tundra vegetation, whose variation was controlled by heavy and very heavy precipitation in winter (p < 0.01).
The analysis of correlation coefficients indicated the presence of a time lag in the variation of GPP and precipitation frequency. For example, an extreme correlation of circa 0.634 was observed between the summer GPP value of barren land and moderate precipitation in spring. In the tundra area, the same correlation was detected between the winter GPP value of grassland and heavy (0.541) or very heavy precipitation (0.560) in summer.

3.4. Differences in Sensitivity to Precipitation in Land Cover Types

The sensitivity to trace precipitation increased in spring and autumn but decreased in summer and winter (Figure 7). Trace rainfall sensitivity is highest in most areas across seasons (Figure S3); for example, sensitivity increased significantly in the southern Sahara Desert. Most areas exhibited an increasing sensitivity to small precipitation amounts, except for the southern Sahara, where a decrease in this parameter was observed. The sensitivity to precipitation frequency decreased in more than half of the areas in northern latitudes (Figures S4–S8). Generally, the greater the amount of precipitation, the smaller the sensitivity variation.
The sensitivity to precipitation frequency in most land cover types decreased at rates between −0.1/decade and 0.1/decade, although it remained unaltered for large precipitation intensities. In some exceptional cases, the areas with increased sensitivity to heavy and very heavy precipitation in winter were approximately 10.2% and 25.8% larger, respectively, than those with decreased sensitivity. A similar trend was observed in summer, autumn, and winter for the frequency of very heavy precipitation. For all vegetation in winter, the sensitivity to moderate precipitation increased while that to trace precipitation decreased.
In terms of different land cover types, shrubland vegetation was shown to be more sensitive to precipitation frequency than forest, tundra, and barren land vegetation. The average sensitivity of shrub vegetation increased by 0.03/decade and 0.06/decade in spring and summer, respectively, as trace precipitation increased in spring, summer, and autumn, but it decreased in winter. On the contrary, other precipitation categories in shrubland, cropland, forest, and tundra showed a completely opposite trend. Trace precipitation decreased by 0.04/decade on average in winter in all land cover types. The sensitivity of cropland and shrubland to precipitation frequency in summer and autumn decreased, while that of forests increased. The sensitivity of grassland slowly decreased throughout the year except in spring. However, the sensitivity of GPP to precipitation frequency in barren land decreased across seasons.
The results suggested that sensitivity to precipitation varied greatly among land cover types. Cropland was crucially impacted by soil water content during the growing season, which led to variations in yield [48]. This land cover type was also affected by human activity, especially in summer and autumn during harvesting, when GPP values dropped dramatically. Thus, the GPP value of cropland would remain constant as precipitation frequency decreased, and then crops planted for the next year’s harvest and grown in winter. Thus, GPP is negatively correlated with the decrease in precipitation frequency. Taiga forests cover 17 million square kilometers (11% of the Northern Hemisphere). The interception effect of forest leaves [49] may explain why forest growth was scarcely impacted by trace precipitation. In addition, the greater biodiversity found in these forest ecosystems makes them more resistant to climate change. In most land cover types, large precipitation events would increase moisture availability more easily than small ones. Plant production would therefore benefit more from heavy precipitation, particularly during the growing season, which is characterized by relatively high temperatures [50].
Grassland, herbaceous, and gramineous plants seem to retain soil moisture in their dense roots, which create impermeable underlying surfaces where precipitation is collected in streams instead of soil. Therefore, variations in the frequency of heavy precipitation events in grassland are unable to control grassland growth. Tundra species, which are distributed in Arctic and pan-Arctic regions (for example in the northern slope of Alaska, Canada, Russia, and Greenland), receive little precipitation, with the annual total amount being between 280 and 350 mm [51]. Interestingly, the tundra is usually a wet environment because the low temperatures slow down water evaporation and, at the same time, the frozen water layers in the permafrost limit water infiltration. This means that heavy precipitation can penetrate through the deep soil and support the growth of dwarf shrubs in summer, but this water is not able to enter the soil as easily in winter.
In addition to climate factors, plant community may also affect the spatial and temporal variations of precipitation sensitivity. For example, plant biodiversity is generally high in humid climate conditions [33,39,52]. In arid and semi-arid regions, the lack of response to small reductions in water supply could be due to the plant species present and their resistances, as communities are adapted to variable precipitation in arid environments. Therefore, a precipitation reduction of 25% or less may not affect these plant communities in the short term.

