Tools for Modeling Climate Change Impacts on Nutrient Loadings

Tools for Modeling Climate Change Impacts on Nutrient Loadings

ABSTRACT

The impacts of climate change on watershed ecosystems and hydrologic processes are complex.  This complex interaction could be explored using integrated system that uses watershed models with a range of components representing climate, soil, land use, lakes, rivers and coastal waters that, eventually, could assess the impacts of catchment change and the interaction between processes.  To date, projecting changes in the hydrological processes, and thereby water resources, has generally been studied by using an approach that combines hydrological models with GCMs.  This approach takes climate model projections, downscales the outputs to be appropriate and reasonable input variables to drive hydrologic models that are then used in simulations of system operations.  Many of these studies of climate change have focused on its effect in the next 100 years and model projections out to year 2100.  This review would look at the different tools that are available to model the effects of climate change on the loading of nitrogen (N) and phosphorous (P).

Introduction

A fundamental understanding of the key biogeochemical and hydrologic processes under a changing climate is critical to managing N and P (Salas and Subburayalu 2020).  A review of literature has shown that many watershed hydrological and water quality models (Table 1) have been adopted to simulate nutrient cycles and determine total maximum daily load caused by anthropogenic activities and current hydrologic conditions.  A number of typical distributed hydrological process-based models that consider the impact of spatial distribution of rainfall and underlying surface (e.g. soil and nutrient processes) in subwatershed scale are: Soil and Water Assessment Tool (SWAT: Srinivasam et al. 1993, Arnold et al. 1994); Agricultural Nonpoint Source model (AGNPS: Pease et al. 2010); Dynamic   Watershed Simulation Model (DWSM: Borah et al. 2001); Hydrologic Simulation Program Fortran (HSPF: Bicknell et al. 1997); LTHIA (Ma 2004); INCA (Wade et al. 2002a); HYDRA (Coe 1998), among others.  Many of these models are able to assess long time series of nutrient fluxes, relying on spatial variability of model input parameters (Singh and Frevert 2002, Shen et al. 2012).  Models that avoid spatial variability (e.g. land use and climate) could have reduced confidence in model predictions (Weller and Baker 2014, Sharifi et al. 2016).

The importance of process-based, distributed, and temporal models has grown in popularity over the years due to spatial data availability and the advances in computing that could find solutions fast through simulations (Borah and Bera 2003).  However, due to extensive input data and calibration requirements, simulation accuracy, and degree of intricacy and flexibility of some models, data availability could still be a major concern (Robson 2014).  Further, systematic biases exist in some of the models in varying degrees when compared to actual climatic conditions (Rinke et al. 2006).  These impediments define the strengths and weaknesses of the model simulation.  Therefore, validation of climate model outputs particularly in the context of their applicability for regional hydrological impact analysis is very essential (Mohammed et al. 2015).   

In the last decade or so, research has been conducted on the application of artificial neural networks (ANNs) as predictors of nutrient loads from a watershed.   ANN, being a highly nonlinear approximator, has become an alternative to these physically based models in estimating N and P loadings (Nour et al. 2008, Kim et al. 2012, Tang et al. 2016).  ANN has been used effectively in various research projects in water protection and water management, such as, estimating surface water and groundwater level fluctuations (Mohanty et al. 2010), examining the relationship between land use/land cover and various water quality parameters (Kalin et al. 2010), water quality assessment (Fogelman et al. 2006, Dogan et al. 2009), optimization of wastewater treatment processes (Iliadis and Maris 2007).  Kim et al. (2012) compared ANN against SWAT and GWLF and found that ANN was computationally efficient and was capable of mimicking the output estimations of SWAT and GWLF accurately with relatively small errors.  However, since ANN does not need assumption and detailed understanding of the physical characteristics of input variables, the major concern of using ANN is selecting the best combination of these input variables (Noori and Kalin 2016).   Other shortcomings of ANN include slowly learning speed, convergence to local minimum, overfitting, and the components of the model’s complex structure (Okkan and Serbes 2012, Huang et al. 2014) that could lead to poor performance and high uncertainty of the predictive model (Jajarmizadeh et al. 2015).

Table 1. List of watershed hydrological and water quality models predicting nutrient dynamics.

