Review of Decision Support Systems and Allocation Models for Integrated Water Resources Management Focusing on Joint Water Quantity-Quality
Abstract
Introduction
Allocation Models and Decision Support Systems for IWRM
Concepts of IWRM
Theoretical Foundation of Water Allocation Models
Main characteristics | Types | Advantages | Disadvantages | Examples/references |
---|---|---|---|---|
DESCRIPTIVE-ORIENTED MODELS | ||||
Show what will occur if a certain plan is adopted. Help to predict how different management types applied to a certain infrastructure react not only under specific adverse conditions (dry weather, flooding, etc.) but also to long-term changes. | ||||
1. Sequentially simulate flows, allocations and volumes at the different network nodes and links for each period.a 2. Evaluate performance of the water system over a long period of time. 3. Provide realism and flexibility in representing reservoirs’ operational rules as well as water demands and priorities of use. | DBRS (descriptive based on rules simulation): Without optimization algorithms (simulation based on Rules of Ad Hoc Models). | 1. Use of structured programming language to develop interactive procedures that represent all the connections between water requirements and management rules for different network stretches, typically from upstream to downstream, identifying water flows and volumes throughout the network. 2. Potential to reproduce water management systems and mechanisms with high precision. | Ad Hoc algorithms (specifically developed to simulate real system conditions) are not easily built, since they realistically represent all the system’s operational rules. | IRAS (Loucks et al. 1995) |
DODS: With optimization algorithms. | 1. Solve distinct optimization problems for each time period to direct water flow, control reservoir volume and allocate water through the network of nodes and links. 2. Relatively user friendly and flexible. | 1. Often requires simplification of math formulation which limits the representation of complex rules of reservoir operation and the entire system, institutional limits, and existing infrastructure. 2. Complex operational rules may be difficult to represent, thus hindering replication results. | OASIS (Randall et al. 1997) | |
PRESCRIPTIVE-ORIENTED MODELS | ||||
Determine the plan that must be adopted to satisfy a certain goal or decision criterion. | ||||
1. Maximize or minimize a specific function-goal to supply values for decision variables simultaneously throughout all time periods, considering all inflows relative to full time horizon under analysis. 2. Usually deal with limited time analysis. 3. Include simulation components, even if rudimentary, to calculate hydrological flows and levels of quality constituents. | PHDC (prescriptive hydrological decision criteria): Decision criterion for hydrological optimization. | Identify levels of releases and optimum allocations among the uses that maximize or minimize hydrological indicators, thus reflecting a specific management goal. | Fewer details concerning hydrological interactions among the main sources of water and their uses are described, given data complexity and volume as well as specificity of the required analysis to support water policy decisions. | HEC-PRM (Ferreira and Lund 1994) |
PEDC (prescriptive economic decision criteria): Decision criteria for economic optimization. | 1. Can incorporate social and economic values in water resources allocation. 2. Can identify economically optimum water allocations as well as associated marginal values such as: shade-prices and opportunity costs. 3. Can incorporate other criteria such as equality into the optimization models through restrictions. | Hydrological interactions are described with fewer details given data complexity and volume. Economic analysis is less specific. | MITSIM (Strzepek 1981) | |
AQUARIUS (Diaz and Brown 1997) |
Theoretical Foundation of DSS Used for IWRM
Review of Models and Decision Support Systems for IWRM
Methodology
Studies Tabulation
Major challenge | Description |
---|---|
In-stream and off-stream intersectoral allocation and use | Applications for in-stream uses include hydropower, navigation, and recreation. Off-stream uses are usually related to consumption, e.g., irrigated agriculture or urban supply. To allocate water efficiently, in-stream flow values must be incorporated into the allocation process. |
Conjunctive use of groundwater and surface water | Support for integrated groundwater management with surface water. |
Institutions, water markets, and pricing | These support the design and evaluation of economic instruments, such as water and collection markets, and thus also support the establishment of regulatory frameworks, as well as institutions using economic theory. Regulatory instruments can also be designed and evaluated based on their modeling. |
Conflict resolution, transboundary management, and sustainability | These identify efficient allocations, account for “virtual water” values and interregional exchanges between economic sectors. From these values, conditions can arise for collaboration and adaptability that can help to resolve local, regional, and cross-border conflicts. |
Managing for climate change and drought | These consider extreme events such as droughts and floods, as well as climate change. In general, these situations are included as scenarios. |
Land use management: floods and water quality | These consider land use management and its impact on water quantity and quality. |
Source: Adapted from Harou et al. (2009).
