Hydrological Research Research Articles

Hydrological Research of the Turkestan Branch of the Russian Geographical Society in the Study of the Aral Sea Basin at the Turn of the 19—20th Centuries

The article is devoted to the history of Aral Sea expeditions organized by the Turkestan section of the Russian Geographical Society at the turn of the 19th — 20th centuries. The study analyzes the theoretical and practical importance of geographical and hydrological study of the Aral Sea basin by the Turkestan section of the Russian Geographical Society, conditioned by the political and economic interests of the Russian Empire in the Central Asian region. L. S. Berg carried out the most important scientific studies of the Aral Sea in the period from 1899 to 1906; the scientist realized scientific projects on geography on the basis of which the practical solutions to the problems of socio-economic development of the region were proposed. The author concludes that science, scientific expeditions, researches of the Russian Geographical Society scientists influenced the political and economic course of imperial Russia in Central Asia. The article will allow to take a new look at the problems of colonization of imperial outskirts and will show the mechanisms of interaction of science, power, and society in the Central Asian region.

Оценка изменений речного стока Ленкоранской природной области

In the conditions of climate change and increase of anthropogenic load upon environment ,one of the main direction of scientific hydrological research in the nearest decades will be assessment of water resources. The paper presents the results of assessment of the Lenkoran Nature Region river water status change over the period from 1990 to 2017 based on observation data from 13 closing ranges. The computations were carried out with taking into account runoff data of the rivers. Considerable number of the points has short observation series or gaps in observations; therefore, we in advance have reduced short series to the single many-year period. Runoff from the territories that had not been covered by hydrometric measurements was determined by computations by the analog rivers’ runoff or by the runoff maps. When assessing water resources for a number of ranges affected by economic activities, we have used the data on water abstraction and disposal and results of researches on assessment of evaporation from the catchment surface. The carried out computations resulted in statement that natural average many-year annual runoff of the Lenkoran Nature Region rivers is 0.993 km3.

Application of HEC-HMS Model for Flow Simulation for Dungun River Basin

Abstract: The uncertainty in the weather will cause droughts and extreme floods. It can affect agriculture, natural resources, ecosystems, and losses. To overcome water-related problems resulting from climate change and careful care should be given to operations management, reservoirs and water catchment. The purpose of this study is to develop a rainfall runoff model and to assess the runoff potential for the catchment and to calibrate and validate the model and using the calibrated values for future hydrological research. The Hydrological Modelling System (HEC-HMS) was used for the river basin of Sungai Dungun (1463.34 km2), Terengganu. To compute infiltration, transform, and flow routing, methods like Initial and Constant, Clark Unit hydrograph and lag routing were chosen, respectively. The rainfall data for this study were obtained from the Department of Irrigation and Drainage Malaysia (DID), covering from year 2007 to 2018. There are three rainfall gauging stations and one stream flow data stations in the study area. Rainfall-runoff simulation had been conducted using different data set for calibration and validation. Preliminary data shows that there is a clear difference between the observed and simulated peak flows. Model calibration with optimization methods and sensitivity analysis was performed to obtain the optimal parameters for this catchment. The values of the parameters obtained from the calibration show a reasonable difference in peak flow. Model validation using optimized parameter values showed reasonable difference in peak flow. Generally the results of the study showed a good simulation between observed and estimated value with NSE = 0.85, R2 = 0.86, Relative Error Peak = -4.14% and Relative Error Volume = -22.5%

Fusion of In-Situ Soil Moisture and Land Surface Model Estimates Using Localized Ensemble Optimum Interpolation over China

