​​​​​​​​​Lakes in the Arctic are important reservoirs of heat with much lower albedo and larger absorption of solar radiation than surrounding tundra vegetation. Under climate warming scenarios, we expect Arctic lakes to further accelerate thawing underlying permafrost. Previous studies of Arctic lakes have focused on ice cover and thickness, the ice decay process, catchment hydrology, lake water balance, and eddy covariance measurements, but little work has been done in the Arctic to model lake heat balance. We applied the LAKE model to simulate water temperatures in three Arctic lakes in Northern Alaska over several years. The LAKE model is a one-dimensional that explicitly solves vertical profiles of water state variables on a grid. We used a combination of meteorological data from local and remote weather stations, as well as data derived from remote sensing, to drive the model. We validated simulated water temperatures with data of observed lake temperatures at several depths. Our validation of the LAKE model completes a necessary step toward modeling changes in Arctic lake ice regimes, lake heat balance, and thermal interactions with permafrost. Our results showed that winter precipitation and lake ice plays an important role in forming water temperatures over the winter period. Our findings suggest that reduction in the lake ice thickness and ice time period could lead to more heat storage by lakes and further warming of the Arctic.

This paper examines the impact of climate-sea level controls on the vegetation and evolution of the Niger Delta during the Late Quaternary. The extraneous controls on the environment outlined in this context confirm a direct link between vegetation dynamics (pollen data), sediment supply, and the landscape evolution of the Niger Delta between 20 ka and 6.5 ka. Two phases of sedimentation are recognized based on multiple proxies analyzed in three gravity cores obtained from the shallow offshore at ~40 m water depth. Phase I records abundant occurrences of Poaceae, Cyperaceae, and Podocarpus pollen from a dry hinterland, charred grass cuticles, nonmarine alga Pediastrum, high Ti/Zr ratio, and lower sedimentation from 20-11.7 ka. Phase II records an expansion of mangrove vegetation, high Fe/S ratio, and increase in planktonic foraminifera between 11.7 ka and 6.5 ka. This second phase is attributed to sea-level rise and higher sedimentation during the development of delta plain and mangrove vegetation on the gently sloping shelf. These sequential records provide a new clue about the link between the evolutionary stages of the Niger Delta landscape and vegetation dynamics during two distinct time-bound intervals, which potentially delineate the boundary between two Marine Isotope Stages: MIS2 (late glacial period) and MIS1 (interglacial period).

Due to the advancement in satellite and remote sensing technologies, a number of satellite precipitation products (SPPs) are easily accessible online at free of cost. These precipitation products have a huge potential for hydro-meteorological applications in data-scare catchments. However, the use of such products is still limited owing to their lack of accuracy in capturing the ground truth. To improve the accuracy of these products, we have developed a quantile based Bayesian model averaging (QBMA) approach to merge the satellite precipitation products. QBMA is a probabilistic approach to assign optimal weights to the SPPs depending on their relative performances. The QBMA approach is compared with simple model averaging and one outlier removed. TRMM, PERSIANN-CDR, CMORPH products were experimented for QBMA merging during the monsoon season over India’s coastal Vamsadhara river basin. QBMA optimal weights were trained using 2001 to 2013 daily monsoon rainfall data and validated for 2014 to 2018. Results indicated that QBMA approach with bias corrected precipitation inputs outperformed the other merging methods. On monthly evaluation, it is observed that all the products perform better during July and September than that in June and August. The QBMA approaches do not have any significant improvement over the SMA approach in terms of POD. However, the bias-corrected QBMA products have lower FAR. The developed QBMA approach with bias-corrected inputs outperforms the IMERG product in terms of RMSE.

