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Study region: The largest inland river basin in China (the Tarim River Basin) Study focus: This study introduces a novel classification scheme for dry and warm snow droughts with focus on their duration, severity, and intensity. The Nonparametric Standardized SWE Index (NSWEI) and the Standardized Precipitation Index (SPI) were employed to assess these drought types. The optimal parameters-based geographical detector (OPGD) model and machine learning (including random forest model and Shapley-XGBoost algorithm) were applied to reveal the spatial and time dynamic driving forces of droughts. New hydrological insights for the regions: Results indicated that warm-type droughts occurred more frequently and with greater intensity, exhibiting a larger spatial coverage compared to dry-type droughts, which can be attributed to warm-type droughts dominated by temperature and vapor pressure deficit, whereas dry-type are predominantly influenced by relative humidity and solar radiation. Moreover, the dynamics of snow droughts exhibited a contrasting pattern between the south (experiencing an upward trend) and the north (experiencing a descending trend). In the south, dry-type droughts were exacerbated by the strength of North Pacific Oscillation (PDO)-precipitation coupling, while warm-type droughts are influenced by the strength of North Atlantic Oscillation (NAO)-precipitation coupling and El Niño Southern Oscillation (ENSO)-precipitation.

Understanding the propagation characteristics and driving factors from meteorological drought to hydrological drought is essential for alleviating drought and for early warning systems regarding drought. This study focused on the Weihe River basin (WRB) and its two subregions (the Jinghe River (JRB) and the middle reaches of the Weihe River (MWRB)), utilizing the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) to characterize meteorological and hydrological drought, respectively. Based on Copula theory and conditional probability, a quantification model for the propagation time (PT) of meteorological–hydrological drought was constructed. The dynamic characteristics of PT on annual and seasonal scales were explored. Additionally, the influences of different seasonal meteorological factors and underlying surface factors on the dynamic changes in PT were analyzed. The main conclusions were as follows: (1) The PT of meteorological–hydrological drought was characterized by faster propagation during the hot months (June–September) and slower propagation during the cold months (December to March of the following year); (2) Under the same level of hydrological drought, as the level of meteorological drought increases, the PT of the drought shortens. The propagation thresholds of meteorological to hydrological drought in the WRB, the JRB, and the MWRB are −0.69, −0.81, and −0.78, respectively. (3) In the dynamic changes in PT, the WRB showed a non-significant decrease; however, both the JRB and the MWRB exhibited a significant increase in PT across different drought levels. (4) The influence of the water and heat status during spring, summer, and winter on PT was more pronounced, while in autumn, the impact of the basin’s water storage and discharge status was more significant in the JRB and the MWRB.

Extreme drought and flood events, as well as their combined events, pose significant challenges to global sustainable socio-economic development and ecological health. However, the impact of dry–wet combination events (DWCEs) on vegetation vulnerability remains to be investigated. The Loess Plateau (LP) was selected as the study area to explore the response time of vegetation to precipitation index changes by optimal correlation coefficient; then, the impact of different DWCEs on vegetation vulnerability under moderate and severe scenarios was analyzed; finally, a vegetation loss probability model was constructed based on the copula function and Bayesian framework, to quantify the vegetation loss probability under DWCEs stress. The results indicate that: (1) normalized difference vegetation index (NDVI) shows an upward trend in spring, summer, and autumn, with the proportion of areas are 90.5%, 86.2%, and 95.4%, respectively, and show an insignificant trend in winter; (2) the response time of vegetation to precipitation index changes tends to be one or two seasons; (3) moderate scenarios have more influence than severe scenarios, dry-to-wet events (DWEs), wet-to-dry events (WDE) and continuous dry events (CDE) in spring-summer have a significant impact on summer vegetation of Ningxia and Shanxi, and WDE and CDE have a higher impact on autumn vegetation. (4) in terms of the probability of vegetation loss, DWE, and CDE cause higher losses to summer vegetation, while WDE and CDE cause higher losses to autumn vegetation. This study quantifies the impact of adjacent seasonal DWCE stress on future vegetation vulnerability.

