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Root zone soil moisture (RZSM) is critical for water resource management, drought monitoring and sub-seasonal flood climate prediction. While RZSM is not directly observable from space, several RZSM products are available and widely used at global and continental scales. This study conducts a comprehensive and quantitative evaluation of eight RZSM products using observations from 58 in situ soil moisture stations over the Huai River basin (HRB) in China. Attention is drawn to the potential factors that contribute to the uncertainties of model-based RZSM, including the errors in atmospheric forcing, vegetation parameterizations, soil properties and spatial scale mismatch. The results show that the Global Land Data Assimilation System Catchment Land Surface Model (GLDAS_CLSM) outperforms the other RZSM products with the highest correlation coefficient (R= 0.69) and the lowest unbiased root mean square error (ubRMSE = 0.018 m 3 m -3), while SMOS Level 4 (L4) RZSM shows the worst performance among eight RZSM products. The RZSM products based on land surface models generally perform better in the wet season than in the dry season due to the enhanced ability to capture of the temporal dynamics of in situ observations in the wet season and the inertia of remaining high soil moisture values even in the dry season, while the SMOS L4 RZSM product, derived from SMOS L3 surface moisture (SSM) combined with an exponential filter method, performs better in the dry season due to the attenuated ground microwave radiation signal caused by the increased water vapour absorption and scattering in the wet season. The underestimated SMOS L3 SSM triggers the underestimation of RZSM in SMOS L4. The overestimated RZSM products based on land surface models could be associated with the overestimated precipitation amounts and frequency, the underestimated air temperature, and the underestimated ratio of transpiration to the total terrestrial evapotranspiration. In addition, the biased soil properties and flawed vegetation parameterizations affect the hydrothermal transport processes represented in different land surface models (LSMs) and lead to inaccurate soil moisture simulation. The scale mismatch between point and footprint also introduces representative errors. The comparison of frequency of normalized soil moisture between RZSM products and in situ observations indicates that the LSMs should focus on reducing the frequency of wet soil moisture, increasing the frequency of dry soil moisture and the ability to capture the frequency peak of soil moisture. The study provides some insights into how to improve the ability of land surface models to simulate the land surface states and fluxes by taking into account the issues mentioned above. Finally, these results can be extrapolated to other regions located in similar climate zones, as they share similar precipitation patterns that dominate the terrestrial water cycle. [ABSTRACT FROM AUTHOR]

Root zone soil moisture (RZSM) is critical for water resource management, drought monitoring and sub-seasonal flood climate prediction. RZSM is not directly observable from space, but several RZSM products are available and widely used at global and continental scales. This paper presents a comprehensive quantitative evaluation of eight RZSM products over the Huai River Basin (HRB) in China. A direct validation is performed using observations from 58 in situ soil moisture stations from 1 April 2015 to 31 March 2020. Attention is drawn to the potential factors that increase the uncertainties of model-based RZSM, such as errors in atmospheric forcing (precipitation, air temperature), soil properties, and spatial scale mismatch. The results show that the Global Land Data Assimilation System Catchment Land Surface Model (GLDAS_CLSM) performs best among all RZSM products with the highest correlation coefficient (R) and the lowest unbiased root mean square error (ubRMSE): 0.69 and 0.018 m3 m−3, respectively. All RZSM products tend to overestimate in situ soil moisture values, except for the Soil Moisture and Ocean Salinity (SMOS) L4 product, which underestimates RZSM. The underestimation of Surface Soil Moisture (SSM) in SMOS L3, caused by underestimated physical surface temperature and overestimated ERA interim soil moisture, triggers the underestimation of RZSM in SMOS L4. The overestimation of RZSM by the other products can be explained by the overestimation of precipitation, the frequency of precipitation events (drizzle effects) and the underestimation of air temperature. In addition, the overestimation of soil clay content and the underestimation of soil sand content in different LSMs lead to higher soil moisture values. The intercomparison of the eight RZSM products shows that MERRA-2 and SMAP L4 RZSM have the highest correlation, which can be attributed to the fact that both products use the catchment land surface model and the atmospheric forcing provided by the Goddard Earth Observing System Model, version 5 (GEOS-5), although the versions differ slightly. [ABSTRACT FROM AUTHOR]

Damage from climate change is widespread throughout the world. This change has brought about calamities, the most prevalent of which is the emergence of numerous droughts which are increasingly threatening human lives. In this paper, we studied the spatial and temporal variations of drought under the effect of climate change in the Huaibei Plain, which is a very important agricultural zone in China. Drought has attracted increasing attention in research due to its heavy impact on agriculture, the environment, livelihood, and food security. The SPEI (Standardized Precipitation Evapotranspiration Index) has been used in this study to express and identify drought events in the Huaibei Plain due to climate change. A general circulation model (GCM), HadGEM2-AO, which was the most appropriate for the study area's precipitation simulation, and three Representative Concentration Pathways (RCP), RCP 2.6, RCP 4.5, and RCP 8.5, were used to analyze and compare the drought effect for the baseline (1985–2017) and the future climate scenarios (2025–2090). At 3 and 6 months, the SPEI successfully detects agricultural drought in temporal and spatial variation. However, according to the analysis, more severe agricultural drought events are foreseen in the future than in the baseline because of climate change. SPEI performed better than SPI in detecting drought in the baseline and simulated data due to increased evapotranspiration. Between the SPEI-3 and SPEI-6, the Pearson coefficient correlation reveals a positive association. The Mann-Kendall test was used to cover the two studied periods in order to establish the drought trend. Both decreasing and increasing trends, in different timescales, were detected by Sen's Slope in the baseline and future periods with all RCPs. [ABSTRACT FROM AUTHOR]

