Mapping favorable groundwater potential recharge zones using a GIS-based analytical hierarchical process and probability frequency ratio model:A case study from an agro-urban region of Pakistan

In Punjab(Pakistan),the increasing population and expansion of land use for agriculture have severely exploited the regional groundwater resources.Intensive pumping has resulted in a rapid decline in the level of the water table as well as its quality.Better management practices and artificial recharge are needed for the development of sustainable groundwater resources.This study proposes a methodology to delineate favorable groundwater potential recharge zones(FPRI)by integrating maps of groundwater potential recharge index(PRI)with the DRASTIC-based groundwater vulnerability index(VI).In order to evaluate both indexes,different thematic layers corresponding to each index were overlaid in ArcGIS.In the overlay analysis,the weights(for various thematic layers)and rating values(for sub-classes)were allocated based on a review of published literature.Both were then normalized and modified using the analytical hierarchical process(AHP)and a frequency ratio model respectively.After evaluating PRI and FPRI,these maps were validated using the area under the curve(AUC)method.The PRI map indicates that 53%of the area assessed exists in very low to low recharge zones,22%in moderate,and 25%in high to excellent potential recharge zones.The VI map indicates that 38%of the area assessed exists in very low to low vulnerability,33%in moderate,and 29%in high to very high vulnerability zones.The FPRI map shows that the central region of Punjab is moderately-to-highly favorable for recharge due to its low vulnerability and high recharge potential.During the validation process,it was found that the AUC estimated with modified weights and rating values was 79%and 67%,for PRI and VI indexes,respectively.The AUC was less when evaluated using original weights and rating values taken from published literature.Maps of favorable groundwater potential recharge zones are helpful for planning and implementation of wells and hydraulic structures in this region.

Coupling numerical simulation with remotely sensed information for the study of frozen soil dynamics

The acquisition of spatial-temporal information of frozen soil is fundamental for the study of frozen soil dynamics and its feedback to climate change in cold regions.With advancement of remote sensing and better understanding of frozen soil dynamics,discrimination of freeze and thaw status of surface soil based on passive microwave remote sensing and numerical simulation of frozen soil processes under water and heat transfer principles provides valuable means for regional and global frozen soil dynamic monitoring and systematic spatial-temporal responses to global change.However,as an important data source of frozen soil processes,remotely sensed information has not yet been fully utilized in the numerical simulation of frozen soil processes.Although great progress has been made in remote sensing and frozen soil physics,yet few frozen soil research has been done on the application of remotely sensed information in association with the numerical model for frozen soil process studies.In the present study,a distributed numerical model for frozen soil dynamic studies based on coupled water-heat transferring theory in association with remotely sensed frozen soil datasets was developed.In order to reduce the uncertainty of the simulation,the remotely sensed frozen soil information was used to monitor and modify relevant parameters in the process of model simulation.The remotely sensed information and numerically simulated spatial-temporal frozen soil processes were validated by in-situ field observations in cold regions near the town of Naqu on the East-Central Tibetan Plateau.The results suggest that the overall accuracy of the algorithm for discriminating freeze and thaw status of surface soil based on passive microwave remote sensing was more than 95%.These results provided an accurate initial freeze and thaw status of surface soil for coupling and calibrating the numerical model of this study.The numerically simulated frozen soil processes demonstrated good performance of the distributed numerical model based on the coupled water-heat transferring theory.The relatively larger uncertainties of the numerical model were found in alternating periods between freezing and thawing of surface soil.The average accuracy increased by about 5%after integrating remotely sensed information on the surface soil.The simulation accuracy was significantly improved,especially in transition periods between freezing and thawing of the surface soil.