4. Discussion

4.1. Reasons for the Variations in Precipitation Frequency and GPP Values

4.1.1. Variation in Precipitation Frequency

Current global climate change causes precipitation to vary in intensity and frequency. Changes in precipitation frequency can directly affect vegetation and surface soil moisture. Overall, precipitation frequency in the Northern Hemisphere decreased while precipitation intensity increased from 1982 to 2015. Wet air contributes to precipitation in areas covered with vegetation, and more precipitation can support more evaporation (for example, in forests [53]), while it rarely rains in dry areas or barren land [53]. This study showed that the frequency of trace precipitation was highest in the tundra, which is distributed mostly in the Arctic and northern latitudes. Lower temperatures contribute to low saturated vapor pressure, which causes droplets to be easily released from clouds and form precipitation, although this phenomenon is also controlled by wind direction [54]. However, numerous studies have reported the “dry soil advantage”, which means that the atmosphere produces more local precipitation over dry soils than over wet soils, because the former may prime the atmosphere for intense convection. This explains the increase in precipitation frequency detected in shrubland [55,56,57].
Undoubtedly, the frequency of precipitation with an intensity between 5 and 10 mm/day obviously decreased in all land cover types, except in barren land. Vegetation coverage in barren land is normally less than 10% of the total area, and no incidences of precipitation intensity >10 mm/day have been reported. Different changes in precipitation frequency were detected among seasons in different land cover types. Interestingly, in cropland, grassland, and barren land, all precipitation frequencies decreased in all seasons. Seasonal precipitation is critical for springtime plant activity, and a direct connection may exist between precipitation frequency and intensity and vegetation productivity in dryland ecosystems [58]. At the same time, soil moisture conditions in different land cover types determine the climatic conditions of local precipitation [59,60].

4.1.2. Variation in GPP Value

Drought is one of the key factors that weakens vegetation productivity [61], which leads to a reduction in the GPP of vegetation to different degrees, especially for barren land. A declining trend in annual GPP was also observed in cropland and shrubland. The reduction in GPP for cropland is partly attributed to the late summer harvest, and the reason for reduction in shrubland GPP is complicated. The findings of reduced summer GPP in shrubs align with previous observations of “browning” in the Arctic [62,63]. The Arctic method effect could lead to shrub invasion and expansion, and the new shrub GPP is lower [64]. The rising temperatures and extreme drought events result in shrubs mortality [65]. Climate change can also induce alterations in land cover types, causing shrubs with high biomass to gradually transform into forests [51].
Tundra in the Northern Hemisphere primarily exists in the pan-Arctic region. The tundra is undergoing shrubification while expanding towards the high-latitude Arctic. Consequently, tundra vegetation GPP is increasing, especially when the restrictions imposed by low temperatures are alleviated in summer, leading to a more rapid increase in GPP. The growth limitations of grasslands in the Northern Hemisphere have large uncertainty. In high latitudes, the growth mechanism resembles that of tundra, while in low latitudes, such as Mongolian grasslands, they are generally driven by drought events.