Study region: Hong Kong is a megalopolis located on the southeast coast of China. Study focus: Megalopolis located in low-lying coastal areas such as Hong Kong is especially vulnerable to compound flood risks from multiple flood drivers (e.g., heavy rainstorms and extreme sea levels). Using long-term hydrological observations and socio-economic data, this study aims to reveal the risks, causes and socio-economic impacts of compound floods from precipitation and sea level in Hong Kong. New hydrological insights for the region: We find that the most extreme sea levels are mostly accompanied by heavy precipitation and thus lead to compound floods, while the most extreme precipitation events are less likely to coincide with extreme sea levels. TCs are the major trigger of compound floods with fractional contributions ranging from 48.8% to 83.8% in the gauges. Typhoon Wanda (1962) triggered the most severe compound flood with a joint return period of 178 years which is much shorter than the return period (>10,000 years) without the consideration of the dependence between the extremes. During the past decades, Hong Kong has experienced an increase in compound flood hazards, which is mainly attributed to the mean sea level rise. Socio-economic data reveals that TCs associated with compound floods are more destructive than TCs that induced single-type floods, highlighting the urgent need to establish a multi-hazard flood risk management strategy.

Meteorological drought is a continuous spatiotemporal phenomenon that poses a serious threat to water resource security. Dynamic evolution and multivariable frequency analysis of meteorological drought are important for effective drought mitigation and risk management. Therefore, this study aims to analyze meteorological drought events in northwestern China between 1960 and 2018 based on the standardized precipitation evapotranspiration index (SPEI) through a three-dimensional identification method. This study investigates the meteorological drought dynamic evolution on different time and space scales and evaluates the frequency analysis considering the spatiotemporal variability based on Copula. The results show that SPEI presents an upward trend in Northwestern China. A trend towards increased humidity is observed in arid regions, contrasted by a trend towards aridification in semi-arid and semi-humid areas, indicating that the spatial distribution of drought in the study area tends towards homogenization. The possibility of high-intensity drought events occurring in the same area was relatively low, whereas low-intensity drought events were frequent. Additionally, this study analyzes the dynamic migration process of individual drought events from a three-dimensional perspective. Neglecting any one drought variable could significantly underestimate the occurring probability of severe drought events. Therefore, a multivariable frequency analysis considering the spatiotemporal variability plays a crucial role in the formulation of drought prevention and mitigation strategies, as well as drought forecasting.

Under the background of global climate warming, meteorological drought disasters have become increasingly frequent. Different vegetation types exhibit varying responses to drought, thus, exploring the heterogeneity of the impact of meteorological drought on vegetation is particularly important. In this study, we focused on Inner Mongolia (IM) as the research area and employed Standardized Precipitation Evapotranspiration Index (SPEI) and Vegetation Health Index (VHI) as meteorological drought and vegetation indices, respectively. The Breaks for Additive Seasons and Trend algorithm (BFAST) was utilized to reveal the dynamic characteristics of both meteorological drought and vegetation changes. Additionally, the Pixel-Based Trend Identification Method (PTIM) was employed to identify the trends of meteorological drought and vegetation during spring, summer, autumn, and the growing season. Subsequently, we analyzed the correlation between meteorological drought and vegetation growth. Finally, the response of vegetation growth to various climate factors was explored using the standardized multivariate linear regression method. The results indicated that: (1) During the study period, both SPEI and VHI exhibited a type of interrupted decrease. The meteorological drought was aggravated and the vegetation growth was decreased. (2) Deserts and grasslands exhibited higher sensitivity to meteorological drought compared to forests. The strongest correlation between SPEI-3 and VHI was observed in desert and grassland regions. In forest areas, the strongest correlation was found between SPEI-6 and VHI. (3) The r between severity of meteorological drought and status of vegetation growth was 0.898 (p < 0.01). Vegetation exhibits a more pronounced response to short-term meteorological drought events. (4) Evapotranspiration is the primary climatic driving factor in the IM. The findings of this study provide valuable insights for the rational utilization of water resources, the formulation of effective irrigation and replenishment policies, and the mitigation of the adverse impacts of meteorological drought disasters on vegetation growth in the IM.

