Water Extent Research Articles

Assessment of UAS Photogrammetry and Planet Imagery for Monitoring Water Levels around Railway Tracks

High water levels near railway tracks can be a major factor affecting the safety of train passage. Water conditions near the tracks are normally monitored through visual inspections. However, this method is limited in spatial coverage and may not provide comparable information over time. We evaluated the utility of satellite imagery (Planet Dove constellation at 3 m pixel size) at the landscape level to assess overall water surface area along railway tracks. Comparatively, we evaluated the use of Structure- from-Motion 3D point clouds and high spatial detail orthomosaics (3 cm) generated from a commercial off-the-shelf Unmanned Aerial System (UAS) (DJI M300 RTK) for measuring vertical water level changes and extent of surface water, respectively, within the right-of-way of a railway line in Ontario, Canada, in areas prone to high water level and flooding. Test sites of varied lengths (~180 m to 500 m), were assessed four times between June and October 2021. Our results indicate that the satellite imagery provides a large-scale overview regarding the extent of open water in wetlands at long distances from the railway tracks. Analysis of the UAS derived 3D point cloud indicates that changes in water level can be determined at the centimeter scale. Furthermore, the spatial error (horizontal and vertical alignments) between the multi-temporal UAS data collections between sites was less than 3 cm. Our research highlights the importance of using consistent UAS data collection protocols, and the significant potential of commercial off-the-shelf UAS systems for water level monitoring along railway tracks.

Integrating remote sensing derived indices and machine learning algorithms for precise extraction of small surface water bodies in the lower Thoubal river watershed, India

Surface water resources have witnessed various challenges namely shortage of water availability, deterioration in water quality and reduction of water bodies due to climate change and anthropogenic activities. Thus, extracting surface water bodies accurately and examining trend in their areal extent assumes greater significance. This study makes a novel attempt to utilize remote sensing derived indices and machine learning algorithms to extract and examine spatio-temporal changes in surface water bodies in the lower Thoubal river watershed. Spectral water indices were derived using Landsat data for the years 1989, 1997, 2005, 2013 and 2020 and were fed into machine learning algorithms for precise extraction of surface water bodies. The random forest model exhibited superior performance in terms of surface water extraction. The Mann-Kendall test was then used to examine the trend of surface water bodies during 1989–2020. The analysis revealed a significant declining trend in water extent over the past three decades. Future simulations using a Markov-Chain model also projected a decreasing trend in water bodies for the years 2030 and 2040 in both wet and dry months. The morphological pattern of the surface water bodies was assessed using morphological spatial pattern analysis (MSPA). It has also shown decrease in area under water bodies. These changes in the dynamics of surface water bodies may be attributed to human encroachment on wetlands for settlement and farming purposes, damming of streamflow, garbage dumping and climate change. Thus, a comprehensive approach encompassing water conservation, demand management, supply augmentation, legislation and long-term planning is essential for fostering sustainable water resource management. The findings of the study may serve as a valuable record for decision-making process and policy responses. We argue that the methodology applied in the study may help in protecting and conserving neglected small surface water bodies in other geographical regions.

Long-term change and geospatial patterns of river ice cover and navigability in Southcentral Alaska detected with remote sensing

ABSTRACT People who travel on ice-covered rivers to access traditional lands and resources can be profoundly impacted by effects of climate change on river ice seasonality. We used remote sensing, bolstered by citizen science, to assess trends and geospatial patterns of the ice cover in the Copper River Basin of Southcentral Alaska. Our analysis of Landsat imagery from water years (WYs) 1973 to 2021 suggests a severely diminishing season of river ice travel (delayed or incomplete freezeup, early breakup) due to increasing air temperatures. The weekly probability of an adequate ice cover for river crossings declined by an average of 53 percentage points. Ice extent was closely related to accumulated freezing degree days (AFDD). AFDDOct-Apr decreased by 15% since WY 1943, a significant warming trend. We mapped the spatiotemporal variation of ice and open water extent with multispectral and synthetic aperture radar (SAR) imagery (Sentinel-2, Sentinel-1). We identified reaches with more reliable opportunities for winter access and others susceptible to extensive open water, differences related to flow energy and channel form. The results of this study can support local decision making and adaptation in response to rapidly changing river ice conditions, and our approach can be applied elsewhere to document change and improve travel safety.

