Showing papers in "Hydrology Research in 2021"

Flood damage and risk assessment for urban area in Malaysia

Abstract: In recent years, flood risk map has been widely accepted as a tool for flood mitigation. The risk of flooding is normally illustrated in terms of its hazard (flood inundation maps), while vulnerability emphasizes the consequences of flooding. In developing countries, published studies on flood vulnerability assessment are limited, especially on flood damage. This paper attempts to establish a flood damage and risk assessment framework for Segamat town in Johor, Malaysia. A combination of flood hazard (flood characteristics), exposure (value of exposed elements), and vulnerability (flood damage function curve) were used for estimating the flood damage. The flood depth and areal extent were obtained from flood modeling and mapping using HEC-HMS/RAS and Arc GIS, respectively. Expected annual damage (EAD) for residential areas (50,112 units) and commercial areas (9,318 premises) were RM12.59 million and RM2.96 million, respectively. The flood hazard map shows that Bandar Seberang area (46,184 properties) was the most affected by the 2011 flood. The flood damage map illustrates similar patterns, with Bandar Seberang suffering the highest damage. The damage distribution maps are useful for reducing future flood damage by identifying properties with high flood risk.

Graphical-statistical method to explore variability of hydrological time series.

Abstract: Due to increasing concern on developing measures for predictive adaptation to climate change impacts on hydrology, several studies have tended to be conducted on trends in climatic data. Conventionally, trend analysis comprises testing the null hypothesis H0 (no trend) by applying the Mann–Kendall or Spearman’s rho test to the entire time series. This leads to lack of information about hidden short-durational increasing or decreasing trends (hereinafter called sub-trends) in the data. Furthermore, common trend tests are purely statistical in nature and their results can be meaningless sometimes, especially when not supported by graphical exploration of changes in the data. This paper presents a graphical-statistical methodology to identify and separately analyze sub-trends for supporting attribution of hydrological changes. Themethod is based on cumulative sum of differences between exceedance and non-exceedance counts of data points. Through themethod, it is possible to appreciate that climate variability comprises large-scale random fluctuations in terms of rising and falling hydro-climatic sub-trendswhich can be associatedwith certain attributes. Illustration on how to apply the introduced methodology was made using data over the White Nile region in Africa. Links for downloading a tool called CSD-VAT to implement the presented methodology were provided.

Application of temporal convolutional network for flood forecasting

Abstract: Rainfall–runoff modeling is a complex nonlinear time-series problem in the field of hydrology. Various methods, such as physical-driven and data-driven models, have been developed to study the highly random rainfall–runoff process. In the past 2 years, with the advancement of computing hardware resources and algorithms, deep-learning methods, such as temporal convolutional network (TCN), has been shown good prospects in time-series prediction tasks. The aim of this study is to develop a prediction model based on TCN structure to simulate the hourly rainfall–runoff relationship. We use two datasets in the Jingle and Kuye watersheds to test the model under different network structures and compare with the other four models. The results show that the TCN model outperforms the EIESM, artificial neural network, and long short-term memory and improves the flood forecasting accuracy at different foreseeable periods. It is shown that the TCN has a faster convergence rate and is an effective method for hydrological forecasting.

Climate change impacts on summer flood frequencies in two mountainous catchments in China and Switzerland

Abstract: Flood events are difficult to characterize if available observation records are shorter than the recurrence intervals, and the non-stationarity of the climate adds additional uncertainty. In this study, we use a hydrological model coupled with a stochastic weather generator to simulate the summer flood regime in two mountainous catchments located in China and Switzerland. The models are set up with hourly data from only 10–20 years of observations but are successfully validated against 30–40-year long records of flood frequencies and magnitudes. To assess the climate change impacts on flood frequencies, we re-calibrate the weather generator with the climate statistics for 2021–2050 obtained from ensembles of bias-corrected regional climate models. Across all assessed return periods (10–100 years) and two emission scenarios, nearly all model chains indicate an intensification of flood extremes. According to the ensemble averages, the potential flood magnitudes increase by more than 30% in both catchments. The unambiguousness of the results is remarkable and can be explained by three factors rarely combined in previous studies: reduced statistical uncertainty due to a stochastic modelling approach, hourly time steps and the focus on headwater catchments where local topography and convective storms are causing runoff extremes within a confined area.

