A comparison of MODIS and NOHRSC snow‐cover products for simulating streamflow using the Snowmelt Runoff Model

Remote sensing is an important source of snow‐cover extent for input into the Snowmelt Runoff Model (SRM) and other snowmelt models. Since February 2000, daily global snow‐cover maps have been produced from data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS). The usefulness of this snow‐cover product for streamflow prediction is assessed by comparing SRM simulated streamflow using the MODIS snow‐cover product with streamflow simulated using snow maps from the National Operational Hydrologic Remote Sensing Center (NOHRSC). Simulations were conducted for two tributary watersheds of the Upper Rio Grande basin during the 2001 snowmelt season using representative SRM parameter values. Snow depletion curves developed from MODIS and NOHRSC snow maps were generally comparable in both watersheds: satisfactory streamflow simulations were obtained using both snow‐cover products in larger watershed (volume difference: MODIS, 2·6%; NOHRSC, 14·0%) and less satisfactory streamflow simulations in smaller watershed (volume difference: MODIS, −33·1%; NOHRSC, −18·6%). The snow water equivalent (SWE) on 1 April in the third zone of each basin was computed using the modified depletion curve produced by the SRM and was compared with in situ SWE measured at Snowpack Telemetry sites located in the third zone of each basin. The SRM‐calculated SWEs using both snow products agree with the measured SWEs in both watersheds. Based on these results, the MODIS snow‐cover product appears to be of sufficient quality for streamflow prediction using the SRM in the snowmelt‐dominated basins. Copyright © 2005 John Wiley & Sons, Ltd.     

Simulation of snowmelt runoff in ungauged basins based on MODIS: a case study in the Lhasa River basin

It is theoretically and practically significant to conduct snowmelt runoff simulations and hydrological research for high-elevation regions. The Lhasa River basin, an ungauged basin, is a typical alpine headwater region where snowmelt runoff contributes significantly to its stream flow. In this study, the snowmelt period, defined by the snow cover curves obtained at different altitudinal zones based on Moderate-Resolution Imaging Spectroradiometer (MODIS) and Digital Elevation Model data, occurred from March 6 to July 12 in the basin. The snowmelt processes were simulated with the Snowmelt Runoff Model (SRM) in 2002 and 2003 for calibration and validation, respectively. The coefficients of determination (R ² ) were 0.86 and 0.87 for calibration and validation, respectively, and the Nash–Sutcliffe coefficients were both 0.80, which indicate reasonable performances in simulating hydrological processes in the Lhasa River basin. The simulated snowmelt at altitudes below 5,000 m accounts for most of the snowmelt. And the simulated snowmelt runoff contributed 3–6 % to the total runoff. The sensitivity of individual parameters was analysed and ranked as follows: α and γ > C _S  > C _R  > T _crit . In short, the SRM based on MODIS remotely sensed data performed well for the ungauged Lhasa River basin.     

Factors controlling specific sediment yield in the upper Indus River basin,northern Pakistan

Estimates of sediment yield are essential in water resources analysis, modelling and engineering, in investigations of continental denudation rates, and in studies of drainage basin response to changes in climate and land use. The availability of high resolution, global environmental datasets offers an opportunity to examine the relationships between specific sediment yield (SY_sp) and drainage basin attributes in a geographical information system (GIS) environment. This study examines SY_sp at 14 long‐term gauging stations within the upper Indus River basin. Twenty‐nine environmental variables were derived from global datasets, the majority with a 1 × 1 km resolution. The SY_sp ranges from 194 to 1302 t km^?2 yr^?1 for sub‐basins ranging from 567 to 212 447 km². The high degree of scatter in SY_sp is greatly reduced when the stations are divided into three groups: upper, glacierized sub‐basins; lower, monsoon sub‐basins; and the main Indus River. Percentage snow/ice cover (LC_s) emerges as the single major land cover control for SY_sp in the high mountainous upper Indus River basin. A regression model with percentage snow/ice cover (LC_s) as the single independent variable explains 73·4% of the variance in SY_sp for the whole Indus basin. A combination of percentage snow/ice cover (LC_s), relief and climate variables explains 98·5% of the variance for the upper, glacierized sub‐basins. For the lower monsoon region, a regression model with only mean annual precipitation (P) explains 99·4% of the variance. Along the main Indus River, a regression model including just basin relief (R) explains 92·4% of the variance in SY_sp. Based on the R²_adj and P‐value statistics, the variables used are capable of explaining the majority of variance in the upper Indus River basin. Copyright © 2007 John Wiley & Sons, Ltd.     