Using water sources extent during inundation as a reliable predictor for vegetation zonation in a natural wetland floodplain

Distinctive zones of inundation water during floods were shown to originate from different sources in some major floodplains around the world. Recent research showed that the zonation of water in rivers and floodplains is related to vegetation patterns. In spite of this, water source zones were not used for vegetation modeling due to difficulties in their delineation. In this study, we used simulation results of a fully-coupled groundwater-surface water integrated hydrological model (IHM) HydroGeoSphere and the Hydraulic Mixing-Cell method to provide standard hydrological predictors (e.g. water depth, inundation length, groundwater depth, exchange flux) and the extent of inundation zones having a certain water source (discharged groundwater, river, rainfall, and snowmelt). These variables were used to train a vegetation model for the lower Biebrza floodplain (about 290 km2) using vegetation maps from 1960, 1980, and 2000. We used a one-at-a-time (OAT) approach, where each map was validated based on a model trained on the remaining two maps to obtain realistic error estimates. We also used a fractional approach in which a fraction of each map was used for training and validation. The single model from the fractional approach was used to assess the importance of predictors and to predict vegetation for the 20th century and for the 21st century using IHM simulation forced by the Twentieth Century Reanalysis data and EURO-CORDEX RCP 2.6, 4.5, and 8.5 model ensembles. The model which used both water sources extent and standard predictors performed the best overall and was sensitive to the future trends. The extent of river water within the inundation area was by far the most important vegetation predictor. The models that neither used the water sources extent predictors nor the exchange flux were not able to predict the trends of areas covered by certain vegetation types under future climate. The advantage of the water sources extent predictors was their ability to represent the spatial effect of local hydrological phenomena. This was not possible with the standard predictors, because they show only the source of the phenomena (e.g. groundwater discharge zone), but do not indicate the actual area affected by its physical and chemical properties, which is more relevant for vegetation development. Our results highlight the relevance of using water extent predictors due to their ability to explain spatiotemporal ecological processes, such as vegetation development. We suggest to use water extent predictors in modelling for developing more accurate decision support for wetland floodplains.

Characterizing water body changes in Poyang lake using multi-source remote sensing data

This study analyzed changes in water body characteristics of Poyang Lake using remote sensing data (Sentinel-2, Sentinel-3 and ICESat-2) and machine learning methods (random forest, support vector machine and classification and regression tree). Furthermore, the relationship between the water area and water level was analyzed. The main findings are as follows: (1) the peak water range in 2016 and 2017 was significantly larger than in 2018 and 2019. The water extent is large in the summer (maximum ∼2650 km2 in July 2016) and small in the winter (minimum ∼600 km2 in February 2017); (2) the spatial scale of water level fluctuated widely, while the temporal scale showed seasonal characteristics. Water level showed seasonal characteristics, with the higher water levels in summer (20.5 m in July 2016) and lower water levels in winter (8.8 m in October 2019); and (3) there was a strong agreement between water level and extent from 2016 to 2019, for which two models fitted by the logarithmic method had a small root mean square error (R12 = 0.67, RMSE1 = 9.02; R22 = 0.55; RMSE2 = 4.88).

Accurate estimation of surface water volume in tufa lake group using UAV-captured imagery and ANNs

Quantitative acquisition of surface water volume of tufa lake group is significant for protecting the tufa landscapes. Limited by low spatial resolution, satellite remote sensing failed to extract the surface water distribution of small and scattered tufa lakes. In recent years, Unmanned Aerial Vehicle (UAV) remote sensing has been increasingly applied to survey aquatic resources and environments, posing great potential for accurately measuring surface water volume of tufa lake group. In this paper, taking the Shuzheng Lakes in Jiuzhai Valley of China as the study site, we used UAV-captured images and Artificial Neural Network (ANN) models to precisely estimate the surface water volume of the tufa lake group. Digital Surface Model (DSM) and orthomosaic were first generated using aerial triangulation. Next, the two classification networks were trained to separate water from vegetation, and then the water extents of individual tufa lakes were extracted. Subsequently, the water depth map of each tufa lake was retrieved using the transferred shallow and deep bathymetry networks. Lastly, the total surface water volume of tufa lake group was achieved by summing the products of pixel unit area and the water depth of each pixel. As results, the surface water volume of tufa lake group estimated by the optimum model combination was approximately 181,360 m3, and the corresponding R-squared was 0.92. These demonstrated the effectiveness of utilizing UAV-based remote sensing and integrated ANN models to accurately estimate surface water volume of tufa lakes.