An updated national-scale assessment of trends in UK peak river flow data: how robust are observed increases in flooding?

Abstract: A cluster of recent floods in the UK has prompted significant interest in the question of whether floods are becoming more frequent or severe over time. Many trend assessments have addressed this in recent decades, typically concluding that there is evidence for positive trends in flood magnitude at the national scale. However, trend testing is a contentious area, and the resilience of such conclusions must be tested rigorously. Here, we provide a comprehensive assessment of flood magnitude trends using the UK national flood dataset (NRFA Peak Flows). Importantly, we assess trends using this full dataset as well as a subset of near-natural catchments with high-quality flood data. While headline conclusions are useful for advancing national flood-risk policy, for on-the-ground flood-risk estimation it is important to unpack these local changes to determine how climate-driven trends compare with those from the wider dataset that are subject to a wide range of human disturbances and data limitations. We also examine the sensitivity of reported trends to changes in study time window using a ‘multitemporal’ analysis. We find that the headline claim of increased flooding generally holds up regionally to nationally, although we show a much more complicated picture of spatio-temporal variability. While some reported trends, such as increased flooding in northern and western Britain, appear to be robust, trends in other regions are more mixed spatially and temporally – for example, trends in recent decades are not necessarily representative of longerterm change, and within regions (e.g. in southeast England) increasing and decreasing trends can be found in close proximity. While headline conclusions are useful for advancing national flood-risk policy, for flood-risk estimation it is important to unpack these local changes, and the results and methodological toolkit provided here could provide such supporting information to practitioners.

Flood routing via a copula-based approach

Abstract: Floods are among the most common natural disasters that if not controlled may cause severe damage and high costs. Flood control and management can be done using structural measures that should be designed based on the flood design studies. The simulation of outflow hydrograph using inflow hydrograph can provide useful information. In this study, a copula-based approach was applied to simulate the outflow hydrograph of various floods, including the Wilson River flood, the River Wye flood and the Karun River flood. In this regard, two-dimensional copula functions and their conditional density were used. The results of evaluating the dependence structure of the studied variables (inflow and outflow hydrographs) using Kendall's tau confirmed the applicability of copula functions for bivariate modeling of inflow and outflow hydrographs. The simulation results were evaluated using the root-mean-square error, the sum of squared errors and the Nash–Sutcliffe efficiency coefficient (NSE). The results showed that the copula-based approach has high performance. In general, the copula-based approach has been able to simulate the peak flow and the rising and falling limbs of the outflow hydrographs well. Also, all simulated data are at the 95% confidence interval. The NSE values for the copula-based approach are 0.99 for all three case studies. According to NSE values and violin plots, it can be seen that the performance of the copula-based approach in simulating the outflow hydrograph in all three case studies is acceptable and shows a good performance.

Detecting the dominant contributions of runoff variance across the source region of the Yellow River using a new decomposition framework

Abstract: Quantifying the contributions of climatic variables to runoff variance is still a great challenge to water resource management. This study adopted an extended Budyko framework to investigate the effects of terrestrial water storage changes (ΔS) on runoff variance across the source region of the Yellow River, China, during the period of 2003–2014. A new decomposition framework based on the extended Budyko framework was proposed to effectively quantify the contributions of different climatic variables including precipitation, PET and ΔS to runoff variance. The results demonstrated that the extended Budyko framework showed a better performance in presenting the water and energy balance than the original Budyko framework, especially at fine time scales. Meanwhile, the variance in runoff estimated by the new decomposition framework was close to that of runoff observations, indicating that this framework can effectively capture the variation in runoff during 2003–2014. It was also found that precipitation was the most important factor that contributed to runoff changes, while PET made a slightly smaller contribution compared to precipitation. Notably, the results also emphasized the important effects of ΔS on runoff variance at fine time scales, which was useful to better understand the interactions between atmospheric and hydrological processes for regions.

