Simulation of Pollution Load at Basin Scale Based on LSTM-BP Spatiotemporal Combination Model

Accurate simulation of pollution load at basin scale is very important for controlling pollution. Although data-driven models are increasingly popular in water environment studies, they are not extensively utilized in the simulation of pollution load at basin scale. In this paper, we developed a data-driven model based on Long-Short Term Memory (LSTM)-Back Propagation (BP) spatiotemporal combination. The model comprises several time simulators based on LSTM and a spatial combiner based on BP. The time series of the daily pollution load in the Zhouhe River basin during the period from 2006 to 2017 were simulated using the developed model, the BP model, the LSTM model and the Soil and Water Assessment Tool (SWAT) model, independently. Results showed that the spatial correlation (i.e., Pearson’s correlation coefficient is larger than 0.5) supports using a single model to simulate the pollution load at all sub-basins, rather than using independent models for each sub-basin. Comparison of the LSTM-BP spatiotemporal combination model with the BP, LSTM and SWAT models showed that the performance of the LSTM model is better than that of the BP model and the LSTM model can obtain comparable performance with the SWAT model in most cases, whereas the performance of the LSTM-BP spatiotemporal combination model is much better than that of the LSTM and SWAT models. Although the variation of the simulated pollution load with the LSTM-BP model is high under different hydrological periods and precipitation intensities, the LSTM-BP model can track the temporal variation trend of pollution load accurately (i.e., the RMSE is 6.27, NSE is 0.86 and BIAS is 19.46 for the NH3 load and the RMSE is 20.27, NSE is 0.71 and BIAS 36.87 is for the TN load). The results of this study demonstrate the applicability of data-driven models, especially the LSTM-BP model, in the simulation of pollution load at basin scale.

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A Review of SWAT Model Application in Africa

Water ◽

10.3390/w13091313 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1313

Author(s):

George Akoko ◽

Tu Hoang Le ◽

Takashi Gomi ◽

Tasuku Kato

Keyword(s):

Climate Change ◽

Land Use ◽

Water Resources ◽

Hydrological Modeling ◽

Assessment Tool ◽

Swat Model ◽

Data Availability ◽

Mitigation Measures ◽

Model Parameterization ◽

Basin Scale

The soil and water assessment tool (SWAT) is a well-known hydrological modeling tool that has been applied in various hydrologic and environmental simulations. A total of 206 studies over a 15-year period (2005–2019) were identified from various peer-reviewed scientific journals listed on the SWAT website database, which is supported by the Centre for Agricultural and Rural Development (CARD). These studies were categorized into five areas, namely applications considering: water resources and streamflow, erosion and sedimentation, land-use management and agricultural-related contexts, climate-change contexts, and model parameterization and dataset inputs. Water resources studies were applied to understand hydrological processes and responses in various river basins. Land-use and agriculture-related context studies mainly analyzed impacts and mitigation measures on the environment and provided insights into better environmental management. Erosion and sedimentation studies using the SWAT model were done to quantify sediment yield and evaluate soil conservation measures. Climate-change context studies mainly demonstrated streamflow sensitivity to weather changes. The model parameterization studies highlighted parameter selection in streamflow analysis, model improvements, and basin scale calibrations. Dataset inputs mainly compared simulations with rain-gauge and global rainfall data sources. The challenges and advantages of the SWAT model’s applications, which range from data availability and prediction uncertainties to the model’s capability in various applications, are highlighted. Discussions on considerations for future simulations such as data sharing, and potential for better future analysis are also highlighted. Increased efforts in local data availability and a multidimensional approach in future simulations are recommended.

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Use of decision Tables to Simulate Management in Ecohydrological Models

10.20944/preprints201805.0156.v1 ◽

2018 ◽

Author(s):

Jeffrey G. Arnold ◽

Katrin Bieger ◽

Michael J. White ◽

Raghavan Srinivasan ◽

John A. Dunbar ◽

...

