Evaluation and hydrological application of precipitation estimates derived from PERSIANN‐CDR,TRMM 3B42V7, and NCEP‐CFSR over humid regions in China

Satellite‐based and reanalysis quantitative precipitation estimates are attractive for hydrologic prediction or forecasting and reliable water resources management, especially for ungauged regions. This study evaluates three widely used global high‐resolution precipitation products [Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Climate Data Record (PERSIANN‐CDR), Tropical Rainfall Measuring Mission 3B42 Version 7 (TRMM 3B42V7), and National Centers for Environment Prediction‐Climate Forecast System Reanalysis (NCEP‐CFSR)] against gauge observations with seven statistical indices over two humid regions in China. Furthermore, the study investigates whether the three precipitation products can be reliably utilized as inputs in Soil and Water Assessment Tool, a semi‐distributed hydrological model, to simulate streamflows. Results show that the precipitation estimates derived from TRMM 3B42V7 outperform the other two products with the smallest errors and bias, and highest correlation at monthly scale, which is followed by PERSIANN‐CDR and NCEP‐CFSR in this rank. However, the superiority of TRMM 3B42V7 in errors, bias, and correlations is not warranted at daily scale. PERSIANN‐CDR and 3B42V7 present encouraging potential for streamflow prediction at daily and monthly scale respectively over the two humid regions, whilst the performance of NCEP‐CFSR for hydrological applications varies from basin to basin. Simulations forced with 3B42V7 are the best among the three precipitation products in capturing daily measured streamflows, whilst PERSIANN‐CDR‐driven simulations underestimate high streamflows and high streamflow simulations driven by NCEP‐CFSR mostly are overestimated significantly. In terms of extreme events analysis, PERSIANN‐CDR often underestimates the extreme precipitation, so do extreme streamflow simulations forced with it. NCEP‐CFSR performs just the reverse, compared with PERSIANN‐CDR. The performance pattern of TRMM 3B42V7 on extremes is not certain, with coexisting underestimation and overestimation. Copyright © 2016 John Wiley & Sons, Ltd.     

Reliability of reanalysis and remotely sensed precipitation products for hydrological simulation over the Sefidrood River Basin,Iran

ABSTRACT

Hydrological models require different inputs for the simulation of processes, among which precipitation is essential. For hydrological simulation, four different precipitation products – Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE); European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim); Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) real time (RT); and Precipitation Estimation from Remotely Sensed Information using Arti?cial Neural Networks (PERSIANN) – are compared against ground-based datasets. The variable infiltration capacity (VIC) model was calibrated for the Sefidrood River Basin (SRB), Iran. APHRODITE and ERA-Interim gave better rainfall estimates at daily time scale than other products, with Nash-Sutcliffe efficiency (NSE) values of 0.79 and 0.63, and correlation coefficient (CC) of 0.91 and 0.82, respectively. At the monthly time scale, the CC between all rainfall datasets and ground observations is greater than 0.9, except for TMPA-RT. Hydrological assessment indicates that PERSIANN is the best rainfall dataset for capturing the streamflow and peak flows for the studied area (CC: 0.91, NSE: 0.80).     

Evaluating precipitation products for hydrologic modeling over a large river basin in the Midwestern USA

ABSTRACT

We evaluated precipitation estimates, TRMM (Tropical Rainfall Measuring Mission 3B42V7), CFSR (Climate Forecast System Reanalysis), GHCN-D (Global Historical Climatology Network-Daily Version 3.24), and Daymet, using the Soil and Water Assessment Tool (SWAT). The suitability and quality of TRMM, CFSR and Daymet in forcing the SWAT-based hydrological model was examined by means of model calibration. A calibrated TRMM-driven model slightly overestimated streamflow, while a calibrated CFSR-driven model performed worst. The Daymet-driven model performance was as good as the GHCN-D-driven model in reproducing observations. In addition, the temperature was far less sensitive compared with precipitation in driving SWAT. TRMM 3B42V7 showed great potential in streamflow simulation. The results and findings from this study provide new insights into the suitability of precipitation products for hydrological and climate impact studies in large basins, particularly those in typical climates and physiographic settings similar to the Midwestern USA.     

