Semi-distributed Land Use-based Runoff Process Research Articles

An Ensemble Climate-Hydrology Modeling System for Long-Term Streamflow Assessment in a Cold-Arid Watershed

Climate change can bring about substantial alternatives of temperature and precipitation in the spatial and temporal patterns. These alternatives would impact the hydrological cycle and cause flood or drought events. This study has developed an ensemble climate-hydrology modeling system (ECHMS) for long-term streamflow assessment under changing climate. ECHMS consists of multiple climate scenarios (two global climate models (GCMs) and four representative concentration pathways (RCPs) emission scenarios), a stepwise-cluster downscaling method and semi-distributed land use-based runoff process (SLURP) model. ECHMS is able to reflect the uncertainties in climate scenarios, tackle the complex relationships (e.g., nonlinear/linear, discrete/continuous) between climate predictors and predictions without functional assumption, and capture the combination of snowmelt– and rainfall–runoff process with a simplicity of operation. Then, the developed ECHMS is applied to Kaidu watershed for analyzing the changes of streamflow during the 21st century. Results show that by 2099, the temperature increment in Kaidu watershed is mainly contributed by the warming in winter and spring. The precipitation will increase obviously in spring and autumn and decrease in winter. Multi-year average streamflow would range from 105.6 to 113.8 m3/s across all scenarios during the 21st century with an overall increasing trend. The maximum average increasing rate is 2.43 m3/s per decade in October and the minimum is 0.26 m3/s per decade in January. Streamflow change in spring is more sensitive to climate change due to its complex runoff generation process. The obtained results can effectively identify future streamflow changing trends and help manage water resources for decision makers.

Future water quality analysis of the Anseongcheon River basin, Korea under climate change

The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) predicted that recent extreme hydrological events would affect water quality and aggravate various forms of water pollution. To analyze changes in water quality due to future climate change, input data (precipitation, average temperature, relative humidity, average wind speed and sunlight) were established using the Representative Concentration Pathways (RCP) 8.5 climate change scenario suggested by the AR5 and calculated the future runoff for each target period (Reference:1989-2015; I: 2016-2040; II: 2041-2070; and III: 2071-2099) using the semi-distributed land use-based runoff processes (SLURP) model. Meteorological factors that affect water quality (precipitation, temperature and runoff) were inputted into the multiple linear regression analysis (MLRA) and artificial neural network (ANN) models to analyze water quality data, dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), suspended solids (SS), total nitrogen (T-N) and total phosphorus (T-P). Future water quality prediction of the Anseongcheon River basin shows that DO at Gongdo station in the river will drop by 35% in autumn by the end of the 21st century and that BOD, COD and SS will increase by 36%, 20% and 42%, respectively. Analysis revealed that the oxygen demand at Dongyeongyo station will decrease by 17% in summer and BOD, COD and SS will increase by 30%, 12% and 17%, respectively. This study suggests that there is a need to continuously monitor the water quality of the Anseongcheon River basin for long-term management. A more reliable prediction of future water quality will be achieved if various social scenarios and climate data are taken into consideration.

Identifying water resources management strategies in adaptation to climate change under uncertainty

In this study, an integrated simulation-based allocation modeling system (ISAMS) is developed for identifying water resources management strategies in response to climate change. The ISAMS incorporates global climate models (GCMs), a semi-distributed land use-based runoff process (SLURP) model, and a multistage interval-stochastic programming (MISP) approach within a general framework. The ISAMS can not only handle uncertainties expressed as probability distributions and interval values but also reveal climate change impacts on water resources allocation under different projections of GCMs. The ISAMS is then applied to the Kaidu-kongque watershed with cold arid characteristics in the Tarim River Basin (the largest inland watershed basin in China) for demonstrating its efficiency. Results reveal that different climate change models corresponding to various projections (e.g., precipitation and temperature) would lead to changed water resources allocation patterns. Variations in water availability and demand due to uncertainties could result in different water allocation targets and shortages. A variety of decision alternatives about water allocations adaptive to climate change are generated under combinations of different global climate models and ecological water release plans. These findings indicate that understanding the uncertainties in water resources system, building adaptive methods for generating sustainable water allocation patterns, and taking actions for mitigating water shortage problems are key adaptation strategies responding to climate change.

