Search Results (122)

Global climate change has intensified the frequency and severity of drought events, posing significant threats to agricultural sustainability, particularly for water-sensitive crops such as tea. In northern China, where precipitation is unevenly distributed and evapotranspiration rates are high, tea plantations frequently experience water stress, leading to reduced yields and declining quality. Therefore, accurately simulating soil water content (SWC) is essential for drought forecasting, soil moisture management, and the development of precision irrigation strategies. However, due to the high complexity of soil–vegetation–atmosphere interactions in field conditions, the practical application of the HYDRUS-1D model in northern China remains relatively limited. To address this issue, a three-year continuous monitoring campaign (2021–2023) was conducted in a coastal area of northern China, covering both young tea plantations and adjacent grasslands. Based on the measured meteorological and soil data, the HYDRUS-1D model was used to simulate SWC dynamics across 10 soil layers (0–100 cm). The model was calibrated and validated against observed SWC data to evaluate its accuracy and applicability. The simulation results showed that the model performed reasonably well, achieving an R² of 0.739 for the tea plantation and 0.878 for the grassland, indicating good agreement with the measured values. These findings demonstrate the potential of physics-based modeling for understanding vertical soil water processes under different land cover types and provide a scientific basis for improving irrigation strategies and water use efficiency in tea-growing regions. Full article

Accurate estimation of actual evapotranspiration (ET) is critical for understanding hydrothermal cycles and ecosystem functioning in arid regions, where water scarcity governs ecological resilience. To address persistent gaps in ET quantification, this study integrates multi-source remote sensing data, energy balance modeling, and ground-based validation that significantly enhances spatiotemporal ET accuracy in the vulnerable desert steppe ecosystems. The study utilized meteorological data from several national stations and Landsat-8 imagery to process monthly remote sensing images in 2019. The Surface Energy Balance System (SEBS) model, chosen for its ability to estimate ET over large areas, was applied to derive modeled daily ET values, which were validated by a large-weighted lysimeter. It was shown that ET varied seasonally, peaking in July at 6.40 mm/day, and reaching a minimum value in winter with 1.83 mm/day in December. ET was significantly higher in southern regions compared to central and northern areas. SEBS-derived ET showed strong agreement with lysimeter measurements, with a mean relative error of 4.30%, which also consistently outperformed MOD16A2 ET products in accuracy. This spatial heterogeneity was driven by greater vegetation coverage and enhanced precipitation in the southeast. The steppe ET showed a strong positive correlation with surface temperatures and vegetation density. Moreover, the precipitation gradients and land use were primary controllers of spatial ET patterns. The process-based SEBS frameworks demonstrate dual functionality as resource-optimized computational platforms while enabling multi-scale quantification of ET spatiotemporal heterogeneity; it was therefore a reliable tool for ecohydrological assessments in an arid steppe, providing critical insights for water resource management and drought monitoring. Full article

The Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), and European Space Agency-Climate Change Initiative (ESA–CCI) soil moisture (SM) products are widely used in agricultural drought monitoring, water resource management, and climate analysis applications. However, the performance of these SM products varies significantly across regions and environmental conditions, due to in sensor characteristics, retrieval algorithms, and the lack of localized calibration. This study proposes a multi-source long short-term memory (MLSTM) for improving ASCAT, ESA–CCI, and SMAP SM products by combining in-situ SM measurements and four key auxiliary variables: precipitation (PRE), land surface temperature (LST), fractional vegetation cover (FVC), and evapotranspiration (ET). First, the in-situ measured data from four in-situ observation networks were corrected using the LSTM method to match the grid sizes of ASCAT (0.1°), ESA–CCI (0.25°), and SMAP (0.1°) SM products. The RPE, LST, FVC, and ET were used as inputs to the LSTM to obtain loss data against in-situ SM measurements. Second, the ASCAT, ESA–CCI, and SMAP SM datasets were used as inputs to the LSTM to generate loss data, which were subsequently corrected using LSTM-derived loss data based on in-situ SM measurements. When the mean squared error (MSE) loss values were minimized, the improvement for ASCAT, ESA–CCI, and SMAP products was considered the best. Finally, the improved ASCAT, ESA–CCI, and SMAP were produced and evaluated by the correlation coefficient (R), root mean square error (RMSE), and standard deviation (SD). The results showed that the RMSE values of the improved ASCAT, ESA–CCI, and SMAP products against the corrected in-situ SM data in the OZNET network were lower, i.e., 0.014 cm³/cm³, 0.019 cm³/cm³, and 0.034 cm³/cm³, respectively. Compared with the ESA–CCI and SMAP products, the ASCAT product was greatly improved, e.g., in the SNOTEL network, the Root Mean-Square Deviation (RMSD) values of 0.1049 cm³/cm³ (ASCAT) and 0.0662 cm³/cm³ (improved ASCAT). Overall, the MLSTM-based algorithm has the potential to improve the global satellite SM product. Full article

