Search

Satellite- and reanalysis-based precipitation products are important data source for precipitation, particularly in areas with a sparse gauge network. Here, five open-access precipitation products, including the newly released China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) reanalysis dataset and four widely used bias-adjusted satellite precipitation products [SPPs; i.e., Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 Version 7 (TMPA 3B42V7), Climate Prediction Center (CPC) morphing technique satellite-gauge blended product (CMORPH-BLD), Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR)], were assessed. These products were first compared with the gauge observed data collected for the upper Huaihe River basin, and then were used as forcing data for streamflow simulation by the Xin’anjiang (XAJ) hydrological model under two scenarios with different calibration procedures. The performance of CMADS precipitation product for the Chinese mainland was also assessed. The results show that: (1) for the statistical assessment, CMADS and CMORPH-BLD perform the best, followed by TMPA 3B42V7, CHIRPS, and PERSIANN-CDR, among which the correlation coefficient (CC) and root-mean-square error (RMSE) values of CMADS are optimal, although it exhibits certain significant negative relative bias (BIAS; −22.72%); (2) CMORPH-BLD performs the best in capturing and detecting rainfall events, while CMADS tends to underestimate heavy and torrential precipitation; (3) for streamflow simulation, the performance of using CMADS as input is very good, with the highest Nash-Sutcliffe efficiency (NSE) values (0.85 and 0.75 for calibration period and validation period, respectively); and (4) CMADS exhibits high accuracy in eastern China while with significant negative BIAS, and the performance declines from southeast to northwest. The statistical and hydrological evaluations show that CMADS and CMORPH-BLD have high potential for observing precipitation. As high negative BIAS values showed up in CMADS evaluation, further study on the error sources from original data and calibration algorithms is necessary. This study can serve as a reference for selecting precipitation products in data-scarce regions with similar climates and topography in the Global Precipitation Measurement (GPM) era.

Mercury is, because of its high vapor pressure and its prevalence in the atmosphere as atoms, an interesting geophysical tracer gas, also with potential archaeological applications. According to historical records dating back 2200 years, the mausoleum chamber of the “Terracotta Army Emperor” Qin in Xi´an, China, contains large amounts of liquid mercury, considered as an elixir of life at the time. We here report on measurements of the atmospheric contents of atomic mercury above the tomb mound performed with a mobile differential absorption lidar (light detection and ranging) system. Our measurements, which were performed from three different locations around the mound, indeed indicate elevated atmospheric mercury levels, with localizations, which correlate with previous in situ soil sampling results. Concentrations up to 27 ng/m3 were observed, significantly higher than the typical general pollutant level in the area which was found to be around 5–10 ng/m3. An out-flux of about 5×10−8 kg/s was estimated. Highly volatile mercury may be escaping through cracks, which developed in the structure over time, and our investigation supports ancient chronicle records on the tomb, which is believed never to have been opened/looted. Our findings also have bearings on the proposed use of mercury as a tracer gas for valuable ores and geothermal resource exploration, and also bring problematics around reliable nuclear waste long-term underground storage to mind.

Precipitation in arid mountainous areas is characterized by low rainfall intensity and large spatial heterogeneity, which challenges satellite-based monitoring by the spaceborne sensors. This study aims to comparatively evaluate the detection ability of spatiotemporal patterns and extremes of rainfall by a range of mainstream satellite precipitation products [TMPA, Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), and PERSIANN–Climate Data Record (PERSIANN-CDR)] over a typical arid mountainous basin of China, benchmarking against rain gauge data from 2000 to 2015. Results showed that satellite precipitation estimates had relatively low accuracy at the daily scale, while a significant improvement of correlation coefficient (CC; >0.6) and a significant reduction of relative root-mean-square error (RRMSE

Evaluating the accuracy of precipitation products is essential for many applications. The traditional method for evaluation is to calculate error metrics of products with gauge measurements that are considered as ground-truth. The multiplicative triple collocation (MTC) method has been demonstrated powerful in error quantification of precipitation products when ground-truth is not known. This study applied MTC to evaluate five precipitation products in Germany: two raw satellite-based (CMORPH and PERSIANN), one reanalysis (ERA-Interim), one soil moisture-based (SM2RAIN-ASCAT), and one gauge-based (REGNIE) products. Evaluation was performed at the 0.5° -daily spatial-temporal scales. MTC involves a log transformation of data, necessitating dealing with zero values in daily precipitation. Effects of 12 different strategies for dealing with zero value on MTC results were investigated. Seven different triplet combinations were tested to evaluate the stability of MTC. Results showed that different strategies for replacing zero values had considerable effects on MTC-derived error metrics particularly for root mean squared error (RMSE). MTC with different triplet combinations generated different error metrics for individual products. MTC-derived correlation coefficient (CC) was more reliable than RMSE. It is more appropriate to use MTC to compare the relative accuracy of different precipitation products. Based on CC with unknown truth, MTC with different triplet combinations produced the same ranking of products as the traditional method. A comparison of results from MTC and the classic TC with additive error model showed the potential limitation of MTC in arid area or dry time periods with large ratio of zero daily precipitation.

Seasonal snow cover in mountainous regions will affect local climate and hydrology. In this study, we assessed the role of altitude in determining the relative importance of temperature and precipitation in snow cover variability in the Central Tianshan Mountains. The results show that: (a) in the study area, temperature has a greater influence on snow cover than precipitation during most of the time period studied and in most altitudes. (b) In the high elevation area, there is a threshold altitude of 3,900 ± 400 m, below which temperature is negatively correlated whereas precipitation is positively correlated to snow cover, and above which the situation is the opposite. Besides, this threshold altitude decreases from snow accumulated period to snow stable period and then increases from snowmelt period to snow-free period. (c) Below 2,000 m, there is another threshold altitude of 1,400 ± 100 m during the snow stable period, below (above) which precipitation (temperature) is the main driver of snow cover. (Less)

Extreme climate events such as severe droughts and floods have become more frequent and widespread in the 21st Century. Recent studies have revealed the tele-connections between Madden–Julian Oscillation (MJO) and extreme precipitation over different regions such as South America, India and China. This study investigates the influence of MJO on global extreme dry and wet conditions, and how the strength of the relationship changes across the MJO phases over the globe. The Evaporative Stress Index (ESI) calculated from global GLEAM evapotranspiration dataset is used to represent extreme dry and wet conditions. Strong correlations between MJO and extreme dry and wet conditions are found, particularly over monsoon regions such as South Asia, South America and East Africa. The underlying mechanism of the influence of MJO on extreme dry and wet conditions is associated with the variation of precipitation, air temperature and soil moisture modulated by the MJO. The study suggests that MJO impacts on extreme dry and wet conditions should be taken into account in investigation of droughts/floods around the world particularly over monsoon areas.

This study conducted the global sensitivity analysis of the APSIM-Oryza rice growth model under eight climate conditions and two CO2 levels using the extended Fourier Amplitude Sensitivity Test method. Two output variables (i.e. total aboveground dry matter WAGT and dry weight of storage organs WSO) and twenty parameters were analyzed. The ±30% and ±50% perturbations of base values were used as the ranges of parameter variation, and local fertilization and irrigation managements were considered. Results showed that the influential parameters were the same under different environmental conditions, but their orders were often different. Climate conditions had obvious influence on the sensitivity index of several parameters (e.g. RGRLMX, WGRMX and SPGF). In particular, the sensitivity index of RGRLMX was larger under cold climate than under warm climate. Differences also exist for parameter sensitivity of early and late rice in the same site. The CO2 concentration did not have much influence on the results of sensitivity analysis. The range of parameter variation affected the stability of sensitivity analysis results, but the main conclusions were consistent between the results obtained from the ±30% perturbation and those obtained the ±50% perturbation in this study. Compared with existing studies, our study performed the sensitivity analysis of APSIM-Oryza under more environmental conditions, thereby providing more comprehensive insights into the model and its parameters.