The equatorial electrojet (EEJ) is an important manifestation of ionospheric electrodynamics. Day-to-day changes of the EEJ result from E-region dynamo processes that are primarily driven by highly variable atmospheric waves propagating up from the lower and middle atmosphere. Progress has been made in our understanding that upward propagating tides are one of the major contributors to the day-to-day variability in the EEJ, however current models are limited in their ability to capture the vertical propagation of tides from the lower and middle atmosphere to the upper atmosphere due to difficulties to adequately represent many processes that influence it. In this study, we thus propose a new data-driven approach to modeling day-to-day variability by taking advantage of widely available ground-based magnetic field measurements. The new approach based on an ensemble transform adjustment method is applied to the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM) lower boundary conditions (LBCs) at about 97 km altitude in order to make the model’s tidal characteristics to be more consistent with observed magnetic perturbations associated with the EEJ. In this method, TIE-GCM ensemble simulations are driven by high-latitude ionospheric convection and auroral particle precipitation patterns specified by the AMGeO and by atmospheric waves and tides based on MERRA meteorological reanalysis. As part of forward modeling, the 3D Dynamo electrodynamic module is used to calculate magnetic perturbations on the ground and at low Earth orbit altitudes. A detailed analysis of the 21-day period from March 1 to 22, 2009 has shown that the modeled EEJ with the LBCs adjusted using ground-based magnetic perturbation data improves the agreement of the model to independent magnetic field observations from CHAMP. The use of routinely available ground-based magnetometer data to constrain the TIE-GCM LBCs could provide an opportunity to investigate how day-to-day tidal variability drives equatorial electrodynamics variability.

Bangladesh is one of the most vulnerable countries of the world in the event of climate change due to its unique geographical location. This study assessed the impact of climate change on precipitation and temperature extremes over Bangladesh from Coupled Model Intercomparison Project Phase 6 (CMIP6) models under four SSP-RCP (Shared Socioeconomic Pathway-Representative Concentration Pathway) scenarios (SSP126, SSP245, SSP370, and SSP585). At first quantile mapping (QM) method was employed to produce bias-corrected daily data. Then the future changes in climate extremes were assessed using a subset of extreme temperature and precipitation indices devised by the Expert Team on Climate Change Detection and Indices (ETCCDI). For the assessment of precipitation extremes, Consecutive Wet Days (CWD), Number of days with rainfall greater than 10mm (R10mm), Wet Days Precipitation (R95p), total annual rainfall (PRCPTOT), annual maximum 1-day precipitation (Rx1day), and annual maximum 5-day precipitation (Rx5day) have been utilized while for the temperature extremes, frequency of hot days (TX90p) and cool days (TX10p) have been used. The results from the probability density function (PDF) of most of the precipitation indices show rightward shifting in the future indicating a tendency toward wetter conditions. However, the magnitudes of change were different for the selected CMIP6 Global Climate Models (GCMs). The projected increase in CWD is greater over the south-western region of the country while the projected increase in PRCPTOT is greater over the north-eastern region of the country under all scenarios. R10mm shows the highest increase for the SSP585. In response to climate change, the TX90p shows a general increase in this century. However, the frequency of cool days is projected to decrease for most of the SSP scenarios. The results from these analyses present an opportunity to understand the impact of climate change on extreme events in Bangladesh and thus may help the local decision-makers in policy-making, disaster management, and infrastructure planning.

The Dijkstra’s algorithm is employed to find the shortest paths between the epicenter and all the wet nodes on a global triangular unstructured mesh using the phase speed of surface gravity waves and progressive acoustic modes. The phase speed estimator takes into account the simultaneous effects of the slight compressibility of water, sea-bed elasticity, and static compression of the ocean under gravity, leading to precise calculation of the arrival time [1]. Coupled to a mathematical solution for multi-fault rupture along the transect extracted from the shortest path between the source and destination is then employed to characterize the genesis of the tsunami [2]. The effectiveness of the aforementioned algorithms and distinct arrival time of different the tsunami and its precursor components (acoustic modes) at DART and deep ocean acoustic observations can be used for tsunami early warning systems. [1] Abdolali, A., Kadri, U. & J.T. Kirby, 2019, Effect of Water Compressibility, Sea-floor Elasticity, and Field Gravitational Potential on Tsunami Phase Speed, Scientific Reports, Nature, doi:10.1038/s41598-019-52475-0 [2] Williams, B., Kadri, U., & Abdolali, A. (2021). Acoustic–gravity waves from multi-fault rupture. Journal of Fluid Mechanics, 915, A108. doi:10.1017/jfm.2021.101