Automatic mapping of snow cover depletion curves using optical remote sensing data under conditions of frequent cloud cover and temporary snow

Snow cover depletion curves are required for several water management applications of snow hydrology and are often difficult to obtain automatically using optical remote sensing data owing to both frequent cloud cover and temporary snow cover. This study develops a methodology to produce accurate snow cover depletion curves automatically using high temporal resolution optical remote sensing data (e.g. Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Aqua MODIS or National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR)) by snow cover change trajectory analysis. The method consists of four major steps. The first is to reclassify both cloud‐obscured land and snow into more distinct subclasses and to determine their snow cover status (seasonal snow cover or not) based on the snow cover change trajectories over the whole snowmelt season. The second step is to derive rules based on the analysis of snow cover change trajectories. These rules are subsequently used to determine for a given date, the snow cover status of a pixel based on snow cover maps from the beginning of the snowmelt season to that given date. The third step is to apply a decision‐tree‐like processing flow based on these rules to determine the snow cover status of a pixel for a given date and to create daily seasonal snow cover maps. The final step is to produce snow cover depletion curves using these maps. A case study using this method based on Terra MODIS snow cover map products (MOD10A1) was conducted in the lower and middle reaches of the Kaidu River Watershed (19 000 km²) in the Chinese Tien Shan, Xinjiang Uygur Autonomous Region, China. High resolution remote sensing data (charge coupled device (CCD) camera data with 19·5 m resolution of the China and Brazil Environmental and Resources Satellite (CBERS) data (19·5 m resolution), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data with 15 m resolution of the Terra) were used to validate the results. The study shows that the seasonal snow cover classification was consistent with that determined using a high spatial resolution dataset, with an accuracy of 87–91%. The snow cover depletion curves clearly reflected the impact of the variation of temperature and the appearance of temporary snow cover on seasonal snow cover. The findings from this case study suggest that the approach is successful in generating accurate snow cover depletion curves automatically under conditions of frequent cloud cover and temporary snow cover using high temporal resolution optical remote sensing data. Copyright © 2007 John Wiley & Sons, Ltd.     

One‐year‐long runoff forecast by a single snowpack evaluation

A degree‐day‐based model is presented for a 1 year ahead runoff forecast, with 1 day time steps. The input information is a single snowpack evaluation collected at the beginning of the snowmelt season. The snow‐cover dynamics, the key information for long‐term snowmelt forecast, are described by the snow‐line dynamics, i.e. by the movements of the downhill snowpack limit. The snowmelt volume, estimated by the snow‐line dynamics, is the exogenous input of an autoregressive transformation model. The model is calibrated by a least‐squares procedure on the basis of observed daily runoff data and the corresponding measurements of the snowpack volume (one measurement per year). A real‐world case study on the Alto Tunuyan River basin (2380 km², Argentinean Andes) is presented. The 1 year ahead Alto Tunuyan River runoff patterns, computed for both calibration and validation periods, reveal high agreement with observed streamflows. Copyright © 2004 John Wiley & Sons, Ltd.     

Relating snow dynamics and balsam fir stand characteristics,Montmorency Forest,Quebec

Snow water equivalent was measured during three springs on north‐ and south‐exposed sites representing a range of stand structure and development stages of Quebec's balsam fir forest. Maximum snow water equivalent of the season, mean seasonal snowmelt rate, snowmelt season duration and total snowmelt season degree‐day factor were related to canopy height, canopy density, light interception fraction and basal area of the stands using random coefficient models. Seasonal mean snowmelt rate was better explained by stand characteristics (R² from 0·41 to 0·61) than was maximum snow water equivalent (R² from 0·08 to 0·23). The best relationship was found with light interception, which explained 61% of snowmelt rate variability between stands. These relationships were not significantly affected by stand aspect (Pr ≥ S = 0·14 or higher), as snow dynamics seemed less dependent on aspect than on stand characteristics. Snowmelt recovery rates could be used by forest planners to establish an acceptable time step for the harvesting of different parts of a watershed in order to prevent peak flow augmentations. Copyright © 2005 John Wiley & Sons, Ltd.     

