Role In Water Resources Management Research Articles

Community-led Initiatives for Water Resource Management in Sumenep Regency, Indonesia

Water scarcity is a global issue affecting human health, well-being, and the environment. Community participation is vital to managing water resources, particularly in addressing water scarcity. In Indonesia, East Java is one of the provinces that frequently experiences water shortages. This research explores how local communities manage water resources and utilize local knowledge to address the vulnerability to clean water crises in Desa Parsanga, Sumenep, and East Java. This study employed a qualitative approach using interviews, observations, and document analysis as data collection methods. The research findings revealed that the local community's initiative is demonstrated through their participation in the construction of drilled wells. This participation is facilitated through an organization formed by the local community in Desa Parsanga called the Association of Drinking Water Users (HIPPAM). HIPPAM constructed five drilled wells that supply clean water to approximately 500 households. This initiative demonstrates that the local communityplays a strategic role in water resource management and can promote sustainable practices. This research provides insights into the potential of communities and local knowledge in addressing global water issues.

Deep Neural Networks for Predicting the Settlement of Earth Dams Based on the InSAR Outputs

Earth dams play a crucial role in water resource management, necessitating effective maintenance to ensure prolonged functionality and safety. Monitoring these dams traditionally involves methods such as surveying and instrumentation. However, challenges arise from equipment malfunctions and absences, especially in dams affected by environmental changes. In response to these challenges, the cost-effective and adaptable nature of Interferometric Synthetic Aperture Radar (InSAR) has made it a preferred choice for monitoring. Despite their advantages, traditional numerical models like finite element methods have limitations in predicting deformation comprehensively, particularly due to intricate, non-linear correlations involving material type and environmental conditions. To overcome these limitations, this research employs deep learning techniques, specifically Long Short-Term Memory (LSTM) network, to capture intricate relationships and accurately predict dam behavior. Time series data from InSAR, representing settlement, are decomposed into trend and seasonal components using Artificial Neural Networks (ANN) for trend prediction. Furthermore, an LSTM network is utilized to handle the complexity of the seasonal component and its correlation with environmental factors. This network incorporates settlement, precipitation, temperature, and reservoir water level time series as inputs, thereby enhancing prediction accuracy. The research outcome presents a robust solution that holds the promise of increased accuracy and efficiency in predicting, monitoring, and serving as an early warning system for earth dam deformations over time. Such advancements are crucial for ensuring the safety and integrity of critical infrastructure in the face of evolving environmental conditions.

Long-term trends in human-induced water storage changes for China detected from GRACE data

Terrestrial Water Storage (TWS) plays a pivotal role in water resource management by providing a comprehensive measure of both surface water and groundwater availability. This study investigates changes in TWS driven by human activities from 2003 to 2023, and forecasts future TWS trends under various climate change and development scenarios. Our findings reveal a continuous decline in China's TWS since 2003, with an average annual decrease of approximately 1.36 mm. This reduction is primarily attributed to the combined effects of climate change and human activities, including irrigation, industrial water use, and domestic water consumption. Notably, TWS exhibits significant seasonal and annual fluctuations, with variations ranging ±10 mm. For the future period (2024–2030), we project greater disparities between water resource supply and demand in specific years for the Songliao, Southwest, and Yangtze basins. Consequently, future water resource management must prioritize water conservation during wet seasons, particularly in years when supply-demand conflicts for limited water resources intensify. This study is valuable for effective planning and sustainable utilization of water resources.

