Surface Water Changes Research Articles

Image similarity-based gap filling method can effectively enrich surface water mapping information

Satellite monitoring is an effective way to obtain the spatiotemporal information for surface water resources. However, due to problems such as clouds, cloud shadows, and sensor failures, there many gaps in satellite images, posing challenges for the accurate monitoring of surface water changes. Here, a new image-similarity-based gap-filling method using Landsat images, to obtain gapless surface water images, is proposed. The new method, based on a database composed of gapless images, uses the symmetrical difference method to determine the gapless image whose water pixels distribution is most similar to that of the gap image, finally replacing the gapped image pixels with gapless image pixels, to obtain a gap-filled water image. The performance of the new method is evaluated for arid and humid regions via numerical experiments and case verification. Then new method is compared with the existing gap-filling method, using the Aral Sea as an example. The new method’s average overall accuracy for surface water in arid areas is about 0.99, and that of surface water in humid areas is about 0.98. It predicted surface water more accurately than other methods, and with high accuracy (R2 = 0.92). This proposed gap-filling method provides an important technical solution to obtaining long-term high-quality observations of surface water. It can also provide a database of image for accurate water resource management.

Exact simulation of the groundwater flow in anisotropic confined aquifer near time-varying streams by comprehensive new analytical solutions

This paper presents some new analytical solutions to an accurate prediction of the behavior of the groundwater flow in aquifers in response to changes in surface water. The new analytical solutions are obtained using integral transforms. An anisotropic rectangular confined aquifer bounded with four time-varying streams is undertaken. The effects of anisotropy on groundwater head and flow rate near time-varying streams are investigated. Depending on the change rates of the streams level, an anisotropic aquifer may render either lower or higher hydraulic head than an isotropic aquifer. In addition, an anisotropic aquifer has provided less water exchange at the interfaces than an isotropic one. The sensitivity of the hydraulic head to change rate of the streams level in both isotropic and anisotropic aquifers is evaluated. It is shown that the aquifer response is more sensitive to change rate of the streams parallel to y-direction and less sensitive to change rate of the streams parallel to x-direction in an anisotropic aquifer and vice versa in an isotropic aquifer. The results of the present new analytical solutions are compared with numerical model of MODFLOW. The results obtained from the presented solutions showed good agreement with the results of MODFLOW. The results show that the presented new analytical solutions are accurate, robust and efficient. Therefore, the results indicate that the presented new analytical solutions are very effective in the simulation of the groundwater flow in river–aquifer systems. Furthermore, one of the advantages of the new analytical solutions is to investigate the sensitivity analysis of aquifer parameters, which has been carried out in this paper. Also, some other new analytical solutions for steady-state conditions and sudden fall in streams level are provided as well. Feasibility of the proposed new analytical solutions is presented via calculating and simulating the hydraulics of groundwater flow in river–aquifer systems by means of integral transforms.

Influence of Land Surface Temperature and Rainfall on Surface Water Change: An Innovative Machine Learning Approach

The largest reservoir of drinkable water on Earth is surface water. It is crucial for maintaining ecosystems and enabling people to adapt to diverse climate changes. Despite surface freshwater is essential for life, the current research shows a striking lack of understanding in its spatial and temporal dynamics of variations in outflow and storage across a sizable country: India. Numerous restrictions apply to current research, including the use of insufficient machine learning techniques and limited data series. This work uses cutting-edge and SOTA-method to use the available data and machine learning to accurately understand spatial and temporal dynamics of variations in surface freshwater outflow and storage using extended data series. The authors did the examination of thematic maps produced using ArcMap 10.8 from June’2005 to June’2020 using JRC dataset to track changes in the intensity of surface water. Google Earth Engine in Python API has been devised to detect changes in surface water levels and quantifying shifting map trends. Raster image viewing, editing, and calculation are done with ArcMap. For determining the relationship between declines in Surface water levels, changes in rainfall intensity and land surface temperature, variables were averaged over 13 rivers for 15 years. The change in surface water is reliant on independent variables of change in land surface temperature and rainfall intensity. The authors use the correlation between these parameters to achieve an average R-squared adjusted value of 0.402. The study's findings contribute to a better understanding of the matter and can be used across the world.