Does river topography have stage thresholds for maintaining fluvial landforms, and if so how can they be quantified? Geomorphic covariance structure analysis offers a novel, systematic framework for evaluating nested topographic patterns in river corridors. In this study, a threshold in mountain river stage was hypothesized to exist; above this stage landform structure is organized to be freely self-maintaining via flow convergence routing morphodynamics. A 13.2 km segment of the canyon-confined Yuba River, California, was studied using 2944 cross-sections. Geomorphic covariance structure analysis was carried out on a meter-resolution topographic model to test the hypothesis. A critical stage threshold governing flow convergence routing morphodynamics was evident in several metrics. Below this threshold, narrow/high “nozzle” and wide/low “oversized” landforms that are out-of-phase with flow convergence routing morphodynamics dominated (excluding “normal channel”), while above it wide/high “wide bar” and narrow/low “constricted pool” landforms consistent with the flow convergence mechanism were dominant. Three-level nesting of co-located base-bankfull-flood stage landforms was dictated by canyon confinement, with nozzle-nozzle-nozzle nesting as the top permutation, excluding normal channel. In conclusion, this study demonstrates a significantly different and highly effective approach to finding process-based fluvial thresholds that can complement pre-existing methods, such as estimating incipient sediment motion, to get at more powerful dynamics controlling fluvial landforms structure.

Mineral dust particles are ubiquitous in the atmosphere and can be transported vast distances affecting climate, air quality, and human health on a global scale. Mineralogical composition has a substantial impact on dust properties and their effects. Natural dust samples are both fine-grained and composed of many different minerals. Most commonly, X-ray diffraction (XRD) has been used to characterize dust mineralogy; however, this technique is less effective for identifying poorly crystalline or amorphous phases. We used Fourier Transform Infrared (FTIR) spectroscopy as a complementary method to identify minerals and their abundances. Long wave infrared (LWIR) spectra (2.5 to 25 μm) are sensitive to molecular bonds rather than crystallography providing additional details. We performed both XRD and reflectance spectroscopy to characterize 37 atmospheric dust samples collected in Ilam City, Iran. The dominant minerals in these samples are quartz, feldspar (albite), calcite and clays (illite, montmorillonite, kaolinite). LWIR reflectance is strongly dependent on particle size but published data of pure silicate minerals in the size range of the Ilam samples (0-63 μm) still show characteristic signatures between 8 and 10 µm (Salisbury et al. 1991; Wenrich and Christensen, 1996). Surprisingly, diagnostic silicate features were not observed in any of the samples although carbonate and OH bonds in the clay minerals were readily identified. Past studies have shown that porosity, grain size and packing can reduce the spectral contrast in the LWIR and additional effects include grain coatings or the interaction of multiple minerals. We also identify a peak at 7.8 µm which may be attributed to anomalous dispersion or the interaction of quartz and calcite in this spectral range. In order to understand the absence of Si-O features we made transmission measurements of representative samples in KBr pellets. Transmission is not influenced by multiple scattering and should clearly detect fundamental Si-O absorptions. Transmission spectra show broad features that include contributions from all silicate minerals (quartz, feldspar and clays) both near 10 μm and at longer wavelengths. We are using various spectral modeling techniques and will compare abundances derived from reflectance and transmission measurements.

Present work deals with paleogeographic reconstruction through sequence modelling, facies and micro-facies analysis and bio-geochemical investigations of late Archaean Banded Iron Formations (BIFs) from Kushtagi-Hungund Schist Belt (KHSB) of Eastern Dharwar Craton (EDC), South Indian Shield (SIS). This comparatively less metamorphosed schist belt of EDC is correlated to the Bababudan Group of Dharwar Supergroup, Western Dharwar Craton (WDC). The lower and upper age constraints have been established from dating of 3.4-3.1 Ga old underlying Sargur Group and TTG gneisses, and 2.5 Ga old younger granites, intruding the KHSB metasediments, respectively. Further, metavolcanics intercalating with the metasediments of KHSB have been dated at 2750-2670 Ma. In this work, special emphasis has been given to the shaley-BIFs of this schist belt which is comprised of thin (few millimeters to a maximum of 1.2-1.5 cm thick) alternating units of iron rich shale, chert and hematite. Facies analysis and sequence model reveal deep water, offshore paleogeography, where proximal outer shelf is dominated by shaley siliciclastics and distal outer shelf and further deep oceanic succession is occupied by chert-hematite-dominated chemogenic sediment suits. Micro-facies analysis of the shaley-BIFs elucidated the interaction between chemogenic and deep water siliciclastic and volcaniclastic shaley sediments within micro domains. Primary chert layers contain several permineralized structures associated with carbonaceous matters. Recent RAMAN spectrometric analysis (compared to previous data in provided figure) and Carbon-isotopic values (d13C values ranging from -22.08 to -30.84 ‰ -VPDB, n= 12) of corresponding Total Organic Carbon (TOC) (ranging from 0.03 to 0.14 %) from cherts and shales indicate preservation of Archean biogenic remnants. Recent elemental (major and trace) and oxygen isotopic data, associated with the micro-facies systematics of shale-chert-iron oxide units, have been compared to previously published geochemical data sets derived from KHSB and other BIFs of SIS to provide important clues and new insights regarding late Archean ocean water chemistry, redox state, paleoclimate and control of tectonics and provenance on sedimentation pattern, prior to Great Oxygenation Event (GOE).