In recent years, under the influence of climate change and human activities, droughts and floods have occurred frequently in the Yangtze River Basin (YRB), seriously threatening socioeconomic development and ecological security. The topography and climate of the YRB are complex, so it is crucial to develop appropriate drought and flood policies based on the drought and flood characteristics of different sub-basins. This study calculated the water storage deficit index (WSDI) based on the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GFO) mascon model, extended WSDI to the bidirectional monitoring of droughts and floods in the YRB, and verified the reliability of WSDI in monitoring hydrological events through historical documented events. Combined with the wavelet method, it revealed the heterogeneity of climate responses in the three sub-basins of the upper, middle, and lower reaches. The results showed the following. (1) Compared and verified with the Standardized Precipitation Evapotranspiration Index (SPEI), self-calibrating Palmer Drought Severity Index (scPDSI), and documented events, WSDI overcame the limitations of traditional indices and had higher reliability. A total of 21 drought events and 18 flood events were identified in the three sub-basins, with the lowest frequency of drought and flood events in the upper reaches. (2) Most areas of the YRB showed different degrees of wetting on the monthly and seasonal scales, and the slowest trend of wetting was in the lower reaches of the YRB. (3) The degree of influence of teleconnection factors in the upper, middle, and lower reaches of the YRB had gradually increased over time, and, in particular, El Niño Southern Oscillation (ENSO) had a significant impact on the droughts and floods. This study provided a new basis for the early warning of droughts and floods in different sub-basins of the YRB. Full article

Increasing water demand and climate change exacerbate water management challenges in arid and semi-arid regions experiencing water scarcity resulting from low and irregular precipitation and high evapotranspiration. These regions rely on substantial water storage capacity, typically provided by large multi-purpose public reservoirs and small private reservoirs. While public reservoirs are typically monitored, the number, size, and private ownership of small reservoirs complicate effective storage monitoring, hindering efforts to assess water availability during droughts and to allocate water efficiently and equitably. Remote sensing provides a solution to complement existing monitoring systems by offering high spatial and temporal resolution observations. This study introduces a methodology for monitoring the surface area of large and small reservoirs based on optical and radar images from Sentinel-1 and Sentinel-2 satellites. The Normalized Difference Water Index (NDWI) and the Otsu image segmentation method are employed to identify and estimate water body areas, and the Google Earth Engine and programming languages are used to automate the process. The validation results demonstrated correlation for most reservoirs, with slight underestimations at flood peaks. Among the 17 large reservoirs, 16 had an R² value above 0.82, 12 had an RMSE value below 0.8, and 14 had a KGE value above 0.7. For the small reservoirs, the method correctly identified 3224 of the 6370 reservoirs recorded in situ, with greater accuracy in the classes of reservoirs with elevation above 10 m. A total of 7251 reservoirs were mapped, including 4027 not present in the database of the responsible regulatory entity, most with an area of less than 1.8 ha. Performance was better for larger areas (>3 ha), while small areas were underestimated. This methodology offers a practical water management tool adaptable for various-sized surface water bodies, including small, unmonitored water bodies. Full article

