Surface Runoff Components Research Articles

High flow prediction model integrating physically and deep learning based approaches with quasi real-time watershed data assimilation

When operating a flood forecasting system, it is crucial to prioritize both promptness and accuracy, along with reflecting the latest watershed conditions. This study proposes a hybrid model for rainfall-induced high flow prediction that combines an instantaneous physically-based model and a deep learning-based rainfall loss model assimilating with quasi real-time watershed conditions. Its primary objective is to present immediate and reliable high flow predictions, ensuring stability in prediction procedures. The surface runoff component of the model is based on nonlinear instantaneous unit hydrograph (IUH) derived using a dynamic wave model, which considers the nonlinearity between the rainfall excess intensity and the time to peak and peak flow of the IUH. To account for rainfall loss, which can significantly influence rainfall-runoff quantity, Long Short-Term Memory (LSTM) model is employed to establish link between key rainfall loss parameters and the latest watershed conditions obtained from the Global Land Data Assimilation System (GLDAS). The application of the proposed model to 28 high flow events reveals several noteworthy outcomes. Firstly, unlike conventional models that depend on fixed parameter values specific to each event, the proposed model offers immediate, stable, and highly accurate high flow predictions without the need for manual calibration. Secondly, the prioritization of parameters reflecting watershed conditions varies between different watersheds and depends on the rainfall events. These findings indicate the necessity to estimate rainfall loss parameters individually for each rainfall-runoff event in each watershed, as no universal value exists for these parameters. Lastly, despite the limited number of test cases conducted in this study, the LSTM model achieved sufficiently accurate rainfall loss estimation for high flow prediction using only 50 datasets.

Controlling enhanced surface runoff components as a result of a freezing-thawing cycle by inoculating soil bacteria and cyanobacteria

Extreme climatic features, such as the freezing-thawing cycles, impact soil erosion processes and make natural-human linked ecosystems more vulnerable to soil erosion and loss. In order to achieve sustainable development, more efficient and comprehensive efforts are necessary. This entails using several techniques to preserve natural resources and ecosystems, resulting in soil and water conservation and economic success. Recent research has focused on natural-based methods, such as developing soil biological crusts by bacteria and cyanobacteria inoculation for soil stability. Modification of soil hydrologic characteristics owing to the inoculation of indigenous microorganisms, on the other hand, has received little attention. The current study assessed the function of inoculation of native bacteria and cyanobacteria in preventing surface runoff during a physical simulation of a freezing-thawing cycle at the laboratory scale. The research soil was obtained mainly from dry terrain in the Badranlou Area of North Khorasan Province and transported to tiny erosion plots (0.5 × 0.5 m) with a constant slope of 20% in the Tarbiat Modares University's Rain and Erosion Simulation Laboratory. The dominant and non-pathogenic bacteria and cyanobacteria found in the study soil's micro-bank were identified, purified, and proliferated. The bacteria and cyanobacteria were inoculated into the study soil before undergoing a six-replication freezing-thawing cycle. The hydrologic outputs of the treated and control plots were compared after a simulated study rain with an intensity of around 70 mm.h-1 continued for 30 min. The surface runoff components improved significantly (p < 0.001) compared to the control and inoculated treatments following individual and combination inoculation of bacteria and cyanobacteria during a freezing and thawing cycle 60 days after inoculation. As a result, bacteria, cyanobacteria, and combined treatments increased the start time by 102%, 228%, and 266%, respectively, and the time to peak by 26%, 49%, and 56%, respectively, while decreasing the runoff peak by 22%, 48%, and 55%, and the runoff volume by 33%, 65%, and 73%. The findings showed that inoculating soil with bacteria and cyanobacteria is a practical, cost-effective, and environmentally friendly way to improve runoff components in locations prone to freezing and thawing.