Study of the extent and degree of water and heavy foam coverage of streams generated by a firefighting vehicle equipped with an SO3 jet engine

A number of real-scale field tests were carried out on a mobile turbine rescue and firefighting system (MTRFS), equipped with a 10 kN SO3 aircraft turbine, using the energy of exhaust gases to disperse water currents with an intensity of 6 m3min-1. The performed tests have proven the feasibility of applying a fog stream with the surface coverage intensity Iz ≥ 2 mm min−1 at a distance of 90 m and an area of 657 m2. The highest efficiency of the system was obtained at a distance between 25 and 70 m, with the maximum surface coverage intensity being Iz(max) = 14.43 mm min−1. These results indicate a significant potential for the use of water mist generated by jet engines in rescue operations conducted from a distance, e.g. elimination of hazardous substance clouds or operations intended to protect objects exposed to thermal radiation. On the other hand, significantly lower maximum surface coverage intensity generated by the water mist stream than that in the case of using the water monitor generating a droplet stream (Iz(max) = 113.48 mm min−1) indicates a much lower potential for extinguishing group A fires, which require significant wetting of the material. Tests carried out on the traditional vehicle system of supplying solid and dispersed water streams through a Turbo Jet monitor have demonstrated the necessary presence of these systems to achieve optimum operability of vehicles equipped with turbine extinguishing systems.

Fully integrating probabilistic flood forecasts into the decision-making process across southern Quebec, Canada: some factors to consider

Flood forecasts can enable decision-makers to plan for mitigation measures, ensure the safety of people and reduce damages to private and public property. However, while flood forecasting systems are becoming increasingly sophisticated, those technical improvements do not always translate into a reduction of damages. In particular, ensemble and probabilistic streamflow forecasts provide more information regarding the uncertainty, but such forecasts are also more difficult to interpret. The connection between streamflow, water depth and flood extent is also not straightforward, as this connection requires extensive previous experience with a given river reach. Streamflow forecasts can be transformed in predictive flood maps through a hydraulic model, and those maps might have a more intuitive interpretation. However, if the system is designed to also represent the uncertainty of both water depth and extent, the interpretation becomes considerably more complex. Within this context of continuous improvement of flood forecasting systems, a broad consultation (90 participants) of forecast users was conducted across southern Quebec, Canada, with the aim of understanding their perception of flood forecasts and how they use those forecasts for decision-making. Participants responses emphasize the importance of contextualizing the forecasts according to potential consequences, providing an accurate representation of the time evolution of flood events, and facilitating the communication between forecasters and decision-makers. Following this, our main recommendation is to organize workshops to provide training for decision-makers on how to interpret hydrological forecasts correctly. Another recommendation is to implement additional communication channels between forecasters and end-users.

Land–Water Transition Zone Monitoring in Support of Drinking Water Production

Water utilities often use extended open surface water reservoirs to produce drinking water. Biotic and abiotic factors influence the water level, leading to alterations in the concentration of the dissolved substances (in cases of flood or drought), entry of new pollutants (in case of flooding) or reduction in the availability and inflow speed of water to the treatment plant (in case of drought). Spaceborne image analysis is considered a significant surrogate for establishing a dense network of sensors to monitor changes. In this study, renowned inundation mapping techniques are examined for their adaptability to the inland water reservoirs’ conditions. The results, from the Polyphytos open surface water reservoir in northern Greece, showcase the transferability of the workflows with overall accuracies exceeding—in cases—98%. Hydroperiod maps generated for the area of interest, along with variations in the water surface extent over a four-year period, provide valuable insights into the reservoir’s hydrological patterns. Comparison among different inundation mapping techniques for the surface water extent and water level reveal challenges and limitations, which are related to the spatial resolution, the data take frequency and the influence of the landscape synthesis beyond the water reservoir boundaries.

