Hydrological Processes Research Articles

What caused the lag between oxygen-18 and deuterium excess in atmospheric vapor and precipitation during the earlier summer season in southwest China?

A combined interpretation of oxygen-18 (δ18O) and deuterium excess (d-excess) in various archives in the Indian monsoon-dominated region may add value to the isotopic climate significance. Yet, coupling water δ18O and d-excess together is rarely discussed. Here, we present measurements for both δ18O and δ2H in water vapor and precipitation samples collected in the Nujiang River valley, an important moisture corridor for the Indian monsoon in southwestern China, aiming for an improved understanding of the hydrological cycle on seasonal scales. The observation found a significant drop occurred in the earlier summer season for δ18O and d-excess in vapor and precipitation. However, an approximately one-month lag between vapor δ18O and vapor d-excess is observed in seasonal variations, revealing different drivers for the seasonal patterns of vapor d-excess and δ18O. Spatial correlation analysis found that d-excess is mainly modulated by the relative humidity over the core moisture source over the Bay of Bengal (BOB), while the sharp decrease of vapor δ18O corresponds to the onset of the Indian summer monsoon associated with large-scale convective activities, which significantly depleted the vapor heavy isotopes. The finding of the phase difference in the seasonal isotope signals and the underlying mechanism will benefit the study in particularly using both δ18O and d-excess for an improved understanding of the hydrological processes in the Indian monsoon-dominated region, and will provide new insight into studies of paleoclimate rebuilding by using isotope-based proxy as well.

Mesoscale soil moisture measurements along the rover route using the mobile cosmic-ray neutron sensing in the eastern Tibetan Plateau

Water resources in the soil play an essential role in hydrological processes and ecosystem functions on the Tibetan Plateau. However, accurately measuring soil moisture distribution in this region presents challenges due to the diverse ecosystem types, complex terrain, and harsh environmental conditions. In this study, we introduce an approach for estimating mesoscale soil moisture in the Qilian Mountains (QLM) region of the eastern Tibetan Plateau using a cosmic-ray neutron rover. Soil moisture estimates derived from neutron count rates, newly adjusted by vegetation effects, demonstrated good agreement with soil moisture measurements obtained through soil sampling at 26 calibration sites across the region (RMSE = 0.025 g g−1). The calibration parameter N0_NDVI was 443 cpm in the QLM. Utilizing NDVI as vegetation correction method showed potential improvements in the accuracy of converting neutron counts to soil moisture across the diverse mountainous ecosystem types. The newly developed calibration equation provided a high-precision, high spatial resolution soil moisture transect across various landscapes measured by the rover. The average mesoscale soil moisture along the rover route varied by ecosystem types, with values of 0.10 g/g in deserts, 0.17 g/g in grasslands, 0.13 g/g in forests, 0.18 g/g in subalpine shrublands, and 0.20 g/g in croplands. Land cover types emerged as crucial determinants of mesoscale soil moisture variability in the QLM region. These findings offer valuable mesoscale soil moisture data and new insights into soil water information at the transect scale across diverse ecosystem types in the Tibetan Plateau.

Influence of data source and copula statistics on estimates of compound flood extremes in a river mouth environment

Abstract. Coastal and riverine floods are major concerns worldwide as they can impact highly populated areas and result in significant economic losses. In a river mouth environment, interacting hydrological and oceanographical processes can enhance the severity of floods. The compound flood hazards from high sea levels and high river discharge are often estimated using copulas, among other methods. Here, we systematically investigate the influence of different data sources coming from observations and models as well as the choice of copula on extreme water level estimates. While we focus on the river mouth at the city of Halmstad (Sweden), the approach presented is easily transferable to other sites. Our results show that the choice of data sources can considerably impact the results up to 10 % and 15 % for the river time series and 3 % to 4.6 % for the sea level time series under the 5- and 30-year return periods, respectively. The choice of copula can also strongly influence the outcome of such analyses up to 13 % and 9.5 % for the 5-year and 30-year return periods. Each percentage refers to the normalized difference in return level results we can expect when choosing a certain copula or input dataset. The copulas found to statistically best fit our datasets are the Clayton, BB1, and Gaussian (once) ones. We also show that the compound occurrence of high sea levels and river runoff may lead to heightened flood risks as opposed to considering them independent processes and that, in the current study, this is dominated by the hydrological driver. Our findings contribute to framing existing studies, which typically only consider selected copulas and datasets, by demonstrating the importance of considering uncertainties.

