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Improving the accuracy of reference evapotranspiration (RET) estimation is essential for effective water resource management, irrigation planning, and climate change assessments in agricultural systems. The FAO-56 Penman-Monteith (PM-FAO56) model, a widely endorsed approach for RET estimation, often encounters limitations due to the lack of complete meteorological data. This study evaluates the performance of eight empirical models and four machine learning (ML) models, along with their hybrid counterparts, in estimating daily RET within the Gharb and Loukkos irrigated perimeters in Morocco. The ML models examined include Random Forest (RF), M5 Pruned (M5P), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), with hybrid combinations of RF-M5P, RF-XGBoost, RF-LightGBM, and XGBoost-LightGBM. Six input combinations were created, utilizing T max , T min , RH mean , R s , and U 2 , with the PM-FAO56 model serving as the benchmark. Model performance was assessed using four statistical indicators: Kling-Gupta efficiency index (KGE), coefficient of determination (R 2 ), mean squared error (RMSE), and relative root squared error (RRSE). Results indicate that the Valiantzas 2013 (VAL2013b) model outperformed other empirical models across all stations, achieving high KGE and R 2 values (0.95–0.97) and low RMSE (0.32–0.35 mm/day) and RRSE (8.14–10.30%). The XGBoost-LightGBM and RF-LightGBM hybrid models exhibited the highest accuracy (average RMSE of 0.015–0.097 mm/day), underscoring the potential of hybrid ML models for RET estimation in subhumid and semi-arid regions, thereby enhancing water resource management and irrigation scheduling.

Based on four decades of research by Professor Andrew Goudie, this volume provides a state-of-the-art synthesis of our understanding of desert geomorphology. It presents a truly international perspective, with examples from all over the world. Extensively referenced and illustrated, it covers such topics as the importance of past climatic changes, the variability of different desert environments, rock breakdown, wind erosion and dust storm generation, sand dunes, fluvial and slope forms and processes, the role of the applied geomorphologist in desert development and conservation, and the Earth as an analogue for other planetary bodies. This book is destined to become the classic volume on arid and semi-arid geomorphology for advanced students and researchers in physical geography, geomorphology, Earth science, sedimentology, environmental science and archaeology.

Nowadays, there is a large demand for nutrient-dense fruits to promote nutritional and metabolic human health. The production of commercial fruit crops is becoming progressively input-dependent to cope with the losses caused by biotic and abiotic stresses. A wide variety of underutilized crops, which are neither commercially cultivated nor traded on a large scale, are mainly grown, commercialized and consumed locally. These underutilized fruits have many advantages in terms of ease to grow, hardiness and resilience to climate changes compared to the major commercially grown crops. In addition, they are exceptionally rich in important phytochemicals and have medicinal value. Hence, their consumption may help to meet the nutritional needs of rural populations, such as those living in fragile arid and semi-arid regions around the world. In addition, local people are well aware of the nutritional and medicinal properties of these crops. Therefore, emphasis must be given to the rigorous study of the conservation and the nutritional characterization of these crops so that the future food basket may be widened for enhancing its functional and nutritional values. In this review, we described the ethnobotany, medicinal and nutritional values, biodiversity conservation and utilization strategies of 19 climate-resilient important, underutilized fruit crops of arid and semi-arid regions (Indian jujube, Indian gooseberry, lasora, bael, kair, karonda, tamarind, wood apple, custard apple, jamun, jharber, mahua, pilu, khejri, mulberry, chironji, manila tamarind, timroo, khirni).

Water supply is strategic for the development of society. The water distribution in nature follows patterns linked to geographic and territorial issues. Climate fluctuations aggravate shortage problems in semi-arid regions. This study aims to develop a systematic review of research on water scarcity in semi-arid areas through bibliometric methods that allow the analysis of its structure, performance, evolution, and future trends. The methodology considers three phases: (i) literature review, (ii) data cleaning and processing, and (iii) analysis of the research field and future trends. The intellectual structure of water scarcity in semi-arid zones covers 2206 documents with the collaboration of sixty-one countries, distributed in studies carried out in 54 years (1967 to 2021). This field of research has been growing, especially since the 21st century (93.1% of the documents). The countries that study the issue the most are those with high population rates and large consumption patterns, such as the United States and China. There are two central areas of interest led by the terms \"water scarcity\" and \"water stress\" due to the intensive use of the resource for agriculture and the management of the water–energy–climate nexus. Thus, the most significant journals studied relate remote sensing to resource management, and the most cited are related to agriculture. This research made it possible to consider future topics such as the study of anthropogenic effects and climate change, the accuracy and applicability of models, and future trends in conventional and unconventional agriculture and resources.