Distinct Hydrologic Pathways Regulate Perennial Surface Water Dynamics in a Hyperarid Basin

AbstractIn water‐stressed hyperarid basins, questions mount over the impacts of anthropogenic groundwater extraction and climate‐driven perturbations on groundwater‐surface water interactions and the resilience of ecosystem‐critical surface water. Coupling groundwater with surface water observations from Sentinel‐2 data provides an unprecedented opportunity to evaluate surface water connectivity with local aquifers following intense precipitation events in arid basins. Surface water area and groundwater level data were analyzed for trends following precipitation, including peak lag time, post‐peak recession rates, and changes in hydraulic gradients. Results indicate variable connectivity following large precipitation events between surface water change and groundwater level fluctuations in the upgradient freshwater aquifer, whereas the downgradient brine‐to‐brackish area of the aquifer indicated virtually no connectivity with the aquifer. Comparison between precipitation and surface water response indicate distinct responses based on the physical relationship of the surface water body with the brine‐to‐brackish area of the aquifer. Lumped parameter modeling of surface water inundation also constrains the possible hydrologic dynamics of the post‐precipitation response. While modeled influx to surface water seems primarily controlled by watershed hydraulics rather than direct hydraulic connectivity of the aquifers, the relationship between surface water and adjacent groundwater levels coupled with surface water area indicates that local aquifers are primarily connected to the surface water bodies through discharge via subsurface infiltration. Modeling results imply that the existence of brine‐adjacent surface water in arid basins relies on upgradient discharge from freshwater aquifers. Our results further support that marginal surface water systems can serve as a critical recharge mechanism to local aquifers.

Redox Changes during the Past 100 ka in the Deeper Eastern Arabian Sea: A Study Based on Trace Elements and Multivariate Statistical Analysis

The temporal distribution of trace elements in a sediment core (SK117/GC-08) indicates minor changes in oxygenation during the last 100 kilo years in the bottom waters of the deeper eastern Arabian Sea. The high values of Mn, Co, Cu, Mn/Al, Co/Al, Cu/Al, V/Cr, and V/(V + Ni) in the sediments during interglacials and interstitials collectively indicate oxic conditions during warm periods. The high values of Cr, Ni, V, Mo, Cr/Al, Ni/Al, and Ni/Co in sediments during glacials and stadials collectively indicate dysoxic to suboxic conditions during the colder last glacial maximum and during the entire marine isotope stage two. The bottom waters have never experienced anoxic conditions. Multivariate statistics showed the attribution of the trace elements in two factors: cluster 1 (Co, Cu, Mn) was enriched during oxic conditions and cluster 2 (Cr, Mo, Ni, V) was enriched during dysoxic to suboxic conditions. Oxygenation conditions are mainly driven by variations in monsoon-controlled surface water productivity and changes in the flux of circumpolar deep water. The dysoxic to suboxic bottom water conditions developed at the core location during colder climates are very well synchronised with an increased organic matter flux. The main factor that controls the accumulation of the organic-rich sediments in the eastern Arabian Sea during a glacial is the increase in the supply of organic matter from increased primary productivity in the surface waters, controlled by winter monsoon winds, and localized convective mixing. During warmer interglacials and interstadials, the core location has remained well ventilated.