In recent years, flash floods have become increasingly serious. Improving the runoff simulation and forecasting ability of hydrological models is urgent. Therefore, data assimilation (DA) methods have become an important tool. Many studies have shown that the assimilation of remotely sensed soil moisture (SM) data could help improve the simulation and forecasting capability of hydrological models. Still, very few studies have attempted to assimilate SM data from land surface process models into hydrological models to improve model simulation and forecasting accuracy. Therefore, in this study, we used the ensemble Kalman filter (EnKF) to assimilate the China Land Data Assimilation System (CLDAS) SM product into the MISDc model. We also corrected the CLDAS SM and assimilated the corrected SM data into the hydrological model. In addition, the effects of the 5th and 95th percentiles of flow were evaluated to see how SM DA affected low and high flows, respectively. Additionally, we tried to find an appropriate size for the number of ensemble members of the EnKF for this study. The results showed that the EnKF SM DA improved the runoff simulation ability of the hydrological model, especially for the high flows of the model; however, the simulation for the low flows deteriorated. In general, SM DA positively affected the ability of the MISDc model runoff simulation. [ABSTRACT FROM AUTHOR]

The Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) V06 product has been widely studied, but the errors and the source of the errors within IMERG over diverse climate regions still need to be quantified. To this end, the final run gauge-calibrated IMERG V06 (V06C) and uncalibrated IMERG V06 (V06UC) products are comprehensively evaluated here against 2088 precipitation gauges acquired between March 2014 and June 2018 over China. Moreover, V06C and V06UC rainfall estimates are compared against the Precipitation Estimation from Remotely Sensed Imagery using Artificial Neural Networks (PERSIANN)-Climate Data Record (CDR) and the Climate Prediction Center morphing technique (CMORPH) gauge-satellite blended (BLD) products. Continuous statistical indices and two error decomposition schemes are used to quantify their performance. Key results are as follows. (1) Except for V06UC's relatively high underestimation over the Tibetan Plateau (TP) and high overestimation over Xinjiang (XJ), Northeastern China (DB), and Northern China (HB) and CDR's severe overestimation over TP, all four satellite-based precipitation products can generally capture the spatial pattern of precipitation over China. Moreover, the satellite-based precipitation estimates agree better with gauge observations over humid regions than over semi-humid, semi-arid, and arid regions. (2) All the statistical indicators show that CDR has the worst performance, whereas BLD is the best precipitation product. As for the two IMERG products, V06C has improved V06UC's precipitation estimate. Results show that the gauge calibration algorithm (GCA) used in IMERG has active effect in terms of r, POD, and CSI. (3) Within all subregions, all four satellite-based precipitation products demonstrate their worst performance over the arid XJ region which exhibits the highest FAR and lowest POD and CSI values among all regions. (4) In terms of intensity distribution, for summer over China, the four satellite-based precipitation products generally overestimate the frequency of moderate precipitation and light precipitation events (<25 mm/day) and underestimate heavy precipitation events (>42 mm/day). (5) The relative bias ratio (RBR) analysis shows that the contribution of missed precipitation tends to be lower over wetter regions. In addition, for the same climate region, the contribution of missed precipitation is clearly lower in summer than in winter. In summer, false precipitation dominates the total error, whereas missed and false precipitation are the two leading error sources in winter. Future algorithm refinement efforts should focus on decreasing FAR in summer and winter and improving missed snow events during the winter. [ABSTRACT FROM AUTHOR]

As a typical semiarid farming-pastoral ecotone sensitive to the environmennt, the Plain of West Liaohe Basin (WLBP) is currently experiencing drastic environmental changes. To identify how environmental change affect vegetation in the WLBP, we analysed spatiotemporal variation characteristics of Ecological environment factors based on monthly and annual air temperature (T), precipitation (P) and Normalized Difference Vegetation Index (NDVI) from 1982 to 2015. And the correlations between them were investigated by correlation analysis (Simple correlation, partial correlation and complex correlation) at temporal and spatial scale. The results showed that: (1) the vegetation growth of the WLBP showed ameliorated trend, with a change rate of 0.004/yr.; (2) P was more sensitive to NDVI than T; (3) and the influence of hydrothermal changes on vegetation growth was more significant than that of the change of single climate factors at time scales; (4) the effects of anthropogenic factors on vegetation change were 75.07% (1982–1993) and 98.08% (1994–2015), respectively. At the temp-special scales, P&T and land use type change (LUCC) were the main climatic and anthropogenic factors that affect vegetation changes, respectively. [ABSTRACT FROM AUTHOR]

The Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement mission (IMERG) aims to deliver the "best" precipitation estimation from space and has attracted much attention. The Version 05 of IMERG products including the near‐real‐time "Early" and "Late" run products (IMERG‐E and IMERG‐L, respectively), and the post‐real‐time "Final" run IMERG product (IMERG‐F) are assessed at both national and basin scales against gauge observations over Mainland China for a 4‐year period (from April 2014 to March 2018). As control products for comparison, their predecessor Tropical Rainfall Measurement Mission (TRMM) Multi‐satellite Precipitation Analysis (TMPA) products (i.e., TMPA‐RT and TMPA‐V7) are also employed. Components analysis confirms the best performance of IMERG‐F among the five SPEs in three different categories. All five SPEs feature increasing bias and root mean square difference (RMSD) with increasing daily gauge total precipitation, and such issue is less pronounced for IMERG‐F—as evidenced by the lowest bias and RMSD across all precipitation rates. Besides, compared to TMPA, IMERG products exhibit better accuracy in detecting real precipitation evens, especially for light‐to‐medium rain (<60 mm/day), but they do not demonstrate significant improvement in the assessment of severe over/underestimation. In the basin‐scale comparison, all five SPEs catch the key variation feature of basin‐averaged precipitation time series (except TMPA‐RT over Continental River Basin). Overall, IMERG‐F demonstrates the best performance over all nine basins despite the slight overestimation, followed by TMPA‐V7. IMERG‐E and IMERG‐L show performance close to or even better than TMPA‐V7 in terms of the correlation coefficient and RMSD. Key Points: Both near‐real‐time and post‐real time TMPA and IMERG products were evaluated and compared over the Mainland ChinaIMERG‐F exhibits slight overestimation but perform best in three different categoriesIn the basin‐scale comparison, IMERG‐F outperforms TMPA‐V7, with IMERG‐E and IMERG‐L show performance close to or even better than TMPA‐V7 [ABSTRACT FROM AUTHOR]

Agricultural drought threatens global water security, food security, and natural ecosystems. Accurate identification of agricultural drought is a crucial task to mitigate its consequences. However, it is challenging to achieve reliable and accurate regional agricultural drought assessment in both wet and dry climates at the same time. Therefore, the objective of this study is to identify a reliable and accurate agricultural drought index that performs well in both dry and wet climates. Drought indices such as the Standardized Precipitation Index (SPI), the Vegetation Condition Index (VCI), the Soil Moisture Anomaly index (SMA), and the Drought Severity Index (DSI) were calculated and compared against in situ drought information devised by official sources in China. The results showed that: (1) DSI based on the Global Land Data Assimilation System (GLDAS) products performed the best in identifying agricultural drought in both dry and wet climate regions of China. (2) Agricultural regions such as Northern arid and semiarid regions, Northeast China Plain, Huang-Huai-Hai Plain, and Loess Plateau, experienced moderate and severe agricultural droughts with a frequency of 20%. (3) The frequency of agricultural droughts observed in Northern arid and semi-arid regions and Northeast China Plain has slowed significantly over the last two decades with a significance level of 0.01. On the other hand, the number of agricultural droughts has increased in Yunnan-Guizhou Plateau since 2002. [Display omitted] • The accuracy of the five drought indices was explored. • The drought results were validated with in situ data from official sources. • Drought Severity Index based on GLDAS has better performance in China. • The agricultural drought in the north moderated and the south aggravated in China. [ABSTRACT FROM AUTHOR]

The West Liaohe Plain (WLP) is a typical crop-pastoral ecotone of the semiarid area in Northwestern, China. Land use/cover change (LUCC) of the WLP might endanger this ecosystem, triggering long-lasting environmental concerns. LUCC data for China (1980–2020) and hydrometeorological data were analyzed to reveal factors contributing to change and explore sustainable development opportunities. The results show that characteristics of the main land-use types in the WLP have changed significantly, especially cultivated land area, which increased by 15.2% and 6.79% during the periods 1980–1995 and 2000–2020, respectively. Response relationships were observed due to natural (precipitation, temperature, and runoff) and anthropogenic (economy) factors and LUCC. Between 2000 and 2020, the impact of anthropogenic factors on cultivated land was stronger than on grassland at the class and landscape level, using the landscape indices which were selected, including percent of landscape (PLAND), number of patches (NP), largest patch index (LPI), and Shannon's evenness index (SHEI). Expansion of cultivated land from 1990 to 1995 was not only related to anthropogenic factors but also to hydrological & climatic factors. The results of this study have the potential to influence sustainable land resource development. [Display omitted] • LUCC in study area is affected by natural and anthropogenic factors. • Response between environmental factors and LUCC has periodicity. [ABSTRACT FROM AUTHOR]