Storm event analysis of four forested catchments on the Atlantic coastal plain using a modified SCS-CN rainfall-runoff model

In this study, we calibrated and tested the Soil Conservation Service Curve Number (SCS-CN) based Modified Sahu-Mishra-Eldo (MSME) model for predicting storm event direct runoff (Qtot) and its soil saturation coefficient α as a threshold antecedent moisture condition for partitioning into overland surface and shallow subsurface runoff components. The model calibration was performed using 36 storm events from 2008 to 2015 on a 160-ha low-gradient forested watershed (WS80) on poorly drained soil. The model was further validated without calibration using data from 2011 to 2015 on two sites [115 ha (Conifer) and 210 ha (Eccles Church)] and from 2008 to 2011 on a third site, the 100-ha Upper Debidue Creek (UDC), all similar forested watersheds on the Atlantic Coastal Plain, USA. The calibrated MSME model was able to accurately predict the estimated Qtot_pred for the WS80 watershed, with calculated Nash-Sutcliffe efficiency coefficient (NSE), RMSE-standard deviation ratio (RSR), and percent bias (PBIAS) of 0.80, 0.44, and 16.7%, respectively. By applying the same calibrated α value of 0.639 from the WS80 to two other similar poorly drained watersheds, the MSME model satisfactorily predicted the estimated Qtot_pred for both the Eccles Church (NSE = 0.64; RSR = 0.57; PBIAS = 28.9%) and Conifer (NSE = 0.60; RSR = 0.58; PBIAS = 21.3%) watersheds, respectively. The MSME model, however, yielded unsatisfactory results (NSE = -0.13, RSR = 2.06, PBIAS = 616.3%) on the UDC watershed with coarse-textured soils, indicating the possible association of the α coefficient with soil subsurface texture. Based on the analysis of event rainfall and pre-event water table elevation, and linking them with the calibrated α coefficient that describes the proportion of saturated depth in a soil profile, it was found that rainfall was the main determining factor for overland runoff generation. These results demonstrate the MSME model’s potential to predict direct runoff in poorly drained forested watersheds, which serve as a reference for urbanizing coastal landscapes in a changing climate.

Calibration of a Distributed Hydrological Model (VIC-3L) Based on Global Water Resources Reanalysis Datasets

The lack of observed streamflow datasets for calibration of rainfall-runoff models imposes substantial problems for their applicability, especially in poorly gauged or ungauged river basins. Developing satellite technologies and increasing computational powers over the past decades, have provided an environment for researchers to simulate several water balance components globally using these datasets and assimilation techniques. Due to importance of accurate hydrologic modeling, this study aims to investigate the applicability of global water resources reanalysis (GWRR) datasets including surface soil moisture (SSM), evapotranspiration (ET), and surface runoff (SR) components for calibration of the macro-scale hydrological model (VIC-3L) over the SefidRood basin (SRB) in Iran at different calibration scenarios. Results show that in the case of using SSM datasets, the model’s performance in the simulation of streamflow hydrograph, with the NSE value higher than 0.65, is better than using other datasets. Among different datasets, the SSM based on LISFLOOD and HBV are the best ones for calibration of VIC-3L model over SRB. In contrast, using ET datasets aren’t so reliable for hydrological calibration in the study area. Furthermore, in the cases of using SSM and surface runoff datasets, the model tends to overestimation of low-flows, while, ET datasets are more reliable for simulation of such these flows. Also, findings displayed that the combination of ET and SSM datasets for hydrological calibration performed better than using only one dataset. In conclusion, this research gives useful and applied insights in the applicability of GWRR data sources for hydrological modeling and water resources studies, especially in data limited regions.

A novel method for cold-region streamflow hydrograph separation using GRACE satellite observations

Abstract. Streamflow hydrograph analysis has long been used for separating streamflow into baseflow and surface runoff components, providing critical information for studies in hydrology, climate and water resources. Issues with established methods include the lack of physics and arbitrary choice of separation parameters, problems in identifying snowmelt runoff, and limitations on watershed size and hydrogeological conditions. In this study, a Gravity Recovery and Climate Experiment (GRACE)-based model was developed to address these weaknesses and improve hydrograph separation. The model is physically based and requires no arbitrary choice of parameters. The new model was compared with six hydrograph separation methods provided with the U.S. Geological Survey Groundwater Toolbox. The results demonstrated improved estimates by the new model particularly in filtering out the bias of snowmelt runoff in baseflow estimate. This new model is specifically suitable for applications over large watersheds which is complementary to the traditional methods that are limited by watershed size. The output from the model also includes estimates for watershed hydraulic conductivity and drainable water storage, which are useful parameters in evaluating aquifer properties, calibrating and validating hydrological and climate models, and assessing regional water resources.