Analysis of interactive effects of DEM resolution and basin subdivision level on runoff simulation in Kaidu River Basin, China

Uncertainties in spatial data associated with basin topography, drainage networks, and land cover characteristics may affect the performance of runoff simulation. Such uncertainties are mainly derived from selection of digital elevation model (DEM) resolution and basin subdivision level. This study focuses on assessing the effects of DEM resolution and basin subdivision level on runoff simulation with a semi-distributed land use-based runoff process model. Twenty-four scenarios based on various DEM resolutions and subdivision levels are analyzed for the Kaidu River Basin. Results can be used for quantifying the uncertainty of input data about spatial information on model simulation, disclosing the interaction between DEM resolution and subdivision level, as well as identifying the optimal system inputs. Results show that the model performance could be enhanced with the increased subdivision level. Results also reveal that the interaction of DEM resolution and subdivision level has slight effects on modeling outputs. Multi-objective fuzzy evaluation is used to further examine the uncertainty in DEM resolution and basin subdivision level on model performance. The results indicate an optimal combination of input parameters is suitable for Kaidu River Basin which could lead to more reliable results of the hydrological simulation.

Development of an inexact-variance hydrological modeling system for analyzing interactive effects of multiple uncertain parameters

Summary Uncertainty assessment of hydrological model parameters has become one of the main topics due to their significant effects on prediction in arid and semi-arid river basins. Incorporation of uncertainty assessment within hydrological models can facilitate the calibration process and improve the degree of credibility to the subsequent prediction. In this study, an inexact-variance hydrological modeling system (IVHMS) is developed for assessing parameter uncertainty on modeling outputs in the Kaidu River Basin, China. Through incorporating the techniques of type-2 fuzzy analysis (T2FA) and analysis of variance (ANOVA) within the semi-distributed land use based runoff processes (SLURP) model, IVHMS can quantitatively evaluate the individual and interactive effects of multiple uncertain parameters expressed as type-2 fuzzy sets in the hydrological modeling system. The modeling outputs indicate a good performance of SLURP model in describing the daily streamflow at the Dashankou hydrological station. Uncertainty analysis is conducted through sampling from fuzzy membership functions under different α-cut levels. The results show that, under a lower degree of plausibility (i.e. a lower α-cut level), intervals for peak and average flows are both wider; while intervals of peak and average flows become narrower under a higher degree of plausibility. Results based on ANOVA reveal that (i) precipitation factor (PF), one of main factors dominating the runoff processes, should be paid more attention in order to enhance the model performance; (ii) retention constant for fast store (RS) controls the amount and timing of the outflow from saturated zone and has a highly nonlinear effect on the average flow; (iii) the interaction between retention constant for fast store (RF) and maximum capacity for fast store (MF) has statistically significant (p

Assessing parameter uncertainty in semi-distributed hydrological model based on type-2 fuzzy analysis: a case study of Kaidu River Basin

In this study, a type-2 fuzzy simulation method (TFSM) is developed for modeling hydrological processes associated with vague information through coupling type-2 fuzzy analysis technique with the semi-distributed land use based runoff processes (SLURP) model. TFSM can handle fuzzy sets with uncertain membership function related to hydrological modeling parameters and reveal the effects of such uncertain parameters on the hydrological processes. Streamflow calibration and verification are performed using the hydrological data for the Kaidu River Basin, China. The statistical values of Nash–Sutcliffe efficiency, determination coefficient, and deviation of volume indicate a good performance of SLURP in describing the streamflow at the outlet of the Kaidu River Basin. Based on TFSM, the effects of four uncertain parameters such as precipitation factor (PF), maximum capacity for fast store, retention constant for fast store (RF), and retention constant for slow store, on the hydrological processes are analyzed under different α-cut levels. Results demonstrate that the uncertainty associated with PF has significant effect on the simulated streamflow, while the uncertainty associated with RF has slight effect among the four parameters. These findings are helpful for improving efficiency in hydrological prediction and enhancing the model applicability.