Freshwater Mussels, Ecosystem Services, and Clean Water Regulation in Minnesota: Formulating an Effective Conservation Strategy

Freshwater mussels are threatened with extirpation in North America. They are a sentinel species for ecosystem function and contribute towards many ecosystem services. As mussels require clean water to survive, and since conserving ecosystem services is implicit in the federal Clean Water Act, incorporating mussel conservation into state water policies could serve multiple conservation goals. In this paper we conduct a comprehensive critical review of three topics related to freshwater mussels: their contribution to ecosystem services, their links with water quality, and threats to their survival from water pollutants and extent of protection available from regulation. In so doing, we identify gaps between the water quality requirements of mussels and the protection provided by current water regulation to help inform clean water and conservation goals in Minnesota. We find freshwater mussels to be generally sensitive to a wide variety of pollutants, and particularly to nutrients such as total nitrogen and total phosphorus and to major ions such as chloride. In addition, we find that current state water quality standards may not be sufficiently protective of mussels. We formulate a framework for determining an effective conservation strategy for mussels in Minnesota based on ecological and economic criteria to ensure adequate conservation at a reasonable cost.

Developing a High-Resolution Seamless Surface Water Extent Time-Series over Lake Victoria by Integrating MODIS and Landsat Data

To effectively monitor the spatio–temporal dynamics of the surface water extent (SWE) in Lake Victoria, this study introduced a novel methodology for generating a seamless SWE time series with fine resolution by integrating daily a Moderate-resolution Imaging Spectroradiometer (MODIS) and Landsat imagery. In the proposed methodology, daily normalized difference vegetation index (NDVI) time series data with 30 m resolution were first generated based on the constructed pixel-by-pixel downscaling models between the simultaneously acquired MODIS-NDVI and Landsat-NDVI data. In the compositing process, a Minimum Value Composite (MinVC) algorithm was used to generate monthly minimum NDVI time series, which were then segmented into a seamless SWE time series of the years 2000–2020 with 30 m resolution from the cloud background. A comparison with the existing Landsat-derived JRC (European Joint Research Centre) monthly surface water products and altimetry-derived water level series revealed that the proposed methodology effectively provides reliable descriptions of spatio–temporal SWE dynamics. Over Lake Victoria, the average percentage of valid observations made using the JRC’s products was only about 70% due to persistent cloud cover or linear strips, and the correlation with the water level series was poor (R2 = 0.13). In contrast, our derived results strongly correlated with the water level series (R2 = 0.54) and efficiently outperformed the JRC’s surface water products in terms of both space and time. Using the derived SWE data, the long-term and seasonal characteristics of lake area dynamics were studied. During the past 20 years, a significant changing pattern of an initial decline followed by an increase was found for the annual mean SWE, with the lowest area of 66,386.57 km2 in 2006. A general seasonal variation in the monthly mean lake area was also observed, with the largest SWE obtained during June–August and the smallest SWE observed during September–November. Particularly in the spring of 2006 and the autumn of 2020, Lake Victoria experienced intense episodes of drought and flooding, respectively. These results demonstrate that our proposed methodology is more robust with respect to capturing spatially and temporally continuous SWE data in cloudy conditions, which could also be further extended to other regions for the optimal management of water resources.

User-Relevant Land Cover Products for Informed Decision-Making in the Complex Terrain of the Peruvian Andes

Land cover in mountainous regions is shaped by a complex web of stressors arising from natural and anthropogenic processes. The co-design process implemented with regional stakeholders in this study highlighted persistent data gaps and the need for locally relevant (thematic, spatial, and temporal) data products, which global alternatives still fail to deliver. This study describes the development of a land cover database designed for the Junín National Reserve (JNR) in Peru as a precursor of a broader effort designed to serve Andean wetland ecosystems. The products were created using Random Forest models leveraging Sentinel-1 and Sentinel-2 data and trained using a large database of in situ data enhanced by the use of high-resolution commercial imagery (Planet). The land cover basemap includes eight classes (two of vegetation) with an overall accuracy of 0.9 and Cohen’s Kappa of 0.93. A second product further subdivided vegetation into locally meaningful vegetation classes, for a total of four types (overall accuracy of 0.85). Finally, a surface water product (snapshot and frequency) delivered a representation of the highly variable water extent around Lake Junín. It was the result of a model incorporating 150 Sentinel-1 images from 2016 to 2021 (an overall accuracy of 0.91). The products were successfully employed in identifying 133 ecosystem services provided by the different land cover classes existing in the JNR. The study highlights the value of participatory monitoring and open-data sharing for enhanced stewardship of social-ecological systems.

Effects of disturbances and environmental changes on an aridland riparian generalist.

