Hydrological Prediction Research Articles

Hybrid hydrological modeling for large alpine basins: a semi-distributed approach

Abstract. Alpine basins are important water sources for human life, and reliable hydrological modeling can enhance the water resource management in alpine basins. Recently, hybrid hydrological models, coupling process-based models and deep learning (DL), have exhibited considerable promise in hydrological simulations. However, a notable limitation of existing hybrid models lies in their failure to incorporate spatial information within the basin and describe alpine hydrological processes, which restricts their applicability in hydrological modeling in large alpine basins. To address this issue, we develop a set of hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing embedded neural networks (ENNs) to parameterize and replace different internal modules. The proposed models are tested on three large alpine basins on the Tibetan Plateau. A climate perturbation method is further used to test the applicability of the hybrid models to analyze the hydrological sensitivities to climate change in large alpine basins. Results indicate that proposed hybrid hydrological models can perform well in predicting runoff processes and simulating runoff component contributions in large alpine basins. The optimal hybrid model with Nash–Sutcliffe efficiencies (NSEs) higher than 0.87 shows comparable performance to state-of-the-art DL models. The hybrid model also exhibits remarkable capability in simulating hydrological processes at ungauged sites within the basin, markedly surpassing traditional distributed models. In addition, the results also show reasonable patterns in the analysis of the hydrological sensitivities to climate change. Overall, this study provides a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and improves our understanding about the hydrological sensitivities to climate change in large alpine basins.

Long-term AI prediction of ammonium levels in rivers using transformer and ensemble models

This study provides a cutting-edge machine learning approach to forecast ammonium (NH4+) levels in River Lee London. Ammonium concentrations were predicted over several time intervals using a complete dataset that includes temperature, turbidity, chlorophyll, dissolved oxygen, conductivity, and pH. Our technique captures the intricate connections between environmental conditions and ammonium concentrations using developed algorithms, including Temporal Fusion Transformer (TFT), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) levels versus the important factors, considerably improving prediction accuracy. The novel aspect of this study is the utilisation of the TFT model for multi-horizon forecasting, which offers high accuracy and interpretability in hydrological predictions by combining convolutional components with an attention mechanism. The study also demonstrates the effectiveness of the TFT model in capturing short-term fluctuations while retaining accuracy over long time periods, which is a major difficulty in environmental modelling. The models used, have exceptional forecasting skills, predicting 150, 200, 365, 730, and 1095 days based on daily average and 12, 24 and 30 months based on monthly average. This dual-scale model combines flexibility and resilience, making it an effective tool for forecasting both short- and long-term environmental changes. The RF model excelled in long-term forecasts, sustaining high R-squared (R²) (0.97) values and low root mean square error (RMSE) (0.18), and the second best one was the XGBoost with optimiser with R2 of (0.92) and RMSE of (0.25) with forecasting 1095 days. The results also found that whilst the TFT captured the fluctuations in the short-term, it struggled with the longer-term predictions due to data granularity. The XGBoost model did remarkably well in monthly forecasts up to 12 months, maintaining low RSME. The findings also highlight the necessity of proactive water management techniques to reduce the risk of potential ecological effects, including hypoxia and oxygen depletion. The findings support resource managers in addressing prospective ammonium toxicity concerns such as oxygen depletion and ecological stress.

Estimating the sensitivity of the Priestley–Taylor coefficient to air temperature and humidity

Abstract. The Priestley–Taylor (PT) coefficient (α) is generally set as a constant value or is fitted as an empirical function of environmental variables, and it can bias the evaporation estimation or hydrological projections under global warming. By using an atmospheric boundary layer model, this study derives a theoretical and parameter-free equation for estimating α as a function of air temperature (T) and specific humidity (Q). With observations from several waterbodies and non-water-limited land sites, we demonstrate that, in addition to estimating the value of α well, the derived expressions can also capture the sensitivity of α to T and Q, that is, dα/dT and dα/dQ. α is generally negatively associated with T and Q, in which regard T plays a more fundamental role in controlling α behaviors. Based on climate model data, we further show that this negative relationship between α and T is of great importance for long-term hydrological predictions. We also provide a lookup graph for practical and broad uses to directly find the values of dα/dT and dα/dQ under specific conditions. Overall, the derived expression gives a physically clear and straightforward approach to quantify changes in α, which is essential for PT-based hydrological simulation and projections.

