Quantifying the impact of landscape changes on hydrological variables in the alpine and cold region using hydrological model and remote sensing data

Quantifying the impact of landscape on hydrological variables is essential for the sustainable development of water resources. Understanding how landscape changes influence hydrological variables will greatly enhance the understanding of hydrological processes. Important vegetation parameters are considered in this study by using remote sensing data and VIC-CAS model to analyse the impact of landscape changes on hydrology in upper reaches of the Shule River Basin (URSLB). The results show there are differences in the runoff generation of landscape both in space and time. With increasing altitude, the runoff yields increased, with approximately 79.9% of the total runoff generated in the high mountains (4200–5900 m), and mainly consumed in the mid-low mountain region. Glacier landscape produced the largest runoff yields (24.9% of the total runoff), followed by low-coverage grassland (LG; 22.5%), alpine cold desert (AL; 19.6%), mid-coverage grassland (MG; 15.6%), bare land (12.5%), high-coverage grassland (HG; 4.5%) and shrubbery (0.4%). The relative capacity of runoff generation by landscapes, from high to low, was the glaciers, AL, LG, HG, MG, shrubbery and bare land. Furthermore, changes in landscapes cause hydrological variables changes, including evapotranspiration, runoff and baseflow. The study revealed that HG, MG, and bare land have a positive impact on evapotranspiration and a negative impact on runoff and baseflow, whereas AL and LG have a positive impact on runoff and baseflow and a negative impact on evapotranspiration. In contrast, glaciers have a positive impact on runoff. After the simulation in four vegetation scenarios, we concluded that the runoff regulation ability of grassland is greater than that of bare land. The grassland landscape is essential since it reduced the flood peak and conserved the soil and water.     

Ressi experimental catchment: Ecohydrological research in the Italian pre-Alps

Ressi is a small (2.4 ha) forested catchment located in the Italian pre-Alps. The site became an experimental catchment to investigate the water fluxes in the soil–plant–atmosphere continuum and the impact of vegetation on runoff generation in 2012. The elevation of the catchment ranges from 598 to 721 m a.s.l. and the climate is humid temperate. The bedrock consists of rhyolites and dacites; the soil is a Cambisol. The catchment is covered by a dense forest, dominated by beech, chestnut, maple, and hazel trees. The field set up includes measurements of the rainfall in an open area, streamflow at the outlet, soil moisture at various depths and locations, and depth to water table in six piezometers at a 5- or 10-min interval. Samples of precipitation, stream water, shallow groundwater and soil water are collected monthly for tracer analysis (stable isotopes (²H and ¹⁸O), electrical conductivity and major ions), and during selected rainfall–runoff events to determine the contribution of the various sources to runoff. Since 2017, soil and plant water samples have been collected to determine the sources of tree transpiration. Data collected in the period 2012–2016 are publicly available. Data collection is ongoing, and the data set is expected to be updated on an annual basis to include the most recent measurements.     

Distinguishing streamflow trends caused by changes in climate,forest cover,and permafrost in a large watershed in northeastern China

Understanding how rivers respond to changes in land cover, climate, and subsurface conditions is critical for sustainably managing water resources and ecosystems. In this study, long‐term hydrologic, climate, and satellite data (1973–2012) from the Upper Tahe River watershed (2359 km²) in the Da Hinggan Mountains of northeast China were analysed to quantify the relative hydrologic effects of climate variability (system input) and the combined influences of forest cover change and permafrost thaw (system characteristics) on average annual streamflow (system response) using 2 methods: the sensitivity‐based method and the Kendall–Theil robust line method. The study period was subdivided into a forest harvesting period (1973–1987), a forest stability period (1988–2001), and a forest recovery period (2002–2012). The results indicated that the combined effects of forest harvesting and permafrost thaw on streamflow (+ 47.0 mm) from the forest harvesting period to the forest stability period was approximately twice as large as the effect associated with climate variability (+20.2 mm). Similarly, from the forest stability period to the forest recovery period, the decrease in average annual streamflow attributed to the combined effects of forest recovery and permafrost thaw (?38.0 mm) was much greater than the decrease due to climate variability (?22.2 mm). A simple method was used to separate the distinct impacts of forest cover change and permafrost thaw, but distinguishing these influences is difficult due to changes in surface and subsurface hydrologic connectivity associated with permafrost thaw. The results highlight the need to consider multiple streamflow drivers in future watershed and aquatic ecosystem management. Due to the ecological and hydrological susceptibility to disturbances in the Da Hinggan Mountains, forest harvesting will likely negatively impact ecohydrological processes in this region, and the effects of forest species transition in the forest recovery process should be further investigated.     