Geochemistry pollution status and ecotoxicological risk assessment of heavy metals in the Pahang River sediment after the high magnitude of flood event

Abstract: The present work is aimed at assessing the aftermath effects of the 2014 flood tragedy on the distribution, pollution status and ecological risks of the heavy metals deposited in the surface river sediment. A series of environmental pollution indexes, specifically the enrichment factor (EF), geo-accumulation index (Igeo), contamination factor (CF), modified degree of contamination (mCd), pollution load index (PLI), potential ecological risk index (PERI) and sediment quality guidelines (SQGs) have been adopted. Results revealed that the freshly deposited sediments collected soon after the flood event were dominated by Cu, Fe, Pb, Ni, Zn, Cr and Cd, with the average concentrations of 38.74, 16,892, 17.71, 4.65, 29.22, 42.36 and 0.29 mg/kg, respectively. According to the heavy metal pollution indexes, Pahang River sediments were moderately to severely contaminated with Pb, Ni, Cu, Zn and Cr, while Cd with the highest risk of 91.09 was the predominant element that illustrated an aesthetic ecological risk to the water body after the tragic flood event. The findings highlighted a critical deterioration of the heavy metals content, driven by the catastrophic flood event, which has drastically altered their geochemical cycles, sedimentary pollution status and biochemical balance of the river's environment.

Influences of the timing of extreme precipitation on floods in Poyang Lake, China

Abstract: Changes in the timing of extreme precipitation have important ramifications for public safety and storm water management, but it has not received much attention in relation to flooding. This study analyzed the changes in the timing of extreme precipitation in the Poyang Lake basin and projected its future changes for the period 2020–2099. The study also quantified the influences of changes in the timing of peak flows on lake floods based on a hydrodynamic model. The results showed that peak rainfall in the Poyang Lake basin had occurred on later dates during the period 1960–2012, and it is this change that caused a delay in peak streamflows from five rivers in the lake basin. Moreover, the effects of these changes are expected to be more prominent during 2020–2099; for example, the rate of delay will be about 2.0 days per 10 years both for peak rainfall and for streamflow in the Poyang Lake basin. The hydrodynamic simulation further showed that a delay of peak streamflows from five rivers would significantly increase the flood level and outflow of the lake and also prolong the duration of floods. These results indicate that the risk of floods in Poyang Lake is likely to increase in the future, therefore making flood control in this region more challenging.

Evaluating three commonly used infiltration methods for permeable surfaces in urban areas using the SWMM and STORM

Abstract: Climate change and urbanization increase the pressure on combined sewer systems in urban areas resulting in elevated combined sewer overflows, degraded water quality in receiving waters, and changing stream flows. Permeable surfaces offer infiltration potential, which can contribute to alleviate the runoff to combined sewer systems. The variation in urban soil characteristics and the initial moisture conditions before a rainfall event are important factors affecting the infiltration process and consequently runoff characteristics. In this study, the urban hydrological models SWMM and STORM are used to evaluate the Green-Ampt, Horton, and Holtan infiltration methods for three urban sandy soils. A sensitivity analysis was carried out on a set of key parameter values. In addition, long-term simulations were conducted to evaluate the ability to account for initial soil moisture content. The results showed that the Holtan method’s ability to account for both available storage capacity and maximum infiltration rate, as well as evapotranspiration in the regeneration of infiltration capacity, gave the best result with regard to runoff behaviour, especially for long-term simulations. Furthermore, the results from the urban sandy soils with different infiltration rate at saturation, together with a high sensitivity to the degree of sensitivity for maximum infiltration rate under dry conditions and minimum infiltration rate under wet conditions, indicate that field measurements of infiltration rate should be carried out at saturation for these soils.