AMSR‐E algorithm for snowmelt onset detection in sub‐arctic heterogeneous terrain

The onset of snowmelt in the upper Yukon River basin, Canada, can be derived from brightness temperatures (T_b) obtained by the Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) on NASA's Aqua satellite. This sensor, with a resolution of 14 × 8 km² for the 36·5 GHz frequency, and two to four observations per day, improves upon the twice‐daily coverage and 37 × 28 km² spatial resolution of the Special Sensor Microwave Imager (SSM/I). The onset of melt within a snowpack causes an increase in the average daily 36·5 GHz vertically polarized T_b as well as a shift to high diurnal amplitude variations (DAV) as the snow melts during the day and re‐freezes at night. The higher temporal and spatial resolution makes AMSR‐E more sensitive to sub‐daily T_b oscillations, resulting in DAV that often show a greater daily range compared to SSM/I. Therefore, thresholds of T_b > 246 K and DAV > ± 10 K developed for use with SSM/I have been adjusted for detecting the onset of snowmelt with AMSR‐E using ground‐based surface temperature and snowpack wetness relationships. Using newly developed thresholds of T_b > 252 K and DAV > ± 18 K, AMSR‐E derived snowmelt onset correlates well with SSM/I observations in the small subarctic Wheaton River basin through the 2004 and 2005 winter/spring transition. In addition, the onset of snowmelt derived from AMSR‐E data gridded at a higher resolution than the SSM/I data indicates that finer‐scale differences in elevation and land cover affect the onset of snowmelt and are detectable with the AMSR‐E sensor. On the basis of these observations, the enhanced resolution of AMSR‐E is more effective than SSM/I at delineating spatial and temporal snowmelt dynamics in the heterogeneous terrain of the upper Yukon River basin. Copyright © 2007 John Wiley & Sons, Ltd.     

A temperature‐precipitation‐based model of thirty‐year mean snowpack accumulation and melt in Oregon,USA

High‐resolution, spatially extensive climate grids can be useful in regional hydrologic applications. However, in regions where precipitation is dominated by snow, snowmelt models are often used to account for timing and magnitude of water delivery. We developed an empirical, nonlinear model to estimate 30‐year means of monthly snowpack and snowmelt throughout Oregon. Precipitation and temperature for the period 1971–2000, derived from 400‐m resolution PRISM data, and potential evapotranspiration (estimated from temperature and day length) drive the model. The model was calibrated using mean monthly data from 45 SNOTEL sites and accurately estimated snowpack at 25 validation sites: R² = 0·76, Nash‐Sutcliffe Efficiency (NSE) = 0·80. Calibrating it with data from all 70 SNOTEL sites gave somewhat better results (R² = 0·84, NSE = 0·85). We separately applied the model to SNOTEL stations located < 200 and ≥ 200 km from the Oregon coast, since they have different climatic conditions. The model performed equally well for both areas. We used the model to modify moisture surplus (precipitation minus potential evapotranspiration) to account for snowpack accumulation and snowmelt. The resulting values accurately reflect the shape and magnitude of runoff at a snow‐dominated basin, with low winter values and a June peak. Our findings suggest that the model is robust with respect to different climatic conditions, and that it can be used to estimate potential runoff in snow‐dominated basins. The model may allow high‐resolution, regional hydrologic comparisons to be made across basins that are differentially affected by snowpack, and may prove useful for investigating regional hydrologic response to climate change. Published in 2011 by John Wiley & Sons, Ltd.     