4.1.3. Relationship between GPP Values and Precipitation Frequency

Seasonal precipitation in environments where rainfall is scarce and varies in intensity and frequency may have major effects on plant physiological and morphological traits, regulating vegetation responses and community interactions [66]. The highest vegetation growth rate is generally observed between June and September in the Northern Hemisphere, when roots are more able to absorb minerals from soil water. However, the rapid warming of the pan-Arctic has led to the melting of sea ice and increased precipitation. Clouds, fog, and water vapor have delayed the rise in surface temperature and hindered the photosynthesis of surface vegetation [67], which may have led to the negative correlation between the frequency of pan-Arctic precipitation and vegetation GPP. This study showed that precipitation in spring and summer did not affect GPP in cropland, possibly due to human activity (such as irrigation) in the primary growth period of crops. When crops were harvested at the end of summer, GPP would not be affected by precipitation frequency. Precipitation in winter ensures the presence of enough soil water content, which hardly evaporates because of the low temperatures, facilitating crop growth the following year.
In forests, the GPP values showed a negative correlation with the frequency of trace precipitation. This may be partially explained by that fact that heavy precipitation penetrates through the soil layer and improves the soil water potential while light precipitation reaches mainly the canopy and quickly evaporates. The same pattern was observed in grassland. Additionally, trace precipitation can infiltrate the surface soil and dissolve soil minerals and organic matter. When the rate of evaporation surpasses precipitation, soil moisture rapidly evaporates, carrying salts upward from the soil bottom. This phenomenon results in soil salinization [68,69]. The higher the frequency of trace precipitation, the more pronounced the degree of soil salinization becomes. The tundra GPP is not highly correlated with the precipitation frequency in winter; because Arctic tundra growth is limited by low temperatures, changes in precipitation no longer impact GPP in winter.
A time lag between GPP value and precipitation frequency was detected. Shrubs, the typical woody plants found in arid and semi-arid areas, are characterized by special water use strategies; they present different physiological and morphological features to cope with the harsh conditions and are resilient to drought [70]. The ability of shrubs to convert precipitation into phytomass can be greatly improved [71,72]. There was a high correlation between GPP and moderate precipitation in the spring. The precipitation conditions in the spring could promote seed germination and germination in the following spring. In addition, reduced precipitation does not necessarily lead to lower vegetation productivity, as it still promotes the absorption of minerals and soil water [73], partially explaining the negative correlation between precipitation frequency and GPP value.

4.2. Differences in Sensitivity to Precipitation across Seasons

The impact of precipitation on vegetation can vary significantly depending on plant growth phases [74] and plant species. For example, desert trees were shown to use more water accumulated in winter than desert shrubs [75]. Precipitation frequency and sensitivity to it decrease in spring and winter, as cold climate conditions with less precipitation slow down the growth of plants. In summer, sufficient precipitation and radiation supported the vegetation carbon sink, reducing the sensitivity of plant communities to frequency changes.
For shrubland, when the frequency of small to heavy precipitation decreases in summer for shrubs, GPP and their sensitivity also decreases. Only the sensitivity to trace precipitation increases. This suggests that substantial precipitation is needed to ensure shrub growth when the temperature is favorable. An increased frequency of trace precipitation during summer can hinder shrub growth. In autumn, while the frequency of precipitation continues to decline, and GPP rises, sensitivity decreases. From September to November, as temperatures gradually drop, arid conditions become more suitable for shrub growth.
In winter, precipitation frequency declined or remained stable, and sensitivity increased in all vegetation areas. This may be explained by the fact that, on the one hand, as vegetation withers and GPP values drop in winter in environments such as cropland, grassland, and barren land, changes in precipitation cannot promote GPP growth; on the other hand, vegetation in evergreen forests needs large precipitation amounts to penetrate through the soil and improve soil water content. The seasonal divergence of water availability [76] may explain why sensitivity increased with precipitation frequency in summer. A previous study showed that during midsummer, many plants use water that originated during winter [77], implying that winter precipitation may play an important role in regulating growing season dynamics, particularly in the context of the shifting seasonal precipitation inputs projected to occur with climate change [78].
In this study, shrubland showed strong seasonal sensitivity diversity, possibly because of solar radiation, precipitation intensity, and temperature, shrub drought, and plant mortality. Another reason may be that vegetation growing in arid areas all year round is adapted to drought conditions; for example, shrubland tends to use water accumulated in winter to support recovery and development during times with less precipitation. The little variation in vegetation sensitivity may also be related to the number of bud banks, which ensure the stability of vegetation growth during drought [79,80]. Strong evapotranspiration and low precipitation intensity in barren land converge to cause lesser plant productivity than in other land cover types and GPP sensitivity to precipitation frequency unresponsiveness until GPP exceeded the threshold at which vegetation could be recovered. The sensitivity of vegetation to large and heavy precipitation is almost unchanged, especially in forests, grassland, and barren land. The forest ecosystem is relatively stable, and precipitation events will not impact GPP in the short term. The grassland has stronger root systems where water could not easily be saturated into soil. The measurement of the resistance of vegetation to precipitation changes should also consider topography and soil hydraulic characteristics, and whether the decrease in precipitation frequency leads to the duration of soil available water below the withering point is also an important factor that cannot be ignored. Alternatively, it is possible that the continuous increase of potential evaporation caused by global warming also weakened the role of precipitation frequency in the aridity change on a regional scale [81].