Keyword(s):

Water Stress ◽

Land Management ◽

Assessment Tool ◽

Swat Model ◽

Regulated River ◽

Irrigation Application ◽

Reservoir Models ◽

Basin Scale ◽

Rule Sets ◽

Decision Tables

Decision tables have been used for many years in data processing and business applications to simulate complex rule sets. Several computer languages have been developed based on rule systems and they are easily programmed in several current languages. Land management and river-reservoir models simulate complex land management operations and reservoir management in highly regulated river systems. Decision tables are a precise yet compact way to model the rule sets and corresponding actions found in these models. In this study, we discuss the suitability of decision tables to simulate management in the river basin scale Soil and Water Assessment Tool (SWAT+) model. Decision tables are developed to simulate automated irrigation and reservoir releases. A simple auto irrigation application of decision tables was developed using plant water stress as a condition for irrigating corn in Texas. Sensitivity of the water stress trigger and irrigation application amounts were shown on soil moisture and corn yields. In addition, the Grapevine Reservoir near Dallas, Texas was used to illustrate the use of decision tables to simulate reservoir releases. The releases were conditioned on reservoir volumes and flood season. The release rules as implemented by the decision table realistically simulated flood releases as evidenced by a daily NSE (Nash-Sutcliffe Efficiency) of 0.52 and a percent bias of -1.1%. Using decision tables to simulate management in land, river and reservoir models was shown to have several advantages over current approaches including: 1) mature technology with considerable literature and applications, 2) ability to accurately represent complex, real world decision making, 3) code that is efficient, modular and easy to maintain, and 4) tables that are easy to maintain, support, and modify.

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The performance of LSTM models from basin to continental scales

10.5194/egusphere-egu2020-8855 ◽

2020 ◽

Author(s):

Frederik Kratzert ◽

Daniel Klotz ◽

Günter Klambauer ◽

Grey Nearing ◽

Sepp Hochreiter

Keyword(s):

Machine Learning ◽

Large Scale ◽

Short Term Memory ◽

Regional Scale ◽

Data Driven ◽

Hydrological Models ◽

Short Term ◽

Term Memory ◽

Basin Scale ◽

Long Short Term Memory

Simulation accuracy among traditional hydrological models usually degrades significantly when going from single basin to regional scale. Hydrological models perform best when calibrated for specific basins, and do worse when a regional calibration scheme is used.&#160;One reason for this is that these models do not (have to) learn hydrological processes from data. Rather, they have a predefined model structure and only a handful of parameters adapt to specific basins. This often yields less-than-optimal parameter values when the loss is not determined by a single basin, but by many through regional calibration.The opposite is true for data driven approaches where models tend to get better with more and diverse training data. We examine whether this holds true when modeling rainfall-runoff processes with deep learning, or if, like their process-based counterparts, data-driven hydrological models degrade when going from basin to regional scale.Recently, Kratzert et al. (2018) showed that the Long Short-Term Memory network (LSTM), a special type of recurrent neural network, achieves comparable performance to the SAC-SMA at basin scale. In follow up work Kratzert et al. (2019a) trained a single LSTM for hundreds of basins in the continental US, which outperformed a set of hydrological models significantly, even compared to basin-calibrated hydrological models. On average, a single LSTM is even better in out-of-sample predictions (ungauged) compared to the SAC-SMA in-sample (gauged) or US National Water Model (Kratzert et al. 2019b).LSTM-based approaches usually involve tuning a large number of hyperparameters, such as the number of neurons, number of layers, and learning rate, that are critical for the predictive performance. Therefore, large-scale hyperparameter search has to be performed to obtain a proficient LSTM network.&#160;&#160;However, in the abovementioned studies, hyperparameter optimization was not conducted at large scale and e.g. in Kratzert et al. (2018) the same network hyperparameters were used in all basins, instead of tuning hyperparameters for each basin separately. It is yet unclear whether LSTMs follow the same trend of traditional hydrological models to degrade performance from basin to regional scale.&#160;In the current study, we performed a computational expensive, basin-specific hyperparameter search to explore how site-specific LSTMs differ in performance compared to regionally calibrated LSTMs. We compared our results to the mHM and VIC models, once calibrated per-basin and once using an MPR regionalization scheme. These benchmark models were calibrated individual research groups, to eliminate bias in our study. We analyse whether differences in basin-specific vs regional model performance can be linked to basin attributes or data set characteristics.References:Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall&#8211;runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005&#8211;6022, https://doi.org/10.5194/hess-22-6005-2018, 2018.&#160;Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089&#8211;5110, https://doi.org/10.5194/hess-23-5089-2019, 2019a.&#160;Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., & Nearing, G. S.: Toward improved predictions in ungauged basins: Exploiting the power of machine learning. Water Resources Research, 55. https://doi.org/10.1029/2019WR026065, 2019b.