According to the widely accepted definition of drought, meteorological and hydrological droughts originally develop from rainfall and runoff deficits, respectively. Runoff deficit is mainly derived from rainfall deficit, and the propagation from meteorological drought to hydrological drought is critical for agricultural water management. Nevertheless, the characteristics and dynamics of drought propagation in the spatiotemporal scale remain unresolved. To this end, the characteristics and dynamics of drought propagation in different seasons and their linkages with key forcing factors are evaluated. In this study, meteorological and hydrological droughts are characterized by the Standardized Precipitation Index (SPI) and the Standardized Runoff Index (SRI), respectively. Propagation time is identified by the corresponding timescale of the maximum correlation coefficient between the SPI and the SRI. Then, a 20-year sliding window is adopted to explore the propagation dynamic in various seasons. Furthermore, the multiple linear regression model is established to quantitatively explore the influence of meteorological factors, underlying surface features and teleconnection factors on the propagation time variations. The Wei River Basin, a typical Loess Plateau watershed in China, is selected as a case study. Results indicate the following: (1) the propagation time from meteorological to hydrological drought is shorter in summer (2 months) and autumn (3 months), whereas it is longer in spring (8 months) and winter (13 months). Moreover, the propagation rates exhibit a decreasing trend in warm seasons, which, however, show an increasing trend in cold seasons; (2) a significant slowing propagation in autumn is mainly caused by the decreasing soil moisture and precipitation, whereas the non-significant tendency in summer is generally induced by the offset between insignificant increasing precipitation and significant decreasing soil moisture; (3) the replenishment from streamflow to groundwater in advance prompts the faster propagation from meteorological to hydrological drought in spring and winter and (4) teleconnection factors have strong influences on the propagation in autumn, in which Arctic Oscillation, El Niño-Southern Oscillation and Pacific Decadal Oscillation mainly affect participation, arid index and soil moisture, thereby impacting drought propagation. HIGHLIGHTS The propagation dynamics at a seasonal timescale were explored.; The impacts of diverse factors on the propagation dynamics were investigated. Autumn exhibits a significant increasing propagation time mainly induced by decreasing soil moisture and precipitation.; Winter shows a significant decreasing propagation time mainly caused by earlier groundwater supply.; Teleconnection factors exert strong influences on the propagation process in autumn.;

The study of the feedback relationship between vegetation and soil water is of great significance for improving the understanding of hydrological process, soil and water conservation and water resources utilization. However, the mutual feedback between vegetation and soil moisture had remained unresolved. To this end, the feedback relationships between NDVI and soil moisture (SM) over the Loess Plateau (LP) were studied by using the Granger causality method. Three layers of soil moisture data were used, with depths of 0–10 cm (SM1), 10–40 cm (SM2) and 40–100 cm (SM3), respectively. The spatial distribution characteristics of causality between NDVI and SM1/ SM2/SM3 were further analyzed. Results indicated that (1) NDVI and SM1 were positively correlated in the whole region of the LP, while NDVI and SM2/SM3 were negatively correlated in the southeast of the region; (2) compared with SM2 and SM3, SM1 and NDVI had a more significant mutual feedback relationship; (3) after the program of “Grain for Green” on the LP, the vegetation increased, the feedback relationship between NDVI and SM1 was further enhanced, and the mutual feedback relationship was the most obvious in the key areas of revegetation. In general, this study sheds new insights into the feedback between vegetation and soil moisture, which can help guide the adjustment of local agricultural structure and ecological restoration.

