Short-term streamflow prediction is essential for managing flood early warning and water resources systems. Although numerical models are widely used for this purpose, they require various types of data and experience to operate the model and often tedious calibration processes. Under the digital revolution, the application of data-driven approaches to predict streamflow has increased in recent decades. In this work, multiple linear regression (MLR) and random forest (RF) models with three different input combinations are developed and assessed for multi-step ahead short-term streamflow predictions, using 14 years of hydrological datasets from the Kulim River catchment, Malaysia. Introducing more precedent streamflow events as predictor improves the performance of these data-driven models, especially in predicting peak streamflow during the high-flow event. The RF model (Nash-Sutcliffe efficiency (NSE): 0.599-0.962) outperforms the MLR model (NSE: 0.584-0.963) in terms of overall prediction accuracy. However, with the increasing lead-time length, the models' overall prediction accuracy on the arrival time and magnitude of peak streamflow decrease. These findings demonstrate the potential of decision tree-based models, such as RF, for short-term streamflow prediction and offer insights into enhancing the accuracy of these data-driven models.
Recently, there is an upsurge in flood emergencies in Nigeria, in which their frequencies and impacts are expected to exacerbate in the future due to land-use/land cover (LULC) and climate change stressors. The separate and combined forces of these stressors on the Gongola river basin is feebly understood and the probable future impacts are not clear. Accordingly, this study uses a process-based watershed modelling approach - the Hydrological Simulation Program FORTRAN (HSPF) (i) to understand the basin's current and future hydrological fluxes and (ii) to quantify the effectiveness of five management options as adaptation measures for the impacts of the stressors. The ensemble means of the three models derived from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are employed for generating future climate scenarios, considering three distinct radiative forcing peculiar to the study area. Also, the historical and future LULC (developed from the hybrid of Cellular Automata and Markov Chain model) are used to produce the LULC scenarios for the basin. The effective calibration, uncertainty and sensitivity analyses are used for optimising the parameters of the model and the validated result implies a plausible model with efficiency of up to 75 %. Consequently, the results of individual impacts of the stressors yield amplification of the peak flows, with more profound impacts from climate stressor than the LULC. Therefore, the climate impact may trigger a marked peak discharge that is 48 % higher as compared to the historical peak flows which are equivalent to 10,000-year flood event. Whilst the combine impacts may further amplify this value by 27 % depending on the scenario. The proposed management interventions such as planned reforestation and reservoir at Dindima should attenuate the disastrous peak discharges by almost 36 %. Furthermore, the land management option should promote the carbon-sequestering project of the Paris agreement ratified by Nigeria. While the reservoir would serve secondary functions of energy production; employment opportunities, aside other social aspects. These measures are therefore expected to mitigate feasibly the negative impacts anticipated from the stressors and the approach can be employed in other river basins in Africa confronted with similar challenges.
Atmospheric air temperature is the most crucial metrological parameter. Despite its influence on multiple fields such as hydrology, the environment, irrigation, and agriculture, this parameter describes climate change and global warming quite well. Thus, accurate and timely air temperature forecasting is essential because it provides more important information that can be relied on for future planning. In this study, four Data-Driven Approaches, Support Vector Regression (SVR), Regression Tree (RT), Quantile Regression Tree (QRT), ARIMA, Random Forest (RF), and Gradient Boosting Regression (GBR), have been applied to forecast short-, and mid-term air temperature (daily, and weekly) over North America under continental climatic conditions. The time-series data is relatively long (2000 to 2021), 70% of the data are used for model calibration (2000 to 2015), and the rest are used for validation. The autocorrelation and partial autocorrelation functions have been used to select the best input combination for the forecasting models. The quality of predicting models is evaluated using several statistical measures and graphical comparisons. For daily scale, the SVR has generated more accurate estimates than other models, Root Mean Square Error (RMSE = 3.592°C), Correlation Coefficient (R = 0.964), Mean Absolute Error (MAE = 2.745°C), and Thiels' U-statistics (U = 0.127). Besides, the study found that both RT and SVR performed very well in predicting weekly temperature. This study discovered that the duration of the employed data and its dispersion and volatility from month to month substantially influence the predictive models' efficacy. Furthermore, the second scenario is conducted using the randomization method to divide the data into training and testing phases. The study found the performance of the models in the second scenario to be much better than the first one, indicating that climate change affects the temperature pattern of the studied station. The findings offered technical support for generating high-resolution daily and weekly temperature forecasts using Data-Driven Methodologies.
