RESUMEN

This study assesses the performance of the Weather Research and Forecasting-Hydrological modeling system (WRF-Hydro) in the simulation of street-scale flood inundation. The case study is the Hackensack River Watershed in New Jersey, US, which is part of the operational Stevens Flood Advisory System (SFAS), a one-way coupled hydrodynamic-hydrologic system that currently uses the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) to simulate streamflow. The performance of the 50-m gridded WRF-Hydro model was assessed for potential integration into the operational SFAS system. The model was calibrated with the dynamically dimensioned search algorithm using streamflow observations. The model performance was assessed using (i) streamflow observations, (ii) USGS HWMs, and (iii) crowdsourced data on street inundation. Results show that WRF-Hydro outperformed the HEC-HMS model. WRF-Hydro over and underestimated flood inundation extent due to the inaccuracy of the synthetic rating curves and the modeling structure errors. An agreement was noticed between WRF-Hydro and crowdsourced data on flood extent.

RESUMEN

The total organic carbon (OC) from plant litter in riparian zones is an important nutrient source for aquatic organisms and plays a crucial role in the nutrient cycling of river ecosystems. Nevertheless, the total amount of OC in dammed rivers gradually decreases, and the restoration methods are rarely researched. A hypothesis was proposed that the periodic inundation altered the process of OC release from plant litter. To explore the impact of periodic inundation on OC release from litter in the riparian zone, litter bags in situ tests were conducted in the Yalong River. Three inundation treatments were conducted for the test samples, which were NS (never submerged by water), PIS (periodic submerged), and PMS (permanent submerged). Results indicated that the amount of OC released from litters in PIS treatment was about 1.1 times that in PMS treatment, and about 2.1 times that in NS treatment. The average release rate coefficient k of PIS treatment (at mean water level) was the highest (12.8 × 10-4 d-1), followed by PMS treatment (11.0 × 10-4 d-1), and NS treatment (5.6 × 10-4 d-1), which demonstrated that the periodic inundation was critical for OC release. The mean water level was a demarcation line where there was a significant difference in the release of OC in the riparian zone (p < 0.05). Flow velocity alone could account for 84% of the variation in OC release rate, while the flow velocity and inundation duration together could achieve an explanatory degree of 86%. This research can provide a valuable scientific basis for the protection and restoration of river ecosystems, especially for the recovery of OC concentration in dammed rivers.

Asunto(s)

RESUMEN

Climate change and intensified human activities are exacerbating the frequency and severity of extreme precipitation events, necessitating more precise and timely flood risk assessments. Traditional models often fail to dynamically and accurately assess flood risks due to their static nature and limited handling of spatiotemporal variations. This study confronts these challenges head-on by developing a novel coupled hydrological-hydrodynamic model integrated with a Block-wise use of the TOPMODEL (BTOP) and the Rainfall-Runoff-Inundation (RRI) model. This integrated approach enables the rapid acquisition of high-precision flood inundation simulation results across large-scale basins, addressing a significant gap in dynamic flood risk assessment and zoning. A critical original achievement of this research lies in developing and implementing a comprehensive vertical-horizontal combined weighting method that incorporates spatiotemporal information for dynamic evaluation indicators, significantly enhancing the accuracy and rationality of flood risk assessments. This innovative method successfully addresses the challenges posed by objective and subjective weighting methods, presenting a balanced and robust framework for flood risk evaluation. The findings from the Min River Basin in China, as a case study, demonstrate the effectiveness of the BTOP-RRI model in capturing the complex variations in runoff and the detailed simulations of flood processes. The model accurately identifies the timing of these peaks, offering insights into the dynamic evolution of flood risks and providing a more precise and timely assessment tool for policymakers and disaster management authorities. The flood risk assessment results demonstrate good consistency with the actual regional conditions. In particular, high-risk areas exhibit distinct characteristics along the river channel, with the distribution area significantly increasing with a sudden surge in runoff. Intense precipitation events expand areas classified as moderate and high risk, gradually shrinking as precipitation levels decrease. This study significantly advances flood risk assessment methodologies by integrating cutting-edge modeling techniques with comprehensive weighting strategies. This is essential for improving the scientific foundation and decision-making processes in regional flood control efforts.