On the usefulness of remote sensing input data for spatially distributed hydrological modelling: case of the Tarim River basin in China

The ecological situation of the Tarim River basin in China seriously declined since the early 1950s, mainly due to a strong increase in water abstraction for irrigation purposes. To restore the ecological system and support sustainable development of the Tarim River basin region in China, more hydrological studies are demanded to properly understand the processes of the watershed and efficiently manage the water resources. Such studies are, however, complicated due to the limited data availability, especially in the mountainous headwater regions of the Tarim River basin. This study investigated the usefulness of remote sensing (RS) data to overcome that lack of data in the spatially distributed hydrological modelling of the basin. Complementary to the conventional station‐based (SB) data, the RS products that are directly used in this study include precipitation, evapotranspiration and leaf area index. They are derived from raw image data of the Chinese Fengyun meteorological satellite and from the Moderate Resolution Imaging Spectroradiometer (MODIS). The MODIS land surface temperature was used to calculate the atmospheric temperature lapse rate to describe the temperature dependency on topographical variations. Moreover, MODIS‐based snow cover images were used to obtain model initial conditions and as validation reference for the snow model component. Comparison of model results based on RS input versus conventional SB input exhibited similar results in terms of high and low river runoff extremes, cumulative runoff volumes in both runoff and snow melting seasons and spatial and temporal variability of snow cover. During summer time, when the snow cover shrinks in the permanent glacier region, it was found that the model resolution influences the model results dramatically, hence, showing the importance of detailed (RS based) spatially distributed input data. Copyright © 2011 John Wiley & Sons, Ltd.     

Incorporating accumulated temperature and algorithm of snow cover calculation into the snowmelt runoff model

An accurate simulation of snowmelt runoff is of much importance in arid alpine regions. Data availability is usually an obstacle to use energy‐based snowmelt models for the snowmelt runoff simulation, and temperature‐based snowmelt models are more appealing in these regions. The snow runoff model is very popular nowadays, especially in the data sparse regions, because only temperature, precipitation and snow cover data are required for inputs to the model. However, this model uses average temperature as index, which cannot reflect the snowmelt simulation in the high altitude band. In this study, the snow runoff model is modified on the basis of accumulated active temperature. Snow cover calculation algorithm is added and is no longer needed as input but output. This makes the model able to simulate long‐time runoff and long‐time snow cover variation in every band. An examination of the improved model in the Manas River basin showed that the model is effective. It can reproduce the behaviour of the hydrology and can reflect the actual snow cover fluctuation. Copyright © 2012 John Wiley & Sons, Ltd.     

Spatio‐temporal controls of snowmelt and runoff generation during rain‐on‐snow events in a mid‐latitude mountain catchment

A network of 30 standalone snow monitoring stations was used to investigate the snow cover distribution, snowmelt dynamics, and runoff generation during two rain‐on‐snow (ROS) events in a 40 km² montane catchment in the Black Forest region of southwestern Germany. A multiple linear regression analysis using elevation, aspect, and land cover as predictors for the snow water equivalent (SWE) distribution within the catchment was applied on an hourly basis for two significant ROS flood events that occurred in December 2012. The available snowmelt water, liquid precipitation, as well as the total retention storage of the snow cover were considered in order to estimate the amount of water potentially available for the runoff generation. The study provides a spatially and temporally distributed picture of how the two observed ROS floods developed in the catchment. It became evident that the retention capacity of the snow cover is a crucial mechanism during ROS. It took several hours before water was released from the snowpack during the first ROS event, while retention storage was exceeded within 1 h from the start of the second event. Elevation was the most important terrain feature. South‐facing terrain contributed more water for runoff than north‐facing slopes, and only slightly more runoff was generated at open compared to forested areas. The results highlight the importance of snowmelt together with liquid precipitation for the generation of flood runoff during ROS and the large temporal and spatial variability of the relevant processes. Copyright © 2015 John Wiley & Sons, Ltd.     

Improvement of distributed snowmelt energy balance modeling with MODIS‐based NDSI‐derived fractional snow‐covered area data

Describing the spatial variability of heterogeneous snowpacks at a watershed or mountain‐front scale is important for improvements in large‐scale snowmelt modelling. Snowmelt depletion curves, which relate fractional decreases in snow‐covered area (SCA) against normalized decreases in snow water equivalent (SWE), are a common approach to scale‐up snowmelt models. Unfortunately, the kinds of ground‐based observations that are used to develop depletion curves are expensive to gather and impractical for large areas. We describe an approach incorporating remotely sensed fractional SCA (FSCA) data with coinciding daily snowmelt SWE outputs during ablation to quantify the shape of a depletion curve. We joined melt estimates from the Utah Energy Balance Snow Accumulation and Melt Model (UEB) with FSCA data calculated from a normalized difference snow index snow algorithm using NASA's moderate resolution imaging spectroradiometer (MODIS) visible (0·545–0·565 µm) and shortwave infrared (1·628–1·652 µm) reflectance data. We tested the approach at three 500 m² study sites, one in central Idaho and the other two on the North Slope in the Alaskan arctic. The UEB‐MODIS‐derived depletion curves were evaluated against depletion curves derived from ground‐based snow surveys. Comparisons showed strong agreement between the independent estimates. Copyright © 2010 John Wiley & Sons, Ltd.     

