An understanding of the spatiotemporal behaviour of Meteorological drought (MD) and Hydrological drought (HD) is crucial for analysing how drought propagation occurs. Here, drought events were treated as three-dimensional grid structures spanning space (latitude and longitude) and time. 31 years (1971-2001) of global MD and HD events were analysed for evidence of propagation, and the most severe 20 MD events explored in detail. From the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) data archive, precipitation data was used for identifying MD events and an ensemble of simulated runoff from several global hydrological models used for detecting HD events. A technique was developed based on overlapping of the spatial and temporal coverage of MD and HD events, to establish propagation, and to calculate several propagation features. In three dimensions, the transformation from MD to HD was characterised based on delayed instigation, elongated duration, and dampened intensity of the HD event. Additionally, pooling of MD events that resulted in one or multiple branched HD events were identified. Results indicate that minor MD events with short durations and small areas generally do not exhibit propagation. The frequency of HD events with drought duration of 6-12-months is higher than that of MD events with 6-12-month duration. Out of 1740 extreme MD events identified for the 31-year period, 272 events propagated and resulted in 395 extreme HD events. Propagation features for the 20 most severe MD events show substantial variation based on geographical location highlighting the influence of regional climatic and hydrological conditions. This study advances the understanding of global drought propagation mechanisms by addressing key methodological challenges and providing a structured framework for future large-scale drought assessments.

With hydrological extremes becoming more frequent and intense in a changing world, the impact on livelihoods, infrastructure, and economies is crucial. River flow data is a valuable resource and can be used to understand and analyse trends in both flow and extreme events. It is essential to systematically examine trends and anomalies within river flow across the globe. To capture the true natural trends, the river flow data should be from natural catchments and free from anthropogenic influences, such as the construction of dams, alterations in land use, and extraction of water from rivers. Special attention must be directed towards delineating these factors to enhance our understanding of the complex dynamics governing river systems. Existing challenges in attributing trends in river flows to climate change demands for a comprehensive, worldwide Reference Hydrometric Network (RHN) with minimal human impacts, to ensure integrity of climate change signals in river flow data. This global initiative, the Reference Observatory of Basins for INternational hydrological climate change detection (ROBIN) is a global collaboration to bring together the first global RHN. Currently consisting of partners from almost 30 countries spanning every continent, the first iteration of the ROBIN dataset is now available – a consistently defined network of over 3,000 near-natural catchments. The ROBIN team estimated the first truly global analysis of trends in river flows using near-natural catchments for periods of 40 (1975-2016) and 60 (1956-2016) years. This research showcases the first global drought assessment using the subset of ROBIN network, investigating variations in river flow trends and their impact on drought events, and trends at a global scale. The research focused on the spatial and temporal variability of trends and drought characteristics in different countries and hydro-belts across the ROBIN network. It also shows the great potential of serving as benchmark for future hydrological trend assessments. Efforts are ongoing to broaden the ROBIN network to bring together more countries, incorporating additional catchments representing diverse geographical characteristics. With the support of international organizations such as WMO, UNESCO, and IPCC, ROBIN establishes the groundwork for a sustainable network of catchments, enabling comprehensive assessments of climate-induced trends, variability, and occurrences of drought on a global scale. This initiative makes a substantial contribution to enhancing our understanding of the impact of climate change on river flows and the corresponding global patterns of drought.

The capacity of aquifers to store water and the stability of infrastructure can each be adversely influenced by variations in groundwater levels and subsequent land subsidence. Along the south bank of the River Thames, the Battersea neighbourhood of London is renovating a vast 42-acre (over 8 million sq ft) former industrial brownfield site to become host to a community of homes, shops, bars, restaurants, cafes, offices, and over 19 acres of public space. For this renovation, between 2016 and 2020, a significant number of bearing piles and secant wall piles, with diameters ranging from 450 mm to 2000 mm and depths of up to 60 m, were erected inside the Battersea Power Station. Additionally, there was considerable groundwater removal that caused the water level to drop by 2.55 ± 0.4 m/year between 2016 and 2020, as shown by Environment Agency data. The study reported here used Sentinel-1 C-band radar images and the persistent scatterer interferometric synthetic aperture radar (PSInSAR) methodology to analyse the associated land movement for Battersea, London, during this period. The average land subsidence was found to occur at the rate of −6.8 ± 1.6 mm/year, which was attributed to large groundwater withdrawals and underground pile construction for the renovation work. Thus, this study underscores the critical interdependence between civil engineering construction, groundwater management, and land subsidence. It emphasises the need for holistic planning and sustainable development practices to mitigate the adverse effects of construction on groundwater resources and land stability. By considering the Sustainable Development Goals (SDGs) outlined by the United Nations, particularly Goal 11 (Sustainable Cities and Communities) and Goal 6 (Clean Water and Sanitation), city planners and stakeholders can proactively address these interrelated challenges.