Study of Climate Change Impact on Flood Frequencies: A Combined Weather Generator and Hydrological Modeling Approach*

Abstract Climate change is expected to lead to more frequent and intensive flooding problems for watersheds in the south part of China. This study presented a coupled Long Ashton Research Station Weather Generator (LARS-WG) and Semidistributed Land Use–Based Runoff Processes (SLURP) approach for flood frequency analysis and applied it to the Heshui watershed, China. LARS-WG, as a weather generator, was used to offer 46 sets of climate data from seven general circulation models (GCMs) under various emission scenarios (i.e., A1B, B1, and A2) over near-term and future periods (i.e., T1, 2011–30; T2, 2046–65; and T3, 2080–99). SLURP is a continuous, spatially distributed hydrological model that uses parameters from physiographic data to simulate the hydrological cycle from precipitation to runoff. Flood frequency analysis based on Pearson type III distributions was followed to analyze statistics of annual peaks. The final results (from ensembles of multimodels and multiscenarios) indicated that the magnitudes of a 200-yr return flood for T1, T2, and T3 would increase by 5.23%, 4.08%, and 12.92%, respectively, in comparison to the baseline level; those under the most extreme condition (i.e., worst scenario) would be 25.18%, 31.00%, and 44.46%, respectively. Various GCMs and emission scenarios suggested different results. But the ECHAM5/Max Planck Institute Ocean Model was found to give a more worrying intensification of flood risks and the Commonwealth Scientific and Industrial Research Organisation Mark, version 3.0, and the Community Climate System Model, version 3, were relatively conservative. The study results were useful in helping gain insight into the flood risks and its uncertainty under future climate change conditions for the Heshui watershed, and the proposed methodology is also applicable to many other watersheds in Southeast Asia with similar climatic conditions.

Climate Change Impact Assessment on Han River Long Term Runoff in South Korea Based on RCP Climate Change Scenario

The 2007 World Economic Forum (WEF) referred to climate change as the overriding problem we face. Concerns have been raised about how global warming would accelerate future climate change and its consequences. Many climate change studies expect the possible occurrence of extreme high temperature, increase in heavy rains and strong typhoons in the near future. Currently, climate change scenarios are used to prepare an appropriate plan for these phenomena under climate change. The main purpose of this paper is to suggest and evaluate an operational method of assessing the potential impact of climate change on hydrologic components and water resources at the regional scale. Future runoff was simulated using high resolution Regional Circulation Model (RCM) (12.5 × 12.5 km) Representative Concentration Pathway (RCP) scenario operated by the Korea Meteorological Administration (KMA) and a semi-distribution model or SLURP (Semi-distributed Land Use-based Runoff Process). The study was carried out on the Han River including its nine dams. The study found that runoff characteristics, especially annual distribution, could change. The discharge in July tends to decrease while runoff can increase in August and September. The flow duration curve was estimated and compared with observed data and simulated daily runoff data for Paldang-dam to evaluate the effect of climate change. The analysis of the flow duration curve shows that the mean average low flow increased while the average wet and normal flow decreased under the climate change scenario.

Effects of watershed subdivision level on semi-distributed hydrological simulations: case study of the SLURP model applied to the Xiangxi River watershed, China