Model	Description and Application	Major Components	Sources
Soil and Water Assessment Tool (SWAT)	-          Assesses long term impacts of management practices and climate change scenarios in complex watersheds -          Uses Modified Universal Soil Loss Equation (MUSLE: Williams and Berndt 1977) to compute sediment yield. -          Uses three organic pools (residue, stable, and active nitrogen) and two inorganic pools (ammonia and nitrate) to model nitrogen -          Uses two organic pools (fresh residue and humic substance) and three mineral pools (labile in solution, labile on the soil surface and fixed in soil) to model phosphorous	Hydrology, Weather, Sedimentation, Soil Temperature, Crop Growth, Nutrients, Pesticides, and Agricultural Management	Arnold et al. (1994) Arnold et al. (2012) Arnold et al. (2015)
Generalized Watershed Loading Function (GWLF)	-          Simulates runoff and sediment delivery using the Curve Number Method and the Universal Soil Loss Equation (USLE: Wischmeier and Smith, 1978) -          Uses export coefficients for different land use and land cover classes to estimate nutrient loadings -          Uses daily time steps for weather data and water balance calculations and produces discharge and nutrient loads at monthly scale by aggregating daily model estimates into monthly values -          Suitable for estimating source loads and total discharges at seasonal and inter-annual time-scale	Hydrology, Weather, Sedimentation, Land Use/Cover Distribution, Crop Growth, Nutrients, Basin Size	Haith and Shoemaker (1987)
Chesapeake Watershed Model (CWM)	-          Assesses which alternate scenarios of LULC and land management practices can best meet nutrient and sediment reduction goals. -          Based on Hydrologic Simulation Program FORTRAN (HSPF: Bicknell et al. 2001) -          Nutrient and sediment loads from major LULCs and the load responses to management practices are simulated with integrated submodels.	Hydrology, Weather, Sedimentation, Land Use/Cover Distribution, Crop Growth, Nutrients	Linker et al. (1999)
Soil and Water Integrated Model (SWIM)	-          Assesses and simulates hydrological processes, vegetation, erosion and nutrient cycles at the catchment scale, based on SWAT -          Interaction between vegetation and nutrient supply is modelled by the plant uptake of nutrients, release of residuals entering mineralization process, and by using N and P stress factors -          Further developments are focused on climate and land-use change impact assessment	Hydrology, Weather, Sedimentation, Soil Temperature, Crop Growth, Nutrients, Pesticides, and Agricultural Landscapes	Krysanova et al. (2000) Krysanova et al. (2015)
Agricultural Nonpoint Source model (AGNPS)	-          Distributed, continuous-simulation, watershed-scale nonpoint source pollution model developed especially for agricultural catchments -          Uses daily time steps to simulate nutrient loads and hydrological cycle (e.g., runoff and erosion) by combining irrigation system as the main hydrological component -          Uses the Conservation Service-Curve Number (SCS-CN) approach to model runoff and the Revised Universal Soil Loss Equation (RUSLE: Renard et al. 1997) to compute erosion	Hydrology, Weather, Sedimentation, Soil Temperature, Crop Growth, Nutrients, and Agricultural Landscapes	Pease et al. (2010) Bingner et al. (2011)
Watershed Assessment Model (WAM)	-          Simulates and assesses the environmental effects of various land use changes and associated land use practices within complex watersheds -          Simulates water quality including particulate and soluble phosphorus, particulate and soluble nitrogen (NO3, NH4, and organic N), total suspended solids, and biological oxygen demand -          Simulates surface water and ground water flow allowing for the assessment of flow and pollutant loading for a tributary reach at both the daily and hourly time increment as necessary -          Uses imbedded models including Groundwater Loading Effects of Agricultural Management Systems (GLEAMS: Knisel 1993), Everglades Agricultural Area Model (EAAMod: SWET 2008), and two submodels written specifically for WAM	Hydrology, Weather,  Soils, Crop Growth, Nutrients, and Topography	Bottcher et al. (2012)
Dynamic   Watershed Simulation Model  (DWSM)	-          Simulates propagation of flood waves, entrainment and transport of sediment, and all agricultural chemicals commonly used in agricultural and rural watersheds -          Uses short-term simulation that could simulate the runoff process dynamically	Hydrology,  Soil Erosion, Sediment Transport, Nutrient and Pesticide Transport	Borah et al. (2001) Borah et al. (2004)
Long-Term Hydrological Impact Analysis (L-THIA)	-          Assesses the impacts on runoff, recharge and nonpoint source pollution resulting from past or proposed land use changes -          Uses daily runoff derived from long -term climate records, soil data, current land use and curve number to approximate the relative hydrologic and water quality impacts of different land use scenarios	Hydrology, Land Use, Nonpoint Source Pollution, Soils, Management Practices	Ma (2004)
Integrated Nitrogen in Catchments (INCA)	-          Simulates the nitrogen export from different land-use types within a river system, and the in-stream nitrate and ammonium concentrations at a daily time-step -          Uses loads rather than concentrations to allow a more robust tracking of mass conservation when using numerical integration -          Integrates soil-water retention volumes into the interface to permit multiple crop and vegetation growth periods and fertilizer applications	Hydrology, Soils, Nitrate Weather	Wade et al. (2002a)
Integrated Catchments Model for Phosphorus (INCA-P)	-          Simulates phosphorus dynamics in both the plant/soil system and the stream. -          Simulates spatial variations in phosphorus export from different land use types within a river system using a semi-distributed representation, thereby accounting for the impacts of different land management practices, such as organic and inorganic fertilizer and wastewater applications -          Simulates Organic and Inorganic Phosphorus concentrations in the land phase, and Total Phosphorus (dissolved plus particulate phosphorus) concentrations in the in-stream phase	Hydrology, Soils, Phosphorus, Weather, Management Practices	Wade et al. (2002b)
Hydrological Routing Algorithm (HYDRA)	-          Simulates seasonal variations in river discharge and lake and wetland area, and allows direct representation of man-made dams and reservoirs -          Uses monthly mean or daily runoff, precipitation, and evaporation from either GCM output or observations -          Diagnoses GCM output and assesses important changes in water availability and discharge to the oceans due to anthropogenic, or naturally occurring, climate change	Climate, Hydrology, Soils, Land Use/Cover Distribution, Topography	Coe (1999)
Hydrologic Simulation Program Fortran (HSPF)	-          Simulates hydrological system and associated nutrient states of the pervious and impervious land, stream and reservoir -          Integrates simulation of land and soil contaminant runoff processes with in-stream hydraulic and sediment-chemical interactions -          Nutrients are predicted in sub-daily time step	Climate, Hydrology, Soils, Land Use/Cover Distribution, Topography	Bicknell et al. (1997)

