Catchment In China Research Articles

Investigating hydrological processes using explainable deep-learning models

This paper examines how meteorological factors influence streamflow. It is crucial for water resources planning and management. It develops three deep learning models (LSTM, RF and GBM) and compares their performance with multiple linear regression (MLR). LSTM is identified as the best model, and SHAP is used to analyze the contributions of different factors to streamflow. The model is applied to the Tang River catchment in China. The results indicate that: 1) DL models outperform MLR in nonlinear regression; 2) the ranking of meteorological factors contributing to streamflow is: precipitation, evaporation, relative humidity, sunshine duration, ground temperature, and wind speed; 3) these factors affect streamflow within a 12-day lead time, with significant contributions closer to the prediction date; 4) during droughts, precipitation has a reduced effect on streamflow, suggesting groundwater recharge is likely recharged by groundwater. The model enhances our understanding of how meteorological factors interact with streamflow dynamics.

Antibiotic resistome dynamics in agricultural river systems: Elucidating transmission mechanisms and associated risk to water security

Usage of antibiotics in agriculture has increased dramatically recently, significantly raising the influx of antibiotic resistance genes (ARGs) into river systems through organic manure runoff, seriously threatening water security. However, the dynamics, transmission mechanisms, and potential water security risk of ARGs, as well as their response to land use spatial scale and seasonal variations in agricultural river systems remain unclear. To address these challenges, this work employed metagenomic technique to systematically evaluate the pollution and dissemination of ARGs in overlying water and sediment within a typical agricultural catchment in China. The results demonstrated significant differences between overlying water and sediment ARGs. Overlying water dominated by multidrug ARGs exhibited higher diversity, whereas sediment predominantly containing sulfonamide ARGs had higher abundance. The dynamics of ARGs in overlying water were more responsive to seasonal variations compared to sediment due to greater changes in hydrodynamics and nutrient conditions. The profiles of ARGs in overlying water were largely regulated by microbiota, whereas mobile genetic elements (MGEs) were the main forces driving the dissemination of ARGs in sediment. The variation in dissemination mechanisms led to different resistance risks, with sediment presenting a higher resistance risk than overlying water. Furthermore, Mantel test was applied to discover the impact of land use spatial scale and composition on the transmission of ARGs in river systems. The findings showed that cultivated land within 5 km of the riverbank was the key influencing factor. Cultivated land exacerbated ARGs spread by increasing MGEs abundance and nutrient concentrations, resulting in the abundance of ARGs in high-cultivated sites being twice that in low-cultivated sites, and raising the regional water security risk, with a more pronounced effect in sediment. These findings contribute to a better understanding of ARGs dissemination in agricultural watersheds, providing a basis for implementing effective resistance control measures and ensuring water security.

Prominent Role of Sulfate Reduction in Considerable Sulfur Retention in a Subtropical Soil

AbstractThe sulfur biogeochemical cycle controls the sulfur dynamics in the soil. In contrast to almost all deposited sulfur leaching out in temperate catchments, approximately 80% of the deposited S is retained in the catchments in subtropical regions. However, the mechanisms for sulfur retention were unclear, hindering the understanding of potential threats of legacy sulfur with environmental changes. Here, we demonstrated that sulfate reduction (as sulfide fixed in soil) was a prominent yet overlooked mechanism for sulfur retention in addition to the widely recognized sulfate adsorption in soil, based on a study on soil sulfur storage and stable isotope signatures within entire soil profiles in a typical subtropical catchment in China. Using a dual‐isotopic model, the sulfate reduction flux was further determined to be 30% of the S deposition. Due to a lot of deposited sulfur fixed via sulfate reduction, the release of soil legacy sulfur would be less in response to decreasing sulfur deposition compared to the projections only considering adsorption. However, the remobilization of large amounts of reduced S should be regarded as a threat to the environment under climate change in the future.