This study investigates the impacts of watershed subdivision on hydrological simulations. The SLURP (Semi-distributed Land Use-based Runoff Processes) model was employed in the Xiangxi River watershed, China, with eight watershed subdivision schemes. Under each scheme, the SLURP model was calibrated using both manual and automatic methods to simulate watershed hydrological processes over the period 1991–1998. The results indicate that model performance could be enhanced by increasing the degree of subdivision. However, there is a threshold level, beyond which no significant improvements can be obtained. The simulated water balance components and runoff contributions from different land types were found to vary among subdivision levels. Differences were slight when the watershed was divided into more than 73 sub-watersheds. These results were partly caused by the altitude-based precipitation correction, where the reduced generalization of terrain characteristics could lead to increasing consideration of small watersheds in the weighted Thiessen polygon technique; thus, the elevation-correction adjustments in deriving distributed meteorological inputs were increased. Parameter sensitivity analysis revealed that increasing the subdivision level was likely to enhance model efficiency. However, the difference became negligible among fine subdivisions. Although a threshold was identified in this study, determination of an appropriate subdivision level should also be based on the modelling objective, the quality of model inputs, the complexity of the watershed system and the resulting uncertainty.Editor D. Koutsoyiannis; Associate editor I. NalbantisCitation Han, J.-C., Huang, G.-H., Zhang, H., Li, Z., and Li, Y.-P., 2013. Effects of watershed subdivision level on semi-distributed hydrological simulations: case study of the SLURP model applied to the Xiangxi River watershed, China. Hydrological Sciences Journal, 59 (1), 108–125.

Bayesian uncertainty analysis in hydrological modeling associated with watershed subdivision level: a case study of SLURP model applied to the Xiangxi River watershed, China

Uncertainty analysis in hydrological modeling would help to better implement decision-making related to water resources management, which relies heavily on hydrologic simulations. However, an important concern will be raised over the uncertainty associated with watershed subdivision broadly applied in distributed/semi-distributed hydrological models since scale issues would significantly affect model performance, and thus, lead to dramatic variations in simulations. To fully understand the uncertainty associated with watershed subdivision level, however, is still a tough work confronting researchers because of complex modeling processes and high computation requirements. In this study, we analyzed this uncertainty within a formal Bayesian framework using a Markov Chain Monte Carlo method based on Metropolis–Hastings algorithm. In a case study using the semi-distributed land use-based runoff processes hydrologic model in the Xiangxi River watershed, results showed that the variation in the simulated discharges due to parameter uncertainty was much smaller than that due to parameter and model uncertainty under different watershed subdivision levels defined using aggregated simulation areas (ASAs). However, the posterior probability distribution of model parameters varied in response to subdivision levels, and four parameters (i.e. maximum infiltration rate, retention constant for slow store, maximum capacity for slow store, and retention constant for fast store) were identified with smaller uncertainty. Although the uncertainty in the simulated discharge due to parameter and model uncertainty varied little across subdivisions, the simulation uncertainty only due to parameter uncertainty was found to be reduced through increasing the subdivisions. In addition, the coarsest subdivision level (7 ASAs) was not sufficient for obtaining satisfying simulations in the Xiangxi River watershed, but inappreciable improvement was achieved through increasing the level among finer subdivisions. Moreover, it was demonstrated that increasing subdivision level would have no advantage of improving the reliability of hydrological simulations beyond the threshold (45 ASAs). The findings of this research may shed light on the design of operational hydrological forecasting in the Three Gorges Reservoir region with profound socio-economic implications.

The Potential Effects of Climate Change on Streamflow in Rivers Basin of Korea Using Rainfall Elasticity

In this paper, the rainfall elasticity of streamflow was estimated to quantify the effects of climate change on 5 river basins. Rainfall elasticity denotes the sensitivity of annual streamflow for the variations of potential annual rainfall. This is a simple, useful method that evaluates how the balance of a water cycle on river basins changes due to long-term climate change and offers information to manage water resources and environment systems. The elasticity method was first used by Schaake in 1990 and is commonly used in the United States and Australia. A semi-distributed hydrological model (SLURP, semi-distributed land use-based runoff processes) was used to simulate the variations of area streamflow, and potential evapotranspiration. A nonparametric method was then used to estimate the rainfall elasticity on five river basins of Korea. In addition, the A₂ (SRES IPCC AR4, Special Report on Emission Scenarios IPCC Fourth Assessment Report ) climate change scenario and stochastic downscaling technique were used to create a high-resolution weather change scenario in river basins, and the effects of climate change on the rainfall elasticity of each basin were then analyzed.