Effects of temporal scales and space mismatches on the TRMM 3B42 v7 precipitation product in a remote mountainous area

The accurate measurement of precipitation is essential to understanding regional hydrological processes and hydrological cycling. Quantification of precipitation over remote regions such as the Tibetan Plateau is highly unreliable because of the scarcity of rain gauges. The objective of this study is to evaluate the performance of the satellite precipitation product of tropical rainfall measuring mission (TRMM) 3B42 v7 at daily, weekly, monthly, and seasonal scales. Comparison between TRMM grid precipitation and point‐based rain gauge precipitation was conducted using nearest neighbour and bilinear weighted interpolation methods. The results showed that the TRMM product could not capture daily precipitation well due to some rainfall events being missed at short time scales but provided reasonably good precipitation data at weekly, monthly, and seasonal scales. TRMM tended to underestimate the precipitation of small rainfall events (less than 1 mm/day), while it overestimated the precipitation of large rainfall events (greater than 20 mm/day). Consequently, TRMM showed better performance in the summer monsoon season than in the winter season. Through comparison, it was also found that the bilinear weighted interpolation method performs better than the nearest neighbour method in TRMM precipitation extraction.     

Assessment of the effectiveness of a multi-site stochastic weather generator on hydrological modelling in the Red Deer River watershed,Canada

ABSTRACT

To improve the convergence of multiple-site weather generators (SWGs) based on the brute force algorithm (MBFA), a genetic algorithm (GA) is proposed to search the overall optimal correlation matrix. Precipitation series from weather generators are used as input to the hydrological model, the soil and water assessment tool (SWAT), to generate runoff over the Red Deer watershed, Canada for further runoff analysis. The results indicate that the SWAT model using SWG-generated data accurately represents the mean monthly streamflow for most of the months. The multi-site generators were capable of better representing the monthly streamflow variability, which was notably underestimated by the single-site version. In terms of extreme flows, the proposed method reproduced the observed extreme flow with smaller bias than MBFA, while the single-site generator significantly underestimated the annual maximum flows due to its poor capability in addressing partial precipitation correlations.     

Evaluating the SWAT's snow hydrology over a Northern Quebec watershed

The snow treatment becomes an important component of Soil and Water Assessment Tool (SWAT)’s hydrology when spring flows are dominated by snow melting. However, little is known about SWAT's snow hydrology performance because most studies using SWAT were conducted in rainfall‐driven catchments. To fill this gap, the present study aims to evaluate the ability of SWAT in simulating snow‐melting‐dominated streamflow in the Outardes Basin in Northern Quebec. SWAT performance in simulating snowmelt is evaluated against observed streamflow data and compared to simulations from the operationally used Streamflow Synthesis and Reservoir Regulation (SSARR) model over that catchment. The SWAT 5‐year calibration showed a satisfactory performance at the daily and seasonal time scales with low volume biases. The SWAT validation was conducted over two (17‐year and 15‐year) periods. Performances were similar to the calibration period in simulating the daily and seasonal streamflows again with low model biases. The spring‐snowmelt‐generated peak flow was accurately simulated by SWAT both in magnitude and timing. When SWAT's results are compared to SSARR, similar performances in simulating the daily discharges were observed. SSARR simulates more accurately streamflow generated at the snowmelt onset whereas SWAT better predicts streamflow in summer, fall and winter. SWAT provided reasonable streamflow simulations for our snow‐covered catchment, but refinement of the process‐driven baseflow during the snowmelt onset could improve spring performances. Therefore, SWAT becomes an attractive tool for evaluating water resources management in Nordic environments when a distributed model is preferred or when water quality information (e.g. temperature) is required. Copyright © 2013 John Wiley & Sons, Ltd.     

