Rice Science ›› 2023, Vol. 30 ›› Issue (4): 276-293.DOI: 10.1016/j.rsci.2023.03.010
收稿日期:2022-10-07
接受日期:2023-03-02
出版日期:2023-07-28
发布日期:2023-05-26
. [J]. Rice Science, 2023, 30(4): 276-293.
| Evaluation criterion | Description | Reference |
|---|---|---|
| RMSE | Root mean square error (RMSE) is used to compare the difference between observed and simulated data. An RMSE that tends towards 0 indicates a good performance of the model | Amiri et al, |
| nRMSE | Normalized root mean square error (nRMSE) gives the percentage difference between the observed and simulated data. The lower the nRMSE value, the better the model performs | Amiri et al, |
| MAE | Mean absolute error (MAE) is an error index that can be interpreted in the same way as the RMSE | Sar and Mahdi, |
| d-index | d-index has values between 0 and 1, with 1 as the value expressing perfect agreement between the observed and simulated data | Buddhaboon et al, |
| PBIAS | Percentage of bias (PBIAS) measures the average tendency of measured variables to be under-estimated or over-estimated by the model. The optimal value for PBIAS is 0. A positive value indicates a bias of the model toward under-estimation, while a negative value indicates a bias toward over-estimation | Sar and Mahdi, |
| NSE | Nash-Sutcliffe efficiency coefficient (NSE) expresses the relative magnitude of the residual variance as a function of the variance of the observations. An NSE equal to 1 indicates a perfect match between the measured and simulated values | Jha et al, |
| ME | Modeling efficiency (ME) varies between minus infinity to 1.0. A negative ME means the mean value of the experimental data is a better predictor than the model, whereas an ME of 1.0 signifies a perfect model agreement with observations | Hasan and Rahman, |
| r² | Coefficient of determination (r²) expresses the degree of collinearity between the simulated and observed data. It describes the proportion of variance in the measured data that is explained by the model, with higher values revealing less error variance. As a general rule, an r² > 0.5 is considered acceptable | Amiri et al, |
| P(t) | P(t) is the significance of paired t-test | Zheng et al, |
| α, β | α is the intercept of linear relation between simulated and measured values. β is the slope of linear relation between simulated and measured values | Amiri et al, |
| SD | Standard deviation (SD) is the mean of measured or simulated values in whole population | Amiri et al, |
Table 1. Evaluation criteria used in parameterization of crop models.
| Evaluation criterion | Description | Reference |
|---|---|---|
| RMSE | Root mean square error (RMSE) is used to compare the difference between observed and simulated data. An RMSE that tends towards 0 indicates a good performance of the model | Amiri et al, |
| nRMSE | Normalized root mean square error (nRMSE) gives the percentage difference between the observed and simulated data. The lower the nRMSE value, the better the model performs | Amiri et al, |
| MAE | Mean absolute error (MAE) is an error index that can be interpreted in the same way as the RMSE | Sar and Mahdi, |
| d-index | d-index has values between 0 and 1, with 1 as the value expressing perfect agreement between the observed and simulated data | Buddhaboon et al, |
| PBIAS | Percentage of bias (PBIAS) measures the average tendency of measured variables to be under-estimated or over-estimated by the model. The optimal value for PBIAS is 0. A positive value indicates a bias of the model toward under-estimation, while a negative value indicates a bias toward over-estimation | Sar and Mahdi, |
| NSE | Nash-Sutcliffe efficiency coefficient (NSE) expresses the relative magnitude of the residual variance as a function of the variance of the observations. An NSE equal to 1 indicates a perfect match between the measured and simulated values | Jha et al, |
| ME | Modeling efficiency (ME) varies between minus infinity to 1.0. A negative ME means the mean value of the experimental data is a better predictor than the model, whereas an ME of 1.0 signifies a perfect model agreement with observations | Hasan and Rahman, |
| r² | Coefficient of determination (r²) expresses the degree of collinearity between the simulated and observed data. It describes the proportion of variance in the measured data that is explained by the model, with higher values revealing less error variance. As a general rule, an r² > 0.5 is considered acceptable | Amiri et al, |
| P(t) | P(t) is the significance of paired t-test | Zheng et al, |
| α, β | α is the intercept of linear relation between simulated and measured values. β is the slope of linear relation between simulated and measured values | Amiri et al, |
| SD | Standard deviation (SD) is the mean of measured or simulated values in whole population | Amiri et al, |
| Crop model | Calibrated parameter |
|---|---|
| Oryza2000 | Development rate in juvenile, photo-sensitive, panicle and reproductive phases; parameters of a junction to calculate specific leaf area; fraction of shoot dry matter portioned to leaves, stem and panicles; leaf death coefficient as a function of development stage |
| Aquacrop FAO | Maximum canopy growth; maximum and minimum root length; recovery time after transplanting; time from transplanting to flowering, senescence, and maturity; length of flowering stage; reference harvest index; water productivity normalized for reference evapotranspiration and CO2 |
| DSSAT/CERES-Rice | Measured plant parameters to estimate rice genetic coefficients: dates of key phenological stages (panicle initiation, heading date, flowering and physiological maturity date and maturity days after planting), yield and yield components |
Table 2. Crop parameters used in rice models for parameterization.