Asunto(s)

RESUMEN

As urbanization progresses and the impacts of climate change become more pronounced, urban flooding has emerged as a critical challenge for resilient cities, particularly concerning urban underground spaces where flooding can lead to significant loss of life and property. Drawing upon a comprehensive review of global research on underground space flood simulation and evacuation, this paper undertakes the modelling of inundation in a substantial underground area during the extraordinary rainfall event on 7 September 2023, in Shenzhen, China. Specifically, it introduces a two-step method to simulate the coupled surface-underground inundation process with high accuracy. The study simulates the inflow processes in three types of underground spaces: parking lots, metro stations, and underpasses. Utilizing the specific force per unit width evaluation, the research examines how varying flood barrier heights influence evacuation time and inundation risk. Subsequently, the paper proposes corresponding evacuation strategies based on the obtained findings. By highlighting the vulnerability of urban underground spaces to flooding, the study underscores the urgent need for further research in this domain.

Asunto(s)

RESUMEN

Afforestation is an acknowledged method for rehabilitating deteriorated riparian ecosystems, presenting multiple functions to alleviate the repercussions of river damming and climate change. However, how ecosystem multifunctionality (EMF) responds to inundation in riparian afforestation ecosystems remains relatively unexplored. Thus, this article aimed to disclose how EMF alters with varying inundation intensities and to elucidate the key drivers of this variation based on riparian reforestation experiments in the Three Gorges Reservoir Region in China. Our EMF analysis encompassed wood production, carbon storage, nutrient cycling, decomposition, and water regulation under different inundation intensities. We examined their correlation with soil properties and microbial diversity. The results indicated a substantial reduction in EMF with heightened inundation intensity, which was primarily due to the decline in most individual functions. Notably, soil bacterial diversity (23.02%), soil properties such as oxidation-reduction potential (ORP, 11.75%), and temperature (5.85%) emerged as pivotal variables elucidating EMF changes under varying inundation intensities. Soil bacterial diversity and ORP declined as inundation intensified but were positively associated with EMF. In contrast, soil temperature rose with increased inundation intensity and exhibited a negative correlation with EMF. Further insights gleaned from structural equation modeling revealed that inundation reduced EMF directly and indirectly by reducing soil ORP and bacterial diversity and increasing soil temperature. This work underscores the adverse effects of dam inundation on riparian EMF and the crucial role soil characteristics and microbial diversity play in mediating EMF in response to inundation. These insights are pivotal for the conservation of biodiversity and functioning following afforestation in dam-induced riparian habitats.

Asunto(s)

RESUMEN

Quantifying flood risks through a cascade of hydraulic-cum-hydrodynamic modelling is data-intensive and computationally demanding- a major constraint for economically struggling and data-scarce low and middle-income nations. Under such circumstances, geomorphic flood descriptors (GFDs), that encompass the hidden characteristics of flood propensity may assist in developing a nuanced understanding of flood risk management. In line with this, the present study proposes a novel framework for estimating flood hazard and population exposure by leveraging GFDs and Machine Learning (ML) models over severely flood-prone Ganga basin. The study incorporates SHapley Additive exPlanations (SHAP) values in flood hazard modeling to justify the degree of influence of each GFD on the simulated floodplain maps. A set of 15 relevant GFDs derived from high-resolution CartoDEM are forced to five state-of-the-art ML models; AdaBoost, Random Forest, GBDT, XGBoost, and CatBoost, for predicting flood extents and depths. To enumerate the performance of ML models, a set of twelve statistical metrics are considered. Our result indicates a superior performance of XGBoost (κ = 0.72 and KGE = 82%) over other ML models in flood extent and flood depth prediction, resulting in about 47% of the population exposure to high-flood risks. The SHAP summary plots reveal a pre-dominance of Height Above Nearest Drainage during flood depth prediction. The study contributes significantly in comprehending our understanding of catchment characteristics and its influence in the process of sustainable disaster risk reduction. The results obtained from the study provide valuable recommendations for efficient flood management and mitigation strategies, especially over global data-scarce flood-prone basins.