Co-analysis of total suspended particles and discharge reveals the dynamic of mercury input into glacier meltwater runoff in the northern Tibetan Plateau

Climate warming has accelerated glacier melting, releasing legacy pollutants such as mercury (Hg) into aquatic ecosystems. While the relationship between Hg in glacier meltwater runoff, total suspended particles (TSP), and runoff discharges has been established, the underlying inter-relationships and governing factors remain poorly understood. To address this knowledge gap, we conducted a continuous fixed-point sampling at Laohugou No. 12 Glacier in the northern Tibetan Plateau from June to September 2019 spanning the entire glacier ablation season. Our study analyzed the variations of Hg partition in the meltwater runoff and conducted a comprehensive co-analysis of Hg with TSP and discharge to uncover the dominant factors of Hg input into meltwater runoff. The concentration of total Hg (THg) in the meltwater runoff ranged from 0.7 to 112.6 ng/L, with an average concentration of 26.6 ± 25.1 ng/L. Particulate Hg (PHg) was found to be the predominant partition, while dissolved Hg (DHg) exhibited a notable increase in June and September. THg concentration significantly correlated with TSP concentration (r = 0.94, P < 0.01), exceeding the correlation with discharge (r = 0.76, P < 0.01) during the entire ablation period. However, further examination during varying hydrological periods revealed differing associations among Hg speciation concentrations, TSP concentration, and discharge. During the rising limb of the hydrograph, THg (r = 0.86, P < 0.01) and PHg concentrations (r = 0.87, P < 0.01) exhibited a significant correlation with TSP concentration, primarily driven by TSP, implying that Hg availability determines the Hg input into meltwater runoff. Conversely, during the recession limb of the hydrograph, THg concentration was primarily influenced by discharge (r = 0.85, P < 0.01). PHg (r = 0.84, P < 0.01) and TSP (r = 0.97, P < 0.01) concentrations were strongly influenced by discharge, indicating that hydraulic action is the dominant factor affecting Hg input. Our study elucidated the impact of glacier hydrological processes on Hg transport, revealing the dominant factors of Hg input during different hydrological periods. This contributes to a deeper understanding of Hg input into meltwater runoff and improves predictions of Hg export through glacier melt in high mountain regions.

Linking basin-scale hydrology with climatic parameters in western Himalaya: Application of satellite data, temperature index modelling and in-situ observations