Factors influencing calibration of a semi-distributed mixed runoff hydrological model: A study on nine small mountain catchments in China

Study Region: Nine mountain catchments (5 in northern and 4 in southern China).Study Focus: Flash floods in small mountain catchments are some of the most frequent and most destructive natural disasters of China. Mixed runoff hydrological model is a popular method for flood warning and forecasting. However, the key factors affecting runoff components in hydrological model parameter calibration need to be further studied. In this study, four different overland flow routing techniques are implemented into a semi-distributed mixed runoff hydrological model system (MRHMS), which are the triangle unit hydrograph (TUH), geomorphological instantaneous unit hydrograph (GIUH), geomorphoclimatic instantaneous unit hydrograph (GcIUH) and kinematic wave (KW). MRHMS-KW is the previously developed spatiotemporally-mixed runoff (SKBY) model.New hydrological insights for the region: The study finds that sensitivity parameters of the MRHMS in northern and southern China are related to their main runoff components. Different parameter orders in automatic calibration of MRHMS result in different model performances, and the suggested parameter orders are consistent with the sensitivity of parameters in northern and southern China. Soil moisture and primary runoff components in northern and southern China differ significantly. Only MRHMS-GcIUH can achieve similar performance in northern and southern catchments. MRHMS-GcIUH therefore enhances our understanding of runoff formation mechanisms in the heterogeneous hydroclimatic landscape of China and is a promising tool for a nationwide flood prediction and flood warning system.

Balancing water quality impacts and cost-effectiveness for sustainable watershed management

Balancing clean water and economic growth remains challenge to achieve the Sustainable Development Goals (SDGs) issued by the United Nations. In this study, the water quality impacts and cost-effectiveness analysis of catchment actions were integrated by a new proposed watershed management framework (WAM-SDGs). The source appointment and control practices were incorporated based on physical-based model and heuristic algorithm. The tradeoffs between different targets were balanced using multiple attribute decision-making (MADM) and Pareto score approaches. The method was tested in two paired catchments including typical urban and agricultural catchments in China. The results indicated that 1) the cost-effectiveness and reliability of catchment action was improved by 4% and 3.3% compare to traditional methods. 2) The restriction of urban pollution is mainly attributable to strong spatial heterogeneity, while that of agricultural pollution control is attributed to lack of investment. 3) For water quality improvement, sources and sinks of pollutants are quantified and used as priority areas for catchment action. Due to high pollution intensity and control reliability, urban catchments should be given priority and agricultural catchments are more cost-effectiveness when fund is no longer a limiting factor. The economic-based and water-quality-based protection strategies are suggested for urban and agricultural catchment, respectively. This new framework could be extended to other catchments for global sustainable development.

An improved non-point source pollution model for catchment-scale hydrological processes and phosphorus loads

Non-point source (NPS) pollutants may cause water quality deterioration and eutrophication, posing a significant threat to aquatic systems. Understanding and modelling surface and sub-surface hydrological processes and nutrient cycles are therefore essential for water resources management and pollution control. This paper presents a new hydrological-water quality model for application in humid and semi-humid catchments in China, considering both hydrological processes, and nutrient transport and transformation. The model divides the soil into three layers, and each layer is implemented with individual computational procedures for soil wetness and nutrient process. Nutrient transport is driven by hydrological processes and follows the same pathways as water flow in the model: surface runoff, infiltration, and outflow from individual soil layers. River channels are described separately with routines to account for the turnover of nutrients. Model parameters are selected according to literature/open-sourced data or estimated from soil texture or land use types. The model is applied to simulate the hydrological processes and phosphorus transport in the Tongyang River Basin in China and the model performance is confirmed by comparing the predicted discharge and total phosphorus concentrations with measured data at the catchment outlets of Tonghe River and Yanghe River over 3 years. Uncertainty analysis has been further carried out using the GLUE method to demonstrate sensitivity of the simulation results to the selection of model parameters. After model calibration, the predicted results are found to compare well with field observations in terms of flow discharge and total phosphorus concentration. From sensitivity analysis, it is found that the recession coefficient of channel system (CS) and the pollutant recession coefficient of surface runoff (pKS) are the most influential hydrological and water quality parameters that control the arrival time and duration of flood peak and nutrient concentration peak, respectively.