Spatial-temporal variability of snow cover over the Amur River Basin inferred from MODIS daily snow products in recent decades

MODIS snow products MOD10A1\MYD10A1 provided us a unique chance to investigate snow cover as well as its spatial-temporal variability in response to global changes from regional and global perspectives.By means of MODIS snow products MOD10A1\MYD10A1 derived from an extensive area of the Amur River Basin,mainly located in the Northeast part of China,some part in far east area of the former USSR and a minor part in Republic of Mongolia,the reproduced snow datasets after removal of cloud effects covering the whole watershed of the Amur River Basin were generated by using 6 different cloud-effect-removing algorithms.The accuracy of the reproduced snow products was evaluated with the time series of snow depth data observed from 2002 to 2010 within the Chinese part of the basin,and the results suggested that the accuracies for the reproduced monthly mean snow depth datasets derived from 6 different cloud-effect-removing algorithms varied from 82%to 96%,the snow classification accuracies(the harmonic mean of Recall and Precision)was higher than 80%,close to the accuracy of the original snow product under clear sky conditions when snow cover was stably accumulated.By using the reproduced snow product dataset with the best validated cloud-effect-removing algorithm newly proposed,spatial-temporal variability of snow coverage fraction(SCF),the date when snow cover started to accumulate(SCS)as well as the date when being melted off(SCM)in the Amur River Basin from 2002 to 2016 were investigated.The results indicated that the SCF characterized the significant spatial heterogeneity tended to be higher towards East and North but lower toward West and South over the Amur River Basin.The inter-annual variations of SCF showed an insignificant increase in general with slight fluctuations in majority part of the basin.Both SCS and SCM tended to be slightly linear varied and the inter-annual differences were obvious.In addition,a clear decreasing trend in snow cover is observed in the region.Trend analysis(at 10%significance level)showed that 71%of areas between 2,000 and 2,380 m a.s.l.experienced a reduction in duration and coverage of annual snow cover.Moreover,a severe snow cover reduction during recent years with sharp fluctuations was investigated.Overall spatial-temporal variability of Both SCS and SCM tended to coincide with that of SCF over the basin in general.

Editorial for the special issue in memoriam to Prof.Xie Zichu

Prof.Xie Zichu passed away on January 25,2020 in Changsha,Hunan Province,China at the age of 83 years old.Being one of the internationally renowned and highly respected glaciologists of China,this issue is dedicated with all respect in memory of him for his life-long effort and contribution to advance studies on glaciology in China.He has undertaken field investigations on glacier mass balance.

Modeling hydrological consequences of 21st-Century climate and land use/land cover changes in a mid-high latitude watershed

The Naoli River Basin(NRB),a pivotal agricultural production area in China,is poised to undergo substantial impacts on water resources due to projected climate and land use/cover(LULC)changes.Despite its significance in the context of China’s expanding farmland construction in the NRB,there exists limited research on the potential repercussions of future shifts in runoff,soil water content(SWC),and evapotranspiration(ET)on crop productivity and water availability(both in terms of quantity and timing).This study employs future LULC maps and an ensemble of ten CMIP6 Global Climate Models(GCMs)across three scenarios to drive the well-calibrated distributed hydrological model,ESSI-3.The objective of present study is aimed on projecting hydrological consequences under climate and land use/land cover changes in near-term(2026–2050),middle-term(2051–2075),and far-term(2076–2100)future in comparison to the baseline period of 1990–2014.Results consistently indicate an increase trend in annual average ET,runoff,and SWC in the NRB across all three future periods under the three SSP scenarios.LULC changes emerge as the primary driver influencing regional hydrological processes in the near future.Notably,under high-emission scenarios,monthly runoff and SWC are projected to significantly increase in March but decrease in April during the middle and far future periods compared to the baseline.This shift is attributed to the anticipated warming of winter and spring,leading to a transition in peak snowmelt from April to March.Concurrently,the expansion of cropland intensifies crop evapotranspiration demand,potentially exacerbating water stress during the early stages of crop growth in April.The findings underscore the importance of addressing the substantial impacts of climate change and land use planning on regional water cycling processes.Early planning to mitigate water shortages during the initial stage of future crop growth is crucial for ensuring food security and managing water-related challenges in the NRB and neighboring mid-high latitude regions.