Criteria (see note 1) | Model | Quality model | DSS name | Location | Reference | |||||
---|---|---|---|---|---|---|---|---|---|---|
EC | EQ | SU | SOC | ORIENT/STRUC | DODS approaches main objective | |||||
Conflict resolution, transboundary management, and sustainability | ||||||||||
X | X | X | — | X | PEDC/L+D | — | No specific name | — | Syr Darya River Basin, Central Asia | Cai et al. (2002) |
X | X | — | — | PEDC/N+D | — | — | — | Dong Nai River Basin, Vietnam | Ringler et al. (2006) | |
— | — | — | — | X | DODS/N+D | See Table | QUAL2K | MOSDEW | Germany, West Africa, and Central Asia basins | Gaiser et al. (2008) |
X | — | — | — | X | PEDC/N+D | — | Streeter–Phelps | — | Pirapama River Basin, Brazil | Alcoforado de Moraes et al. (2010) |
— | — | — | — | X | DODS/N+S | See Table | GESCAL module | AQUATOOL | Araguari River Basin, Brazil | Salla et al. (2014) |
— | X | X | X | — | DBRS/N+D | — | — | SDSS | Ichkeul Basin, Tunisia | Chakroun et al. (2015) |
X | X | — | — | — | PEDC/N+S | — | — | — | Santa Cruz River Basin, USA/Mexico | Ghosh et al. (2017) |
— | — | X | — | — | DBRS/N+S | — | — | — | Neshanic River Watershed | Giri et al. (2018) |
X | — | — | — | X | PEDC/N+D | — | Mike ECO Lab | — | Kelani River, Sri Lanka | Gunawardena et al. (2018) |
X | — | X | — | — | PEDC/N+D | — | — | — | Mekong River Basin | Ringler et al. (2004) |
X | X | X | — | — | PEDC/L+D | — | — | — | Sefidrud River Basin, Iran | Roozbahani et al. (2020) |
— | — | X | X | — | PHDC/N+D | — | — | — | Wei River Basin and Han River Basin, China | Ma et al. (2021) |
— | — | — | X | — | PHDC/N+D | — | — | HEAL | Four interlinked river basins in Northeast of Brazil | Alcoforado de Moraes et al. (2021) |
Conjunctive use of groundwater and surface water | ||||||||||
— | — | — | — | X | DODS/N+D | See Table | QUAL2E | — | Arkansas River Basin, EUA | Dai and Labadie (2001) |
— | — | X | — | — | DBRS/L+D | — | — | Web-based DSS prototype | Ganges River, India and Bangladesh | Salewicz and Nakayama (2004) |
— | — | X | — | — | DODS/L+D | See Table | — | REALM | The Goulburn and the Melbourne Water Supply System, Australia | Perera et al. (2005) |
X | — | — | X | — | PEDC/N+D | — | — | — | São Francisco River Basin, Brazil | Maneta et al. (2009) |
X | — | — | — | — | PEDC/N+D | — | — | — | Semiarid region with a Mediterranean climate | Zhu et al. (2015) |
X | X | X | — | — | PEDC/N+S | — | — | — | Jucar Basin, Spain | Kahil et al. (2016b) |
— | — | X | X | X | PHDC/N+D | — | No specific name | — | Theoretical region | Abdulbaki et al. (2017) |
— | X | X | X | — | PHDC/L+D | — | — | — | South Florida | Mirchi et al. (2018) |
— | — | X | — | — | DODS/L+D | See Table | — | WEAP21 | Urmia Lake basin | Sprague (1980) |
— | — | X | X | X | PHDC/L+D | — | No specific name | — | Haihe River Basin, China | Martinsen et al. (2019a) |
— | — | X | — | — | PHDC/L+D | — | — | — | Zayandehrud River Basin, Iran | Chakraei et al. (2021) |
Institutions, water markets and pricing | ||||||||||
X | — | — | — | — | PEDC/N+D | — | — | — | The Brantas River Basin, Indonesia | Rodgers et al. (2003) |
— | — | X | — | X | DODS/L+D | See Table | Quality Module WEAP21 | WEAP21 | Sacramento River | Yates et al. (2005) |
— | — | X | X | — | DODS/L+D | See Table | — | — | California’s Friant-Kern | Marques et al. (2006) |
— | X | X | X | — | DBRS/L+D | — | — | — | South Africa | van Heerden et al. (2008) |
X | X | X | — | — | PEDC/N+D | — | — | — | Rio Grande | Ward and Pulido-Velázquez (2008) |
— | — | X | — | — | DODS/N+D | See Table | — | — | Rio Grande | Sandoval-Solis and McKinney (2014) |
— | — | X | X | — | DODS/L+D | See Table | — | — | Middle Guadiana Basin, Spain | Blanco-Gutiérrez et al. (2013) |