Land data assimilation (DA) is an effective method to provide high-quality spatially and temporally continuous soil moisture datasets that are crucial in weather, climate, hydrological, and agricultural research. However, most existing land DA applications have used remote sensing observations, and are based on one-dimensional (1D) analysis, which cannot be directly employed to reasonably assimilate the recently expanded in-situ soil moisture observations in China. In this paper, a two-dimensional (2D) localized ensemble-based optimum interpolation (EnOI) scheme for assimilating in-situ soil moisture observations from over 2200 stations into land surface models (LSMs) is introduced. This scheme uses historical LSM simulations as ensemble samples to provide soil moisture background error covariance, allowing the in-situ observation information to be propagated to surrounding pixels. It is also computationally efficient because no additional ensemble simulations are needed. A set of ensemble sampling and localization length scale sensitivity experiments are performed. The EnOI performs best for in-situ soil moisture fusion over China with an ensemble sampling of hourly soil moisture from the previous 7 days and a localization length scale of 100 km. Following the evaluation, simulations for in-situ soil moisture fusion are also performed from May 2016 to September 2016. The EnOI analysis is notably better than that without in-situ observation fusion, as the wet bias of 0.02 m3 m−3 is removed, the root-mean-square error (RMSE) is reduced by about 37%, and the correlation coefficient is increased by about 25%. Independent evaluation shows that the EnOI analysis performs considerably better than that without fusion in terms of bias, and marginally better in terms of RMSE and correlation.

A review of hydrologic signatures and their applications

AbstractHydrologic signatures are quantitative metrics or indices that describe statistical or dynamical properties of hydrologic data series, primarily streamflow. Hydrologic signatures were first used in eco‐hydrology to assess alterations in flow regime, and have since seen wide uptake across a variety of hydrological fields. Their applications include extracting biologically relevant attributes of streamflow data, monitoring hydrologic change, analyzing runoff generation processes, defining similarity between watersheds, and calibrating and evaluating hydrologic models. Hydrologic signatures allow us to extract meaningful information about watershed processes from streamflow series, and are therefore seeing increasing use in emerging information‐rich areas such as global‐scale hydrologic modeling, machine learning, and large‐sample hydrology. This overview paper describes the background and development of hydrologic signature theory, reviews hydrologic signature use across a variety of applications, and discusses ongoing hydrologic signature research including current challenges.This article is categorized under: Science of Water > Science of Water

Development of a 250-m Downscaled Land Surface Temperature Data Set and its Application to Improving Remotely Sensed Evapotranspiration Over Large Landscapes in Northern China

Satellite-derived land surface temperature (LST) is critical for retrieving terrestrial evapotranspiration (ET); however, its availability is limited by low spatial resolution and inclement weather conditions. This study develops a spatiotemporal regression strategy that can downscale 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) LST product to 250-m resolution and simultaneously gap-fill the missing values. The proposed methodology synergistically uses random forest (RF) model and geographically weighted regression, which are, respectively, available for demonstrating the nonlinear correlation between LST and explanatory variables and for calibrating the RF-derived residuals. The study is conducted across a region of ~1.49 million square kilometers in northern China. The coupled model creates a 250-m spatial resolution LST product with the root-mean-square error (RMSE) of 2.32 and 1.87 K when compared with field observations and reference Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST, respectively. Meanwhile, it minimizes the constraint of LST availability due to inclement weather conditions with RMSE of 2.69 and 2.31 K relative to field observations and reference images, respectively. The results further reveal that remote-sensing-derived ET using the 250-m downscaled LST data is fairly accurate with the relative errors of 6%–9% as evaluated with flux measurements. The 250-m modeled ET retrievals exhibit a more intense hydrological response to the water use conditions compared with the 1-km remotely sensed ETs and Noah land surface model ETs. This study may benefit land surface hydrology research and water resource management.