Current Features and Dynamics of Nutrient Balance in the Kud’ma River Basin. 1. Annual Nutrient Balance

The main components of the annual water and nutrient balance are determined for the basin of the Kud’ma River, which has a landscape structure typical of the southern part of the forest zone. The contribution of the natural and anthropogenic factors to nutrient inputs into the Kud’ma River and their export from the catchment area is estimated. It is shown that forest tree waste is among the major natural factors of nutrient input into the Kud’ma River catchment area, while animal wastes are among anthropogenic factors. Nutrient export with river runoff, including its surface and underground components, is evaluated. It is found that the export of nitrogen and phosphorus from the catchment area has increased considerably in the recent years because of changes in the hydroclimatic conditions, which have caused a decrease in the surface runoff component and a considerable increase in its underground component.

Testing the potential of soil moisture observations to estimate rainfall in a soil tank experiment

An experimental loamy sand soil tank (12 m long × 1.5 m wide × 1.5 m deep) with a slope of 3° was designed to conduct artificial rainfall–runoff experiments and continuously monitor soil moisture (SM), rainfall, surface runoff (SR) and subsurface runoff (SSR). A total of 28 rainfall-runoff events are analysed to investigate the capability of estimating rainfall from SM observations through SM2RAIN algorithm which is then adjusted in SM2RAIN-exp according to the conditions of the artificial rainfall-runoff experiment, i.e., including the SR and SSR component. The main purpose of this study is to test the underlying hypotheses of SM2RAIN algorithm in a controlled experiment to better understand the most important processes driving the relationship between SM and rainfall.Results suggest that SM2RAIN-exp demonstrates satisfactory performance in estimating rainfall with respect to the observed data. When the aggregation intervals of rainfall increase, the SM2RAIN-exp performances improve. The integration of 10 cm and 30 cm SM data yields performance scores better than using the sensor at 10 cm and 30 cm depth alone. The lagged response of 30 cm SM sensor has a slight influence on the results. In addition, the major contribution to the estimated rainfall is provided by the term incorporating the SM variations (~85%), followed by the SR (12%), which is different from the hypothesis of neglecting SR made in the classical SM2RAIN. By comparing the results obtained by SM2RAIN and SM2RAIN-exp, SM2RAIN-exp is proved more stable and accurate. The recognition of the more suitable soil depth needed for obtaining good results, and of the important SR contribution, could help to clarify some important aspects related to the usage of satellite surface SM data for estimating rainfall.

Biogenic Runoff from the Nizhny Novgorod Area

The methodical approaches to the integral estimation of the biogenic diffuse export from urbanized areas have been developed and tested, using the Nizhny Novgorod area as an example in the absence of direct hydrological observations. The conditionally natural runoff of the Rakhma River, the catchment of which is mostly occupied by the present-day Nizhny Novgorod area, has been reproduced based on the RosHydroMet data on the Sundovik River-analog runoff for 2010–2017. The interannual distribution of the Rakhma River runoff has been analyzed, and the variations in the groundwater and surface runoff components have been estimated with regard to the joint impact of climatic and anthropogenic factors. The surface component of the Rakhma River runoff has been shown to increase twice while the groundwater component, to decrease by 38%. In this case the groundwater runoff value remains rather large (63 mm per year), accounting for 33% of the total river runoff. The calculations of the biogen export by the river into the Cheboksary Reservoir in different seasons, less the biogen input with wastewater, have been illustrated. The present-day diffuse export of biogens from the Nizhny Novgorod area has been roughly estimated. The export of mineral nitrogen (195 kg km–2) is maximal in the cold season and that of phosphorus (11 kg km–2), during the spring flood, respectively.