Anthropogenic climate change and ecosystem disturbances can detrimentally affect habitats and species. Areas with concentrated biodiversity, such as aridland riparian zones, often yield the greatest number of vulnerable species. A better understanding of ecological and environmental relationships can guide more effective conservation strategies. We used both visual transects and external (tape) radio telemetry to study the behavioral and spatial ecology of black-necked gartersnakes (Thamnophis cyrtopsis; n = 81)-a dietary generalist yet aquatic habitat specialist-in a heterogenous aridland riparian zone of lower Sabino Canyon, Tucson, Arizona, between 2018 and 2021. Our objectives were to (1) understand how extrinsic conditions influence population ecology dynamics, including immediately prior to and after major disturbances and environmental extremes; (2) analyze behavioral activity and microhabitat usage in relation to environmental factors; and (3) assess the efficacy of a less-invasive telemetry strategy. Between late spring 2020 and early summer 2021, ecosystem disturbances included near-record heat and drought, wildfire, and low overwinter precipitation. Many aquatic habitats either completely dried or were spatially disjunct; gartersnake prey species were noticeably sparse. Extreme drought rapidly shifted to excessive flooding during the 2021 monsoon that brought above-average streamflow magnitude and duration. Between 2019 and 2021, we observed a dramatic decline in T. cyrtopsis; odds of detection reduced by 92.8% (CI [56.0-99.1%]). Strong spatiotemporal links relative to the extent and timing of available surface water appear important. Prior to the onset of monsoonal stream recharge in early summer, shallow and drying aquatic habitats are used as parturition sites and foraging grounds; all age classes took advantage to corral fishes trapped in isolated and shrinking pools. Ambient conditions had varying effects on gartersnake behaviors. Variation in microhabitat assemblages occurred with distance from water, activity level, and developmental age class. Interestingly, associations remained consistent across seasons and years, which suggests a reliance on heterogenous habitat structure. Sampling techniques complemented each other, however, bioclimatic parameters rendered limitations and should be considered in methodological decisions. Overall, disadvantageous responses to major disturbances and climatic extremes by a presumably adaptable generalist like T. cyrtopsis are concerning. Insights from long-term monitoring of responses by common yet environmentally sensitive species such as T. cyrtopsis may serve to more broadly highlight demographic challenges that other taxa with similar semi-aquatic life histories may face in changing systems. Such information could inform more effective conservation management strategies in warming and drying ecosystems.

Construction of High Spatiotemporal Continuity Surface Water Bodies Dataset in the Haihe River Basin

Surface water bodies exhibit high dynamic variability on seasonal and interannual scales, and high spatiotemporal resolution water bodies extent data are crucial for studying surface water bodies’ evolution. Existing surface water bodies datasets are mainly based on optical data acquisition, which has the advantages of long temporal coverage and convenience but is susceptible to cloud contamination, leading to low spatiotemporal continuity. Although microwave remote sensing data are not affected by clouds, early SAR acquisition and short temporal coverage limit its use. Therefore, existing surface water bodies datasets face the problem of insufficient spatiotemporal resolution or low continuity. This research integrates Sentinel-2 optical data and Sentinel-1 Synthetic Aperture Radar (SAR) observations to reconstruct the surface water bodies dataset with a 6-day and 10-meter spatiotemporal resolution. Then, the proposed method introduces a spatiotemporal correlation model and predicts the land cover (water or land) of Sentinel-2 cloudy pixels, which improves the spatiotemporal continuity of the reconstructed surface water bodies dataset further. Based on the proposed method, we construct the Haihe River Water Dataset (HRWD) from 2016 to 2020 with a 6-day and 10-meter spatiotemporal resolution. Compared with the European Commission’s Joint Research Centre’s (JRC’s) Global Surface Water Explorer and Global Surface Water Extent Dataset (GSWED), the HRWD shows a rational accuracy (e.g., the overall accuracy of the HRWD is more than 93%) and a better spatiotemporal continuity, which provide an improved performance in identifying and monitoring surface water bodies in the Haihe River Basin. This indicates that the proposed method can improve the spatiotemporal continuity of surface water body mapping and meet the needs of accurate and long-term quantitative observation of the distribution of large-scale and high spatiotemporal continuity surface water bodies.