Inter-annual variability in snow cover depletion patterns and atmospheric circulation indices in the Upper Irtysh basin,Central Asia

The Irtysh River is the main water resource of Eastern Kazakhstan and its upper basin is severely affected by spring floods each year, primarily as a result of snowmelt. Knowledge of the large-scale processes that influence the timing of these snow-induced floods is currently lacking, but critical for the management of water resources in the area. In this study, we evaluated the variability in winter–spring snow cover in five major sub-basins of the Upper Irtysh basin between 2000 and 2017 as a possible explanatory factor of spring flood events, assessing the time of peak snow cover depletion rate and snow cover disappearance from the moderate-resolution imaging spectroradiometer (MODIS) MOD10A2 data set. We found that on average, peak snow cover retreat occurs between 22 March and 14 April depending on the basin, with large interannual variations but no clear trend over the MODIS period, while our comparative analysis of longer-term snow cover extent from the National Oceanic and Atmospheric Administration Climate Data Record data set suggests a shift to earlier snow cover disappearance since the 1970s. In contrast, the annual peak snow cover depletion rate displays a weak increasing trend over the study period and exceeded 5,900 km²/day in 2017. The timing of snow disappearance in spring shows significant correlations of up to 0.82 for the largest basin with winter indices of the Arctic Oscillation (AO) over the region. The primary driver is the impact of the large-scale pressure anomalies upon the mean spring (MAM) air temperatures and resultant timing of snow cover disappearance, particularly at elevations 500–2,000 m above sea level. This suggests a lagged effect of this atmospheric circulation pattern in spring snow cover retreat. The winter AO index could therefore be incorporated into long-term runoff forecasts for the Irtysh. Our approach is easily transferable to other similar catchments and could support flood management strategies in Kazakhstan and other countries.     

Seasonal snowpack dynamics and runoff in a cool temperate forest: lysimeter experiment in Niigata,Japan

Seasonal snowpack dynamics are described through field measurements under contrasting canopy conditions for a mountainous catchment in the Japan Sea region. Microclimatic data, snow accumulation, albedo and lysimeter runoff are given through the complete winter season 2002–03 in (1) a mature cedar stand, (2) a larch stand, and (3) a regenerating cedar stand or opening. The accumulation and melt of seasonal snowpack strongly influences streamflow runoff during December to May, including winter baseflow, mid‐winter melt, rain on snow, and diurnal peaks driven by radiation melt in spring. Lysimeter runoff at all sites is characterized by constant ground melt of 0·8–1·0 mm day⁻¹. Rapid response to mid‐winter melt or rainfall shows that the snowpack remains in a ripe or near‐ripe condition throughout the snow‐cover season. Hourly and daily lysimeter discharge was greatest during rain on snow (e.g. 7 mm h⁻¹ and 53 mm day⁻¹ on 17 December) with the majority of runoff due to rainfall passing through the snowpack as opposed to snowmelt. For both rain‐on‐snow and radiation melt events lysimeter discharge was generally greatest at the open site, although there were exceptions such as during interception melt events. During radiation melt instantaneous discharge was up to 4·0 times greater in the opening compared with the mature cedar, and 48 h discharge was up to 2·5 times greater. Perhaps characteristic of maritime climates, forest interception melt is shown to be important in addition to sublimation in reducing snow accumulation beneath dense canopies. While sublimation represents a loss from the catchment water balance, interception melt percolates through the snowpack and contributes to soil moisture during the winter season. Strong differences in microclimate and snowpack albedo persisted between cedar, larch and open sites, and it is suggested further work is needed to account for this in hydrological simulation models. Copyright © 2005 John Wiley & Sons, Ltd.     