| Crop model | Calibrated parameter |
|---|---|
| Oryza2000 | Development rate in juvenile, photo-sensitive, panicle and reproductive phases; parameters of a junction to calculate specific leaf area; fraction of shoot dry matter portioned to leaves, stem and panicles; leaf death coefficient as a function of development stage |
| Aquacrop FAO | Maximum canopy growth; maximum and minimum root length; recovery time after transplanting; time from transplanting to flowering, senescence, and maturity; length of flowering stage; reference harvest index; water productivity normalized for reference evapotranspiration and CO2 |
| DSSAT/CERES-Rice | Measured plant parameters to estimate rice genetic coefficients: dates of key phenological stages (panicle initiation, heading date, flowering and physiological maturity date and maturity days after planting), yield and yield components |
Fig. 1. Example of a methodological framework for climate change impact studies on agricultural systems. GCM, Global climate model; RCM, Regional climate model; RCP, Representative concentration pathway.
| Reference | Main result | Method | Study period | |
|---|---|---|---|---|
| Baseline | Horizon | |||
| Liersch et al, | Decrease in floodable area | RCM, SRES, hydrological model SWIM | 1971-2000 | 2011-2050 |
| Robles-Morua et al, | Increase in availability of irrigation water | RCM, SRES, downscaling approach, hydrological model HEC-HMS | 1990-2000 | 2031-2040 |
| Ye et al, | Increase in CWR & IWR | RCM, SRES, water balance calculation | 1951-1980 | 2011-2040, 2071-2100 |
| Cho et al, | Increase in IWR; increase in availability of irrigation water | GCM multimodel, RCP 4.5 & 8.5, reservoir model HOMWRS | 1976-2005 | 2011-2040 |
| Hong et al, | Decrease in IWR; increase in cET and NIR | RCM, RCP 4.5 and 8.5, water balance calculation | 1981-2010 | 2011-2040 (2025) 2041-2070 (2055) 2071-2100 (2085) |
| Acharjee et al, | Decrease in ETc; increase in IWR | GCM, RCP 4.5 and 8.5, Crop model FAO-CropWat | 1980-2013 | 2050, 2080 |
| Ding et al, | Increase in IWR | GCM, RCP 2.6, 4.5 and 8.5, water balance model | 1961-2012 | 2020, 2050, 2080 |
| Sun et al, | Decrease in IWR | GCM, RCP 2.6, 4.5 and 8.5, water balance calculation | 1996-2005 | 2020, 2030, 2040 |
| Rowshon et al, | Increase in IWR; decrease in irrigation water availability | GCM, RCP 4.5, 6.0 and 8.5, graphical user interface development environment/modèle agrohydrologique intelligent utilisant visual basic for applications | 1976-2005 | 2020, 2050, 2080 |
| Ahmed, | Decrease in the irrigation water availability | RCM, RCP 4.5 and 8.5, crop model FAO-Aquacrop | 1960-1990 | 2040-2070 |
| Ding et al, | Increase in NIR | GCM, RCP 8.5, Crop model ORYZA v3 | 2011-2040, 2041-2070, 2071-2100 | |
| Zheng et al, | Increase in blue water footprint; decrease in water productivity | GCM, RCP 2.6, 4.5, 6.0 and 8.5, crop model ORYZA2000 | 1961-2010 | 2020, 2050, 2080 |
Table 3. Main results of climate change impact studies on rice irrigation water requirements.
| Reference | Main result | Method | Study period | |
|---|---|---|---|---|
| Baseline | Horizon | |||
| Liersch et al, | Decrease in floodable area | RCM, SRES, hydrological model SWIM | 1971-2000 | 2011-2050 |
| Robles-Morua et al, | Increase in availability of irrigation water | RCM, SRES, downscaling approach, hydrological model HEC-HMS | 1990-2000 | 2031-2040 |
| Ye et al, | Increase in CWR & IWR | RCM, SRES, water balance calculation | 1951-1980 | 2011-2040, 2071-2100 |
| Cho et al, | Increase in IWR; increase in availability of irrigation water | GCM multimodel, RCP 4.5 & 8.5, reservoir model HOMWRS | 1976-2005 | 2011-2040 |
| Hong et al, | Decrease in IWR; increase in cET and NIR | RCM, RCP 4.5 and 8.5, water balance calculation | 1981-2010 | 2011-2040 (2025) 2041-2070 (2055) 2071-2100 (2085) |
| Acharjee et al, | Decrease in ETc; increase in IWR | GCM, RCP 4.5 and 8.5, Crop model FAO-CropWat | 1980-2013 | 2050, 2080 |
| Ding et al, | Increase in IWR | GCM, RCP 2.6, 4.5 and 8.5, water balance model | 1961-2012 | 2020, 2050, 2080 |
| Sun et al, | Decrease in IWR | GCM, RCP 2.6, 4.5 and 8.5, water balance calculation | 1996-2005 | 2020, 2030, 2040 |
| Rowshon et al, | Increase in IWR; decrease in irrigation water availability | GCM, RCP 4.5, 6.0 and 8.5, graphical user interface development environment/modèle agrohydrologique intelligent utilisant visual basic for applications | 1976-2005 | 2020, 2050, 2080 |
| Ahmed, | Decrease in the irrigation water availability | RCM, RCP 4.5 and 8.5, crop model FAO-Aquacrop | 1960-1990 | 2040-2070 |
| Ding et al, | Increase in NIR | GCM, RCP 8.5, Crop model ORYZA v3 | 2011-2040, 2041-2070, 2071-2100 | |
| Zheng et al, | Increase in blue water footprint; decrease in water productivity | GCM, RCP 2.6, 4.5, 6.0 and 8.5, crop model ORYZA2000 | 1961-2010 | 2020, 2050, 2080 |
Fig. 2. Number and percentage of studies reviewed that assessed climate change impacts on irrigation water requirement in rice.