Impacts of climate and land-use changes on the migration of non-point source nitrogen and phosphorus during rainfall-runoff in the Jialing River Watershed, China

Summary The loss of nitrogen and phosphorus via non-point source (NPS) pollution in the Jialing River Watershed has become the main pollution sources of river waters in the Three Gorges reservoir area in the past decades, while climatic conditions and human activities directly affect changes of rainfall-runoff and land use types which are closely related to NPS pollution. This study is to assess the impact of climate change on hydrological behavior considering future land-use types and rural residential area and their propagation to NPS pollution loads. An integrated pollution load model composed of regional climate, the Semi-distributed Land Use based Runoff Processes (SLURPs) hydrological model and the improved export coefficient approach within a single framework was explored and developed to assess impacts of climate and land-use changes on NPS pollution load. Climate data for the Special Report on Emissions Scenarios (SRESs) future scenario B2 from Met Office Hadley center were generated and used as the input data for the runoff and NPS load evaluation of the Jialing River Watershed and the Markov process was used to forecast changes of land use types, respectively. Simulations of present and future regional NPS pollution from land use, livestock and poultry breeding, and agricultural population over the Jialing River Watershed were performed to investigate the potential impacts of global climate change on river water quality using the established model. Results demonstrate that: (1) Annual pollution load would obviously change due to variations of runoff and livestock and poultry breeding, the largest growth months in one year for total nitrogen (TN) and total phosphorus (TP) load are both in June, which is in accordance with changes of rainfall amount. (2) The impacts of global climate change on pollution load are relatively greater when compared to the impacts of future livestock and poultry breeding increase or agricultural population reduction; the effects from runoff increment leads to approximately 28.6% and 22.5% increases of TN and TP pollution load, respectively. And (3) the impacts of land-use change have shown insignificant effect due to soil conservation measures, whereas the impacts of rural residential area account for great proportion changes, of which about 5% of its increases are contributed to the increase of livestock and poultry breeding; but the biggest contribution rate is still from the output of different land use types. These data will be useful and valuable in evaluating potential NPS pollution load for the control of watershed pollution in the future and understanding its migration and transformation in a large-scale watershed where meteorological and underlying surface data are mutative.

Load estimation and source apportionment of nonpoint source nitrogen and phosphorus based on integrated application of SLURP model, ECM, and RUSLE: a case study in the Jinjiang River, China

The nonpoint source (NPS) pollution is difficult to manage and control due to its complicated generation and formation. Load estimation and source apportionment are an important and necessary process for efficient NPS control. Here, an integrated application of semi-distributed land use-based runoff process (SLURP) model, export coefficients model (ECM), and revise universal soil loss equation (RUSLE) for the load estimation and source apportionment of nitrogen and phosphorus was proposed. The Jinjiang River (China) was chosen for the evaluation of the method proposed here. The chosen watershed was divided into 27 subbasins. After which, the SLURP model was used to calculate land use runoff and to estimate loads of dissolved nitrogen and phosphorus, and ECM was applied to estimate dissolved loads from livestock and rural domestic sewage. Next, the RUSLE was employed for load estimation of adsorbed nitrogen and phosphorus. The results showed that the 12,029.06 t a(-1) pollution loads of total NPS nitrogen (TN) mainly originated from dissolved nitrogen (96.24 %). The major sources of TN were land use runoff, which accounted for 45.97 % of the total, followed by livestock (32.43 %) and rural domestic sewage (17.83 %). For total NPS phosphorous (TP), its pollution loads were 570.82 t a(-1) and made up of dissolved and adsorbed phosphorous with 66.29 and 33.71 % respectively. Soil erosion, land use runoff, rural domestic sewage, and livestock were the main sources of phosphorus with contribution ratios of 33.71, 45.73, 14.32, and 6.24 % respectively. Therefore, land use runoff, livestock, and soil erosion were identified as the main pollution sources to influence loads of NPS nitrogen and phosphorus in the Jinjiang River and should be controlled first. The method developed here provided a helpful guideline for conducting NPS pollution management in similar watershed.