Overview to the Hard X-ray Modulation Telescope (Insight-HXMT) Satellite

As China’s first X-ray astronomical satellite, the Hard X-ray Modulation Telescope (HXMT), which was dubbed as Insight-HXMT after the launch on June 15, 2017, is a wide-band(1-250 ke V) slat-collimator-based X-ray astronomy satellite with the capability of all-sky monitoring in 0.2-3 Me V. It was designed to perform pointing, scanning and gamma-ray burst(GRB)observations and, based on the Direct Demodulation Method (DDM), the image of the scanned sky region can be reconstructed.Here we give an overview of the mission and its progresses, including payload, core sciences, ground calibration/facility, ground segment, data archive, software, in-orbit performance, calibration, background model, observations and some preliminary results.

Insight-HXMT observations of the first binary neutron star merger GW170817

Finding the electromagnetic （EM） counterpart of binary compact star merger, especially the binary neutron star （BNS） merger, is critically important for gravitational wave （GW） astronomy, cosmology and fundamental physics. On Aug. 17, 2017, Advanced LIGO and Fermi/GBM independently triggered the first BNS merger, GW170817, and its high energy EM counterpart, GRB 170817A, respectively, resulting in a global observation campaign covering gamma-ray, X-ray, UV, optical, IR, radio as well as neutrinos. The High Energy X-ray telescope （HE） onboard Insight-HXMT （Hard X-ray Modulation Telescope） is the unique high-energy gamma-ray telescope that monitored the entire GW localization area and especially the optical counterpart （SSS17a/AT2017gfo） with very large collection area （M000 cm2） and microsecond time resolution in 0.2-5 MeV. In addition, Insight-HXMT quickly implemented a Target of Opportunity （TOO） observation to scan the GW localization area for potential X-ray emission from the GW source. Although Insight-HXMT did not detect any significant high energy （0.2-5 MeV） radiation from GW170817, its observation helped to confirm the unexpected weak and soft nature of GRB 170817A. Meanwhile, Insight-HXMT/HE provides one of the most stringent constraints （-10-7 to 104 erg/cm2/s） for both GRB170817A and any other possible precursor or extended emissions in 0.2-5 MeV, which help us to better understand the properties of EM radiation from this BNS merger. Therefore the observation of Insight-HXMT constitutes an important chapter in the full context of multi-wavelength and multi-messenger observation of this historical GW event.

The Medium Energy X-ray telescope (ME) onboard the Insight-HXMT astronomy satellite

The Medium Energy X-ray telescope(ME) is one of the three main telescopes on board the Insight hard X-ray modulation telescope(Insight-HXMT) astronomy satellite. ME contains 1728 pixels of Si-PIN detectors sensitive in 5-30 ke V with a total geometrical area of 952 cm^2. The application specific integrated circuit(ASIC) chip, VA32TA6, is used to achieve low power consumption and low readout noise. The collimators define three kinds of field of views(FOVs) for the telescope, 1°×4°, 4°×4°,and blocked ones. Combination of such FOVs can be used to estimate the in-orbit X-ray and particle background components.The energy resolution of ME is ~3 ke V at 17.8 ke V(FWHM) and the time resolution is 255 μs. In this paper, we introduce the design and performance of ME.

Separating the impacts of climate variability,land-use change and large reservoir operations on streamflow in the Yangtze River basin,China,using a hydrological modeling approach

Separating the individual effects of climate variability and human activities on streamflow is more important than just knowing their combined effects.In this paper,using a scenario-based hydrological simulation approach,the streamflow changes caused by climate variability and two different types of human activities(i.e.land-use change and large reservoirs operations)as well as the contribution rates of these three factors over 272 sub-basins in the Yangtze river basin were quantified and compared among 5 different periods(i.e.1988–1992(P1),1993–1997(P2),1998–2002(P3),2003–2007(P4)and 2008–2012(P5)).Results demonstrate that,at the annual scale,climate variability played a leading role in the change in outflow of most sub-basins.With regard to the seasonal variations in discharge at Datong station,climate factors played a predominant role during P1-P2 and P2-P3.Since the Three Gorges Reservoir began operating in 2003,the discharge was enhanced by reservoirs in January-May and reduced by reservoirs in JulyDecember.Reservoir and climate factors codetermined seasonal streamflow change during P3-P4 and P4-P5.Land-use change made the smallest contribution to seasonal discharge fluctuations.This study can support decision-making in regional water resources planning and management.