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Evaluating the influence of hydrological condition on the phosphorus loads in an agricultural river basin using the SWAT model

Hydrology Research ◽

10.2166/nh.2021.165 ◽

2021 ◽

Author(s):

Cui Jian ◽

Yue Zhao ◽

Wenchao Sun ◽

Yan Chen ◽

Bo Wu ◽

...

Keyword(s):

River Basin ◽

Pollution Control ◽

Assessment Tool ◽

Swat Model ◽

River Basin Management ◽

Hydrological Conditions ◽

Basin Scale ◽

Phosphorus Loads ◽

Using Data ◽

And Control

Abstract Excessive phosphorus is an important cause of eutrophication. For river basin management, source identification and control of nonpoint source (NPS) pollution are difficult. In this study, to explore influences of hydrological conditions on phosphorus, the Soil and Water Assessment Tool (SWAT) model is applied to the Luanhe River basin in North China. Moreover, influences of the spatial scale of the livestock and poultry amount data on estimations of phosphorus loads are also discussed. The results show that applying town-level livestock and poultry amount data allows the model to perform better when estimating phosphorus loads, indicating that using data at a finer administrative level is necessary. For the typical wet year, the estimated annual phosphorus load was 2.6 times that in the typical dry year. Meanwhile, the contribution of pollution in summer to the annual load is greater in the wet year than that in the dry year. The spatial distributions of subbasins with high unit loads of phosphorus differ under different hydrological conditions, meaning that critical areas for pollution control vary with the wetness of each year. All these findings indicate that for pollution control at basin scale, considering the seasonal and interannual variabilities in hydrological conditions is highly demanded.

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Evaluation and Hydrological Application of CMADS against TRMM 3B42V7, PERSIANN-CDR, NCEP-CFSR, and Gauge-Based Datasets in Xiang River Basin of China

Water ◽

10.3390/w10091225 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1225 ◽

Cited By ~ 19

Author(s):

Xichao Gao ◽

Qian Zhu ◽

Zhiyong Yang ◽

Hao Wang

Keyword(s):

River Basin ◽

Bayesian Model Averaging ◽

Assessment Tool ◽

Swat Model ◽

Model Averaging ◽

Tropical Rainfall Measuring Mission ◽

Probability Of Detection ◽

Climate Data ◽

Xiang River ◽

Better Than

Satellite-based and reanalysis precipitation products provide a practical way to overcome the shortage of gauge precipitation data because of their high spatial and temporal resolution. This study compared two reanalysis precipitation datasets (the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS), the National Centers for Environment Prediction Climate Forecast System Reanalysis (NCEP-CFSR)) and two satellite-based datasets (the Tropical Rainfall Measuring Mission 3B42 Version 7 (3B42V7) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR)) with observed precipitation in the Xiang River basin in China at two spatial (grids and the whole basin) and two temporal (daily and monthly) scales. These datasets were then used as inputs to a SWAT model to evaluate their usefulness in hydrological prediction. Bayesian model averaging was used to discriminate dataset performance. The results show that: (1) for daily timesteps, correlations between reanalysis datasets and gauge observations are >0.55, better than satellite-based datasets; The bias values of satellite-based datasets are <10% at most evaluated grid locations and for the whole baseline. PERSIANN-CDR cannot detect the spatial distribution of rainfall events; the probability of detection (POD) of PERSIANN-CDR at most evaluated grids is <0.50; (2) CMADS and 3B42V7 are better than PERSIANN-CDR and NCEP-CFSR in most situations in terms of correlation with gauge observations; satellite-based datasets are better than reanalysis datasets in terms of bias; and (3) CMADS and 3B42V7 simulate streamflow well for both daily (The Nash-Sutcliffe coefficient (NS) > 0.70) and monthly (NS > 0.80) timesteps; NCEP-CFSR is worst because it substantially overestimates streamflow; PERSIANN-CDR is not good because of its low NS (0.40) during the validation period.