Analysis of Seasonal Water Characteristics and Water Quality Responses to the Land Use/Land Cover Pattern: A Case Study in Tianjin, China

As the second largest city in northern China, Tianjin has a unique geographical and social status. Following its rapid economic development, Tianjin is experiencing high levels of surface water pollution. The land use/land cover (LULC) pattern has a considerable impact on hydrological cycling and pollutant transmission, and thus on regional water quality. A full understanding of the water quality response to the LULC pattern is critical for water resource management and improvement of the natural environment in Tianjin. In this study, surface water monitoring station data and LULC data from 2021 to 2022 were used to investigate the surface water quality in Tianjin. A cluster analysis was conducted to compare water quality among monitoring stations, a factor analysis was conducted to identify potential pollution sources, and an entropy weight calculation was used to analyze the impact of the land use pattern on water quality. The mean total nitrogen (TN) concentration exceeded the class Ⅴ water quality standard throughout the year, and the correlation coefficient of the relationship between dissolved oxygen (DO) and pH exceeded 0.5 throughout the year, with other water quality parameters showing seasonal changes. On the basis of their good water quality, the water quality monitoring stations near large water source areas were distinguished from those near areas with other LULC patterns via the cluster analysis. The factor analysis results indicated that the surface water in Tianjin suffered from nutrient and organic pollution, with high loadings of ammonia nitrogen (NH3N), TN, and total phosphorus (TP). Water pollution was more serious in areas near built-up land, especially in the central urban area. The entropy weight calculation results revealed that water, built-up land, and cultivated/built-up land had the greatest impact on NH3N, while cultivated land had the greatest impact on electrical conductivity (EC). This study discusses the seasonal changes of surface water and impact of land use/land cover pattern on water quality at a macro scale, and highlighted the need to improve surface water quality in Tianjin. The results provide guidance for the sustainable utilization and management of local water resources.

Surface water changes in China's Yangtze River Delta over the past forty years

Surface water security is a challenge in highly urbanized, economically active, and high water consumption regions. Exploring the supply and demand of surface water resources in developed is conducive to promoting the sustainable socioeconomic development of these agglomerations. The Yangtze River Delta (YRD) region is one of the most important world-class urban in China. However, it is unclear how surface water has responded to underlying impacts of climate change and anthropogenic water exploitation over the past decades. Here, we monitored the spatiotemporal characteristics of surface water and investigated the driving forces and regional impact of surface water changes. We found that (1) the trend in surface water body area in China's YRD was stable over the past 40 years; (2) precipitation and the digital elevation model best determined the spatial distribution of surface water in the YRD; (3) the construction of reservoirs was the main factor for the increase of surface water area; and (4) the increase in population density was the main driver of the decrease in surface water area. Human activities deplete and artificially increase surface water supply, which tends to offset; human activities have decreased water conservation services and soil and water conservation services of the YRD.

Responses of Free-Living Planktonic Bacterial Communities to Experimental Acidification and Warming

Climate change driven by human activities encompasses the increase in atmospheric CO2 concentration and sea-surface temperature. Little is known regarding the synergistic effects of these phenomena on bacterial communities in oligotrophic marine ecosystems that are expected to be particularly vulnerable. Here, we studied bacterial community composition changes based on 16S rRNA sequencing at two fractions (0.1-0.2 and >0.2 μm) during a 10- day fully factorial mesocosm experiment in the eastern Mediterranean where the pH decreased by ~0.3 units and temperature increased by ~3 °C to project possible future changes in surface waters. The bacterial community experienced significant taxonomic differences driven by the combined effect of time and treatment; a community shift one day after the manipulations was noticed, followed by a similar state between all mesocosms at the third day, and mild shifts later on, which were remarkable mainly under sole acidification. The abundance of Synechococcus increased in response to warming, while the SAR11 clade immediately benefited from the combined acidification and warming. The effect of the acidification itself had a more persistent impact on community composition. This study highlights the importance of studying climate change consequences on ecosystem functioning both separately and simultaneously, considering the ambient environmental parameters.

