Uncertainty Characteristics of Climatic Potential Productivity in Guizhou Based on Normal Cloud Model

doi:10.11924/j.issn.1000-6850.casb2026-0284

Abstract

Abstract:

To deepen the understanding of the uncertain changes in climate resources in karst areas of the Yangtze River Basin, based on meteorological data from 78 national stations in Guizhou from 1961 to 2024, this research employs a sliding time window (30a) and a backward cloud algorithm to systematically analyze the evolution of normal cloud model parameters (expectation Ex, entropy En, and hyper-entropy He) for temperature production potential (Y_t), precipitation production potential (Y_p), evapotranspiration production potential (Y_e), and standard climatic potential productivity (Y_s). The main conclusions are as follows. (1) Overall, thermal resources (Y_t expectation value of 19087.25 kg/hm²) determine the upper limit of production potential in Guizhou, while water conditions are the primary limiting factor. Y_p exhibits the highest randomness and fuzziness and is the main source of uncertainty for Y_s. (2) Regarding temporal trends, the expectation value of Y_t shows the fastest increase (138.2 kg/hm²·10a), while the expectation value of Y_p shows a significant decrease (-80.3 kg/hm²·10a), leading to a slow increase in Y_s (36.4 kg/hm²·10a). The entropy and hyper-entropy of Y_p exhibit the strongest increasing trends, indicating intensified instability in precipitation. Abrupt changes were concentrated between 1976 and 1988, coinciding with the interdecadal climate transition. The differences before and after the abrupt changes are consistent with the long-term trends, and the trends of increasing Y_t, decreasing Y_p, and increasing fuzziness of Y_s are projected to continue for 6 to 11 years (H>0.75). (3)Spatially, empirical orthogonal function (EOF) decomposition reveals a composite characteristic of global response (first mode variance 47.0% to 96.3%) and local differentiation (second mode reflecting topographic influences). The normal cloud model effectively characterizes the baseline, fluctuation, and fuzzy boundaries of climatic potential productivity, offering a new approach for the multi-dimensional assessment of regional agro-climatic resources.

Key words: climatic potential productivity, expectation, randomness, fuzziness, spatiotemporal variation

CLC Number:

P467

REN Qingfeng, ZHANG Dongping, WANG Jie, WANG Hongbin, HUANG Fulin, XIONG Xinyi, MA Lei. Uncertainty Characteristics of Climatic Potential Productivity in Guizhou Based on Normal Cloud Model[J]. Chinese Agricultural Science Bulletin, 2026, 42(17): 131-140.

Figures/Tables 10

References 41

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H值	持性续强度	H值	反持续性强度
0.80<H≤1.00	很强	0≤H<0.20	很强
0.75<H≤0.80	强	0.20≤H<0.25	强
0.65<H≤0.75	较强	0.25≤H<0.35	较强
0.55<H≤0.65	较弱	0.35≤H<0.45	较弱
0.50<H≤0.55	很弱	0.45≤H<0.50	很弱

H值	持性续强度	H值	反持续性强度
0.80<H≤1.00	很强	0≤H<0.20	很强
0.75<H≤0.80	强	0.20≤H<0.25	强
0.65<H≤0.75	较强	0.25≤H<0.35	较强
0.55<H≤0.65	较弱	0.35≤H<0.45	较弱
0.50<H≤0.55	很弱	0.45≤H<0.50	很弱

	Ex			En			He
	H值	持续（反持续）时间/a	持续（反持续）强度	H值	持续（反持续）时间/a	持续（反持续）强度	H值	持续（反持续）时间/a	持续（反持续）强度
Y_t	0.82	6	很强	0.60	9	较弱	0.83	8	很强
Y_p	0.76	8	强	0.62	6	较弱	0.61	7	较弱
Y_e	0.50	-	无	0.51	7	很弱	0.42	6	较弱
Y_s	0.65	7	较弱	0.59	7	较弱	0.81	11	很强

	Ex			En			He
	H值	持续（反持续）时间/a	持续（反持续）强度	H值	持续（反持续）时间/a	持续（反持续）强度	H值	持续（反持续）时间/a	持续（反持续）强度
Y_t	0.82	6	很强	0.60	9	较弱	0.83	8	很强
Y_p	0.76	8	强	0.62	6	较弱	0.61	7	较弱
Y_e	0.50	-	无	0.51	7	很弱	0.42	6	较弱
Y_s	0.65	7	较弱	0.59	7	较弱	0.81	11	很强

	Ex			En			He
	第一模态	第二模态	前两个模态	第一模态	第二模态	前两个模态	第一模态	第二模态	前两个模态
Y_t	96.34	2.54	98.88	77.33	13.07	90.39	46.95	23.67	70.63
Y_p	61.25	24.19	85.44	64.98	16.55	81.53	72.64	8.76	81.40
Y_e	76.94	11.19	88.12	83.22	6.57	89.79	85.05	6.16	91.21
Y_s	62.94	20.43	83.38	83.12	6.54	89.66	79.43	6.98	86.40