Uncertainties in representing dryland water availability

Uncertainties in quantifying and representing vegetation productivity

At the plot scale, harvesting all aboveground herbaceous biomasses at the end of the growing season is considered to be the most direct and accurate way to estimate annual ANPP (Fahey and Knapp, Reference Fahey and Knapp2007). For woody plants, allometric methods are typically used to estimate the aboveground biomass (e.g., Clark et al., Reference Clark, Brown, Kicklighter, Chambers, Thomlinson and Ni2001). Because these methods indirectly incorporate the loss of productivity by grazing, browsing or senescence, they may underestimate primary production. Furthermore, these plot-scale measurements require extensive replication to capture the strong small-scale heterogeneity of dryland vegetation.

At the ecosystem scale, vegetation productivity is often estimated from eddy covariance flux tower-based CO₂ measurements and is represented by gross primary production (GPP). Flux towers offer high temporal resolution measurements of GPP over relatively large ecological footprints (e.g., hundreds of meters to kilometers). Tower-based GPP measurements are usually considered to be the gold standard to benchmark and validate the carbon cycle in land surface models. However, GPP is not a directly observable variable, and it is deduced from net ecosystem exchange (NEE) measurements using different methods partitioning NEE into GPP and total ecosystem respiration (e.g., daytime partitioning vs. nighttime partitioning) and considerable biases can occur in tower-based GPP estimates (Keenan et al., Reference Keenan, Migliavacca, Papale, Baldocchi, Reichstein, Torn and Wutzler2019). Ideally, GPP can be further partitioned into net primary production (NPP) if total ecosystem respiration can be partitioned into root and heterotrophic respiration. However, this is challenging to achieve for flux tower measurements and rarely done.

At very large spatial scales, remote sensing-based estimates of biomass are often a necessity. The Normalized Difference Vegetation Index (NDVI, an estimate of “greenness”) is often used as a surrogate for vegetation productivity, but there are potential issues using NDVI to represent vegetation productivity in drylands. First, remote sensing data, especially satellite-based remote sensing data, often have a coarse spatial resolution (e.g., MODIS NDVI resolution is 500 m and Landsat NDVI is 30 m), which homogenizes local variability in vegetation production (e.g., trees, shrubs and herbaceous composition). Second, NDVI does not account for tissue loss by grazing or senescence and thus potentially underestimates vegetation production. That is why people often use peak NDVI or integrate multiple measurements over the growing season to minimize this bias. Other satellite-derived direct productivity indicators (e.g., MODIS GPP and NPP) generally perform poorly in drylands. This poor performance in drylands is related to the limited amount of dryland ground truth data used to calibrate and drive light use efficiency models (Smith et al., Reference Smith, Biederman, Scott, Moore, He, Kimball, Yan, Hudson, Barnes and MacBean2018). Our ability to constrain nearly every variable in the light use efficiency models, such as absorbed photosynthetically active radiation (APAR) by plant canopies or the efficiency at converting APAR to carbohydrates (ε), remains limited in dryland systems because of data scarcity as well as the structural and functional heterogeneities in many dryland ecosystems (e.g., sparse canopies, C₃/C₄ composition) (Smith et al., Reference Smith, Biederman, Scott, Moore, He, Kimball, Yan, Hudson, Barnes and MacBean2018). The use of solar-induced chlorophyll fluorescence (SIF) to estimate productivity has increased recently because it is directly linked to plant photosynthesis (Sun et al., Reference Sun, Gu, Wen, van der Tol, Porcar-Castell, Joiner, Chang, Magney, Wang, Hu, Rascher, Zarco-Tejada, Barrett, Lai, Han and Luo2023a, Reference Sun, Wen, Gu, Joiner, Chang, van der Tol, Porcar-Castell, Magney, Wang, Hu, Rascher, Zarco-Tejada, Barrett, Lai, Han and Luo2023b). However, currently, there are no direct SIF observations from satellites and all available satellite SIF products are from space missions that were designed to monitor atmospheric trace gasses. Satellite SIF data are therefore indirect estimates that build on a set of assumptions that are affected by a suite of factors including meteorology (Song et al., Reference Song, Wang and Wang2021). SIF measurements from towers or unmanned aerial vehicles could be one solution, but tower-based SIF measurements are still limited and lacking standardized processing and retrieval methods (Sun et al., Reference Sun, Wen, Gu, Joiner, Chang, van der Tol, Porcar-Castell, Magney, Wang, Hu, Rascher, Zarco-Tejada, Barrett, Lai, Han and Luo2023b). More importantly, it has been argued that SIF data availability and applications currently outpace the growth in the mechanistic understanding of SIF dynamics (Sun et al., Reference Sun, Gu, Wen, van der Tol, Porcar-Castell, Joiner, Chang, Magney, Wang, Hu, Rascher, Zarco-Tejada, Barrett, Lai, Han and Luo2023a).