The Effect of Negative Samples on the Accuracy of Water Body Extraction Using Deep Learning Networks

Water resources are important strategic resources related to human survival and development. Water body extraction from remote sensing images is a very important research topic for the monitoring of global and regional surface water changes. Deep learning networks are one of the most effective approaches and training data is indispensable for ensuring the network accurately extracts water bodies. The training data for water body extraction includes water body samples and non-water negative samples. Cloud shadows are essential negative samples due to the high similarity between water bodies and cloud shadows, but few studies quantitatively evaluate the impact of cloud shadow samples on the accuracy of water body extraction. Therefore, the training datasets with different proportions of cloud shadows were produced, and each of them includes two types of cloud shadow samples: the manually-labeled cloud shadows and unlabeled cloud shadows. The training datasets are applied on a novel transformer-based water body extraction network to investigate how the negative samples affect the accuracy of the water body extraction network. The evaluation results of Overall Accuracy (OA) of 0.9973, mean Intersection over Union (mIoU) of 0.9753, and Kappa of 0.9747 were obtained, and it was found that when the training dataset contains a certain proportion of cloud shadows, the trained network can handle the misclassification of cloud shadows well and more accurately extract water bodies.

Surface Water Changes and Water Depletion of Lake Hamrin, Eastern Iraq, Using Sentinel-2 Images and Geographic Information Systems

Iraq’s lakes and rivers are currently exposed to drought because of climatic and political conditions. This study uses satellite images and geographic information systems to display the water depletion and the change in the surface water area of Hamrin Lake. Sentinel-2 imagery of 2018 and 2022 was used to analyze the surface water area changes and water depletion. A comparison was made by studying the changes that happened between 2018 and 2022 respectively. Moreover, a supervised classification technique was implemented using the Support Vector Machine algorithm (SVM). Besides the shrinkage of Hamrin Lake was determined based on 2017 to 2022 Sentinel-2 images and the Normal Difference Water Index (NDWI). Findings showed an extreme change occurred in the surface water area of the lake, which had decreased by 73% in study years and changed to land in 2022. Based on the resultant maps clear alteration is notable in the shape of the lake from north to southeast where the maximum retraction occurred. The surface water area was 144 km<sup>2</sup> in 2018 which decreased to 39 km<sup>2</sup> in 2022 and was concentrated in the western part of the lake. Besides, the lake's capacity has become 234 million cubic meters, and the amount of water in it is very little while its capacity is more than two billion cubic meters. This sharp drop in the water level in the lake indicates that what is flowing to the lake is less than what is released from it, especially due to the lack of rain. In addition to the drought, the neighboring countries have taken measures to change courses, cut off some rivers, and build dams, all of which are additional influential factors that impose quick intervention and action to prevent the lake from drying out completely.

Historical water body changes using spatio temporal indices - a case study of Dakshina Kannada

The spatiotemporal shift of surface water affects the structure, performance, and development of the ecosystem in Dakshina Kannada as well as its agricultural, economic, and social development. In order to better recognize the long-standing variations in the surface water area in Dakshina Kannada, the study used all available Landsat images, the modified Normalized Difference Water Index (MNDWI), the Enhanced Vegetation Index (EVI), and the Normalized Difference Water Index (NDWI) to map the open-surface water from 2014 to 2020 in the Google Earth Engine (GEE) cloud platform. The study looked at precipitation, temperature, and irrigated land to see how weather variation and anthropological actions affect long-standing surface water changes. The following are the outcomes. (1) The Dakshina Kannada, the extreme, periodic, and yearly average water body area values have all decreased during the last 8 years. In the meantime, the number of extreme, periodic, and lasting waterbodies has been steadily increasing. (2) The maximum water body area influences the variance of surface water area in the Dakshina Kannada, whereas the number of minimum water bodies influences the variation of water body number. (3) The area and number of water bodies are statistically significantly improved by rainfall, which also significantly improves water body area and number. The city's water surface area has changed significantly during the last 8 years, according to this study.