Correlation analysis is the common method to evaluate the relationship between two variables; however, it may sometimes cause spurious correlations. Specifically, in the field of hydrometeorology, with the impacts of climate change and human activities, correlation analysis is difficult to identify the true relationship between variables, and thus, causality analysis should be adopted instead. This study analyzed the causal relationship between meteorological drought and hydrological drought in different climatic regions of China by using convergent cross mapping (CCM). We improved the identification of CCM convergence by using the coefficient of variation and applied it in the field of large-scale hydrometeorology. The results of correlation analysis were compared, and the applicability of causality analysis was explored. The results revealed that: In Southeast China, the correlation and causality between meteorological drought and hydrological drought were both large. In Northeast China and central Qinghai–Tibet Plateau, the correlation between meteorological drought and hydrological drought was small, but the causality was large. In view of the spurious correlation, introducing causality analysis can better explain the relationship between meteorological drought and hydrological drought, especially in areas with snowmelt runoff. Overall, CCM can provide valuable causal information from common time series in the field of large-scale hydrometeorology and has a wide range of application values. However, causality analysis cannot explain the positive or negative relationship between variables. Therefore, when analyzing the relationship between variables, the advantages of the two methods should be given full play.

Droughts are among the most damaging environmental disasters that may have destructive damages on societal properties and lives. Generally, socio-economic drought occurs when water resources systems could not fulfil the water demand. Additionally, it is not to be overlooked the role of local reservoirs in modifying uneven distribution of water and coping with climatic extremes. This study examined the evolution characteristics of the socio-economic droughts via applying a Multivariate Standardized Reliability and Resilience Index (MSRRI). Furthermore, the influences of anomalous atmospheric circulation on the socio-economic droughts were explored through adopting the cross wavelet analysis to investigate the meteorological driving force behind the socio-economic droughts. Results mainly indicated that (1) the MSRRI has proven to be effective in evaluating socio-economic droughts for its integration of inflow-demand reliability and water storage resilience indexes; (2) the MSRRI series in Datong River Basin (DRB) has a non-significant increasing trend at annual scale with apparent periods (17 and 22 years) and (3) the comprehensive effects of ENSO, EASM and PNA contribute to the socio-economic drought variations, and the ENSO has strongest impacts than others. The findings in this study benefit local socioeconomic drought mitigation and water resources planning and management.

Quantifying changes in the vulnerability of vegetation to various drought stresses in different seasons is important for rational and effective ecological conservation and restoration. However, the vulnerability of vegetation and its dynamics in a changing environment are still unknown, and quantitative attribution analysis of vulnerability changes has been rarely studied. To this end, this study explored the changes of vegetation vulnerability characteristics under various drought stresses in Xinjiang and conducted quantitative attribution analysis using the random forest method. In addition, the effects of ecological water transport and increased irrigation areas on vegetation vulnerability dynamics were examined. The standardized precipitation index (SPI), standardized precipitation-evapotranspiration index (SPEI), and standardized soil moisture index (SSMI) represent atmospheric water supply stress, water and heat supply stress, and soil water supply stress, respectively. The results showed that: (1) different vegetation types responded differently to water stress, with grasslands being more sensitive than forests and croplands in summer; (2) increased vegetation vulnerability under drought stresses dominated in Xinjiang after 2003, with vegetation growth and near-surface temperature being the main drivers, while increased soil moisture in the root zone was the main driver of decreased vegetation vulnerability; (3) vulnerability of cropland to SPI/SPEI/SSMI-related water stress increased due to the rapid expansion of irrigation areas, which led to increasing water demand in autumn that was difficult to meet; and (4) after ecological water transport of the Tarim River Basin, the vulnerability of its downstream vegetation to drought was reduced.