Assessment of alternative adsorption models and global sensitivity analysis to characterize hexavalent chromium loss from soil to surface runoff

AbstractWe investigate our ability to assess transfer of hexavalent chromium, Cr(VI), from the soil to surface runoff by considering the effect of coupling diverse adsorption models with a two‐layer solute transfer model. Our analyses are grounded on a set of two experiments associated with soils characterized by diverse particle size distributions. Our study is motivated by the observation that Cr(VI) is receiving much attention for the assessment of environmental risks due to its high solubility, mobility, and toxicological significance. Adsorption of Cr(VI) is considered to be at equilibrium in the mixing layer under our experimental conditions. Four adsorption models, that is, the Langmuir, Freundlich, Temkin, and linear models, constitute our set of alternative (competing) mathematical formulations. Experimental results reveal that the soil samples characterized by the finest grain sizes are associated with the highest release of Cr(VI) to runoff. We compare the relative abilities of the four models to interpret experimental results through maximum likelihood model calibration and four model identification criteria (i.e., the Akaike information criteria [AIC and AICC] and the Bayesian and Kashyap information criteria). Our study results enable us to rank the tested models on the basis of a set of posterior weights assigned to each of them. A classical variance‐based global sensitivity analysis is then performed to assess the relative importance of the uncertain parameters associated with each of the models considered, within subregions of the parameter space. In this context, the modelling strategy resulting from coupling the Langmuir isotherm with a two‐layer solute transfer model is then evaluated as the most skilful for the overall interpretation of both sets of experiments. Our results document that (a) the depth of the mixing layer is the most influential factor for all models tested, with the exception of the Freundlich isotherm, and (b) the total sensitivity of the adsorption parameters varies in time, with a trend to increase as time progresses for all of the models. These results suggest that adsorption has a significant effect on the uncertainty associated with the release of Cr(VI) from the soil to the surface runoff component.

Hydrological responses to land degradation in the Northwest Benin Owena River Basin, Nigeria

Adequate insights into how land degradation alters the hydrology of river basins will help forecast the hydrological impacts of land cover change dynamics, thereby providing unique information required for sustainable river basin management. This study attempts to utilize a multi-dimensional methodology involving the application of the physically based Soil and Water Assessment Tool (SWAT) hydrological model and Partial Least Square regression (PLSR) statistical model to evaluate the response of the hydrological components within the Northwest Benin Owena River Basin (NWBORB) in Nigeria to land degradation. Using the historic land cover maps of 1986, 2002, and 2015, covering the basin, the SWAT model was employed to simulate the hydrological components for each historic year. The PLSR model was utilized to establish a response relationship between land cover changes and hydrological component modification within the basin. Results showed that between 1986 and 2015, 18.56% of the forest was lost, with a major portion (16.19%) gained by plantation. Consequently, annual water yield and surface runoff increased by 18.28% and 17.26% respectively, while annual base flow and actual evapotranspiration decreased by 22.58% and 21.72% respectively. The reduction in forest is strongly correlated with an increase in plantation (−0.833), surface runoff component (−0.723) and water yield (−0.532). Also, expansion of plantation land is strongly correlated with surface runoff components (0.877) and negatively correlated with base flow component (−0.573). Variable Importance of the Projections (VIP) from the PLSR model showed forest (VIP = 1.23), Plantation land (VIP = 1.02) as the most important land cover variables altering the basins' local hydrology. The study clearly shows that forest preservation plays an important role in the regulation of water resources within a river basin. This methodology can be replicated for poorly gauged river basins provided the land cover maps and stream discharge data are available.