4.3. Data Reliability

Under the assumption that the total global annual precipitation does not change significantly, we conclude that changes in precipitation frequency will affect the carbon sequestration capacity of vegetation. Precipitation frequency will directly affect vegetation leaves, such as washing away leaf dust and improving photosynthesis efficiency. In addition, changes in the frequency of precipitation also act directly on the topsoil, creating complex interactions with it. Surface soil moisture is prone to evaporation, so changes in soil moisture are partly reflected in changes in precipitation frequency. Therefore, this study takes this as a starting point to discuss the sensitivity of GPP to precipitation frequency. Additionally, air temperature also causes vegetation GPP changes and vegetation growth [82,83,84], especially in cold regions. To address this point, the partial correlation between GPP and precipitation frequency was analyzed when mean temperature was controlled (Figure S9). The partial correlation coefficient was calculated using ppcor R package in R 4.3.2. It found that the spatial correlations of the marginal one were similar to those when annual mean temperature was controlled.
In addition, for vegetation with short root systems, root soil moisture is mainly controlled by precipitation, while for vegetation with long root systems, such as forests, soil moisture in the root zone is not only affected by precipitation, but also by deep percolation, changes in soil water potential, soil structure, topographic factors, etc. The complexity of influencing factors prevents us from taking into account the impact of all environmental factors on sensitivity analysis. Merely using precipitation frequency as the input variable for the XGBoost model may restrict a comprehensive understanding of vegetation growth processes. However, this study mainly discusses the direct impact of precipitation frequency on vegetation GPP and ignores the influence of external factors on soil moisture and other factors on sensitivity analysis.
Moreover, global land cover types were classed into six categories in GLASS-GLC, which could be insufficient to account for variations in the responses of different vegetation types. Vegetation roots have large spatial variability in the same land cover types, and there is limited flexibility and diversity of vegetation species communities. In the present paper, we only investigated sensitivity for different land cover types, which did not consider root conditions. Human impact also affects vegetation GPP changes. For example, irrigation mainly affects the GPP change of cropland. However, according to GLASS-GLC product, cropland accounted for only 8% (570,554 grids to 7,083,833 grids) of the global vegetation area in 2015, and more than 80% of the world’s cropland relied on rainwater [85]. Therefore, in the grid analysis, the impact of human irrigation was ignored.
Precipitation volume has large spatial variability, and there is not a consistent standard. According to the research and experiments, we artificially set 5 mm as the step size to divide different precipitation intensities. More sub-categories may be needed and suitable for areas with sparse precipitation. However, there is almost no vegetation distribution in arid areas. Therefore, this study did not consider more sub-categories, which would be involved in future research on arid regions.