Due to limited spatial and temporal in-situ runoff data availability, Himalaya-Karakoram (HK) glaciohydrology has a significant knowledge gap between large-scale and small-scale runoff modelling studies. This study reconstructs longest basin-wide runoff series in Chandra-Bhaga Basin by applying a high-resolution glaciohydrological model SPHY (Spatial Processes in Hydrology) over 1950–2022. Two-tier model calibration is done using in-situ basin-wide runoff (1973–2006) and MODIS snow cover (2003–2018). Model validation is done against in-situ Chhota Shigri Glacier catchment-wide runoff (2010–2015). The modelled mean annual basin-wide runoff is 60.21 ± 6.17 m3/s over 1950–2022, with maximum runoff in summer-monsoon months, peaking in July (182.69 m3/s). Glacier runoff (ice melt + snowmelt over glacier) contributes maximum (39%) followed by equal contributions from snowmelt runoff from non-glacierized basin area and baseflow (25%), while rainfall-runoff contributes minimum (11 %) to total runoff. There is a significant volumetric increase by ∼7% from pre- (59.17 m3/s) to post-2000 (63.47 m3/s) mainly because of early onset of snowmelt post-2000 that resulted in a hydrograph shift by ∼25 days earlier in spring. The glacier runoff is overestimated by 3% from RGI 7.0 inventory compared to different manually delineated inventories over 1950–2022, because of higher glacierized area from RGI 7.0. The precipitation shows a negative trend, but total runoff shows a positive trend due to positive trend of temperature that resulted in more glacier runoff and rainfall-runoff for basin over last 72 years. Basin-wide runoff is mainly governed by summer temperature which directly controls the amount of glacier and snowmelt runoffs and is supported by summer rainfall. This study highlights importance of basin-scale model calibration with in-situ data in large scale studies and stresses the need for in-situ observations in high-altitude Himalayan region. Basin-scale calibrated model parameters are transferable to glacier catchment scale within Chandra-Bhaga Basin, showing the model robustness at a small catchment scale.

Reflooding the coupled human and natural system of the Waza-Logone Floodplain, Cameroon

The rewilding framework is used to guide the restoration of ecological processes in natural systems, but the framework can also be used in the restoration of social and ecological processes in coupled human and natural systems. We use the case of the large-scale reflooding of the Waza-Logone Floodplain in Cameroon three decades ago as an example of rewilding a coupled human and natural system. Drawing on studies that have been conducted of the Logone Floodplain and Waza National Park over the last five decades, we discuss the reflooding efforts, review the long-term impact of the reflooding, and reflect on the assumptions of the reflooding effort. Our review shows that restoring the hydrological and ecological processes benefitted human populations but was not sufficient for supporting wildlife; and, political dynamics impact ecological processes and must be considered for rewilding to succeed.

Soil Water Flow Affected by Vegetation Root Water Uptake in the Semiarid Region of Mu Us Sandy Land, China

ABSTRACTVegetation plays an important role in the management of water resources and of the terrestrial vegetation degradation in semiarid regions of China. Detailed descriptions of the actual hydrological process of vegetation root water uptake (RWU) are lacking, and the dynamics of interfaces involved in the process of RWU have not received enough attention. A field in situ experiment with bare land and vegetated land was implemented to investigate soil water flow in vadose zones affected by vegetation RWU. The results revealed that, with the influence of RWU, the soil moisture content was redistributed and impacted evaporation and infiltration; therefore, even the roots were driven to utilize groundwater under intense potential evapotranspiration and water stress. Notably, the thicker vadose zones were beneficial for preserving water resources for bare land, but Salix reduced soil water storage by 1191.13 L compared with bare land, which was adverse for water resource development in semiarid regions. The results provide the basis for the protection of the vegetation environment and water resource management in arid regions.

Lake sediment record of eolian activity on the eastern Tibetan Plateau since 15 cal ka BP

Atmospheric dust has important influences on atmospheric circulation, global biogeochemical cycles, and hydrological processes. However, understanding the history of dust storms on the Tibetan Plateau (TP) remains challenging due to the lack of suitable geological archives. Lakes in dust-influenced regions act as dust repositories, offering the opportunity to trace the history of dust emissions and eolian activity. Here we present a synthesis of eolian activity on the eastern TP covering the past 15,000 years. It is based on records of grain size and n-alkanes from a sediment core from Gahai lake, which we combined with published pollen and other records from the same core, to reconstruct variations in surface runoff and eolian activity in this region. Our results indicate a correlation between vegetation conditions and eolian activity during different periods. Increased eolian activity occurred during the transition from the last deglaciation to the early Holocene, due to suboptimal vegetation conditions. Between 7.5 and 3.5 cal ka BP (ka), higher moisture levels resulted in the dominance of arboreal vegetation, which suppressed eolian activity. However, after 3.5 ka a sustained intensification of eolian activity occurred in the Gahai area, which was linked to decreasing vegetation cover, reduced regional humidity, and growing human impacts, especially in the eastern plateau, in southern Gansu. In recent decades, human interventions have suppressed eolian activity. Additionally, a ∼ 1435-year cyclicity in our record, and other regional records, suggests a link between increased eolian activity on the eastern TP and ice-rafting events in the North Atlantic. Generally, Holocene eolian dynamics were primarily influenced by the regional vegetation and climatic conditions which were controlled by the atmospheric circulation. However, in the late Holocene, climatic shifts and human influences had a synergistic effect which intensified the eolian activity, highlighting the important role of humans on recent dust dynamics in this region.

