Spatio-Temporal Vulnerability of Irrigated Agroecosystems in the Turkestan Region to Drought: An Integrated Assessment Based on Copula Theory, SPEI, NDVI, and NDSI

Aigul Tokbergenova, Ulan Mukhtarov, Aisara Assanbayeva, Maulken Askarova, Aizhan N. Mussagaliyeva, Kanat Zulpykharov, Ruslan Salmurzauly, Bekzat Bilalov, Darkhan Zhanbayev

2026

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.3390/su18157793
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

Climate change, increasing aridification, and water scarcity are intensifying the vulnerability of irrigated agroecosystems in Central Asia to drought and secondary soil salinization. In this study, we assessed the spatio-temporal vulnerability of irrigated land in the Turkestan Region (Kazakhstan) using SPEI-3, NDVI, NDSI, Landsat imagery (2000–2025), spatial analysis, and copula modeling. A strong positive relationship was found between SPEI-3 and NDVI (Pearson’s r = 0.861; Spearman’s r = 0.851), indicating vegetation is highly sensitive to moisture availability. Moderate and severe droughts reduced NDVI values by 15–25%, exceeding 30% in the most vulnerable areas, while persistent salinity hotspots were identified in the southern and southeastern irrigated zones. Copula modeling quantified the relationship between SPEI-3 and NDVI using 23 paired annual observations (2000–2022). The best-fitting model, selected using the Akaike Information Criterion (AIC = −31.074) and Bayesian Information Criterion (BIC = −29.939), revealed a nonlinear and asymmetric dependence between drought conditions and vegetation response. The probability of concurrent drought and vegetation degradation reached 0.55–0.70 in the most vulnerable areas. Integrated vulnerability mapping identified the Maktaaral, Zhetysay, Shardara, and Otyrar districts as the most vulnerable territories. The proposed framework supports drought vulnerability assessment and sustainable management of irrigated agroecosystems under climate change.