Groundwater-dependent ecosystems (GDEs) in the lower reaches of arid inland river basins of Northwest China have degraded because of a decrease in water supply caused by excessive socio-economic development in the middle reaches of the river. The degraded GDEs have resulted in serious eco-environmental problems, such as drying of terminal lakes, river channel shifts, declines in riparian forest, desert activation, intensification of desert dust storms, and salinization of secondary soils. These problems have further hindered local and regional efforts at sustainable development. An ecosystem conservation and restoration project was initiated in 2000 in order to deal with the eco-environmental problems. The project has been underway for 15 years, but problems still exist. Using the Heihe River and Tarim River basins as examples, this paper explains the ecological–environmental problems and their causes, describes with the status of ecosystem restoration and conservation, analyzes the problems in the ongoing ecosystem conservation and restoration, and suggests strategies for achieving sustainable ecosystem restoration in the lower reaches of the arid inland river basins of Northwest China. [ABSTRACT FROM AUTHOR]

Six global climate models (GCMs) from Coupled Model Intercomparison Project Phase 5 under three Respectively Concentration Pathways (RCP2.6, RCP4.5 and RCP8.5) were used to assess the impact of climate change on streamflow for the Huangnizhuang catchment (HNZ) in China. Change factor method was used for bias correction between GCM outputs and observations and the SWAT model was used to simulate the hydrological processes. The results indicated that the SWAT model performed well in the study catchment with a monthly Nash-Sutcliffe efficiency (NS) of 0.93 and 0.91 and daily NS of 0.63 and 0.68 for calibration and validation periods respectively. Their corresponding relative errors were −2.2 and 8.9, and −2.6 and 8.5 % respectively. The ensemble of multi-GCMs projected an increase of precipitation in the middle and end of twenty-first century over the HNZ, ranging from −2.4 to 9 %. However, streamflow is likely to decline in the future, ranging −6.9 to 0.8 %, mainly due to an increase of evapotranspiration in a warming world, as air temperature shows steadily increases for all the GCMs and RCPs. Average monthly streamflow from six GCMs are likely to increase in August and September but decline from October to June. The associated uncertainties of the reported results were also discussed. It includes, but is not limit to, different GCMs, emissions scenarios, downscaling techniques as well as hydrological simulations. The results of this study can inform planning of long-term basin water management strategies taking into account global change scenarios. [ABSTRACT FROM AUTHOR]

This study aims to identify the parameters that are most important in controlling the Noah land surface model (LSM), the analysis of parameter interactions, and the evaluation of the performance of parameter optimization using the parameter estimation software PEST. We found it necessary to analyze parameter sensitivity in order to properly simulate hydrological variables such as latent heat flux in the Huaihe River Basin, China. The parameters under study in the Noah LSM link thermodynamic and hydrological parts into a complete model. To our knowledge, this parameter interaction in the Noah LSM has never been studied before. There are, however, several studies concerning the influence of vegetation types and climate conditions on parameter sensitivity of the Noah LSM. Three sensitivity analysis methods, the including local sensitivity analysis method SENSAN, regional sensitivity analysis, and Sobol's method, were tested. Five experimental sites in the Huaihe River Basin were chosen to perform the simulations. The results show that the Noah LSM parameter sensitivities were impacted by the choice of the analysis method. The local method SENSAN often produced significant differences in results compared to the two global methods. The parameter interactions investigated made a significant contribution towards elucidating how one process influences another in the Noah LSM. The results show that parameters were not transferable solely based on vegetation types but also rely on climate conditions. According to the sensitivity analysis results, four sensitive parameters were chosen to be optimized using the PEST method. PEST is a widely used method for estimating parameters in models. Root-mean-square error was used to evaluate the effect of the optimization. Generally in all sites, the optimized parameters values perform better than the original parameter values. [ABSTRACT FROM AUTHOR]

The Laohahe River basin, located in northeastern China, was selected as a case study to quantify the magnitude of changes in land use and land cover (LULC) during the period from the 1970s to the 2010s and its quantitative effects on surface hydrology, based on hydrologic modeling of a distributed Soil and Water Assessment Tool ( SWAT) model and catchment-scale spatial information analyses from remotely sensed data. Land cover maps with 30-m resolution from 1979, 1989, 1999, and 2007, interpreted from Landsat images, were used to analyze LULC changes during the last decades. The observed daily hydro-meteorological data from 1970 to 2006 were divided into four periods: 1970-1979, 1980-1989, 1990-1999, and 2000-2006. The SWAT model was utilized for each period with four LULC scenarios, which were developed by using the four LULC maps. Annual and monthly surface runoff and actual evapotranspiration (AET) were selected as important hydrologic elements to indicate the hydrologic response to LULC changes. The results revealed that distinct land cover changes occurred in the basin; the most important change was the conversion among vegetation cover classes of cropland, forest land, and grassland. Surface runoff always decreased as the LULC scenarios changed from 1976 to 2007 during all periods, but AET did not change regularly following the present LULC changes. Multiple regression equations between quantitative changes of LULC and hydrologic elements were developed. The equations indicated that the changes in three vegetation cover classes of grassland, cropland, and forest areas significantly affected hydrological elements and the increases of vegetation cover class areas all led to decreases in surface runoff and increases in AET. Moreover, given the same quantitative area change, the effects of cropland on hydrologic elements were the strongest, the effects of forest land were the second strongest, and the effects of grassland were the third. The effects of LULC changes on the seasonal distribution of hydrologic elements were also investigated. The results demonstrated that LULC changes have less influence on surface runoff and AET in nonflood seasons, but more influence in flood seasons, especially in July, August, and September, when the crops grow best. The results of this study improved the understanding of hydrologic responses to LULC changes and provided needed knowledge for agricultural decisions and the management of land use and integrated water resources. [ABSTRACT FROM AUTHOR]