Catchment response to climate and land use changes in the Upper Blue Nile sub-basins, Ethiopia

The impacts of climate and land development on streamflow and water balance components were analyzed in the Tana and Beles watersheds by using the Soil and Water Assessment Tool (SWAT). Streamflow response to simultaneous future land-use and land-cover (fLULC) and climate change (fCC) scenarios on the seasonal scale varied among the key water abstraction locations. The General Circulation Models (GCMs) average simulation of short-term climate indicated wetter and warmer climatic condition compared to that in the baseline period (1971/1980–2013). The near–future climate scenario would intensify extreme flow by increasing rainy season flow and reducing dry period flow. However, conversion of cultivation land on steep slope into forest might mitigate these extreme flows. At the outlet of Tana watershed, streamflow response would be amplified under concurrent scenarios of fLULC and fCC; but the streamflow would have an augmenting response at the outlet of the Beles watershed. Compared to response due to fCC alone, the streamflow and surface runoff components under combined fLULC and fCC scenarios would be alleviated in sub-catchments subject to conversion of cultivation in steep slope into forest land. The present results have significances for water resource management and land use planning in the Tana and Beles watersheds, and for other regions encountering identical pressures from climate change and LULC dynamics. In view of ongoing land use and climate dynamics, environmental policies must be carried out to cope with the potential changes of hydrologic regime. Moreover, catchment management should be adapted to changing hydrological regimes at different water abstraction points.

Characterizing the climatic water balance dynamics and different runoff components in a poorly gauged tropical forested catchment, Nicaragua

ABSTRACTThe water balance dynamics and runoff components of a tropical forested catchment (46 km2) on the southwestern Pacific coast of Nicaragua were studied combining hydrometry, geological characterization and hydrochemical and isotopic tracers (three-component hydrograph separation). The climatic water balance was estimated for 2010/11, 2011/12 and 2012/13 with net values of 811 mm year-1, 782 mm year-1 and –447 mm year-1, respectively. Runoff components were studied at different spatial and temporal scales, demonstrating that different sources and temporal contributions are controlled by dominant landscape elements and antecedent rainfall. In forested sub-catchments, permeable soils, stratigraphy and steep slopes favour subsurface stormflow generation contributing 50% and 53% to total discharge. At catchment scale, landscape elements such as smooth slopes, wide valleys, deeper soils and water table allow groundwater recharge during rainfall events. Groundwater dominates the hydrograph (50% of total discharge) under dry prior conditions. However, low soil infiltration capacity generates a larger surface runoff component (42%) under wet prior conditions which dominates total discharge. Our results show that forested areas are important to reduce surface runoff and thus soil degradation, which is relevant for the design of water management plans.Editor D. Koutsoyiannis Associate editor D. Gerten

Investigation into impacts of land-use changes on floods in the upper Huaihe River basin, China

Abstract. To investigate the agricultural land-use change on flood regime, the upper Huaihe River basin above the Dapoling station was selected as the case study site. Based on topography, land-use, hydrological and meteorological data in 1990s and 2010s, the improved distributed Xinanjiang model, with potential evapotranspiration being computed by coupling a dual-source evapotranspiration model with a simplified plant growth model, was adopted to simulate the daily and hourly rainfall-runoff processes over 1990s and 2010s, and then the effects of land-use change on flood volume, flood peak, occurring time of flood peak, the percentage of surface runoff component were investigated respectively. The results was interesting and indicated that impacts of land-use change on flood characteristics varied significantly with land-use types. The outputs could provide valuable references for flood risk management and water resources management in the Huaihe River basin.

Scalability of grid- and subbasin-based land surface modeling approaches for hydrologic simulations

This paper investigates the relative merits of grid-and subbasin-based land surface modeling approaches for hydrologic simulations, with a focus on their scalability (i.e., ability to perform consistently across spatial resolutions) in simulating runoff generation. Simulations are produced by the grid- and subbasin-based Community Land Model at 0.125°, 0.25°, 0.5°, and 1° spatial resolutions over the U.S. Pacific Northwest. Using the 0.125° simulation as the “reference” solution, statistical metrics are calculated by comparing simulations at 0.25°, 0.5°, and 1° resolutions with the 0.125° simulation for each approach. Statistical significance test results suggest significant scalability advantage for the subbasin-based approach compared to the grid-based approach. Basin level annual average relative errors of surface runoff at 0.25°, 0.5°, and 1° resolutions compared to the 0.125° simulation are 3%, 4%, and 6% for the subbasin-based configuration and 4%, 7%, and 11% for the grid-based configuration, respectively. The scalability advantages are more pronounced during winter/spring and over mountainous regions. The source of runoff scalability is found to be related to the scalability of major meteorological and land surface parameters of runoff generation. More specifically, the subbasin-based approach is more consistent across spatial scales than the grid-based approach in snowfall/rainfall partitioning because of scalability related to air temperature and surface elevation. Scalability of a topographic parameter used in runoff parameterization also contributes to improved scalability of the rain-driven saturated surface runoff component, particularly during winter. Hence, this study demonstrates the importance of spatial structure for multiscale modeling of hydrological processes.