A novel hybrid XAJ-LSTM model for multi-step-ahead flood forecasting

Abstract: The conceptual hydrologic model has been widely used for flood forecasting, while long short-term memory (LSTM) neural network has been demonstrated a powerful ability to tackle time-series predictions. This study proposed a novel hybrid model by combining the Xinanjiang (XAJ) conceptual model and LSTM model (XAJ-LSTM) to achieve precise multi-step-ahead flood forecasts. The hybrid model takes flood forecasts of the XAJ model as the input variables of the LSTM model to enhance the physical mechanism of hydrological modeling. Using the XAJ and the LSTM models as benchmark models for comparison purposes, the hybrid model was applied to the Lushui reservoir catchment in China. The results demonstrated that three models could offer reasonable multi-step-ahead flood forecasts and the XAJ-LSTM model not only could effectively simulate the long-term dependence between precipitation and flood datasets, but also could create more accurate forecasts than the XAJ and the LSTM models. The hybrid model maintained similar forecast performance after feeding with simulated flood values of the XAJ model during horizons to . The study concludes that the XAJ-LSTM model that integrates the conceptual model and machine learning can raise the accuracy of multi-step-ahead flood forecasts while improving the interpretability of data-driven model internals.

Assessing the impact of PET estimation methods on hydrologic model performance

Abstract: Evapotranspiration is a necessary input and one of the most uncertain hydrologic variables for quantifying the water balance. Key to accurately predicting hydrologic processes, particularly under data scarcity, is the development of an understanding of the regional variation of the impact of potential evapotranspiration (PET) data inputs on model performance and parametrization. This study explores this impact using four different potential evapotranspiration products (of varying quality). For each data product, a lumped conceptual rainfall–runoff model (GR4J) is tested on a sample of 57 catchments included in the MOPEX data set. Monte Carlo sampling is performed, and the resulting parameter sets are analyzed to understand how the model responds to differences in the forcings. Test catchments are classified as energyor water-limited using the Budyko framework and by ecoregion, and the results are further analyzed. While model performance (and parameterization) in water-limited sites was found to be largely unaffected by the differences in the evapotranspiration inputs, in energy-limited sites model performance was impacted as model parameterizations were clearly sensitive to evapotranspiration inputs. The quality/reliability of PET data required to avoid negatively impacting rainfall–runoff model performance was found to vary primarily based on the water and energy availability of catchments.

A collaborated framework to improve hydrologic ecosystem services management with sparse data in a semi-arid basin

Abstract: Applying various models to assess hydrologic ecosystem services (HESs) management has the potential to encourage efficient water resources allocation. However, can a single model designed on these principles be practical to carry out hydrologic ecosystem services management for all purposes? We address this question by fully discussing the advantages of the variable infiltration capacity (VIC) model, the soil and water assessment tool (SWAT), and the integrated valuation of ecosystem services and tradeoffs (InVEST) model. The analysis is carried both qualitatively and quantitatively at the Yixunhe River basin, China, with a semi-arid climate. After integrating the advantages of each model, a collaborated framework and model selection method have been proposed and validated for optimizing the HESs management at the data sparse scenario. Our study also reveals that the VIC and SWAT model presents the better runoff reproducing ability of the hydrological cycle. Though the InVEST model has less accuracy in runoff simulation, the interannual change rate is similar to the other two models. Furthermore, the InVEST model (1.08 billion m) has larger simulation result than the SWAT model (0.86 billion m) for the water yield, while both models have close results for assessment of sediment losses.

Relative impacts of increases of solar radiation and air temperature on the temperature of surface water in a shallow, eutrophic lake

Abstract: We monitored lake surface water temperatures from 1992 to 2019 in Lake Kasumigaura, a shallow lake in Japan. We hypothesized that increases of shortwave radiation had increased surface water temperatures and heat fluxes more than had the increases of air temperature. We used the heat flux analyses and the sensitivity analyses to test the hypothesis. The fluxes of solar radiation gradually increased during the study period in a manner consistent with the phenomenon of global brightening. The increase was especially apparent in the spring. The rate of increase of surface water temperature was especially significant in May. Air temperature did not significantly increase in May, but it increased significantly in June (0.40 °C decade−1). A sensitivity analysis of the heat fluxes at the lake surface (shortwave radiation, longwave radiation, latent heat flux, and sensible heat flux) revealed that surface water temperature was more sensitive to changes of shortwave radiation than to air temperature during the spring. Although other factors such as inflows of groundwater and river water may also have impacted surface water temperatures, the increase of solar radiation appeared to be the major factor responsible for the increase of surface water temperature during the spring in Lake Kasumigaura.