When hydrology model parameters are determined, a traditional data assimilation method (such as Kalman filter) and a hydrology model can estimate the root zone soil water with uncertain state variables (such as initial soil water content). The simulated result can be quite good. However, when a key soil hydraulic property, such as the saturated hydraulic conductivity, is overestimated or underestimated, the traditional soil water assimilation process will produce a persistent bias in its predictions. In this paper, we present and demonstrate a new multi-scale assimilation method by combining the direct insertion assimilation method, particle swarm optimisation (PSO) algorithm and Richards equation. We study the possibility of estimating root zone soil water with a multi-scale assimilation method by using observed in situ data from the Wudaogou experiment station, Huaihe River Basin, China. The results indicate there is a persistent bias between simulated and observed values when the direct insertion assimilation surface soil water content is used to estimate root zone soil water contents. Using a multi-scale assimilation method (PSO algorithm and direct insertion assimilation) and an assumed bottom boundary condition, the results show some obvious improvement, but the root mean square error is still relatively large. When the bottom boundary condition is similar to the actual situation, the multi-scale assimilation method can well represent the root zone soil water content. The results indicate that the method is useful in estimating root zone soil water when available soil water data are limited to the surface layer and the initial soil water content even when the soil hydraulic conductivities are uncertain. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The article presents a study which investigates the impact of biological soil crusts (BSCs) formation and revegetation programme on soil hydrological processes in the Shapotou region in China. It states that HYDRUS-ID model simulations and field measurements were used to examine the impact. It mentions that the study found that BSCs formation changes the patterns of soil water storage and increases its moisture content. Moreover, the changes are evident in plant community succession.

Abstract: In the hilly regions of China, developing sustainable agriculture requires implementing conservation management practices that prevent soil erosion and conserve soil and water resources. In the semiarid northwest Loess Plateau, the primary conservation management practice is terracing. Numerical simulation of soil water dynamics in terraces is potentially an efficient means of investigating the effects of terrace design on moisture retention, but little information is available on the accuracy of such simulations. In this work, we evaluated the accuracy of HYDRUS-2D simulations of water infiltration and redistribution in fallow, level, dryland terraces located in the Loess Plateau. The simulated soil water content distributions were in good agreement with experimental data. Modeling analyses showed that about one-third of the evaporative water losses occurred from the terrace riser surface. To prevent such losses, it is advisable to mulch the riser and minimize the riser surface area. The simulations also demonstrated that with other dimensions equal, wide terraces retain more water on a percentage basis than narrow ones due to a lower evaporating surface area are per unit volume of water storage. With other design considerations being equal, wide beds and minimal riser surface areas will likely enhance water capture and retention. Future analyses of terrace moisture dynamics may additionally include simulations of root water uptake, surface ponding, and runoff. [Copyright &y& Elsevier]

• A flash drought identification method is developed, and it includes rapid intensification and drought period and tracks flash drought paths in time and space. • This is the first specific study of the regional characteristics of flash droughts in a humid and semi-humid region. • Flash droughts in the Huaibei Plain most likely occur in May, August and October. • The main flash drought transfer pathway in the Huaibei Plain is an east to west transition, accounting for 44% of the flash droughts. Flash droughts are regional phenomena that can manifest in large areas with rapid intensification for a period of time. Few studies have considered the spatial pathways of flash droughts or their drought period. This study uses the five criteria based on the standardized evaporative stress ratio (SESR) method to identify flash droughts, and when a SESR value recovers to the 20th percentile, the flash drought is considered to terminate. To define spatially continuous flash droughts accurately, the usual order of first calculating drought patches and then identifying flash droughts is reversed to first identify flash droughts on the grid and then determine flash drought patches. In addition, this study tracks the spatial paths of flash droughts via centroid transfers of flash drought patches. Using MOD16 data, the methodology is evaluated by analyzing the regional characteristics of flash droughts in the Huaibei Plain of China from 2001 to 2019. The flash droughts in this region most frequently tracked in the northeast and west. The average flash drought duration was 31 days, of which the rapid intensification period was 18 days and drought period was 14 days. Flash drought events in this region mostly occurred in May, August and October, and east to west transition and non-transitions, which accounted for 44% and 22%, respectively, were the main spatial track paths. Only 26% of flash drought events transitioned to long term drought events. This study expands our knowledge of the evolution process of flash droughts to space-time dimensions, which is essential for flash droughts early warning and agricultural water management. [ABSTRACT FROM AUTHOR]