— | — | — | X | — | DBRS/L+D | — | — | — | Haihe River Basin, China | White et al. (2015) |
X | — | X | — | — | PEDC/N+S | — | — | — | Nar River Basin, England | Garbe and Beevers (2017) |
X | X | X | — | — | PEDC/N+S | — | — | — | Malian River Basin, China | Gao et al. (2019) |
X | X | — | — | PEDC/L+D | — | — | — | California’s major water supply system | Draper et al. (2003) | |
X | — | — | — | — | PEDC/L+D | — | — | — | Euphrates river basin in Turkey and Syria | Tilmant et al. (2008) |
In-stream and off-stream intersectoral allocation and use | ||||||||||
— | — | X | — | — | DODS/N+D | See Table | — | NILE DSS | Atankwidi catchment, West Africa | Georgakakos (2007) |
— | — | X | X | — | DODS/L+D | See Table | — | — | Musi River, India | George et al. (2011) |
— | — | X | — | — | DBRS/N+D | — | — | IRAS-2010 | Thames basin, England | Matrosov et al. (2011) |
X | — | — | — | X | PEDC/L+D | — | Streeter–Phelps | — | Ziya River, China | Davidsen et al. (2015) |
— | X | X | — | — | DODS/L+D | See Table | — | — | Zayandehrud River Basin, Iran | Safavi et al. (2015) |
X | — | X | — | — | PEDC/N+D | — | — | — | Aral Sea Basin, Central Asia | Bekchanov et al. (2015) |
X | — | — | — | X | PEDC/L+D | — | No specific name | — | Kaidu-Kongque River Basin, China | Zeng et al. (2016) |
— | X | — | X | — | PHDC/N+S | — | — | — | Qujiang River Basin, China | Hu et al. (2016) |
— | — | — | X | — | PHDC/N+D | — | — | — | Iran’s central desert | Habibi Davijani et al. (2016) |
X | — | X | — | — | PEDC/L+D | — | — | SEWEM | Aral Sea Basin, Central Asia | Bekchanov and Lamers (2016) |
— | — | X | X | — | PHDC/L+D | — | — | — | Generic | Zhang and Vesselinov (2017) |
X | X | X | — | — | PEDC/N+D | — | — | See note 2 | São Francisco River Basin, Brazil | Souza da Silva and Alcoforado de Moraes (2018) |
— | X | X | X | — | DBRS/L+D | — | — | BRIM | Bow River Basin, Canada | Wang et al. (2019) |
— | — | X | — | — | PHDC/L+D | — | — | — | Fuhe River basin, China | Yan et al. (2020) |
X | — | — | — | — | PEDC/N+S | — | — | — | São Marcos River Basin, Brazil | Bof et al. (2021) |
Land use management: floods and water quality | ||||||||||
— | X | X | — | X | DBRS/N+D | — | Modified QUAL2E-UNCAS | Modified MODSIM and QUAL2E-UNCAS | Piracicaba River Basin, Brazil | de Azevedo et al. (2000) |
— | — | — | — | X | DBRS/L+D | — | QUAL2E | GIBSI | Chaudière River basin, Canada | Rousseau et al. (2000) |
— | — | X | X | X | DBRS/N+D | — | MULINO Quality Module | MULINO DSS - mDSS | Dyle, Belgium; Caia, Portugal; Vahlui, Romania; Bure and Yare, England; Vela and Cavallino Catchments, Italy | Mysiak et al. (2005) |
— | — | X | — | X | DBRS/N+D | — | No specific name | — | Jiaojiang River Basin, China | Zhang et al. (2010) |
— | — | — | — | X | PHDC/N+S | — | SWAT quality modules (CREAMS, QUAL2E, WASP) | — | Wenyu River Catchment, China | Zhang et al. (2011) |
— | — | X | — | X | DODS/N+D | See Table | GESCAL module | AQUATOOL | Jucar Basin, Spain | Paredes-Arquiola et al. (2010) |
X | — | — | — | X | PEDC/N+D | — | 0 | — | Maipo River Basin | Rosegrant et al. (2000) |
— | — | — | — | X | DODS/N+D | See Table | SWAT | — | Europe | Abbaspour et al. (2015) |
— | — | — | — | X | PHDC/N+S | — | HEQM Module (WQM) | — | Huai River Basin, China | Zhang et al. (2016) |
X | X | — | — | X | PEDC/N+S | — | No specific name | — | Sinos River Basin, Brazil | Dalcin and Fernandes Marques (2020) |
Managing for climate change and drought | ||||||||||
— | — | X | — | X | DBRS/N+D | — | DSM2-SJR/QUAL/ APSIDE | Modular Modeling System/Object User Interface | San Joaquin River Basin | Quinn et al. (2004) |
— | — | — | X | X | DODS/L+D | See Table | No specific name | — | Galego River Basin, Spain | Graveline and de Recherches (2014) |