Estimations of Crop Losses Due to Flood Using Multiple Sources of Information and Models: The Case Study of the Panaro River

Floods and droughts are the events that most threaten crop production; however, the impact of floods on crops is still not fully understood and often under-reported. Nowadays, multiple sources of information and approaches support the estimation of agricultural losses due to floods. This study aims to understand the differences in agricultural loss estimates provided by two conceptually different approaches (crop models and expert-based models), evaluating their sensitivity to flood hazard inputs. We investigated the challenges in flood agricultural loss assessments referring to a case study for which, in addition to model simulations, information from surveys and on-site inspections were available. Two crop models (APSIM and WOFOST) and the expert-based model AGRIDE-c were applied to evaluate agricultural yield losses after the flood event of the Panaro River (Emilia-Romagna, Northern Italy) that took place on the 6 December 2020. Two modelling tools were used to reproduce the event: the hydraulic model HEC-RAS and the image-based tool FwDET. Additionally, surveys among local farmers were conducted in the aftermath of the event to evaluate the flood features (water depth, extent and duration) and crop losses. The main findings of the study are that APSIM and WOFOST provide similar estimates of yield losses, while AGRIDE-c tends to underestimate yield losses when the losses over the entire study area are evaluated. The choice of the flood simulation technique does not influence the loss estimation since the difference between the yield loss estimates retrieved from the same model initialized with HEC-RAS or FwDET was always lower than 2%. Information retrieved from the surveys was not sufficient to validate the damage estimates provided by the models but could be used to derive a qualitative picture of the event. Therefore, further research is needed to understand how to effectively incorporate this kind of information in agricultural loss estimation.

Impacts of climatic variability on surface water area observed by remotely sensed imageries in the Red River Basin

Recent wetting in the Northern Great Plain (NGP) exerted strong influences on lakes and wetlands. However, the influence of recent increase in precipitation on spatiotemporal variation of surface water area is poorly understood in the Red River Basin (RRB, northern United States and southern Canada). Here, we used a high-resolution global surface water dataset to understand spatiotemporal dynamics of the annual, total, permanent, and seasonal water extent in RRB. Monthly surface water area is investigated to detect the change in seasonal surface water extent. We found four distinct phases of variation in surface water: Phase 1 (1990–2001, wetting); Phase 2 (2002- 2005, dry); Phase 3 (2006–2013, recent wetting); and Phase 4 (2014–2019, recent drying). A bare land to a permanent and seasonal water area switch is observed during Phase 1, while the other phases have experienced relatively little fluctuation. Findings have implications for nutrient concentration assessment in lakes and wetlands.

Time-series analysis of Sentinel-1/2 data for flood detection using a discrete global grid system and seasonal decomposition

Automated flood detection using earth observation data is a crucial task for efficient flood disaster management. Current solutions to identify flooded areas usually rely on calculating the difference between new observations and static, pre-calculated water extents derived by either single acquisitions or timely aggregated products. Such pre-calculated datasets, however, lack representation of real-world seasonality and short-term changes in trend. In this paper we present a complete workflow to automatically detect hydrological extreme events and their spatial extent, which automatically adapts to local seasonality and trend. For that we rely on a novel combination of well-established algorithms and tools to detect anomalies in time-series of water extent across large study areas. The data is binned into a discrete global grid system H3, which greatly simplifies aggregation across spatial and temporal resolutions. For each grid cell of an H3 resolution we perform a time-series decomposition using Seasonal and Trend decomposition using Loess (STL) of the cell’s proportion which is covered with surface water. All cells receive an anomaly score, calculated with extended isolation forest (EIF) on the residuals for each step in time. A burst of anomalies represents a hydrological extreme event like a flood or low water level. The presented methodology is applied on Sentinel-1/2 data for two study areas, one near Sukkur, Pakistan and the other one in Mozambique. The detected anomalies correlate with reported floods and seasonal variations of the study areas. The performance of the process and the possibility to use different H3 resolutions make the proposed methodology suitable for large scale monitoring.