Droughts accompanied by high temperatures are becoming increasingly frequent across Europe and globally. Both individual trees and entire forest ecosystems are exposed to drought stress, with prolonged drought periods leading to increased tree mortality. Therefore, continuous monitoring, data collection, and analysis of tree mortality are essential prerequisites for understanding the complex interactions between climate and trees. This study examined the effects of short-term and prolonged (multi-year) droughts on the mortality of individual trees and forests in Serbia. The analysis was based on datasets from our previous research on the influence of drought and drought duration on individual tree mortality in Serbian forest ecosystems, supplemented with new data collected through the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests). Additionally, we incorporated data from the public enterprise (PE) “Srbijašume”, which manages forests in Central Serbia, focusing on random yields resulting from natural disasters (droughts). These data enabled a comparative assessment of the findings on increased mortality and drought impact at both the individual tree level and the stand level. This study identifies key similarities and differences in tree mortality trends based on drought duration and examines their correlations within the same time frame (2004–2023). By analysing climatic conditions across Serbia, we provide evidence of the interaction between drought periods and increased forest mortality, which we further confirmed by calculating the Standardized Precipitation Evapotranspiration Index (SPEI). We also address the tree species that were most sensitive to the effects of drought. Our findings indicate that prolonged (multi-year) droughts, accompanied by high temperatures, have significantly contributed to increased tree mortality over the past decade. Successive multi-year droughts pose a substantial threat to both individual trees and entire forests, producing more severe and persistent responses compared to those caused by single-year droughts, which forests and individual trees are generally more capable of tolerating. Moreover, due to prolonged drought stress, trees weaken, leading to delayed mortality that may manifest several years after the initial drought event. The observed increase in tree mortality has been found to correlate with rising temperatures and the growing frequency of prolonged droughts over the past decade. Especially, intense droughts in the growing season (April–September) have a very negative impact on forests. Full article

Droughts have been exacerbated by climate change, posing significant risks to ecosystems, hydrology, agriculture, and human society. In this paper, we present the development and evaluation of a high-resolution 1 km SPEI (Standardized Precipitation-Evapotranspiration Index) dataset to enhance drought monitoring at finer spatial scales. The high-resolution SPEI datasets, derived using high-resolution TPDC precipitation and satellite-based MODIS potential evapotranspiration data, were compared with a coarse-resolution 50 km SPEI dataset derived from CRU measurements, as well as vegetation health indices (VHIs) and root zone soil moisture (SM), over two climatically contrasting regions in China: Northeast China (NEC) and Southwest China (SWC). The evaluation highlights the MODIS-based high-resolution SPEI’s ability to capture regional drought dynamics and improved correlation with vegetation and soil moisture dynamics. NEC, with its relatively flat topography and recent experience of significant droughts, and SWC, characterized by complex terrain and high precipitation variability, provided ideal testbeds for examining the performance of the 1 km SPEI. The results demonstrate that the high-resolution dataset offered superior spatial detail in detecting drought conditions, making it valuable for agricultural planning and water resource management in diverse climates. Full article

The interaction between trees’ water needs during drought and the signals that appear in their canopies is not fully understood. The first visually detectable signs, which we describe as early warning signals in tree canopies, are often not noticeable at first glance. When these signs become widely apparent, tree decline is already underway. In this study, we focus on identifying early visible signs of drought stress in the tree crowns, such as very small leaves, premature needle/leaf discolouration and abscission, and defoliation. We provide guidance on recognising initial signs, offer specific examples, and comprehensively analyse each signal. Our focus is on signs in the tree crowns that appear during intense and prolonged droughts, which we confirmed by calculating the Standardised Precipitation Evapotranspiration Index (SPEI). Our findings are based on 20 years (2004–2024) of continuous fieldwork and data collection from permanent sample plots in Serbia, which was conducted as part of the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests). We also conducted a comprehensive review of the literature and key findings related to the early signs we address. This research was further motivated by the signs observed in the tree crowns during the summer of 2024 due to extreme climatic events, which classify this year as one of the hottest recorded in Serbia. However, we still cannot conclusively determine which specific trees will die back based solely on these early warning signals, as some trees manage to withstand severe drought conditions. Nonetheless, the widespread appearance of these indicators is a clear warning of significant ecosystem instability, potentially leading to the decline of individual trees or larger groups. Full article