Discharge‐sediment processes of the Zhadang glacier on the Tibetan Plateau measured with a high frequency data acquisition system

In high elevation cold regions of the Tibetan Plateau, suspended sediment transfer from glacier meltwater erosion is one of the important hydrological components. The Zhadang glacier is a typical valley‐type glacier in the Nyainqentanglha Mountains on the Tibetan Plateau. To make frequent and long period records of meltwater runoff and sediment processes in the very high elevation and isolated regions, an automatic system was installed near the glacier snout (5400 m a.s.l) in August 2013, to measure the transient discharge and sediment processes at 5‐min interval, which is shorter than the time span for the water flow to traverse the catchment from the farthest end to the watershed outlet. Diurnal variations of discharge, and suspended sediment concentration (SSC) were recorded at high frequency for the Zhadang glacier, before suspended sediment load (SSL) was computed. Hourly SSC varied from the range of 0.2 kg/m³ to 0.5 kg/m³ (at 8:00–9:00) to the range of 2.0 kg/m³ to 4.0 kg/m³ (at 17:00–18:00). The daily SSL was 32.24 t during the intense ablation period. Hourly SSC was linearly correlated with discharge (r = 0.885**, n = 18, p < 0.01). A digit‐eight hysteresis loop was observed for the sediment transport in the glacier area. Air temperature fluctuations influence discharge, and then result in the sediment variations. The results of this study provide insight into the responses of suspended sediment delivery processes with a high frequency data in the high elevation cold regions. Copyright © 2016 John Wiley & Sons, Ltd.     

Unraveling complex hydrogeological processes in Andean basins in south‐central Chile: An integrated assessment to understand hydrological dissimilarity

Groundwater storage, drainage, and interbasin water exchange are common hydrological processes but often difficult to quantify due to a lack of local observations. We present a study of three volcanic mountainous watersheds located in south‐central Chile (~36.9 ° S) in the Chillán volcanic complex (Chillán, Renegado, and Diguillín river basins). These are neighboring basins that are similar with respect to the metrics normally available for characterization everywhere (e.g., precipitation, temperature, and land cover). In a hydrological sense, similar (proportional) behavior would be expected if these catchments would be characterized with this general information. However, these watersheds show dissimilar behavior when analyzed in detail. The surface water balance does not fit for any of these watersheds individually; however, the water balance of the whole system can be explained by likely interbasin water exchanges. The Renegado river basin has an average annual runoff per unit of area on the order of 60–65% less than those of the Diguillín and Chillán rivers, which is contradictory to the hydrological similarity among the basins. To understand the main processes that control streamflow generation, two analyses were performed: (a) basin metrics (land cover, geologic, topographic, and climatological maps) and hydro‐meteorological data analyses and (b) a water balance model approach. The analyses contribute to a plausible explanation for the hydrogeological processes in the system. The soils, topography, and geology of the Chillán–Renegado–Diguillín system favor the infiltration and groundwater movements from the Renegado river basin, mainly to the neighboring Diguillín basin. The interbasin water exchanges affect hydrological similarity and explain the differences observed in the hydrological processes of these three apparently similar volcanic basins. The results highlight the complexity of hydrological processes in volcanic mountainous systems and suggest that a simple watershed classification approach based on widely available data is insufficient. Simple local analyses such as specific flow analysis with a review of the geology and morphology can contribute to a better understanding of the hydrology of volcanic mountainous areas.     