Coarsening a numerical mesh using methods of multiphase flow property upscaling is essential in almost any modeling application due to computational limits. In CO2 storage modeling, after injection wells have been shut off or far away from the wells, CO2 migrates upwards under the influence of gravity and capillary forces. Upscaling permeability (k), relative permeability (kr) and capillary pressure (Pc) is required for grid coarsening these models. However, it has been shown that this is a difficult problem and using conventional upscaling methods such as the common capillary limit upscaling approach leads to large modeling errors. This work presents a new upscaling method based on an effective property formula for k, power law averaging in the capillary limit for kr, and an optimization approach for Pc. The new method is tested on various example cases and coarse-grid simulations are shown to match fine-grid ones with sufficient accuracy. The challenge of upscaling the flows is found to be related to entry pressure trapping and the optimization upscaled Pc is shown to have a unique structure allowing to model the trapping. The method is global, requiring a fine-grid simulation for calibration of the optimized parameters. However, we show that the method reduces computational time dramatically if calibrated parameters are used in similar cases in which the fine-grid solution is unknown, such as for varying k realizations.

Volcanic ash detection is important for effective emergency management. Ash emitted by volcanoes is injected at high altitudes into the atmosphere, dispersed and driven by wind and extended for kilometres away from volcanoes. It can affect communities in different ways including health, agriculture, the environment and air traffic. Thus, detecting and tracking the volcanic ash clouds using satellite images can help civil protection authorities and volcanic ash advisory centres respond rapidly to a volcanic event. Satellite based detection of volcanic ash has traditionally entailed the implementation of radiances, reflectances, brightness temperatures (BT), brightness temperature differences (BTD), beta ratios and cloud objects tracking tools. The aim of this study is to test machine learning algorithms on satellite data in order to identify volcanic ash in the atmosphere as an attempt to enhance the space-borne ash detection capabilities especially in ambiguous cases, where image classification is hard, such as when ash, clear land and cirrus clouds coexist within a scene. We used a set of 10 satellite images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the satellites Terra and Aqua just after the 4 June 2011 eruption of the Puyehue Cordon Caulle Volcanic Complex to train and test two supervised classification algorithms: K-Nearest Neighbours and Support Vector Machine, for volcanic ash detection in the atmosphere. We first built a database that consists of 217,859 pixels identified and labelled, on the basis of visual interpretation of color composites, according to seven classes: volcanic ash above the emission centre, land and sea, meteorological clouds above land and sea, and clear sky above land and sea. For each of the seven classes, sample pixel values were retrieved for six features, which are given by the BT at 11𝝁m and the BTDs between 11 and 12 𝝁m, 11 and 3.9 𝝁m, 11 and 6.7 𝝁m, 11 and 8.5 𝝁m and a BTD that combines the BTs measured at 11, 12 and 8.5 𝝁m. We show preliminary results on statistical analysis of the training datasets and performance of both algorithms for volcanic ash detection above land and sea including verification tests with MODIS datasets that cover the April 2015 eruption of Volcan Calbuco (Chile).

Climate change will amplify erosion rates as erosive power of rainfall will likely increase due to change in rainfall characteristics (e.g., energy, intensity, duration as well as frequency). According to Intergovernmental Panel on Climate Change Fourth Assessment report (IPCC AR4), it is projected that by mid-21st Century, the rainfall across southeastern US will both increase and decrease in intensity, which will substantially affect rainfall erosivity. Few studies have estimated the impact of climate change (e.g., rainfall intensity) on rainfall erosivity across US and around the world. However, previously published erosion indices have discrepancies due to differences in methodologies (e.g., primarily omission of small and low rainfall intensity) adopted in those studies. Therefore, the objective of this study was to estimate change in erosion indices for the period 2030-2059 using the benchmark rainfall indices established for southeastern region of US. Hourly precipitation data were retrieved from NA-CORDEX under Representative concentration pathways (RCP) 8.5. Results on change in erosion indices as a result of climate change will be presented.