X | X | X | — | X | PEDC/N+S | — | No specific name | — | Dongjiang River Basin, China | Zhou et al. (2015) |
— | X | X | X | — | PHDC/N+D | — | — | — | Orb River Basin, France | Girard et al. (2015) |
X | — | — | — | — | PEDC/N+S | — | — | — | Heihe River Basin, China | Li et al. (2016) |
X | — | X | — | — | PEDC/N+S | — | — | — | Karkheh River Basin, Iran | Fereidoon and Koch (2018) |
— | X | X | X | — | DBRS/N+D | — | — | — | Zayandehrud River Basin, Iran | Safaei et al. (2013) |
Note: EC = Economic optimization; SU = sustainability; EQ = equality; SOC = social welfare/economic assessment; and QQ = water quantity-quality. This model is recently available in the HEAL System (Souza da Silva and Alcoforado de Moraes 2021).
DSS name | Database loose/tight coupling | Model base-criteria | Scenarios/sensitivity analysis | User interface developed | Concepts considered (see note 1) | Reference | |
---|---|---|---|---|---|---|---|
Y/N | Applied models | ||||||
Conflict resolution, transboundary management, and sustainability | |||||||
MOSDEW** | Loose | Yes | (1) Downscaling model of climate scenarios output from GCM, (2) agroeconomy: ACRE, (3) discharge groundwater: MODFLOW, LARSIM (HBV), (4) water quality: QUAL 2K, MONERIS, (5) cultivation methods, income, diffuse pollution: EPIC, SLISYS, (6) water supply: WEAP, and (7) fresh water ecology: CASIMIR | Two different official development scenarios | Yes | Gaiser et al. (2008) | |
AQUATOOL | Tight | No | 1. Water balance: SIMGES 2. Quality model: GESCAL 3. Discharge groundwater: HBV (for rural subbasins). Parameters of HBV calibrated using evolutionary algorithms | Analysis of the sensitivity of the variables of state to changes in the values of the coefficients of re-aeration, decomposition of carbonaceous organic matter and of organic nitrogen among others | No | Salla et al. (2014) | |
SDSS | Tight | Yes | 1. Scenarios generation: MODICHKEUL 2. Other models were not named | Four scenarios: current conditions with the water demand projected to 2015, additional transferred from a dam, construction of one additional dam, construction of two additional dams | No | Chakroun et al. (2015) | |
HEAL | Strong | Yes | Criteria based on water allocation strategies | Scenarios regarding water allocation for users and water storage | Yes | SOC | Alcoforado de Moraes et al. (2021) (See note 2) |
Conjunctive use of groundwater and surface water | |||||||
Web-based DSS prototype | Tight | No | The names of the models are not available in the paper | Three scenarios: average, better than average and worse than average monsoon conditions | Yes | SU | Salewicz and Nakayama (2004) |
REALM | Loose | Yes | REALM models | Hydrological scenarios with capacities and penalty functions | Yes | SU | Perera et al. (2005) |
WEAP21 | Tight | Yes | (1) Surface-hydrologic, groundwater, water temperature, and allocation models developed in WEAP21, and (2) climate change: HadCM3 and LARS-WG to downscale the data | Three future emission scenarios based on the IPCC and five water management scenarios: current situation, crop pattern change, improving the conveyance and distribution efficiency, among others | Yes | SU | Sprague (1980) |
Institutions, water markets and pricing | |||||||
WEAP21 | Tight | Yes | Surface-hydrologic, groundwater, water temperature, and allocation models developed in WEAP21 | Three scenarios: base, increase in irrigated area of 35%, and 50% reduction in irrigated area | Yes | Yates et al. (2005) | |
In-stream and off-stream intersectoral allocation and use | |||||||