English

Catchment response as consequence of changes in vegetation cover and land use management could not be well explained by statistical methods alone. At the same time, long range periodic and trend components of time series are not adequately predicted by watershed modeling. Therefore, joint application of statistical time series analysis and watershed modeling better help to understand the underlying climate variability and catchment dynamics. In this paper, an attempt has been made to examine the effects of climate variability and catchment dynamics at two agricultural watersheds situated in Rift Valley lakes basin of Ethiopia. Distributed hydrologic modeling is used to characterize catchment dynamics whereas statistical methods (time-trend, double mass curve, flow duration curve analysis) are applied to explain the accompanying climate variability. The simulated surface runoff component increased progressively since 1970s. Percentage annual surface runoff varies from 10 to 23% at Bilate, and 16% to over twofold at Hare watersheds. Statistical time-trend analysis reveals that annual streamflow do not show significant monotonic trend, whereas, extreme daily streamflow at Alaba Kulito of Bilate catchment is characterized by increasing trend during the analysis period. Recurrent yet statistically weaker change point years are found and are independent of each other in two watersheds and hence they are governed by land use attributes unique to respective watersheds that influence overland flow. A rising slope of rainfall-runoff double mass curve during post-1992 and 1994 period at Bilate and Hare watersheds respectively supports increasing trend of streamflow that is not fully explained by time-trend analysis. Time-segmented FDCs of monthly streamflow at Bilate shows increased quantile estimates of high flows for similar level of exceedance probability for recent years. The resulting runoff variability over the analysis period is attributed to climate variability and altered land use/cover conditions, the latter being dominant in the watersheds. &nbsp; Key words: Land use dynamics, runoff, watershed modeling, trend analysis, climate change.

Contrasts between estimates of baseflow help discern multiple sources of water contributing to rivers

Abstract. This study compares baseflow estimates using chemical mass balance, local minimum methods, and recursive digital filters in the upper reaches of the Barwon River, southeast Australia. During the early stages of high-discharge events, the chemical mass balance overestimates groundwater inflows, probably due to flushing of saline water from wetlands and marshes, soils, or the unsaturated zone. Overall, however, estimates of baseflow from the local minimum and recursive digital filters are higher than those based on chemical mass balance using Cl calculated from continuous electrical conductivity measurements. Between 2001 and 2011, the baseflow contribution to the upper Barwon River calculated using chemical mass balance is between 12 and 25% of the annual discharge with a net baseflow contribution of 16% of total discharge. Recursive digital filters predict higher baseflow contributions of 19 to 52% of discharge annually with a net baseflow contribution between 2001 and 2011 of 35% of total discharge. These estimates are similar to those from the local minimum method (16 to 45% of annual discharge and 26% of total discharge). These differences most probably reflect how the different techniques characterise baseflow. The local minimum and recursive digital filters probably aggregate much of the water from delayed sources as baseflow. However, as many delayed transient water stores (such as bank return flow, floodplain storage, or interflow) are likely to be geochemically similar to surface runoff, chemical mass balance calculations aggregate them with the surface runoff component. The difference between the estimates is greatest following periods of high discharge in winter, implying that these transient stores of water feed the river for several weeks to months at that time. Cl vs. discharge variations during individual flow events also demonstrate that inflows of high-salinity older water occurs on the rising limbs of hydrographs followed by inflows of low-salinity water from the transient stores as discharge falls. The joint use of complementary techniques allows a better understanding of the different components of water that contribute to river flow, which is important for the management and protection of water resources.