Using dense soil moisture (SM) measurements in the upper Huai River basin of China, this study evaluated the spatial patterns of L-band satellite-based SM products, including Soil Moisture Active Passive (SMAP) L3, Soil Moisture and Ocean Salinity (SMOS) L3 and the European Space Agency’s Climate Change Initiative (ESA CCI) SM products. The mean difference (MD), root mean squared error (RMSE), unbiased root mean square error (ubRMSE) and Pearson correlation coefficient (R), were used in the evaluation. The evaluation results presented that SMAP and ESA CCI products can well capture the temporal variation of SM at single points quite well, with average R values of 0.51 and 0.46, respectively. And SMAP had the highest overall accuracy among the three satellite-based products in study area. We also analyzed the correlations between the four accuracy indexes and six environmental factors including the proportions of five land use/land cover types (i.e. water bodies, paddy fields, construction land, dryland and forest) and the average NDVI (Normalized Difference Vegetation Index) in 2016 in each grid. Analysis showed that the proportions of paddy fields and water bodies in each grid had significant positive correlations with MD, RMSE and ubRMSE, while NDVI, and the proportions of dryland and construction land had significant negative correlations with these three indexes. The significant correlations between the accuracy of SMAP, SMOS and ESA CCI SM products and environmental factors indicate that there exist systematic biases in these products, which can provide valuable insights into algorithm improvements.

Spatial and temporal patterns of trends in annual and seasonal precipitation over Germany during 1951–2013 were analysed using the ensemble empirical mode decomposition (EEMD) method. Three widely used and recognized high-resolution observation-based gridded precipitation products, the Climatic Research Unit (CRU) time-series data, Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis data and the EU-FP6 project ENSEMBLES derived data set (EOBS), were used and compared. Comparison among different products showed that the CRU used a considerably lower number of gauge stations (maximum 36) in the construction of gridded product over Germany and should be used with great caution for other applications, and overall CRU presented less precipitation and smaller inter-annual variability; GPCC and EOBS agreed well in the spatial and temporal distribution of seasonal and annual precipitation, as well as the identified trends. The spatiotemporal analysis of trends showed that trends in precipitation during 1951–2013 were, in most cases, highly nonlinear and varying over time. In particular, the spring, summer and autumn precipitation showed large variations in trends. Therefore, the assumption of linear trends on which previous studies were based is invalid. Annual precipitation showed increasing trends in over 66% of Germany (particularly northern and eastern Germany), and the magnitude of increasing trends were generally enhanced over time, e.g. GPCC showed that the average magnitude of increasing trends rose from 6.3% for the 1951–1980 period to 12% for the 1951–2013 period. There were considerable spatial and temporal variabilities in trends in seasonal precipitation totals over Germany. This study is among one of the first studies applying the EEMD method to a comprehensive analysis of time-varying trends in precipitation over Germany, which is expected to improve our understanding of the complex and nonlinear regional climate system. (Less)

Rapid warming in northern high latitudes during the past two decades may have profound impacts on the structures and functioning of ecosystems. Understanding how ecosystems respond to climatic change is crucial for the prediction of climate-induced changes in plant phenology and productivity. Here we investigate spatial patterns of polynomial trends in ecosystem productivity for northern (> 30 °N) biomes and their relationships with climatic drivers during 2000–2018. Based on a moderate resolution (0.05°) of satellite data and climate observations, we quantify polynomial trend types and change rates of ecosystem productivities using plant phenology index (PPI), a proxy of gross primary productivity (GPP), and a polynomial trend identification scheme (Polytrend). We find the yearly-integrated PPI (PPIINT) shows a high degree of agreement with an OCO-2-based solar‐induced chlorophyll fluorescence GPP product (GOSIF-GPP) for distinct spatial patterns of trend types of ecosystem productivities. The averaged slope for linear trends of GPP is found positive across all the biomes, among which deciduous broadleaved and evergreen needle-leaved forests show the highest and lowest rates respectively. The evergreen needle-leaved forests, low shrub, and permanent wetland show linear trends in PPIINT over more than 50% of the covered area and permanent wetland also shows a large fraction of the area with the quadratic and cubic trends. Spatial patterns of linear trends for growing season sum of temperature, precipitation, and photosynthetic active radiation have been quantified. Based on the partial correlations between PPIINT and climate drivers, we found that there is a consistent shift of dominant drivers from temperature or radiation to precipitation across all the biomes except the permeant wetland when the trend type of ecosystem productivity changes from linear to non-linear. This may imply precipitation changes in recent years may determine the linear or non-linear responses of ecosystem productivity to climate change. Our results highlight the importance of understanding how changes in climatic drivers may affect the overall responses of ecosystems productivity. Our findings will facilitate the sustainable management of ecosystems accounting for the resilience of ecosystem productivity and phenology to future climate change.

Soil moisture content is an important hydrological and climatic variable with applications in a wide range of domains. The high spatial variability of soil moisture cannot be well captured from conventional point-based in-situ measurements. Remote sensing offers a feasible way to observe spatial pattern of soil moisture from regional to global scales. Microwave remote sensing has long been used to estimate Surface Soil Moisture Content (SSMC) at lower spatial resolutions (>1km), but few accurate options exist in the higher spatial resolution (

Accurate and timely monitoring of streamflow and its variation is crucial for water resources management in watersheds. This study aimed at evaluating the performance of two process-driven conceptual rainfall-runoff models (HBV: Hydrologiska Byrans Vattenbalansavdelning, and NRECA: Non Recorded Catchment Areas) and seven hybrid models based on three artificial intelligence (AI) methods (adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), and group method of data handling (GMDH)) in simulating streamflow in four river basins in Indonesia. HBV and NRECA were developed based on precipitation data. Various combinations of 1-month lagged precipitation data together with outputs of HBV and NRECA were used for developing ANFIS and SVM models, and the best results of ANFIS and SVM formed the inputs to GMDH. Results showed that AI-based hybrid models have generally led to more accurate streamflow estimates compared with HBV and NRECA, and the GMDH model had the best performance at Cipero, Kedungdowo, Notog, and Sukowati stations, with RMSEs of 12.21, 6.07, 20.35, and 24.2 m3 s−1, respectively. More accurate estimation of peak values in training set at Cipero and Sukowati stations, and in both training and testing sets at Kedungdowo station was another advantage of GMDH. Hybrid models based on AI methods can be suitable alternatives to hydrological models, particularly in watersheds where there is a lack of measured data (e.g. climatic parameters, land cover-plant growth data, soil data, stream conditions, and properties of groundwater aquifers), provided that appropriate inputs are used.

Amongst the impacts of climate change, those arising from extreme hydrological events are expected to cause the greatest impacts. To assess the changes in temperature and precipitation and their impacts on the discharge in the upper Yangtze Basin from pre-industrial to the end of 21st century, four hydrological models were integrated with four global climate models. Results indicated that mean discharge was simulated to increase slightly for all hydrological models forced by all global climate models during 1771&ndash, 1800 and 1871&ndash, 1900 relative to the 1971&ndash, 2000 reference period, whereas the change directions in mean discharge were not consistent among the four global climate models during 2070&ndash, 2099, with increases from HadGEM2-ES and MIROC5, and decreases from GFDL-ESM2M and IPSL-CM5A-LR. Additionally, our results indicated that decreases in precipitation may always result in the decrease in mean discharge, but increases in precipitation did not always lead to increases in discharge due to high temperature rise. The changes in extreme flood events with different return intervals were also explored. These extreme events were projected to become more intense and frequent in the future, which could have potential devastating impacts on the society and ecosystem in this region.

The multiple-year drought that started in 2011 and reached climax in 2015 was the most severe and prolonged one in the semiarid northeastern (NE) Brazil in recent decades. This study aimed to investigate the reservoir surface water volume (SWV) variation in NE Brazil from 2009 to 2017 in four representative regions covering a total area of approximately 10,000 km2 there and encompassing 2,140 reservoirs (areas range from 0.003 to 21 km2). High-resolution (10 m) digital elevation models (DEMs) were generated from the TanDEM-X data acquired during October–December 2015 to represent the reservoirs' bathymetric maps. The water extents in the reservoirs were delineated from high-resolution (6.5 m) RapidEye images acquired during 2009–2017. The combination of the aforementioned two variables yielded reservoir SWV with an accuracy of 0.64 × 106–1.06 × 106 m3, corresponding to 3.1%–5.6% of the maximum SWV in the reservoirs. The results showed that: 1) 81%–99% of the reservoirs in the four regions were from the groups with maximum water extent 50 ha and contributed 40%–98% to the regional SWV; 2) From 2009 to 2017, reservoir SWV in the four regions decreased at the rates of 2.3 × 106–17.8 × 106 m3/year; and 3) The SWV in the reservoirs responded differently to the regional terrestrial water budget, i.e. the differences between precipitation and evapotranspiration (P-ET). This study filled the data gap of bathymetric maps for the 2140 reservoirs, regardless of their sizes and macrophyte coverage. The SWV variations derived in those reservoirs over a period covering the recent drought can support better preparedness for drought in NE Brazil and better understanding of the regional hydrology in semi-arid regions.