State updating in a distributed hydrological model by ensemble Kalman filtering with error estimation

For flood simulation in small- and medium-sized catchments, discharge observations may be used to update model states of a distributed hydrological model to improve performance. The ensemble Kalman filter (EnKF) has been widely used for hydrological assimilation due to its relative simplicity and robustness. An advantage of the EnKF is that it is easy to include different sources of uncertainty, therefore the choice of error model is crucial for the application of the EnKF assimilation. This paper describes an EnKF assimilation scheme for estimating error models using the maximum a posteriori estimation method (MAP). We test this scheme in two small and medium-sized catchments in China with different characteristics, and in addition compared the performance differences under two kinds of rainfall forcing. We show that MAP is beneficial in specifying error models and providing reliable ensemble spread. The assimilation scheme can effectively ameliorate the degradation of distributed hydrological model performance due to uncalibrated model parameters and/or poor quality of input data.

A comparison of CMIP5 and CMIP6 climate model projections for hydrological impacts in China

Abstract Global climate model (GCM) outputs from Coupled Model Inter-comparison Project Phase 5 (CMIP5) were widely used to investigate climate change impacts last 10 years. It is important to know whether Coupled Model Inter-comparison Project Phase 6 (CMIP6) is more reliable than CMIP5. Number of studies compared GCMs from two CMIPs in simulating climate variables, but they are not in the field of hydrology for large quantities of watersheds. The objective of this study is to compare CMIP5 and CMIP6 climate model projections in projecting hydrological changes between future (2071–2100) and historical (1976–2005) periods and inter-model variability of hydrological impacts for the future period over 343 catchments in China's mainland. The results show that the GCMs in CMIP6 show more increase in daily mean temperature and mean annual precipitation. However, GCMs in CMIP6 and CMIP5 show similar increases in mean and peak streamflow. Moreover, GCMs in CMIP6 show less inter-model variability for streamflow in southern and northeastern China, but more in central China, which is consistent to that for precipitation after bias correction. Overall, this comparison provides the consistency of future change and uncertainty in predicted streamflow with climate simulations, which bring confidence for hydrological impact studies using CMIP6.

Sulfuric acid weathering counteracts CO2 drawdown from silicate weathering in mountainous catchments from southwest China

Sulfide oxidation, coupled with carbonate weathering is thought to be a source of CO2 over geological timescales to offset CO2 drawdown from silicate weathering. Its impact on the carbon budget of weathering at the catchment scale is generally constrained by utilizing riverine water chemistry and the isotope value (δ34S) of sulfate. However, the multiple origins of riverine sulfate and the high variability of sulfide-derived δ34S certainly limit the determination of sulfate sources and thus our understanding of the role of sulfuric acid-induced weathering in the carbon budget. In this study, to quantify the carbon budget between weathering by carbonic and sulfuric acids in mountainous catchments (the Yinjiang River, the Shiqian River, and the Yuqing River) in southwest China, a Monte Carlo inversion model is employed for major element concentrations and sulfate δ34S (δ34SSO4). This inversion quantitatively partitions the cations and sulfate between different sources, and further identifies the pyrite-derived δ34S and the flux ratios of alkalinity to dissolved inorganic carbon (ΔALK/ΔDIC). Inversion results reveal that the majority of major cations (>90 %) is derived from carbonate (calcite and dolomite) dissolution, and that riverine sulfate budget is predominately contributed by pyrite oxidation and precipitation. In the Yinjiang River, the average δ34SSO4 is 8.4 ‰ in the upper and middle reaches, but declines significantly to 0.4 ‰ in the lower reaches. In the Shiqian River and Yuqing River, δ34SSO4 mainly cluster around 3.6–5.7 ‰ and 5.5–8.5 ‰, respectively. Inversion results suggest that the primary controls on δ34SSO4 are the variability of lithology and the variation in the proportional contribution of pyrite oxidation to riverine sulfate. In the Yinjiang River, the fraction of H2SO4-driven weathering is highly variable, and higher in the lower reaches than in the upper reaches, attributing to distinct underlying lithology in the catchment (a widespread distribution of coal-bearing strata). The weathering fluxes with 1 < ΔALK/ΔDIC < 2 evidenced that the negative alkalinity flux from carbonate weathering driven by sulfuric acid from both pyrite oxidation and acid deposition counteracts much of the alkalinity flux from silicate weathering. This is implicated on the timescale between marine carbonate precipitation and sulfate reduction (>5–10 kyr and <10 Myr), indicating that weathering in these catchments acts as short-term sinks of atmospheric CO2 but long-term sources of it. Moreover, reanalysis of prior datasets of karst-dominated catchments in China also highlight the important role of pyrite oxidation and acid precipitation in the carbon budget.