It is of great challenge to accurately predict flash floods for small to medium catchments (SMC) in mountainous areas, for which parameter calibration strategies are crucial for model performance. This study investigates the influence of calibration parameter selection on flash flood simulations using a rainfall–runoff model, MISDc-2L (Modello Idrologico Semi-Distribuito in continuo–2 layers), at hourly scale for SMC in the Huai River basin of China over the 2010–2015 period. We investigated model performances under different calibration schemes, where different amounts of model parameters were selected for the calibration procedure. The model clearly performed better in the case involving calibration of partial sensitive parameters than that of a full parameter set with respect to the peaks, the hydrographs and the base-flow of flood simulation, especially after including maximum water capacity (W_max) in the calibration. This finding was consistently valid under different model calibration experiments, including single event, "split-sample" test and combined events at different flood magnitude levels. We further found that the model performed better for high magnitude flood events than medium and low ones, but clear improvements can be achieved for low and medium magnitude flood events with careful calibration parameter selection. Our study suggests that calibration parameter selection is important for flash flood event simulations with the MISDc-2L model for SMC in the Huai River basin of China; specifically, the reduction in calibration parameter amount and the inclusion of W_max in calibration remarkably improve flood simulation. [ABSTRACT FROM AUTHOR]

• The effect of climate change on groundwater contributions to winter wheat are investigated. • The exchanges between soil water and groundwater will increase in the future. • Climate change alters groundwater contributions to the root zone of winter wheat during each growth period, especially in the wet season. • Groundwater contributions remove the need for irrigation under RCP 4.5 in wet and normal growing seasons. Winter wheat is the predominant crop in the Huaibei Plain of Anhui Province, China. The Huaibei Plain has a transitional climate and a shallow water table. As global warming accelerates, climate change in the area may impact agricultural production. In shallow groundwater regions, capillary rise can be large, and it can act as an important water resource for crop use. However, it is not clear how climate change will impact groundwater depths and crops. Based on local weather data from 1960 to 2010, a general circulation model, HadGEM2-AO, which performed best in simulating precipitation in the study area, and three Representative Concentration Pathways (RCPs, RCP 2.6, RCP 4.5 and RCP 8.5), the validated numerical model HYDRUS-1D was used to estimate the daily groundwater contribution to the crop root zone in 2011–2100 of different hydrological growing seasons. The largest groundwater contribution and deep drainage occurred at a water table depth of 1.5 m. The groundwater contribution to crop root zones was largest for RCP 2.6, and the deep drainage was largest for RCP 8.5. The results indicated that climate change could alter the distribution of the groundwater contribution in each growing period. Meanwhile, the phenophases of winter wheat for RCP 2.6 were delayed while they were advanced for RCP 4.5 and RCP 8.5. In wet and normal seasons, there was no need for irrigation for RCP 4.5 during the whole growing season. However, in a dry season, a small amount of irrigation was still needed during the maturity stage for RCP 2.6. In general, the total irrigation amounts needed for winter wheat growth were reduced for all hydrological conditions with the three RCPs. [ABSTRACT FROM AUTHOR]

Sub-seasonal drought forecasting is crucial for early warning in estimating agricultural production and optimizing irrigation management, as forecasting skills are relatively weak during this period. Soil moisture exhibits stronger persistence compared to other climate system quantities, which makes it especially influential in shaping land-atmosphere feedback, thus supplying a unique insight into drought forecasting. Relying on the soil moisture memory, this study investigates the combination of multiple deep-learning modules for sub-seasonal drought indices hindcast in the Huai River basin of China, using long-term ERA5-Land soil moisture records with a noise-assisted data analysis tool. The inter-compared deep-learning models include a hybrid model and a committee machine framework. The results show that the performance of the committee machine framework can be improved with the help of series decomposition and the forecasting skill is not impaired with the lead time increases. Overall, this study highlights the potential of combining deep-learning models with soil moisture memory analysis to improve sub-seasonal drought forecasting. • The soil moisture anomaly index has reliable predictability at a sub-seasonal scale. • The noise-assisted tool enhances the forecasting skill of the committee models. • The committee model shows stable positive skills at all lead times. [ABSTRACT FROM AUTHOR]