The rapid expansion of oil palm is a major driver of deforestation and other associated damage to the climate and ecosystem in tropical regions, especially Southeast Asia. It is therefore necessary to precisely detect and monitor oil palm plantations to safeguard the ecosystem services and biodiversity of tropical forests. Compared with optical data, which are vulnerable to cloud cover, the Sentinel-1 dual-polarization C-band synthetic aperture radar (SAR) acquires global observations under all weather conditions and times of day and shows good performance for oil palm detection in the humid tropics. However, because accurately distinguishing mature and young oil palm trees by using optical and SAR data is difficult and considering the strong dependence on the input parameter values when detecting oil palm plantations by employing existing classification algorithms, we propose an innovative method to improve the accuracy of classifying the oil palm type (mature or young) and detecting the oil palm planting area in Sumatra by fusing Landsat-8 and Sentinel-1 images. We extract multitemporal spectral characteristics, SAR backscattering values, vegetation indices, and texture features to establish different feature combinations. Then, we use the random forest algorithm based on improved grid search optimization (IGSO-RF) and select optimal feature subsets to establish a classification model and detect oil palm plantations. Based on the IGSO-RF classifier and optimal features, our method improved the oil palm detection accuracy and obtained the best model performance (OA = 96.08% and kappa = 0.9462). Moreover, the contributions of different features to oil palm detection are different; nevertheless, the optimal feature subset performed the best and demonstrated good potential for the detection of oil palm plantations.

Permafrost extends 40% of the Qinghai-Tibet Plateau (QTP), a region which contains the headwaters of numerous major rivers in Asia. As an aquiclude, permafrost substantially controls surface runoff and its hydraulic connection with groundwater. The freeze–thaw cycle in the active layer significantly impacts soil water movement direction, velocity, storage capacity, and hydraulic conductivity. Under the accelerating warming on the QTP, permafrost degradation is drastically altering regional and even continental hydrological regimes, attracting the attention of hydrologists, climatologists, ecologists, engineers, and decision-makers. A systematic review of permafrost hydrological processes and modeling on the QTP is still lacking, however, leaving a number of knowledge gaps. In this review, we summarize the current understanding of permafrost hydrological processes and applications of some permafrost hydrological models of varying complexity at different scales on the QTP. We then discuss the current challenges and future opportunities, including observations and data, the understanding of processes, and model realism. The goal of this review is to provide a clear picture of where we are now and to describe future challenges and opportunities. We concluded that more efforts are needed to conduct long-term field measurements, employ more advanced observation technologies, and develop flexible and modular models to deepen our understanding of permafrost hydrological processes and to improve our ability to predict the future responses of permafrost hydrology to climate changes.

Projected increases in temporal precipitation variability, including intra-annual and interannual variability, will likely have important impacts on terrestrial ecosystem productivity. The direction and magnitude of these impacts and how they vary across biomes, however, remain largely uncertain. Here, we review published literature that investigated the effect of different characteristics of temporal precipitation variability on vegetation productivity. We first reviewed commonly used methods, including manipulation experiments, process-based modeling, and data-driven analysis, and further discussed their strengths and limitations. Then, we summarized state-of-the-art research on this topic by categorizing the results based on the characteristics of temporal precipitation variability. Given the same amount of growing season precipitation, a more extreme precipitation regime, characterized as fewer but larger precipitation events, tends to have a negative impact on vegetation productivity of most ecosystems except xeric grasslands and wet-cold forests. Precipitation in the early growing season was found to be particularly important to vegetation productivity. Greater interannual precipitation variability tends to decrease vegetation productivity, but the reported patterns are complex, as both concave-up and concave-down precipitation-productivity relations were found. Despite the progress made so far, critical challenges and knowledge gaps remain, such as the global-scale impacts across different biomes, the role of biological adaption, and the contribution of individual precipitation events. Future research needs to combine manipulation experiments across a broad spectrum of ecosystem types and environmental gradients with model-data integration strategies to disentangle the interactions between abiotic and biotic factors controlling vegetation responses to precipitation variability. This article is categorized under: Science of Water > Hydrological Processes Water and Life > Nature of Freshwater Ecosystems. (Less)

Glacier retreat caused by global warming alters the hydrological regime and poses far-reaching challenges to water resources and nature conservation of the headwater of Yangtze River, and its vast downstream regions with dense population. However, there is still lack of a robust modeling framework of the “climate-glacier-streamflow” in this water tower region, to project the future changes of glacier mass balance, glacier geometry, and the consequent impacts on runoff. Moreover, it is imperative to use the state-of-the-art sixth phase Coupled Model Intercomparison Project (CMIP6) to assess glacio-hydrology variations in future. In this study, we coupled a glacio-hydrological model (FLEXG) with a glacier retreat method (Δh-parameterization) to simulate glacio-hydrological processes in the Dongkemadi Glacier (over 5155 m.a.s.l), which has the longest continuous glacio-hydrology observation on the headwater of Yangtze River. The FLEXG-Δh model was forced with in-situ observed meteorological data, radar ice thickness, remote sensing topography and land cover data, and validated by measured runoff. The results showed that the model was capable to simulate hydrological processes in this glacierized basin, with Kling-Gupta efficiency (IKGE) of daily runoff simulation 0.88 in calibration and 0.70 in validation. Then, forcing by the bias-corrected meteorological forcing from the eight latest CMIP6 Earth system models under two climate scenarios (RCP2.6 and RCP8.5), we assessed the impact of future climate change on glacier response and its hydrological effects. The results showed that, to the end of simulation in 2100, the volume of the Dongkemadi Glacier would continuously retreat. For the RCP2.6 and RCP8.5 scenarios, the glacier volume will decrease by 8.7 × 108 m3 (74%) and 10.8 × 108 m3 (92%) respectively in 2100. The glacier runoff will increase and reach to peak water around 2060 to 2085, after this tipping point water resources will likely decrease.

Laser-induced fluorescence is a powerful measurement method for determining the concentration of organic pollutants as well as the amount of algae in water. It can be applied in remote sensing of natural waters and has the advantages of high speed and sensitivity. In this paper, we present a compact handheld fluorosensor system for water quality assessment. The power of the technique is demonstrated in measurements at a river system in South China.

Model realism testing is of vital importance in science of hydrology, in terms of realistic representation of hydrological processes and reliability of future prediction. We conducted three modeling case studies in cold regions of China, i.e. the upper Heihe River basin, the Urumqi Glacier No.1 basin, and the Yigong Zangbu River basin, to test the importance of stepwise modeling and internal fluxes validation to improve model realism.In the upper Heihe River basin, we used four progressively more complex hydrological models (FLEXL, FLEXD, FLEXT0 and FLEXT), to stepwisely account for distributed forcing inputs, tailor-made model structure for different landscapes, and the realism constraints of parameters and fluxes. We found that the stepwise modeling framework helped hydrological processes understanding, and the tailor-made model structure and realism constraints improved model transferability to two nested basins.In the Urumqi Glacier No. 1 basin, with 52% of the area covered by glaciers, we developed a conceptual glacier-hydrological model (FLEXG) and tested its performance to reproduce the hydrograph, and separate the discharge into contributions from glacier and nonglacier areas, and establish estimates of the annual glacier mass balance (GMB), the annual equilibrium line altitude (ELA), and the daily snow water equivalent (SWE). We found that the FLEXG model, involving effects of topography aspect, was successfully transferred and upscaled to a larger catchment without recalibration.In the Yigong Zangbu River basin, with 41.4% glacier area, we designed three models (FLEXD, FLEX-S, FLEX-SG) to stepwisely understand the impact of snow, glacier to reproduce historic streamflow. We found that by involving snow and glacier modules, the model performance was dramatically improved. Although the daily streamflow of FLEX-SG reached up to 0.93 Kling-Gupta Efficiency (KGE) in calibration, it significantly overestimated snow cover area (SCA) and glacier mass balance (GMB). With satellite measured precipitation lapse rate, we improved FLEX-SG model realism not only to reproduce hydrography but also SCA and GMB.