| Method | Main result | Study period | Reference | |
|---|---|---|---|---|
| Baseline | Horizon | |||
| Reanalyze climate data/RCM, DSSAT/CERES-Rice | Decreased yields without adaptation | 1980-1998 | 2041-2059 | Ahmed et al, |
| DSSAT/CERES-Rice | Negative effects of maximum temperatures on yields | 1967-2007 | Zhang et al, | |
| RCM, RCP 4.5 & 8.5, GLAM-Rice, DSSAT/CERES-Rice | Increased yields with fertilization; decrease in yields without adaptation | 1991-2000 | 2020, 2040, 2080 | Chun et al, |
| GCM, RCP 8.5, DSSAT/CERES-Rice | Decreased yields | 2014 | 2040, 2050, 2060 | Dias et al, |
| Statistical models | Increased yields | 1980-2012 | Liu et al, | |
| GCM, RCP 8.5, ORYZA2000 | Increased yields with adaptation and CO2 fertilization; decreased yields without adaptation | 2000 | 2070 | van Oort and Zwart, |
| RCM, SRES, DSSAT/CERES-Rice | Effects of temperature and transplanting date on yields | 2008 | 2030, 2050, 2070 | Hasan and Rahman, |
| GCM, RCP 4.5 & 8.5, DSSAT/CERES-Rice | Decreased yields; increased yields with CO2 fertilization | 1981-2010 | 2040-2069 | Kontgis et al, |
Table 4. Main results of studies on climate change impact on rice yields.
| Method | Main result | Study period | Reference | |
|---|---|---|---|---|
| Baseline | Horizon | |||
| Reanalyze climate data/RCM, DSSAT/CERES-Rice | Decreased yields without adaptation | 1980-1998 | 2041-2059 | Ahmed et al, |
| DSSAT/CERES-Rice | Negative effects of maximum temperatures on yields | 1967-2007 | Zhang et al, | |
| RCM, RCP 4.5 & 8.5, GLAM-Rice, DSSAT/CERES-Rice | Increased yields with fertilization; decrease in yields without adaptation | 1991-2000 | 2020, 2040, 2080 | Chun et al, |
| GCM, RCP 8.5, DSSAT/CERES-Rice | Decreased yields | 2014 | 2040, 2050, 2060 | Dias et al, |
| Statistical models | Increased yields | 1980-2012 | Liu et al, | |
| GCM, RCP 8.5, ORYZA2000 | Increased yields with adaptation and CO2 fertilization; decreased yields without adaptation | 2000 | 2070 | van Oort and Zwart, |
| RCM, SRES, DSSAT/CERES-Rice | Effects of temperature and transplanting date on yields | 2008 | 2030, 2050, 2070 | Hasan and Rahman, |
| GCM, RCP 4.5 & 8.5, DSSAT/CERES-Rice | Decreased yields; increased yields with CO2 fertilization | 1981-2010 | 2040-2069 | Kontgis et al, |
| Crop system model | Predicted impact | Reference |
|---|---|---|
| Crop Environment Resource Synthesis-Rice | Nutrient and water processes in the soil; effects of temperature, solar radiation and precipitation on yield and growth; effects of temperatures on yields; effects of CO2 on yields; yields | Zhang et al, |
| General Large Area Model-Rice | Regional yields | Chun et al, |
| Organizing Carbon and Hydrology in Dynamic Ecosystems | Growing period | Wang et al, |
| ORYZA2000 | Yields | van Oort and Zwart, |
Table 5. Main rice models used to study climate change impacts and predicted impacts.
| Crop system model | Predicted impact | Reference |
|---|---|---|
| Crop Environment Resource Synthesis-Rice | Nutrient and water processes in the soil; effects of temperature, solar radiation and precipitation on yield and growth; effects of temperatures on yields; effects of CO2 on yields; yields | Zhang et al, |
| General Large Area Model-Rice | Regional yields | Chun et al, |
| Organizing Carbon and Hydrology in Dynamic Ecosystems | Growing period | Wang et al, |
| ORYZA2000 | Yields | van Oort and Zwart, |
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