Long‐term trend of precipitation and runoff in Korean river basins

The spatial and temporal variations of precipitation and runoff for 139 basins in South Korea were investigated for 34 years (1968–2001). The Precipitation‐Runoff Modelling System (PRMS) was selected for the assessment of basin hydrologic response to varying climates and physiology. A non‐parametric Mann–Kendall's test and regression analysis are used to detect trends in annual, seasonal, and monthly precipitation and runoff, while Moran's I is adapted to determine the degree of spatial dependence in runoff trend among the basins. The results indicated that the long‐term trends in annual precipitation and runoff were increased in northern regions and decreased in south‐western regions of the study area during the study period. The non‐parametric Mann–Kendall test showed that spring streamflow was decreasing, while summer streamflow was increasing. April precipitation decreased between 15% and 74% for basins located in south‐western part of the Korean peninsula. June precipitation increased between 18% and 180% for the majority of the basins. Trends in seasonal and monthly streamflow show similar patterns compared to trends in precipitation. Decreases in spring runoff are associated with decreases in spring precipitation which, accompanied by rising temperatures, are responsible for reducing soil moisture. The regional patterns of precipitation and runoff changes show a strong to moderate positive spatial autocorrelation, suggesting that there is a high potential for severe spring drought and summer flooding in some parts of Korea if these trends continue in the future. Copyright © 2008 John Wiley & Sons, Ltd.     

Merging multi-source precipitation products or merging their simulated hydrological flows to improve streamflow simulation

Satellite and reanalysis precipitation products are widely utilized for streamflow simulation, which is one critical hydrological application, especially for ungauged regions. Possible ways to improve streamflow simulation are investigated in this study by merging multi-source precipitation products, or directly merging streamflow simulated with different precipitation products. Two satellite-based precipitation products, Tropical Rainfall Measuring Mission (3B42 Version 7) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and one reanalysis precipitation product, National Centers for Environment Prediction-Climate Forecast System Reanalysis (NCEP-CFSR) are selected. Bayesian model averaging (BMA) is used to merge multi-source precipitation estimates and streamflow simulations. The results show that merging multi-source precipitation products made little difference to improve streamflow simulation. Merging multi-source streamflow simulations using the BMA generally achieved better performance on streamflow simulation, indicating that this approach is more efficient than merging multi-source precipitation products.     

Changes in hydrology and streamflow as predicted by a modelling experiment forced with climate models

Changes in precipitation and temperature have direct effects on crop water use, water stress, crop yield, evapotranspiration, water nutrient dynamics and other indicators. This study, built on a modelling framework with the Soil and Watershed Assessment Tool (SWAT) model for the Raccoon River Watershed in central Iowa, a typical US Midwestern agricultural watershed, examines the watershed response to changes in meteorological inputs from an ensemble of ten global climate models under the A1B scenario. Changes in climate were directly applied to observations (the delta change method) assuming that the estimates of climate change are reliable even if the simulated current climate may be biased. The ensemble average for the mid‐century (1946–1965) predicted 0.7% increase in daily precipitation (monthly variation from ?11.3% to +19.5%) and 2.78 °C increase in average temperature over the entire watershed. These predictions were translated through a well‐calibrated SWAT modelling setup into 22% decrease in snowfall, 16% decrease in surface runoff, 18% decrease in baseflow, 8% increase in evapotranspiration and 17% decrease in total water yield. The spatial impact at the subwatershed level revealed a wide variation (but no defined trend) with decrease in water yield that ranged from 10% to 23%. Flow near the watershed outlet (Van Meter, Iowa) is expected to decline by 17% on an average annual basis with the highest impact occurring during summer months with a maximum 39% reduction in August. Changes in climate were found to have a clear and significant impact signal of decreasing streamflow at the watershed outlet with far‐reaching implication for drinking water supplies for the central Iowa communities. Copyright © 2013 John Wiley & Sons, Ltd.     

