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Spatially varying coefficients of SRI and the dominant land types (a-d) in the Dongjiang River Basin, including rainfed cropland with herbaceous cover (L11) (a), mosaic tree and shrub (L100) (b), shrubland (L120) (c), and grassland (L130) (d). Subbasins with red color (positive relationship) are the places where increased land proportion potentially increase drought risks, while more land proportion potentially mitigate drought risks in subbasins with blue color (negative relationship). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Spatially-invariant land use and cover changes (LUCC) are not suitable for managing non-stationary drought conditions. Therefore, developing a spatially varying framework for managing land resources is necessary. In this study, the Dongjiang River Basin in South China is used to exemplify the significance of spatial heterogeneity in land planning o...
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... (Table E1) is required to select the land types that are statistically significant to SRI. The dominant land types from the GWPCA results having the p-value less than 0.01 were possible variables for the further GTWR analysis. These variables were 'rainfed cropland with herbaceous cover', 'mosaic tree and shrub', 'shrubland', and 'grassland'. Fig. 5 showed the GTWR spatially varying coefficients of these four land types and the SRI in the DRB. In general, there were two spatial patterns of coefficients of land types and SRI, including 1) homogeneous impacts with either positive or negative coefficients only over the basin, and 2) heterogeneous impacts with opposite correlations ...
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... coefficients only over the basin, and 2) heterogeneous impacts with opposite correlations over the northern and southern subbasins. Specifically, over the whole basin, 'rainfed cropland with herbaceous cover' had positive relationships with the SRI, which indicated that more rainfed herbaceous crops increased the SRI and lowered the drought risk (Fig. 5a). Conversely, 'mosaic tree and shrub' had negative relationships with the SRI (Fig. 5b), which suggested that more 'mosaic tree and shrub' caused lower SRI values and thereby higher drought risks. At the subbasins, shrub and grassland showed the similar patterns in terms of their relationships with the SRI. In the northern DRB, the ...
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... over the northern and southern subbasins. Specifically, over the whole basin, 'rainfed cropland with herbaceous cover' had positive relationships with the SRI, which indicated that more rainfed herbaceous crops increased the SRI and lowered the drought risk (Fig. 5a). Conversely, 'mosaic tree and shrub' had negative relationships with the SRI (Fig. 5b), which suggested that more 'mosaic tree and shrub' caused lower SRI values and thereby higher drought risks. At the subbasins, shrub and grassland showed the similar patterns in terms of their relationships with the SRI. In the northern DRB, the positive coefficients indicated that more shrub and grassland were related to higher SRI ...
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... to the implemented GGP in China. At the same time, a reduced trend of SRI can be observed in Fig. F1. Hence, the 'rainfed cropland with herbaceous cover' and the SRI had a similar variation trend, while the percentage of 'mosaic tree and shrub' and the SRI changed in an opposite trend. These observations are consistent with the GTWR results in Fig. 5a and b. On a smaller scale (around 20,630 km 2 ), the percentages of 'shrubland' and 'grassland' were less in the north than those in the south (Fig. 6c and d). At the same time, the northern areas, as cold spots of SRI, had lower SRI values (Fig. 3b). In other words, the northern regions have less shrub and grassland and also have ...
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... of 'shrubland' and 'grassland' were less in the north than those in the south (Fig. 6c and d). At the same time, the northern areas, as cold spots of SRI, had lower SRI values (Fig. 3b). In other words, the northern regions have less shrub and grassland and also have lower SRI values, which have general consistencies of the GTWR results in Fig. 5c and ...
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... rates also explain why the extents of shrub and grassland were positively related to SRI in the north. On the other hand, in the south region where high amounts of forest (e.g. broadleaved evergreen and deciduous trees) were still transformed to shrubland (Fig. 6e), the relationship between shrubland areas and the SRI was negative (Fig. 5c), which was opposite to the north. This contrast can be attributed to the solar insolation differences in the DRB. The low-lying and flat areas in the southern part receives relatively more solar radiation than the rugged and sloppy terrain located in the northern counterpart. Hence, more evaporation and greater runoff reductions occur ...