Although global- and catchment-scale hydrological models are often shown to accurately simulate long-term runoff time-series, far less is known about their suitability for capturing hydrological extremes, such as droughts. Here we evaluated runoff simulations from nine catchment scale hydrological models (CHMs) and eight global scale hydrological models (GHMs) for eight large catchments: Upper Amazon, Lena, Upper Mississippi, Upper Niger, Rhine, Tagus, Upper Yangtze and Upper Yellow. The simulations were conducted within the framework of phase 2a of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). We evaluated the ability of the CHMs, GHMs and their respective ensemble means (Ens-CHM and Ens-GHM) to simulate observed monthly runoff and hydrological droughts over 31 years (1971–2001). Observed and simulated hydrological drought events were identified using the Standardised Runoff Index (SRI) and were classified based on intensity. Our results show that for all eight catchments, CHMs out-performed GHMs in monthly runoff estimation showing a better representation of observed runoff than GHMs. The number of drought events identified under different drought categories (i.e. SRI values of -1 to -1.49, -1.5 to -1.99, and ≤-2) varied significantly between models. All the models, as well as the two ensemble means present limited ability to accurately simulate severe drought events in all eight catchments, in terms of their timing and intensity. By analysing the monthly runoff time-series for several extreme droughts over the historical period, we identify room for improvement in the models so that extreme droughts may ultimately be better represented by both CHMs and GHMs.

The Corona Virus Disease (COVID)-19 pandemic led to the death of countless lives worldwide, which forced most countries and cities to impose a shutdown, bringing a halt to major human activities. While this shutdown caused a significant economic crisis, resulting in loss of livelihood to many people, it caused relief to the environment. Delhi in India is amongst the highest air-contaminated cities worldwide, and the COVID-19 shutdown helped improve air quality. This paper studied the variation in air quality for Wazirpur, Delhi, during shutdown in 2020 and a similar time-period in 2019. The data was acquired from the Central Pollution Control Board (CPCB) open-access portal for six air contaminants viz. Carbon-monoxide (CO), Nitrogen-dioxide (NO2), Ozone (O3), Particulate Matter (PM10 and PM2.5), and Sulphur-dioxide (SO2). Inferential statistical analysis was done to determine the trend in air quality variation during the shutdown compared to the previous year. Mean, standard deviation, percentage difference, linear regression and correlation analysis were made, and variable reduction in most air contaminants was noted. It was noted that for most of our observed time, the concentration of NO2, O3, PM10, PM2.5 and SO2 in 2020 is lower than in 2019, while the concentration of CO is greater in 2020 than the corresponding time in 2019. The maximum decline was observed for PM10 (70.5%) during phase-1, while the maximum increase was observed in CO (32.3%) during phase-1. As the shutdown restrictions were eased out, an increase in the air contaminants was also noted.

Crucial changes in urban climate can be witnessed due to rapid urbanisation of cities across the world. It is important to find a balance between urban expansion and thermal environment quality to guarantee sustainable urban development. Thus, it is a major research priority to study the urban heat island (UHI) in various fields, i.e., climate change urban ecology, urban climatology, urban planning, mitigation and management, urban geography, etc. The present study highlighted the interrelationship between land surface temperature (LST) and the abundance of impervious cover and green cover in the Varanasi city of Uttar Pradesh, India. For this purpose, we used various GIS and remote-sensing techniques. Landsat 8 images, land-use–land-cover pattern including urban/rural gradients, and grid- and metric-based multi-resolution techniques were used for the analysis. From the study, it was noticed that LST, density of impervious cover, and density of green cover were correlated significantly, and an urban gradient existed over the entire city, depicting a typical UHI profile. It was also concluded that the orientation, randomness, and aggregation of impervious cover and green cover have a strong correlation with LST. From this study, it is recommended that, when planning urban extension, spatial variation of impervious cover and green cover are designed properly to ensure the comfort of all living beings as per the ecological point of view.

Evapotranspiration is a key component of hydrological cycle which together with precipitation determines the water availability within a region. The present study aims to investigate spatiotemporal patterns in evapotranspiration (ET) over the period 1981–2015. The monthly time series of ET obtained from Noah Land Surface Model of GLDAS was aggregated to seasonal and annual time series to carry out the analyses. The response of ET to climate variability is examined by analysing the relationship between trend in ET and key climate parameters (temperature and precipitation) at annual and seasonal timescale. The significance of trend is tested using nonparametric Mann–Kendall test at 5% significance level. Magnitude of trends is defined by slope parameter of linear regression line. The results show an increase in ET over major portion of the basin except for the upper reaches for all the seasons. In the upper reaches (Himalayan region), negative trends prevail in ET at both seasonal and annual temporal scale. However, the trends in precipitation and mean temperature are found to be increasing over the upper Ganga basin. The results reveal the existence of factors other than climate parameters controlling the variations in ET, especially in the upper reaches of Ganga basin located in the Himalayan region.