Evapotranspiration (ET) plays a crucial role in the surface water cycle and energy balance, and accurate ET estimation is essential for study in various domains, including agricultural irrigation, drought monitoring, and water resource management. Remote sensing (RS) technology presents an efficient approach for estimating ET at regional scales; however, existing RS retrieval algorithms for ET are intricate and necessitate a multitude of parameters. The land surface temperature–vegetation index (LST-VI) space method and statistical regression by machine learning (ML) offer the benefits of simplicity and straightforward implementation. This study endeavors to identify the optimal long-term sequence LST-VI space method and ML for ET estimation under conditions of limited observed variables, (LST, VI, and near-surface air temperature). A comparative analysis of their performance is undertaken using ground-based flux observations and MOD16 ET data. The findings can be summarized as follows: (1) Long-term remote sensing data can furnish a more comprehensive background field for the LST-VI space, achieving superior fitting accuracy for wet and dry edges, thereby enabling precise ET estimation with the following metrics: correlation coefficient (r) = 0.68, root mean square error (RMSE) = 0.76 mm/d, mean absolute error (MAE) = 0.49 mm/d, and mean bias error (MBE) = −0.14 mm. (2) ML generally produces more accurate ET estimates, with the Random Forest Regressor (RFR) demonstrating the highest accuracy: r = 0.79, RMSE = 0.61 mm/d, MAE = 0.42 mm/d, and MBE = −0.02 mm. (3) Both ET estimates derived from the LST-VI space and ML exhibit spatial distribution characteristics comparable to those of MOD16 ET data, further attesting to the efficacy of these two algorithms. Nevertheless, when compared to MOD16 data, both approaches exhibit varying degrees of underestimation. The results of this study can contribute to water resource management and offer a fresh perspective on remote sensing estimation methods for ET. Full article

Over the last century, significant climate changes, including more intense droughts and floods, have impacted agriculture and socio-economic development, particularly in rain-dependent regions like Marrakech–Safi (MS) in Morocco. Limited data availability complicates the accurate monitoring and assessment of these natural hazards. This study evaluates the role of satellite data in drought monitoring in the MS region using rain gauge observations from 18 stations, satellite-based precipitation estimates from Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), and temperatures from the fifth generation of the atmospheric global climate reanalyzed Era5-Land data. The Standardized Precipitation Evapotranspiration Index (SPEI) was calculated at various timescales to characterize droughts. Statistical analysis was then performed to assess the correlation between the SPEI and the cereal yields. The results show that CHIRPS effectively monitors droughts, demonstrating strong statistically significant correlations (r ~ 0.9) with the observed data in the plains, the plateaus, Essaouira–Chichaoua Basin, and the coastal zones, along with a good BIAS score and lower root mean square error (RMSE). However, discrepancies were observed in the High Atlas foothills and the mountainous regions. Correlation analysis indicates the significant impact of droughts on agricultural productivity, with strong correlations between the Standardized Yield Residual Series (SYRS) and SPEI-6 in April and SPEI-12 in June (r ~ 0.80). These findings underscore the importance of annual and late-season precipitation for cereal yields. Analysis provides valuable insights for decision-makers in designing adaptation strategies to enhance small-scale farmers’ resilience to current and projected droughts. Full article

To more precisely monitor drought, a new remote sensing-based drought index, the Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll fluorescence Dryness Index (VMFDI), with a spatial resolution of 1 km based on vapor pressure deficit (VPD), soil moisture (SM), and sun-induced chlorophyll fluorescence (SIF) data was constructed via a three-dimensional spatial distance model, and it was used to monitor dryness in the Yellow River Basin during 2003–2020. The spatiotemporal variations in and main factors of the VMFDI and agroecosystem responses were analyzed via the Theil–Sen median and Mann–Kendall tests and Liang–Kleeman information flow. The results revealed the following: (1) The VMFDI effectively monitors regional drought and is more sensitive than other indices like the standardized precipitation evapotranspiration index (SPEI) and GRACE drought severity index and single variables. (2) VMFDI values fluctuated seasonally in the Yellow River Basin, peaking in August and reaching their lowest in March. The basin becomes drier in winter but wetter in spring, summer, and autumn, with the middle and lower reaches, particularly Shaanxi and Gansu, being drought-prone. The VMFDI values in the agroecosystem were lower. (3) SM and VPD dominated drought at the watershed and agroecosystem scales, respectively. Key agroecosystem indicators, including greenness (NDVI), gross primary productivity (GPP), water use efficiency (WUE), and leaf area index (LAI), were negatively correlated with drought (p < 0.05). When VPD exceeded a threshold range of 7.11–7.17 ha, the relationships between these indicators and VPD shifted from positive to negative. The specific VPD thresholds in maize and wheat systems were 8.03–8.57 ha and 7.15 ha, respectively. Suggestions for drought risk management were also provided. This study provides a new method and high-resolution data for accurately monitoring drought, which can aid in mitigating agricultural drought risks and promoting high-quality agricultural development. Full article