NILE DSS | Tight | Yes | Includes a database, a set of models: 1. River simulation and management 2. Agricultural planning: GT-AgroPlan 3. Hydrologic modeling: Sacramento type watershed models 4. Remote sensing: Georgia Tech rainfall estimation method | Two development scenarios: current condition and four large hydropower projects built and operated using dynamic inflow forecasts and multi-reservoir control methods | Yes | SU | Georgakakos (2007) |
IRAS-2010 | Tight | Yes | 1- IRAS-2010 linked to a generic user interface HydroPlatform | — | Yes | SU | Matrosov et al. (2011) |
SEWEM | Loose | No | SEWEM Models | Two scenarios: optimal levels of irrigation benefits without and with considering the energy production and consumption | No | Bekchanov and Lamers (2016) | |
BRIM | Loose | Yes | Model structures in the BRIM - the water supply, population, municipal, agricultural environmental, and recreational sectors - are adapted from the Invitational Drought Tournament (IDT) Model | Three water demand scenarios related to four conditions: licensed allocations; four modified common indices; five industrial management policies; and three management gaming scenarios | Yes | Wang et al. (2019) | |
Land use management: floods and water quality | |||||||
GIBSI | Tight | Yes | (1) Hydrological model: HYDROTEL, (2) Model erosion and sediment transport: USLE + Yalin’s sediment transport equation, (3) Agricultural-chemical transport: based on SWATs algorithms, and (4) Water quality: QUAL2E | Two scenarios: a timber harvest and a municipal clean water program | Yes | Rousseau et al. (2000) | |
MULINO DSS - mDSS | Loose | Yes | The names of the models are not available in the paper, but the main model is composed of user interface, decision models, hydrologic models, data management and reporting system | Nine types of analysis: tornado diagram, pairwise comparison of option and criteria, simple average weighting, among others | No | Mysiak et al. (2005) | |
AQUATOOL | Tight | No | 1. Water balance: SIMGES 2. Quality model: GESCAL | Three scenarios: current, medium- and long-term scenarios from the River Basin Plan | Yes | Paredes-Arquiola et al. (2010) | |
Modified MODSIM and QUAL2E-UNCAS | Loose | Yes | Modified MODSIM and QUAL2E-UNCAS | Five alternatives composed of different levels of urban sewage and industrial wastewater treatments, release and storage in existing reservoirs and flow augmentation from new reservoirs | No | de Azevedo et al. (2000) | |
Managing for climate change and drought | |||||||
Modular Modeling System/Object User Interface | Tight | Yes | (1) Climate simulations: HadCM and PCM, (2) water allocation and streamflow simulation: CALSIM-II, (3) agricultural production and drainage salinity: APSIDE, and (4) river flow and water quality simulation: extension of DSM2 [DSM2-SJR (HYDRO)], QUAL (based on QUAL2E) | Six scenarios: wet and warm climatic trends (Hadley Centre Model) and cool and dry projections (Pacific Climate Model) for 2025, 2065 and 2090 | Yes | Quinn et al. (2004) |
Note: EC = Economic optimization; SU = sustainability; EQ = equality; SOC = social welfare/economic assessment; and QQ = water quantity and quality. The HEAL system incorporates the economic criteria in the São Francisco river basin study case (Souza da Silva and Alcoforado de Moraes 2021).
Results and Discussion
Criteria | Physical/engineering criteria | Economic criteria | ||||
---|---|---|---|---|---|---|
SU | EC | EQ | SOC | |||
Economic criteria | EC | 15 | 8 | — | 9 | 1 |
EQ | 15 | 4 | 9 | — | 7 | |
SOC | 14 | 4 | 1 | 7 | — | |
Physical/engineering criteria | SU | — | 11 | 15 | 15 | 14 |
11 | — | 8 | 4 | 4 |
Note: EC = Economic optimization; SU = sustainability; EQ = equality; SOC = social welfare/economic assessment; and QQ = water quantity and quality.