Alternative climate products, such as gauge-based gridded data, ground-based weather radar, satellite precipitation and climate reanalysis products, are being increasingly applied for hydrological modelling. This review aims to summarize the studies that have evaluated alternative climate products within Soil and Water Assessment Tool (SWAT) applications and to propose future research directions, primarily for modelers who wish to study limited gauge, ungauged or transnational river basins. A total of 126 articles have been identified since 2004, the majority of which have been published within the last five years. About 58% of the studies were conducted in Asia, mostly in China and India, while another 14% were reported for United States studies. CFSR and TRMM are the most popular applied products in SWAT modelling, followed by PERSIANN, CMADS, APHRODITE, CHIRPS and NEXRAD. Generally, the performance of climate products is region-dependent; e.g., CFSR typically performs well in the United States and South America, but performs more poorly for Asia, Africa and mountainous basin conditions, as compared to other products. In contrast, the CMADS, TRMM, APRHODITE and NEXRAD have shown the strongest capability for supporting SWAT modelling in these regions. However, most of the evaluated products contain only precipitation input; therefore, merging reliable precipitation with CFSR-temperature is recommended for hydro-climatic modelling. Future research directions include: (1) examination of optimal combinations; e.g. CHIRPS-precipitation and CFSR-temperature, for simulating streamflow in different types of river basins; (2) development of a standardized validation scheme which incorporates the commonly accepted products, statistical approaches and temperature variables; (3) further evaluation of existing climate data products to accurately capture extreme events, pattern and indices as well as WGEN statistics; (4) improvement of climate data in terms of averaging approach, bias correction and additional factors or indices integration; and (5) bias correction of CMIP6 climate projections using the optimal climate data combinations.
In Australia, droughts are recurring events that tremendously affect environmental, agricultural and socio-economic activities. Southern Queensland is one of the most drought-prone regions in Australia. Consequently, a comprehensive drought vulnerability mapping is essential to generate a drought vulnerability map that can help develop and implement drought mitigation strategies. The study aimed to prepare a comprehensive drought vulnerability map that combines drought categories using geospatial techniques and to assess the spatial extent of the vulnerability of droughts in southern Queensland. A total of 14 drought-influencing criteria were selected for three drought categories, specifically, meteorological, hydrological and agricultural. The specific criteria spatial layers were prepared and weighted using the fuzzy analytical hierarchy process. Individual categories of drought vulnerability maps were prepared from their specific indices. Finally, the overall drought vulnerability map was generated by combining the indices using spatial analysis. Results revealed that approximately 79.60% of the southern Queensland region is moderately to extremely vulnerable to drought. The findings of this study were validated successfully through the receiver operating characteristics curve (ROC) and the area under the curve (AUC) approach using previous historical drought records. Results can be helpful for decision makers to develop and apply proactive drought mitigation strategies.
Population increase and the demand for infrastructure development such as construction of highways and road widening are intangible, leading up to mass land clearing. As flat terrains become scarce, infrastructure expansions have moved on to hilly terrains, cutting through slopes and forests. Unvegetated or bare slopes are prone to erosion due to the lack of or insufficient surface cover. The combination of exposed slope, uncontrolled slope management practices, poor slope planning and high rainfall as in Malaysia could steer towards slope failures which then results in landslides under acute situation. Moreover, due to the tropical weather, the soils undergo intense chemical weathering and leaching that elevates soil erosion and surface runoff. Mitigation measures are vital to address slope failures as they lead to economic loss and loss of lives. Since there is minimal or limited information and investigations on slope stabilization methods in Malaysia, this review deciphers into the current slope management practices such as geotextiles, brush layering, live poles, rock buttress and concrete structures. However, these methods have their drawbacks. Thus, as a way forward, we highlight the potential application of soil bioengineering methods especially on the use of whole plants. Here, we discuss the general attributions of a plant in slope stabilization including its mechanical, hydrological and hydraulic effects. Subsequently, we focus on species selection, and engineering properties of vegetation especially rooting structures and architecture. Finally, the review will dissect and assess the ecological principles for vegetation establishment with an emphasis on adopting the mix-culture approach as a slope failure mitigation measure. Nevertheless, the use of soil bioengineering is limited to low to moderate risk slopes only, while in high-risk slopes, the use of traditional engineering measure is deemed more appropriate and remain to be the solution for slope stabilization.