Spatial aggregation of time‐variant stream water ages in urbanizing catchments

We calibrated an integrated flow–tracer model to simulate spatially distributed isotope time series in stream water in a 7.9‐km² catchment with an urban area of 13%. The model used flux tracking to estimate the time‐varying age of stream water at the outlet and both urbanized (1.7 km²) and non‐urban (4.5 km²) sub‐catchments over a 2.5‐year period. This included extended wet and dry spells where precipitation equated to >10‐year return periods. Modelling indicated that stream water draining the most urbanized tributary was youngest with a mean transit time (MTT) of 171 days compared with 456 days in the non‐urban tributary. For the larger catchment, the MTT was 280 days. Here, the response of urban contributing areas dominated smaller and more moderate runoff events, but rural contributions dominated during the wettest periods, giving a bi‐modal distribution of water ages. Whilst the approach needs refining for sub‐daily time steps, it provides a basis for projecting the effects of urbanization on stream water transit times and their spatial aggregation. This offers a novel approach for understanding the cumulative impacts of urbanization on stream water quantity and quality, which can contribute to more sustainable management. Copyright © 2015 John Wiley & Sons, Ltd.     

Remote estimation of terrestrial evapotranspiration by Landsat 5 TM and the SEBAL model in cold and high‐altitude regions: a case study of the upper reach of the Shule River Basin,China

Evapotranspiration (ET) is an important expenditure in water and energy balances, especially on cold and high‐altitude land surfaces. Daily ET of the upper reach of the Shule River Basin was estimated using Landsat 5 TM data and the Surface Energy Balance Algorithm for Land (SEBAL) model. Based on observations made at the Suli station, the algorithms of land surface temperature and soil heat flux in SEBAL were modified. Land surface temperature was retrieved and compared with ground truth via three methods: the radiative transfer equation method, the mono‐window algorithm, and the single‐channel method. We selected the best of these methods, mono‐window algorithm, for estimating ET. The average error of daily ET estimated by the modified SEBAL model and measured by the eddy covariance system was 16.4%, with a root‐mean‐square error of 0.52 mm d^?1. The estimated ET means were 3.09, 2.48, and 1.48 mm d^?1 on June 9 (DOY 160), June 25 (DOY 176), and July 27 (DOY 208) of the year 2010, respectively. The average estimated ET on the glacier surface of all days was more than 3 mm d^?1, a measurement that is difficult to capture in‐situ and has rarely been reported. This study will improve the understanding of water balance in cold, high‐altitude regions. Copyright © 2016 John Wiley & Sons, Ltd.     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

Water source dynamics of high Arctic river basins

Arctic river basins are amongst the most vulnerable to climate change. However, there is currently limited knowledge of the hydrological processes that govern flow dynamics in Arctic river basins. We address this research gap using natural hydrochemical and isotopic tracers to identify water sources that contributed to runoff in river basins spanning a gradient of glacierization (0–61%) in Svalbard during summer 2010 and 2011. Spatially distinct hydrological processes operating over diurnal, weekly and seasonal timescales were characterized by river hydrochemistry and isotopic composition. Two conceptual water sources (‘meltwater’ and ‘groundwater’) were identified and used as a basis for end‐member mixing analyses to assess seasonal and year‐to‐year variability in water source dynamics. In glacier‐fed rivers, meltwater dominated flows at all sites (typically >80%) with the highest contributions observed at the beginning of each study period in early July when snow cover was most extensive. Rivers in non‐glacierized basins were sourced initially from snowmelt but became increasingly dependent on groundwater inputs (up to 100% of total flow volume) by late summer. These hydrological changes were attributed to the depletion of snowpacks and enhanced soil water storage capacity as the active layer expanded throughout each melt season. These findings provide insight into the processes that underpin water source dynamics in Arctic river systems and potential future changes in Arctic hydrology that might be expected under a changing climate. Copyright © 2013 John Wiley & Sons, Ltd.