Hydrological modelling is an important tool to improve our understanding of hydrological processes of river basins and to predict impacts of climate change and environmental change on water resources. Precipitation is a key component of the hydrological cycle, and the most important driver/input data for hydrological models. Accurate precipitation measurements at desirable temporal and spatial resolution are essential for achieving reasonable performance of hydrological modelling. Compared to the conventional measurements from point-based rain gauge stations, remote sensing of precipitation with satellite sensors and ground-based radar can expand observational coverage and provide regional precipitation at varying temporal and spatial resolutions. Radars can provide sampling at very high resolution but also tend to contain significant errors in precipitation estimates. The Deutscher Wetterdienst (DWD; German Weather Service) developed the RADOLAN (RADar-OnLine-ANeichung) method (a real-time, gauge-adjustment and correction procedure) to generate precipitation estimates (termed as RADOLAN product) from the German Doppler radar network. More recently (2017), the DWD published a reanalysis of radar data to generate RADKLIM (RADarKLIMatologie) precipitation product using upgraded correction algorithms and additional offline gauge adjustment. This study presents the first assessment of the performance of two high spatial resolution (1 km) radar-based precipitation products (RADOLAN and RADKLIM) in streamflow simulation using the hydrological model SWAT (Soil and Water Assessment Tool) in Germany. We also evaluate the performance of conventional point-based rain gauge data and a satellite precipitation product in driving SWAT for streamflow simulation. The selected satellite product is CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) because of its well reported good performance and the relative higher spatial resolution (0.05°). The Vils Basin located in Bavaria, Germany is chosen as the study area. Performance of investigated precipitation product is assessed by comparing simulated streamflow using calibrated SWAT model against measured streamflow at basin outlet at both daily and monthly time scales. The model calibration is performed using the SWAT-CUP program with measured streamflow. Different calibration procedures are also investigated to analyze the influence on model performance. This study presents and discusses the accuracy and uncertainty of using ground-based radar and satellite precipitation products in driving SWAT model for daily and monthly streamflow simulation. Our findings are expected to provide beneficial feedback to product developers for further improvements, and to inform local end-users about the quality of investigated precipitation products.

Precipitation is an important component of the water cycle. Precipitation is characterized with high temporal and spatial variability. Accurate measurements of precipitation at high spatiotemporal resolution are essential for many applications in the fields of hydrology, meteorology and ecology. The traditional rain gauge stations provide direct measurements of rainfall at the surface but at a limited scale; rain gauge measurements are often considered as point-based measurements that are insufficient to represent the spatial variability of rainfall over a certain region, especially in the case of sparse rain gauge network. Satellite remote sensing has been developing with great ability of being used for estimating various water cycle components at different temporal and spatial scales. Considerable efforts have been made to develop satellite precipitation products at different spatial and temporal resolutions over the global or quasi-global scale. The majority of global/quasi-global precipitation products are at the spatial resolution of 0.25° (~25 km) with very few products at 0.05°-0.10° resolution. The usefulness of satellite precipitation products has been increasingly recognized but the relative coarse spatial resolution is still a limitation for many applications such as hydrological modelling at basin scales that generally require precipitation data at a desirable higher spatial resolution (e.g. 1 km). Over recent years, numerous spatial downscaling procedures/methods have been proposed to obtain precipitation products at higher spatial resolution. The relationships between precipitation and various auxiliary land-surface variables were explored and incorporated into spatial downscaling procedures using a large range of regression algorithms. Advanced machine learning and geostatistical methods have also been innovatively used to develop spatial downscaling procedures. The aim of this study is to present a comprehensive review of studies on spatial downscaling of satellite precipitation products over the recent years. We will summarize the proposed spatial downscaling methods, investigated auxiliary land-surface variables and the evaluation strategy. The performance of spatial downscaling methods in studied regions and their applications will be compared and discussed in terms of advantages and limitations. Finally, we will conclude this paper with outlook on future research needs and associated challenges about spatial downscaling of satellite precipitation products.

Both flood magnitude and frequency might change under the changing environment. In this study, a procedure combining statistical methods, flood frequency analysis and attribution analysis was proposed to investigate the response of floods to urbanization and precipitation change in the Qinhuai River Basin, an urbanized basin located in Southeast China, over the period from 1986 to 2013. The Mann–Kendall test was employed to detect the gradual trend of the annual maximum streamflow and the peaks over threshold series. The frequency analysis was applied to estimate the changes in the magnitude and frequency of floods between the baseline period (1986–2001) and urbanization period (2002–2013). An attribution analysis was proposed to separate the effects of precipitation change and urbanization on flood sizes between the two periods. Results showed that: (1) there are significant increasing trends in medium and small flood series according to the Mann–Kendall test; (2) the mean and threshold values of flood series in the urbanization period were larger than those in the baseline period, while the standard deviation, coefficient of variation and coefficient of skewness of flood series were both higher during the baseline period than those during the urbanization period; (3) the flood magnitude was higher during the urbanization period than that during the baseline period at the same return period. The relative changes in magnitude were larger for small floods than for big floods from the baseline period to the urbanization period; (4) the contributions of urbanization on floods appeared to amplify with the decreasing return period, while the effects of precipitation diminish. The procedure presented in this study could be useful to detect the changes of floods in the changing environment and conduct the attribution analysis of flood series. The findings of this study are beneficial to further understanding interactions between flood behavior and the drivers, thereby improving flood management in urbanized basins.

A novel mobile laser radar system was used for mapping gaseous atomic mercury (Hg0) atmospheric pollution in the Wanshan district, south of Tongren City, Guizhou Province, China. This area is heavily impacted by legacy mercury from now abandoned mining activities. Differential absorption lidar measurements were supplemented by localized point monitoring using a Lumex RA-915M Zeeman modulation mercury analyzer. Range-resolved concentration measurements in different directions were performed. Concentrations in the lower atmospheric layers often exceeded levels of 100 ng/m3 for March conditions with temperature ranging from 5 °C to 20 °C. A flux measurement of Hg0 over a vertical cross section of 0.12 km2 resulted in about 29 g/h. Vertical lidar sounding at night revealed quickly falling Hg0 concentrations with height. This is the first lidar mapping demonstration in a heavily mercury-polluted area in China, illustrating the lidar potential in complementing point monitors.

Surface water is vital resources for terrestrial life, while the rapid development of urbanization results in diverse changes in sizes, amounts, and quality of surface water. To accurately extract surface water from remote sensing imagery is very important for water environment conservations and water resource management. In this study, a new Multi-Band Water Index (MBWI) for Landsat 8 Operational Land Imager (OLI) images is proposed by maximizing the spectral difference between water and non-water surfaces using pure pixels. Based on the MBWI map, the K-means cluster method is applied to automatically extract surface water. The performance of MBWI is validated and compared with six widely used water indices in 29 sites of China. Results show that our proposed MBWI performs best with the highest accuracy in 26 out of the 29 test sites. Compared with other water indices, the MBWI results in lower mean water total errors by a range of 9.31%–25.99%, and higher mean overall accuracies and kappa coefficients by 0.87%–3.73% and 0.06–0.18, respectively. It is also demonstrated for MBWI in terms of robustly discriminating surface water from confused backgrounds that are usually sources of surface water extraction errors, e.g., mountainous shadows and dark built-up areas. In addition, the new index is validated to be able to mitigate the seasonal and daily influences resulting from the variations of the solar condition. MBWI holds the potential to be a useful surface water extraction technology for water resource studies and applications.

Terrestrial water storage (TWS) is a crucial indicator of regional water balance and water resources changes. Due to limited hydrological observations, we combined the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) products using the Long Short-Term Memory (LSTM) neural network to monitor the TWS changes from April 2002 to March 2020 over the closed Qaidam Basin in northwest China and examined the impacts of climate and meteorological changes on TWS variations. The results indicated that the LSTM model, driven by the cumulative precipitation, temperature, and Global Land Data Assimilation System datasets, was reliable for use in reconstruction of the GRACE products in the closed basin. The TWS variations featured seasonal variation characteristics and a significant upward trend at internal-annual scales, which were tested via linear statistics and a modified Mann–Kendall method. The increasing trend is likely to remain strongly sustainable in the near future with a Hurst index over 0.75 in most regions. Moreover, the TWS oscillation has a periodicity and nonlinearity increase trend of 0.43 mm/month as observed using ensemble empirical mode decomposition analysis, and the TWS components (including snow water equivalent, soil moisture, and groundwater) demonstrate discordant increasing trends in the basin. Under climate change conditions, teleconnection factors have strong impacts on TWS variability, particularly for the Pacific Decadal Oscillation index with a significant negative correlation by cross wavelet transform technology. Nonetheless, the increase in TWS is primarily influenced by precipitation increases and is more sensitive to the accumulated precipitation in this region. In this study, the GRACE products in combination with GRACE-FO data may help us to better understand the spatiotemporal characterization of TWS in Qaidam Basin, which will provide an important support for the water resource management and ecological environment protection in such data-scarce regions.