Citations
... To overcome these limitations, Emerging Hot Spot Analysis (EHSA) is considered a viable approach. Currently, EHSA has been utilized in studying temporal and spatial changes in diverse fields, such as surface evapotranspiration rate [22], prediction of hydrological drought risk [23], and surface deformation [24]. Furthermore, Liu [25] investigated the spatial heterogeneity of WCF in the Yangtze River Basin using EHSA. ...
Precisely delineating the spatiotemporal heterogeneity of water conservation services function (WCF) holds paramount importance for watershed management. However, the existing assessment techniques exhibit common limitations, such as utilizing only multi-year average values for spatial changes and relying solely on the spatial average values for temporal changes. Moreover, traditional research does not encompass all WCF values at each time step and spatial grid, hindering quantitative analysis of spatial heterogeneity in WCF. This study addresses these limitations by utilizing an improved water balance model based on ecosystem type and soil type (ESM-WBM) and employing the EFAST and Sobol’ method for parameter sensitivity analysis. Furthermore, a space–time cube of WCF, constructed using remote-sensing data, is further explored by Emerging Hot Spot Analysis for the expression of WCF spatial heterogeneity. Additionally, this study investigates the impact of two core parameters: neighborhood distance and spatial relationship conceptualization type. The results reveal that (1) the ESM-WBM model demonstrates high sensitivity toward ecosystem types and soil data, facilitating the accurate assessment of the impacts of ecosystem and soil pattern alterations on WCF; (2) the EHSA categorizes WCF into 17 patterns, which in turn allows for adjustments to ecological compensation policies in related areas based on each pattern; and (3) neighborhood distance and the type of spatial relationships conceptualization significantly impacts the results of EHSA. In conclusion, this study offers references for analyzing the spatial heterogeneity of WCF, providing a theoretical foundation for regional water resource management and ecological restoration policies with tailored strategies.
... The ERA5-Land dataset boasts a high temporal-spatial resolution (monthly, 0.1 • ×0.1 • ) and an extensive time period spanning from 1950 to the present (Hersbach et al., 2020). Previous studies have affirmed the suitability of this dataset for drought evaluating globally (Vicente-Serrano et al., 2022) and regionally (Dong et al., 2022;Fan et al., 2021;Zhou et al., 2021). Moreover, prior studies have validated the ERA5-Land precipitation and runoff data against station-based measurements, demonstrating the robust applicability of ERA5-Land dataset in the MRB (Luo et al., 2023). ...
... ERA5 datasets have been previously used in South China for various studies, providing a valuable supplement to observed datasets and demonstrating reliable results. For instance, they have been utilized to study drought propagations in the Pearl River Basin (Fan et al., 2021;Zhou et al., 2021) and, agricultural drought in South China (Zhang et al., 2021b), whereas Jiang et al. (2021a), observed that ERA5 correlated well with gauge analysis regarding the geographical distribution of typhoon precipitation centers. ...
Abstract
Understanding the relationship between vegetation and climatic drivers is essential for assessing terrestrial ecosystem patterns and managing future vegetation dynamics. This study examines the effects of local climatic factors and remote large-scale ocean–atmosphere circulations from the Pacific, Atlantic, and Arctic Oceans, as well as the East Asian and Indian summer monsoons, on the spatiotemporal variability of the Normalized Difference Vegetation Index (NDVI) in the karst region of southwest China (KRSC) using Mann-Kendall test, Sen’s slope, cross-correlation, and wavelet analysis. We observed a significant increase in NDVI over karst and non-karst regions from 1981 to 2019, with a notable abrupt shift from 2001 onwards, underscoring the importance of understanding the underlying drivers. The significant correlation and coherence of surface air (TMP) and soil temperatures (ST) with NDVI, especially when analyzed using wavelet methods, indicate their crucial role in vegetation dynamics. Additionally, the broad coherence patterns of AMO and WHWP with NDVI at annual and decadal cycles suggest that ocean–atmosphere interactions also play a significant part. At interannual periodicities, most large-scale indices displayed significant coherence with NDVI. These findings highlight the complexity of NDVI variability, which is better explained by the integration of multiple local and global factors rather than by single variables. The integrated local–global drivers, particularly TMP-ST-AMO-NP-WHWP and PCP-SM-AMO-NP-WHWP with mean coherence of 0.90 and 0.89, respectively, showed the highest mean coherence, emphasizing the need for a multifaceted approach in understanding vegetation changes rather than a single local variable or atmospheric circulation index. These findings have significant implications for policymakers, aiding in better planning and policy formulations considering climate change and atmospheric variability.