Quantifying the Influence of Climatic and Anthropogenic Factors on Multi-Scalar Streamflow Variation of Jialing River, China

Clarifying the impact of driving forces on multi-temporal-scale (annual, quarterly and monthly) runoff changes is of great significance for watershed water resource planning. Based on monthly runoff data and meteorological data of the Jialing River (JLR) during 1982–2020, the Mann–Kendall tendency testing approach was first applied to analyze variation tendencies of multi-timescale runoff. Then, abrupt variation years of runoff were determined using Pettitt and cumulative anomaly mutation testing approaches. The ABCD model was employed for simulating hydrological change processes in the base period and variation period. Finally, influences of climatic and anthropic factors on multi-scalar runoff were computed using the multi-scalar Budyko formula. The following conclusions were drawn in this study: (1) The mutation year of discharge was 1993; (2) the monthly runoff in the JLR presented a “single peak” distribution, and the concentration degree and concentration period in the JLR both showed an insignificant reduction trend; (3) anthropic factors were the dominant factor for spring runoff variations; climatic factors were the dominant factor on annual, summer, fall and winter runoff variations; (4) except for November, climatic factors were the dominant factor causing runoff changes in the other 11 months. This study has important reference value for water resource allocation and flood control decisions in the JLR.

Enhancing ecohydrological simulation with improved dynamic vegetation growth module in SWAT

The Soil and Water Assessment Tool (SWAT) model has been widely used for simulating ecohydrological processes such as streamflow and evapotranspiration (ET) under different land use and climate conditions. The growth of vegetation is a crucial link in the ecohydrological process. However, the model uses a simplified Environmental Policy Impact Climate (EPIC) model, which has limitations in simulation of dynamic vegetation growth. Therefore, this study aims to enhance SWAT by incorporating the forest aging data and optimizing the dormancy and forest vegetation growth modules. The application of this modified model with a case study shows that the accuracy of leaf area index (LAI) simulation has significantly improved (coefficient of determination (R2) increased by 0.16, the Nash-Sutcliffe efficiency (NSE) increased from −0.18 to 0.87, and the absolute value of percent bias (PBIAS) decreased by 50.99%), and with moderate improvement in streamflow simulation (NSE increased by 0.03, PBIAS decreased by 4.60%). Furthermore, optimizing the dynamic growth model of vegetation led to significant increases in LAI (151.58%) and ET (5.25%) values in the forest area, while streamflow and water yield decreased by 4.28% and 4.16%, respectively, across the watershed. The results indicate that dynamic vegetation growth led to increased ET, resulting in a decrease in streamflow and water yield. Overall, our results suggest that the modified SWAT model effectively simulated the process of forest growth from young forest to mature stages, significantly improving the accuracy of simulated hydrological processes. This methodology can be applied to other watersheds to simulate ecohydrological processes and can be valuable in management of ecosystems and water resources at watershed scale.