Analysis of diurnal, seasonal, and annual distribution of urban sub-hourly to hourly rainfall extremes in Germany

Abstract: The timing of short extreme rainstorm, which was usually thought to occur on midsummer afternoons, was investigated to improve future mitigation options for infrastructure and safety from localised flash flooding. Using a peak-over-threshold approach, the timing of 10and 60-min extreme events was filtered from high-resolution rainfall series assessing diurnal, seasonal, and annual distributions and analysed for spatial variations and prevailing atmospheric circulation types (CTs). The diurnal distribution showed a clear deviation from that of the entire rainfall regime. A complex spatial pattern was identified with distinct timing signatures of storms in the northern (mostly afternoon) and southern regions (a bimodal distribution with a second peak in the early morning) of Germany and a more homogenous diurnal distribution of events across the central regions. Most storms occurred in summer, but 42% of 10-min events occurred outside the summer months (June– July–August). A distinct annual clustering of extremes was identified, which varied distinctly between the 10and 60-min extremes, indicating that the sub-hourly and hourly events were far from running conterminously. The timing of extreme events on the investigated time scales was not dominated by the occurrence of specific CTs in most cases, suggesting that other factors control these extremes.

Performance evaluation of potential inland flood management options through a three-way linked hydrodynamic modelling framework for a coastal urban watershed

Abstract: This study proposes a novel comprehensive hydrodynamic flood modelling framework over Mithi river watershed in Mumbai, India, a coastal urban area, to reduce the inundation extent by incorporation of different inland hydraulic scenarios. First, the study addresses the issue of data scarcity by adapting alternate robust techniques to estimate design rainfall, tidal elevation and discharge, the key inputs for a flood model. Following that, a three-way linked flood model has been developed in the MIKE FLOOD platform, considering river, stormwater, overland flow and tidal influence to generate flood inundation and subsequently hazard maps for various inland hydraulic scenarios, by incorporating different feasible cross-sections and lining materials. The flood inundation and hazard maps have been derived for 10-, 50and 200-year return periods of design rainfall, discharge and tide to identify the best possible flood-reducing hydraulic scenario. It is observed that a ‘trapezoidal river cross-section lined with concrete’ relatively maximizes the reduction in flooding extent. The proposed framework can be implemented as an effective flood mitigation strategy in data-scarce, densely populated and space-constrained areas.

Elevation-dependent compensation effects in snowmelt in the Rhine River Basin upstream gauge Basel

Abstract: In snow-dominated river basins, floods often occur during early summer, when snowmelt-induced runoff superimposes with rainfall-induced runoff. An earlier onset of seasonal snowmelt as a consequence of a warming climate is often expected to shift snowmelt contribution to river runoff and potential flooding to an earlier date. Against this background, we assess the impact of rising temperatures on seasonal snowpacks and quantify changes in timing, magnitude and elevation of snowmelt. We analyse in situ snow measurements, conduct snow simulations and examine changes in river runoff at key gauging stations. With regard to snowmelt, we detect a threefold effect of rising temperatures: snowmelt becomes weaker, occurs earlier and forms at higher elevations. Due to the wide range of elevations in the catchment, snowmelt does not occur simultaneously at all elevations. Results indicate that elevation bands melt together in blocks. We hypothesise that in a warmer world with similar sequences of weather conditions, snowmelt is moved upward to higher elevation. The movement upward the elevation range makes snowmelt in individual elevation bands occur earlier, although the timing of the snowmelt-induced runoff stays the same. Meltwater from higher elevations, at least partly, replaces meltwater from elevations below.