Incorporating hydrological constraints with deep learning for streamflow prediction

Streamflow prediction is a fundamental task serving for flood prevention and efficient water resource management. Deep learning-based methods have emerged as the dominant approach and have made remarkable progress in the task. However, these methods ignore the hydrological constraints on streamflow, such as water delay time and streamflow direction, resulting in inaccurate prediction. To address this problem, this study incorporates hydrological constraints with deep learning models to improve streamflow prediction, and develops a hydrological constrained graph convolutional network (HCGCN) model. Specifically, the model designs a time-delayed spatiotemporal directed graph to present the spatiotemporal relationships and hydrological constraints, and develops a hydrological feature aggregation module to adaptively learn the complex spatiotemporal dependencies. In addition, HCGCN proposes a streamflow prediction neural network to predict streamflow by stacking the feature aggregation modules. Experimental results show that HCGCN significantly improves the prediction accuracy in a real streamflow prediction task. This study provides an exploration of introducing hydrological constraints to improve streamflow prediction, and provides a new reference for various hydrological prediction tasks.

Water budgets in an arid and alpine permafrost basin: Observations from the High Mountain Asia

Ground freeze‒thaw processes have significant impacts on infiltration, runoff and evapotranspiration. However, there are still critical knowledge gaps in understanding of hydrological processes in permafrost regions, especially of the interactions among permafrost, ecology, and hydrology. In this study, an alpine permafrost basin on the northeastern Qinghai‒Tibet Plateau was selected to conduct hydrological and meteorological observations. We analyzed the annual variations in runoff, precipitation, evapotranspiration, and changes in water storage, as well as the mechanisms for runoff generation in the basin from May 2014 to December 2015. The annual flow curve in the basin exhibited peaks both in spring and autumn floods. The high ratio of evapotranspiration to annual precipitation (>1.0) in the investigated wetland is mainly due to the considerably underestimated ‘observed’ precipitation caused by the wind-induced instrumental error and the neglect of snow sublimation. The stream flow from early May to late October probably came from the lateral discharge of subsurface flow in alpine wetlands. This study can provide data support and validation for hydrological model simulation and prediction, as well as water resource assessment, in the upper Yellow River Basin, especially for the headwater area. The results also provide case support for permafrost hydrology modeling in ungauged or poorly gauged watersheds in the High Mountain Asia.

Enhanced hydrological drought prediction in the Gediz Basin: integrating meteorological drought via hybrid wavelet-machine learning-random oversampling models using

ABSTRACT In study, meteorological drought ​​was used to estimate the potential hydrological drought that may occur in the Gediz Basin of Turkey. For this purpose, the most effective stream flow gauging station was determined by looking at the correlation values ​​between the meteorological data obtained from the Uşak meteorological station. SPEI values ​​for meteorological drought and SRI values ​​for hydrological drought are calculated for 3-, 6-, 9-, and 12-month periods. Correlation matrices were created between meteorological drought inputs from SPEI(t) to SPEI(t-12) and SRI(t) for use in hydrological drought models for 3-, 6-, 9- and 12-month periods. ML models were developed considering correlation matrices and it was seen that ML model results were not sufficient. For this reason, W-ML models were developed by applying DWT and Optuna hyperparameter analysis. It has been observed that the performance of W-ML models increases. Random oversampling (ROS), which has never been used in drought modeling, was then applied to W-ML models. W-ML-ROS model obtained an R2 value of 0.999 for testing set in 12-month period. Similarly, R2 values ​​for SRI3, SRI6 and SRI9 were obtained as 0.893, 0.851, and 0.940, respectively. Results showed that W-ML-ROS hybrid models can be used to predict hydrological drought from meteorological drought.