A design of experiment aided sensitivity analysis and parameterization for hydrological modeling

To provide a better understanding of the water balance in the Deer River watershed of the Hudson Bay lowlands, the Semi-distributed Land Use-based Runoff Process hydrological model was applied to simulate the runoff over a 20 year period. The purpose of this study is to develop an approach to examine the sensitivity of the ten parameters and their interactions via statistical design of experiment methodology. Using the proposed approach, the contribution of each parameter and how they interact with one another were evaluated. The results indicated that the interaction between “retention constant for fast storage” and “precipitation factor” had the greatest positive impact on the Nash–Sutcliffe efficiency (NSE) and the quadratic factor term of “precipitation factor” had the greatest negative effect on the NSE. The proposed approach provided an effective tool for evaluating the contribution of the input parameters and could also be applied for calibration of other hydrological models.

Hydrological Modeling of Subartic Wetlands: Comparison Between SLURP and WATFLOOD

Abstract Subarctic wetlands span almost one-sixth of the Canadian wetlands and have been acknowledged as an important ecotone between arctic tundra and boreal forest. Their hydrological complexity is determined by the climatological and physiographical characteristics with regard to the spatiotemporal distribution of water resources. In this study, two semi-distributed hydrological models, SLURP (semi-distributed land use-based runoff processes) and WATFLOOD™, were employed to understand their effectiveness in modeling the hydrology of subarctic wetlands. Comparisons of their delineation approaches, formulations, parameters, and simulation results indicated that both models were capable of simulating the hydrological processes. However, differences were also observed. Besides their different segment delineation approaches, snowmelt and spring peak flows simulated by SLURP were 4–7 days earlier than those estimated by WATFLOOD because SLURP predefines snowmelt rates as variables, whereas WATFLOOD applies c...

Field Investigation and Hydrological Modelling of a Subarctic Wetland - the Deer River Watershed

Recently, investigation and conservation of subarctic wetlands has been recognized as an attractive route. To gain insight of the interactions between hydrology and atmosphere of the second largest wetland in Canada - the Hudson Bay Lowlands (HBL), the semi-distributed land use-based runoff process (SLURP) hydrological model was applied to a typical subarctic wetland - the Deer River watershed over a 20-year period (1978-1997). Sensitivity analysis, calibration and validation of the model identified a number of distinguishable hydrological features of subarctic wetlands as well as model deficiencies. Snowmelt was the major source of water recharge in subarctic wetlands and constituted approximately half of the average annual runoff in the Deer River watershed. The peaks of the simulated spring runoff were 34% lower than the observed ones in average which could be attributed to the effects of shallow permafrost that impeded the infiltration of melt water. Runoff of rainfall water during the summer season occurred only during storms due to canopy interception, depression storage, soil porosity, impermeable permafrost, and intensive evapotranspiration. A lag of 2-8 days between the peaks of streamflow and rainfall was observed through both field investigation and modeling results. The numerous seasonally connected ponds/lakes stretching over the middle and lower reach of the watershed behaved as buffers and significantly prolonged the concentration time in summer and fall. The findings will help build a scientific basis for advancing the knowledge of the hydrologic cycle and impacts of climatic changes on sub-arctic wetlands.

Comparison of hydrological responses by two different satellite remotely sensed leaf area indices in a mountainous watershed of South Korea

Vegetation information influences evapotranspiration (ET) and hence affects the hydrological cycle. Understanding the impact of biophysical variable such as Leaf Area Index (LAI) on watershed hydrology can clarify the water balance of a watershed. This study investigates the impact of vegetation information on ET, soil moisture, groundwater recharge and streamflow by using two different sources of spatial LAI data (Terra MODIS and NOAA AVHRR) in the calculation of FAO Penman-Monteith ET. For a 6661.3 km2 mountainous watershed in South Korea, the SLURP (Semi-distributed Land Use-based Runoff Processes) hydrological model was calibrated and verified based on 4 years MODIS LAI data with average Nash-Sutcliffe model efficiency of 0.67. The results indicate that the two watershed ETs vary within 4%, and the deciduous and coniferous ETs vary within 6.8% and 11.2% respectively. The two ETs affected maximum 2.4%, 6.1%, and 8.8% differences of soil moisture, groundwater recharge, and streamflow respectively.