Digital elevation model (DEM) plays a vital role in hydrological modelling and environmental studies. Many essential layers can be extracted from this land surface information, including slope, aspect, rivers, and curvature. Therefore, DEM quality and accuracy will affect the extracted features and the whole process of modeling. Despite freely available DEMs from various sources, many researchers generate this information for their areas from various observations. Sentinal-1 synthetic aperture radar (SAR) images are among the best Earth observations for DEM generation thanks to their availabilities, high-resolution, and C-band sensitivity to surface structure. This paper presents a comparative study, from a hydrological point of view, on the quality and reliability of the DEMs generated from Sentinel-1 data and DEMs from other sources such as AIRSAR, ALOS-PALSAR, TanDEM-X, and SRTM. To this end, pair of Sentinel-1 data were acquired and processed using the SAR interferometry technique to produce a DEM for two different study areas of a part of the Cameron Highlands, Pahang, Malaysia, a part of Sanandaj, Iran. Based on the estimated linear regression and standard errors, generating DEM from Sentinel-1 did not yield promising results. The river streams for all DEMs were extracted using geospatial analysis tool in a geographic information system (GIS) environment. The results indicated that because of the higher spatial resolution (compared to SRTM and TanDEM-X), more stream orders were delineated from AIRSAR and Sentinel-1 DEMs. Due to the shorter perpendicular baseline, the phase decorrelation in the created DEM resulted in a lot of noise. At the same time, results from ground control points (GCPs) showed that the created DEM from Sentinel-1 is not promising. Therefore, other DEMs' performance, such as 90-meters' TanDEM-X and 30-meters' SRTM, are better than Sentinel-1 DEM (with a better spatial resolution).
A simplified modelling approach for illustrating the fate of emerging pollutants can improve risk assessment of these chemicals. Once released into aquatic environments, these pollutants will interact with various substances including suspended particles, colloidal or nano particles, which will greatly influence their distribution and ultimate fate. Understanding these interactions in aquatic environments continues to be an important issue because of their possible risk. In this study, bisphenol A (BPA) in the water column of Bentong River, Malaysia, was investigated in both its soluble and colloidal phase. A spatially explicit hydrological model was established to illustrate the associated dispersion processes of colloidal-bound BPA. Modelling results demonstrated the significance of spatial detail in predicting hot spots or peak concentrations of colloidal-bound BPA in the sediment and water columns as well. The magnitude and setting of such spots were system based and depended mainly on flow conditions. The results highlighted the effects of colloidal particles' concentration and density on BPA's removal from the water column. It also demonstrated the tendency of colloidal particles to aggregate and the impact all these processes had on BPA's transport potential and fate in a river water. All scenarios showed that after 7.5-10 km mark BPA's concentration started to reach a steady state with very low concentrations which indicated that a downstream transport of colloidal-bound BPA was less likely due to minute BPA levels.
Monitoring the groundwater chemical composition and identifying the presence of pollutants is an integral part of any comprehensive groundwater management strategy. The present study was conducted in a part of West Tripura, northeast India, to investigate the presence and sources of trace metals in groundwater and the risk to human health due to direct ingestion of groundwater. Samples were collected from 68 locations twice a year from 2016 to 2018. Mixed Ca-Mg-HCO3, Ca-Cl and Ca-Mg-Cl were the main groundwater types. Hydrogeochemical methods showed groundwater mineralization due to (1) carbonate dissolution, (2) silicate weathering, (3) cation exchange processes and (4) anthropogenic sources. Occurrence of faecal coliforms increased in groundwater after monsoons. Nitrate and microbial contamination from wastewater infiltration were apparent. Iron, manganese, lead, cadmium and arsenic were above the drinking water limits prescribed by the Bureau of Indian Standards. Water quality index indicated 1.5% had poor, 8.7% had marginal, 16.2% had fair, 66.2% had good and 7.4% had excellent water quality. Correlation and principal component analysis reiterated the sources of major ions and trace metals identified from hydrogeochemical methods. Human exposure assessment suggests health risk due to high iron in groundwater. The presence of unsafe levels of trace metals in groundwater requires proper treatment measures before domestic use.