Surface water–groundwater interactions in the Matusagaratí wetland, Panama

The Matusagarati wetland in the Panamanian Darien is one of the largest and most biologically diverse wetland ecosystems in Central America. Despite this, no hydrological studies have been conducted in the area due to its remoteness and difficult access. The aim of this research is to define the relationship between surface water and groundwater through the field and laboratory data obtained from the first monitoring network installed in the Matusagarati wetland. Variations registered in the isotopic relationships in surface water and groundwater show that the wetland receives contributions from rainfall as well as fluvial and estuarine water. There are areas of the wetland where one of these sources predominates, and others where they mix. In the wetland sector downstream the Tuira river, the groundwater presents a combination of freshwaters coming from Filo del Tallo and those coming from the Tuira river, whose high salinity reflects the estuary's influence. Meanwhile, in the upstream sector of the wetland, the groundwater is a mixture of river water and rainwater on the river´s levee but behind it, rainwater predominates. In all cases, groundwater has Rare Earth Elements patterns like those of the Tuira river, highlighting the importance of river flooding as a source and support system for the wetland groundwater. Hydrological research such as this, presents a valuable opportunity to provide sound scientific information to promote sustainable management and environmental conservation of this unique wetland that is rapidly being transformed to cattle pasture and palm and rice cultivation.

Development of bias-correction PERSIANN-CDR models for the simulation and completion of precipitation time series

The availability of continuous long-term precipitation time series with uniform spatial distribution has a significant role in hydrological studies and applications from simple water budget calculation to infrastructure design. However, in numerous parts of the world precipitation observations are sparse, patchy and uneven and hence hydrological research and hydroclimate-related projects are conducted with uncertainty. Therefore, filling gaps in precipitation time series and estimating precipitation in ungauged regions are of prime importance. Satellite-based precipitation measurement methods, among rainfall measuring tools, seem to be the most promising method to provide continuous and high spatiotemporal precipitation data. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) is one of the most commonly used precipitation retrieval algorithms, with long term data and high spatiotemporal resolutions that is employed in this study. Biases in the satellite-based precipitation data, as some studies have reported, demand the development of bias-correction models to enhance the accuracy of the satellite-based estimates. First, the performance of the PERSIANN-CDR algorithm is assessed based on 35 years of monthly, seasonal and annual observed precipitation at 49 stations as the benchmark in Fars province, Iran. Results indicate that while PERSIANN-CDR mimics the precipitation time series with a high degree of correlation, it underestimates the precipitation in the province with the Root Mean Squared Error (RMSE), Relative Mean Error (RME) and BIAS of (15.23 mm, 0.23 and 0.77), (37.68 mm, 0.24 and 0.76) and (96.07 mm, 0.24 and 0.76) for the monthly, seasonal and annual precipitation, respectively. Then, a set of bias-correction models is developed to enhance the PERSIANN-CDR estimates with the reduced monthly, seasonal and annual RMSE, RME and BIAS of (9.09 mm, −0.01 and 1.01), (19.36 mm, −0.01 and 1.01) and (41.23 mm, 0.0 and 1.0), respectively. The Bias-correction models are validated with the precipitation time series that were not used in the development phase. The validated models proved as an effective and robust tool in improving the accuracy of the PERSIANN-CDR datasets. We also demonstrate how the proposed models are used to estimate precipitation at ungauged sites and to fill in the gaps in the ground-based precipitation time series. The proposed models provide the practitioners, planners, and decision-makers with a huge precipitation data bank for various hydro-climatological projects in the study area.

Modeling River Discharge Using Automated River Width Measurements Derived from Sentinel-1 Time Series

Against the background of a worldwide decrease in the number of gauging stations, the estimation of river discharge using spaceborne data is crucial for hydrological research, river monitoring, and water resource management. Based on the at-many-stations hydraulic geometry (AMHG) concept, a novel approach is introduced for estimating river discharge using Sentinel-1 time series within an automated workflow. By using a novel decile thresholding method, no a priori knowledge of the AMHG function or proxy is used, as proposed in previous literature. With a relative root mean square error (RRMSE) of 19.5% for the whole period and a RRMSE of 15.8% considering only dry seasons, our method is a significant improvement relative to the optimized AMHG method, achieving 38.5% and 34.5%, respectively. As the novel approach is embedded into an automated workflow, it enables a global application for river discharge estimation using solely remote sensing data. Starting with the mapping of river reaches, which have large differences in river width over the year, continuous river width time series are created using high-resolution and weather-independent SAR imaging. It is applied on a 28 km long section of the Mekong River near Vientiane, Laos, for the period from 2015 to 2018.