Controls of streamflow generation in small catchments across the snow–rain transition in the Southern Sierra Nevada,California

Processes controlling streamflow generation were determined using geochemical tracers for water years 2004–2007 at eight headwater catchments at the Kings River Experimental Watersheds in southern Sierra Nevada. Four catchments are snow‐dominated, and four receive a mix of rain and snow. Results of diagnostic tools of mixing models indicate that Ca²⁺, Mg²⁺, K⁺ and Cl^? behaved conservatively in the streamflow at all catchments, reflecting mixing of three endmembers. Using endmember mixing analysis, the endmembers were determined to be snowmelt runoff (including rain on snow), subsurface flow and fall storm runoff. In seven of the eight catchments, streamflow was dominated by subsurface flow, with an average relative contribution (% of streamflow discharge) greater than 60%. Snowmelt runoff contributed less than 40%, and fall storm runoff less than 7% on average. Streamflow peaked 2–4 weeks earlier at mixed rain–snow than snow‐dominated catchments, but relative endmember contributions were not significantly different between the two groups of catchments. Both soil water in the unsaturated zone and regional groundwater were not significant contributors to streamflow. The contributions of snowmelt runoff and subsurface flow, when expressed as discharge, were linearly correlated with streamflow discharge (R² of 0.85–0.99). These results suggest that subsurface flow is generated from the soil–bedrock interface through preferential pathways and is not very sensitive to snow–rain proportions. Thus, a declining of the snow–rain ratio under a warming climate should not systematically affect the processes controlling the streamflow generation at these catchments. Copyright © 2012 John Wiley & Sons, Ltd.     

Accelerated continental-scale snowmelt and ecohydrological impacts in the four largest Siberian river basins in response to spring warming

River runoff from the four largest Siberian river basins (the Ob, Yenisei, Lena, and Kolyma) considerably contributes to freshwater flux into the Arctic Ocean from the Eurasian continent. However, the effects of variation in snow cover fraction on the ecohydrological variations in these basins are not well understood. In this study, we analysed the spatiotemporal variability of the maximum snow cover fraction (SCF_max) in the four Siberian river basins. We compared the SCF_max from 2000 to 2016 with data in terms of monthly temperature and precipitation, night-time surface temperatures, the terrestrial water storage anomaly (TWSA), the normalised difference vegetation index (NDVI), and river runoff. Our results exhibit a decreasing trend in the April SCF_max values since 2000, largely in response to warming air temperatures in April. We identified snowmelt water as the dominant control on the observed increase in the runoff contribution in May across all four Siberian river basins. In addition, we detected that the interannual river runoff was predominantly controlled by interannual variations in the TWSA. The NDVI in June was strongly controlled by the timing of the snowmelt along with the surface air temperature and TWSA in June. The rate of increase in the freshwater flux from the four Siberian rivers decreased from 2000 to 2016, exhibiting large interannual variations corresponding to interannual variations in the TWSA. However, we identified a clear increase trend in the freshwater flux of ~4 km³/year when analysing the long-term 39-year historical record (1978–2016). Our results suggest that continued global warming will accelerate the transition towards the earlier timing of snowmelt and spring freshwater flux into the Arctic Ocean. Our findings also highlight the effects of earlier snowmelt on ecohydrological changes in the Northern Hemisphere.     

Melted snow volume control in the snowmelt runoff model using a snow water equivalent statistically based model

Snowmelt is an important component of the river discharge in mountain environments. In the past 40 years, the snowmelt dynamics has been mostly evaluated using degree‐day‐based models like the snowmelt runoff model (SRM). This model has no control on the volume of the melting snow, even if SRM includes as data input the snow‐covered area. This lack explains why the application of SRM may lead to inaccurate snowmelt volume estimations, even if the discharge volumes are accurately reproduced. Here we introduce in SRM the control on the melted snow volume and consider it in the determination of SRM parameters. The total snow volume, accumulated at the end of winter season, is evaluated by a snow water equivalent statistically based model, SWE‐SEM, and used as an estimate of the melting snow during the summer season. The benefit derived from the introduction of the control on the melting snow volume was investigated in the Mallero basin (northern Italy) for the 2003 and 2004 snow melting seasons. The analysis compares the model's results adopting different parameter sets, both considering and ignoring the control on the melting snow volume. Copyright © 2011 John Wiley & Sons, Ltd.     