Evaluating machine learning models in predicting GRI drought indicators (case study: Ajabshir area)

Examining the condition of groundwater resources and the impact of droughts is valuable for effective water resources management. Today, machine learning (ML) models are recognized as one of the useful tools in time series predictions. In this study, the groundwater condition of one of the most important aquifers in northwest Iran was investigated using MODFLOW, followed by estimating the groundwater resource index (GRI) utilizing the multivariate adaptive regression spline (MARS) and least squares support vector regression (LSSVR) for a period between 2001 and 2019. Meteorological and hydrological drought indicators along with precipitation and flow rate were used as input variables for prediction. The simulation results revealed a groundwater level decrease since the aquifer withdrawal amount is more than the recharge amount. Besides, results showed that there is a limited interaction between surface water and groundwater resources, mainly caused by the decrease in the river flow and aquifer groundwater level drop. Both ML models performed well in GRI estimation, using groundwater flow, streamflow drought index, standardized precipitation index, and runoff as input variables. The performance of the MARS model with RMSE, MAE, and NSE error evaluation criteria of 0.37, − 0.19, and 0.83, respectively, exerted slightly better results than LSSVR with RMSE, MAE, and NSE of 0.48, − 0.06, and 0.80, respectively. The findings reveal the appropriate performance of both models in forecasting drought indicators, highlighting the necessity of using ML models in hydrology and drought prediction problems.

Identification of propagation characteristics from meteorological drought to hydrological drought using daily drought indices and lagged correlations analysis

Study regionThe Juam Dam area in Suncheon, Jeollanam-do, South Korea Study focusThis study aims to analyze drought propagation characteristics using time-lagged correlation analysis based on daily drought index, determining the lag time from meteorological drought to propagation into hydrological drought. The target period for correlation analysis is the onset date of the dry spell preceding the occurrence of hydrological drought and the termination date of the dry spell following the termination of the drought for each drought event. The Standardized Precipitation Index (SPI) at various time-scales for meteorological drought and the Standardized Reservoir Supply Index (SRSI) for hydrological drought were applied. New hydrological insights for the regionThis study presents the objectivity and accuracy of drought onset and termination date for drought propagation analysis through daily lagged correlation analysis. Through ROC analysis, SPI90, SPI180, and SPI365 are shown to increase by an average of 16.0 %, 8.8 %, and 6.0 %, respectively. From 1993–2023, long-term hydrological droughts lasting 2 years occurred 5 times, with a maximum duration of 408 days, magnitude −629, and severity −1.76. The daily lag between the multi-scale SPIs and SRSI of individual drought events presents the possibility of predicting hydrological drought through multiple regression analysis. This research provides insights for improving hydrological drought monitoring, prediction, and response strategies through results of individual propagation characteristics of drought events.

Hydrologic Model Prediction Improvement in Karst Watersheds through Available Reservoir Capacity of Karst

This study aimed to enhance flood forecasting accuracy in the Liangfeng River basin, a small karst watershed in Southern China, by incorporating the Available Reservoir Capacity of Karst (ARCK) into the HEC-HMS model. This region is often threatened by floods during the rainy season, so an accurate flood forecast can help decision-makers better manage rivers. As a crucial influencing factor on karstic runoff, ARCK is often overlooked in hydrological models. The seasonal and volatile nature of ARCK makes the direct computation of its specific values challenging. In this study, a virtual reservoir for each sub-basin (total of 17) was introduced into the model to simulate the storage and release of ARCK-induced runoff phenomena. Simulations via the enhanced model for rainfall events with significant fluctuations in water levels during 2021–2022 revealed that the Nash–Sutcliffe efficiency coefficient (NSE) of the average simulation accuracy was improved by more than 34%. Normally, rainfalls (even heavy precipitations) during the dry season either do not generate runoff or cause negligible fluctuations in flow rates due to long intervals. Conversely, relatively frequent rainfall events (even light ones) during the wet season result in substantial runoff. Based on this observation, three distinct types of karstic reservoirs with different retaining/releasing capacities were defined, reflecting variations in both the frequency and volume of runoff during both seasons. As a real-time environmental variable, ARCK exhibits higher and lower values during the dry and rainy seasons, respectively, and we can better avoid the risk of flooding according to its special effects.