... Meanwhile, land use change is one of the main anthropogenic drivers of climate change. Land cover spatial heterogeneity is also among the most significant factors affecting hydrology (Fan et al., 2021;Gao et al., 2018), biology (Atauri and Lucio, 2001;Yoshioka et al., 2017), and phenology (Honnay et al., 2003;Zhang et al., 2019), which are all sensitive to extremes. Thus, projections of extremes obtained by better representation of soil physics will address a more confident assessment of climate vulnerability and adaptation. ...
High temperature extremes accompanied by drought have led to serious ramifications for environmental and socio‐economic systems. Thus, improving the predictability of heat‐wave events is a high priority. One key to achieving this is to better understand land‐atmosphere interactions. Recent studies have documented a hypersensitive regime in the soil moisture‐temperature relationship: when soil dries below a critical low threshold, called the soil moisture breakpoint, air temperatures increase at a greater rate as soil moisture declines. Whether such a hypersensitive regime is rooted in land surface processes and whether this soil moisture breakpoint corresponds to a known plant critical value, the permanent wilting point (WP), below which latent heat flux almost ceases, remains unclear. In this study, we explore the mechanisms linking low soil moisture to high air temperatures. From in situ observations, we confirm that the hypersensitive regime acts throughout the chain of energy processes from land to atmosphere. A simple energy‐balance model indicates that the hypersensitive regime occurs when there is a dramatic drop in evaporative cooling, which happens when soil moisture dries toward the permanent WP, suggesting that the soil moisture breakpoint is slightly above the permanent WP. Precisely how a model represents the relationship between evapotranspiration and soil moisture is found to be essential to describe the occurrence of the hypersensitive regime. Thus, we advocate that weather and climate models should ensure a realistic representation of land‐atmosphere interactions to obtain reliable forecasts of extremes and climate projections, aiding the assessment of heatwave vulnerability and adaptation.
... However, the limitations of these previous studies are using the mean values (multiyear averages are used in describing spatial variation, and spatial averages are used in describing temporal variation), and failing to include all WCFs in each timestep and spatialgrid observation [21]. To address these limitations, emerging hot spot analysis (EHSA) is an effective method that can deal with these limitations, and it has been used to investigate spatiotemporal variations in multiple fields, including hydrological drought risk [22], surface evapotranspiration ratios [21], fire occurrences [23], and surface deformation [24]. The EHSA integrates temporal and spatial patterns and could present the spatial nonstationarity of the WCF, describe the location and pattern of historical changes more accurately, and identify different patterns through trend significance [22]. ...
... To address these limitations, emerging hot spot analysis (EHSA) is an effective method that can deal with these limitations, and it has been used to investigate spatiotemporal variations in multiple fields, including hydrological drought risk [22], surface evapotranspiration ratios [21], fire occurrences [23], and surface deformation [24]. The EHSA integrates temporal and spatial patterns and could present the spatial nonstationarity of the WCF, describe the location and pattern of historical changes more accurately, and identify different patterns through trend significance [22]. Thus, it is used to detect the spatial heterogeneity of the WCF. ...
The water conservation function (WCF), as one of the most critical ecosystem services, has an important impact on the ecological sustainability of a region. Accurately characterizing the spatiotemporal heterogeneity of WCF and further exploring its driving factors are of great significance for river basin management. Here, the WCF of the upper Yangtze River basin (UYRB) from 1991 to 2020 was calculated using the water yield module in the Integrated Valuation of Ecosystem Service and Tradeoffs (InVEST) model. Also, we innovatively applied emerging hot spot analysis (EHSA), which could describe the location and pattern of historical changes more accurately, to investigate the spatiotemporal heterogeneity and evolution of WCF. Based on the Geographical Detector Model (GDM), the main driving factors of WCF and their interactions were revealed. The results showed the following: (1) the WCF in the UYRB experienced a temporal increase at a growth rate of 1.48 mm/a, while remarkable differences were observed across the change rates of sub-watersheds. (2) The spatial variation of the WCF showed a gradual increase from northwest to southeast. Interestingly, the Jinshajing River upstream (JSJU) source area with a low WCF showed an increasing trend (with diminishing cold spots). On the contrary, the downstream regions of the JSJU watershed (with intensifying cold spots) underwent a weakening WCF. (3) Among all driving factors, precipitation (q = 0.701) exhibited the most remarkable prominent impact on the spatial heterogeneity of the WCF. Additionally, the interaction of factors exhibited more explanatory power than each factor alone, such as precipitation and saturated soil hydraulic conductivity (q = 0.840). This research study is beneficial to water resource management and provides a theoretical basis for ecological restoration.