 SWAT Model

Among the current watershed-scale mathematical models, SWAT remains to be the most popular for many hydrological and water quality studies around the globe.  Over the past decade, SWAT has proven to be a versatile and computationally efficient model that could simulate the effects of changes in climate, land-use, and management practices on sediment and pollutant transport, water, and pesticides and nutrient loss over time (Gassman et al. 2007, Niraula et al. 2012, White et al. 2015, Wang et al. 2016, Yen et al. 2016).  The U.S. EPA BASINS (Better Assessment of Science Integrating Point and Nonpoint Sources) software has included SWAT as one component of its modeling framework.  The USDA has used SWAT within the Conservation Effects Assessment Project.  SWAT was also used to analyze water management scenarios in the Hydrologic Modeling of the United States Project (HUMUS) (Arnold et al. 1999).  Also, the application of SWAT has been successful in several agricultural watersheds across many disciplines and under different scenarios (Santhi et al. 2001, Ranjan and Wurbs 2002, Bosch et al. 2004, Saleh and Du 2004, Tripathi et al. 2005, Grunwald and Qi 2006, Setegn et al. 2008, Niraula et al. 2012, Wang et al. 2016).  As of September 2018, over 3500 peer reviewed publications have used SWAT (SWAT Literature Database 2018).  About 20 and 16 publications were specifically associated with modeling nitrogen and phosphorous pollutant loss and transport, respectively.

In the SWAT-modeling approach a watershed is divided into multiple sub-basins using elevation data with both stream network and sub-basin outlets.  Further, the sub-basins are divided into hydrologic response units (HRUs) (Arnold and Fohrer 2005) that lumps land areas with unique soil, land cover and slope combinations.  SWAT simulates the hydrologic cycle based on water balance, which is controlled by climate inputs such as daily precipitation and maximum and minimum air temperature (Neitsch et al., 2005).  For the simulation of N and P, nutrients input data are divided into two groups: nitrogen inputs and phosphorus inputs.  Inputs of each part are classified in terms of nutrient forms and nutrient sources.  For nitrogen, SWAT uses three organic pools (residue, stable, and active nitrogen) and two inorganic pools (ammonia and nitrate).  Mineralization, nitrification, denitrification, and volatilization govern the balance among the different pools (Sharifi et al. 2017).  The nitrate concentrations in runoff, lateral flow, and percolation are functions of the volume of water and the average concentration of nitrate in the soil layer (Neitsch et al. 2005).  Table 2 shows the N input requirements for SWAT against two other watershed hydrological models.  Among the three models, SWAT has the highest number of input N requirements: 33 from 11 sources, compared with 8 inputs from 7 sources for GWLF, and 10 inputs from 4 sources for HSPF.  The main sources of N are mainly soil, runoff, groundwater, sediment, plant uptake, urban source, point source, fertilizer, septic system, surface water bodies, and atmosphere.