Groundwater variation can cause land-surface movement, which in turn can cause significant and recurrent harm to infrastructure and the water storage capacity of aquifers. The capital cities in the England (London) and India (Delhi) are witnessing an ever-increasing population that has resulted in excess pressure on groundwater resources. Thus, monitoring groundwater-induced land movement in both these cities is very important in terms of understanding the risk posed to assets. Here, Sentinel-1 C-band radar images and the persistent scatterer interferometric synthetic aperture radar (PSInSAR) methodology are used to study land movement for London and National Capital Territory (NCT)-Delhi from October 2016 to December 2020. The land movement velocities were found to vary between −24 and +24 mm/year for London and between −18 and +30 mm/year for NCT-Delhi. This land movement was compared with observed groundwater levels, and spatio-temporal variation of groundwater and land movement was studied in conjunction. It was broadly observed that the extraction of a large quantity of groundwater leads to land subsidence, whereas groundwater recharge leads to uplift. A mathematical model was used to quantify land subsidence/uplift which occurred due to groundwater depletion/rebound. This is the first study that compares C-band PSInSAR-derived land subsidence response to observed groundwater change for London and NCT-Delhi during this time-period. The results of this study could be helpful to examine the potential implications of ground-level movement on the resource management, safety, and economics of both these cities.

Unrestrained urbanisation and rapid land use land cover changes can impact underlying aquifer systems, resulting in the instances of land subsidence. Thus, monitoring of groundwater induced land movement is an important part of environmental information systems and helps maintain the safety and economics of a city. Interferometric Synthetic Aperture Radar (InSAR) can facilitate monitoring of land movement and observed boreholes can facilitate groundwater monitoring. In this study, we used Sentinel-1 radar images to obtain land movement using Persistent Scatterer InSAR (PSInSAR) technique in the ENVI SARscape software package. The land movement has been studied between October 2016 and October 2020, using 98 SAR images for Delhi and 100 SAR images for London. This is the first time that such a comparison has been made between these two great cities. The land movement InSAR velocity maps for both these cities showed long-term, decreasing, complex, non-linear patterns in the spatial and temporal domain, with few areas of heave and a fair amount of subsidence. The land movement varied between -18 mm/year to +20 mm/year for Delhi and -10 mm/year to +9 mm/year for London. The underground metro construction played an important role in controlling the land movement pattern of Delhi. Its Phase III metro line was mostly built between the years 2015 and 2020 with 28 underground stations, 11 route extension and 3 new lines, namely Pink, Magenta and Grey lines. Similarly, construction of the northern line extension, the Channel Tunnel Rail Link and the Lee tunnel directly affected the land movement pattern of London. In addition, the ground movement was compared to observed groundwater values obtained from various boreholes across both these cities. The extraction and recharge of groundwater to meet the demands of an ever-increasing population directly affected the land movement patterns in both cities. It was observed that when large volumes of groundwater are extracted, then it leads to land subsidence, and when groundwater is recharged, then surface uplift is witnessed. The reasons for this subsidence pattern are consistent for both these cities in a few places, while they are completely different at some other locations. Delhi has been declared as groundwater critical zone by the government of India, while London is not under critical zone. Delhi is one of the most exploited city with regards to groundwater, owing to its urban fabric and ever-increasing population, and these results reflect that. A similar pressure is exerted on London’s groundwater by its ever-increasing population, which is not recognised by a critical status but is borne out by these results. Along with the groundwater extraction, sub-surface geology, underground construction, and metro extensions all contribute to form a complex land movement pattern. This study can serve as a guideline to government agencies in identifying the areas and extent of groundwater induced land subsidence, so that they can take proper steps to mitigate it.