Performance of GRACE Mascon Solutions in Studying Seismic Deformations

AbstractGRACE (Gravity Recovery and Climate Experiment) mascon (mass concentration) solutions are widely used in cryosphere science and hydrology research. Mascon solutions have been demonstrated to perform analogous to or better than spherical harmonic (SH) solutions at the basin scale. Although the mascon solutions are expected to facilitate an improved local recovery of seismic gravity signals of large earthquakes, the application of mascon solutions to earthquake analysis remains to be investigated. In this study, we examine the performance of the mascon solutions for seismic gravity signals induced by three M9 class (2011 Tohoku‐Oki, 2004 Sumatra, and 2010 Chile) and three smaller earthquakes and compare them with SH solutions and theoretical gravity changes modeled according to the dislocation theory. We analyze the coseismic and postseismic gravity changes and conclude that the mascon solutions contain information almost identical to the SH solutions and retrieve coseismic gravity change signals with resolutions equivalent to Gaussian filter radii of 210–270 km. The mascon solutions have strengthened gravity change signals of approximately twice the strength of the SH solutions. However, mascon solution cannot fully recover the complete seismic deformation since it is still a spatially “band‐limited” data set that uses the spatial smoothing in the aggressive regularization. We found that the mascon solutions contain “seismic‐like” gravity change signals several months before the Tohoku‐Oki and Chile earthquakes occurred.

Evaluation of multi-source precipitation products over the Yangtze River Basin

Precipitation products are good choices to complement ground observations in hydrological research, but their accuracy is uncertain in different areas. This study aims to evaluate the systematic error characteristics of four major precipitation products, namely, Climate Prediction Center MORPHing technique(CMORPH), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Global Land Data Assimilation System (GLDAS), and Tropical Rainfall Measuring Mission(TRMM) 3B42 v7, over the Yangtze River Basin in terms of estimating precipitation amounts and detecting events. The results show that the precipitation products have high spatial and seasonal heterogeneity of error characteristics, and the capability to capture rain occurrence decreases when rainfall intensity increases. GLDAS demonstrated the poorest performance, with the lowest correlation of 0.08 and the largest relative bias of over 25% underestimation. The possibility of GLDAS missing medium and heavy rains (>15 mm/d) reached 50%, and of falsely reporting light rainfall was up to 40%, while CMORPH outperformed the others with the highest consistency (0.39) against the gauge, the smallest root-mean-square error (RMSE) (10.28 mm), and the highest scores for most subregions. Generally, in this study GLDAS proved its inferiority to satellite-based precipitation products for hydrological applications over the Yangtze River Basin.

Stress testing as complement to climate scenarios: recharge scenarios to quantify streamflow drought sensitivity

Abstract. Precipitation deficits and temperature anomalies are often the main cause for low flows and summer streamflow droughts. However, where groundwater is the main contribution to sustain water availability and ecological integrity during dry spells, the role of recharge and catchment storage is crucial to understand streamflow drought sensitivity. Here we introduce recharge stress tests as complement to climate scenarios to characterize and quantify the streamflow drought sensitivities of catchments. The stress tests are presented by applying them to six headwater catchments in Switzerland with various catchment and streamflow characteristics. The stress tests drive the bucket-type hydrological model HBV in a framework, in which pre-drought recharge conditions can be decreased to test how catchments respond to and recover from drought. We identified an upper limit of stress test durations around 12 months as indicator of maximum recharge- and storage-memory for the study catchments. Varying response on stress testing across the catchments suggests different storage properties and thus different recovery times from drought. From the stress test simulations, we found up to 200 d longer summer streamflow droughts with additional streamflow deficits which account for up to 40 d of median flow. Using a worst-case pre-drought recharge in stress test simulation leads to minimum flow reductions of 50 %–80 % compared with the reference simulation. Based on the results we conclude with recommendations for further stress test research in drought hydrology.