Under a changing environment, the effect of climate change and human activities on maize yield is vital for ensuring food security and efficient socio-economic development. The time series of maize yield is generally non-stationary and contains different frequency components, such as long- and short-term oscillations. Nevertheless, there is no adequate understanding of the relative importance of climate change. In addition, human activities on maize yield at multiple timescales remain unclear, which help in further improving maize yield prediction. Based on the ensemble empirical mode decomposition method (EEMD), the method of dependent variable variance decomposition (DVVD) and the Sen-slope method, the effect of climate change including growing-season precipitation and temperature (i.e., GSP, GEP, CDD, GST, GSMAT, and GSMT) and human activities including effective irrigation area (EIA) and the consumption of chemical fertilizers (CCF) on maize yield were explored at multiple timescales during 1979–2015. The Heilongjiang Province, a highly important maize production area in China, was selected as a case study. The results of this work indicate the following: (1) The original maize yield series was divided into 3.1-, 7.4-, 18.5-, and 37-year timescale oscillations and a residual series with an increasing trend, where the 3.1-year timescale (IMF1), the 18.5-year timescale (IMF3), and the increasing trend (R) were dominant; (2) the original sequence was mainly affected by human activities; (3) climate change and human activities had different effects on maize yield at different timescales: The short-term oscillation (IMF1) of maize yield was primarily affected by climate change. However, human activities dominated the mid- and long-term oscillations (IMF3 and R) of maize yield. This study sheds new insight into multiple timescale analysis of the role of climate and human activities on maize yield dynamics.

This work proposes the use of a robust geometrical segmentation algorithm to detect inherent shapes from dense point clouds. The points are first divided into voxels based on their connectivity and normal consistency. Then, the voxels are classified into different types of shapes through a multi-scale prediction algorithm and multiple shapes including spheres, cylinders, and cones are extracted. Next, a hybrid voting RANSAC algorithm is adopted to separate the point clouds into corresponding segments. The point–shape distance, normal difference, and voxel size are all considered as weight terms when evaluating the proposed shape. Robust voxels are weighted as a whole to ensure efficiency, while single points are considered to achieve the best performance in the disputed region. Finally, graph-cut-based optimization is adopted to deal with the competition among different segments. Experimental results and comparisons indicate that the proposed method can generate reliable segmentation results and provide the best performance compared to the benchmark methods.

Abstract Projection of future drought is often involved large uncertainties from climate models, emission scenarios as well as drought definitions. In this study, we investigate changes in future droughts in the conterminous United States based on 97 1/8 degree hydro-climate model projections. Instead of focusing on a specific drought type, we investigate changes in meteorological, agricultural, and hydrological drought as well as the concurrences. Agricultural and hydrological droughts are projected to become more frequent with increase in global mean temperature, while less meteorological drought is expected. Changes in drought intensity scale linearly with global temperature rises under RCP8.5 scenario, indicating the potential feasibility to derive future drought severity given certain global warming amount under this scenario. Changing pattern of concurrent droughts generally follows that of agricultural and hydrological droughts. Under the 1.5 °C warming target as advocated in recent Paris agreement, several hot spot regions experiencing highest droughts are identified. Extreme droughts show similar patterns but with much larger magnitude than the climatology. This study highlights the distinct response of droughts of various types to global warming and the asymmetric impact of global warming on drought distribution resulting in a much stronger influence on extreme drought than on mean drought.