Table 2. Comparison of nitrogen input variables from three watershed hydrological models.  Input could be either supplied by measurements, or calculated by the model using equations.  This is a reconstructed and modified table adapted from Tuo et al. (2015).

Initial Soil Nitrogen	SWAT	GWLF	HSPF
Organic N	X		X
NO3	X		X
NH4			X
Normal fraction of N in plant biomass (crop-specific)	X
Nitrogen in runoff
Dissolved N		X
Nitrogen in groundwater
Dissolved N		X
Nitrogen in sediment
Total N		X
Plant uptake nitrogen
Total N		X
Urban sources
Total N	X	X	X
NO3	X
Point sources
Organic n	X
NO3	X
NO2	X
NH4	X
Dissolved N		X
Fertilizer nitrogen (crop-specific)
Organic N	X
Active organic N
Inorganic N	X
NH4	X
Septic system
Dissolved N in outflow		X
Dissolved N from ponded system		X
Total N	X
NO3	X
NO2	X
Organic N	X
NH4	X
Initial nitrogen in pond
Organic N	X
NO3	X
Initial nitrogen in wetland
Organic N	X
NO3	X
Initial nitrogen in reservoir
Organic N	X
NO3	X		X
NO2	X		X
NH4	X
NH4 + NH3			X
In-Stream Nitrogen
Organic N	X
NO3	X		X
NO2	X		X
NH4	X
NH4 + NH3			X
Atmospheric deposition
NO3 in rain	X	X
NH4 in rain	X
NO3 in dry deposition	X
NH4 in dry deposition	X

 

Phosphorus is divided into two organic pools (fresh residue and humic substance) and three mineral pools (labile in solution, labile on the soil surface and fixed in soil) with decay and mineralization moving P among the pools.  The soluble P concentration in surface runoff is a function of the labile P concentration in the top soil layer, runoff volume, and a partitioning factor.  Concentrations of sediment-bound N and P are functions of sediment yield and organic nutrient concentration in top soil layer (Sharifi et al. 2017).  Table 3 shows the P input requirements for SWAT against two other watershed hydrological models.  Again, among the three models, SWAT has the highest number of input P requirements: 18 from 10 sources, compared with 8 inputs from 7 sources for GWLF, and 5 inputs from 4 sources for HSPF.  The main sources of P are mainly soil, runoff, groundwater, sediment, plant uptake, urban source, point source, fertilizer, septic system, and surface water bodies.

Table 3. Comparison of phosphorous input variables from three watershed hydrological models.  Input could be either supplied by measurements, or calculated by the model using equations. This is a reconstructed and modified table adapted from Tuo et al. (2015).

Initial Soil Phosphorous	SWAT	GWLF	HSPF
Organic P	X		X
PO4			X
Fresh organic P	X
Normal fraction of P in plant biomass (crop-specific)	X
Phosphorus in runoff
Dissolved P		X
Phosphorus in groundwater
Dissolved P		X
Phosphorus in sediment
Total P		X
Plant uptake Phosphorus
Total P		X
Urban Sources
Total P	X	X	X
Point sources
Organic P	X
Soluble P	X
Dissolved P		X
Fertilizer phosphorus (crop-specific)
Organic P	X
Inorganic P	X
Septic system
Dissolved P in outflow		X
Dissolved P from ponded system		X
Total P	X
PO4	X
Organic P	X
Initial phosphorus in pond
Organic P	X
Soluble P	X
Initial phosphorus in wetland
Organic P	X
Soluble P	X
Initial phosphorus in reservoir
Organic P	X
Soluble P	X
PO4			X
In-stream phosphorus
Organic P	X
PO4			X

Model outputs could be categorized as (1) nutrients transport output which displays nutrient fate associated with transport processes in hydrological systems, and (2) nutrient transformation output that shows nutrient fate related to reactions.  Table 4 shows significant differences in N outputs from the three models – SWAT, GWLF, and HSPF.  The nitrogen outputs are subdivided into four groups: soil nitrogen, external nitrogen added to the catchment system, transports, and transformations.  SWAT is the most comprehensive of all as it produces 28 N outputs from eight transport processes, while HSPF simulates 23 N outputs related to six processes. 