Vegetation greenness dynamics in arid and semi-arid regions are sensitive to climate change, which is an important phenomenon in global climate change research. However, the driving mechanism, particularly for the longitudinal and latitudinal changes in vegetation greenness related to climate change, has been less studied and remains poorly understood in arid and semi-arid areas. In this study, we investigated changes in vegetation greenness and the vegetation greenness line (the mean growing season normalized difference vegetation index (NDVI) = 0.1 contour line) and its response to climate change based on AVHRR-GIMMS NDVI3g and the fifth and latest global climate reanalysis dataset from 1982 to 2015 in the arid and semi-arid transition zone of the Mongolian Plateau (ASTZMP). The results showed that the mean growing season NDVI increased from the central west to east, northeast, and southeast in ASTZMP. The vegetation greenness line migrated to the desert during 1982–1994, to the grassland during 1994–2005, and then to the desert during 2005–2015. Vegetation greenness was positively correlated with precipitation and negatively correlated with temperature. The latitudinal variation of the vegetation greenness line was mainly affected by the combination of precipitation and temperature, while the longitudinal variation was mainly affected by precipitation. In summary, precipitation was a key climatic factor driving rapid changes in vegetation greenness during the growing season of the transition zone. These results can provide meaningful information for research on vegetation coverage changes in arid and semi-arid regions.

Aim: Understanding species ability to withstand heat stress is paramount for predicting their response to increasing temperatures and decreasing rainfall. Arid systems are subject to climatic extremes, where plants, being immobile, live on the frontline of climate change. Our aim was to investigate whether: (1) warming tolerance [WT = the difference between a species physiological thermal damage threshold (T₅₀) and the maximum temperature within its distribution (Thab)] for desert plants is higher at high latitudes, as has been shown for terrestrial ectotherms, and (2) if T₅₀ of desert plants better corresponds with broad climatic indicators or species native microhabitats. Location: The Australian Arid Lands Botanic Garden, Port Augusta, South Australia. Methods: Using chlorophyll fluorescence techniques, we measured T₅₀ for 42 Australian arid plant species native to different microhabitats based on water availability. WT was calculated (T₅₀ — Thab) and each metric was compared against microhabitat and broad-scale climatic variables for each species. Results: T₅₀ was unrelated to macro-scale climate or latitude, whereas WT increased for species whose distributions extend into higher latitudes, a pattern hitherto not shown for terrestrial plants. We also found that species adapted to higher water availability in their native microhabitat had significantly lower T₅₀ and WT than species from drier microhabitats. Main conclusions: (1) Warming tolerance increased with latitude, but the strength of this relationship was related to the way WT was quantified, with T hab and latitude being linked. (2) T₅₀ did not correlate with latitude, but both T₅₀ and WT were strongly related to their microhabitats. Specifically, water availability is important, such that even within a desert biome, species associated with 'wetter' microhabitats, may be particularly vulnerable to heat stress. Thus, we show that local-scale patterns better capture plant physiological responses to temperature than broad-scale distributions.

Climate change and human activities have increased droughts, especially overgrazing and deforestation, which seriously threaten the balance of terrestrial ecosystems. The ecological carrying capacity and vegetation cover in the arid zone of Xinjiang, China, are generally low, necessitating research on vegetation response to drought in such arid regions. In this study, we analyzed the spatial and temporal characteristics of drought in Xinjiang from 2001 to 2020 and revealed the response mechanism of SIF to multi-timescale drought in different vegetation types using standardized precipitation evapotranspiration index (SPEI), solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) data. We employed trend analysis, standardized anomaly index (SAI), Pearson correlation, and trend prediction techniques. Our investigation focused on the correlations between GOSIF (a new SIF product based on the Global Orbital Carbon Observatory-2), NDVI, and EVI with SPEI12 for different vegetation types over the past two decades. Additionally, we examined the sensitivities of vegetation GOSIF to various scales of SPEI in a typical drought year and predicted future drought trends in Xinjiang. The results revealed that the spatial distribution characteristics of GOSIF, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) were consistent, with mean correlations with SPEI at 0.197, 0.156, and 0.128, respectively. GOSIF exhibited the strongest correlation with SPEI, reflecting the impact of drought stress on vegetation photosynthesis. Therefore, GOSIF proves advantageous for drought monitoring purposes. Most vegetation types showed a robust response of GOSIF to SPEI at a 9-month scale during a typical drought year, with grassland GOSIF being particularly sensitive to drought. Our trend predictions indicate a decreasing trend in GOSIF vegetation in Xinjiang, coupled with an increasing trend in drought. This study found that compared with that of the traditional greenness vegetation index, GOSIF has obvious advantages in monitoring drought in the arid zone of Xinjiang. Furthermore, it makes up for the lack of research on the mechanism of vegetation GOSIF response to drought on multiple timescales in the arid zone. These results provide strong theoretical support for investigating the monitoring, assessment, and prediction of vegetation response to drought in Xinjiang, which is vital for comprehending the mechanisms of carbon and water cycles in terrestrial ecosystems.