Tropical peatland degradation due to oil palm plantation development has reduced peat's ability to naturally regulate floods. In turn, more severe and frequent flooding on peatlands could seriously impair plantation productivity. Understanding the roles of peatland ecosystems in regulating floods has become essential given the continued pressure on land resources, especially in Southeast Asia. However, the limited knowledge on this topic has resulted in the oversimplifications of the relationships between floods, commercial plantations and peatland sustainability, creating major disagreement among policymakers at different levels in governments, companies, NGOs and society. Hence, this study identifies whether flood policies are integrated within peatland management through a qualitative policy analysis of publicly available papers, government reports, and other official documents that discuss flooding, and/or more in general, hydrology in peatlands. Document analysis was then triangulated with data obtained from several semi-structured discussions. The analysis indicates that the industry on peatlands and the peatland's environmental sustainability could be threatened by increased flooding. We show that, in spite of this, flood policies in SE Asian countries like Malaysia and Indonesia have not been well-integrated into peatland management. We also discuss how the countries could move forward to overcome this problem.
The Transient Electro-Magnetic (TEM) geophysical technique was deployed to map and characterized the subsurface of Pahang River Basin along the East Coast Peninsula Malaysia. The data aimed at differentiating between the massive zones and the weak zones within the region, to also assess and differentiate the subsurface structures and comes up with recommendations for policy decision, formulation and plans on the flooding impact, surface water and groundwater managements, in addition to other environmental related issues ravaging the area. The data presented in this paper, showed the properties of the subsurface rocks underlain the region as beneficial to the Agriculturists; Climatologists; Engineers; Environmentalists; Geoscientists, Hydrologists and Policy formulation officers. The TEM data collection utilized a 100 m x 100 m single loop coil for both the Transmitter (Tx) loop and the Receiver (Rx) loop to produce a total surface area coverage of 10,000 m2 per survey line along a single profile. The total area covered in the data extended across an average area of 30 km x 40 km in parts of Maran, Temerloh and Jerantut districts, within the State of Pahang, East Coast, Peninsula Malaysia. The conductivity data recorded varied from -20 mS/m to about 440 mS/m at a maximum depth of about 375 m. On the other hand, the resistivity data recorded varied from 0 Oh-m to about 1000 Oh-m. The information derived from the data are intended for potential abstraction by the Malaysian Groundwater Management Board; the Department of Mineral and Geoscience; Department of Irrigation and Drainage; the Pahang State Water Board, and the Department of Agriculture.
Climate change-induced spatial and temporal variability of stremflow has significant implications for hydrological processes and water supplies at basin scale. This study investigated the impacts of climate change on streamflow of the Kurau River Basin in Malaysia using a Climate-Smart Decision Support System (CSDSS) to predict future climate sequences. For this, we used 25 reliazations consisting from 10 Global Climate Models (GCMs) and three IPCC Representative Concentration Pathways (RCP4.5, RCP6.0 and RCP8.5). The generated climate sequences were used as input to Soil and Water Assessment Tool (SWAT) to simulate projected changes in hydrological processes in the basin over the period 2021-2080. The model performed fairly well for the Kurau River Basin, with coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE) and Percent Bias (PBIAS) of 0.65, 0.65 and -3.0, respectively for calibration period (1981-1998) and 0.60, 0.59 and -4.6, respectively for validation period (1996-2005). Future projections over 2021-2080 period show an increase in rainfall during August to January (relatively wet season, called the main irrigation season) but a decrease in rainfall during February to July (relatively dry season, called the off season). Temperature projections show increase in both the maximum and minimum temperatures under the three RCP scenarios, with a maximum increase of 2.5 °C by 2021-2080 relative to baseline period of 1976-2005 under RCP8.5 scenario. The model predicted reduced streamflow under all RCP scenarios compared to the baseline period. Compared to 2021-2050 period, the projected streamflow will be higher during 2051-2080 period by 1.5 m3/s except in February for RCP8.5. The highest streamflow is predicted during August to December for both future periods under RCP8.5. The seasonal changes in streamflow range between -2.8% and -4.3% during the off season, and between 0% (nil) and -3.8% during the main season. The assessment of the impacts of climatic variabilities on the available water resources is necessary to identify adaptation strategies. It is supposed that such assessment on the Kurau River Basin under changing climate would improve operation policy for the Bukit Merah reservoir located at downstream of the basin. Thus, the predicted streamflow of the basin would be of importance to quantify potential impacts of climate change on the Bukit Merah reservoir and to determine the best possible operational strategies for irrigation release.