Objectivity verification experiment of the dynamic system response curve method for streamflow simulation

Error correction methods are widely used to improve the performance of streamflow modeling. The Dynamic system response curve (DSRC) is a newly developed error correction method that utilizes error feedback correction to obtain updated variables and thus enhances model accuracy. While the DSRC method is simple and practical, its objectivity has not been systematically demonstrated. To address this issue, the paper proposes an experiment to verify its objectivity. First, three hydrological models (Xinanjiang (XAJ), Hydrologiska Byråns Vattenavdelning (HBV) and vertically hybrid yielding (VHY)) were calibrated and three corrected areal mean rainfall series were obtained by using the DSRC-based models (XAJ-DSRC, HBV-DSRC and VHY-DSRC). The corrected rainfall series from one hydrological model was then used as input to other hydrological models, and its performance was compared to those of using the original rainfall series by five performance measures (Nash-Sutcliffe efficiency coefficient (NSE), root mean squared error (RMSE), percent bias (PBIAS), mean absolute error (MAE) and correlation coefficient (R)). Finally, this objectivity verification experiment was applied to different model combinations for further demonstration. Case studies of the Sunshui River and Shiquan catchments in China showed that when the DSRC-corrected rainfall series from either hydrological model was applied to the original or other hydrological models, an increase in NSE and R values and a decrease in RMSE and MAE values were observed. The absolute values of PBIAS were within 10%. Accordingly, the hypothesis that the DSRC method is objective is not rejected based on the results of the experiment.

Severe Socioeconomic Exposures Due to Enhanced Future Compound Flood-Heat Extreme Hazards in China

As the climate warms, a new hazard, compound flood-heat extreme (CFH) events, characterized by the rapid succession of devastating floods and deadly heat (or vice-versa), are becoming increasingly frequent, threatening infrastructure and ecosystems. However, how this CFH hazard will change under future anthropogenic warming in China and their potential population and economic exposures remains unexamined. Here, we systematically quantify the projected changes in bivariate CHF hazards for 187 catchments in China during the 2071–2100 period relative to the 1985–2014 period and investigate the potential population and gross domestic product (GDP) exposure, by developing a climatic-hydrological-socioeconomic modelling chain. We find that there is a nationwide increase in CFH hazards and the historical 30-year CFH episodes are projected to increase by 10 times in southern catchments. Under the synergistic impacts of changing CFH episodes and population (GDP), a mass of people in southern (0.79–2.13 thousand/km2) and eastern (1.68 thousand/km2) catchments and an enormous sum of GDP in eastern catchments (400–912 million/km2) will be exposed to increasing CFH hazards. Our results highlight the necessity of improving both societal resilience and mitigation solutions to address such weather-related hazards.

Exploring the Influences of Stream Network Structure and Connectivity on Water Environment Risk in China

Stream networks are the transportation channels of pollutants that can significantly influence water environment risk (WER). However, the influences of stream network structure and connectivity (SC) on WER at the national scale and its regional variability have been rarely investigated in China. In this study, the WER was assessed from the grey water footprint of nitrogen and phosphorus in 214 catchments in China. The relationship between WER and SC and its regional variability were analyzed using correlation and grey relational analysis. Results showed that the water environment risk index (RI) in some catchments located in the Hai and Liao River Basins was the highest in China (RI &gt; 0.8). On national scale, longitudinal connectivity (Cl) and cyclical connectivity (Cc) had the strongest influences on WER with grey relational degree index (GRAI) of 0.68 and 0.67, respectively. The average slope (Sr) was the most important in humid zones, whereas Cl and water surface ratio (Rw) had a stronger influence in arid zones. In zones with intensive human activities, Cc, river density (Rd), and the node connection ratio (Rnc) mostly affected WER. The main influenced factors varied significantly among nationwide and different zones, which indicated that climate and human activities played an important role in the spatial variation of the relationship between WER and SC. This study highlights the important role of SC on WER and that the relationship between WER and SC varies with climate and human activities.