RESUMEN

Developing management strategies to safeguard public health and environmental sustainability requires a comprehensive understanding of the solubility and mobility of trace and alkaline metals in the event of seawater flooding. This study investigated the effects of seawater flooding, along the duration of flooding, on the release of trace and alkaline metals (Mn, Fe, Cu, Zn, Ca, K, and Mg) in two calcareous soils (Krome and Biscayne) located in southern Florida. Seawater flooding experiments involved two soil types and four flooding durations (1, 7, 14, and 28 days) replicated three times. Freshwater flooding experiments were also conducted for comparison. After each flooding experiment, soil samples were collected at three depths (15, 30, and 45 cm), and analyzed for selected elements. Comparative analysis revealed significant releases of Mn, Fe, and Zn in both soils flooded by seawater compared to freshwater. In most cases, significant increments were evident as early as 1-day exposure to seawater flooding, which further increased with flooding duration. However, the impacts of seawater flooding had notable differences between the two soils. Seawater flooding in Krome soil for 28 days, resulted in higher Mn, Fe, and Zn contents by 58, 340, and 510% compared with freshwater flooding, while corresponding increases in Biscayne soil were 3.3, 130, and 180%, respectively. Comparable marginal increases in Cu content were observed for both soils. Similarly, seawater flooding increased K, Mg, and Na contents from single-day flooding. The interplay between soil type, column depth, flooding duration, and their interactions proved influential factors in determining Mn, Fe, Cu, and Zn releases, with peak levels typically observed on the 28th day of flooding and at bottom depths. Overall, these findings highlight the release of these elements, raising concerns about potential plant toxicity and groundwater or surface water contamination due to leaching and runoff.

RESUMEN

Hydro-Fluctuation Belt (HFB), a periodically exposed bank area formed by changes in water level fluctuations, is critical for damaging the reservoir wetland landscape and ecological balance. Thus, it is important to explore the mechanism of hydrological conditions on the plant-soil system of the HFB for protection of the reservoir wetland and landscape restoration. Here, we investigated the response of plant community characteristics and soil environment of the HFB of Tonghui River National Wetland Park (China), is a typical reservoir wetland, to the duration of inundation, as well as the correlation between the distribution of dominant plants and soil pH, nutrient contents, and enzyme activity by linear regression and canonical correlation analyses. The results show that as the duration of inundation decreases, the vegetation within the HFB is successional from annual or biennial herbs to perennial herbs and shrubs, with dominant plant species prominent and uneven distribution of species. Soil nutrient contents and enzyme activities of HFB decreased with increasing inundation duration. Dominant species of HFB plant community are related to soil environment, with water content, pH, urease, and available potassium being principle soil environmental factors affecting their distribution. When HFB was inundated for 0-30 days, soil pH was strongly acidic, with available potassium content above 150 mg kg-1 and higher urease activity, distributed with Arundo donax L., Polygonum perfoliatum L., Alternanthera philoxeroides (Mart.) Griseb., and Daucus carota L. communities. When inundated for 30-80 days, soil pH was acidic, with lower available potassium content (50-150 mg kg-1) and urease activity, distributed with Beckmannia syzigachne (Steud.) Fern.+ Polygonum lapathifolium L., Polygonum lapathifolium L., Medicago lupulina L. + Dysphania ambrosioides L. and Leptochloa panicea (Retz.) Ohwi communities. Using the constructed HFB plant-soil correlation model, changes in the wetland soil environment can be quickly judged by the succession of plant dominant species, which provides a simpler method for the monitoring of the soil environment in the reservoir wetland, and is of great significance for the scientific management and reasonable protection of the reservoir-type wetland ecosystem.

