Abstract

Groundwater depth (GWD) is an important factor to sustain the ecological integrity of some ecosystems and is often used as an indicator of environmental quality in dry areas. Single-scale data gained from quadrat surveys is always used to establish a relationship with GWD to determine the optimum GWD. However, the randomness and uncertainty in single-scale data may result in insufficient reliability of results. To overcome this shortage, multiple growth indicators of poplar trees (Populus euphratica) in Hetao Irrigation District, including average crown width (ACW), tree height, diameter at breast height (DBH), mean ring spacing (MRC), and normalized difference vegetation index (NDVI), were acquired by field sampling and remote sensing. These indicators were used to establish relationships with the GWD by considering spatial and temporal variation to identify the optimum GWD. The cloud model was introduced and its three digital features derived from optimum groundwater depth data (expectation: Ex, entropy: En, and super-entropy: He) were calculated to construct the reverse cloud models W (Ex, En, He) for describing ecological GWD to determine the optimum ecological GWD in semi-arid areas. The results show that the optimum GWD range was 1.60–2.20 m. The cloud models obtained on spatial and temporal scales were WS (2.01, 0.07, 0.04) and WT (1.78, 0.10, 0.02), respectively. The resulting comprehensive cloud model WC (1.87, 0.14, 0.03) exhibited better variability, so 1.87 m was taken as the optimum GWD for poplars. This method can determine the regional ecological groundwater level more accurately and effectively, and provide evaluation indicators for the management of regional groundwater.

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