Understanding the changing relationships between vegetation coverage and precipitation/temperature (P/T) and then exploring their potential drivers are highly necessary for ecosystem management under the backdrop of a changing environment. The Jing River Basin (JRB), a typical eco-environmentally vulnerable region of the Loess Plateau, was chosen to identify abrupt variations of the relationships between seasonal Normalized Difference Vegetation Index (NDVI) and P/T through a copula-based method. By considering the climatic/large-scale atmospheric circulation patterns and human activities, the potential causes of the non-stationarity of the relationship between NDVI and P/T were revealed. Results indicated that (1) the copula-based framework introduced in this study is more reasonable and reliable than the traditional double-mass curves method in detecting change points of vegetation and climate relationships; (2) generally, no significant change points were identified during 1982–2010 at the 95% confidence level, implying the overall stationary relationship still exists, while the relationships between spring NDVI and P/T, autumn NDVI and P have slightly changed; (3) teleconnection factors (including Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO), Niño 3.4, and sunspots) have a more significant influence on the relationship between seasonal NDVI and P/T than local climatic factors (including potential evapotranspiration and soil moisture); (4) negative human activities (expansion of farmland and urban areas) and positive human activities (“Grain For Green” program) were also potential factors affecting the relationship between NDVI and P/T. This study provides a new and reliable insight into detecting the non-stationarity of the relationship between NDVI and P/T, which will be beneficial for further revealing the connection between the atmosphere and ecosystems.

The world has experienced urban changes rapidly, and this phenomenon encourages authors to contribute to the United Nations sustainable development goals (SDGs) 2030 and geospatial information. This study presents a proposed algorithm of change detection and extracting the borders of buildings. This proposed algorithm provides a set of instructions to describe the method of solving the problem of how extracting the boundary of buildings from the light detection and ranging (LiDAR) input data incorporating with the firefly and ant colony algorithms. The method has used two different epochs to compare buildings and to identify the type of changes in selected buildings. These changes are based on the newly built or demolished buildings. We also used drone images and mask the region-based convolutional neural network (R-CNN) method to compare the results of roof extraction of buildings vs. the proposed algorithm. This study shows that the proposed algorithm identifies the changes of all buildings with higher accuracy of extracting border of buildings than the existing methods, successfully. This study also determines that the amount of root mean square error (RMSE) is 2.40 m2 when we use LiDAR. This proposed algorithm contributes to identifying rapidly changed buildings, and it is helpful for global geospatial information of urban management that can add best practice and solution toward the UN SDGs connectivity dilemma of urban settlement, resilience, and sustainability.

Water resources systems are often characterized by multiple objectives. Typically, there is no single optimal solution which can simultaneously satisfy all the objectives but rather a set of technologically efficient non-inferior or Pareto optimal solutions exists. Another point regarding multi-objective optimization is that interdependence and contradictions are common among one or more objectives. Therefore, understanding the competition mechanism of the multiple objectives plays a significant role in achieving an optimal solution. This study examines cascade reservoirs in the Heihe River Basin of China, with a focus on exploring the multi-objective competition mechanism among irrigation water shortage, ecological water shortage and the power generation of cascade hydropower stations. Our results can be summarized as follows: (1) the three-dimensional and two-dimensional spatial distributions of a Pareto set reveal that these three objectives, that is, irrigation water shortage, ecological water shortage and power generation of cascade hydropower stations cannot reach the theoretical optimal solution at the same time, implying the existence of mutual restrictions; (2) to avoid subjectivity in choosing limited representative solutions from the Pareto set, the long series of non-inferior solutions are adopted to study the competition mechanism. The premise of sufficient optimization suggests a macro-rule of ‘one falls and another rises,’ that is, when one objective value is inferior, the other two objectives show stronger and superior correlation; (3) the joint copula function of two variables is firstly employed to explore the multi-objective competition mechanism in this study. It is found that the competition between power generation and the other objectives is minimal. Furthermore, the recommended annual average water shortage are 1492 × 104 m3 for irrigation and 4951 × 104 m3 for ecological, respectively. This study is expected to provide a foundation for selective preference of a Pareto set and insights for other multi-objective research.