A comparison study on distributed hydrological modelling of a subarctic wetland system

Wetlands occupy 14% of the Canadian territory and mainly exist as bogs, fens, swamps, marshes, and shallow water. Recently, arctic and subarctic wetlands have attracted much attention due to their unique hydrological characteristics, and vulnerability to climate condition changes. To gain insight of the interactions between hydrology and atmosphere of the second largest wetland in Canada - the Hudson Bay Lowlands (HBL), extensive field investigations were conducted from 2006 to 2008 in the Deer River watershed near Churchill, Manitoba, Canada. Hydrologic and geographic conditions, such as frost table, soil moisture and temperature, and streamflow were monitored to advance the understanding of the wetland systems. Following up the field investigation, two semi-distributed hydrological models, Semi-distributed Land Use-based Runoff Processes (SLURP) and WATFLOOD, were employed to simulate the water cycle in the Deer River watershed. They were further compared from the aspects of modelling structures, formulations, parameters, and simulation results. Regardless the distinct simulation concepts (i.e., aggregated simulation area in SLURP and group response unit in WATFLOOD), the results indicated that snowmelt and peaks of spring runoffs simulated by SLURP were earlier than those simulated by WATFLOOD. This may be explained by the exponentially increasing snowmelt rate adopted by SLURP. Lack of considering the existence of permafrost and seasonal ponds in both models tended to underestimate the peaks of spring runoffs. It was also observed that the Morton CRAE method used in SLURP slightly underestimated the summertime evapotranspiration, meanwhile it was overestimated by the Hargreaves Equation employed in WATFLOOD. This study not only helped to fill the knowledge gaps in how well the two widely used models could fit the requirements of subarctic wetlands modelling, but also showed their strength and limitations as well as the potential for improvement.

Simulating streamflow response to climate scenarios in central Canada using a simple statistical downscaling method

Despite the importance of surface water in Manitoba, Canada, there has been relatively little investigation of the impact of climate change on the hydrology of the province. This study exam- ines streamflow characteristics in northern Manitoba basins (Taylor and Burntwood River basins) under climate scenarios generated by global climate models (GCM) from 2 agencies (the UK Hadley Centre and the Canadian Centre for Climate Modelling and Analysis). The hydrological model SLURP (Semi-distributed Land Use-based Runoff Processes) was run with perturbed meteorological data based on the GCM simulations under 2 greenhouse gas emission scenarios (A2 and B2) for 2 future periods (2041-2070 and 2071-2099). A method to incorporate the changed variability in future temperature and precipitation to the climate scenarios was also tested. All scenarios project wetter and warmer climates, and simulation results show that the mean annual runoff is likely to increase in every scenario. The largest seasonal increase occurs in late spring and the least in winter and sum- mer. The daily flow exceeded by 80% of the total records is also projected to substantially increase, resulting in much fewer days with extreme low flow compared to the control period. Results indicate that the combination of a hydrological model with simple GCM-based scenarios can produce results comparable to those obtained from regional climate modelling. Explicit incorporation of changed variability in climate scenarios results in higher runoff than consideration of only changes in means.

Use of the North American Regional Reanalysis for Hydrological Modelling in Manitoba

This study investigates the applicability of temperature and precipitation data from the North American Regional Reanalysis (NARR) for hydrological modelling of selected watersheds in northern Manitoba. For the specific region, it is found that NARR temperature and precipitation data are in much better agreement with observations than a popular global reanalysis data set. The hydrological model SLURP (Semi-distributed Land Use-based Runoff Processes) was set up and calibrated for three catchments (Burntwood, Taylor and Sapochi), using meteorological data from weather stations. When the calibrated models were run with temperature and precipitation data from NARR, runoff was underestimated by approximately 20%. The SLURP model was then recalibrated using the NARR temperature and precipitation data as input. This eliminated much of the bias and provided a goodness-of-fit that was only slightly inferior to simulations with observed weather data. This suggests that SLURP can be adequately calibrated with NARR data and used for modelling hydrological processes in northern Manitoba where weather stations are scarce.