... Previous studies have suggested that the precipitation and runoff derived from the ERA5-Land reanalysis dataset are reasonable for calculating drought indices, such as the SPI and SRI (Zhou et al., 2021b;Huang et al., 2021;Fan et al., 2021). To evaluate the ERA5-Land precipitation and GloFAS discharge data in the LMRB, the reanalysis data were validated with ground observations. ...
The Lancang-Mekong River Basin (LMRB) has been particularly vulnerable to serious and successive droughts during the last decades. Nevertheless, the characteristics of spatiotemporal variations in meteorological drought (MD) and hydrological drought (HD) with an emphasis on drought propagation have yet to be thoroughly investigated. In this study, based on reanalysis data, the standardized precipitation index (SPI) and standardized runoff index (SRI) were employed to comprehensively examine the evolution and propagation characteristics of MD and HD. Spearman rank correlation and wavelet analyses were employed for the correlation and propagation of MD to HD. The influencing factors of the drought propagation time (DPT) were also explored. The results indicate that: (1) During 1950–2021, the regions with worsening drought conditions were primarily in the Lancang River Basin in Yunnan Province, the highlands in north Laos, and the Khorat Plateau in Thailand. (2) The drought characteristics varied significantly on different timescales. For the longer timescales, the MDs and HDs corresponded to fewer drought events but longer durations and larger severities. The average duration and severity of HD were higher than MD on all selected timescales. (3) The DPTs from MD to HD exhibited noticeable spatial variability ranging from 2 to 9 months. In addition, there were statistically positive correlations between the MD and HD in each sub-region. (4) Precipitation was the dominant factor influencing the spatial distribution of DPTs at the basin scale, while the catchment properties, represented by the land use and elevation, had non-negligible influences on the DPTs. (5) Human activities weakened the correlations between MD and HD. Meanwhile, the DPTs were prolonged as a result of human influence. Our findings highlight the characteristics of HD response to MD in the diverse sub-regions of the LMRB and provide crucial information for early warning and improvement of drought resilience in this transboundary international river.
... Thus, precipitation, temperature, NDVI, and DFI in each temporal and spatial interval were aggregated in these cubes. There were 17 pattern classes of spatial-temporal modes, which are the hot spots and cold spots of new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating, or historical, respectively [57]. ...
... The above data and methods were used to analyze the impact of vegetation belt movement on wildfires in the Mongolian Plateau over the last 40 years ( Figure 2). consecutive, intensifying, persistent, diminishing, sporadic, oscillating, or historical, respectively [57]. ...
The frequency and intensity of fires are increasing because of warmer temperatures and increased droughts, as well as climate-change induced fuel distribution changes. Vegetation in environments, such as those in the mid-to-high latitudes and high elevations, moves to higher latitudes or elevations in response to global warming. Over the past 40 years, the Mongolian Plateau has been arid and semi-arid, with a decrease in growing season vegetation in the southwest and an increase in growing season vegetation in the northeast. The northward movement of vegetation has brought fires, especially in the Dornod, Sukhbaatar, and Kent provinces near the Kent Mountains, and has become more obvious in the past 20 years. The occurrence of a dead fuel index (DFI) with high probability is distributed in northern Mongolia, the border area between China and Mongolia, and the forest-side meadow-steppe region of the Greater Khingan Mountains. These findings suggest that vegetation is moving northward because of climate change and this presents a challenge of future warming spreading fire northward, adding material to the study of the relationship between the northward movement of global vegetation and fires.
... Previous studies have suggested that the precipitation and runoff derived from the ERA5-Land reanalysis dataset are reasonable for calculating drought indices, such as the SPI and SRI (Zhou et al., 2021b;Huang et al., 2021;Fan et al., 2021). To evaluate the ERA5-Land precipitation and GloFAS discharge data in the LMRB, the reanalysis data were validated with ground observations. ...