Projections of hydrological changes are associated with large uncertainties from different sources, which should be quantified for an effective implementation of water management policies adaptive to future climate change. In this study, a modeling chain framework to project future hydrological changes and the associated uncertainties in the Xijiang River basin, South China, was established. The framework consists of three emission scenarios (ESs), four climate models (CMs), four statistical downscaling (SD) methods, four hydrological modeling (HM) schemes, and four probability distributions (PDs) for extreme flow frequency analyses. Direct variance method was adopted to analyze the manner by which uncertainty sources such as ES, CM, SD, and HM affect the estimates of future evapotranspiration (ET) and streamflow, and to quantify the uncertainties of PDs in future flood and drought risk assessment. Results show that ES is one of the least important uncertainty sources in most situations. CM, in general, is the dominant uncertainty source for the projections of monthly ET and monthly streamflow during most of the annual cycle, daily streamflow below the 99.6% quantile level, and extreme low flow. SD is the most predominant uncertainty source in the projections of extreme high flow, and has a considerable percentage of uncertainty contribution in monthly streamflow projections in July–September. The effects of SD in other cases are negligible. HM is a non-ignorable uncertainty source that has the potential to produce much larger uncertainties for the projections of low flow and ET in warm and wet seasons than for the projections of high flow. PD contributes a larger percentage of uncertainty in extreme flood projections than it does in extreme low flow estimates. Despite the large uncertainties in hydrological projections, this work found that future extreme low flow would undergo a considerable reduction, and a noticeable increase in drought risk in the Xijiang River basin would be expected. Thus, the necessity of employing effective water-saving techniques and adaptive water resources management strategies for drought disaster mitigation should be addressed. [ABSTRACT FROM AUTHOR]

• A real-time ice-jam risk assessment system was developed to better regulate reservoir discharges. • The modelling system improves ice-jam flood predictions considering the influence of reservoir regulation. • Machine learning with deterministic modelling provides more accurate ice-jam flood predictions of regulated rivers. • The modelling system was successfully verified for the Sanhuhekou bend reach regulated by the Sanshenggong reservoir. To effectively alleviate ice-jam flood disasters, it is necessary to carry out hazard assessments and predictions of ice-jam flooding influenced by the operational scheme of a reservoir. However, traditional hydrologic flood routing techniques cannot effectively address the huge uncertainties caused by the many factors that lead to ice-jam flooding. In this paper, a hazard assessment system for regulating flood discharge schemes is developed; it is composed of a machine learning (ML) model, Long Short-Term Memory (LSTM), and a river-ice dynamic model (RIVICE) within a probabilistic method. The modelling system is to aid in the challenge of predicting ice-jam flooding downstream of reservoirs. The LSTM model forecasts the downstream flow under the operational discharge scheme and, combined with the RIVICE model, the backwater level profile of ice jams can be forecasted. Furthermore, a set of backwater level profiles can be provided by probabilistic modelling, and the probability of ice-jam flood inundation can be calculated by comparing backwater levels with the elevation of the river bank; this information can be used to warn of the hazard induced by operational discharges to better aid in the preparedness and mitigation of ice-jam floods. This system was tested successfully for the ice-cover breakup period in the spring of 2008 and 2018 along the Sanhuhekou bend reach of the Yellow River in China. [ABSTRACT FROM AUTHOR]

In recent years, due to the intensification of human activities, water ecological problems are gradually increasing. As the third largest freshwater lake in China, Lake Taihu is an important drinking water source for several densely populated cities in China. The prominent water ecological problem in this area is mainly Cyanobacteria Bloom. Cyanobacterial blooms have been erupting which have affected local residents' drinking water and caused losses to the national economy. Based on the interpretation results of MODIS data in the Lake Taihu region from 2004 to 2014, this paper analyzes the main driving factors of cyanobacterial bloom are phosphorus and potassium through the correlation analysis of nitrogen, phosphorus, potassium and cyanobacteria area, normalizes nutrient, and identifies that the water level of Lake Taihu is the influencing factor of cyanobacterial bloom. A Lake Taihu cyanobacteria bloom hazard function is constructed to quantitatively assess the losses (economic losses) caused by cyanobacterial blooms from 2001 to 2012, supporting for cyanobacteria control management in Lake Taihu. [Display omitted] • The S-shaped curve shape of cyanobacteria bloom outbreak process is deduced. • Establish the disaster function relationship, clarify the physical meaning of relevant parameters. • The cyanobacteria disaster function of Taihu Lake is constructed to quantitatively evaluate the disaster loss. [ABSTRACT FROM AUTHOR]

Available phosphorus (AP) deficiency in soil is an important limiting factor in agricultural and forestry production. Thus, microbes with high P mobilization capacity are of great concern, especially for acidic soil where most P is fixed by Fe. Camellia oleifera is a typical acidic soil–planted oil crop with a high status in forestry economic systems in China. In this study, Streptomyces sp. CoT10 was screened and identified from C. oleifera endophytes. CoT10 could effectively mobilize different P resources, particularly showing a high solubility of 72.49 mg L−1 for FePO 4 , and was prominent in the production of different siderophores. Here, we initially assumed and confirmed that the Fe–P mobilization function of CoT10 was directly mediated by its siderophores. Application of CoT10 in C. oleifera could benefit P fraction transformation by mobilizing insoluble P and Fe, thus improving 15.35% AP content in soil and promoting plant growth with high P and Fe acquisition. In addition, CoT10 inoculation improved microbial diversity, with the proportions of some dominant groups, such as Actinobacteria, Proteobacteria, and Firmicutes, obviously enriched by 21.32%, 7.93%, and 38.95%, respectively. In addition, redundancy analysis (RDA), network analysis and functional prediction indicated that CoT10 inoculation improved the microbial interactions in C. oleifera rhizosphere soil and balanced the soil microbial P- and Fe-related functional roles. The results of Streptomyces sp. CoT10 and its siderophores provide a valuable approach to regulate P mobilization in iron-rich soil for the sustainable and eco-friendly development of plant-related industries. [Display omitted] • Endophyte Streptomyces sp. CoT10 mobilizes insoluble Fe-fixed P through siderophores. • CoT10 broadly improves AP content for soils from various regions in southern China. • CoT10 benefits soil bacterial community and P-cycling function. • Microbial siderophores provide a novel approach to solve P-deficient problem in soil. [ABSTRACT FROM AUTHOR]