Visualization analysis of rainfall-induced landslides hazards based on remote sensing and geographic information system-an overview

In recent years,RS and GIS technologies have played an increasingly important role in various aspects of rainfall induced landslide research.In order to systematically understand their application situation,this paper extensively used various visualization analysis technologies for in-depth analysis of 1,161 documents collected by the WOS data platform in the past 27 years by combining quantitative and qualitative methods.Then,this article focuses on sub domain analysis from four aspects:landslide detection and monitoring,prediction models,sensitivity mapping,and risk assessment.The study found that the number of literature in thisfield has steadily increased and is expected to continue to rise.This literature review has attracted widespread attention from the academic community,but it challenging to meet research needs.Frequent and effective cooperationis between countries,institutions,and authors is very beneficial for promoting progress in thisfield.The future development direction is a new intelligent hybrid model that integrates multiple research methods.This study can provide researchers in thisfield with the core research force,hot topic evolution,and future development trends of future rainfall-induced landslides and contribute to landslide prevention and control decision-making and achieving the United Nations’sustainable development goals.

Evaluation of neural network models for landslide susceptibility assessment

Identifying and assessing the disaster risk of landslide-prone regions is very critical for disaster prevention and mitigation.Owning to their special advantages,neural network algorithms have been widely used for landslide susceptibility mapping(LSM)in recent decades.In the present study,three advanced neural network models popularly used in relevant studies,i.e.artificial neural network(ANN),one dimensional convolutional neural network(1D CNN)and recurrent neural network(RNN),were evaluated and compared for LSM practice over the Qingchuan County,Sichuan province,China.Extensive experimental results demonstrated satisfactory performances of these three neural network models in accurately predicting susceptible regions.Specifically,ANN and 1D CNN models yielded quite consistent LSM results but slightly differed from those of RNN model spatially.Nevertheless,accuracy evaluations revealed that the RNN model outperformed the other two models both qualitatively and quantitatively but its complexity was relatively high.Experiments concerning training hyper-parameters on the performance of neural network models for LSM suggested that relatively small batch size values with Tanh activation function and SGD optimizer are essential to improve the performance of neural network models for LSM,which may provide a thread to help those who apply these advanced algorithms to improve their efficiency.

In-orbit performance of ME onboard Insight-HXMT in the first 5 years

Introduction The medium-energy X-ray telescope(ME)is a collimated X-ray telescope onboard the Insight hard X-ray modulation telescope(Insight-HXMT)satellite.It has 1728 Si-PIN pixels readout using 54 low noise application-specific integrated circuits(ASICs).ME covers the energy range of 5–30 keV and has a total detection area of 952cm2.The typical energy resolution of ME at the beginning of the mission is 3 keV at 17.8 keV(full width at half maximum,FWHM),and the time resolution is 255μs.In this study,we present the in-orbit performance of ME in its first 5 years of operation.Methods The performance of ME was monitored using onboard radioactive sources and astronomical X-ray objects.ME carries six 241Am radioactive sources for onboard calibration,which can continuously illuminate the calibration pixels.The long-term performance evolution of ME can be quantified using the properties of the accumulated spectra of the calibration pixels.In addition,observations of the Crab Nebula and the pulsar were used to check the long-term evolution of the detection efficiency as a function of energy.Conclusion After 5 years of operation,742cm2 of the Si-PIN pixelswere stillworking normally.The peak positions of 241Am emission lines gradually shifted to the high-energy region,implying a slow increase in ME gain of 1.43%.A comparison of the ME spectra of the Crab Nebula and the pulsar shows that the E–C relations and the redistribution matrix file are still acceptable for most data analysis works,and there is no detectable variation in the detection efficiency.