Resolving spatially complex interactions between hydrodynamics and biogeochemical processing in a large reservoir with metalimnetic oxygen deficits

Deoxygenation in the deep water of stratified lakes and reservoirs due to climate warming and human activities poses a threat to crucial limnetic ecosystem services. Metalimnetic oxygen minima (MOM) typically occurs during the mid to late stages of limnetic stratification. To investigate the mechanism of MOM during stably stratified periods in the Panjiakou Reservoir, northern China, four years of in-situ monitoring was conducted in parallel with longitudinal-vertical 2D modeling. The model simulated the hydrodynamic, water quality, algal, and DO processes in 2017 and 2020, and was calibrated and verified by the measurements. Three scenarios were designed to test the impact of hydrological processes and the high oxygen-consuming sedimentation zones (HOCSZ). We concluded that the stratification and the corresponding advection generated in the metalimnion are the driving forces of MOM in the reservoir, and the limnetic eutrophication-related benthic HOCSZ are the seedbeds of the metalimnetic hypoxia. This unique spatial setting driving the biogeochemical processing and MOM dynamics required a new generation of modelling, incorporating spatial inhomogeneities of sediment characteristics and a sophisticated resolution of hydrodynamics. Metalimnetic horizontal advection, hypolimnetic upwelling currents, sedimentation hot spots and the formation of high oxygen consumption zones became the major initiation processes leading to MOM. The numerical modeling not only represented the occurrence, development, and extinction of MOM but also comprehensively explained the spatial differences in the vertical morphology of MOM and its inter-annual variations. Our studies help to identify management strategies to mitigate the impacts of MOM on aquatic ecological security and drinking water quality, thus supporting the optimization of reservoir regulations.

Combined effects of soil colloid and soil extracellular enzymes on nitrogen loss from sloping farmland

The extracellular enzyme plays a crucial role in nitrogen (N) conversion. Soil colloid serves as an important transporter of N transport in hydrological processes. This study investigated soil colloid-mediated N loss co-transporting with soil extracellular enzymes. Five simulated rainfall experiments were conducted under four tillage treatments in a purple sloping farmland in Sichuan, China. The N concentrations, soil mineral colloids, and four carbon (C), N and phosphorus (P) acquisition extracellular enzymes (βG, AP, NAG, and LAP) in surface runoff and interflow were measured. The results showed that cross-slope tillage with straw returning practices significantly reduced the concentrations of TN, PN, NH4+, and DON in surface runoff. The activities of N and P acquisition enzymes in interflow were higher than in surface runoff, while C acquisition enzymes showed the opposite trend. The BG and AP enzymes dominated in surface runoff, while AP and NAG dominated in interflow. The concentrations of fine soil mineral colloids (SMC, φ<1 μm) and coarse mineral colloids (CMC, φ＞1 μm) in interflow were higher than that in surface runoff. The extracellular enzymes were found to co-transport with soil colloid migration during the hydrologic process. The involvement of colloid in extracellular enzyme migration in surface runoff was primarily due to SMC, while in interflow, it was the joint action of SMC and CMC. Surface runoff is always in N and P limits, while interflow is only in the P limits. With a SEM combined model quantitatively analysis, we found the synergistic transport of soil colloid and extracellular enzymes significantly impacted TN loss, explaining 95 % and 55 % of the differences between surface runoff and interflow N loss pathways. This emphasizes the importance of understanding the co-transport mechanism between soil colloid and extracellular enzymes in N loss processes.

A coupled regional-scale numerical model for hydrological processes and interactions between groundwater and surface water in a controlled drainage district