Enhancing Hydrological Variable Prediction through Multitask LSTM Models

Deep learning models possess the capacity to accurately forecast various hydrological variables, encompassing flow, temperature, and runoff, notably leveraging Long Short-Term Memory (LSTM) networks to exhibit exceptional performance in capturing long-term dynamics. Nonetheless, these deep learning models often fixate solely on singular predictive tasks, thus overlooking the interdependencies among variables within the hydrological cycle. To address this gap, our study introduces a model that amalgamates Multitask Learning (MTL) and LSTM, harnessing inter-variable information to achieve high-precision forecasting across multiple tasks. We evaluate our proposed model on the global ERA5-Land dataset and juxtapose the results against those of a single-task model predicting a sole variable. Furthermore, experiments explore the impact of task weight allocation on the performance of multitask learning. The results indicate that when there is positive transfer among variables, multitask learning aids in enhancing predictive performance. When jointly forecasting first-layer soil moisture (SM1) and evapotranspiration (ET), the Nash–Sutcliffe Efficiency (NSE) increases by 19.6% and 4.1%, respectively, compared to the single-task baseline model; Kling–Gupta Efficiency (KGE) improves by 8.4% and 6.1%. Additionally, the model exhibits greater forecast stability when confronted with extreme data variations in tropical monsoon regions (AM). In conclusion, our study substantiates the applicability of multitask learning in the realm of hydrological variable prediction.

Quantile mapping technique for enhancing satellite-derived precipitation data in hydrological modelling: a case study of the Lam River Basin, Vietnam

ABSTRACT Accurate precipitation is crucial for hydrological modelling in sparse gauge regions like the Lam River Basin (LRB) in Vietnam. Gridded precipitation data from satellite and numerical models offer significant advantages in such areas. However, satellite precipitation estimates (SPEs) are subject to uncertainties, especially in high variable of topography and precipitation. This study focuses on enhancing the accuracy of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), Climate Prediction Center morphing technique (CMORPH) using the Quantile Mapping (QM) technique, aligning the cumulative distribution functions of the observed precipitation data with those of the SPEs, and assessing the impact on hydrological predictions. The study highlights that the post-correction IMERG precipitation using QM performs better than other data sets, enhancing the hydrological model's performance for the LRB at different temporal scales. Nash–Sutcliffe efficiency values increased from 0.60 to 0.77, surpassing the original IMERG's 0.52 to 0.74, and correlation coefficients improved from 0.79 to 0.89 (compared with the previous 0.75–0.86) for hydrological modelling. Additionally, Percent Bias (PBIAS) decreased from approximately −1.66 to −2.21% (contrasting with the initial −20.22 and 4.6%) with corrected SPEs. These findings have implications for water resource management and disaster risk reduction initiatives in Vietnam and other countries.

Ensemble hydrological predictions at an intraseasonal scale through a statistical–dynamical downscaling approach over southwestern Amazonia

ABSTRACT We developed and analyzed the performance of an ensemble forecasting system for the Madeira River basin, the largest sub-basin of the Amazon, with forecasts up to 30 days under different hydrometeorological conditions. We used outputs from the regional Eta model of precipitation and global climatological data as inputs to a large-scale hydrological model. Bias correction of precipitation through quantile mapping significantly improved the results, achieving a hit rate >70%. The system demonstrated the ability to discriminate between high, medium, and low flow conditions. Forecast performance is better for larger catchment areas. This system is expected to increase decision-making efficiency for flood and drought situations in the largest Amazon tributary.