Contribution of anthropogenic and hydroclimatic factors on the variation of surface water extent across the contiguous United States

Human pressure and climate variability are significantly threatening freshwater resources, with cascading effects on societies and ecosystems. In this context, it is crucial to understand the anthropogenic and climatic impacts on surface water dynamics. Here, we examine the interaction between the variation of surface water extent and the change in five potential concurrent drivers across river basins of the contiguous United States (CONUS) during the period 1984–2020. In particular, built-up area, population, and irrigated land are regarded as the anthropogenic drivers, while hydroclimatic drivers are represented by precipitation and potential evapotranspiration (PET). We perform statistical analyses in order to quantify the change in the considered variables and then identify significantly different spatial patterns and possible interrelations. Results show that almost 79% (169 out of 204 river basins) of the CONUS experienced an expansion of surface water extent mainly in the continental and temperate climatic regions (mean expansion 158.33 km2). Increasing precipitation is found to be the most widespread driver of the gain in surface water extent, affecting nearly 70% of river basins. The remaining 35 river basins of the CONUS, mostly located in the arid southwestern region of the country, faced a reduction in surface water extent (mean reduction −146.73 km2). The expansion of built-up areas and increasing PET resulted to contribute to the loss of surface water in all the river basins, followed by population growth (in ∼75% of the river basins), decreasing precipitation (in ∼60% of the river basins, all situated in southwestern US), and irrigated land expansion (in ∼55% of the river basins). Our findings shed light on the potential impacts of the variability of anthropogenic and hydroclimatic factors on hydrology and surface water resources, which could support predictive adaptation strategies that ensure water conservation.

Assessing surface-groundwater interactions for sustaining spring wetlands of the Great Artesian Basin, Australia

The Great Artesian Basin (GAB) is one of the world’s largest actively recharging aquifers. Groundwater discharges from the GAB sustain numerous spring wetlands, which have great ecological, scientific, and socio-economic significance. However, groundwater extraction and variation over time have had an impact on the quantity and area of spring wetlands with a 38% decline in extent since 1900. A major barrier to understanding variability in surface–groundwater interactions in spring wetlands of the GAB is the lack of observational data across critical spatial and temporal scales. Satellite observations have the ability to overcome this barrier and allow the evaluation of spring wetland responses to groundwater storage (GWS) variation. We investigated how GWS, and its associated drivers such as evapotranspiration (ET), soil moisture storage (SMS), and rainfall, in the GAB, influence the extent of surface water at five spring supergroups (Eulo, Barcaldine, Flinders, Springsure, and Springvale). We used satellite observations (2002–2017) to assess ET, SMS, rainfall, the normalized difference vegetation index (NDVI) and the modified normalized difference water index (mNDWI) for observing surface–groundwater interactions. NDVI responded positively to the GWS variation over the GAB and varies from sub-basin to sub- basin, with higher correlations in the Carpentaria sub-basin and some parts of Central and Western Eromanga. GWS variations was correlated with ET, SMS, rainfall, NDVI, mNDWI and surface water level (SWL). After a strong La Niña began in 2010, we uncovered relatively higher linear relationships between different components (ET, SMS, rainfall, NDVI, mNDWI, and SWL) and GWS variation (R2 > 0.50) than before the La Niña (R2 < 0.50), with the Flinders spring supergroup being the exception. NDVI and SMS are found to be the most significant predictor variables among ET, rainfall, SWL, and mNDWI components to influence GWS. This study provides improved understanding of surface-ground water interaction in spring wetlands and the influence of different hydrological components on variation in spring wetland extent in the GAB region.

Rainwater harvesting systems: An urban flood risk mitigation measure in arid areas

Rainwater harvesting (RWH) systems have been developed to compensate for shortage in the water supply worldwide. Such systems are not very common in arid areas, particularly in the Gulf Region, due to the scarcity of rainfall and their reduced efficiency in covering water demand and reducing water consumption rates. In spite of this, RWH systems have the potential to reduce urban flood risks, particularly in densely populated areas. This study aimed to assess the potential use of RWH systems as urban flood mitigation measures in arid areas. Their utility in the retention of stormwater runoff and the reduction of water depth and extent were evaluated. The study was conducted in a residential area in Bahrain that experienced waterlogging after heavy rainfall events. The water demand patterns of housing units were analyzed, and the daily water balance for RWH tanks was evaluated. The effect of the implementation of RWH systems on the flood volume was evaluated with a two-dimensional hydrodynamic model. Flood simulations were conducted in several rainfall scenarios with different probabilities of occurrence. The results showed significant reductions in the flood depth and flood extent, but these effects were highly dependent on the rainfall intensity of the event. RWH systems are effective flood mitigation measures, particularly in urban arid regions short of proper stormwater control infrastructure, and they enhance the resilience of the built environment to urban floods.