The volcanic eruption that began on 19 March 2021 at Fagradalsfjall is the first one to occur on the Reykjanes Peninsular for nearly 800 years and in Fagradalsfjall for about 6000 years. The feeder-dike was injected from a magma reservoir whose top is at about 10 km depth below the surface (but the reservoir itself reaches much greater depths). The dike formation involved at least two roof ruptures and resulting dike segments. The first occurred on 24 February and the second on 14 March 2021. The first rupture, marked by earthquakes of M23 close to the contact between the roof and the magma, occurred in the eastern half of the toppart of the reservoir. An injected dike segment propagated towards the surface but became arrested at the depths of 0.5-2 km. As its vertical propagation became arrested, while continuing to receive magma, the segment spread laterally, reaching an overall maximum dike strike-dimension (length) of about 10 km. The second rupture, also marked by earthquakes of M2-3, occurred about 1 km to the west of the first rupture. The injected dike segment following the second rupture eventually resulted in the dike propagating to the surface to feed the eruption which started on 19 March. We estimate the average vertical rate of the feeder-dike propagation at about 0.02 m s-1. This is an order of magnitude lower than common rates of lateral dike propagation in rift zones, yet similar to the average rate during the Bardarbunga (Iceland) 2014 dike propagation (around 0.04 m s-1). The initial volcanic fissure fed by the second dike segment had a length of less than 200 m and an opening of a fraction of a metre. Subsequently, several more ‘dike-fingers’ reached the surface and generated volcanic fissure segments. The total length of the discontinuous, segmented fissure is many hundred metres – but only one crater is presently active. Using the aspect ratio of the fissure and basic fracture mechanics, we estimate the magmatic overpressure (driving pressure) at the beginning of the eruption as about 3 MPa. The low driving pressure and small fissure opening displacement and length are in harmony with the very low volumetric flow (effusion) rate of about 10 m3 s-1.

The increasing availability of surface water inundation data has encouraged modelers and managers to include small yet abundant surface water storage systems (e.g., wetlands and other landscape depressions) in process-based models. Yet, these model applications have been largely limited to small- to meso- watershed scales, with drainage areas ranging from a few hectares to several thousand square kilometers. The conventional practice of overlooking these surface water storage systems in basin-scale (e.g., >10,000 m2) hydrologic modeling may be missing the total picture of flood and drought hazards. To fill this gap, we developed a 30-m resolution topography-based wetland and depression storage (maximum surface area and storage volume) database for the Upper Mississippi, Ohio, and Missouri River Basins ⎼ encompassing the 2.35 million km2 upstream domain of the Mississippi River system. Further, we integrated this depression dataset into a process-based model to simulate sub-catchment and river reach-scale hydrologic fluxes (surface runoff, soil wetness, evapotranspiration) and flows (streamflow). Compared with a “no depression” conventional model constructed for the Missouri and Upper Mississippi River Basins, our exploratory analyses demonstrate that a depression-integrated model (i) significantly alters the spatial patterns and magnitudes of water yields, (ii) improves streamflow simulation accuracy, and (iii) provides realistic spatial distributions of landscape wetness conditions. These emerging findings provide us with new insights into the effects of small surface water storage and stimulates a reassessment of current practices for basin-scale hydrologic modeling and water management.

Since the late 70’s, successive satellite missions have been monitoring the sun’s activity, recording total solar irradiance observations. These measurements provide estimates of the Earth’s energy imbalance, i.e. the difference of energy absorbed and emitted by our planet. With this amount of TSI data, solar irradiance reconstruction models can be better validated which can also improve studies looking at past climate reconstructions (e.g., Maunder minimum). Various algorithms have been proposed to merge the various TSI measurements recorded over the last 4 decades. We develop a 3-step algorithm based on data fusion, including a stochastic noise model to take into account the short and long-term correlations. We develop a wavelet filter in order to eliminate specific correlations introduced by the data fusion. Comparing with previous products,the mean value difference is below 0.1 W/m2and the discrepancy with the solar minima is mostly below 0.05 W/m2. Next, we model the frequency spectrum of this 40-year TSI composite time series with a Generalized Gauss-Markov model(with white noise) due to an observe flattening at high frequencies. It allows us to fit a linear trend in these TSI time series by joint inversion with the stochastic noise model via a maximum-likelihood estimator. Our results show that the amplitude of such trend is ∼ -0.009+/-0.01 W/(m2.yr). We conclude that the trend in these composite time series is mostly an artifact due to the solar noise.

