**1. Introduction**

Global warming and the increase of extreme weather events are having a serious impact on the structure, function, and processes of global ecosystems [1], and have become a focal issue of common concern to governments, the public, and the scientific community. In IPCC AR6 [2], it is reported that the frequency and intensity of some extreme weather and climate events will continue to increase under medium and high emission scenarios, and the increased extreme events (e.g., droughts, heat waves, and heavy rainfall) will affect 25−40% of global ecosystem structure and function.

The terrestrial ecosystem carbon cycle is a key process driving ecosystem change, and changes in the ecosystem carbon cycle are sensitive to climate change. China is one of the most sensitive and vulnerable regions to climate change. Climate change

**Citation:** Wang, S.; Zhang, Q.; Yue, P.; Wang, J.; Yang, J.; Wang, W.; Zhang, H.; Ren, X. Precipitation-Use Efficiency and Its Conversion with Climate Types in Mainland China. *Remote Sens.* **2022**, *14*, 2467. https://doi.org/10.3390/rs14102467

Academic Editors: Massimo Menenti, Yaoming Ma, Li Jia and Lei Zhong

Received: 23 March 2022 Accepted: 19 May 2022 Published: 20 May 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

decreased the capacity of carbon storage [3], and extreme climate events such as drought, extreme heat, and extreme precipitation all have serious impacts on the carbon cycle of terrestrial ecosystems. Droughts have weakened vegetation growth [4], and prolonged and persistent droughts have reduced carbon accumulation, causing grassland ecosystems in Inner Mongolia to change from a carbon sink to a carbon source in a typical year [5]. Heat waves and droughts significantly reduced regional GPPs and crop yields in summer 2013 [6]. The ice storm in early 2008 also resulted in a decrease in annual evapotranspiration and GPP in southeastern China [7].

Precipitation-use efficiency (PUE) describes the response of net primary productivity (NPP) to the temporal and spatial distribution of precipitation. PUE is a comprehensive physiological and ecological index for evaluating the appropriate degree of vegetation growth while reflecting the carbon and water cycles and the carbon–water relationship in the ecosystem [8]. Research on the characteristics of PUE and its control mechanism can help evaluate and predict the impact of global changes on the carbon–water cycle of ecosystems and provide a theoretical basis for regional plant protection and restoration. PUE distribution and changes are affected by several factors, such as topography, soil conditions, climate change, and human activities. Climate change is the most important and active factor [9,10]. Earlier studies addressed the response of the spatiotemporal PUE pattern to climate change but did not reach a consistent conclusion due to different temporal and spatial scales. Some studies concluded that the PUE spatially decreases with increasing drought and potential evapotranspiration and increases with increasing precipitation [11]. However, other studies concluded that the PUE spatially decreases with increasing precipitation [12,13]. It has also been demonstrated that there is no obvious relationship between the spatial PUE distribution and precipitation [14]. In addition, some studies have reported that the PUE exhibits a unimodal distribution that first increases and then decreases with increasing precipitation and reaches its peak at a specific precipitation value [13,15,16]. The feedback mechanisms between the water-carbon cycle of ecosystems and climate are relatively complex, and the response of NPP and PUE to climate change has a large spatial and temporal heterogeneity. China's vast land area, complex topography, diverse climate and vegetation types, and high spatial and temporal variability in precipitation and ecosystem carbon fluxes hinder the accurate assessment of carbon fluxes [17–20]. Earlier studies focused mostly on small local areas with precipitation or temperature limits or specific vegetation types [11,16,21,22], and little attention has been paid to variations in PUE along a climatic gradient. At the same time, prior studies mostly focused on the effects of single climate factors on PUE, lacking a comprehensive understanding of the specific contributions of each climate factor and regional differences [16,20,21], which cannot fully reveal the difference and transformation characteristics of PUE with climate and vegetationgradient distribution, thereby limiting the in-depth understanding of PUE characteristics and driving forces in different regions.

NPP is a key for calculating the PUE. There are several methods for obtaining the NPP. In situ measurements have high data accuracy but are limited by the amount of data and are time-consuming and labor-intensive. Thus, they can only be used during surveys of small areas. Model estimation is an effective means of obtaining NPP on a regional or global scale. NPP estimation models can be roughly divided into three categories: ecophysiological process models, light-use efficiency models, and climate statistical models [23]. Ecophysiological process models simulate NPP based on the ecophysiological characteristics and growth mechanisms of plants [24]. Representative models include the Biome-BGC, CEVSA, and BEPS models. This type of model has strong mechanisms and is systematic. However, they are complex, and the required parameters are many and difficult to obtain. Light-use efficiency models use photosynthesis from vegetation and a resource balance view as the theoretical basis. They apply remote sensing data to drive ecological models for NPP simulation on regional or global scales and have been used worldwide. Representative models are the CASA and Glo-PEM models. Although this type of model has clear mechanisms and complete structures, the assignment and correction of

model parameters are complicated, with large uncertainties. There are three main types of parameters based on the input of the light-use efficiency model: solar radiation data, maximum light-use efficiency, and environmental factors. The algorithms and required data for each parameter type are diverse. Different calculation methods have substantial differences in simulating vegetation NPP, especially in simulating environmental factors and maximum light use efficiency, because it is difficult to assign a large number of soil parameters to certain regions or special geomorphic types [25]. Additionally, these models are completely dependent on the availability and quality of remote sensing data [26]; in regions with strong spatial heterogeneity and complex terrain, the accuracy of the model is highly uncertain.

Climate statistical models estimate NPP based on the correlation between plant growth and environmental factors. Representative models include the Miami, Thornthwaite memorial, Chicago, and comprehensive models. Although these models lack a mechanism, they are simple, intuitive, and highly applicable. Hence, they constitute the easiest and most convenient method for estimating NPP. Since their development in the 1970s, they have been applied in vegetation NPP research worldwide [20], particularly in relation to large-scale research. Various landatmosphere mutual observation experiments also provide the possibility for model calibration, thereby continuously improving the accuracy of these models.

In the context of global climate change, the characteristics and changes of China's regional PUE are not ye<sup>t</sup> fully understood, as well as the regional differences and driving forces of the PUE response to climate change. What are the spatial distribution characteristics of the carbon flux in mainland China? How do environmental factors relate to PUE? Does PUE spatial conversion occur with climate type? What is the possible mechanism of action? Therefore, our primary objectives were to: (1) improve the ETa calculation model by comprehensively considering the water and energy conditions in different regions; (2) analyze the characteristics of ETa, NPP, and PUE in different climate regions of China; (3) reveal the driving forces, transformation characteristics, and control mechanism of the PUE distribution.

### **2. Materials and Methods**

### *2.1. Study Area*

This study focused on mainland China. There are grea<sup>t</sup> climatic differences between east and west and south and north of China. Precipitation decreases from southeast to northwest, and climate transitions from humid to arid, presenting a basic pattern of humid in the east and south, but arid in the west and north, and there is a narrow zonal climate transition zone between humid and arid climate regions. We divided the study region into three sub-regions according to the distribution of average annual precipitation (P): the arid region (P < 200 mm), transition zone (600 > P ≥ 200 mm), and humid region (P ≥ 600 mm) [27]. The distribution of meteorological and flux stations and sub-regions is shown in Figure 1.

**Figure 1.** Locations of 693 meteorological stations (black solid dots), 44 flux stations (red stars), and the three sub-regions (i.e., arid region, transition zone, and humid region) (**a**), and the Köppen-Geiger climatic zones (**b**).