Research on hydrological survey and forecasting model based on rolling sampling Markov chain

Abstract Hydrological survey is of great significance in flood disaster warning, hydropower engineering construction, and efficient utilization of watershed resources. Due to the seasonal and random fluctuations of hydraulic resources, it is difficult to accurately predict waterway runoff. Based on this, this article proposes a hydrological survey and prediction method based on rolling sampling Markov chain. Firstly, based on historical hydrological and meteorological data, establish a watershed runoff model and summarize and analyze the characteristics of the patterns; Then, based on the first-order Markov chain model, the relationship between adjacent days is considered. According to seasonal and meteorological factors, the historical data is rolling sampled in a 10 day sampling interval, and a multi state transition probability matrix is established to construct a year-round time-series runoff forecasting model; Finally, taking the Jinsha River Basin as an example, the annual runoff situation of the waterway was simulated. The calculation results have verified the effectiveness of the proposed method, indicating that it can simulate runoff well and provide certain guidance for watershed management and construction.

How to Achieve a 50% Reduction in Nutrient Losses From Agricultural Catchments Under Different Climate Trajectories?

AbstractUnder persistent eutrophication of European water bodies and a changing climate, there is an increasing need to evaluate best‐management practices for reducing nutrient losses from agricultural catchments. In this study, we set up a daily discharge and water quality model in Hydrological Predictions of the Environment for two agricultural catchments representative for common cropping systems in Europe's humid continental regions to forecast the impacts of future climate trajectories on nutrient loads. The model predicted a slight increase in inorganic nitrogen (IN) and total phosphorus (TP) loads under RCP2.6, likely due to precipitation‐driven mobilization. Under RCP4.5 and RCP8.5, the IN loads were forecasted to decrease from 16% to 26% and 21%–50% respectively, most likely due to temperature‐driven increases in crop uptake and evapotranspiration. No distinct trends in TP loads were observed. A 50% decrease in nutrient loads, as targeted by the European Green Deal, was backcasted using a combination of management scenarios, including (a) a 20% reduction in mineral fertilizer application, (b) introducing cover crops (CC), and (c) stream mitigation (SM) by introducing floodplains. Target TP load reductions could only be achieved by SM, which likely results from secondary mobilization of sources within agricultural streams during high discharge events. Target IN load reductions were backcasted with a combination of SM, fertilizer reduction, and CC, wherein the required measures depended strongly on the climatic trajectory. Overall, this study successfully demonstrated a modeling approach for evaluating best‐management practices under diverging climate change trajectories, tailored to the catchment characteristics and specific nutrient reduction targets.

Prescriptive analytics for low and high water management in German waterways

Abstract The real-time management of multi-purpose storage reservoirs aims at an efficient operation of existing hydraulic infrastructure. This management process can be structured as a prescriptive analytics setup that considers both current and predicted system states to recommend actions and outline potential implications. In application to a reservoir and river system, it combines hydrological modelling components for the system schematization, observations and data assimilation for the identification of the current system state, meteorological and hydrological predictions as well as optimization-based techniques to support decision-making regarding reservoir operations. In this paper, we present the application of such a framework to the short-term management of the Eder and Diemel storage reservoirs. These reservoirs are operated by the German Federal Waterways and Shipping Administration (WSV) with the primary goal to support navigation in the River Weser during low flow periods. In addition, partially conflicting objectives such as flood protection, energy generation and recreation are considered. The implementation includes an explicit consideration of forecast uncertainty and its impact on the decision-making by using probabilistic forecasts in combination with a multi-stage stochastic optimization approach. We demonstrate the applicability of the approach based on low and high water use cases. Special attention is paid on the benefits of the probabilistic forecast in combination with the multi-stage stochastic optimization versus a deterministic setup. It provides an explicit translation of the forecast uncertainty in the decision variables, in this case the reservoir releases helping the operators to better anticipate the range of future release decisions. Furthermore, the stochastic approach is expected to provide more stable decisions in an operational setting, based on more stable forecasts by considering various possible realizations of the future instead of picking a single one, which gets random after 4–5 days.