Controlled drainage (CD) has emerged as an effective strategy to prevent field waterlogging and mitigate drought during crop growth by altering the hydrological process, while few studies have quantified its effect on groundwater-surface water interactions and groundwater recharge at a regional scale. In this study, a new model is developed for the coupled numerical simulation of water flow in ditches, soil, and underground aquifers under CD conditions. The domain is partitioned into horizontal sub-areas and two vertical layers, with ditches discretized into segments based on groundwater flow grids. The Saint-Venant equations, Richards equation, and MODFLOW are applied to describe water movement processes in ditches, soil, and underground aquifers, respectively. The proposed model is calibrated and validated using five years of observations of ditch water levels (DWL) and groundwater levels, demonstrating excellent agreement with observed data. Subsequently, water balance components of the shallow aquifer below 1 m depth (GW), 1 m of root layer soil profile (SW), and ditch water (DW) under seven scenarios with different control schemes during flood and dry seasons are analyzed to quantify variations in hydrological processes caused by CD. The results show that CD significantly impacts water within SW, GW, and DW, soil evaporation, infiltration, and net recharge from GW to SW (GtoS) by altering regional groundwater table depth (GWT) through the exchange between DW and GW (DtoG and GtoD). GWT and DW show moderate correlation with GtoD, while their correlation with DtoG varies significantly under different rainfall conditions. Smaller precipitation results that precipitation and GWT have a higher linear correlation with water balance components during the dry season compared to the flood season. The correlation values (r) between transpiration (T) and GWT range from −0.99 to 0.96 and −1 to −0.85 during the flood and dry seasons, respectively, indicating a pronounced influence of CD on crop growth. On average, a 1 m increase in DWL control scheme leads to a 0.51 m increase in DWL, and regional GWT decreases by 0.35 m and 0.24 m during flood and dry seasons, respectively. Furthermore, for every 1 m decrease in GWT, net GtoS increases by 36.1 mm and 28.1 mm, respectively, resulting in an increase in SW by 13.9 mm and 11.8 mm, respectively. Considering the varying main crop water stress and the impact of CD under different rainfall conditions, an appropriate CD scheme needs to be adjusted accordingly. These findings provide a quantitative reference for the effect of CD on regional hydrological process and serve as a typical case for similar areas aiming to implement CD strategies for waterlogging and drought control.

Quantification of soil water content by machine learning using enhanced high-resolution ERT

The accurate acquisition of soil water content is a fundamental cornerstone of research into hydrological processes and agricultural engineering. Electrical Resistivity Tomography (ERT) has been validated for hydrological studies and soil monitoring. The establishment of a quantitative relationship between ERT resistivity data and soil water content is usually based on rock physics models. However, the applicability of such models in complex environments and the acquisition of the relevant parameters pose a certain challenge. In addition, the spatial resolution of ERT limits its application in soil moisture assessment. Therefore, a machine learning-based approach is proposed in this study to determine the quantitative relationship between resistivity and soil water content. We investigate the integration of three machine learning models (KNN, RF, XGBOOST) with ERT to predict the water content of clay soils. The results show that the RF model achieves an R2 of 0.92 with an RMSE of 0.41. To improve the ERT resolution, a new data collection method (MRU) is introduced in this study by increasing the density of ERT data collection. A comparative analysis is conducted between traditional ERT data collection methods and the MRU approach in terms of soil water content prediction accuracy. The results show that the MRU method of data collection improves the accuracy of soil water content prediction by an average of 57% compared to traditional methods. This study confirms the feasibility of using machine learning models to establish mappings between resistance and water content and shows that the MRU data collection method for ERT effectively improves the accuracy of predicting soil water content. These results provide a new perspective for hydrological process research and agricultural monitoring technology.

Petroleum induces soil water repellency and impedes the infiltration and evaporation processes in sandy soil