A concerted research effort to advance the hydrological understanding of tropical páramos

AbstractPáramos, a neotropical alpine grassland‐peatland biome of the northern Andes and Central America, play an essential role in regional and global cycles of water, carbon, and nutrients. They act as water towers, delivering water and ecosystem services from the high mountains down to the Pacific, Caribbean, and Amazon regions. Páramos are also widely recognized as a biodiversity and climate change hot spots, yet they are threatened by anthropogenic activities and environmental changes. Despite their importance for water security and carbon storage, and their vulnerability to human activities, only three decades ago, páramos were severely understudied. Increasing awareness of the need for hydrological evidence to guide sustainable management of páramos prompted action for generating data and for filling long‐standing knowledge gaps. This has led to a remarkably successful increase in scientific knowledge, induced by a strong interaction between the scientific, policy, and (local) management communities. A combination of well‐established and innovative approaches has been applied to data collection, processing, and analysis. In this review, we provide a short overview of the historical development of research and state of knowledge of the hydrometeorology, flux dynamics, anthropogenic impacts, and the influence of extreme events in páramos. We then present emerging technologies for hydrology and water resources research and management applied to páramos. We discuss how converging science and policy efforts have leveraged traditional and new observational techniques to generate an evidence base that can support the sustainable management of páramos. We conclude that this co‐evolution of science and policy was able to successfully cover different spatial and temporal scales. Lastly, we outline future research directions to showcase how sustainable long‐term data collection can foster the responsible conservation of páramos water towers.

Applicability Evaluation of Multisource Satellite Precipitation Data for Hydrological Research in Arid Mountainous Areas

Global Satellite Mapping of Precipitation (GSMaP), Climate Hazards Group InfraRed Preconception with Station data (CHIRPS), Tropical Rain Measurement Mission Multisatellite Precipitation Analysis (TRMM 3B42 V7) and Rainfall Estimation from Soil Moisture Observations (SM2RAIN) are satellite precipitation products with high applicability, but their applicability in hydrological research in arid mountainous areas is not clear. Based on precipitation and runoff data, this study evaluated the applicability of each product to hydrological research in a typical mountainous basin (the Qaraqash River basin) in an arid region by using two methods: a statistical index and a hydrological model (Soil and Water Assessment Tool, SWAT). Simulation results were evaluated by Nash efficiency coefficient (NS), relative error (PBIAS) and determination coefficient (R2). The results show that: (1) The spatial distributions of precipitation estimated by these four products in the Qaraqash River basin are significantly different, and the multi-year average annual precipitation of GSMaP is 97.11 mm, which is the closest to the weather station interpolation results. (2) On the annual and monthly scales, GSMaP has the highest correlation (R ≥ 0.82) with the observed precipitation and the smallest relative error (BIAS < 6%). On the seasonal scale, the inversion accuracy of GSMaP in spring, summer and autumn is significantly higher than other products. In winter, all four sets of products perform poorly in estimating the actual precipitation. (3) Monthly runoff simulations based on SM2RAIN and GSMaP show good fitting (R2 > 0.6). In daily runoff simulation, GSMaP has the greatest ability to reproduce runoff changes. The study provides a reference for the optimization of precipitation image data and hydrological simulation in data-scarce areas.