A key challenge to environmental flow assessment in many rivers is to evaluate how much of the discharge flow should be retained in the river in order to maintain the integrity and valued features of riverine ecosystems. With the increasing impact of climate change and human activities on riverine ecosystems, the natural flow regime paradigm in many rivers has become non-stationary conditions, which is a new challenge to the assessment of environmental flow. This study presents a useful framework to (1) detect change points in runoff time series using two statistical methods (Mann-Kendall test method and heuristic segmentation method), (2) adjust data of the changed period against the original flow series into a stationary condition using a procedure of reconstruction; and (3) incorporate inter- and intra-annual streamflow variability with adjusted streamflow to evaluate environmental flow. The Jialing to Han inter-basin water transfer project was selected as the case study. Results indicate that a change point of 1994 was identified, revealing that the stationarity of annual streamflow series is invalid. The variations of reconstructed streamflow series are roughly consistent with original streamflow series, especially in the maximum/minimum values and rise/fall rates, but the mean value of reconstructed streamflow series is increased. The reconstructed streamflow series would further serve to eliminate the non-stationary of original streamflow, and incorporating the inter- and intra-annual variability would upgrade the ecosystem fitness. Selecting different criteria for the conservation of riverine ecosystems can have significantly different consequences, and we should not focus on the protection of specific objectives that will inevitably affect other aspects. This study provides a useful framework for environmental flow assessment and can be applied to a wide range of instream flow management approaches to protect the riverine ecosystem.

A more accurate hourly prediction of day-ahead wind power can effectively reduce the uncertainty of wind power integration and improve the competitiveness of wind power in power auction markets. However, due to the inherent stochastic and intermittent nature of wind energy, it is very difficult to sharply improve the multi-step wind power forecasting (WPF) accuracy. According to theory of direct and recursive multi-step prediction, this study firstly proposes the models of R (recursive)-VMD (variational model decomposition)-LSTM (long short-term memory) and D (direct)-VMD-LSTM for the hourly forecast of day-ahead wind power by using a combination of a novel and in-depth neural network forecasting model called LSTM and the variational model decomposition (VMD) technique. The data from these model tests were obtained from two real-world wind power series from a wind farm located in Henan, China. The experimental results show that LSTM can achieve more precise predictions than traditional neural networks, and that VMD has a good self-adaptive ability to remove the stochastic volatility and retain more adequate data information than empirical mode decomposition (EMD). Secondly, the R-VMD-LSTM and D-VMD-LSTM are comparatively studied to analyze the accuracy of each step. The results verify the effectiveness of the combination of the two models: The R-VMD-LSTM model provides a more accurate prediction at the beginning of a day, while the D-VMD-LSTM model provides a more accurate prediction at the end of a day.

Quantifying the relative contributions of climate variability and human activity to streamflow change is important for effective water resource use and management. Four sub-catchments of the Wei River Basin (WRB) in the Loess Plateau in China were selected as the study region, where the evolution of parameter α from the latest Budyko equation (Wang-Tang equation) was explored using an 11-year moving window. The elasticity of streamflow was derived from the climatic aridity index, represented by the ratio of annual potential evaporation ( E P ) to annual precipitation ( P ), and catchment characteristics as represented by α . The effects of climate change and human activities on streamflow change during 1971–2010 were quantified with climate elasticity and decomposition methods. The contributions of different types of human activities to streamflow were further empirically determined using the water and soil conservation method. Results indicate that (1) under the same climate condition ( P and E P ), a higher value of α caused an increase in evaporation rate ( E / P ) and a decrease in runoff. Changes in these hydrological variables led to a subsequent reduction in streamflow in the WRB; (2) The absolute value of the precipitation elasticity was larger than the potential evaporation elasticity, indicating that streamflow change was more sensitive to precipitation; (3) The results based on the two methods were consistent. Climate change and human activities contributed to the decrease in streamflow by 29% and 71%, respectively, suggesting that human activities have exerted more profound impacts on streamflow in the study region; (4) Contributions of different water and soil conservation measures to streamflow reduction were calculated and sorted in descending order: Irrigation, industrial and domestic consumption, terrace, afforestation, reservoirs, check-dams, then grass-planting.

The authors wish to make a correction to their paper [...]