One of the measures for the effective reuse of reclaimed asphalt is the rejuvenation by tung oil and waste cooking oil, recently recognised as possible alternatives. The research objectives were the comprehensive investigation of base and SBS modified bitumen, and course-graded asphalt mixtures with different contents of these agents, in terms of standard Marshall stability, dynamic stability, freeze–thaw and low-temperature static indirect tension strength, cyclic stiffness and fatigue, and anti-aging performance. Most of the test results showed that the rejuvenated mixtures had better performance, and the specimens with tung oil performed better than those with waste cooking oil. The cyclic indirect tensile stiffness modulus and fatigue results indicated that stiffness that the rejuvenated specimens had lower stiffness, while those rejuvenated by waste cooking oil had better fatigue performance. As the result, the tung oil and waste cooking oil were identified as potentially effective rejuvenators, with tung oil having generally better performance.

In various conceptual models, the shape parameter (β) of the storage capacity curve, representing the non-linear relationship between relative soil moisture and runoff, determines runoff yield given certain circumstances of rainfall and antecedent soil moisture. In practice, β is typically calibrated for individual catchments and for different purposes, which limits more systematic understanding and also prediction in ungauged basins. Moreover, its regionalization and linkage to catchment characteristics is also not well understood, especially in relation to large-sample datasets. In this study, we used 404 catchments in the USA to explore β regionalization and attributes in relation to key catchment characteristics: elevation, slope, depth-to-bedrock, soil erodibility, forest cover, urban area, aridity index, catchment area, and stream density. We found a clear regionalized pattern of β, coherent with topography. Comparisons between β and various features demonstrated that slope has the largest impact. Land-cover, soil, geology, and climate also have an impact, but with lower correlation coefficients. This finding not only reveals spatial variation in β, but also deepens our understanding of its linkage to catchment features and flood flows. Moreover, the results provide a useful reference for decision-makers for flood prevention and mitigation.

Northeast China, the most important region for commercial grain farming in China, is vulnerable to drought due to high fluctuation in monthly rainfall. Timely, accurate and effective drought monitoring is very essential for securing the output of grain farming. In this study, three widely used drought indices were compared using satellite soil moisture data and their capability for drought monitoring was evaluated in Northeast China. Three indices are Normalized Monthly Precipitation Anomaly Percentage (NPA), Vegetation Health Index (VHI) and Normalized Vegetation Supply Water Index (NVSWI). In order to know the relationship between rainfall and drought especially over different local climate zones and give a more detail strategy for crop irrigation, a time-lag relationship is investigated based on the TRMM 3B43 monthly precipitation and NVSWI. Tendency rate (slope) was used to characterize the change of drought events in the region for the period 2001–2014. Results showed that: (1) the three selected indices were suitable for drought monitoring, with NVSWI performing the best in terms of the highest correlation with soil moisture, (2) an obvious time-lag was observed between rainfall and drought with the time lag being one month for all three climate zones, (3) drought occurred more frequently in spring and winter, while in summer drought occurred more easily in the west than in the east in Northeast China, (4) overall the frequency of drought was decreasing from 2001 to 2014 in Northeast China.

Drought in Australia has widespread impacts on agriculture and ecosystems. Satellite-based Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) has great potential to monitor and assess drought impacts on vegetation greenness and health. Various FAPAR products based on satellite observations have been generated and made available to the public. However, differences remain among these datasets due to different retrieval methodologies and assumptions. The Quality Assurance for Essential Climate Variables (QA4ECV) project recently developed a quality assurance framework to provide understandable and traceable quality information for Essential Climate Variables (ECVs). The QA4ECV FAPAR is one of these ECVs. The aim of this study is to investigate the capability of QA4ECV FAPAR for drought monitoring in Australia. Through spatial and temporal comparison and correlation analysis with widely used Moderate Resolution Imaging Spectroradiometer (MODIS), Satellite Pour l&rsquo, Observation de la Terre (SPOT)/PROBA-V FAPAR generated by Copernicus Global Land Service (CGLS), and the Standardized Precipitation Evapotranspiration Index (SPEI) drought index, as well as the European Space Agency&rsquo, s Climate Change Initiative (ESA CCI) soil moisture, the study shows that the QA4ECV FAPAR can support agricultural drought monitoring and assessment in Australia. The traceable and reliable uncertainties associated with the QA4ECV FAPAR provide valuable information for applications that use the QA4ECV FAPAR dataset in the future.

A drone-based system for monitoring of laser-induced fluorescence from the aquatic environment was constructed. Fixed-range remote-sensing demonstration measurements were performed, and field recordings of natural river water fluorescence, oil-slicks as well as dye-marked natural water volumes were taken at drone flying heights of about 10 m. Our fluorosensor, weighing only 1.5 kg, and carried by a commercial drone, illustrates how airborne remote sensing based on fluorescence can be made cost-effective and readily applicable, while presently only in ambient low-light-level conditions.

As an important part of the farmland ecosystem, crop residues provide a barrier against water erosion, and improve soil quality. Timely and accurate estimation of crop residue coverage (CRC) on a regional scale is essential for understanding the condition of ecosystems and the interactions with the surrounding environment. Satellite remote sensing is an effective way of regional CRC estimation. Both optical remote sensing and microwave remote sensing are common means of CRC estimation. However, CRC estimation based on optical imagery has the shortcomings of signal saturation in high coverage areas and susceptibility to weather conditions, while CRC estimation using microwave imagery is easily influenced by soil moisture and crop types. Synergistic use of optical and microwave remote sensing information may have the potential to improve estimation accuracy. Therefore, the objectives of this study were to: (i) Analyze the correlation between field measured CRC and satellite derived variables based on Sentinel-1 and Sentinel-2, (ii) investigate the relationship of CRC with new indices (OCRI-RPs) which combine optical crop residues indices (OCRIs) and radar parameters (RPs), and (iii) to estimate CRC in Yucheng County based on OCRI-RPs by optimal subset regression. The correlations between field measured CRC and satellite derived variables were evaluated by coefficient of determination (R2) and root mean square error (RMSE). The results showed that the normalized difference tillage index (NDTI) and radar indices 2 (RI2) had relatively higher correlations with field measured CRC in OCRIs and RPs (R2 = 0.570, RMSE = 6.560% and R2 = 0.430, RMSE = 7.052%, respectively). Combining OCRIs with RPs by multiplying each OCRI with each RP could significantly improve the ability of indices to estimate CRC, as NDTI × RI2 had the highest R2 value of 0.738 and lowest RMSE value of 5.140%. The optimal model for CRC estimation by optimal subset regression was constructed by NDI71 × σ V V 0 and NDTI × σ V H 0 , with a R2 value of 0.770 and a RMSE value of 4.846%, which had a great improvement when compared with the best results in OCRIs and RPs. The results demonstrated that the combination of optical remote sensing information and microwave remote sensing information could improve the accuracy of CRC estimation.