NOAA Satellite Soil Moisture Operational Product System (SMOPS) Version 3.0 Generates Higher Accuracy Blended Satellite Soil Moisture

Soil moisture plays a vital role for the understanding of hydrological, meteorological, and climatological land surface processes. To meet the need of real time global soil moisture datasets, a Soil Moisture Operational Product System (SMOPS) has been developed at National Oceanic and Atmospheric Administration to produce a one-stop shop for soil moisture observations from all available satellite sensors. What makes the SMOPS unique is its near real time global blended soil moisture product. Since the first version SMOPS publicly released in 2010, the SMOPS has been updated twice based on the users’ feedbacks through improving retrieval algorithms and including observations from new satellite sensors. The version 3.0 SMOPS has been operationally released since 2017. Significant differences in climatological averages lead to remarkable distinctions in data quality between the newest and the older versions of SMOPS blended soil moisture products. This study reveals that the SMOPS version 3.0 has overwhelming advantages of reduced data uncertainties and increased correlations with respect to the quality controlled in situ measurements. The new version SMOPS also presents more robust agreements with the European Space Agency’s Climate Change Initiative (ESA_CCI) soil moisture datasets. With the higher accuracy, the blended data product from the new version SMOPS is expected to benefit the hydrological, meteorological, and climatological researches, as well as numerical weather, climate, and water prediction operations.

Corrigendum: Hydrology Research 50 (6), 1645–1664: Difference in the bed load transport of graded and uniform sediments during floods: An experimental investigation, Khabat Khosravi, Amir H. N. Chegini, Andrew D. Binns, Prasad Daggupati and Luca Mao, https://doi.org/10.2166/nh.2019.078

Corrigendum: <i>Hydrology Research</i> 50 (6), 1645–1664: Difference in the bed load transport of graded and uniform sediments during floods: An experimental investigation, Khabat Khosravi, Amir H. N. Chegini, Andrew D. Binns, Prasad Daggupati and Luca Mao, https://doi.org/10.2166/nh.2019.078

Delineation of groundwater flow and estimation of lake water flushing time using radium isotopes and geochemistry in an arid desert: The case of Badain Jaran Desert in western inner Mongolia (CHN)

The Badain Jaran Desert, located in western Inner Mongolia in northwestern China, is characterized by the coexistence of high megadunes and numerous lakes. The spatial distribution of groundwater systems and supply sources of lakes have yet to be elucidated. Ra isotopes and geochemistry of water samples from 17 lakes and their peripheral groundwater were investigated. The characteristics of the geochemical behaviors of Ra isotopes, including activities, activity ratios, main sources and sinks, and geochemistry both of groundwater and lake water, suggest that there are two groundwater flow systems in the studied area: the southern-central and the northern groundwater flow systems. The southern-central groundwater is supplied from mountains farther to the south and the northern groundwater supplied from areas farther to the north. We developed mass balance models for 228Ra in fresh and saline lakes. The equation for the saline lakes has three input terms: groundwater discharge, atmospheric deposition and diffusion from bottom sediments; and two outputs: radioactive decay and coprecipitation. The freshwater lake equation has one input term: radionuclide supply from groundwater; and two output terms: adsorption onto particles in the lake and radioactive decay. Mass balance calculations for 228Ra give discharge fluxes of 0.03 × 106–7.72 × 107 m3 yr−1 for the lakes. The flushing times of the lakes were estimated to be 0.05–9.23 yrs. The results of this study promote scientific understanding of the hydrologic cycle and water resource evaluation in the Badain Jaran Desert and provide a paradigm for radium isotope application in hydrological research in arid areas.

Using Remote Sensing Data‐Based Hydrological Model Calibrations for Predicting Runoff in Ungauged or Poorly Gauged Catchments

AbstractBecause remote sensing (RS) data are spatially and temporally explicit and available across the globe, they have the potential to be used for predicting runoff in ungauged catchments and poorly gauged regions, a challenging area of research in hydrology. There is potential to use remotely sensed data for calibrating hydrological models in regions with limited streamflow gauges. This study conducts a comprehensive investigation on how to incorporate gridded remotely sensed evapotranspiration (AET) and water storage data for constraining hydrological model calibration in order to predict daily and monthly runoff in 30 catchments in the Yalong River basin in China. To this end, seven RS data calibration schemes are explored and compared to direct calibration against observed runoff and traditional regionalization using spatial proximity to predict runoff in ungauged catchments. The results show that using bias‐corrected remotely sensed AET (bias‐corrected PML‐AET data) for constraining model calibration performs much better than using the raw remotely sensed AET data (nonbias‐corrected AET obtained from PML model estimate). Using the bias‐corrected PML‐AET data in a gridded way is much better than using lumped data and outperforms the traditional regionalization approach especially in headwater and large catchments. Combining the bias‐corrected PML‐AET and GRACE water storage data performs similarly to using the bias‐corrected PML‐AET data only. This study demonstrates that there is great potential in using bias‐corrected RS‐AET data to calibrating hydrological models (without the need for gauged streamflow data) to estimate daily and monthly runoff time series in ungauged catchments and sparsely gauged regions.