Daily Runoff Prediction Based on FA-LSTM Model

Accurate and reliable short-term runoff prediction plays a pivotal role in water resource management, agriculture, and flood control, enabling decision-makers to implement timely and effective measures to enhance water use efficiency and minimize losses. To further enhance the accuracy of runoff prediction, this study proposes a FA-LSTM model that integrates the Firefly algorithm (FA) with the long short-term memory neural network (LSTM). The research focuses on historical daily runoff data from the Dahuangjiangkou and Wuzhou Hydrology Stations in the Xijiang River Basin. The FA-LSTM model is compared with RNN, LSTM, GRU, SVM, and RF models. The FA-LSTM model was used to carry out the generalization experiment in Qianjiang, Wuxuan, and Guigang hydrology stations. Additionally, the study analyzes the performance of the FA-LSTM model across different forecasting horizons (1–5 days). Four quantitative evaluation metrics—mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and Kling–Gupta efficiency coefficient (KGE)—are utilized in the evaluation process. The results indicate that: (1) Compared to RNN, LSTM, GRU, SVM, and RF models, the FA-LSTM model exhibits the best prediction performance, with daily runoff prediction determination coefficients (R2) reaching as high as 0.966 and 0.971 at the Dahuangjiangkou and Wuzhou Stations, respectively, and the KGE is as high as 0.965 and 0.960, respectively. (2) FA-LSTM model was used to conduct generalization tests at Qianjiang, Wuxuan and Guigang hydrology stations, and its R2 and KGE are 0.96 or above, indicating that the model has good adaptability in different hydrology stations and strong robustness. (3) As the prediction period extends, the R2 and KGE of the FA-LSTM model show a decreasing trend, but the whole model still showed feasible forecasting ability. The FA-LSTM model introduced in this study presents an effective new approach for daily runoff prediction.

Multiple conceptual hydrological models for simulating streamflow in data-sparse river basins: an application of the Vietnamese Cau river basin

ABSTRACT Streamflow plays a critical role in water resources management, requiring precise estimation, especially in data-sparse river basins. In this study, multiple hydrological models (HYMOD, GR4J, and NAM) are presented and employed to simulate streamflow in the data-sparse Vietnamese Cau river basin at the Thac Rieng station, providing an illustrative example. Four indices named Nash–Sutcliffe efficiency (NSE), correlation coefficient (r), Kling–Gupta efficiency (KGE), and root mean square error (RMSE) are implemented to quantitatively assess model performance. Firstly, using the available hydrometeorological data from 1 January 1960 to 31 December 1981, parameters in each model are determined using the Shuffled Complex Evolution University of Arizona optimization method. The results show that three models reproduced acceptably observed streamflow, with NSE ranging from 0.65 to 0.75, r and KGE varying between 0.72 and 0.87, and RMSE is less than 6.5% of the observed streamflow magnitude. Secondly, the NAM model was found as the most suitable for simulating observed streamflow in the studied river basin. Thirdly, three models were applied to simulate streamflow in the period from 1 January 1982 to 31 December 2022, revealing similar magnitudes of four statistical indicators of observed and estimated streamflow. The capability of the three models in simulating streamflow in data-sparse river basins is finally discussed.

The road to net-zero in Canada through regionalization and segmentation of GHG emissions and the role of water resources management

The road to net-zero in Canada through regionalization and segmentation of GHG emissions and the role of water resources management

Residual Temporal Convolutional Network With Dual Attention Mechanism for Multilead-Time Interpretable Runoff Forecasting.

As a pivotal subfield within the domain of time series forecasting, runoff forecasting plays a crucial role in water resource management and scheduling. Recent advancements in the application of artificial neural networks (ANNs) and attention mechanisms have markedly enhanced the accuracy of runoff forecasting models. This article introduces an innovative hybrid model, ResTCN-DAM, which synergizes the strengths of deep residual network (ResNet), temporal convolutional networks (TCNs), and dual attention mechanisms (DAMs). The proposed ResTCN-DAM is designed to leverage the unique attributes of these three modules: TCN has outstanding capability to process time series data in parallel. By combining with modified ResNet, multiple TCN layers can be densely stacked to capture more hidden information in the temporal dimension. DAM module adeptly captures the interdependencies within both temporal and feature dimensions, adeptly accentuating relevant time steps/features while diminishing less significant ones with minimal computational cost. Furthermore, the snapshot ensemble method is able to obtain the effect of training multiple models through one single training process, which ensures the accuracy and robustness of the forecasts. The deep integration and collaborative cooperation of these modules comprehensively enhance the model's forecasting capability from various perspectives. Ablation studies conducted validate the efficacy of each module, and through multiple sets of comparative experiments, it is shown that the proposed ResTCN-DAM has exceptional and consistent performance across varying lead times. We also employ visualization techniques to display heatmaps of the model's weights, thereby enhancing the interpretability of the model. When compared with the prevailing neural network-based runoff forecasting models, ResTCN-DAM exhibits state-of-the-art accuracy, temporal robustness, and interpretability, positioning it at the forefront of contemporary research.