Comparison of radar and gauge precipitation data in watershed models across varying spatial and temporal scales

Precipitation is a key control on watershed hydrologic modelling output, with errors in rainfall propagating through subsequent stages of water quantity and quality analysis. Most watershed models incorporate precipitation data from rain gauges; higher‐resolution data sources are available, but they are associated with greater computational requirements and expertise. Here, we investigate whether the Multisensor Precipitation Estimator (MPE or Stage IV Next‐Generation Radar) data improve the accuracy of streamflow simulations using the Soil and Water Assessment Tool (SWAT), compared with rain gauge data. Simulated flows from 2002 to 2010 at five timesteps were compared with observed flows for four nested subwatersheds of the Neuse River basin in North Carolina (21‐, 203‐, 2979‐, and 10 100‐km² watershed area), using a multi‐objective function, informal likelihood‐weighted calibration approach. Across watersheds and timesteps, total gauge precipitation was greater than radar precipitation, but radar data showed a conditional bias of higher rainfall estimates during large events (>25–50 mm/day). Model parameterization differed between calibrations with the two datasets, despite the fact that all watershed characteristics were the same across simulation scenarios. This underscores the importance of linking calibration parameters to realistic processes. SWAT simulations with both datasets underestimated median and low flows, whereas radar‐based simulations were more accurate than gauge‐based simulations for high flows. At coarser timesteps, differences were less pronounced. Our results suggest that modelling efforts in watersheds with poor rain gauge coverage can be improved with MPE radar data, especially at short timesteps. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.     

Multi‐variable and multi‐site calibration and validation of SWAT in a large mountainous catchment with high spatial variability

Many methods developed for calibration and validation of physically based distributed hydrological models are time consuming and computationally intensive. Only a small set of input parameters can be optimized, and the optimization often results in unrealistic values. In this study we adopted a multi‐variable and multi‐site approach to calibration and validation of the Soil Water Assessment Tool (SWAT) model for the Motueka catchment, making use of extensive field measurements. Not only were a number of hydrological processes (model components) in a catchment evaluated, but also a number of subcatchments were used in the calibration. The internal variables used were PET, annual water yield, daily streamflow, baseflow, and soil moisture. The study was conducted using an 11‐year historical flow record (1990–2000); 1990–94 was used for calibration and 1995–2000 for validation. SWAT generally predicted well the PET, water yield and daily streamflow. The predicted daily streamflow matched the observed values, with a Nash–Sutcliffe coefficient of 0·78 during calibration and 0·72 during validation. However, values for subcatchments ranged from 0·31 to 0·67 during calibration, and 0·36 to 0·52 during validation. The predicted soil moisture remained wet compared with the measurement. About 50% of the extra soil water storage predicted by the model can be ascribed to overprediction of precipitation; the remaining 50% discrepancy was likely to be a result of poor representation of soil properties. Hydrological compensations in the modelling results are derived from water balances in the various pathways and storage (evaporation, streamflow, surface runoff, soil moisture and groundwater) and the contributions to streamflow from different geographic areas (hill slopes, variable source areas, sub‐basins, and subcatchments). The use of an integrated multi‐variable and multi‐site method improved the model calibration and validation and highlighted the areas and hydrological processes requiring greater calibration effort. Copyright © 2005 John Wiley & Sons, Ltd.     

Comparison of daily and sub-daily SWAT models for daily streamflow simulation in the Upper Huai River Basin of China

Despite the significant role of precipitation in the hydrological cycle, few studies have been conducted to evaluate the impacts of the temporal resolution of rainfall inputs on the performance of SWAT (soil and water assessment tool) models in large-sized river basins. In this study, both daily and hourly rainfall observations at 28 rainfall stations were used as inputs to SWAT for daily streamflow simulation in the Upper Huai River Basin. Study results have demonstrated that the SWAT model with hourly rainfall inputs performed better than the model with daily rainfall inputs in daily streamflow simulation, primarily due to its better capability of simulating peak flows during the flood season. The sub-daily SWAT model estimated that 58 % of streamflow was contributed by baseflow compared to 34 % estimated by the daily model. Using the future daily and 3-h precipitation projections under the RCP (Representative Concentration Pathways) 4.5 scenario as inputs, the sub-daily SWAT model predicted a larger amount of monthly maximum daily flow during the wet years than the daily model. The differences between the daily and sub-daily SWAT model simulation results indicated that temporal rainfall resolution could have much impact on the simulation of hydrological process, streamflow, and consequently pollutant transport by SWAT models. There is an imperative need for more studies to examine the effects of temporal rainfall resolution on the simulation of hydrological and water pollutant transport processes by SWAT in river basins of different environmental conditions.     