Multivariate Hydrological Modeling Based on Long Short-Term Memory Networks for Water Level Forecasting

In the Department of Chocó, flooding poses a recurrent and significant challenge due to heavy rainfall and the dense network of rivers characterizing the region. However, the lack of adequate infrastructure to prevent and predict floods exacerbates this situation. The absence of early warning systems, the scarcity of meteorological and hydrological monitoring stations, and deficiencies in urban planning contribute to the vulnerability of communities to these phenomena. It is imperative to invest in flood prediction and prevention infrastructure, including advanced monitoring systems, the development of hydrological prediction models, and the construction of hydraulic infrastructure, to reduce risk and protect vulnerable communities in Chocó. Additionally, raising public awareness of the associated risks and encouraging the adoption of mitigation and preparedness measures throughout the population are essential. This study introduces a novel approach for the multivariate prediction of hydrological variables, specifically focusing on water level forecasts for two hydrological stations along the Atrato River in Colombia. The model, utilizing a specialized type of recurrent neural network (RNN) called the long short-term memory (LSTM) network, integrates data from hydrological variables, such as the flow, precipitation, and level. With a model architecture featuring four inputs and two outputs, where flow and precipitation serve as inputs and the level serves as the output for each station, the LSTM model is adept at capturing the complex dynamics and cross-correlations among these variables. Validation involves comparing the LSTM model’s performance with linear and nonlinear Autoregressive with Exogenous Input (NARX) models, considering factors such as the estimation error and computational time. Furthermore, this study explores different scenarios for water level prediction, aiming to utilize the proposed approach as an effective flood early warning system.

Is comprehensive event sampling necessary for constraining process models of water quality? A comparison of high and low frequency phosphorus sampling programs for constraining the HYPE water quality model

Model parameter calibration is an important step in process-based watershed water quality modelling. Calibration is commonly performed using readily available water quality data from long-term monitoring programs that collect samples periodically at a relatively low frequency (i.e. monthly). Higher frequency synoptic and targeted event data is becoming available from more intensive monitoring programs, yet there remains little consensus on whether relatively lower frequency data are sufficient to constrain process-based watershed water quality models. To investigate the effects of using water quality data from differing sampling regimes for calibration, we use an implementation of HYPE (HYdrological Predictions for the Environment), a process-based watershed water quality model, in Southern Ontario, Canada. HYPE was calibrated with two stream water quality datasets, one associated with the long-term Ontario Provincial (Stream) Water Quality Monitoring Network and characterized by routine approximately monthly sampling frequency, and one associated with the Multi-Watershed Nutrient Study and characterized by semi-regular synoptic sampling during baseflow and at increased frequency (∼4 to 8 h) during event flow. Performances varied widely between sites, with validation ranges of daily predictions having Nash-Sutcliffe Efficiencies (NSE) from > −1 to 0.48 for flow and > −1 to 0.98 for total Phosphorus. Medians for daily data during validation ranged from 0.15 to 0.24 for flow and from 0.19 to 0.36 for total phosphorus. We found that model performance and simulated total phosphorus loads were similar for the two calibration datasets, potentially suggesting that the dataset with approximately monthly water quality sampling was adequate to constrain the HYPE model. We attribute this to a combination of similar levels of statistical variability in the calibration datasets and prior knowledge in the HYPE model structure. Furthermore, while model performance was similar when HYPE was calibrated with datasets of differing sampling strategies, there was a large range in model performance within each modelling domain. Results suggest that this range in performance could be due in part to poor representation of cold-weather processes in the HYPE model, as sites with higher mean annual temperature and fewer freezing days had better model performance.

Rainfall-Runoff Simulation for Ungauged Watershed: A Case of Bessre Watershed, Duhok Province, Iraq