Quantifying the Regulation Capacity of the Three Gorges Reservoir on Extreme Hydrological Events and Its Impact on Flow Regime in a Changing Climate

AbstractThe Three Gorges Reservoir (TGR) is one of the world's largest hydropower projects and plays an important role in water resources management in the Yangtze River. For the sake of disaster prevention and catchment management, it is crucial to understand the regulation capacity of the TGR on extreme hydrological events and its impact on flow regime in a changing climate. This study obtains historical inflows of the TGR from 1961 to 2019 and uses a distributed hydrological model to simulate the future inflows from 2021 to 2070. These data are adopted to drive a machine learning‐based TGR operation model to obtain the simulated outflow with TGR operation, which are then compared with the natural flow without TGR operation to assess the impact of TGR. The results indicate that the average flood peaks and total flooding days in the historical period could have been reduced by 29.2% and 53.4% with the operation of TGR. The relative declines in drought indicators including duration and intensity were generally less than 10%. Faced with more severe extreme hydrological events in the future, the TGR is still expected to alleviate floods and droughts, but cannot bring them down to historical levels. The impact of TGR operation on flow regime will also evolve in a changing climate, potentially altering the habitats of river ecosystems. This study proposes feasible methods for simulating the operation of large reservoirs and quantifying the impact on flow regime, and provides insights for integrated watershed management in the upper Yangtze River basin.

Rolling forecast of snowmelt floods in data-scarce mountainous regions using weather forecast products to drive distributed energy balance hydrological model

Snowmelt flood forecasting plays a crucial role in water resource management and utilization in arid regions. However, the accuracy of current snowmelt flood forecasts falls short of requirements due to complex mountainous terrain and limited observational data. In this study, We conducted a snowmelt flood simulation and forecasting experiment in a mountainous watershed in northern Xinjiang. First, we used site observations, ERA5-land reanalysis data and WorldClim data to produce historical meteorological forcing, calibrated the VIC-CAS model and improved routing model using historical observed runoff data, and conducted a detailed simulation of the hourly snowmelt flood process in the basin. Then, the latest grid forecast weather product developed by the China Meteorological Administration was integrated into the system to conduct a rolling forecast test of the snowmelt floods in the next 72 h during the snowmelt period in 2023. The remotely sensed snow cover and the freezing level measured by the sounding balloon were assimilated into the model in real time to correct the deviation of runoff simulation. The results show that during the model calibration period, the KGE of the simulated monthly runoff in the Kayertes River basin (KRB) from 2006 to 2011 was 0.83 and the NSE was 0.86. In the continuous hourly runoff simulation during the 2022 snowmelt period, the model well captured the intraday fluctuations of snowmelt runoff in the KRB, with the daily peak flow error within ± 20 % and the peak occurrence time controlled within ± 4 h. In the rolling forecast test in 2023, the daily peak flow error is within ± 20 % (the peak occurrence error during the peak period is even controlled within ± 5 %), and the peak occurrence time is within ± 4 h. Real-time assimilation of freezing level measured by sounding balloons and remotely sensed snow cover can effectively correct the deviations in simulated temperature and snow cover. This study can provide insights for runoff prediction in other high-altitude mountainous areas where data are scarce.

Breach Analysis of Earthen Dam

Failure of a levee or dam breach can result in severe economic losses and loss of human life. To minimize these consequences, it's imperative to assess the risks associated with levee breaches. This involves accurately predicting the volume of water released during a breach and simulating the propagation of flood waves across the floodplain to create inundation maps. Despite extensive observation and research into representing dam breaches and deriving crucial breach parameters like width, height, and side slope, there is no universally accepted method for determining these parameters. This study aims to evaluate existing methodologies for dam breach analyses, highlighting their advantages, limitations, and the challenges that lie ahead in this field. Earthen dams play a crucial role in water resource management and flood control. However, breaches in these structures pose significant risks to surrounding communities and infrastructure. This study explores the causes of earthen dam breaches, focusing on hydraulic, geotechnical, and structural factors. Through a comprehensive analysis of historical case studies and numerical modeling techniques, we elucidate the underlying mechanisms leading to dam failures. Furthermore, this research investigates effective mitigation strategies to enhance the resilience of earthen dams against breach incidents. By identifying key vulnerabilities and implementing targeted interventions, we aim to minimize the likelihood and impact of future breaches, ensuring the safety and sustainability of earthen dam infrastructure. Keywords— Breach analysis; Geotechnical Engineering; Earthen dam and Gangapur Dam

A new interpretable streamflow prediction approach based on SWAT-BiLSTM and SHAP.