5. Conclusions

Precipitation frequency directly affects the fluctuation frequency of surface soil moisture, washes the leaves to improve photosynthesis, and regulates the temperature in the region, which has a large effect on vegetation growth. Based on the XGBoost model, this study analyzed the sensitivity of vegetation productivity to five precipitation frequencies at different precipitation intensities and in various land cover types in the Northern Hemisphere between 1982 and 2015.
The results showed that vegetation growth was sensitive to precipitation frequency, and seasonal differences were revealed. Spatially, the higher the amount of precipitation, the smaller the area affected by precipitation frequency. Overall, during the study period, vegetation was less sensitive to the frequency of precipitation, with the largest downward trend for shrubs and the least fluctuation in barren land. Shrubland exhibited strong diversity of seasonal sensitivity. During the growing season, cropland, shrubland, and forests were more sensitive to the frequency of trace precipitation, while other land cover types were less sensitive. This may because heavy precipitation is needed for shrub growth in summer, and trace precipitation can hinder shrub growth. In regard to seasonal precipitation frequency in different land cover types, the results showed a time lag and differences in vegetation communities. Precipitation frequency did not crucially affect cropland growth during the growing season. Forest growth depended on heavy precipitation events during the cool season and small precipitation events during the warm season. The productivity of barren land species was significantly correlated with variations in precipitation frequency, but sensitivity did not vary significantly. These results can help fill the gap of considering only the amount of precipitation and ignoring the impact of frequency and seasonal changes on different land cover types.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/rs16010021/s1, Figure S1: M–K trend of GPP values for each 0.25°×0.25° grid during the four seasons between 1982 and 2015; Figure S2: Decade trend of GPP values during each season from 1982 to 2015 in every vegetation type; Figure S3: M–K trends of total precipitation frequency for each 0.25°×0.25° grid across the four seasons from 1982 to 2015; Figure S4: Decade trends of precipitation frequency during each season from 1982 to 2015 in every vegetation type; Figure S5: Spatial distribution of the Pearson correlation coefficient between annual GPP values and total precipitation frequency from 1982 to 2015; Figure S6: Pearson correlation coefficients between annual GPP values and precipitation frequency across seasons in every vegetation type; Figure S7: Decade M–K trends of sensitivity to different precipitation frequencies during each season from 1982 to 2015 in every vegetation type; Figure S8: M–K trend of sensitivity to the frequency of very heavy precipitation from 1982 to 2015; Figure S9: Spatial distribution of the partial correlation coefficient between annual GPP values and total precipitation frequency from 1982 to 2015 when annual mean temperature was controlled.

Author Contributions

Software, S.X.; supervision, S.X. and G.W.; writing—original draft, S.X. and G.W. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Key Research and Development Program of China (2022YFF0801302) and the National Natural Science Foundation of China (41930970 and 42077421).

Data Availability Statement

Monthly global GPP data can be accessed at https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1789 (accessed on 12 October 2022). The Annual Dynamics of Global Land Cover dataset can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.913496 (accessed on 2 November 2022). The daily precipitation dataset ERA5 of the generation ECMWF atmospheric reanalysis can be accessed at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels (accessed on 21 November 2022).