Hydrological probabilistic forecasting based on deep learning and Bayesian optimization algorithm

Abstract: Obtaining accurate runoff prediction results and quantifying the uncertainty of the forecasting are critical to the planning and management of water resources. However, the strong randomness of runoff makes it difficult to predict. In this study, a hybrid model based on XGBoost (XGB) and Gaussian process regression (GPR) with Bayesian optimization algorithm (BOA) is proposed for runoff probabilistic forecasting. XGB is first used to obtain point prediction results, which can guarantee the accuracy of forecast. Then, GPR is constructed to obtain runoff probability prediction results. To make the model show better performance, the hyper-parameters of the model are optimized by BOA. Finally, the proposed hybrid model XGB-GPR-BOA is applied to four runoff prediction cases in the Yangtze River Basin, China and compared with eight state-of-the-art runoff prediction methods from three aspects: point prediction accuracy, interval prediction suitability and probability prediction comprehensive performance. The experimental results show that the proposed model can obtain high-precision point prediction, appropriate prediction interval and reliable probabilistic prediction results on the runoff prediction problems.

Effectiveness assessment of urban waterlogging mitigation for low impact development in semi-mountainous regions under different storm conditions

Abstract: To assess the urban waterlogging mitigation effectiveness on low impact development (LID) in semimountainous regions, the Storm Water Management Model (SWMM) of a semi-mountainous region combined with GIS was generalized. The SWMM was calibrated and validated through maximum seeper depth of the checkpoints, and various LID scenarios have been designed according to local conditions. The discharge processes of outlets, surface runoff, peak flow and peak time were analyzed in different scenarios. The results show that: all the flow processes of outlets in the LID scenario are gentler than that in the status quo scenario, and the effectiveness of LIDs in semimountainous regions are different from that in plain regions because of the slope influence; in semimountainous regions, the LID effectiveness on surface runoff reduction decreases with the increase in rainfall return period or the extension of rainfall duration, but remains almost unchanged with the increase in rainfall peak coefficient; the LID effectiveness on control peak flow reduction is not remarkable with the change in rainfall characteristics, and the LID effectiveness on peak time delay is poor. This research can provide decision support for regional small-scale measures of urban waterlogging mitigation and reduction in semi-mountainous regions.

Performance of snow density measurement systems in snow stratigraphies

Abstract: Gravimetric and dielectric permittivity measurement systems (DMS) are applied to measure snow density, but few studies have addressed differences between the two measurement systems under complex snowpack conditions. A field experiment was conducted to measure the snow density using the two measurement systems in stratigraphical layers of different densities, liquid water content (LWC), hardness, and shear strength, and the performance of the two measurement systems was analyzed and compared. The results showed that the snow density from the DMS tended to underestimate by 9% in the dry snowpack and overestimate by 3% in the wet snowpack, expressed as the percentage of the mean density from the gravimetric measurement system (GMS). Compared with the GMS, the DMS has relatively low precision and accuracy in the dry snowpack and similar precision and accuracy in the wet snowpack. The accuracy and precision of the two measurement systems increased with the increase of hardness and shear strength of snow in the dry snowpack, but the accuracy and precision measured of the DMSs increased with the decrease of hardness and shear strength of snow in wet snowpack. The results will help field operators to choose a more reasonable measurement system based on snowpack characteristics to get reliable density data and optimize

Spatiotemporal geostatistical analysis of precipitation combining ground and satellite observations

Abstract: Precipitation data are useful for the management of water resources as well as flood and drought events. However, precipitation monitoring is sparse and often unreliable in regions with complicated geomorphology. Subsequently, the spatial variability of the precipitation distribution is frequently represented incorrectly. Satellite precipitation data provide an attractive supplement to ground observations. However, satellite data involve errors due to the complexity of the retrieval algorithms and/or the presence of obstacles that affect the infrared observation capability. This work presents a methodology that combines satellite and ground observations leading to improved spatiotemporal mapping and analysis of precipitation. The applied methodology is based on space-time regression kriging. The case study refers to the island of Crete, Greece, for the time period of 2010-2018. Precipitation data from 53 stations are used in combination with satellite images for the reference period. This work introduces an improved spatiotemporal approach for precipitation mapping.