Streamflow is a crucial variable for assessing the available water resources for both human and environmental use. Accurate streamflow prediction plays a significant role in water resource management and assessing the impacts of climate change. This study explores the potential of coupling conceptual hydrological models based on physical processes with machine learning algorithms to enhance the performance of streamflow simulations. Four coupled models, namely SWAT-Transformer, SWAT-LSTM, SWAT-GRU, and SWAT-BiLSTM, were constructed in this research. SWAT served as a transfer function to convert four meteorological features, including precipitation, temperature, relative humidity, and wind speed, into six hydrological features: soil water content, lateral flow, percolation, groundwater discharge, surface runoff, and evapotranspiration. Machine learning algorithms were employed to capture the underlying relationships between these ten feature variables and the target variable (streamflow) to predict daily streamflow in the Sandu-River Basin (SRB). Among the four coupled models and the calibrated SWAT model, SWAT-BiLSTM exhibited the best streamflow simulation performance. During the calibration period (training period), it achieved R2 and NSE values of 0.92 and 0.91, respectively, and maintained them at 0.90 during the validation period (testing period). Additionally, the performance of all four coupled models surpassed that of the calibrated SWAT model. Compared to the tendency of the SWAT model to underestimate streamflow, the absolute values of PBIAS for all coupled models are below 10%, which indicates that there is no significant systematic bias evident. SHapley Additive exPlanations (SHAP) were used to analyze the impact of different feature variables on streamflow prediction. The results indicated that precipitation contributed the most to streamflow prediction, with a global importance of 29.7%. Hydrological feature variable output by the SWAT model played a dominant role in the Bi-LSTM's prediction process. Coupling conceptual hydrological models with machine learning algorithms can significantly enhance the predictive performance of streamflow. The application of SHAP improves the interpretability of the coupled models and enhances researchers' confidence in the prediction results.

The multiscale nexus among land use-land cover changes and water quality in the Suquía River Basin, a semi-arid region of Argentina

Agricultural intensification and urban sprawl have led to significant alterations in riverscapes, and one of the critical consequences is the deterioration of water quality with significant implications for public health. Therefore, the objectives of this study were the assessment of the water quality of the Suquía River, the assessment of LULC change at different spatial scales, and the analysis of the potential seasonal correlation among LULC change and Water Quality Index (WQI). The Sample Sites (SS) 1 and 2 before Cordoba city had the highest WQI values while from SS3 the WQI decreased, with the lowest WQI close to the wastewater treatment plant (SS7) after Cordoba city. From SS8 in a agricultural context, the WQI increases but does not reach the original values. In light of analysis carried out, the correlation between water quality variables and the different LULC classes at the local and regional scales demonstrated that WQI is negatively affected by agricultural and urban activities, while natural classes impacted positively. The spatialization of the results can help strongly in assessing and managing the diffusion of point and non-point pollution along the riverscape. The knowledge gained from this research can play a crucial role in water resources management, which supports the provision of river ecosystem services essential for the well-being of local populations.

Winter Snowpack and Its Role in Water Resource Management and Ecosystem Function

Winter Snowpack and Its Role in Water Resource Management and Ecosystem Function

Control of unstable water flow in open channels

The analysis and modeling of hydrodynamic processes in open channels are crucial for effective water resource management and hydraulic structure design. This study investigates unsteady water flow in main channels using the Saint-Venant equations. Analytical and numerical methods were employed to model various flow scenarios. The developed mathematical model effectively describes and analyzes flow characteristics such as discharge, free surface elevation, and flow velocity. The obtained results have practical applications in water resource management, flood forecasting, and infrastructure design for water conservation. Overall, this work contributes to the advancement of knowledge in open channel hydraulics and provides a basis for developing more accurate and reliable water system modeling methods. These equations allow for the simulation of hydrodynamic processes in rivers, channels, and reservoirs, enabling the prediction of water level fluctuations, flow velocities, and pressure distribution under various operating conditions. This plays a vital role in water resource management and the design of hydraulic structures.