Generalization of an Encoder-Decoder LSTM model for flood prediction in ungauged catchments

Flood prediction in ungauged catchments is usually conducted by hydrological models that are parameterized based on nearby and similar gauged catchments. As an alternative to this process-based modelling, deep learning (DL) models have demonstrated their ability for prediction in ungauged catchments (PUB) with high efficiency. Catchment characteristics, the number of gauged catchments, and their level of hydroclimatic heterogeneity in the training dataset used for model regionalization can directly affect the model’s performance. Here, we study the generalization ability of a DL model to these factors by applying an Encoder-Decoder Long Short-Term Memory neural network for a 6-hour lead-time runoff prediction in 35 mountainous catchments in China. By varying the available number of catchments and model settings with different training datasets, namely local, regional, and PUB models, we evaluated the generalization ability of our model. We found that both quantity (i.e. number of gauged catchments available) and heterogeneity of the training dataset used for the DL model are important for improving model performance in the PUB context, due to a data synergy effect. The assessment of the sensitivity to catchment characteristics showed that the model performance is mainly correlated to the local hydro-climatic conditions; the more arid the region, the more likely it is to have a poor model performance for prediction in ungauged catchments. The results suggest that the regional ED-LSTM model is a promising method to predict streamflow from rainfall inputs in PUB, and outline the need for preparing a representative training dataset.

Estimating the annual runoff frequency distribution based on climatic conditions and catchment characteristics: A case study across China

Estimation of the annual runoff frequency distribution is an essential basis for water resource management. This study proposes a framework for estimating the annual runoff frequency distribution across 252 catchments in China based on climatic conditions and catchment characteristics from 1956 to 2000. The Budyko land-specific parameter n, which intergrates influences other than the mean climate conditions, is firstly estimated based on easily ascertainable catchment characteristics without the requirements of having long-term runoff observations. Second, the annual runoff statistical parameters, namely, the mean value and standard deviation (STD), are derived based on the Budyko rainfall-runoff model with the central moment method. Finally, the annual runoff on any recurrence interval is obtained by the Pearson-III frequency function. Results show that the parameter n can be estimated from the catchment average slope, longitude, and climatic seasonality index. The estimated statistical parameters of annual runoff have acceptable agreement with observed values (mean value: R2 ∼0.94, STD: R2 ∼0.91, and both relative errors <10%). In addition, estimated annual runoff at each catchment for typical wet and dry years (25% and 75% ranked percentiles) coincides well with observed values, with R2 of 0.92–0.93 and relative errors less than 10%. This result indicates the robustness of this framework for estimating the annual runoff frequency distribution, which provides a simple and effective tool for ungauged or poorly gauged catchments.

Estimating the Routing Parameter of the Xin’anjiang Hydrological Model Based on Remote Sensing Data and Machine Learning

The parameters of hydrological models should be determined before applying those models to estimate or predict hydrological processes. The Xin’anjiang (XAJ) hydrological model is widely used throughout China. Since the prediction in ungauged basins (PUB) era, the regionalization of the XAJ model parameters has been a subject of intense focus; nevertheless, while many efforts have targeted parameters related to runoff yield using in-site data sets, classic regression has predominantly been applied. In this paper, we employed remotely sensed underlying surface data and a machine learning approach to establish models for estimating the runoff routing parameter, namely, CS, of the XAJ model. The study was conducted on 114 catchments from the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) data set, and the relationships between CS and various underlying surface characteristics were explored by a gradient-boosted regression tree (GBRT). The results showed that the drainage density, stream source density and area of the catchment were the three major factors with the most significant impact on CS. The best correlation coefficient (r), root mean square error (RMSE) and mean absolute error (MAE) between the GBRT-estimated and calibrated CS were 0.96, 0.06 and 0.04, respectively, verifying the good performance of GBRT in estimating CS. Although bias was noted between the GBRT-estimated and calibrated CS, runoff simulations using the GBRT-estimated CS could still achieve results comparable to those using the calibrated CS. Further validations based on two catchments in China confirmed the overall robustness and accuracy of simulating runoff processes using the GBRT-estimated CS. Our results confirm the following hypotheses: (1) with the help of large sample of catchments and associated remote sensing data, the ML-based approach can capture the nonstationary and nonlinear relationships between CS and the underlying surface characteristics and (2) CS estimated by ML from large samples has a robustness that can guarantee the overall performance of the XAJ mode. This study advances the methodology for quantitatively estimating the XAJ model parameters and can be extended to other parameters or other models.