Rapid and accurate mapping of the spatial distribution of cotton fields is helpful to ensure safe production of cotton fields and the rationalization of land-resource planning. As cotton is an important economic pillar in Xinjiang, accurate and efficient mapping of cotton fields helps the implementation of rural revitalization strategy in Xinjiang region. In this paper, based on the Google Earth Engine cloud computing platform, we use a random forest machine-learning algorithm to classify Landsat 5 and 8 and Sentinel 2 satellite images to obtain the spatial distribution characteristics of cotton fields in 2011, 2015 and 2020 in the Ogan-Kucha River oasis, Xinjiang. Unlike previous studies, the mulching process was considered when using cotton field phenology information as a classification feature. The results show that both Landsat 5, Landsat 8 and Sentinel 2 satellites can successfully classify cotton field information when the mulching process is considered, but Sentinel 2 satellite classification results have the best user accuracy of 0.947. Sentinel 2 images can distinguish some cotton fields from roads well because they have higher spatial resolution than Landsat 8. After the cotton fields were mulched, there was a significant increase in spectral reflectance in the visible, red-edge and near-infrared bands, and a decrease in the short-wave infrared band. The increase in the area of oasis cotton fields and the extensive use of mulched drip-irrigation water saving facilities may lead to a decrease in the groundwater level. Overall, the use of mulch as a phenological feature for classification mapping is a good indicator in cotton-growing areas covered by mulch, and mulch drip irrigation may lead to a decrease in groundwater levels in oases in arid areas.

1. Demographic recruitment processes, such as seed germination and seedling emergence, are critical transitional phases to the re-establishment of degraded plant populations, but often fail due to rainfall not supporting plant requirements. Using species from the widespread arid Australian perennial grass genus Triodia, we investigated the interactions of seeds in different dormancy states and their functional germination envelope in response to water stress after simulated pulse rainfall events. 2. Seed dormancy was alleviated in Triodia species to varying degrees by wet/dry cycling or by removing floret structures from seeds. The seeds were then exposed to different rainfall frequency and quantity events mimicking the 25th, median, 75th and 95th percentile rainfall events found in natural habitats for the study species in the north-west Australian arid zone. 3. Under 95th percentile rainfall conditions recruitment was highest, but still limited to 35% germination and 10% emergence of cleaned seeds (i.e. the least dormant state evaluated). This was related to the functional germination envelope as indicated by more negative base water potential thresholds (Ψb50) for cleaned seeds (≥ -0.33 MPa) compared to intact florets (≥ -0.26 MPa). As a result, the maximum cumulative time where soil water potentials were optimal for germination (Ψsoil ≥ Ψb50) was 1.6-2.6 times longer for cleaned seeds in large frequent rainfall events when compared to intact florets. Furthermore, seed dormancy, that usually prolongs seed survival, was linked to a short-term reduction in seed viability, which may further reduce recruitment rates. 4. Synthesis and applications. Our findings indicate that large frequent rainfall events raised soil water potentials above critical thresholds for germination and are important for successful plant establishment. If recruitment bottlenecks are a result of seed dormancy and variable rainfall for arid grass species, then this study shows benefits for alleviating seed dormancy prior to seeding in restoration sites, as this increases the environmental envelope for germination.

The Tibetan Plateau is known as the “third pole of the world,” and plateau oases are a key component of plateau ecosystems. Under natural conditions, the ecosystems on the Tibetan Plateau contain relatively low levels of heavy metals. However, the overexploitation of resources by humans for production and living has affected the quality of soils in the Qinghai-Tibet region, whereby the environmental capacity is decreasing. The oases in the arid zone of Delingha-Wulan County were selected as the study area for determining the environmental capacity of soil heavy metals in the Tibetan Plateau. The results indicated that the six key heavy metals in the study region ranked in the following overall order of static environmental capacity: Zn > Pb > Cu > As > Hg > Cd. High values of the elemental residual capacity occurred mainly in the Chachaxiangka area, whereas low values occurred mainly in the Wulan area. The geological background of the research area had a statistically significant on the residual capabilities of all six elements. Except for Pb, the other five elements were significantly affected by soil type and land use. This study revealed the soil-carrying capacity of oases in arid zones. The findings reported herein provide a scientific foundation for safeguarding the ecosystem in the oases of the arid zone in the Qinghai-Tibetan region.