Compound dry and hot extremes (CDHE) during the Indian summer monsoon significantly affect agriculture. Due to climate change, the frequency, spatial extent and severity of CDHE have changed over several parts of the world. Understanding the variability of CDHE is critical for designing adaptation strategies to reduce the adverse impacts on agricultural systems. In particular, traditional assessments have focused on the variability of frequency and spatial extent using the quantile-based approach. However, counting the number of events excess over the threshold helps to understand the variability in frequency and spatial extent but fails to detect the changes in the severity. Further, limited studies have investigated the changes in CDHE severity over India. Hence, in the present study, the variability of CDHE severity is assessed during the summer monsoon from 1951 to 2020 over homogenous regions of India using a copula-based Standardized Compound Event Indicator. A significant increase in the severity of CDHE during the summer season was found in eight homogenous regions out of ten. The most vulnerable regions are northeast India and peninsular India, and interestingly, a significant decrease in the severity is observed for the north rain-belt Western Himalayan region. In addition, a significant increase in the spatial extent of the CDHE severe category is also found in all the homogenous regions over the past three decades. This study highlights that severe CDHE is associated with a high risk of severe agricultural drought for a large part of the country. Uni-variate assessments based on precipitation or temperature can underestimate the risks associated with CDHE if there is a strong dependence among the drivers.

Data publication with DOI assignment has become common practice. The Citation Service for Coupled Model Intercomparison Project Phase 6 (CMIP6) was requested by the scientific panel WGCM, which is part of the World Climate Research Programme (WCRP) to enable references of the CMIP6 data in the upcoming Sixth IPCC Assessment Report (AR6). Expectations for data citations include aspects of data usage metrics to receive credit and reproducibility of published research findings. These two are difficult to combine, because a meaningful data usage metrics requires information on large data collections such as experiment data while reproducibility relies on individual datasets. In addition, data usage metrics rely on data users citing the data in the reference list of a scholarly publication and the publisher to provide data references in the articles’ metadata. Neither is a given. Organizations like the Coalition for Publishing Data in the Earth and Space Sciences (COPDESS) play an important role for the required ongoing community change. The Reliquaries approach together with PID Graph implementations being developed in the Data Citation Community of Practice subgroup shows promise for combining reproducability and credit expectations. The contribution will present survey results on CMIP6 participants’ expectations, discuss gaps in the current citation system and investigate, how new ideas can help to close these gaps. Reference: Stockhause, M. and Lautenschlager, M., 2017. CMIP6 Data Citation of Evolving Data. Data Science Journal, 16, p.30. DOI: http://doi.org/10.5334/dsj-2017-030

Urbanization increases runoff, sediment, and nutrient loadings to freshwaters, causing flooding, harmful algal blooms, and increased costs for drinking water filtration. Most watershed models use runoff quality data from specific land uses; coupled with a variety of methods for quantifying loads, the most common being Build-up and Wash-off (BUWO) equations for which loading is a function of antecedent dry period (ADP). However, several studies have shown there is no significant correlation between urban runoff quality and ADP, suggesting predictions based solely on landuse and ADP are questionable. The objective of this study is to discover which parameters, climatological or catchment characteristics, are most significant and should be included in the BUWO relationship. Stormwater quality data was obtained from the National Stormwater Quality Database (NSQD), which is the largest and most recent database of its type in the U.S. Bayesian Network Structure Learner (BNSL) was used to assess the relationships between catchment characteristics, climatological information, and runoff quality for each land use. Given the optimal BN structure, it was determined which parameters affect water quality event mean concentrations the most, and which the least. The results demonstrated that for some constituents (i.e. particulate nitrogen, particulate phosphorous, and sediments), other factors (such as rainfall depth and duration, surface slope) exert a more important influence on urban runoff quality than ADP.