Analysis of Vegetation Coverage Evolution and Degradation under Coal Mine Construction in Permafrost Region

The ecological environment in permafrost regions is very sensitive to climate change and human activities. The effects of coal mining on the vegetation in permafrost regions have been poorly studied. Herein, on the basis of a field survey in the Juhugen mining area of Qilian Mountain, China, we investigated and quantified the influence of open-pit coal mining on vegetation coverage degradation in permafrost areas. According to the NDVI and field survey, the vegetation coverage was divided into five levels from low to high in the Arc GIS platform. Compared with the area not affected by coal mining, vegetation degradation was significant in the coal-mining-affected area, especially in the high-vegetation-coverage area. The vegetation coverage in Level 5 decreased from 51.99% to 21.35%. According to the conversion matrix, the transfer-out area in high coverage was larger, while the transfer-in area in low vegetation coverage was larger. The transfer-out area of five levels was significant in levels 2–5, accounting for 36.1% to 62.8% of the total area. The transfer-in area of five levels was significant in levels 1–4, accounting for 55.2% to 75.0% of the total area. Moreover, the ground surface temperature and water change were monitored in the vegetation degradation area. The results showed that the above degradation was related to an increase in the ground surface temperature and a decrease in the ground surface moisture.

Unsustainable Anthropogenic Activities: A Paired Watershed Approach of Lake Urmia (Iran) and Lake Van (Turkey)

Water availability in lakes must be studied in order to better manage ecosystems within lake basins and meet economic development needs. Despite being Iran’s largest lake, Lake Urmia’s water level and surface area have declined dramatically over the past two decades. During the same period, Lake Van in Turkey maintained a relatively stable water level and surface area. As a result, comparing factors related to water level and surface area in these lakes, which have similar geographical and climate conditions but different management policies, can be an appropriate way to identify the causes of water declines in Lake Urmia. Comparing these variables may help explain observed differences in lake behavior between 2000 and 2016. Hydrometric and climatic parameters, as well as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), were used to achieve this goal. Changes in precipitation, temperature, and evapotranspiration in both lakes show essentially identical trends, but this is not a convincing explanation for Lake Urmia’s water surface changes. The results revealed that dam construction and water diversion projects, the expansion of irrigated agriculture, and the lake’s shallow depth in most parts were the primary causes of Lake Urmia’s shrinkage compared to Lake Van.

Middle Risle River Critical Zone Observatory: monitoring karst-process evolution and its impact on surface water and on aquatic ecosystems

Abstract Rivers in karstic environments are known to be greatly influenced by surface water–groundwater interactions, with significant localized inflows during floods from springs, or with losses that can dry up rivers. The Middle Risle River is frequently affected by the development of sinkholes in a chalk karst area (Normandy, France). In the 2010s, two new major sinkholes in the riverbed caused a complete loss of water into the underlying phreatic aquifer, causing the river to dry up over several kilometres. The resulting changes in hydrogeological processes and surface water–groundwater interaction greatly affected water quality, water use and water-dependent ecosystems, causing a political crisis in this river-dependent touristic valley. To understand these phenomena and improve crisis management, the Middle Risle Critical Zone Observatory was set up to enhance monitoring, surveying and/or modelling of groundwater and river levels, river and spring flow, water temperature and conductivity, and ecosystem characteristics (fish, macro-invertebrates and vegetation). The results showed notable impacts on fish, macro-invertebrates and vegetation, some plants proving to be reliable indicators of surface-water–groundwater interaction. The dynamics of local hydrogeological processes were assessed and linked to the measured effects on ecosystems and water supply. Inverse modelling based on an analytical solution of the diffusive wave equation assessed lateral flow during floods, quantifying the spatial–temporal variability of surface-water and groundwater exchanges. It also highlighted the important role of karst zones in both storage and flood-peak attenuation processes, thereby protecting downstream villages against floods.