Table 5 shows significant differences in P outputs from the three models – SWAT, GWLF, and HSPF.  The P outputs are subdivided into four groups: phosphorus, external phosphorus added to the catchment system, transport, and transformations.  Again, SWAT is the most comprehensive of the three in terms of P outputs with 19 for 6 transport processes, while HSPF simulates 12 P outputs related to 6 processes.  

Table 4.  Nitrogen outputs from SWAT, GWLF, and HSPF watershed hydrological models. This is a reconstructed and modified table adapted from Tuo et al. (2015).

Soil Nitrogen	SWAT	GWLF	HSPF
Organic N			X
NO3			X
NH4			X
Add in
Organic N from residue	X
Nitrogen applied in fertilizer	X
NO3 added to soil profile by rain	X
Surface Runoff
Total N in sediment		X	X
Organic N in sediment	X		X
NH4 in sediment			X
NO3 in water	X
NH4 in water			X
Nitrogen from Urban area by wash off
Total N	X	X	X
Nitrogen from septic system
Dissolved N		X
Total N	X
Nitrogen from point source
Dissolved N		X
Interflow
NO3	X		X
NH4			X
Leaching by percolation
NO3	X		X
NH4			X
Groundwater Flow
NO3	X		X
NH4			X
Dissolved N		X
From first soil layer to surface by evaporation
NO3	X
Surface Water Body Systems
Organic N transported with water into reach	X
Organic N transported with water out of reach	X
NO3 transported with water into reach	X		X
NO3 transported with water out of reach	X		X
NH4 transported with water into reach	X		X
NH4 transported with water out of reach	X		X
NO2 transported with water into reach	X		X
NO2 transported with water out of reach	X		X
Concentration of organic N in pond	X
Concentration of NO3 in pond	X
Concentration of organic N in wetland	X
Concentration of NO3 in wetland	X
Organic N transported into reservoir	X
Organic N transported out of reservoir	X
NO3 transported into reservoir	X		X
NO3 transported out of reservoir	X		X
NO2 transported into reservoir	X		X
NO2 transported out of reservoir	X		X
NH4 transported into reservoir	X		X
NH4 transported out of reservoir	X		X
Fixation	X
Nitrification	X		X
Ammonia volatilization	X		X
Denitrification	X		X
Ionization			X
Mineralization/immobilization
Fresh organic N to mineral N	X
Active organic N to mineral N	X
N transferred between active organic N and stable organic N	X
NO3 to Organic N			X
NH4 to Organic N			X
Organic N to NH4			X
Decomposition	X		X
Plant uptake
Inorganic N	X
NH4			X
NO3			X
In-stream reaction (related to Algae or plankton)
organic N	X		X
NH4	X
NH4 + NH3			X
NO3	X		X
NO2	X		X
Adsorption/Desorption
NH4			X
Nitrogen settling/sinking in ponds, wetland and reservoir
Total N	X		X
Reservoir system chemical and biochemical transformation			X

Table 5.  Phosphorus outputs from SWAT, GWLF, and HSPF watershed hydrological models. This is a reconstructed and modified table adapted from Tuo et al. (2015).

Soil Phosphorus	SWAT	GWLF	HSPF
Organic P			X
PO4			X
Add in
Phosphorus (mineral and organic) applied in fertilizer	X
Organic P from residue	X
Transport
Surface Runoff
Total P in sediment
Organic P in sediment	X		X
Mineral P in sediment	X
PO4 in sediment			X
Soluble P in water	X
PO4 in water			X
Phosphorus from Urban area by wash off
Total P	X	X	X
Phosphorus from septic system
Dissolved P		X
Total P	X
Phosphorus from point source
Dissolved P		X
Interflow
PO4			X
Leaching by percolation
Soluble P	X
PO4			X
Groundwater flow
Soluble P	X
PO4			X
Dissolved P		X
Surface Water Body Systems
Organic P transported with water into reach	X
Organic P transported with water out of reach	X
Mineral P transported with water into reach	X
Mineral P transported with water out of reach	X
Inflow PO4 to reach			X
Outflow PO4 from reach			X
Concentration of organic P in pond	X
Concentration of mineral P in pond	X
Concentration of organic P in wetland	X
Concentration of mineral P in wetland	X
Organic P transported into reservoir	X
Organic P transported out of reservoir	X
Mineral P transported into reservoir	X
Mineral P transported out of reservoir	X
Inflow PO4 to reservoir			X
Outflow PO4 from reservoir			X
Transformation
Decomposition	X		X
Mineralization/ Immobilization
Fresh organic to mineral P	X
Organic to labile mineral P	X
PO4 to Organic P			X
Organic P to PO4			X
Sorption
P transferred between Labile and active mineral P	X
P transferred between Active and stable mineral P	X
Plant uptake
Inorganic P	X
PO4			X
In-stream reaction(related to Algae or plankton)
Organic P	X		X
Soluble P	X
PO4			X
Adsorption/desorption
PO4			X
Phosphorus settling/sinking in ponds, wetland and reservoir
Total P	X		X
Reservoir system chemical and biochemical transformation			X