Catchment-scale, high-resolution, hydraulic models and habitat maps – a salmonid's perspective

The advent of remotely-sensed high-resolution imagery has led to the development of methods to map river bathymetry. In this study, we utilized high-resolution imagery to map river depth and quantify hydraulic habitats at the catchment scale (>1000 km2) during low flows. Using 0.3-m airborne multi-spectral imagery (resampled to 0.5 m), we mapped contiguous river depth (124 km) within a well-established Atlantic Salmon (Salmo salar) and Brook Trout (Salvelinus fontinalis) river – The Little Southwest Miramichi, New Brunswick. We built image-derived depth maps with and without field data calibration. The model without field calibration data (flow resistance equation‐based imaging of river depths) accurately described river depths (R 2 = 72.7; RMSE = 0.167 m; n = 762); however, it overestimated shallow depths. The field-calibrated model removed shallow depth errors (R 2 = 76.4; RMSE = 0.155 m; n = 762). We mapped velocity using a relationship between river geometry and discharge, and coalesced the field-calibrated depth and velocity maps to create Froude and Reynolds number maps. Finally, we performed an unsupervised classification model to delineate the hydraulically relevant habitat units for salmonids. This approach provides an unprecedented view of catchment-scale hydraulic habitats that will advance both hydrological process research and river resources management.

Estimating Regional Soil Moisture Distribution Based on NDVI and Land Surface Temperature Time Series Data in the Upstream of the Heihe River Watershed, Northwest China

Temporal and spatial variability of soil moisture has an important impact on hydrological processes in mountainous areas. Understanding such variability requires soil moisture datasets at multiple temporal and spatial scales. Remote sensing is a very effective method to obtain surface (~5 cm depth) soil moisture at the regional scale but cannot directly measure soil moisture at deep soil layers (&gt;5 cm depth) currently. This study chose the upstream of the Heihe River Watershed in the Qilian Mountain Ranges in Northwest China as the study area to estimate the profile soil moisture (0–70 cm depth) at the regional scale using satellite Vegetation Index (NDVI) and Land Surface Temperature (LST) products. The study area was divided into 31 zones according to the combination of altitude, vegetation and soil type. Long-term in situ soil moisture observation stations were set up at each of the zones. Soil moisture probe, ECH2O, was used to collect soil moisture at five layers (0–10, 10–20, 20–30, 30–50 and 50–70 cm) continuously. Multiple linear regression equations of time series MODIS (Moderate-resolution Imaging Spectroradiometer) NDVI, LST and soil moisture were developed for each of the five soil layers at the 31 zones to estimate the soil moisture (0–70 cm) on a regional scale with a spatial resolution of 1 km2 and a temporal resolution of 16-d from October, 2013 to September, 2016. The correlation coefficient R of the regression equations was between 0.47 and 0.94, the RMSE was 0.03, indicating that the estimation method based on the MODIS NDVI and LST data was suitable and could be applied to alpine mountainous areas with complex topography, soil and vegetation types. The overall pattern of soil moisture spatial distribution indicated that soil moisture was higher in the eastern region than in the western region, and the soil moisture content in the whole study area was 14.5%. The algorithm and results provide novel applications of remote sensing to support soil moisture data acquisition and hydrological research in mountainous areas.