Detecting land surface water changes in the Upper Mzingwane sub-catchment using remotely sensed data

Abstract Globally, water is acknowledged as indispensable. It is essential for both human life and environmental needs. However, surface water resources are threatened by human and climatic influences, which may result in changes in size and density. This study aimed to evaluate the effectiveness of the normalised difference water index (NDWI), modified normalised difference water index (MNDWI) and automated water extraction index (AWEI) in detecting land surface water changes using Landsat satellite data. The results showed that the AWEI performed considerably better than the MNDWI and NDWI for extracting water surface area in the Upper Mzingwane sub-catchment, with an overall accuracy of 0.93 and a kappa coefficient of 0.82. The MNDWI and NDWI had overall accuracy/kappa values of 0.88/0.74 and 0.89/0.73, respectively. The AWEI can enhance surface water features while effectively suppressing or eliminating pollution and noise from surrounding vegetation and muddy soil. NDWI/MDWI water information is often mixed with pollution noise, vegetation and muddy soil, overestimating the area of water. All the applied indices indicate a progressive loss in the surface area of the water bodies in the sub-catchment. The decrease in water surface area could be a result of degradation, as it coincided with a decrease in vegetation cover and an increase in degraded areas. Future research needs to investigate the hydrological response of the sub-catchment to the potential influence of climate, variability, change, and land use land cover (LULC) changes.

Monitoring of Hydrological Resources in Surface Water Change by Satellite Altimetry

Satellite altimetry technology has unparalleled advantages in the monitoring of hydrological resources. After decades of development, satellite altimetry technology has achieved a perfect integration from the geometric research of geodesy to the natural resource monitoring research. Satellite altimetry technology has shown great potential, whether solid or liquid. In general, this paper systematically reviews the development of satellite altimetry technology, especially in terms of data availability and program practicability, and proposes a multi-source altimetry data fusion method based on deep learning. Secondly, in view of the development prospects of satellite altimetry technology, the challenges and opportunities in the monitoring application and expansion of surface water changes are sorted out. Among them, the limitations of the data and the redundancy of the program are emphasized. Finally, the fusion scheme of altimetry technology and deep learning proposed in this paper is presented. It is hoped that it can provide effective technical support for the monitoring and application research of hydrological resources.

Examining ground and surface water changes in response to environmental variables, land use dynamics, and socioeconomic changes in Canada

Canada's abundant and high-quality water resources support a growing human population, as well as thriving industrial and agricultural economies. However, recent intense drought conditions have raised concerns for current water resource availability. Patterns of long-term ground and surface water (GSW) changes, and their response to environmental conditions, land-use dynamics, and socioeconomic changes are not well-understood across this large and diverse country. To address this crucial gap, we identified regions of ground and surface water (GSW) changes in all the Provinces of Canada between 2002 and 2016 from the Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) datasets. We explored the relationships between GSW changes and environmental, socioeconomic, and land-use dynamics over time. We found that all the Provinces of Canada gained a net 4.46 mm Liquid Water Equivalent (LWE) per year, equivalent to a total increase of 66.9 mm LWE. GSW increases were significantly associated with the normalized difference vegetation index and evapotranspiration rates. In contrast, GSW declines were significantly related to deforestation rate, urban expansion, and economic development (median household income). Despite apparent widespread post-drought recovery detected from 2002 to 2016, the rapid GSW declines were also observed in almost all of Western Canada and part of Ontario, amounting to a net loss of 66.13 mm. This indicates that a pronounced drought had emerged. It is anticipated that Canada will be experiencing more frequent and severe droughts under ongoing climate change and increasing demand for water resources.

Changes in extent of open-surface water bodies in China's Yellow River Basin (2000–2020) using Google Earth Engine cloud platform

Dynamics of open surface water is of great significance to understand the impacts of global changes and human activities on water resources. However, long-term changes of open-surface water bodies in the Yellow River Basin have remained poorly characterized. In the study, 26,681 high-quality Landsat images were processed using the Google Earth Engine (GEE) cloud platform to study the long-term changes of the open water bodies in the Yellow River Basin from 2000 to 2020. A percentile-based image synthesis method was used to collect training samples. The multi-index water body extraction rule (MIWER) was proposed to rapidly extract surface water bodies. The results show that (1) The MIWER is suitable for long-term and large-scale surface water mapping based on Landsat images; (2) The average area of permanent and seasonal water in the last 20 years is 9062.59 km 2 and 6918.83 km 2 , respectively, accounting for 56.7% and 43.3% of the total surface water in the Yellow River Basin, respectively; (3) From 2000–2020, both permanent and seasonal water in the whole Yellow River Basin and sub-basins have increased. In general, the total open surface water shows an increasing trend with obvious spatial heterogeneity; (4) The changes of surface water in the Yellow River Basin are related to climate change and intensive human activities, but the influencing factors vary in different regions. The findings of this study demonstrate a novel robust, low-cost water extraction method that satisfies the requirements of terrestrial water inundation mapping and management across the Yellow River Basin. The methodology of this study is expected to be extended to global surface waters, providing valuable information for global surface water resource planning and management.