Vegetation growth in Soil and Water Assessment Tool (SWAT) is a crucial process for quantifying ecohydrological modelling, as it influences evapotranspiration, interception, soil erosion and biomass production. The simplified version of Environmental Policy Integrated Climate (EPIC) in SWAT was originally designed for temperate regions and naturally based on temperature to simulate growth cycles of vegetation. However, tropical or subtropical vegetation growth is mainly controlled by rainfall. Due to this limitation, current SWAT simulations in tropics and subtropics have been facing a series of problems on vegetation dormancy, water balance and sediment yield. Therefore, we proposed an approach to enhance the modelling of SWAT vegetation dynamics with remotely sensed leaf area index (LAI), to finally increase the applicability of SWAT in tropical or subtropical areas. Spatially and temporally continuous LAI products (1 day, 500 m) from Moderate Resolution Imaging Spectroradiometer (MODIS) observations were integrated into SWAT to replace the LAI simulated by built-in EPIC module. Two advanced filter algorithms were employed to derive a downscaled LAI (30 m) to keep a consistent spatial scale with the size of Hydrological Response Units (HRU) and open data (i.e. SRTM, 30 m), and the source code of the plant growth module were correspondingly modified to incorporate the downscaled LAI into SWAT. To examine the performance of our proposed approach, a case study was conducted in a representative middle-scale (6384 km 2) subtropical watershed of Meichuan basin, China, and detailed analysis was performed to investigate its ecohydrological effects, such as streamflow, sediment yield and LAI dynamics from 2001 to 2014. Model performances were compared among three scenarios: (1) original SWAT, (2) SWAT with a corrected plant dormancy function, and (3) modified SWAT after integration of MODIS LAI (our proposed method). Results showed that the modified SWAT took advantage of downscaled MODIS LAI and produced more reasonable seasonal curves of vegetation cover factor (C) of plants than the original model. Correspondingly, the modified SWAT substantially improved streamflow and sediment simulations. The findings demonstrated that SWAT model can be a useful tool for simulating ecohydrological process for subtropical ecosystems when integrated with our proposed method.

Precipitation and air temperature are key drivers of watershed models. Currently there are many open-access gridded precipitation and air temperature datasets at different spatial and temporal resolutions over global or quasi-global scale. Motivated by the scarcity and substantial temporal and spatial gaps in ground measurements in Africa, this study evaluated the performance of three open-access precipitation datasets (i.e. CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), TRMM (Tropical Rainfall Measuring Mission) and CFSR (Climate Forecast System Reanalysis)) and one air temperature dataset (CFSR) in driving Soil and Water Assessment Tool (SWAT) model in simulation of daily and monthly streamflow in the upper Gilgel Abay Basin, Ethiopia. The “best” available measurements of precipitation and air temperature from sparse gauge stations were also used to drive SWAT model and the results were compared with those using open-access datasets. After a comprehensive comparison of a total of eight model scenarios with different combinations of precipitation and air temperature inputs, we draw the following conclusions: (1) using measured precipitation from even sparse available stations consistently yielded better performance in streamflow simulation than using all three open-access precipitation datasets; (2) using CFSR air temperature yielded almost identical performance in streamflow simulation to using measured air temperature from gauge stations; (3) among the three open-access precipitation, overall CHIRPS yielded best performance. These results suggested that the CHIRPS precipitation available at high spatial resolution (0.05°) together with CFSR air temperature can be a promising alternative open-access data source for streamflow simulation in this data-scarce area in the case of limited access to desirable gauge data.

Lake ice is a robust indicator of climate change. The availability of information contained in Moderate Resolution Imaging Spectroradiometer daily snow products from 2000 to 2017 could be greatly improved after cloud removal by gap filling. Thresholds based on open water pixel numbers are used to extract the freezeup start and breakup end dates for 58 lakes on the Tibetan Plateau (TP); 18 lakes are also selected to extract the freezeup end and breakup start dates. The lake ice durations are further calculated based on freezeup and breakup dates. Lakes on the TP begin to freezeup in late October and all the lakes start the ice cover period in mid‐January of the following year. In late March, some lakes begin to break up, and all the lakes end the ice cover period in early July. Generally, the lakes in the northern Inner‐TP have earlier freezeup dates and later breakup dates (i.e., longer ice cover durations) than those in the southern Inner‐TP. Over 17 years, the mean ice cover duration of 58 lakes is 157.78 days, 18 (31%) lakes have a mean extending rate of 1.11 day/year, and 40 (69%) lakes have a mean shortening rate of 0.80 day/year. Geographical location and climate conditions determine the spatial heterogeneity of the lake ice phenology, especially the ones of breakup dates, while the physico‐chemical characteristics mainly affect the freezeup dates of the lake ice in this study. Ice cover duration is affected by both climatic and lake specific physico‐chemical factors, which can reflect the climatic and environmental change for lakes on the TP.

Soil freezing/thawing is an important mechanism to control soil water and heat redistribution in mid-to-high latitudes. Salt in the agricultural soil from mid-to-high latitudes can alter characteristics of soil freezing/thawing cycle and then affect soil thermal and hydrological processes in winter and finally cause salinization in spring. To quantify the impacts of soil salinization on soil water and heat transport in saline soils, we conducted field experiments on soil water and heat dynamics in two typical agricultural regions of northern China with different climate and soil conditions. The coupled soil heat and water model—CoupModel has been extended to account for the dynamic impacts of salt on freezing point depression. The newly-added module improved the representation of soil freezing point depression by significantly improving model performance between simulated and measured soil temperatures, especially around freezing point, with mean error (ME) for the soil temperature at various depths reduced by 16% to 77% for the entire winter period. With a systematic model calibration approach, processes related to energy balance and soil freezing/thawing have been well constrained for both study sites with different characteristics for soil hydrology and energy balance. The model generally showed good performance with respect to soil moisture and temperature for both the calibration and validation periods. Our study has demonstrated a new modeling approach to successfully account for the impacts of salt on soil freezing/thawing and the new module can be a useful tool to address the salinization problems in mid-to-high latitudes with respect to climate change and water management.

Model realism is of vital importance in science of hydrology, in terms of realistic representation of hydrological processes and reliability of future prediction. Here, we employed a stepwise modeling approach that leverages flexible model structures and multi-source observations for robust streamflow simulation and internal variables validation with improved model realism. This framework is demonstrated in Yigong Zangbu River (YZR) basin, a data scarce glacier basin in the upper Brahmaputra River. We designed six experiments (Exp1–6) to use modeling as a tool to understand hydrological processes in this remote cold basin with extremely high altitude. In Exp1, we started with a distributed rainfall-runoff model (FLEXD) - representing the case that snow and glacier processes were ignored. Then, we stepwisely added snow and glacier processes into FLEXD, denoted as FLEXD-S (Exp2) and FLEXD-SG (Exp3), respectively, and such improvement of model structure led to significantly improved streamflow estimates. To explore the impact of different precipitation forcing on model performance, FLEXD-SG was driven by Theissen average (Exp3) and three individual stations’ precipitation (Exp4–6). The model realism was tested by observed hydrograph, snow cover area (SCA) and glacier mass balance (GMB). Results showed that a robust and realistic hydrological modeling system was achieved in Exp6. In this modeling study, we learned that: 1) stepwise modeling is effective in investigating catchment behavior, and snow and glacier melting are the dominant hydrological processes in the YZR basin; 2) internal variables validation is beneficial to test model realism in data scarce basin; 3) the FLEXD-SG model calibrated by only one year hydrograph is sufficient to reproduce snow and glacier variations; 4) precipitation of a single station as forcing data could outperform Theissen average; 5) based on the well tested model configuration in Exp6, we analyzed simulated results, and reconstructed the long term hydrography (1961–2013), to support the potential competence for decision making on water resources management in practice. The proposed framework may significantly improve our skills in hydrological modeling over data-poor regions.

Although the Tropical Rainfall Measurement Mission (TRMM) has come to an end, the evaluation of TRMM satellite precipitation is still of great significance for the improvement of the Global Precipitation Measurement (GPM). In this paper, the hydrological utility of TRMM Multi-satellite Precipitation Analysis (TMPA) 3B42 RTV7/V7 precipitation products was evaluated using the variable infiltration capacity (VIC) hydrological model in the upper Yangtze River basin. The main results show that (1) TMPA 3B42V7 had a reliable performance in precipitation estimation compared with the gauged precipitation on both spatial and temporal scales over the upper Yangtze River basin. Although TMPA 3B42V7 slightly underestimated precipitation, TMPA 3B42RTV7 significantly overestimated precipitation at daily and monthly time scales, (2) the simulated runoff by the VIC hydrological model showed a high correlation with the gauged runoff and lower bias at daily and monthly time scales. The Nash&ndash, Sutcliffe coefficient of efficiency (NSCE) value was as high as 0.85, the relative bias (RB) was &minus, 6.36% and the correlation coefficient (CC) was 0.93 at the daily scale, (3) the accuracy of the 3B42RTV7-driven runoff simulation had been greatly improved by using the hydrological calibration parameters obtained from 3B42RTV7 compared with that of gauged precipitation. A lower RB (14.38% vs. 66.58%) and a higher CC (0.87 vs. 0.85) and NSCE (0.71 vs. &minus, 0.92) can be found at daily time scales when we use satellite data instead of gauged precipitation data to calibrate the VIC model. However, the performance of the 3B42V7-driven runoff simulation did not improve in the same operation accordingly. The cause might be that the 3B42V7 satellite products have been adjusted by gauged precipitation. This study suggests that it might be better to calibrate the parameters using satellite data in hydrological simulations, especially for unadjusted satellite data. This study is not only helpful for understanding the assessment of multi-satellite precipitation products in large-scale and complex areas in the upper reaches of the Yangtze River, but also can provide a reference for the hydrological utility of the satellite precipitation products in other river basins of the world.