Lag effect between water input and vegetation response

Although water is the most limiting factor controlling ANPP in drylands, vegetation growth sometimes lags behind precipitation inputs both within and between years (He et al., Reference He, Li, Wang, Xie and Ye2021). During a growing season, peak rates of photosynthesis often occur several days after a rain event (e.g., Thomey et al., Reference Thomey, Collins, Friggens, Brown and Pockman2014) and depend on event size and duration of soil moisture (Vargas et al., Reference Vargas, Collins, Thomey, Johnson, Brown, Natvig and Friggens2012). This lag effect of precipitation on ANPP can be highly complex. Sala et al. (Reference Sala, Gherardi, Reichmann, Jobbagy and Peters2012) hypothesized that the effects of dry years, for example, carried over to reduce ANPP the following year despite higher precipitation inputs because of structural, biochemical or compositional changes. Indeed, using flux tower data, Petrie et al. (Reference Petrie, Peters, Yao, Blair, Burruss, Collins, Derner, Gherardi, Hendrickson and Sala2018) reported that the correlation between ANPP and precipitation in a given year was influenced by the sequence of prior conditions. For example, ANPP often increased with the length of multiyear wet periods, such that the importance of the amount of current-year precipitation declined. Others have shown that drought legacies reduce belowground bud banks and limit the capacity for vegetation to respond to increases in precipitation (Luo et al., Reference Luo, Muraina, Griffin-Nolan, Te, Qian, Yu, Zuo, Wang, Knapp, Smith, Han and Collins2023). These lagged responses obscure a strong relationship between vegetation productivity and water input rate or preclude its existence.

Nonlinear effect

Most previous studies primarily employed equation-based and linear approaches to investigate the relationship between water availability indicators (e.g., precipitation) and vegetation productivity. However, a nonlinear relationship could occur both spatially because more mesic or colder drylands are less water limited and temporally because other factors limit vegetation growth when water is plentiful (e.g., Hsu et al., Reference Hsu, Powell and Adler2012; Knapp et al., Reference Knapp, Ciais and Smith2017; Rudgers et al., Reference Rudgers, Chung, Maurer, Moore, Muldavin, Litvak and Collins2018). Based on long-term data from 48 grassland sites in Mongolia, Sasaki et al. (Reference Sasaki, Collins, Rudgers, Batdelger, Baasandai and Kinugasa2023) applied an equation-free, nonlinear time-series analysis to examine the relationship between precipitation and vegetation productivity. The results are counterintuitive in that they found that productivity responded positively to annual precipitation in mesic regions but negatively in arid regions (Sasaki et al., Reference Sasaki, Collins, Rudgers, Batdelger, Baasandai and Kinugasa2023), likely due to lagged effects. Additionally, productivity responded negatively to interannual variability in precipitation in mesic regions but positively in arid regions (Sasaki et al., Reference Sasaki, Collins, Rudgers, Batdelger, Baasandai and Kinugasa2023), as has been demonstrated elsewhere both empirically (Cleland et al., Reference Cleland, Collins, Dickson, Farrer, Gross, Gherardi, Hallett, Hobbs, Hsu and Turnbull2013; Gherardi and Sala, Reference Gherardi and Sala2019) and through modeling studies (Hou et al., Reference Hou, Litvak, Rudgers, Jiang, Collins, Pockman, Hui, Niu and Luo2021). These results indicate that the response of vegetation productivity to water availability may often be nonlinear and state dependent. Nonlinear responses, lag effects and state-dependent variables create multiple challenges for translating complex relationships into modeling frameworks that can effectively predict vegetation response to water availability under current and future climates.

Impact from non-water factors

Depending on locations and season, other meteorological factors such as temperature, relative humidity and vapor pressure deficit (VPD) could also play a role in moderating the relationship between ANPP and water availability (Novick et al., Reference Novick, Ficklin, Stoy, Williams, Bohrer, Oishi, Papuga, Blanken, Noormets, Sulman, Scott, Wang and Phillips2016; Knapp et al., Reference Knapp, Condon, Folks, Sturchio, Griffin‐Nolan, Kannenberg, Gill, Hajek, Siggers and Smith2024; Novick et al., Reference Novick, Ficklin, Grossiord, Konings, Martínez-Vilalta, Sadok, Trugman, Williams, Wright, Abatzoglou, Dannenberg, Gentine, Guan, Johnston, Lowman, Moore and McDowell2024; Wright and Collins, Reference Wright and Collins2024). For example, in the Namib Desert, vegetation growth is significantly impacted by temperature based on more than 20 years of satellite observations (Qiao and Wang, Reference Qiao and Wang2022). In this case, the temperature impact is negative, meaning that plant growth was reduced as the temperature increased, and temperature modulates the vegetation response to water availability. Given that temperature is increasing globally, but predicted changes in precipitation are spatially variable, more work is needed to address the potential interactions associated with coupled changes in both soil water and atmospheric demand across drylands. Besides meteorological factors, soil nutrients such as soil nitrogen and phosphorus availability play a significant role in affecting the relationship between ANPP and water availability in drylands (Yahdjian et al., Reference Yahdjian, Gherardi and Sala2011; Brown and Collins, Reference Brown and Collins2024). For example, low soil nitrogen constrains the vegetation response to water availability in African savanna ecosystems (Wang et al., Reference Wang, D’Odorico, Ries, Caylor and Macko2010). In addition to these physical factors, biological factors such as competition and herbivory (Maestre et al., Reference Maestre, Le Bagousse-Pinguet, Delgado-Baquerizo, Eldridge, Saiz, Berdugo, Gozalo, Ochoa, Guirado and García-Gómez2022) could further moderate the relationship between ANPP and water availability.