Revising precipitation – water storages – vegetation signatures with GRACE-based data assimilation

• Assessing modeled water – vegetation dynamics by observation-based data sets. • Observation-based data derived from GRACE assimilation and remote sensing. • Duration for precipitation refilling water storages is shorter modeled than observed. • Model overestimates water storage amount contributing to vegetation growth. • Insights will help modelers improve model structures; e.g. climate events like ENSO. The availability of freshwater is highly influenced by climate change, extreme climate events and by anthropogenic use. Countries where a large part of the population depends on the agricultural sector, such as South Africa, are strongly affected by changes in climate, which emphasizes that water is an essential source for food production and drinking water. To analyze changes in surface and subsurface water, model simulations and in situ data are commonly used. However, both have limitations, for example, the models rely on potentially erroneous forcing data and insufficient process representations and the in situ data do not represent the larger-scale weather and climate due to spatial and temporal heterogeneity. This can be mitigated by assimilating remote-sensed satellite data into models. In this research, we build a realistic picture of the water and its propagation (measured between peak times) from fluxes as precipitation, to its way through the storages and its impact on vegetation at the 50 km scale by using observation-based data. The observations are derived from MODIS remote-sensing and integrating GRACE total water storage anomaly (TWSA) observations into a hydrological model via data assimilation. Our objective is to identify shortcomings in model simulations by confronting them with the (synthesized) observations. Moreover, we demonstrate the importance of integrating observations into the models. We base these comparisons on signatures or sub-signals (e.g., temporal lags and annual amplitudes) that we derive via regression analysis, principal component analysis, sensitivity analysis and correlation analysis from the synthetic data and the model output. Our main results show that correlations and signatures in real observations are found weaker as compared to what is simulated in the model, e.g. for the contribution of precipitation to groundwater. Lag times between precipitation and surface and groundwater storage peaks are observed to be longer than in the model. The observed propagation of soil water from storages to vegetation is often shorter than in the model, while for groundwater it is longer. We believe our findings will be highly relevant for modelers; the gained knowledge can be used to improve models. In addition, we feel our study underlines the potential of GRACE assimilation into hydrological models.

Determination and Estimation of Water Surface Change With Landsat Data and Machine Learning Algorithms; A Case Study in Lake Marmara

Water resources play an important role in the continuity of life. Therefore, it is necessary to map water resources and monitor changes. Remote sensing technologies provide important data in the monitoring, control, and protection studies of water resources. These data are important for planners in studies related to water bodies. In this study, the change of the water surface of Marmara Lake, located in Gölmarmara district, 70 km from Manisa, was determined. In addition, an estimation study of the future spatial change of Marmara Lake was carried out. In this direction, the surface areas were obtained as a result of the analysis of the Landsat 7 images of the study area for the years 2002-2021 with the unsupervised classification method. In addition, precipitation, temperature, and LST data of the area were obtained with the help of Google Earth Engine. RBF Regressor, Linear Regression, Additive Regression, and MultiLayer PerceptronCS methods were used to make the most accurate estimation using the data obtained. Using the data between 2002 and 2012, the change between 2013 and 2021 was determined. When the results were examined, it was observed that the best estimation was obtained with MultiLayer Perceptron CS with R2= 0.91. As a result of the estimation study carried out for the years 2022 and 2026 with this method, it is predicted that the lake will shrink much more and reach 1.56 km2.