Solar-induced chlorophyll fluorescence (SIF) offers significant potential as a novel approach for quantifying carbon and water cycling in coastal wetland ecosystems across multiple spatial scales. However, the mechanisms governing these biogeochemical processes remain insufficiently understood, largely due to the periodic influence of tidal inundation. In this study, we investigated the effects and underlying mechanisms of meteorological and tidal factors on the relationships between canopy-level solar-induced chlorophyll fluorescence at 760 nm (SIF₇₆₀) and key ecosystem processes, including gross primary productivity (GPP) and evapotranspiration (ET), in coastal wetlands. These processes are critical components of the ecosystem carbon and water cycles. Our approach involved a comparative analysis of simulations from the Soil Canopy Observation, Photochemistry and Energy Fluxes (SCOPE) model with field measurements. The results showed that: (1) simulations of SIF₇₆₀ improved following observation-based calibration of the fluorescence photosynthesis module in the SCOPE model; (2) under optimal moisture and temperature conditions (VPD 1.2–1.4 kPa and temperatures of 20–23 °C for air, soil, and water), the simulations of GPP, ET, and SIF₇₆₀ were most accurate, although salinity stress reduced performance. GPP simulations tended to overestimate under drought stress but improved at higher air temperatures (30–32 °C); (3) during tidal inundation, the SIF₇₆₀-GPP relationship weakened while the SIF₇₆₀-ET strengthened. The range of significant correlations between SIF₇₆₀, water levels, and temperature narrowed, with both relationships becoming more complex due to salinity stress. These findings suggest that tidal inundation can alleviate temperature stress on photosynthesis and transpiration; however, it also decreases photosynthetic efficiency and alters radiative transfer processes due to elevated salinity and water levels. These factors are critical considerations when using SIF to monitor GPP and ET dynamics in coastal wetlands. This study demonstrated that the tidal dynamics significantly affected the SIF₇₆₀-GPP and SIF₇₆₀-ET relationships, underscoring the necessity of incorporating tidal influences in the application of SIF remote sensing for monitoring GPP and ET dynamics. The results of this study not only contribute to a deeper understanding of the mechanisms linking SIF₇₆₀ with GPP and ET but also provide new insights into the development and refinement of SIF-based remote sensing for carbon quantification in coastal blue-carbon ecosystems on a large-scale domain. Full article

Estimating daily continuous evapotranspiration (ET) can significantly enhance the monitoring of crop stress and drought on regional scales, as well as benefit the design of agricultural drought early warning systems. However, there is a need to verify the models’ performance in estimating the spatiotemporal continuity of long-term daily evapotranspiration (ET_d) on regional scales due to uncertainties in satellite measurements. In this study, a thermal-based two-surface energy balance (TSEB) model was used concurrently with Terra/Aqua MODIS data and the ERA5 atmospheric reanalysis dataset to calculate the surface energy balance of the soil–canopy–atmosphere continuum and estimate ET at a 1 km spatial resolution from 2000 to 2022. The performance of the model was evaluated using 11 eddy covariance flux towers in various land cover types (i.e., savannas, woody savannas, croplands, evergreen broadleaf forests, and open shrublands), correcting for the energy balance closure (EBC). The Bowen ratio (BR) and residual (RES) methods were used for enforcing the EBC in the EC observations. The modeled ET was evaluated against unclosed ET and closed ET (ET_BR and ET_RES) under clear-sky and all-sky observations as well as gap-filled data. The results showed that the modeled ET presented a better agreement with closed ET compared to unclosed ET in both Terra and Aqua datasets. Additionally, although the model overestimated ET_d across all different land cover types, it successfully captured the spatiotemporal variability in ET. After the gap-filling, the total number of days compared with flux measurements increased substantially, from 13,761 to 19,265 for Terra and from 13,329 to 19,265 for Aqua. The overall mean results including clear-sky and all-sky observations as well as gap-filled data with the Aqua dataset showed the lowest errors with ET_RES, by a mean bias error (MBE) of 0.96 mm.day⁻¹, an average mean root square (RMSE) of 1.47 mm.day⁻¹, and a correlation (r) value of 0.51. The equivalent figures for Terra were about 1.06 mm.day⁻¹, 1.60 mm.day⁻¹, and 0.52. Additionally, the result from the gap-filling model indicated small changes compared with the all-sky observations, which demonstrated that the modeling framework remained robust, even with the expanded days. Hence, the presented modeling framework can serve as a pathway for estimating daily remote sensing-based ET on regional scales. Furthermore, in terms of temporal trends, the intra-annual and inter-annual variability in ET can be used as indicators for monitoring crop stress and drought. Full article