Fine resolution (hourly rainfall) of rainfall series for various hydrological systems is widely used. However, observed hourly rainfall records may lack in the quality of data and resulting difficulties to apply it. The utilization of Bartlett-Lewis rectangular pulse (BLRP) is proposed to overcome this limitation. The calibration of this model is regarded as a difficult task due to the existence of intensive estimation of parameters. Global optimization algorithms, named as artificial bee colony (ABC) and particle swarm optimization (PSO) were introduced to overcome this limitation. The issues and ability of each optimization in the calibration procedure were addressed. The results showed that the BLRP model with ABC was able to reproduce well for the rainfall characteristics at hourly and daily rainfall aggregation, similar to PSO. However, the fitted BLRP model with PSO was able to reproduce the rainfall extremes better as compared to ABC.
In the past few decades, there has been a rapid growth in the concentration of nitrogenous compounds such as nitrate-nitrogen and ammonia-nitrogen in rivers, primarily due to increasing agricultural and industrial activities. These nitrogenous compounds are mainly responsible for eutrophication when present in river water, and for 'blue baby syndrome' when present in drinking water. High concentrations of these compounds in rivers may eventually lead to the closure of treatment plants. This study presents a training and a selection approach to develop an optimum artificial neural network model for predicting monthly average nitrate-N and monthly average ammonia-N. Several studies have predicted these compounds, but most of the proposed procedures do not involve testing various model architectures in order to achieve the optimum predicting model. Additionally, none of the models have been trained for hydrological conditions such as the case of Malaysia. This study presents models trained on the hydrological data from 1981 to 2017 for the Langat River in Selangor, Malaysia. The model architectures used for training are General Regression Neural Network (GRNN), Multilayer Neural Network and Radial Basis Function Neural Network (RBFNN). These models were trained for various combinations of internal parameters, input variables and model architectures. Post-training, the optimum performing model was selected based on the regression and error values and plot of predicted versus observed values. Optimum models provide promising results with a minimum overall regression value of 0.92.
An accuracy in the hydrological modelling will be affected when having limited data sources especially at ungauged areas. Due to this matter, it will not receiving any significant attention especially on the potential hydrologic extremes. Thus, the objective was to analyse the accuracy of the long-term projected rainfall at ungauged rainfall station using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model. The SDSM was used as a climate agent to predict the changes of the climate trend in Δ2030s by gauged and ungauged stations. There were five predictors set have been selected to form the local climate at the region which provided by NCEP (validated) and CanESM2-RCP4.5 (projected). According to the statistical analyses, the SDSM was controlled to produce reliable validated results with lesser %MAE (<23%) and higher R. The projected rainfall was suspected to decrease 14% in Δ2030s. All the RCPs agreed the long term rainfall pattern was consistent to the historical with lower annual rainfall intensity. The RCP8.5 shows the least rainfall changes. These findings then used to compare the accuracy of monthly rainfall at control station (Stn 2). The GIS-Kriging method being as an interpolation agent was successfully to produce similar rainfall trend with the control station. The accuracy was estimated to reach 84%. Comparing between ungauged and gauged stations, the small %MAE in the projected monthly results between gauged and ungauged stations as a proved the integrated SDSM-GIS model can producing a reliable long-term rainfall generation at ungauged station.
This study developed 0.05° × 0.05° land-only datasets of daily maximum and minimum temperatures in the densely populated Central North region of Egypt (CNE) for the period 1981-2017. Existing coarse-resolution datasets were evaluated to find the best dataset for the study area to use as a base of the new datasets. The Climate Prediction Centre (CPC) global temperature dataset was found to be the best. The CPC data were interpolated to a spatial resolution of 0.05° latitude/longitude using linear interpolation technique considering the flat topography of the study area. The robust kernel density distribution mapping method was used to correct the bias using observations, and WorldClim v.2 temperature climatology was used to adjust the spatial variability in temperature. The validation of CNE datasets using probability density function skill score and hot and cold extremes tail skill scores showed remarkable improvement in replicating the spatial and temporal variability in observed temperature. Because CNE datasets are the best available high-resolution estimate of daily temperatures, they will be beneficial for climatic and hydrological studies.