Asunto(s)

RESUMEN

In a previous study, an experimental oversight led to the accumulation of water filling a container housing diapausing bumblebee queens. Surprisingly, after draining the water, queens were found to be alive. This observation raises a compelling question: can bumblebee queens endure periods of inundation while overwintering underground? To address this question, we conducted an experiment using 143 common eastern bumblebee (Bombus impatiens) queens placed in soil-filled tubes and subjected to artificially induced diapause in a refrigerated unit for 7 days. Tap water was then added to the tubes and queens (n = 21 per treatment) were either maintained underwater using a plunger-like apparatus or left to float naturally on the water's surface for varying durations (8 h, 24 h or 7 days) while remaining in overwintering conditions. Seventeen queens served as controls. After the submersion period, queens were removed from water, transferred to new tubes with soil and kept in cold storage for eight weeks. Overall, queen survival remained consistently high (89.5 ± 6.4%) across all treatments and did not differ among submersion regimes and durations. These results demonstrate the remarkable ability of diapausing B. impatiens queens to withstand submersion under water for up to one week, indicating their adaptations to survive periods of flooding in the wild.

Asunto(s)

RESUMEN

Floods cause substantial losses to life and property, especially in flood-prone regions like northwestern Bangladesh. Timely and precise evaluation of flood impacts is critical for effective flood management and decision-making. This research demonstrates an integrated approach utilizing machine learning and Google Earth Engine to enable real-time flood assessment. Synthetic aperture radar (SAR) data from Sentinel-1 and the Google Earth Engine platform were employed to generate near real-time flood maps of the 2020 flood in Kurigram and Lalmonirhat. An automatic thresholding technique quantified flooded areas. For land use/land cover (LULC) analysis, Sentinel-2's high resolution and machine learning models like artificial neural networks (ANN), random forests (RF) and support vector machines (SVM) were leveraged. ANN delivered the best LULC mapping with 0.94 accuracy based on metrics like accuracy, kappa, mean F1 score, mean sensitivity, mean specificity, mean positive predictive value, mean negative value, mean precision, mean recall, mean detection rate and mean balanced accuracy. Results showed over 600,000 people exposed at peak inundation in July-about 17% of the population. The machine learning-enabled LULC maps reliably identified vulnerable areas to prioritize flood management. Over half of croplands flooded in July. This research demonstrates the potential of integrating SAR, machine learning and cloud computing to empower authorities through real-time monitoring and accurate LULC mapping essential for effective flood response. The proposed comprehensive methodology can assist stakeholders in developing data-driven flood management strategies to reduce impacts.

Asunto(s)

RESUMEN

Microtopography plays a critical role in road inundation during urban flood events. The microtopography in this paper was defined as terrain-scale features that encompass surface roughness, slope, road network and urban building layout. This paper aims to explore the mechanism of depression storage and road inundation under different microtopography. Simulations under 4 rainfall intensities (144.0- 182.88 mm/h) and 14 slope combinations (four transverse slope and five longitudinal slope) were implemented in an 800 by 70 cm local model. The correlation heat map directly reflected that longitudinal slope had higher influence on drainage than other factors. Then real topographical and hydrological data was applied to predict road inundation with five different extreme rainfall events in Jiangning District (Nanjing City, China). The microtopography characteristics of frequent inundation road were extracted, which further verified the conclusions of the local model. Results show that: the microtopography depressions drainage process could be divided into six main stages: filling stage, interaction stage, unstable drainage stage, stable flow stage, drainage stage and stage of drainage end. Water was stored on depressions of road, and the storage volume and discharge efficiency were affected by the surface relief and slope. The emergence of slope provided an altered path and power for water drainage. Only 0.3 % slope could contribute a 28.4 % to discharge efficiency. Upon comparation, the best combination for drainage was 2.0 % transverse slope with 3.0 % longitudinal slope. These findings provided meaningful insights and perspectives for urban flood hazard mitigation and were a more detailed reference for road design.