This study evaluated the suitability of the latest retrospective Integrated Multi-satellitE Retrievals for Global Precipitation Measurement V06 (IMERG) Final Run product with a relatively long period (beginning from June 2000) for drought monitoring over mainland China. First, the accuracy of IMERG was evaluated by using observed precipitation data from 807 meteorological stations at multiple temporal (daily, monthly, and yearly) and spatial (pointed and regional) scales. Second, the IMERG-based standardized precipitation index (SPI) was validated and analyzed through statistical indicators. Third, a light–extreme–light drought-event process was adopted as the case study to dissect the latent performance of IMERG-based SPI in capturing the spatiotemporal variation of drought events. Our results demonstrated a sufficient consistency and small error of the IMERG precipitation data against the gauge observations with the regional mean correlation coefficient (CC) at the daily (0.7), monthly (0.93), and annual (0.86) scales for mainland China. The IMERG possessed a strong capacity for estimating intra-annual precipitation changes; especially, it performed well at the monthly scale. There was a strong agreement between the IMERG-based SPI values and gauge-based SPI values for drought monitoring in most regions in China (with CCs above 0.8). In contrast, there was a comparatively poorer capability and notably higher heterogeneity in the Xinjiang and Qinghai-Tibet Plateau regions with more widely varying statistical metrics. The IMERG featured the advantage of satisfactory spatiotemporal accuracy in terms of depicting the onset and extinction of representative drought disasters for specific consecutive months. Furthermore, the IMERG has obvious drought monitoring abilities, which was also complemented when compared with the Precipitation Estimation from the Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) 3B42V7. The outcomes of this study demonstrate that the retrospective IMERG can provide a more competent data source and potential opportunity for better drought monitoring utility across mainland China, particularly for eastern China.

A continuous-wave (CW) Scheimpflug underwater multi-spectral lidar system was constructed to monitor aquatic fauna with spatial, spectral, and temporal resolution. Utilizing a 1 W 414 nm diode laser and a detection set-up with a reflective grating, measurements of shrimp pleopod movements at fixed range, and the swimming of small fish trapped in a clear tube were performed in a 5 m ×0.6 m ×0.6 m water tank. The spatial resolution is about 5 mm, the spectral resolution is 10 nm (from 400 nm to 700 nm), and with proper binning of the CCD, a read-out repetition rate up to 150 Hz can be reached. The experimental results demonstrate that the underwater Scheimpflug lidar system has great potential for detailed monitoring of the small aquatic fauna in oceanic environments.

Under climate change and increasing water demands, groundwater depletion has become regional and global threats for water security, which is an indispensable target to achieving sustainable developments of human society and ecosystems, especially in arid and semiarid regions where groundwater is a major water source. In this study, groundwater depletion of 2003–2016 over Xinjiang in China, a typical arid region of Central Asia, is assessed using the gravity recovery and climate experiment (GRACE) satellite and the global land data assimilation system (GLDAS) datasets. In the transition of a warm-dry to a warm-wet climate in Xinjiang, increases in precipitation, soil moisture and snow water equivalent are detected, while GRACE-based groundwater storage anomalies (GWSA) exhibit significant decreasing trends with rates between-3.61 ± 0.85 mm/a of CSR-GWSA and −3.10 ± 0.91 mm/a of JPL-GWSA. Groundwater depletion is more severe in autumn and winter. The decreases in GRACE-based GWSA are in a good agreement with the groundwater statistics collected from local authorities. However, at the same time, groundwater abstraction in Xinjiang doubled, and the water supplies get more dependent on groundwater. The magnitude of groundwater depletion is about that of annual groundwater abstraction, suggesting that scientific exploitation of groundwater is the key to ensure the sustainability of freshwater withdrawals and supplies. Furthermore, GWSA changes can be well estimated by the partial least square regression (PLSR) method based on inputs of climate data. Therefore, GRACE observations provide a feasible approach for local policy makers to monitor and forecast groundwater changes to control groundwater depletion.

In recent years, many studies have focused on the changes of partial or single African ecosystems and the drivers of those changes. However, focusing only on partial or single ecosystems has limited the understanding of the relationships between the vegetation and climate changes in all of the African ecosystems. In this study, the temporal trends of the satellite-derived annual mean leaf area index (GLASS-LAI) were analyzed, and the inter-annual relationships were developed between the annual mean LAI and the climate variables (precipitation and temperature) for the time period ranging from 1982 to 2013. Additionally, this study applied seasonal curves and step-wise multiple regression methods to investigate the relationships between intra-annual LAI and climate changes. It was found that the GLASS-LAI over half of Africa had shown general significant greening or browning trends during the period from 1982 to 2013. From the results of inter-annual analysis, with mean annual precipitation lower than 600 mm, the greening of the savannas and grasslands in the Sahel was found to highly correspond with the increased precipitation. In contrast, the evergreen broadleaf forests in the Gulf of Guinea and Congo Basin showed strongly positive responses to the annual temperature when the mean annual temperature was below 25 °C. In regard to the intra-annual responses, the precipitation with 1-month lags was found to be helpful for the vegetation growth, with the exception of the evergreen broadleaf. The results of this research study indicated that the different land-covers in Africa had displayed clear differences in their annual trends during the examined 32-year period and had responded differently to the inter- and intra-annual climate drivers. This difference was evident by the characteristics of the vegetation covers and the geographic distributions. Therefore, further examinations of these differences can potentially improve the understanding of the land surface-atmosphere interactions among the different African ecosystems.

The preliminary remote laser-induced breakdown spectroscopy measurement of metals is performed on a mobile lidar system. The Nd:YAG laser synchronized with an ICCD spectrometer, the detection range can be achieved up to 45 m.

Lake Tana is the largest lake in Ethiopia, and its lake outflow is the source of the Blue Nile River that provides vital water resources for many livelihoods and downstream/international stakeholders. Therefore, it is essential to quantify and monitor the water balance of Lake Tana. However, Lake Tana is poorly gauged, with more than 50% of Lake Tana Basin being ungauged from in-situ measurements, making it difficult to quantify the lake inflow from surrounding basins. The lack of in-situ measurements highlights the need for the innovative application of satellite remote sensing. This study explores how freely accessible satellite remote sensing can be used to complement routine weather data to quantify the water balance of Lake Tana and its surrounding catchments. This study particularly investigates whether the outflow from Lake Tana can be estimated with sufficient accuracy as the residual of the lake water balance. Monthly inflow into lake was computed as the total runoff from the surrounding catchments; the runoff was estimated as the residual of the land-based catchment water balance using satellite precipitation improved with an integrated downscaling-calibration procedure, satellite evapotranspiration, and a correction term for changes in land total storage (soil moisture storage and deep percolation). The outflow from Lake Tana was estimated as the residual of lake water balance by combining satellite-based lake precipitation, changes in water storage, and lake inflow with estimated lake evaporation. Evaluation using limited available measurements showed that estimated annual runoff for two gauged subbasins agreed well with measurements, with differences within 4%. The estimated annual outflow from Lake Tana was also close to measured outflow, with a difference of 12%. However, the estimated monthly runoff from catchments and monthly lake outflow were unsatisfactory, with large errors.