Can Satellite and Atmospheric Reanalysis Products Capture Compound Moist Heat Stress-Floods?

Satellite-retrieved and model-based reanalysis precipitation products with high resolution have received increasing attention in recent decades. Their hydrological performance has been widely evaluated. However, whether they can be applied in characterizing the novel category of extreme events, such as compound moist heat-flood (CMHF) events, has not been fully investigated to date. The CMHF refers to the rapid transition from moist heat stress to devastating floods and has occurred increasingly frequently under the current warming climate. This study focuses on the applicability of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and the fifth generation of European Reanalysis (ERA5-Land) in simulating CMHF events over 120 catchments in China. Firstly, the precipitation accuracy of IMERG and ERA5-Land products is appraised for each catchment, using the gridded in situ meteorological dataset (CN05.1) as a baseline. Then, the ability of IMERG and ERA5-Land datasets in simulating the fraction, magnitude, and decade change of floods and CMHFs is comprehensively evaluated by forcing the XAJ and GR4J hydrological models. The results show that: (a) the IMERG and ERA5-Land perform similarly in terms of precipitation occurrences and intensity; (b) the IMERG yields discernably better performance than the ERA5-Land in streamflow simulation, with 71.7% and 50.8% of catchments showing the Kling–Gupta efficiency (KGE) higher than 0.5, respectively; (c) both datasets can roughly capture the frequency, magnitude, and their changes of floods and CMHFs in recent decades, with the IMERG exhibiting more satisfactory accuracy. Our results indicate that satellite remote sensing and atmospheric reanalysis precipitation can not only simulate individual hydrological extremes in most regions, but monitor compound events such as CMHF episodes, and especially, the IMERG satellite can yield better performance than the ERA5-Land reanalysis.

Seasonal isotopic cycles used to identify transit times and the young water fraction within the critical zone in a subtropical catchment in China

Transit times and the young water fraction (Fyw, the proportion of the water younger than a certain age threshold, τyw) can help characterize the hydrological behaviors within a catchment. Previous studies of water age have concentrated on individual catchment hydrological components, such as river water, groundwater, plant water or soil water, which hinders the dynamic linking of various hydrological components in the critical zone. In this study, the isotopic compositions of precipitation, soil water, xylem water, river water, and groundwater in a subtropical catchment in China were continuously monitored for two years. We found that the seasonal isotopic cycles of shallow soil water (0–60 cm), xylem water and river water had different attenuations and phase shifts relative to those of precipitation. There were no obvious seasonal isotopic cycles of deep soil water (60–130 cm) and groundwater. Additionally, the transit time distributions (TTDs) of soil water at the depth of 0–10 cm, xylem water and river water in the catchment varied between the wet year and normal year. With increasing depth, the mean transit times (MTTs) of soil water increased and the Fyw decreased. For xylem water and soil water, the MTTs and Fyw in the wet year were shorter and larger than those in the normal year, respectively. The river water consisted of a small proportion of fast flow components and a large proportion of slow flow components, with the young water accounting for 16% in the wet year and 21% in the normal year. Our results contribute to an improved understanding of water ages, flow paths and linkages of different hydrological components in the critical zone at the catchment scale.