Acknowledgments

Thanks to the National Key Research and Development Program of China and the National Natural Science Foundation of China for the financial support of this study.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. M–K trend of GPP values for each 0.25° × 0.25° grid for the four seasons (a–d) and annual mean (e) from 1982 to 2015. The “+” symbols in each sub-figure indicate statistically significant grids at the 0.05 level; (f) indicates the annual mean of the GPP value and the corresponding long-term trend for six vegetation types, with the shadows showing the 95% confidence interval.
Figure 1. M–K trend of GPP values for each 0.25° × 0.25° grid for the four seasons (a–d) and annual mean (e) from 1982 to 2015. The “+” symbols in each sub-figure indicate statistically significant grids at the 0.05 level; (f) indicates the annual mean of the GPP value and the corresponding long-term trend for six vegetation types, with the shadows showing the 95% confidence interval.
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Figure 2. Decade trend of GPP values during each season and the annual mean from 1982 to 2015 in every vegetation type. The error bars (mean ± 1.96 × SE) represent 95% confidence intervals.
Figure 2. Decade trend of GPP values during each season and the annual mean from 1982 to 2015 in every vegetation type. The error bars (mean ± 1.96 × SE) represent 95% confidence intervals.
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Figure 3. M–K trends of total precipitation frequency for each 0.25° × 0.25° grid across the four seasons from 1982 to 2015. The “+” symbols in each sub-figure indicate the statistically significant grids at the 0.05 level.
Figure 3. M–K trends of total precipitation frequency for each 0.25° × 0.25° grid across the four seasons from 1982 to 2015. The “+” symbols in each sub-figure indicate the statistically significant grids at the 0.05 level.
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Figure 4. Decade trends of precipitation frequency during each season from 1982 to 2015 in every vegetation type. The error (mean ± 1.96 × SE) bars represent 95% confidence intervals.
Figure 4. Decade trends of precipitation frequency during each season from 1982 to 2015 in every vegetation type. The error (mean ± 1.96 × SE) bars represent 95% confidence intervals.
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Figure 5. Spatial distribution of the Pearson correlation coefficient between annual GPP values and total precipitation frequency from 1982 to 2015. The red and blue colors in the lower left pie chart represent negatively correlated and positively correlated areas as a percentage of total area, respectively. The “+” symbol in the map indicates statistical significance at the 0.05 level.
Figure 5. Spatial distribution of the Pearson correlation coefficient between annual GPP values and total precipitation frequency from 1982 to 2015. The red and blue colors in the lower left pie chart represent negatively correlated and positively correlated areas as a percentage of total area, respectively. The “+” symbol in the map indicates statistical significance at the 0.05 level.
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Figure 6. Pearson correlation coefficients between annual GPP values and precipitation frequency across seasons in every vegetation type. The letters T, S, S, A, and W in the horizontal ordinate stand for total year, spring, summer, autumn, and winter, respectively; P1–P5 indicate trace, small, moderate, heavy, and very heavy; * and ** in the T row indicate statistical significance at the 0.05 and 0.01 levels, respectively, obtained from the stepwise regression between precipitation frequency and GPP value from 1982 to 2015.
Figure 6. Pearson correlation coefficients between annual GPP values and precipitation frequency across seasons in every vegetation type. The letters T, S, S, A, and W in the horizontal ordinate stand for total year, spring, summer, autumn, and winter, respectively; P1–P5 indicate trace, small, moderate, heavy, and very heavy; * and ** in the T row indicate statistical significance at the 0.05 and 0.01 levels, respectively, obtained from the stepwise regression between precipitation frequency and GPP value from 1982 to 2015.
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Figure 7. Decade M–K trends of sensitivity to different precipitation frequencies during each season from 1982 to 2015 in every vegetation type. The error bars (mean ± 1.96 × SE) represent 95% confidence intervals.
Figure 7. Decade M–K trends of sensitivity to different precipitation frequencies during each season from 1982 to 2015 in every vegetation type. The error bars (mean ± 1.96 × SE) represent 95% confidence intervals.
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Xue, S.; Wu, G. Sensitivities of Vegetation Gross Primary Production to Precipitation Frequency in the Northern Hemisphere from 1982 to 2015. Remote Sens. 2024, 16, 21. https://doi.org/10.3390/rs16010021

AMA Style

Xue S, Wu G. Sensitivities of Vegetation Gross Primary Production to Precipitation Frequency in the Northern Hemisphere from 1982 to 2015. Remote Sensing. 2024; 16(1):21. https://doi.org/10.3390/rs16010021

Chicago/Turabian Style

Xue, Shouye, and Guocan Wu. 2024. "Sensitivities of Vegetation Gross Primary Production to Precipitation Frequency in the Northern Hemisphere from 1982 to 2015" Remote Sensing 16, no. 1: 21. https://doi.org/10.3390/rs16010021

APA Style

Xue, S., & Wu, G. (2024). Sensitivities of Vegetation Gross Primary Production to Precipitation Frequency in the Northern Hemisphere from 1982 to 2015. Remote Sensing, 16(1), 21. https://doi.org/10.3390/rs16010021

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