Estimating the distribution of snow water equivalent using remotely sensed snow cover data and a spatially distributed snowmelt model: A multi-resolution,multi-sensor comparison

Time series of fractional snow covered area (SCA) estimates from Landsat Enhanced Thematic Mapper (ETM+), Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High Resolution Radiometer (AVHRR) data were combined with a spatially distributed snowmelt model to reconstruct snow water equivalent (SWE) in the Rio Grande headwaters (3419 km²). In this reconstruction approach, modeled snowmelt over each pixel is integrated during the period of satellite-observed snow cover to estimate SWE. Due to underestimates in snow cover detection, maximum basin-wide mean SWE using MODIS and AVHRR were, respectively, 45% and 68% lower than SWE estimates obtained using ETM+ data. The mean absolute error (MAE) of SWE estimated at 100-m resolution using ETM+ data was 23% relative to observed SWE from intensive field campaigns. Model performance deteriorated when MODIS (MAE = 50%) and AVHRR (MAE = 89%) SCA data were used. Relative to differences in the SCA products, model output was less sensitive to spatial resolution (MAE = 39% and 73% for ETM+ and MODIS simulations run at 1 km resolution, respectively), indicating that SWE reconstructions at the scale of MODIS acquisitions may be tractable provided the SCA product is improved. When considering tradeoffs between spatial and temporal resolution of different sensors, our results indicate that higher spatial resolution products such as ETM+ remain more accurate despite the lower frequency of acquisition. This motivates continued efforts to improve MODIS snow cover products.     

Rainfall–runoff–inundation analysis of the 2010 Pakistan flood in the Kabul River basin

Abstract

Pakistan has suffered a devastating flood disaster in 2010. In the Kabul River basin (92 605 km²), large-scale riverine and flash floods caused destructive damage with more than 1100 casualties. This study analysed rainfall–runoff and inundation in the Kabul River basin with a newly developed model that simulates the processes of rainfall–runoff and inundation simultaneously based on two-dimensional diffusion wave equations. The simulation results showed a good agreement with an inundation map produced based on MODIS for large-scale riverine flooding. In addition, the simulation identified flash flood-affected areas, which were confirmed to be severely damaged based on a housing damage distribution map. Since the model is designed to be used even immediately after a disaster, it can be a useful tool for analysing large-scale flooding and to provide supplemental information to agencies for relief operations.

Editor Z.W. Kundzewicz

Citation Sayama, T., Ozawa, G., Kawakami, T., Nabesaka, S. and Fukami, K., 2012. Rainfall–runoff–inundation analysis of the 2010 Pakistan flood in the Kabul River basin. Hydrological Sciences Journal, 57 (2), 298–312.     

Hydrological response to timber harvest in northern Idaho: implications for channel scour and persistence of salmonids

The potential for forest harvest to increase snowmelt rates in maritime snow climates is well recognized. However, questions still exist about the magnitude of peak flow increases in basins larger than 10 km² and the geomorphic and biological consequences of these changes. In this study, we used observations from two nearly adjacent small basins (13 and 30 km²) in the Coeur d'Alene River basin, one with recent, relatively extensive, timber harvest, and the other with little disturbance in the last 50 years to explore changes in peak flows due to timber harvest and their potential effects on fish. Peak discharge was computed for a specific rain‐on‐snow event using a series of physical models that linked predicted values of snowmelt input to a runoff‐routing model. Predictions indicate that timber harvest caused a 25% increase in the peak flow of the modelled event and increased the frequency of events of this magnitude from a 9‐year recurrence interval to a 3·6‐year event. These changes in hydrologic regime, with larger discharges at shorter recurrence intervals, are predicted to increase the depth and frequency of streambed scour, causing up to 15% added mortality of bull trout (Salvelinus confluentus) embryos. Mortality from increased scour, although not catastrophic, may have contributed to the extirpation of this species from the Coeur d'Alene basin, given the widespread timber harvest that occurred in this region. Copyright © 2008 John Wiley & Sons, Ltd.