Over the years, sedimentation has posed a great danger to the storage capacity of hydropower reservoirs. Good understanding of the transport system and hydrological processes in the dam is very crucial to its sustainability. Under optimal functionality, the Shiroro dam in Northern Nigeria can generate ∼600 MW, which is ideally sufficient to power about 404,000 household. Unfortunately, there have not been reliable monitoring measures to assess yield in the upstream, where sediments are sourced into the dam. In this study, we applied the Soil and Water Assessment Tool (SWAT) to predict the hydrological processes, the sediment transport mechanism and sediment yield between 1990 and 2018 in Kaduna watershed (32,124 km2) located upstream of the dam. The model was calibrated and validated using observed flow and suspended sediment concentration (SSC) data. Performance evaluation of the model was achieved statistically using Nash-Sutcliffe (NS), coefficient of determination (r2) and percentage of observed data (p-factor). SWAT model evaluation using NS (0.71), r2 (0.80) and p-factors of 0.86 suggests that the model performed satisfactorily for streamflow and sediment yield predictions. The model identified the threshold depth of water (GWQMN.gw) and base flow (ALPHA_BF.gw) as the most sensitive parameters for streamflow and sediment yield estimation in the watershed. Our finding showed that an estimated suspended sediment yield of about 84.1 t/ha/yr was deposited within the period under study. Basins 67, 71 and 62 have erosion prone area with the highest sediment values of 79.4, 75.1 and 73.8 t/h respectively. Best management practice is highly recommended for the dam sustainability, because of the proximity of erosion-prone basins to the dam.
Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The prediction of leachate amounts is elementary in sustainable waste management and leachate treatment processes, before discharging to surrounding environment. In developing countries, the accurate evaluation of leachate generation rates has often considered a challenge due to the lack of reliable data and high measurement costs. Leachate generation is related to several factors, including meteorological data, waste generation rates, and landfill design conditions. The high variations in these factors lead to complicating leachate modeling processes. This study aims at identifying the key elements contributing to leachate production and developing various AI-based models to predict leachate generation rates. These models included Artificial Neural Network (ANN)-Multi-linear perceptron (MLP) with single and double hidden layers, and support vector machine (SVM) regression time series algorithms. Various performance measures were applied to evaluate the developed model's accuracy. In this study, input optimization process showed that three inputs were acceptable for modeling the leachate generation rates, namely dumped waste quantity, rainfall level, and emanated gases. The initial performance analysis showed that ANN-MLP2 model-which applies two hidden layers-achieved the best performance, then followed by ANN-MLP1 model-which applies one hidden layer and three inputs-while SVM model gave the lowest performance. Ranges and frequency of relative error (RE%) also demonstrate that ANN-MLP models outperformed SVM models. Furthermore, low and peak flow criterion (LFC and PFC) assessment of leachate inflow values in ANN-MLP model with two hidden layers made more accurate values than other models. Since minimizing data collection and processing efforts as well as minimizing modeling complexity are critical in the hydrological modeling process, the applied input optimization process and the developed models in this study were able to provide a good performance in the modeling of leachate generation efficiently.
Data on the micrometeorological parameters and Energy Fluxes at an intertidal zone of a Tropical Coastal Ocean was carried out on an installed eddy covariance instruments at a Muka head station in the north-western end of the Pinang Island (5°28'06''N, 100°12'01''E), Peninsula Malaysia. The vast source of the supply of energy and heat to the hydrologic and earth׳s energy cycles principally come from the oceans. The exchange of energies via air-sea interactions is crucial to the understanding of climate variability, energy, and water budget. The turbulent energy fluxes are primary mechanisms through which the ocean releases the heat absorbed from the solar radiations to the environment. The eddy covariance (EC) system is the direct technique of measuring the micrometeorological parameters which allow the measurement of these turbulent fluxes in the time scale of half-hourly basis at 20 Hz over a long period. The data being presented is the comparison of the two-year seasonality patterns of monsoons variability on the measured microclimate variables in the southern South China Sea coastal area.
Satellite imagery reveals flowstripes on Foundation Ice Stream parallel to ice flow, and meandering features on the ice-shelf that cross-cut ice flow and are thought to be formed by water exiting a well-organised subglacial system. Here, ice-penetrating radar data show flow-parallel hard-bed landforms beneath the grounded ice, and channels incised upwards into the ice shelf beneath meandering surface channels. As the ice transitions to flotation, the ice shelf incorporates a corrugation resulting from the landforms. Radar reveals the presence of subglacial water alongside the landforms, indicating a well-organised drainage system in which water exits the ice sheet as a point source, mixes with cavity water and incises upwards into a corrugation peak, accentuating the corrugation downstream. Hard-bedded landforms influence both subglacial hydrology and ice-shelf structure and, as they are known to be widespread on formerly glaciated terrain, their influence on the ice-sheet-shelf transition could be more widespread than thought previously.