Hydrological risk assessment for Mangla Dam: compound effects of instant flow and precipitation peaks under climate change, using HEC-RAS and HEC-GeoRAS

Dams play a pivotal role in water resource management by storing and supplying water for a multitude of purposes. However, the looming threat of dam breach floods necessitates meticulous research and the simulation of potential failure scenarios. These endeavors are essential not only for comprehending the gravity of dam break floods but also for identifying vulnerable regions and informing emergency response strategies and land-use planning initiatives. This study employs a two-dimensional hydraulic model within the HEC-RAS (Hydrologic Engineering Center and River Analysis System) software to conduct an extensive dam breach analysis specifically focusing on the Mangla Dam located in Azad Kashmir, Pakistan. The analysis encompasses the prediction of various breach parameters, including the hydrograph of the breach flood, peak flow rates, arrival times of the flood, and the creation of inundation maps. Of primary concern is the Probable Maximum Floo, which drives the dam collapse model under unsteady flow conditions, accounting for both piping and overtopping failure scenarios. This study discerns the breach outflow hydrograph through the utility of HEC-RAS tools and evaluates hydraulic conditions at critical downstream locations. To dynamically route flood waves, the breach outflow hydrographs are harnessed. Furthermore, the HEC-RAS model is executed with breach parameters derived from five distinct empirical approaches, with ensuing outcomes subjected to rigorous comparative analysis. A comprehensive sensitivity study pertaining to breach parameters is also carried out to ascertain the sensitivity of peak flow and maximum stage. The results reveal peak flow rates of 174,850 m3/s and 177,850 m3/s in the downstream vicinity adjacent to the dam, translating into corresponding flooded areas of 379 km2 and 394 km2 attributable to piping and overtopping failures, respectively. The analysis of Land Use Land Cover data demonstrates that in the event of piping failure, 217 km2 of agricultural land and 56 km2 of urban areas would be completely submerged. Conversely, overtopping failure would inundate 220 km2 of agricultural land and 59 km2 of urban areas. The utilization of advanced remote sensing data, combined with flood modeling insights, equips engineers and stakeholders with invaluable knowledge. This knowledge, in turn, underpins strategic planning and well-informed decision-making processes, essential for addressing the potential global repercussions of similar catastrophes.

Estimation of the deep drainage for irrigated cropland based on satellite observations and deep neural networks

The deep drainage of irrigated cropland, which is one of the main causes of water losses in irrigation, plays an important role in water resource management. However, its magnitudes and variations remain unclear at regional scales. In this study, we established a remote sensing deep drainage model for irrigated cropland (ICDD_RS model) based on satellite observations and a deep neural network (DNN). First, deep drainage for natural vegetation was calculated by the water balance equation. Then, the ICDD_RS model was established by linking the calculated deep drainage and remote sensing data for natural vegetation based on a DNN. Finally, the model was used to predict the deep drainage of irrigated cropland through remote sensing data. The model was tested in the Heihe River Basin of China and the results showed the following: (1) The ICDD_RS model can well describe the relationship between deep drainage and selected influencing factors (0.83 < R2 < 0.86). At the site scale, it is highly consistent with in-situ observations with R2 values of 0.86, 0.74, and 0.71 for the Daman, Yingke, and Pingchuan stations, respectively. At the catchment scale, a low bias of 12% is obtained. (2) Deep drainage is large, accounting for 6–17% of the irrigation water. This model should be valuable in providing more detailed and effective information on agricultural water resource management.

Novel Threshold Self-Regulating Water Extraction Method

Water resources are crucial for human activities and sustainable socioeconomic development. Understanding surface water information can play a key role in water resource management, which affects the global water cycle and ecological environments. Considering the Hailar River water body as an example, this study proposes a new threshold self-learning water body extraction method (TSLWEM) based on modified normalized difference water index (MNDWI) data. The optimal water extraction thresholds determined by the TSLWEM algorithm for four test images were −0.0030, 0, 0.1990, and −0.0800. The TSLWEM algorithm effectively identified the target water body with recognition accuracies of 98.08%, 99.93%, 93.39%, and 93.20% for the four test images. Moreover, it can accurately identify small tributaries, such as lakes and rivers. The TSLWEM algorithm is suitable for Landsat 8 Operational Land Imager (OLI) data, which can effectively monitor and map complex surface water in temperate and semiarid regions while improving the accuracy of water body identification. The study’s findings provide technical support for the protection of water resources as well as their rational utilization and monitoring.