Incorporating data-driven technologies into agriculture presents a promising approach to optimizing crop production, especially in regions dependent on irrigation, where escalating heat waves and droughts driven by climate change pose increasing challenges. Recent advancements in sensor technology have introduced diverse methods for assessing irrigation needs, including meteorological sensors for calculating reference evapotranspiration, belowground sensors for measuring plant available water, and plant sensors for direct water status measurements. Among these, infrared thermometry stands out as a non-destructive remote sensing method for monitoring transpiration, with significant potential for integration into drone- or satellite-based models. This study applies infrared thermometry to develop a crop water stress index (CWSI) model for European hazelnuts (Corylus avellana), a key crop in Oregon, the leading hazelnut-producing state in the United States. Utilizing low-cost, open-source infrared thermometers and data loggers, we aim to provide hazelnut farmers with a practical tool for improving irrigation efficiency and enhancing yields. The CWSI model was validated against plant water status metrics such as stem water potential and gas exchange measurements. Our results show that when stem water potential is below −6 bar, the CWSI remains under 0.2, indicating low plant stress, with corresponding leaf conductance rates ranging between 0.1 and 0.4 mol m² s⁻¹. Additionally, un-irrigated hazelnuts were stressed (CWSI > 0.2) from mid-July through the end of the season, while irrigated plants remained unstressed. The findings suggest that farmers can adopt a leaf conductance threshold of 0.2 mol m² s⁻¹ or a water potential threshold of −6 bar for irrigation management. This research introduces a new CWSI model for hazelnuts and highlights the potential of low-cost technology to improve agricultural monitoring and decision-making. Full article

Amid global climate change, recurrent drought events pose significant challenges to regional water resource management and the sustainability of socio-economic growth. Thus, understanding drought characteristics and regional development patterns is essential for effective drought monitoring, prediction, and the creation of robust adaptation strategies. Most prior research has analyzed drought events independently in spatial and temporal dimensions, often overlooking their dynamic nature. In this study, we employ a three-dimensional methodology that accounts for spatiotemporal continuity to identify and extract meteorological drought events based on a 3-month standardized precipitation evapotranspiration index (SPEI3). Measured by the SPEI3 index, the incidence of drought increased in the middle part of the basin, especially in some parts of Sichuan and Yunnan province, and the frequency of drought events decreased in the upper reaches. We evaluate drought events within the Yangtze River basin from 1980 to 2016 by examining five variables: chronology, extent, severity, duration, and epicenter locations. The results show that a total of 97 persisting drought events lasting at least 3 months have been identified in Yangtze River basin. Most events have a duration between 4 and 7 months. The findings indicate that while the number of drought events in the Yangtze River basin has remained unchanged, the intensity, duration, and severity of these events have shown a slight increase from 1980 to 2016. The drought events gradually moved from the western and southeastern parts of the basin to the central region. The most severe drought event occurred between January 2011 and October 2011, with a duration of 10 months and an affected area of 0.94 million km², impacting over fifty percent of the basin. Changes in wetness and dryness in the Yangtze River basin are closely related to El Niño/Southern Oscillation (ENSO) events, with a positive correlation between the intensity of cold events and the probability of extreme drought. This study enhances our understanding of the dynamics and evolution of drought events in the Yangtze River basin, providing crucial insights for better managing water resources and developing effective adaptation strategies. Full article