Hybrid approach for flood susceptibility assessment in a flood-prone mountainous catchment in China

• We developed a hybrid approach integrating physical-based flood models with random forest for flood susceptibility assessment. • We identified the relatively significant flood-inducing factors. • The geographical and hydromorphological factors had greater effects on riverine flooding. • This flood susceptibility assessment is suitable in ungauged areas. Flooding is one of the most destructive natural hazards that has caused catastrophic effects worldwide. Recently, machine learning methods have become widespread in flood susceptibility assessments due to their efficiency and robustness. However, flood inventory data acquired from historical flooding records and field surveys in existing studies are sparse and not enough to develop accurate machine learning models. Thus, we developed a hybrid approach that integrates hydrodynamic model (HEC-HMS/RAS), rapid flood model (height above the nearest drainage, HAND) and machine learning model (random forest, RF) for flood susceptibility assessment. The proposed approach was then implemented in the Xinluo watershed, a flood-prone mountainous catchment in China. The results showed that the spatially continuous flood inventory map generated from HEC-HMS/RAS model satisfies the big data requirements of the RF model, and the proposed hybrid approach demonstrates the effectiveness and efficiency in flood susceptibility assessment with overall accuracy and area under the ROC curve values of 0.915 and 0.912, respectively. The geographical and hydromorphological factors had greater effects on riverine flooding with average contribution of 27.8% and 27.6%. In comparison, the average contribution of land cover and meteorological factors were 24.3% and 20.3%, respectively. The method proposed in this study is effective in assessing and forecasting flood susceptibility and can facilitate resilience planning as well as integrated flood management.

Hydrogeological processes and hydrochemical effects in the Manas river catchment, Northwest China, over the past 60 years

The arid inland regions around the world are suffering from extreme water shortages and ecological crises due to the impacts of climate change and enhanced human activities. This study used multiple methods including trend and abrupt analysis, correlation analysis, water-balance analysis, and hydrochemical analysis to evaluate the hydrogeological processes and hydrochemical effects in 5 periods in the Manas River catchment, a typical arid inland catchment in China. The results show that groundwater storage in the catchment decreased by 5.45 × 108 m3 over the past 60 years, while glacial melt, snowmelt, and precipitation showed an increasing trend since the 1980 s. A drop in the groundwater level in the alluvial fan and a 28 % reduction in spring discharge in the lower alluvial fan since 1959 were caused by reduced river infiltration owing to the water diversion projects. Meanwhile, the concentrations of SO42- and Cl- in the alluvial fan groundwater have more than doubled over the period 1957–2018 as a result of the decrease of both groundwater level and flow rate. Long-term over-exploitation of groundwater associated with irrigation after 1978 strongly depleted the groundwater storage and altered the flow path of groundwater in the oasis zone. In addition, the expansion of arable land and increased temperatures enhanced evapotranspiration and salinization of shallow groundwater in the oasis and desert zones. Based on the results, we propose making full use of natural underground reservoirs to increase infiltration of river water and flood water in the alluvial fan zone, and reducing the arable land area to ensure the sustainability of water resources and ecology in the middle and lower reaches of the catchment. The results will provide reference for the sustainable development of water resources and environmental protection in arid inland regions and similar catchments worldwide.

Characterizing the groundwater storage–discharge relationship of small catchments in China

Abstract Baseflow recession is an essential part of the hydrological cycle, as it transfers unconfined aquifer storage to runoff. This study derived the parameterization of the baseflow recession from recession curves extracted from 382 catchments in China. The recession parameters of recession curves in power-law form, which control the shape of the curves and reflect the net effects of hillslope on the runoff decline process, are estimated by the baseflow recession analysis. The results show that the ranges of recession coefficient α and recession exponent β across China are 0–0.70 and 0.57–3, respectively. Most of the α values range between 0 and 0.20, and most of the β values range between 1 and 2. Generally, the α values of relatively dry catchments are higher than that of the wet catchments, and the distribution pattern of β values is opposite to that of α values. Statistical analysis is used to construct parametric equations for the recession parameters, indicating that catchment area, field capacity, etc., are essential for predicting α and β. In addition, the transplantation results of parametric equations show that equations can be applied to the estimation of α and β in other catchments. The results provide data support for storage estimation of data-scarce catchments.