... In hydrological studies, EHSA is rarely used. To our knowledge, Fan et al. (2021) used this method to investigate the spatiotemporal changes in hydrological drought risk. This study is the first attempt to integrate the EHSA with the Budyko framework in the land planning field. ...
Land use planning regulates surface hydrological processes by adjusting land properties with varied evapotranspiration ratios. However, a dearth of empirical spatial information hampers the regulation of place-specific hydrological processes. Therefore, this study proposed a Local Land Use Planning framework for EvapoTranspiration Ratio regulations (ETR-LLUP), which was tested for the developments of spatially-varied land use strategies in the Dongjiang River Basin (DRB) in Southern China. With the first attempt at integrating the Emerging Hot Spots Analysis (EHSA) with the Budyko framework, the spatiotemporal trends of evapotranspiration ratios based on evaporative index and dryness index, from 1992 to 2018, were illustrated. Then, representative land-cover types in each sub-basin were defined using Geographically Weighted Principal Component Analysis, in two wet years (1998 and 2016) and three dry years (2004, 2009, and 2018), which in turn were identified using the Standard Precipitation Index. Finally, Geographically Weighted Regressions (GWRs) were used to detect spatially-varied relationships between land-cover proportions and evaporative index in both dry and wet climates. Results showed that the DRB was consistently a water-limited region from 1992 to 2018, and the situation was getting worse. We also identified the upper DRB as hotspots for hydrological management. Forests and croplands experienced increasingly water stress compared to other vegetation types. More importantly, the spatial results of GWR models enabled us to adjust basin land use by 1) expanding and contracting a combination of ‘mosaic natural vegetation’ and ‘broadleaved deciduous trees’ in the western and eastern parts of the basin, respectively; and 2) increasing ‘broadleaved evergreen trees’ in the upstream parts of the basin. These spatially-varied land use strategies based on the ETR-LLUP framework allow for place-specific hydrological management during both dry and wet climates.
... html. Past studies have verified the reliability of the selected datasets on drought parameters across the globe (Bai et al., 2016;Chen et al., 2020;Fan et al., 2021;Ho et al., 2021;McNally et al., 2017;Raziei et al., 2010;Spennemann et al., 2015;Zhang et al., 2017;Zhou et al., 2021;Zhu et al., 2018). It should be noted that the runoff datasets from all the sources are extracted at a common resolution of 0.5 • Lat × 0.5 • Lon for the analysis. ...
As a costliest natural hazard, drought profoundly affects water resources, agriculture, and socio-economic sectors in India. In spite of large spatio-temporal variability in droughts, the propagation time from meteorological to hydrological droughts is not examined at local scale over India. In this study, the meteorological and hydrological variables are obtained at a grid resolution of 0.5o Lat x 0.5o Lon over India to estimate the time of propagation. Here, five different runoff datasets from ERA5, FLDAS, GLDAS, MEERA2, and NCEP are collected. The Standardised Precipitation Evapotranspiration Index (SPEI), and Standardised Runoff Index (SRI) are estimated under the influence of external global and regional drivers. The drought propagation time is computed in 1170 grids blanketing the entire India based on differences between the initiation to initiation (Δs), peak to peak (Δp) and termination to termination (Δe). In addition, the internal propagation of hydrological drought is estimated with the help of variable motion relationship of speed-time process. The large duration and more severe hydrological droughts are observed mostly over southern and northern parts of India. The drought propagation time varies between 4 to 9 months, 9 to 12 months, and 15 to 20 months in the cases of Δs, Δp, and Δe, respectively. The drought development and recovery duration are computed as 3.1 to 6 months over most of the areas. It is found that locations with the high value (greater than 10 months) of Drought Development Period (DDP) are also having high value of Drought Recovery Period (DRP). The internal propagation of hydrological drought ranges between the magnitude of 0.4 and 0.6 per month over most of the area in India. It is found that the drought propagation and its characteristics are underestimated over most of the regions in India when computed without the external drivers. The present study would provide important drought characteristics at local scale which can assist water managers and policy makers to devise sustainable management practices.