Contamination with petroleum could alter the soil hydrological processes and degrade soil and vegetation. Reclamation of petroleum contaminated soil is limited by the absence of scientific data regarding contaminated soil hydrological properties. In this study, we determined the effects of petroleum contamination on the hydrological properties of sandy soil with different contamination levels (0 g kg−1, 5 g kg−1, 10 g kg−1, 20 g kg−1, and 40 g kg−1), through the Water Drop Penetration Time (WDPT) test, infiltration and evaporation experiment in soil columns. The results showed that petroleum contamination increased the water repellency of sandy soil (hydrophilic) to slightly hydrophobic (5 g kg−1), strongly hydrophobic (10 g kg−1), extremely hydrophobic (20 g kg−1, 40 g kg−1). With the increased water repellency, the average infiltration rate decreased from 5.65 mm min−1 to 2.88, 1.17, 0.24, 0.13 mm min−1, and the accumulated evaporation decreased from 59.62 mm to 55.26, 53.91, 45.03, 37.76 mm. Our study indicated that petroleum contamination reduced the soil infiltrability and evaporation in sandy soil, and the increased water repellency was the main reason for these changes. However, more researches were still deserved to explore the effects of hydrological properties on soil water, rainfall-runoff, and pollutant migration in petroleum contaminated soil, especially in the future of global warming and intensified hydrological cycles. Our findings provide the scientific data for understanding the hydrological properties to remove pollution and restore vegetation successfully in petroleum contaminated soil.

Spatial Distribution of Thaw Depth in Palsas Estimated From Optical Unoccupied Aerial Systems Data

ABSTRACTMaximum seasonal thaw depth, referred to as active layer thickness (ALT), is one of the key parameters used to monitor permafrost conditions. ALT maps based on interpolation of point measurements or derived from coarse or moderate spatial resolution satellite data often hide small‐scale spatial variations in thaw depth resulting from differences in surface characteristics and microtopography. To model and predict changes in hydrological and biogeochemical processes in permafrost areas accurately, high‐resolution remote sensing‐based estimations of ALT are needed. Therefore, we applied random forest (RF) regression on a set of topographical and spectral vegetation indices derived from optical unoccupied aerial systems data, Landsat 8 land surface temperature (LST) data, and field measurements to estimate thaw depths in palsas at three mires in north‐west Finland. We also analyzed differences in thaw depths between mires located at different elevations, between dome and plateau‐shaped palsas, and between different vegetation and surface cover classes. The RF models resulted in root mean square errors from 2.4 to 5.7 cm between predicted and observed thaw depths and the R2 values of 0.57–0.96. Height from the surrounding fen surface and LST were the most important variables in thaw depth models, although high‐accuracy results were also achieved without LST. The mean thaw depths did not differ between the sites with lowest and highest elevation, whereas the thaw depths were significantly deeper in dome‐shaped palsas compared to plateaus. The thaw depths were significantly different between vegetation cover classes only on plateau‐shaped palsas. The results indicate the high impact of the topography on the palsa thaw depth, thus highlighting the importance of accurate elevation models in spatial modeling of palsa ALT. The methodology presented in this study can be applied to other permafrost regions where field measurements of ALT are accompanied with high‐resolution topographical and multispectral data.

Hydrological model calibration in data-deficient basins using satellite altimetry and a hydrodynamic model

Large basins with insufficient hydrological station distribution and lacking sufficient meteorological data, such as water level and discharge, pose significant challenges in developing reliable hydrological models. These models are crucial for addressing water resource-related challenges such as climate change (e.g., floods and droughts) and human-induced activities (e.g., dams) impacts. Recent advancements in satellite altimetry offer an efficient alternative by providing water level data; however, integrating this data into hydrological models remains an ongoing challenge. Here we demonstrate that a properly developed hydrodynamic model can effectively translate the satellite-observed water levels at various river cross-sections (hereafter referred to as virtual stations (VSs)) into the required discharge data, facilitating reliable hydrological modeling for data-deficient basins. Our results, based on the assessment of water level data derived from a hydrodynamic model and satellite altimetry, showed high consistency (NSE > 0.96) in the Lancang-Mekong River (LMR) basin as a case study. We found that the distribution of stations along the mainstream, in terms of their number and distance from each other, can significantly impact the performance of hydrological modeling processes. The results revealed that when a large stretch of the river is not monitored by a station, a large uncertainty occurs in runoff generation, at least for that segment of the river. However, the inclusion of virtual stations in the modeling process improved the overall performance, indicating the advantages of satellite altimetry data in better modeling of data-deficient basins. Our findings revealed that virtual stations can be effectively utilized in physically based and spatially distributed hydrological models to develop a sub-basin modeling approach that addresses the regional contribution of sub-basins to downstream river flow. The integrated modeling framework holds potential for hydrological analysis and water resources management for basins lacking sufficient gauging data and provides insight into how the distribution of stations can influence the calibration of hydrological models.