Model calibration using hydropedological insights to improve the simulation of internal hydrological processes using SWAT+

Soils affect the distribution of hydrological processes by partitioning precipitation into different components of the water balance. Therefore, understanding soil-water dynamics at a catchment scale remains imperative to future water resource management. In this study, the value of hydropedological insights was examined to calibrate a processes-based model. Soil morphology was used as soft data to assist in the calibration of the Soil Water Assessment Tool (SWAT+) model at five different catchment scales (48, 56, 174, 674, and 2421 km²) in the Sabie River catchment, South Africa. The aim of this study was to calibrate the SWAT+ model to accurately simulate long-term monthly streamflow predictions as well as to reflect internal soil hydrological processes using a procedure focusing on hydropedology as a calibration tool in a multigauge system. Results indicated that calibration improved streamflow predictions where R² improved by 2%–8%. Nash-Sutcliffe Efficiency (NSE) improved from negative correlations to values exceeding 0.5 at four of the five catchment scales compared to the uncalibrated model. Results confirm that soil mapping units can be calibrated individually within SWAT+ to improve the representation of hydrological processes. Particularly, the spatial linkage between hydropedology and hydrological processes, which is captured within the soil map of the catchment, can be adequately reflected within the model simulations after calibration. This research will lead to an improved understanding of hydropedology as soft data to improve hydrological modelling accuracy.     

Stream response to precipitation variability: a spectral view based on analysis and modelling of hydrological cycle components

Hydrological processes commonly exhibit long‐term persistence, also known as the ‘Hurst phenomenon’. Here, we examine long‐term precipitation and streamflow time series from the Elbe River Basin to quantify differences in the spectral properties and in the Hurst parameter estimates () of the individual hydrological cycle components. Precipitation‐runoff modelling is performed for the Elbe River sub‐catchment Striegis using the Soil and Water Assessment Tool (SWAT). For 38 daily 50 years long streamflow time series from the Elbe River Basin, baseflow separation and spectral analysis is performed. The results show a spectral shift towards low‐frequency scales (>2 years) from precipitation to baseflow, with a parallel increase of from 0.52 (precipitation) to 0.65 (baseflow). The SWAT model is able to reproduce both, the main low‐frequency mode (≈7 yr.) and the (0.62) of the observed Striegis River flow time series. The baseflow appears to be the main component which shapes the low‐frequency response and of streamflow in the Elbe River Basin to the input precipitation. This conclusion is further confirmed through PMWIN‐MODFLOW groundwater modelling of a hypothetic phreatic stream‐connected aquifer system that consists of various soils (sand, loamy sand and silt). A power shift towards lower frequencies and an increase of for the hydraulic heads is obtained, as the aquifer's lateral dimensions increase and its hydraulic conductivity decreases. The average of the groundwater heads is 0.80, 0.90 and 1.0 for sand, loamy sand and silt aquifers, respectively. Copyright © 2014 John Wiley & Sons, Ltd.     

Response of hydrological processes to land‐cover and climate changes in Kejie watershed,south‐west China

Land‐cover/climate changes and their impacts on hydrological processes are of widespread concern and a great challenge to researchers and policy makers. Kejie Watershed in the Salween River Basin in Yunnan, south‐west China, has been reforested extensively during the past two decades. In terms of climate change, there has been a marked increase in temperature. The impact of these changes on hydrological processes required investigation: hence, this paper assesses aspects of changes in land cover and climate. The response of hydrological processes to land‐cover/climate changes was examined using the Soil and Water Assessment Tool (SWAT) and impacts of single factor, land‐use/climate change on hydrological processes were differentiated. Land‐cover maps revealed extensive reforestation at the expense of grassland, cropland, and barren land. A significant monotonic trend and noticeable changes had occurred in annual temperature over the long term. Long‐term changes in annual rainfall and streamflow were weak; and changes in monthly rainfall (May, June, July, and September) were apparent. Hydrological simulations showed that the impact of climate change on surface water, baseflow, and streamflow was offset by the impact of land‐cover change. Seasonal variation in streamflow was influenced by seasonal variation in rainfall. The earlier onset of monsoon and the variability of rainfall resulted in extreme monthly streamflow. Land‐cover change played a dominant role in mean annual values; seasonal variation in surface water and streamflow was influenced mainly by seasonal variation in rainfall; and land‐cover change played a regulating role in this. Surface water is more sensitive to land‐cover change and climate change: an increase in surface water in September and May due to increased rainfall was offset by a decrease in surface water due to land‐cover change. A decrease in baseflow caused by changes in rainfall and temperature was offset by an increase in baseflow due to land‐cover change. Copyright © 2009 John Wiley & Sons, Ltd.     