RESUMEN

Disaster recovery poses unique challenges for residents reliant on private wells. Flooding events are drivers of microbial contamination in well water, but the relationship observed between flooding and contamination varies substantially. Here, we investigate the performance of different flood boundaries─the FEMA 100 year flood hazard boundary, height above nearest drainage-derived inundation extents, and satellite-derived extents from the Dartmouth Flood Observatory─in their ability to identify well water contamination following Hurricane Florence. Using these flood boundaries, we estimated about 2600 wells to 108,400 private wells may have been inundated─over 2 orders of magnitude difference based on boundary used. Using state-generated routine and post-Florence testing data, we observed that microbial contamination rates were 7.1-10.5 times higher within the three flood boundaries compared to routine conditions. However, the ability of the flood boundaries to identify contaminated samples varied spatially depending on the type of flooding (e.g., riverine, overbank, coastal). While participation in testing increased after Florence, rates were overall still low. With <1% of wells tested, there is a critical need for enhanced well water testing efforts. This work provides an understanding of the strengths and limitations of inundation mapping techniques, which are critical for guiding postdisaster well water response and recovery.

Asunto(s)

RESUMEN

Hydrodynamic models can accurately simulate flood inundation but are limited by their high computational demand that scales non-linearly with model complexity, resolution, and domain size. Therefore, it is often not feasible to use high-resolution hydrodynamic models for real-time flood predictions or when a large number of predictions are needed for probabilistic flood design. Computationally efficient surrogate models have been developed to address this issue. The recently developed Low-fidelity, Spatial analysis, and Gaussian Process Learning (LSG) model has shown strong performance in both computational efficiency and simulation accuracy. The LSG model is a physics-guided surrogate model that simulates flood inundation by first using an extremely coarse and simplified (i.e. low-fidelity) hydrodynamic model to provide an initial estimate of flood inundation. Then, the low-fidelity estimate is upskilled via Empirical Orthogonal Functions (EOF) analysis and Sparse Gaussian Process models to provide accurate high-resolution predictions. Despite the promising results achieved thus far, the LSG model has not been benchmarked against other surrogate models. Such a comparison is needed to fully understand the value of the LSG model and to provide guidance for future research efforts in flood inundation simulation. This study compares the LSG model to four state-of-the-art surrogate flood inundation models. The surrogate models are assessed for their ability to simulate the temporal and spatial evolution of flood inundation for events both within and beyond the range used for model training. The models are evaluated for three distinct case studies in Australia and the United Kingdom. The LSG model is found to be superior in accuracy for both flood extent and water depth, including when applied to flood events outside the range of training data used, while achieving high computational efficiency. In addition, the low-fidelity model is found to play a crucial role in achieving the overall superior performance of the LSG model.

Asunto(s)

RESUMEN

Due to rising land development, mitigating the negative effects of land use change is becoming a problem. Understanding how land development affects flood inundation is critical for long-term water resource management. This study evaluates the land use change in the Konkoure River Basin and its impact on flood inundation. The land use changes were assessed using Landsat image (level 1) in August 2006 and August 2021. In addition, we used GIS and remote sensing applications to assess the degree of changes that took place in the Konkoure watershed. According to the findings, 32.16% of the total area became built-up areas, and 35.51% was converted to other land uses in Konkoure watershed. Konkoure's most significant change is that 29.50% of forest area transformed into built-up areas and other land uses. The rainfall-runoff-inundation model (RRI) based inundation of the Konkoure River Basin was compared to the MODIS extent between 31 August 2006 and 30 August 2021 flood events. Flood inundation variations in the Konkoure watershed were studied in terms of inundation area, peak inundation depth, runoff volume, and the infiltration rate. As a result, the flood inundation area increased from 139.98 to 198.72 km2 and the infiltration rate decrease from 7 to 5 mm/h. Moreover, we used flow duration curves (FDCs) to fully comprehend the streamflow processes. The result indicates that the Konkoure watershed has experienced flooding partly due to land use change.