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Investigate the Applicability of CMADS and CFSR Reanalysis in Northeast China

Water ◽

10.3390/w12040996 ◽

2020 ◽

Vol 12 (4) ◽

pp. 996 ◽

Cited By ~ 2

Author(s):

Limin Zhang ◽

Xianyong Meng ◽

Hao Wang ◽

Mingxiang Yang ◽

Siyu Cai

Keyword(s):

Northeast China ◽

Assessment Tool ◽

Meteorological Data ◽

Swat Model ◽

Poor Performance ◽

Runoff Simulation ◽

Reanalysis Dataset ◽

Climate Forecast System Reanalysis ◽

Maximum Minimum ◽

Better Than

Reanalysis datasets can provide alternative and complementary meteorological data sources for hydrological studies or other scientific studies in regions with few gauge stations. This study evaluated the accuracy of two reanalysis datasets, the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) and Climate Forecast System Reanalysis (CFSR), against gauge observations (OBS) by using interpolation software and statistical indicators in Northeast China (NEC), as well as their annual average spatial and monthly average distributions. The reliability and applicability of the two reanalysis datasets were assessed as inputs in a hydrological model (SWAT) for runoff simulation in the Hunhe River Basin. Statistical results reveal that CMADS performed better than CFSR for precipitation and temperature in NEC with the indicators closer to optimal values (the ratio of standard deviations of precipitation and maximum/minimum temperature from CMADS were 0.92, 1.01, and 0.995, respectively, while that from CFSR were 0.79, 1.07, and 0.897, respectively). Hydrological modelling results showed that CMADS + SWAT and OBS + SWAT performed far better than CFSR + SWAT on runoff simulations. The Nash‒Sutcliffe efficiency (NSE) of CMADS + SWAT and OBS + SWAT ranged from 0.54 to 0.95, while that of CFSR + SWAT ranged from −0.07 to 0.85, exhibiting poor performance. The CMADS reanalysis dataset is more accurate than CFSR in NEC and is a suitable input for hydrological simulations.

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Application of the Soil and Water Assessment Tool (SWAT) at Field Scale: Categorizing Methods and Review of Applications

Transactions of the ASABE ◽

10.13031/trans.13545 ◽

2020 ◽

Vol 63 (2) ◽

pp. 513-522 ◽

Cited By ~ 2

Author(s):

Ritesh Karki ◽

Puneet Srivastava ◽

Tamie L. Veith

Keyword(s):

Best Management Practices ◽

Management Practices ◽

Assessment Tool ◽

Swat Model ◽

Scale Model ◽

Future Research ◽

Field Scale ◽

Basin Scale ◽

Water Assessment ◽

Soil And Water

HighlightsThis review study identified five different ways of setting up a SWAT model for field-scale analysis.Model setup for each field-scale modeling method and examples of application are discussed.Benefits and limitations of each method are discussed.Abstract. Although the Soil and Water Assessment Tool (SWAT) has been widely used as a watershed/basin scale model, recently there has been considerable interest in applying it at the field scale, especially for evaluation of best management practices and for building stakeholder confidence. In this study, a thorough review of the literature on field-scale application of SWAT was conducted. It was determined that there is more than one way of setting up a field-scale SWAT model depending on the spatial scale of the research as well as the research question to be answered. This article provides a detailed review of the methods used for field-scale SWAT modeling along with a summary of applications. This article also discusses the limitations and advantages of each method along with future research needs. The overarching goal of this article is to provide a valuable and time-conserving resource for future researchers interested in field-scale SWAT modeling. Keywords: Arc-SWAT, Field level, Field-scale resolution, Field-scale SWAT, SWAT.

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Improved Estimations of Nitrate and Sediment Concentrations Based on SWAT Simulations and Annual Updated Land Cover Products from a Deep Learning Classification Algorithm

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9100576 ◽

2020 ◽

Vol 9 (10) ◽

pp. 576

Author(s):

Nikiforos Samarinas ◽

Nikolaos Tziolas ◽

George Zalidis

Keyword(s):