Pan evaporation is an important indicator of atmospheric evaporative demand, and its long-term variation is of much concern in studies of climate change. Estimation of evaporation is also important for water budgeting and yet is difficult to quantify because of the combined effects of four meteorological variables: net radiation, wind speed, atmospheric humidity, and air temperature. This work considered the temporal trends of pan evaporation and the meteorological variables that affect them for a station located in Roorkee (India). In this study, observed meteorological data at NIH observatory for the period from 1987 to 2018 was used for trend analysis of the data (rainfall, relative humidity, maximum temperature, minimum temperature, average temperature, wind speed and pan evaporation). Evaporation was also estimated using Penman method, Meyer method and other empirical equations, and compared with the observed evaporation values. Anomalies in the time series of meteorological variables were computed to find out the magnitude of rise or fall in the series. Pettitt-Mann-Whitney (PMW) test for detection of shift in the time series has been carried out, and the trend and shift in meteorological data is correlated with the same in evaporation. Based on this research, a number of conclusions are drawn: (1) minimum temperature and relative humidity have been increasing whereas maximum temperature and wind speed have been decreasing during the period 1987-2018, (2) pan evaporation series has not shown any significant trend, except during post-monsoon when it decreased, (3) significant change points (shifts) in the time series of temperature, relative humidity and wind speed may attribute the influence of fast urbanization and enhanced anthropogenic activities in Roorkee town after creation of Uttarakhand as a separate State in the year 2000.

Groundwater-induced land movement can cause damage to property and resources, thus its monitoring is very important for the safety and economics of a city. London is a heavily built-up urban area and relies largely on its groundwater resource and thus poses the threat of land subsidence. Interferometric Synthetic Aperture Radar (InSAR) can facilitate monitoring of land movement and Gravity Recovery and Climate Experiment (GRACE) gravity anomalies can facilitate groundwater monitoring. For London, no previous study has investigated groundwater variations and related land movement using InSAR and GRACE together. In this paper, we used ENVISAT ASAR C-band SAR images to obtain land movement using Persistent Scatterer InSAR (PSInSAR) technique and GRACE gravity anomalies to obtain groundwater variations between December 2002 and December 2010 for central London. Both experiments showed long-term, decreasing, complex, non-linear patterns in the spatial and temporal domain. The land movement values varied from −6 to +6 mm/year, and their reliability was validated with observed Global Navigation Satellite System (GNSS) data, by conducting a two-sample t-test. The average groundwater loss estimated from GRACE was found to be 9.003 MCM/year. The ground movement was compared to observed groundwater values obtained from various boreholes around central London. It was observed that when large volumes of groundwater is extracted then it leads to land subsidence, and when groundwater is recharged then surface uplift is witnessed. The results demonstrate that InSAR and GRACE complement each other and can be an excellent source of monitoring groundwater for hydrologists.

Space‐time variability of rainfall at local scale is affected by several regional factors such as aerosol concentration, greenhouse gases, land cover changes, etc., along with large scale atmospheric circulations. Predictive ability of regional circulation models can be significantly improved and efficient management of water resources can be assured by identifying dominant variables controlling spatiotemporal variations in rainfall among aforementioned factors. The present study aims to investigate dominant climate system(s) controlling trends in rainfall over Chhattisgarh state (a semi‐arid region) in India over the period of 115 years (1901–2015). Discrete wavelet transform in conjunction with Mann–Kendall test is applied to the rainfall data series at different time scales (monthly, seasonal, annual, pre‐monsoon, monsoon, post‐monsoon and winter) in order to identify the long term trends and dominant periodic components influencing the trend. In the results, negative trends are found to exist in all rainfall time series at majority of districts (except for annual and monsoon rainfall at Bijapur and Sukma district). In addition, analysis of trend in actual evapotranspiration and soil moisture in the region does not exhibits the effect of anthropogenic variables (such as land cover change, irrigation projects, etc.) on the rainfall as significant negative trend are also observed in soil moisture for majority of districts. Overall, 2‐year and 4‐year periodic components have been detected to be dominating the trends in most of the rainfall time series (annual, monsoon, post‐monsoon and winter). On comparing the identified dominating components with the existing climate systems (Atlantic multidecadal oscillations, Indian ocean dipole, Madden‐Julian oscillations, Inter‐Tropical Convergence Zone, etc.), El Niño‐Southern Oscillations has been recognized as predominant climate circulation influencing the rainfall trends over the study region. The study outcomes are expected to improve the regional precipitation forecasts and should be useful in various hydro‐meteorological analyses and decision making at regional scale.

Evapotranspiration is one of the important component in hydrological cycle and plays a major role in water balance studies of tropical and subtropical areas. Estimation of actual evapotranspiration using remote sensing is a step towards the real time spatial mapping of evapotranspiration. However, this estimation being a tedious process requires determination of heat and radiation fluxes with the help of field observations. Real time spatio-temporal mapping of ET will go a long way in solving problems of water use, land use and water allocation. In this paper we used Surface Energy Balance Algorithm for Land (SEBAL) estimating the actual evapotranspiration (ET) for upper Tapi basin (India). Study emphasises on the methodology of the model and energy flux components it operates on.