Asunto(s)

RESUMEN

The rapid development of the city leads to the continuous updating of the land use allocation ratio, particularly during the flood season, which will exacerbate the significant changes in the spatial and temporal patterns of urban flooding, increasing the difficulty of urban flood forecasting and early warning. In this study, the spatial and temporal evolution of flooding in a high-density urban area was analyzed based on the Mike Flood model, and the influence mechanisms of different rainfall peak locations and infiltration rate scenarios on the spatial and temporal characteristics of urban waterlogging were explored. The results revealed that under the same return period, the larger the rainfall peak coefficient, the larger the peak value of inundation volume and inundation area. When the rainfall peak coefficient is small, the higher the return period is, and the larger the peak lag time of the inundation volume is, in which P = 50a, r = 0.2, the peak lag time of the inundation volume reached 32 min and 45 min for the inundation depths H > 0.03 m and H > 0.15 m, respectively. There are also significant differences in the peak lag time of waterlogging inundation volume for different inundation depths. The greater the inundation depth, the longer the peak lag time of the inundation volume, and the higher the return period, the more significant the effect of lag time prolongation. It is worth noting that the increase in infiltration rate may lead to an advance in the peak time of inundation volume and inundation area, and the peak time of the inundation area is overall more obvious than that of inundation volume. The effect of infiltration rate on the peak time of inundation volume for larger inundation depths was relatively large; the peak times of inundation volume and inundation area were advanced by 4-6 min and 4-8 min for H > 0.03 m and H > 0.15 m, respectively, after the increase in infiltration rate, and the higher the rainfall return period, the longer the advance time. The spatial and temporal characteristics of waterlogging under different peak rainfall locations and infiltration capacities obtained in this study can help provide a new perspective for temporal forecasting and warning of urban waterlogging.

Asunto(s)

RESUMEN

Wetlands cycle carbon by being net sinks for carbon dioxide (CO2) and net sources of methane (CH4). Daily and seasonal temporal patterns, dissolved oxygen (DO) availability, inundation status (flooded or dry/partially flooded), water depth, and vegetation can affect the magnitude of carbon uptake or emissions, but the extent and interactive effects of these variables on carbon gas fluxes are poorly understood. We characterized the linkages between carbon fluxes and these environmental and temporal drivers at the Old Woman Creek National Estuarine Research Reserve (OWC), OH. We measured diurnal gas flux patterns in an upstream side channel (called the cove) using chamber measurements at six sites (three vegetated and three non-vegetated). We sampled hourly from 7 AM to 7 PM and monthly from July to October 2022. DO concentrations and water levels were measured monthly. Water inundation status had the most influential effect on carbon fluxes with flooded conditions supporting higher CH4 fluxes (0.39 µmol CH4 m-2 s-1; -1.23 µmol CO2 m-2 s-1) and drier conditions supporting higher CO2 fluxes (0.03 µmol CH4 m-2 s-1; 0.86 µmol CO2 m-2 s-1). When flooded, the wetland was a net CO2 sink; however, it became a source for both CH4 and CO2 when water levels were low. We compared chamber-based gas fluxes from the cove in flooded (July) and dry (August) months to fluxes measured with an eddy covariance tower whose footprint covers flooded portions of the wetland. The diurnal pattern of carbon fluxes at the tower did not vary with changing water levels but remained a CO2 sink and a CH4 source even when the cove where we performed the chamber measurements dried out. These results emphasize the role of inundation status on wetland carbon cycling and highlight the importance of fluctuating hydrologic patterns, especially hydrologic drawdowns, under changing climatic conditions.

RESUMEN

The present study aimed to measure wetland inundation inconsistency level (IIL) at a spatial scale to appraise the potential serviceability in the Mayurakshi river basin of Eastern India. Inconsistency was used for measuring both wetland water presence area and proxy water depth based on historical satellite images from 1988 to 2022. Applying inconsistency assessment, it was tried to assess how water appearance at a pixel is inconsistent and how average proxy water depth is inconsistent to attain. Four manmade and natural floodplain wetland complexes were taken for this. The study revealed about 51-53% and 59-86% manmade and natural wetland losses respectively and the IIL was also found significantly higher (30-50%) in the cases of natural wetlands in pre and post-monsoon seasons. The scenario is worse in pre-monsoon season in the natural wetlands. Inconsistency of water depth anomaly (IWDA) was also significantly increased almost in the same trend. Discharge control through hydro-engineering structures like dams, barrages, and embankments; river and wetland connecting tie channel loss; and loss of groundwater support are some crucial reasons behind the hydrological inconsistency of wetlands. Growing loss and IIL are caused for concerned economic and ecological adversity. So, the findings would be very useful for taking necessary planning for wetland management and restoration.