Estimation of the added value of using rainfall–runoff transformation and statistical models for seasonal streamflow forecasting

Two methods for generating streamflow forecasts in a Sahelian watershed, the Sirba basin, were compared. The direct method used a linear relationship to relate sea-surface temperature to annual streamflow, and then disaggregated on a monthly time scale. The indirect method used a linear relationship to generate annual precipitation forecasts, a temporal disaggregation to generate daily precipitation and the SWAT (Soil and Water Assessment Tool) model to generate monthly streamflow. The accuracy of the forecasts was assessed using the coefficient of determination, the Nash-Sutcliffe coefficient and the Hit score, and their economic value was evaluated using the cost/loss ratio method. The results revealed that the indirect method was slightly more effective than the direct method. However, the direct method achieved higher economic value in the majority of cost/loss situations, allowed for predictions with longer lead times and required less information.     

Trends in hydrological and climatic variables affected by four variations of the Mann-Kendall approach in Urmia Lake basin,Iran

ABSTRACT

The trends in hydrological and climatic time series data of Urmia Lake basin in Iran were examined using the four different versions of the Mann-Kendall (MK) approach: (i) the original MK test; (ii) the MK test considering the effect of lag-1 autocorrelation; (iii) the MK test considering the effect of all autocorrelation or sample size; and (iv) the MK test considering the Hurst coefficient. Identification of hydrological and climatic data trends was carried out at monthly and annual time scales for 25 temperature, 35 precipitation and 35 streamflow gauging stations selected from the Urmia Lake basin. Mann-Kendall and Pearson tests were also applied to explore the relationships between temperature, precipitation and streamflow trends. The results show statistically significant upward and downward trends in the annual and monthly hydrological and climatic variables. The upward trends in temperature, unlike streamflow, are much more pronounced than the downward trends, but for precipitation the behaviour of trend is different on monthly and annual time scales. Furthermore, the trend results were affected by the different approaches. Specifically, the number of stations showing trends in hydrological and climatic variables decreased significantly (up to 50%) when the fourth test was considered instead of the first and the absolute value of the Z statistic for most of the time series was reduced. The results of correlations between streamflow and climatic variables showed that the streamflow in Urmia Lake basin is more sensitive to changes in temperature than those of precipitation. The observed decreases in streamflow and increases in temperature in the Urmia Lake basin in recent decades may thus have serious implications for water resources management under the warming climate with the expected population growth and increased freshwater consumption in this region.

Editor Z. W. Kundzewicz; Associate editor Q. Zhang     

Projecting climate change impacts on stream flow regimes with tracer‐aided runoff models ‐ preliminary assessment of heterogeneity at the mesoscale