Separation of Stormflow Hydrographs in Surface and Subsurface Flow by Perceptual Based Modelling of Channel Inflow Components

The aim of this case study was to determine the origin of stormflow runoff in a 31 ha headwater basin in Western Germany. Therefore, the contribution of infiltration excess and saturation overland flow as well as matrix and preferential flow has been assessed along a deeply incised channel of 300 m length. Measurable parameters and simple al- gorithms were used to assess the flow rate of the different runoff components. Only the scaling parameter cf that repre- sents converging flow processes and controls the actual height of the saturated zone along the channel incision has been calibrated. The results showed that during wet conditions the subsurface flow rates exceed the surface flow rates tremen- dously. In contrast, for the short but intense summer storm the hydrograph consists solely of surface runoff components. Obviously, the parameter cf accounts for the pre-event condition of the catchment. This leads to the conclusion, that the extension of the contributing subsurface space mainly governs stormflow processes. Further investigations shall focus on the relationship between subsurface flow processes and the lateral and vertical extension of the saturated zone.

SWAT模型融雪模块的改进

PDF HTML阅读 XML下载 导出引用 引用提醒 SWAT模型融雪模块的改进 DOI: 10.5846/stxb201207110977 作者: 作者单位: 南京师范大学 地理科学学院 南京,中国科学院寒区旱区环境与工程研究所 兰州,西北师范大学 地理与环境科学学院 兰州,南京师范大学 地理科学学院 南京 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金面上资助项目（91125006）；国家重点实验室开放基金资助项目（SKLFSE201009） Improvement of snowmelt implementation in the SWAT hydrologic model Author: Affiliation: School of Geography Science,Nanjing Normal University,Nanjing,Cold and Arid Regions Environmental and Engineering Research Institute,Chinese Academy of Sciences,Lanzhou,College of Geography and Environment Science,Northwest Normal University,Lanzhou,School of Geography Science,Nanjing Normal University,Nanjing Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:水土评价工具模型（SWAT）是一个具有物理基础的分布式水文模型，利用SCS径流曲线数方法计算地表径流，而采用相对简单的度日因子方法计算融雪径流。因此在湿润半湿润、雨量丰富的平原地区应用SWAT模型进行径流模拟时可以得到较好的模拟结果，但是在干旱半干旱、降水稀少，且春汛期间融雪径流是重要补给来源的高寒山区，模拟的融雪径流明显偏小，不能很好的反映这些地区的融雪过程，导致河道径流模拟精度偏低。FASST模型是具有物理机制的陆面过程模型，其采用能量平衡的方法计算融雪径流，能够较好的模拟复杂地形山区流域的融雪径流。以黑河山区流域为研究区，将FASST模型集成到SWAT模型，改善SWAT模型融雪径流的计算方法。通过对比SWAT模型集成前后莺落峡出山口的河道月径流、融雪径流和地表径流对河道的贡献等几个方面，表明了集成FASST融雪模块的SWAT模型能更好的反映黑河山区流域的融雪径流过程，从而提高河道径流的整体模拟精度。 Abstract:SWAT is a physically explicit distributed hydrologic model, which could simulate hydrological processes such as surface runoff, snowmelt runoff and infiltration with geographic information system(GIS).The surface runoff component in SWAT is implemented by using the SCS curve number and snowmelt is calculated by a relatively simple, empirical degree-day method. Those approaches work well in humid and semi-humid areas where precipitation dominantly controls runoff. In cold and arid regions, for example, in a case study in the Heihe river basin (HRB) of northwest China, however, it revealed those implementations cannot represent the effects of snowmelt in springs and thus impact surface runoff simulations. At large, snowmelt in such areas is underestimated and more discrepancies are consequently introduced to the overall simulation accuracy. The FASST model is a surface process model with explicit physical base, including a snowmelt runoff component that makes use of mass and energy balance equations. There is a snowmelt implementation in FASST, which takes topography, vegetation, soil type, and snow physical properties into account. Existing applications of FASST show good simulations of snowmelt in terrain-complex mountainous watershed and its applicability is also confirmed by an application to the Tangula site of northwest China located in a similar cold and alpine area. This paper proposes a coupling approach to improve the simulation of snowmelt by integrating FASST snowmelt to SWAT. In this approach, when the snowmelt begins to be calculated in SWAT, it will call FASST snowmelt to calculate, return its value to the SWAT corresponding variable and continue remainder SWAT logics. The technical implementation is presented in detail. An application to the upper mountainous HRB is set up to test its performance. There are abundant snow falls in Upper HRB in winters and snowmelt is the primary water source to river in springs that cannot be simulated well by original SWAT. The improvements in comparison to the original were examined from three aspects, namely, streamflow, snowmelt runoff and surface runoff contribution to streamflow. Both snowmelt and surface runoff estimates with the coupled model were increased so that streamflow estimate was more close to that observed. An improvement of monthly streamflow estimation by 0.11 in Nash-Sutcliffe coefficient (NSE) can be achieved. Examination of surface runoff contribution to streamflow also supports its feasibility in estimating snowmelt by the coupled approach. Moreover, by using optimized parameter sets, the monthly streamflow simulation accuracy in the validation period of 2000-2009 can be up to 0.83 in NSE. The results confirm the applicability of the SWAT-FASST coupled approach in cold and alpine watersheds where snowmelt should be taken into account and suggest its significance in improving the simulations in such areas. 参考文献 相似文献 引证文献