Contamination of the urban river network with perfluoroalkyl acids (PFAAs) introduced during river regulations

River regulation has a key role in water resource management, but the introduced pollutants cannot be underestimated. This study reported spatiotemporal variations of perfluoroalkyl acids (PFAAs) significantly affected by river regulations in a standard example of urban river network with bidirectional flow in China. Perfluoroalkyl sulfonic acids (PFSAs), mostly of domestic origin, dominated during discharge, and perfluoroalkyl carboxylic acids (PFCAs), industrial pollutants, during diversion. The estimated PFAA flux into the Yangtze River during discharge was 1.22 × 102 kg with 62.5 % from Taihu Lake and 37.5 % from the river network. And that from the Yangtze River during diversion was 90.2 kg with 72.2 % into Taihu Lake and 27.8 % into the river network. Our findings show that PFAAs can exert pressure on regional water security that most of the urban river network was at medium risk. This study improves understandings of the role of river regulations in urban water networks and provides solid reference for risk assessment.

Virtual Water and Its Role in Water Resource Management

Considering the future water scarcity issues, the control and management of water resources have become critically important, particularly with the rapid population growth in various countries and the rising standards of living. Agriculture, especially in developing countries, is the largest consumer of water. Therefore, the concept of virtual water gains significant importance. Virtual water refers to the hidden water embedded in agricultural products, considering the substantial amount of water required for their growth. For instance, the production of one kilogram of wheat consumes more than 1000 liters of water, termed as virtual water, as it's actually used and, in case of agricultural exports and imports, transferred from one country to another. Nations can reduce the cultivation of crops that consume a lot of water through the management of virtual water, controlling the input and output of water concerning this definition.&#x0D; Virtual water management allows countries to control their water input and output concerning their crops. This concept plays a crucial role in areas facing water scarcity. With efficient management of virtual water, demand for water in different regions (both dry and semi-arid) can be significantly reduced. The term "virtual water" indicates the amount of water used in the production of food items or commercial goods. By minimizing the production of water-intensive products, especially in water-scarce regions, the need for water consumption in those areas decreases significantly. Storing water virtually is a practical way to overcome periods of scarcity, more efficient than artificial reservoirs. The rise in competition for water resources and the increase in demand have made water management a prevalent topic. The transfer of embedded water in agricultural products is a vital component of global water management, particularly in water-scarce regions. The trade of virtual water not only saves water in importing countries but is also considered a global water conservation measure. This article aims to introduce the concept of virtual water and its role in water resource management.

Machine Learning Framework with Feature Importance Interpretation for Discharge Estimation: A Case Study in Huitanggou Sluice Hydrological Station, China

Accurate and reliable discharge estimation plays an important role in water resource management as well as downstream applications such as ecosystem conservation and flood control. Recently, data-driven machine learning (ML) techniques showed seemingly insurmountable performance in runoff forecasting and other geophysical domains, but they still need to be improved in terms of reliability and interpretability. In this study, focusing on discharge estimation and management, we developed an ML-based framework and applied it to the Huitanggou sluice hydrological station in Anhui Province, China. The framework contains two ML algorithms, the ensemble learning random forest (ELRF) and the ensemble learning gradient boosting decision tree (ELGBDT). The SHapley Additive exPlanation (SHAP) was introduced into our framework to interpret the impact of the model features. In our framework, the correlation analysis of the dataset can provide feature information for modeling, and the quartile method was utilized to solve the outlier problem of the dataset. The Bayesian optimization algorithm was adopted to optimize the hyperparameters of the ensemble ML models. The ensemble ML models are further compared with the traditional stage–discharge rating curve (SDRC) method and the single ML model. The results show that the estimation performance of the ensemble ML models is superior to that of the SDRC and the single ML model. In addition, an analysis of the discharge estimation without considering the flow state was performed. This analysis reveals that the ensemble ML models have strong adaptability. The ensemble ML models accurately estimate the discharge, with a coefficient of determination of 0.963, a root mean squared error of 31.268, and a coefficient of correlation of 0.984. Our framework can prove helpful to improve the efficiency of short-term hydrological estimation and simultaneously provide the interpretation of the impact of the hydrological features on estimation results.