Wetlands in drylands have high inter- and intra-annual ecohydrological variations that are driven to a great extent by climate variability and anthropogenic influences. The Ramsar Convention on Wetlands encourages the development of frameworks for national action and international cooperation for ensuring conservation and wise use of wetlands and their resources at local, national and regional scales. However, the implementation of these frameworks remains a challenge. This is mainly due to limited availability of high-resolution data and suitable big data processing techniques for assessing and monitoring wetland ecohydrological dynamics at large spatial scales, particularly in the sub-Saharan African region. The availability of cloud computing platforms such as Google Earth Engine (GEE) offers unique big data handling and processing opportunities to address some of these challenges. In this study, we applied the GEE cloud computing platform to monitor the long-term ecohydrological dynamics of a seasonally flooded part of the Nylsvley floodplain wetland complex in north-eastern South Africa over a 20-year period (2000–2020). The specific objectives of the study were 1) to evaluate wetland ecohydrological dynamics using the 20-year multi-date Landsat composite data coupled with the Random Forest machine learning algorithm, and 2) to establish the major drivers of wetland ecohydrological changes, using selected spectral indices (i.e. Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI) and Normalised Difference Phenology Index (NDPI)) coupled with climate data. The ecohydrology of the wetland changed over time, with some classes increasing twice when compared to the previous measurement, while others decreasing significantly during the study period. Notably, the bare surface class increased at rates of 230% and 350% between 2006–2010 and 2016–2020, respectively. Moreover, the indices showed similar trends throughout the 20-year period, with NDWI having minimum values less than zero in all cases. This implied no surface inundation, although the presence of some wetland vegetation indicated seasonal to semi-permanent soil saturation conditions. A comparative analysis of climate data and remotely sensed indices showed that annual changes of precipitation and evapotranspiration were the main drivers of wetland ecohydrological variations. The findings of the study underscore the relevance of cloud computing artificial intelligence techniques, and particularly the GEE platform, in evaluating wetland ecohydrological dynamics for semi-arid southern African systems which are deteriorating due to the unsustainable use and poor management resulting from limited knowledge about their changes over time.

旱地湿地具有很高的年际和年内生态水文变化，这在很大程度上是由气候变率和人为影响驱动的。《拉姆萨尔湿地公约》鼓励制定国家行动和国际合作框架，以确保在地方、国家和区域范围内保护和明智地利用湿地及其资源。然而，这些框架的实施仍然是一个挑战。这主要是由于在大空间尺度上评估和监测湿地生态水文动态的高分辨率数据和合适的大数据处理技术有限，特别是在撒哈拉以南非洲地区。谷歌地球引擎 (GEE) 等云计算平台的可用性为应对其中一些挑战提供了独特的大数据处理和处理机会。在这项研究中，我们应用 GEE 云计算平台来监测南非东北部 Nylsvley 洪泛区湿地综合体季节性洪水部分在 20 年期间（2000-2020 年）的长期生态水文动态。该研究的具体目标是 1) 使用 20 年多日期 Landsat 复合数据和随机森林机器学习算法评估湿地生态水文动态，以及 2) 使用选定的光谱确定湿地生态水文变化的主要驱动因素指数（即归一化差异植被指数（NDVI），归一化差异水指数 (NDWI) 和归一化差异物候指数 (NDPI)) 以及气候数据。湿地的生态水文随时间发生变化，与之前的测量相比，一些类别增加了两倍，而其他类别在研究期间显着减少。值得注意的是，裸露表面等级在 2006-2010 年和 2016-2020 年间分别以 230% 和 350% 的速度增长。此外，这些指数在整个 20 年期间显示出类似的趋势，NDWI 在所有情况下的最小值都小于零。这意味着没有地表淹没，尽管一些湿地植被的存在表明了季节性到半永久性的土壤饱和状况。气候资料和遥感指数的对比分析表明，降水量和蒸散量的年变化是湿地生态水文变化的主要驱动因素。该研究的结果强调了云计算人工智能技术，特别是 GEE 平台在评估南部非洲半干旱系统的湿地生态水文动态方面的相关性，这些系统由于不可持续的使用和管理不善而恶化，原因是对其了解有限。随着时间的推移而变化。