Models and Decision Support Systems for IWRM Considering Integration of Water Quantity and Quality
Water quality model | DSS | Concepts considered | REF | ||
---|---|---|---|---|---|
Model | Parameters | Highlights | |||
Conflict resolution, transboundary management, and sustainability | |||||
No specific name | Salt emissions | — | — | Cai et al. (2002) | |
QUAL2K | pH, temperature, electrical conductivity, SS, N, and forms of P | QUAL2K is fed by MONERIS, which collects all pollutant emissions and daily discharge values provided by the hydrological model. Diffuse pollution is supplied through the Soil and Land Resources Information System - SLISYS | MOSDEW | Gaiser et al. (2008) | |
Streeter–Phelps | BOD, DO, and P | The self-purification phenomenon makes it possible to use the assimilation capacity and prevent releases above what is endurable by the water body. The model allocated water and effluents in the field guaranteeing the water quality in the watercourse and reservoirs | — | Alcoforado de Moraes et al. (2010) | |
GESCAL module | DO, BOD, organic nitrogen, ammonia, nitrate, and TP | — | AQUATOOL | Salla et al. (2014) | |
Mike ECO Lab | BOD and DO | — | — | Gunawardena et al. (2018) | |
Conjunctive use of groundwater and surface water | |||||
QUAL2E | Quality model of groundwater return flow (calcium and magnesium ions - soil salinity) and 15 water quality constituents | The integration between the quantity (MODSIM) and the quality model (QUAL2E) was a challenge due to the difference in the representation of the network in each model, which was solved through modifications in the QUAL2E source code | — | Dai and Labadie (2001) | |
No specific name | Total dissolved solids, pH, Hardness and Sodium | The model seeks to minimize the total water cost, including the economic cost of treatment and distribution and the associated environmental costs (carbon footprint) | — | Abdulbaki et al. (2017) | |
No specific name | Surface water quality classes | Each run of the model was optimized according to a single objective, with different constraints on secondary objectives to analyze trade-offs between them | — | Martinsen et al. (2019b) | |
Institutions, water markets, and pricing | |||||
Quality Module WEAP21 | Temperature | The water temperature is impacted by hydropower production, which is important for the maintenance of the region as a suitable habitat for anadromous Chinook salmon | WEAP21 | Yates et al. (2005) | |
In-stream and off-stream intersectoral allocation and use | |||||
Streeter–Phelps | BOD | — | — | Davidsen et al. (2015) | |
No specific name | COD, TN, TP | — | — | Zeng et al. (2016) | |
Land use management: floods and water quality | |||||
QUAL2E | Loads and dissolved oxygen, chlorophyll a, dissolved P and organic P, forms of N, BOD and pesticides | — | GIBSI | Rousseau et al. (2000) | |
Modified QUAL2E-UNCAS | DO, BOD, N, P and fecal coliforms | — | Modified MODSIM and QUAL2E-UNCAS | de Azevedo et al. (2000) | |
MULINO Quality Module | Several applications, including nitrate | — | MULINO DSS - mDSS | Mysiak et al. (2005) | |
No specific name | COD, ammonia nitrogen, TN and TP | — | — | Zhang et al. (2010) | |
SWAT quality modules (CREAMS, QUAL2E, WASP) | ammonia nitrogen and COD | — | — | Zhang et al. (2011) | |
GESCAL module | Conductivity, SS, CBOD, DO (second level of complexity), ammonium, and nitrates | According to the authors, AQUATOOL was one of the main instruments used in Spain to analyze water quantity and quality aspects of water resources systems for the compliance with European Water Framework Directive | AQUATOOL | Paredes-Arquiola et al. (2010) | |
SWAT | Point and diffuse sources of N to investigate the nitrate leaching into the groundwater | SWAT can be used to build models to evaluate the effects of alternative management decisions on water resources and non-point source pollution in large river basins | — | Abbaspour et al. (2015) | |
HEQM Module (WQM) | Six hydrological, eleven nitrogen, five COD, and six soil carbon parameters were used to model ammonium–nitrogen | Using a multi-objective evolutionary algorithm, the results showed that a Pareto front was formed, providing different satisfactory solutions for users to choose according to their specific objectives | — | Zhang et al. (2016) | |
No specific name | BOD and Thermotolerant Coliforms | — | — | Dalcin and Fernandes Marques (2020) | |
Managing for climate change and drought | |||||
DSM2-SJR/QUAL/APSIDE | Salinity on the river and the soil and electrical conductivity | — | Modular Modeling System/Object User Interface | Quinn et al. (2004) | |
No specific name | Salt emissions | Simulated the effects of climatic change, increase in water storage capacity and irrigated land extension, modernization of irrigation technique and two global change scenarios on the water scarcity, salinity, and agricultural profits of the catchment area | — | Graveline and de Recherches (2014) | |
No specific name | COD and ammonia nitrogen | — | — | Zhou et al. (2015) |
Note: Lines with bold text represent tools available through DSS. BOD = biochemical oxygen demand; COD = chemical oxygen demand; DO = dissolved oxygen; P = phosphorus; N = nitrogen; SS = suspended solids; TN = total nitrogen; and TP = total phosphorus.
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