SWAT on Simulating Nutrients Under Future Climate

Basin climatic input requirements for SWAT include rainfall, snow, air temperature, solar radiation, humidity/dew point, wind speed, carbon dioxide concentration, evapotranspiration, daily daylight hours and vapor pressure.  These inputs could either be supplied by measurements, or calculated by the model.  To assess the impacts of future climate change on the N and P nutrients in the watershed, observed and downscaled climate data of precipitation, maximum and minimum temperature obtained from the climate modeling in each sub-basin for both the current (past) and future time periods are employed as climate forcing input in the SWAT model.  

Čerkasova et al. (2018) simulated the total P and total N under both the RCP4.5 and RCP8.5 scenarios using SWAT in a large-scale catchment situated mostly in Belarus.  The study found significant seasonal increase of P load in winter and March, following by a decrease in loads for the rest of the studied year.  An overall increase in the modeled N load was observed for both the RCP4.5 and RCP8.5, with a more significant change in RCP4.5.  Čerkasova et al. (2018) concluded that if no mitigation is taken, climate change could have a significant impact on the growing season of the crops, fertilization practice change and human activity, resulting in the possible increase of N and P nutrient loads to the rivers.  An increase of P and N were also observed by Wagena et al. (2018) looking to future scenarios 2045 to 2068 for the sub-watershed of Mahantango Creek in east-central Pennsylvania.  The increase of P was related to the increases in sediment-bound P export during the winter/spring and increased precipitation intensity.  Similarly, the annual increase in N export is due to substantial increases in nitrification during the winter/spring and increased runoff/soil moisture.  Similar findings were seen by Cho et al. (2016) as total P and N were higher during wet season due to increase precipitation.  Yang et al. (2018) utilized 16 GCMs under RCP4.5 and RCP8.5 to simulate the discharge, transport, and transformation of N from all known anthropogenic sources including industries, municipal sewage treatment plants, concentrated and scattered feedlot operations, rural households, and crop production in the Upper Huai River Basin in China.   Yang et al. (2018) found that both emission scenarios could likely to increase both the average and extreme total N loads at the high end in February, May, and November, while the impacts of climate change on August total N loads were more variable.   Using five GCMs under RCP8.5, Lee et al. (2018) quantified the increase of N and concluded that a temperature increase of 5°C would increase N loads in Chesapeake Bay watershed in Maryland and Virginia by 66%.  Greater increase of the rate of N was observed in regions of the watershed that have larger percent of cropland.  In a study of N loads in the Upper Mississippi River Basin using 10 GCMs under the old A1B scenario, Jha et al. (2015) found that N loadings were predicted to increase across all Illinois subwatersheds but in contrast were predicted to decrease for over 40% of the Iowa subwatersheds in response to the future decrease in runoff. 

While the strength of SWAT is having numerous physically-based functions that govern complex hydrologic and nutrient processes, it has weaknesses as well.  Uncertainties in SWAT modeling efforts varied from the parameterization and representation of the natural system to channel scale such as watershed width, depth, and slope (Jha et al. 2005).  Uncertainties from inputs in agricultural land management, which differed between farmers, and uncertainties in river flow and water quality monitoring strategies were also identified (Glavan et al. 2015; Salas and Subburayalu 2019).  Moreover, the large number of system parameters required by SWAT could lead to over-calibration in some cases (Sharifi et al. 2017).   To improve SWAT modeling results on future climate studies, Glavan et al. (2015) suggested integrating improved regional climate models with improved monitoring strategies for P and N, in addition to more investigation on the relationship between soil properties, slope, erosive power of rainfall, land use, land cover and land management. 

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