Sediment clues in flood mitigation: the key to determining the origin, transport, and degree of heavy metal contamination

Abstract: This study seeks to identify sediment sources, quantify erosion rates, and assess water quality status via sediment fingerprinting, the Modified Laser Erosion Bridge (MLEB) method, and various pollution indices (PIs), respectively, in the humid tropics (Malaysia). Geochemical elements were used as tracers in sediment fingerprinting. Erosion rates were measured at 3,241 points that encompass high conservation value forests (HCVFs); logged forests (LFs); mature oil palm (MOP); and mature rubber (MR) plantations. Annual erosion rates were 63.26–84.44, 42.38, 43.76–84.40, and 5.92–59.32 t ha-1 yr-1 in the HCVF, LF, MOP, and MR, respectively. Via sediment fingerprinting, logging and agricultural plantations were identified as the major contributors of the sediment. PIs also indicated the highest level of pollution in those catchments. This study highlighted three main messages: (i) the feasibility and applicability of the multiproxy sediment fingerprinting approach in identifying disaster-prone areas; (ii) the MLEB as a reliable and accurate method for monitoring erosion rates within forested and cultivated landscapes; and (iii) the adaptation of PIs in providing information regarding the status of river water quality without additional laboratory analyses. The combination of these approaches aids in identifying high-risk and disaster-prone areas for the prioritisation of preventive measures in the tropics.

Difference between rainfall and throughfall chemistry for different forest stands in the Qinling Mountains, China

Abstract: This study compared the effects of four forest canopies on throughfall chemistry in the Qinling Mountains, China. Rainfall and throughfall samples were collected in stands of Quercus aliena (Qa) var. Acuteserrata, Pinus tabulaeformis (Pt), P. armandii (Pa), and mixed broad-leaved (Mb) trees from 2009 to 2011. The results indicated that the pH of the rainfall, which was mildly acidic, increased as it passed through the forest canopy. The pH increased more within the broad-leaved forest canopy than the coniferous forest. Concentrations of SO 4 decreased as rainfall passed through the Qa canopy but increased after passing through the other species. The concentrations of NO 3 and Zn, Cd and Pb decreased as rainfall passed through the four canopies. The coniferous forest canopy was more effective than the broad-leaved forest in reducing NO 3 in rainwater. The decreases in Cd concentrations were similar among the four canopies. The Pb concentration decreased the most among the heavy metals, and the order of the decrease was Qa> Pt> Pa>Mb. The results may provide a basis for the selection of tree species for afforestation in water sources in the Qinling Mountains and similar areas.

Assessment of land cover resolution impact on flood modeling uncertainty

Abstract: The main objective of this paper is to evaluate the impact of land cover resolution, in comparison with the digital elevation model (DEM) resolution, on hydrological modeling outputs. Three different basins in the various resolutions of DEM (12.5, 25, 50, 100, 500 and 1,000 m) and land-use maps (250, 1,000 and 2,500 m) were collected in this study, and the hydrological modeling process was performed using the Soil and Water Assessment Tool (SWAT) model. The soil type resolution was 1,000 m for all basins, and the runoff modeling was done based on the Soil Conservation Service Curve Number (SCS-CN) method. The final model outputs showed that the DEM cell size variations affect significantly the topographical characteristics of a catchment such as area, mean slope, river network and time to concentration which alter the flood modeling outputs especially in hilly watersheds (mean slope more than 15%) up to 15% for a DEM cell size of 1,000 m in comparison to 12.5 m. Also, the resolution and spatial distribution of land cover maps which directly specify SCS-CN values, can change the output simulated runoff results up to 49% for a land cover cell size of 2,500 m in comparison to 250 m. These results indicated that the quality of the land cover map is more important than the quality of DEM in hydrological modeling. Also, the results showed that for an identical land-use cell size, the differences between model outputs using DEM cell sizes less than 100 m were not very significant. Furthermore, in all models by increasing the DEM cell size, the simulated runoff depth was decreased.