The northern mid‐high latitudes form a region that is sensitive to climate change, and many areas already have seen – or are projected to see – marked changes in hydroclimatic drivers on catchment hydrological function. In this paper, we use tracer‐aided conceptual runoff models to investigate such impacts in a mesoscale (749 km²) catchment in northern Scotland. The catchment encompasses both sub‐arctic montane sub‐catchments with high precipitation and significant snow influence and drier, warmer lowland sub‐catchments. We used downscaled HadCM3 General Circulation Model outputs through the UKCP09 stochastic weather generator to project the future climate. This was based on synthetic precipitation and temperature time series generated from three climate change scenarios under low, medium and high greenhouse gas emissions. Within an uncertainty framework, we examined the impact of climate change at the monthly, seasonal and annual scales and projected impacts on flow regimes in upland and lowland sub‐catchments using hydrological models with appropriate process conceptualization for each landscape unit. The results reveal landscape‐specific sensitivity to climate change. In the uplands, higher temperatures result in diminishing snow influence which increases winter flows, with a concomitant decline in spring flows as melt reduces. In the lowlands, increases in air temperatures and re‐distribution of precipitation towards autumn and winter lead to strongly reduced summer flows despite increasing annual precipitation. The integration at the catchment outlet moderates these seasonal extremes expected in the headwaters. This highlights the intimate connection between hydrological dynamics and catchment characteristics which reflect landscape evolution. It also indicates that spatial variability of changes in climatic forcing combined with differential landscape sensitivity in large heterogeneous catchments can lead to higher resilience of the integrated runoff response. Copyright © 2012 John Wiley & Sons, Ltd.     

Impact of the spatial variability of daily precipitation on hydrological projections: A comparison of GCM‐ and RCM‐driven cases in the Han River basin,Korea

In this study, we investigate the impact of the spatial variability of daily precipitation on hydrological projections based on a comparative assessment of streamflow simulations driven by a global climate model (GCM) and two regional climate models (RCMs). A total of 12 different climate input datasets, that is, the raw and bias‐corrected GCM and raw and bias‐corrected two RCMs for the reference and future periods, are fed to a semidistributed hydrological model to assess whether the bias correction using quantile mapping and dynamical downscaling using RCMs can improve streamflow simulation in the Han River basin, Korea. A statistical analysis of the daily precipitation demonstrates that the precipitation simulated by the GCM fails to capture the large variability of the observed daily precipitation, in which the spatial autocorrelation decreases sharply within a relatively short distance. However, the spatial variability of precipitation simulated by the two RCMs shows better agreement with the observations. After applying bias correction to the raw GCM and raw RCMs outputs, only a slight change is observed in the spatial variability, whereas an improvement is observed in the precipitation intensity. Intensified precipitation but with the same spatial variability of the raw output from the bias‐corrected GCM does not improve the heterogeneous runoff distributions, which in turn regulate unrealistically high peak downstream streamflow. GCM‐simulated precipitation with a large bias correction that is necessary to compensate for the poor performance in present climate simulation appears to distort streamflow patterns in the future projection, which leads to misleading projections of climate change impacts on hydrological extremes.     

Streamflow response to the rapid retreat of a lake‐calving glacier

There has been increasing attention over the last decade to the potential effects of glacier retreat on downstream discharge and aquatic habitat. This study focused on streamflow variability downstream of Bridge Glacier in the southern Coast Mountains of BC between 1979 and 2014, prior to and during a period in which the glacier experienced enhanced calving and rapid retreat across a lake‐filled basin. Here we combined empirical trend detection and a conceptual‐parametric hydrological model to address the following hypotheses: (1) streamflow trends in late summer and early autumn should reflect the opposing influences of climatic warming (which would tend to increase unit‐area meltwater production) and the reduction in glacier area (which would tend to reduce the total volume of meltwater generated), and (2) winter streamflow should increase because of displacement of lake water as ice flows past the grounding line and calves into the lake basin. In relation to the first hypothesis, we found no significant trends in monthly discharge during summer. However, applying regression analysis to account for air temperature and precipitation variations, weak but statistically significant negative trends were detected for August and melt season discharge. The HBV‐EC model was applied using time‐varying glacier cover, as derived from Landsat imagery. Relative to simulations based on constant glacier extent, model results indicated that glacier recession caused a decline in mean monthly streamflow of 9% in August and 11% in September. These declines in late‐summer streamflow are consistent with the results from our empirical analysis. The second hypothesis is supported by the finding of positive trends for December, January, and February discharge. Despite the modelled declines in late‐summer mean monthly streamflow, recorded discharge data exhibited neither positive nor negative trends during the melt season, suggesting that Bridge Glacier may currently be at or close to the point of peak water. Further analysis of the impact of lake‐terminating glaciers on downstream discharge is needed to refine the peak water model. Copyright © 2016 John Wiley & Sons, Ltd.