The scarcity of measured hydrological data poses a challenge in many developing countries, stemming from insufficiently established gauging stations. Due to the mentioned issue, it is crucial to develop models capable of conducting reliable simulations of runoff behavior, particularly for ungauged catchments. Understanding the intricate relationships in rainfall-runoff modeling is essential for estimating peak flows, a critical aspect in formulating water resources management strategies, which can aid in water resource management and planning. In areas prone to floods performing, an extensive hydrological study becomes necessary. This study determined the outflow discharge at the outlet point of the Bessre Valley Ungauged Catchment (41.4 km2) using the Watershed Modeling System, used by reliable hydrological standards as a graphical interface integrating with the Hydrologic Modeling System (HEC-HMS). Bessre Valley watershed is one of the flood-prone watersheds in the Duhok governorate, mainly due to the terrain’s steep slopes at the upper north and east of the catchment. The catchment was delineated by a Geographic Information System (GIS). Its properties were extracted from a 12.5 m × 12.5 m Digital Elevation Model (DEM), which evaluates the hydrological response of a watershed to two significant storm events: a real rainfall event in March 2020 and a hypothetical 100-year return period event by dividing the watershed into ten sub-basins. Achieving a Nash-Sutcliffe efficiency of 0.895 indicates a high accuracy between observed and simulated peak flows of the real rainfall event of March 2020, underscoring the model's reliability for hydrological predictions. Also, comparing the HEC-HMS model and the Rational Method of (100 YRP event) for calculating peak discharges revealed a mere 2.2% error. Furthermore, the study explores the potential for building additional dams based on discharge volumes from specific sub-basins to enhance flood control and water storage capabilities.

Application of the Data-Driven Method and Hydrochemistry Analysis to Predict Groundwater Level Change Induced by the Mining Activities: A Case Study of Yili Coalfield in Xinjiang, Norwest China

As the medium of geological information, groundwater provides an indirect method to solve the secondary disasters of mining activities. Identifying the groundwater regime of overburden aquifers induced by the mining disturbance is significant in mining safety and geological environment protection. This study proposes the novel data-driven algorithm based on the combination of machine learning methods and hydrochemical analyses to predict anomalous changes in groundwater levels within the mine and its neighboring areas induced after mining activities accurately. The hydrochemistry analysis reveals that the dissolution of carbonate and evaporite and the cation exchange function are the main hydrochemical process for controlling the groundwater environment. The anomalous change in the hydrochemistry characteristic in different aquifers reveals that the hydraulic connection between different aquifers is enhanced by mining activities. The continuous wavelet coherence is used to reveal the nonlinear relationship between the groundwater level change and external influencing factors. Based on the above analysis, the groundwater level, precipitation, mine water inflow, and unit goal area could be considered as the input variables of the hydrological model. Two different data-driven algorithms, the Decision Tree and the Long Short-Term Memory (LSTM) neural network, are introduced to construct the hydrological prediction model. Four error metrics (MAPE, RMSE, NSE and R2) are applied for evaluating the performance of hydrological model. For the NSE value, the predictive accuracy of the hydrological model constructed using LSTM is 8% higher than that of Decision Tree algorithm. Accurately predicting the anomalous change in groundwater level caused by the mining activities could ensure the safety of coal mining and prevent the secondary disaster of mining activities.

Review of the development of hydrological data quality control in Typhoon Committee Members

Nowadays, with the continual development of the science and technology applied in data observation, monitoring and collection, human has more and more means and channels to obtain various data, consequently, the amount of collected and stored data is also getting bigger and bigger. In recent years, hydro-meteorological data have multiplied in some Typhoon Committee (TC) Members. Data-based advanced technology applications in TC, such as application of Artificial Intelligent (AI) and impact-based typhoon disaster forecasting and early warning, has emerged one after another. A consistent and integrated data quality management system is crucial for ensuring accurate hydrological and meteorological analysis and prediction. Considering the importance and urgent necessary, TC working group on hydrology (WGH) conducted a cooperation project on data quality management in the past years with the major objective of improving the capacity of TC Members on integrated data quality control and processing. Despite the significant improvements, the uncertainties and difficulties in processing the full-elements of hydro-meteorological data still persist in hydro-meteorological data. To tackle these challenges and further enhance the data quality management system, the integration of AI technology shows great promise. By examining the data quality management system at World Meteorological Organization (WMO) as a starting point, this paper explored how related organizations in China, Japan, Malaysia, Philippines and Republic of Korea, manage the quality of hydro-meteorological data; reviewed the current status of hydro-meteorological data quality control in TC Members, and discussed the potential areas to be enhanced in future.