Monitoring variations in terrestrial water storage (TWS) is of great significance for the management of water resources. However, it remains a challenge to continuously monitor TWS variations using in situ observations and hydrological models because of a limited number of gauge stations and the complicated spatial distribution characteristics of TWS. In contrast, the Gravity Recovery and Climate Experiment (GRACE) could overcome the aforementioned restrictions, providing a new reliable means of observing TWS variation. Thus, GRACE was employed to investigate TWS variations in Northwest China (NWC) between April 2002 and March 2016. Unlike previous studies, we focused on the interactions of multiple climatic and vegetational factors, and their combined effects on TWS variation. In addition, we also analyzed the relationship between TWS variations and socioeconomic water consumption. The results indicated that (i) TWS had obvious seasonal variations in NWC, and showed significant decreasing trends in most parts of NWC at the 95% confidence level; (ii) decreasing sunshine duration and wind speed resulted in an increase in TWS in Qinghai province, whereas the increasing air temperature, ameliorative vegetational coverage, and excessive groundwater withdrawal jointly led to a decrease in TWS in the other provinces of NWC; (iii) TWS variations in NWC had a good correlation with water storage variations in cascade reservoirs of the upper Yellow River; and (iv) the overall interactions between multiple climatic and vegetational factors were obvious, and the strong effects of some climatic and vegetational factors could mask the weak influences of other factors in TWS variations in NWC. Hence, it is necessary to focus on the interactions of multiple factors and their combined effects on TWS variations when exploring the causes of TWS variations.

It is of great significance for the watershed management department to reasonably allocate water resources and ensure the sustainable development of river ecosystems. The greatly important issue is to accurately calculate instream ecological flow. In order to precisely compute instream ecological flow, flow variation is taken into account in this study. Moreover, the heuristic segmentation algorithm that is suitable to detect the mutation points of flow series is employed to identify the change points. Besides, based on the law of tolerance and ecological adaptation theory, the maximum instream ecological flow is calculated, which is the highest frequency of the monthly flow based on the GEV distribution and very suitable for healthy development of the river ecosystems. Furthermore, in order to guarantee the sustainable development of river ecosystems under some bad circumstances, minimum instream ecological flow is calculated by a modified Tennant method which is improved by replacing the average flow with the highest frequency of flow. Since the modified Tennant method is more suitable to reflect the law of flow, it has physical significance, and the calculation results are more reasonable.

Façade image recognition under complex illumination conditions is crucial for various applications, including urban three-dimensional modeling and building identification. Existing methods relying solely on Red-Green-Blue (RGB) images are prone to texture ambiguity in complex illumination environments. Furthermore, façades display varying orientations and camera viewing angles, resulting in performance issues within the RGB color space. In this study, we introduce an illumination-invariant façade recognition network (I2 -FaçadeNet) that leverages sparsely gated multi-color space experts for enhanced façade image recognition in challenging illumination environments. First, RGB façade images are converted into multi-color spaces to eliminate the ambiguous texture in complex illumination. Second, we train expert networks using separate channels of multi-color spaces. Finally, a sparsely gated mechanism is introduced to manage the expert networks, enabling dynamic activation of expert networks and the merging of results. Experimental evaluations leveraging both the International Society for Photogrammetry and Remote Sensing benchmark data sets and the Shenzhen data sets reveal that our proposed I2 -FaçadeNet surpasses various depths of ResNet in façade recognition under complex illumination conditions. Specifically, the classification accuracy for poorly illuminated façades in Zurich improves by nearly 8%, while the accuracy for over-illuminated areas in Shenzhen increases by approximately 3%. Moreover, ablation studies conducted on façade images with complex illumination indicate that compared to traditional RGB-based ResNet, the proposed network achieves an accuracy improvement of 3% to 4% up to 100% for overexposed images and an accuracy improvement of 3% to 10% for underexposed images. [ABSTRACT FROM AUTHOR]