Asunto(s)

RESUMEN

Sea-level rise (SLR) is influencing coastal groundwater by both elevating the water table and shifting salinity profiles landward, making the subsurface increasingly corrosive. Low-lying coastal municipalities worldwide (potentially 1,546, according to preliminary analysis) are vulnerable to an array of impacts spurred by these phenomena, which can occur decades before SLR-induced surface inundation. Damage is accumulating across a variety of infrastructure networks that extend partially and fully beneath the ground surface. Because the resulting damage is largely concealed and imperceptible, it is largely overlooked as part of infrastructure management and planning. Here, we provide an overview of SLR-influenced coastal groundwater and related processes that have the potential to damage societally critical infrastructure and mobilize urban contamination. In an effort to promote research efforts that can inform effective adaptation and management, we discuss various impacts to critical infrastructure and propose actions based on literature focused specifically on SLR-influenced coastal groundwater.

Asunto(s)

RESUMEN

Humidity is a basic and crucial meteorological indicator commonly measured in several forms, including specific humidity, relative humidity, and absolute humidity. These different forms can be inter-derived based on the saturation vapor pressure (SVP). In past decades, dozens of formulae have been developed to calculate the SVP with respect to, and in equilibrium with, liquid water and solid ice surfaces, but many prior studies use a single function for all temperature ranges, without considering the distinction between over the liquid water and ice surfaces. These different approaches can result in humidity estimates that may impact our understanding of surface-subsurface thermal-hydrological dynamics in cold regions. In this study, we compared the relative humidity (RH) downloaded and calculated from four data sources in Alaska based on five commonly used SVP formulas. These RHs, along with other meteorological indicators, were then used to drive physics-rich land surface models at a permafrost-affected site. We found that higher values of RH (up to 40 %) were obtained if the SVP was calculated with the over-ice formulation when air temperatures were below freezing, which could lead to a 30 % maximum difference in snow depths. The choice of whether to separately calculate the SVP over an ice surface in winter also produced a significant range (up to 0.2 m) in simulated annual maximum thaw depths. The sensitivity of seasonal thaw depth to the formulation of SVP increases with the rainfall rate and the height of above-ground ponded water, while it diminishes with warmer air temperatures. These results show that RH variations based on the calculation of SVP with or without over-ice calculation meaningfully impact physically-based predictions of snow depth, sublimation, soil temperature, and active layer thickness. Under particular conditions, when severe flooding (inundation) and cool air temperatures are present, care should be taken to evaluate how humidity data is estimated for land surface and earth system modeling.

RESUMEN

The application of restoration plans for the Iraqi marshlands is encountering significant challenges due to water scarcity and the impacts of climate change. This paper assesses the impact of water scarcity on the possibility of continuing the application of restoration and sustainable management plans for the main marshlands in Iraq. This assessment was conducted based on the available data and expected situation of available water resources under climate change conditions until the year 2035. Additionally, a satellite image-based index model was prepared and applied for the period 2009-2020 to obtain the spatiotemporal distribution of the restored marshlands. The results show that the shortage in water resources and insufficient inundation rates prevented the adequate application of the restoration plans. Also, applying the scenarios of distributing the deficit equally over all water demand sectors (S1) and according to the percentage of demand for each sector (S2) shows that the expected deficit in available water for the three marshes by the years 2025 and 2035 will be approximately 25% and 32% for S1 and 9% for S2. Consequently, the considered marshes are expected to lose approximately 20 to 33% of their eligible restoration areas. Accordingly, looking for suitable alternatives to support the water resources of these marshes became a very urgent matter and/or recourse to reduce the areas targeted by inundation and being satisfied with the areas that can be sustainable and maintain the current status of the rest of the regions as an emerging ecosystem characterized by lands that are inundated every few years. Accordingly, steps must be urged to develop plans and programs to maintain the sustainability of these emerging ecosystems within the frameworks of climate change and the conditions of scarcity of water resources and water and air pollution to ensure that they are not lost in the future.

Asunto(s)