Super-resolution water body mapping with a feature collaborative CNN model by fusing Sentinel-1 and Sentinel-2 images

Mapping water bodies from remotely sensed imagery is crucial for understanding hydrological and biogeochemical processes. The identification of water extent is mainly dependent on optical and synthetic aperture radar (SAR) images. However, the use of remote sensing for water body mapping is often undermined by the mixed pixel dilemma inherent to traditional hard classification approaches. At the same time, the presence of clouds in optical imagery and speckle noise in SAR imagery, coupled with the difficulty in differentiating between water-like surfaces and actual water bodies, significantly compromise the accuracy of water body identification. This paper proposes a DEEP feature collaborative convolutional neural network (CNN) for Water Super-Resolution Mapping based on Optical and SAR images (DeepOSWSRM), which collaboratively leverages Sentinel-1 and Sentinel-2 imagery to address the challenges of missing data and mixed pixels. The Sentinel-1 image provides complementary water distribution information for the cloudy areas of the Sentinel-2 image, while the Sentinel-2 image enhances the perception capabilities for small water bodies in the Sentinel-1 image. Using PlanetScope imagery as the true reference data, the effectiveness of the proposed method was assessed through two experimental scenarios: one utilizing synthetic coarse-resolution imagery degraded from Sentinel-1 and Sentinel-2 data and another using actual Sentinel-1 and Sentinel-2 data, encompassing both simulated and real cloud conditions. A comparative analysis was conducted against three state-of-the-art CNN-based water mapping methods and two CNN SRM methods. The findings demonstrate that the proposed DeepOSWSRM method successfully produces accurate, fine-resolution water body maps, with its performance mainly benefiting from the fusion of SAR and optical images.

Impact of construction projects on sustainable landscape: a systematic review

Human activities related to land use, such as the transformation of natural landscapes into urban areas and agricultural fields, as well as changes in land management practices, have caused substantial modifications to the Earth's surface. These alterations have had significant consequences for climate, soil, hydrology, biodiversity, and ecosystem processes. The research presented in this study provides a comprehensive summary of previous investigations in the field of landscape research. The main aim of this paper is to make a comprehensive review about the effect of the construction projects on the sustainable landscape , the results found that the projects effect adversely on the landscape and the free re should be increased.

Sensitivity Analysis and Parameterization of Gridded and Lumped Models Representation for Heterogeneous Land Use and Land Cover

Hydrological processes can be highly influenced by changes in land use land cover (LULC), which can make hydrological modelling also very sensitive to land cover characterization. Therefore, obtaining up-to-date LULC data is a crucial process in hydrological modelling, and as such, different sources of LULC data raises questions on their quality and applicability. This is especially true with new data sources, such as citizen science-based land cover maps. Therefore, this research aims to explore the influence of LULC data sources on hydrological models via their parameterization and by performing sensitivity analyses. Kiffissos catchment, in Greece, a poorly gauged and highly urbanized basin including the city of Athens, is the case study area. In total, 12 continuous hydrological models were developed by mainly varying their structure and parametrization (lumped and gridded) and using three LULC datasets: coordination of information on the environment (CORINE), Urban Atlas and Scent (citizen-based). It was found that excess precipitation is negligibly contributed to by soil saturation and is dominated by the runoff over impervious areas. Therefore, imperviousness was the main parameter influencing both sensitivity to land cover and parameterization. Lastly, although the parametrization as lumped and gridded models affected the representation of hydrological processes in pervious areas, it was not relevant in terms of excess precipitation.