A new soil moisture and soil temperature wireless sensor network (the SMN-SDR) consisting of 34 sites was established within the Shandian River Basin in 2018, located in a semi-arid area of northern China. In this study, in situ measurements of the SMN-SDR were used to evaluate 24 different soil moisture datasets grouped according to three categories: (1) single-sensor satellite-based products, (2) multi-sensor merged products, and (3) model-based products. Triple collocation analysis (TCA) was applied to all possible triplets to verify the reliability and robustness of the results. Impacts of different factors on the accuracy of soil moisture products were also investigated, including local acquisition time, physical surface temperature, and vegetation optical depth (VOD). The results reveal that the latest Climate Change Initiative (CCI) -combined product (v06.1, merging extra low-frequency passive microwave data) had the best agreement with in situ measurements from the SMN-SDR, with the lowest ubRMSE (< 0.04 m3/m3) and highest R (> 0.6). Among all single-sensor retrieved soil moisture products, the Soil Moisture Active Passive (SMAP) products performed best in terms of R (> 0.6) and ubRMSE (close to 0.04 m3/m3), with the SMAP-MDCA (Modified Dual Channel Algorithm) being slightly better than the baseline SCA-V (Single Channel Algorithm-Vertical polarization). Importantly, the newly developed SMAP-IB product, which does not use auxiliary data, delivered the best bias statistics and higher VOD values compared with the drier SMAP retrievals, suggesting that the low VOD values (underestimated vegetation effects) may be the major factor causing the dry bias of SMAP products in this study area. It was also found that TCA may systematically overestimate the correlation and underestimate the ubRMSE of soil moisture products as compared with ground-based metrics. TCA-based metrics may vary considerably when using different triplets, due to the TCA assumptions being violated even with the most conservative triplets (in this case an active product, a passive product, and a model-based product). Redundant TCA-based metrics from multiple independent triplets could be averaged to increase the accuracy of final TCA estimates. This study is the first to use in situ measurements from the SMN-SDR to conduct a comprehensive evaluation of commonly used, multi-source soil moisture products. These results are expected to further promote the improvement of satellite- and model-based soil moisture products. • First use of a new ground network to assess different soil moisture products. • There is an obvious deviation between TCA- and ground-based metrics. • SMAP-MDCA retrieval is more accurate than SCA-V in SMN-SDR. • Underestimation of the vegetation effect is the main factor of SMAP underestimation in SMN-SDR. • First evaluation of the newly developed SMAP-IB product. [ABSTRACT FROM AUTHOR]

• A novel method to obtain a reliable gridded surface soil moisture dataset. • The spatially continuous evaluation of satellite-based soil moisture products. • Strong spatial heterogeneity and small seasonal differences between SMOS and SMAP. Soil moisture often governs the exchange of water between the land surface and the atmosphere and, as a result, has a profound effect on the global water and energy cycles. With the launch of the two new L-band satellite missions tasked with retrieving surface soil moisture (i.e., Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active/Passive (SMAP) missions), new possibilities exist for quasi-global soil moisture monitoring. Here, new soil moisture estimates (CLDAS-BPNN), generated by fusing a land surface model soil moisture product (CLDAS) using in-situ observations via a Back Propagation Neural Network (BPNN) method, are used to evaluate four satellite-derived soil moisture products (SMOS-L3, SMOS-IC, SMAP_L3_SM_P, and SMAP_L3_SM_P_E) over the highly irrigated Huai River Basin in China. Results indicate that the post-processing of CLDAS (to generate CLDAS-BPNN) reduces soil moisture errors (particularly bias) and preserves temporal correlations with in-situ observations. Due to extensive irrigation present in the basin, CLDAS-BPNN soil moisture time series have weak seasonal differences and the spatial distribution of their means does not reflect known precipitation patterns. Based on the validation of SMOS and SMAP soil moisture retrievals against CLDAS-BPNN, several key findings can be obtained. First, SMAP retrievals are recommended for locations possessing both valid SMOS and SMAP retrievals. However, SMOS provides generally better spatial support than SMAP. Second, the four satellite retrievals present no significant seasonal variation in time-varying errors, and larger soil moisture underestimation is observed in winter than in summer. Finally, all four satellite products exhibit degraded accuracy in mountainous and forested areas of the Huai River Basin relative to flatter terrain in the Huaibei plain. [ABSTRACT FROM AUTHOR]