The performance of the latest released Integrated Multi-satellitE Retrievals for GPM mission (IMERG) version 5 (IMERG v5) and the TRMM Multisatellite Precipitation Analysis 3B42 version 7 (3B42 v7) are evaluated and compared at multiple temporal scales over a semi-humid to humid climate transition area (Huaihe River basin) from 2015 to 2017. The impacts of rainfall rate, latitude and elevation on precipitation detection skills are also investigated. Results indicate that both satellite estimates showed a high Pearson correlation coefficient (r, above 0.89) with gauge observations, and an overestimation of precipitation at monthly and annual scales. Mean daily precipitation of IMERG v5 and 3B42 v7 display a consistent spatial pattern, and both characterize the observed precipitation distribution well, but 3B42 v7 tends to markedly overestimate precipitation over water bodies. Both satellite precipitation products overestimate rainfalls with intensity ranging from 0.5 to 25 mm/day, but tend to underestimate light (0–0.5 mm/day) and heavy (>25 mm/day) rainfalls, especially for torrential rains (above 100 mm/day). Regarding each gauge station, the IMERG v5 has larger mean r (0.36 for GPM, 0.33 for TRMM) and lower mean relative root mean square error (RRMSE, 1.73 for GPM, 1.88 for TRMM) than those of 3B42 v7. The higher probability of detection (POD), critical success index (CSI) and lower false alarm ratio (FAR) of IMERG v5 than those of 3B42 v7 at different rainfall rates indicates that IMERG v5 in general performs better in detecting the observed precipitations. This study provides a better understanding of the spatiotemporal distribution of accuracy of IMERG v5 and 3B42 v7 precipitation and the influencing factors, which is of great significance to hydrological applications.

The operator of an offshore oilfield located in South China Sea, has been researching for efficient methods to tackle the production constraints from the increasing produced water amount and maximize oil recovery. An ESP assisted downhole oil and water separation system, known as SubSep system, was designed and successfully installed in year 2014. During the operations, the system achieved designed separation performance but went offline due to heavy sand problem. This paper concentrates on sharing the experience of complete cycle of system design, deployment, operation and post-job investigations, and discussing the lessons learned and future improvements for downhole oil and water separation technology. The downhole oil and water separation system features in two independent ESP to operate simultaneously: the lower ESP feeds well fluid into multistage hydrocyclone where oil is separated from water, and enters upper ESP to lift to ground, while water is injected to injection layer. Installed in year 2014, the system is the first successful deployment of downhole oil and water separation technology in South China Sea area. The system has totally operated 480 days, during which various operation methodologies were experimented and outcomes analyzed. In normal operation the separated water collected from sample line in water injection zone showed 99ppm oil, and 75% of water was reduced to ground, which signaled the significant success in water and oil separation. The system went offline when surface water rate increased abnormally and injected water with high oil concentration. Further investigation of pulled system showed clear evidence of abrasions from sand and quarts. Future improvement pathways were identified as applying multiple sand control methods, simplifying completion strings, enhancing chemical injection programs and implementing surface experiments. This paper shares the experience of a complete cycle of design, deployment, operation, and post-job investigations of a downhole oil and water separation system, and provide reference for future improvements and optimizations

Precipitation data at high spatio-temporal resolution are essential for hydrological, meteorological, and ecological research in local basins and regions. The coarse spatial resolution (0.25°) of Tropical Rainfall Measuring Mission (TRMM) 3B43 product is insufficient for practical requirements. In this paper, a multivariable geographically weighted regression (GWR) downscaling method was developed to obtain 1 km precipitation. The GWR method was compared with two other downscaling methods [univariate regression (UR) and multivariate regression (MR)] in terms of the performance of downscaled annual precipitation. Variables selection procedures were proposed for selecting appropriate auxiliary factors in all three downscaling methods. To obtain the monthly 1 km precipitation, two monthly downscaling strategies (annual-based fraction disaggregation method and monthly based GWR method) were evaluated. All analysis was tested in Gansu province, China with a semiarid to arid climate for three typical years. Validation with measurements from 24 rain gauge stations showed that the proposed GWR method performed consistently better than the UR and MR methods. Two monthly downscaling methods were efficient in deriving the monthly precipitation at 1 km. The former method faces the challenge of precipitation spatial heterogeneity and the derived monthly precipitation heavily depends on the annual downscaled results, which could lead to the accumulation of errors. The monthly based GWR method is suitable for downscaling monthly precipitation, but the accuracy of original TRMM 3B43 data would have large influence on downscaling results. It was demonstrated that the proposed method was effective for obtaining both annual and monthly TRMM 1 km precipitation with high accuracy.

Samples of typical street dust were collected from Jiansheng Town, Chongqing City and classified into four groups on the basis of particle size: 0–75, 75–150, 150–300, and 300–1000 μm. The pollutant content and heavy metal composition of the samples were investigated. The particles in the 0–75, 75–150, 150–300, and 300–1000 μm groups had mass fractions of 20.82% ± 8.86%, 24.68% ± 9%, 30.83% ± 9.76%, and 23.68% ± 15.56%, respectively. The highest concentration of nutritional pollutants was detected in the 300–1,000 µm group, with total nitrogen (TN), total phosphorus (TP), and total organic carbon (TOC) concentrations of 3.07 ± 2.48, 0.64 ± 0.39, and 83.79 ± 65.28 g/kg, respectively. The lowest concentration of nutritional pollutants was observed in the 150–300 μm group, with TN, TP, and TOC concentrations of 1.78 ± 1.22, 0.51 ± 0.20, and 36.74 ± 24.64 g/kg, respectively. The nutritional pollutants presented similar mass fractions in the street dust samples with different particle sizes. Vegetation significantly affected the TOC content in the samples. The TOC content of the streets with rich vegetation was 1.90 ± 0.56 times that of the streets with less vegetation. The 150–300 µm group had the highest heavy metal mass percentage. Among the heavy metals detected in street dust, Cd, Cr, and Ni were mainly residuals; Cu and Pb were oxidizable; and Zn was mainly acid-extractable. © 2015 American Institute of Chemical Engineers Environ Prog, 2015

This study aims to investigate separate and combined impacts of land-use and climate variability on hydrological components in the Johor River Basin (JRB), Malaysia. The Mann-Kendall and Sen’s slope tests were applied to detect the trends in precipitation, temperature and streamflow of the JRB. The Soil and Water Assessment Tool (SWAT) was calibrated and validated using measured monthly streamflow data. The validation results showed that SWAT was reliable in the tropical JRB. The trend analysis showed that there was an insignificant increasing trend for streamflow, whereas significant increasing trends for precipitation and temperature were found. The combined (climate + land-use change) impact caused the annual streamflow and evaporation to increase by 4.4% and 1.2%, respectively. Climate (land-use) raised annual streamflow by 4.4% (0.06%) and evaporation by 2.2% (−0.2%). Climate change imposed a stronger impact than land-use change on the streamflow and evaporation. These findings are useful for...

Evaporation is an important component of the water and energy balance of lakes and reservoirs. The energy balance combination method for estimating evaporation requires the heat storage changes (Qt) to be known. The lack of data on water temperature profiles and water depth fluctuations hinders routine computations of Qt. Following successful estimation of the soil heat flux density of land surfaces (G) and heat storage change of urban areas and wetlands from net all wave radiation (Rn), we investigated in this paper whether a similar generic Qt(Rn) empirical relationship can be developed for lakes and reservoirs. A comprehensive literature survey was conducted and experimental datasets for 22 lakes with different characteristics were collected. A linear Qt(Rn) model with a hysteresis function to describe seasonal warming and cooling effects was developed (Qt = a ∗ Rn + b + c ∗ dRn/dt) that fits the 22 independently gathered datasets satisfactory (R2 of 0.83 and RMSE of 22 W m− 2) for bi-weekly and monthly time scales. Predictive models for the coefficients a, b and c were also developed, using Rn and water surface temperature measurements that can be retrieved from routine earth observation measurements. The average R2 between measured and modeled Qt was 0.84 and the RMSE was 37 W m− 2 if predictive models were used for the assessment of lake specific Qt(Rn) functions. Two independent satellite-derived products were explored: the ARC-Lake (ATSR Reprocessing for Climate) product for water surface temperature using the ATSR (Along Track Scanning Radiometer) series data, and the CM SAF (Satellite Application Facility on Climate Monitoring) product for solar radiation Rs based on the AVHRR (Advanced Very High Resolution Radiometer) data. The proposed procedure using purely satellite-derived data as inputs resulted in comparably good Qt estimates as those using in-situ measurements. The new Qt hysteresis model can thus be applied together with satellite measurements for supporting the computation of evaporation from open water bodies based on energy balance equations.