Development, sensitivity and uncertainty analysis of LASH model

Many hydrologic models have been developed to help manage natural resources all over the world. Nevertheless, most models have presented a high complexity regarding data base requirements, as well as, many calibration parameters. This has brought serious difficulties for applying them in watersheds where there is scarcity of data. The development of the Lavras Simulation of Hydrology (LASH) in a GIS framework is described in this study, which focuses on its main components, parameters, and capabilities. Coupled with LASH, sensitivity analysis, parameter range reduction, and uncertainty analysis were performed prior to the calibration effort by using specific techniques (Morris method, Monte Carlo simulation and a Generalized Likelihood Uncertainty Estimation -GLUE) with a data base from a Brazilian Tropical Experimental Watershed (32 km²), in order to predict streamflow on a daily basis. LASH is a simple deterministic and spatially distributed model using long-term data sets, and a few maps to predict streamflow at a watershed outlet. We were able to identify the most sensitive parameters which are associated with the base flow and surface runoff components, using a reference watershed. Using a conservative threshold, two parameters had their range of values reduced, thus resulting in outputs closer to measured values and facilitating automatic calibration of the model with less required iterations. GLUE was found to be an efficient method to analyze uncertainties related to the prediction of mean daily streamflow in the watershed.

Investigation of Internal Functioning of the Radial-Basis-Function Neural Network River Flow Forecasting Models

This paper deals with the challenging problem of hydrological interpretation of the internal functioning of artificial neural networks (ANNs) by extracting knowledge from their solutions. The neural network used in this study is based on the structure of the radial-basis-function neural network (RBFNN), which is considered as an alternative to the multilayer perceptron for solving complex modeling problems. This network consists of input, hidden, and output layers. The network is trained using the daily data of two catchments having different characteristics and from two different regions in the world. The present day and antecedent observed discharges are used as inputs to the network to forecast the flow one day ahead. A range of quantitative and qualitative techniques are used for hydrological interpretation of the internal functioning by examining the responses of the hidden layer nodes. The results of the study show that a single hidden layered RBFNN is an effective tool to forecast the daily flows and that the activation of the hidden layer nodes are far from arbitrary, but appear to represent flow components of the predicted hydrograph. The results of the study confirm that the three nodes in the hidden layer of this model effectively divide the input data space in such a way that the contribution from each node dominates in one of the flow domains—low, medium, or high—and form, in a crude manner, the base flow, interflow and surface runoff components of the hydrograph.