Land Use ◽

Deep Learning ◽

Agricultural Sector ◽

Assessment Tool ◽

Swat Model ◽

Hybrid Approach ◽

Agricultural Watershed ◽

Observation Data ◽

Basin Scale ◽

Land Use Maps

The agricultural sector and natural resources are heavily interdependent, comprising a coherent but complex system. The soil and water assessment tool (SWAT) is widely used in assessing these interdependencies for regional watershed management. However, long-term simulations of agricultural watersheds are considered as not realistic since they have often been performed assuming constant land use over time and are based on the coarse resolution of the existing global or national data. This work presents the first insights of the synergy among SWAT model and deep learning classification algorithms to provide annually updated and realistic model’s parameterization and simulations. The proposed hybrid modelling approach couples the physical process SWAT model with the versatility of Earth observation data-driven non-linear deep learning algorithms for land use classification (Overall Accuracy (OA) = 79.58% and Kappa = 0.79), giving a strong advantage to decision makers for efficient management planning. A validation case at an agricultural watershed located in Northern Greece is provided to demonstrate their synergistic use to estimate nitrate and sediment concentrations that load in Zazari Lake. The SWAT model has been implemented under two different simulations; one with the use of a static coarse land use map and the other with the use of the annual updated land use maps for three consecutive years (2017–2019). The results indicate that the land use changes affect the final estimations resulting to an enhanced prediction performance of 1% and 2% for sediment and nitrate, respectively, when the annual land use maps are incorporated into SWAT simulations. In this context, a hybrid approach could further contribute to addressing challenges and support a data-centric scheme for informed decision making with regard to environmental and agricultural issues on the river basin scale.

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Calibration of SWAT and Two Data-Driven Models for a Data-Scarce Mountainous Headwater in Semi-Arid Konya Closed Basin

Water ◽

10.3390/w11010147 ◽

2019 ◽

Vol 11 (1) ◽

pp. 147 ◽

Cited By ~ 4

Author(s):

Cihangir Koycegiz ◽

Meral Buyukyildiz

Keyword(s):

Assessment Tool ◽

Swat Model ◽

Data Driven ◽

Support Vector ◽

Efficiency Coefficient ◽

Hydrologic Models ◽

Closed Basin ◽

Vector Machines ◽

Physically Based ◽

Validation Stage

Hydrologic models are important tools for the successful management of water resources. In this study, a semi-distributed soil and water assessment tool (SWAT) model is used to simulate streamflow at the headwater of Çarşamba River, located at the Konya Closed Basin, Turkey. For that, first a sequential uncertainty fitting-2 (SUFI-2) algorithm is employed to calibrate the SWAT model. The SWAT model results are also compared with the results of the radial-based neural network (RBNN) and support vector machines (SVM). The SWAT model performed well at the calibration stage i.e., determination coefficient (R2) = 0.787 and Nash–Sutcliffe efficiency coefficient (NSE) = 0.779, and relatively lower values at the validation stage i.e., R2 = 0.508 and NSE = 0.502. Besides, the data-driven models were more successful than the SWAT model. Obviously, the physically-based SWAT model offers significant advantages such as performing a spatial analysis of the results, creating a streamflow model taking into account the environmental impacts. Also, we show that SWAT offers the ability to produce consistent solutions under varying scenarios whereas it requires a large number of inputs as compared to the data-driven models.

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Comparative Water Environment Simulation Study of Two Typical Models with BMPs in a Karst Basin

Agriculture ◽

10.3390/agriculture12010069 ◽

2022 ◽

Vol 12 (1) ◽

pp. 69

Author(s):

Jing Zhang ◽

Peiqi Zhang ◽

Yongyu Song

Keyword(s):

Water Quality ◽

River Basin ◽

Best Management Practices ◽

Management Practices ◽

Assessment Tool ◽

Swat Model ◽

Coefficient Of Determination ◽

Pollution Load ◽

Hspf Model ◽

Lijiang River

Carbonate rocks are widely distributed in southwest China, forming a unique karst landscape. The Lijiang River Basin provides a typical example of an area with concentrated karst. Research on the laws of hydrology and water quality migration in the Lijiang River Basin is important for the management of the water resources of Guilin City and similar areas. In this study, we combined three meteorological data with the soil and water assessment tool (SWAT) model and the hydrological simulation program-Fortran (HSPF) model to simulate the hydrological and water quality processes in the Lijiang River Basin separately. We chose the Nash–Sutcliffe efficiency (NSE) coefficient, coefficient of determination (R2), root mean square error-observations standard deviation ratio (RSR), and mean absolute error (MAE) as the metrics used to evaluate the models. The results, combined with the time-series process lines, indicated that the SWAT model provides a more accurate performance than the HSPF model in streamflow, ammonia nitrogen (NH3-N), and dissolved oxygen (DO) simulations. In addition, we divided the karst and non-karst areas, and we analyzed the differences between them in water balance, sediment transport, and pollution load. We further identified the key source areas of pollution load in the Lijiang River Basin, evaluated the pollution reduction effect of best management practices (BMPs) on surface source pollution, and proposed some pollution control countermeasures. Each scenario, especially returning farmland to forest and creating vegetation buffer zones, reduces the NH3-N and DO pollution load.

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