Statistical Downscaling Model Research Articles

Assessing the impact of climate change by using Mann–Kendall, Pettitt and statistical downscaling model (case study: Tabriz station)

Assessing the impact of climate change by using Mann–Kendall, Pettitt and statistical downscaling model (case study: Tabriz station)

Climate Change Effects on Spatiotemporal Distribution of Precipitation over West Central India: A Statistical Downscaling Approach

The long-term shifts in temperatures and weather patterns are referred to as climate change. Climate change not only leads to long-term shifts in average temperatures but also changes in the spatial and temporal distribution of rainfall over continents. This study aims to predict likely changes in the rainfall pattern induced by climate change over the West Central (WC) region of India. The approach uses a statistical downscaling technique that converts coarse-scale outputs of the global climate model (GCM) to high-resolution future precipitation projections giving refined distribution. The decision support tool that uses a robust statistical downscaling technique viz. statistical downscaling model (SDSM) is used for assessing local climate change impacts. The research includes the study of likely regional climate variability, by integrating historical observational data and empirical relationships between large-scale climate variables and local weather patterns. Historical data and the SDSM, version 4.2, are employed to forecast future rainfall trends. Rainfall data from the India Meteorological Department and the National Centre for Environmental Prediction (NCEP) from 1961 to 2001 are used along with outputs from general circulation models (GCMs), viz. Hadley Centre coupled model, version 3 (HadCM3), and coupled global climate model, version 3 (CGCM3), for the period 1961–2099. Rainfall scenarios are presented for three future time periods (2011–2040, 2041–2070, and 2071–2099). The study indicates a significant increase in the mean annual precipitation across the West Central India region, particularly in the 2050s and 2080s. Mean annual rainfall is projected to rise by 10–19.4% under HadCM3 A2 and B2 scenarios. The HadCM3 indicates the month of September as the month of the highest precipitation in later time periods, whereas it is the month of August, according to CGCM3 simulations. When comparing the results of the two models, HadCM3 gives better results, as indicated by better R2 value in validation. Thus, the analysis gives climate change-induced likely changes in the spatiotemporal distribution of precipitation over the West Central India region. The insight given by the work will be useful for decision making in many sectors like agriculture, water management, disaster risk reduction, and infrastructure planning.

Assessment of Maximum Temperature for Future Time Series over Aurangabad, Maharashtra State, India

Climatic downscaling is an important tool used by many researchers in the field of climate to downscale climatic data available for global area to study the impacts of climate on smaller area. In climatic downscaling, there are mainly two types: 1) Statistical Downscaling and 2) Dynamical Downscaling. In the present study, statistical downscaling has been performed using SDSM (Statistical Downscaling Model) to find out future values of maximum temperatures (Tmax) over Aurangabad region (Latitude: 19.8911° N, Longitude: 75.1545° E). For this downscaling, CanESM2 (Canadian Earth System Model) CMIP5 (Coupled Model Intercomparison Project Phase 5) GCM (General Circulation Model) were selected and downscaling was performed under three different RCPs (Representative Concentration Pathways): RCP 2.6, RCP 4.5 and RCP 8.5. Downscaling model is calibrated and validated successfully over the baseline period 1961-2005 and results are presented graphically as well as statistically. Future downscaling values of maximum temperatures are presented with the help of three future time series: 2020s (2011-2040), 2050s (2041-2070) and 2080s (2071-2099) and it is observed that there is increase in the values of Tmax for the future time series with respect to the baseline period. Further the heat maps are developed for increasing monthly mean daily Tmax values under three RCPs with respect to the baseline period over the duration 2006 to 2013 and compared with increased observed monthly mean daily Tmax values over the same duration. This comparison has been done to assess the pattern of increasing monthly mean daily Tmax of downscaling results over Aurangabad region. All three RCPs are showing increasing values of monthly mean daily Tmax values for the selected three future time series with respect to the baseline period. Decade wise study of downscaling results is also showing increasing values of mean monthly daily Tmax with respect to the baseline period. Tmax values are predicted to be increased under RCP 2.6 by 1.10 0C, under RCP 4.5 by 2.13 0C and under RCP 8.5 by 4.20 0C at the end of 2099 which follows the prediction given by IPCC for global level temperature rise up to 2100.

Statistical downscaling model for future projection of daily IDF relationship by Markov chain and kernel density estimator

ABSTRACT Changes in climate might have a significant impact on rainfall characteristics, including extreme rainfall. This study aims to project the future daily rainfall, preserving most of the rainfall characteristics, including extreme rainfall incorporating climate changes. This paper presents two hybrid semi-parametric statistical downscaling models for future projection of IDF curves. The precipitation flux from seven scenarios of ten GCMs and observed daily rainfall data are considered as predictors and predictand variables, respectively. At site, daily rainfall occurrence is modeled using a two-state first-order Markov chain. Rainfall amounts on each wet day are modelled using a univariate nonparametric kernel density estimator. Two types of amount generation models are presented in this study. The bounded model (KDE-SP) is developed, considering the support for the kernel distribution as positive. In the unbounded model (KDE-Ext), the wet days are reclassified as extreme and non-extreme rainy days. A significant increasing trend can be observed in the future projected intensity–duration–frequency relationships. The maximum increment using empirical distribution is observed as 93.21 and 80.93% on a 5-year return period in the far future for the SSP5-8.5 scenario, using KDE-Ext and KDE-SP models, respectively. Although both methods show similar results, the KDE-Ext model performs better in simulating extreme rainfall.

Development of Statistical Downscaling Model Based on Volterra Series Realization, Principal Components, Climate Classification, and Ridge Regression

This paper applied the fuzzy function approach, combined with the ridge regression model, to produce daily rainfall projections from large-scale climate variables. This study developed a statistical downscaling model based on principal components, c-means fuzzy clustering, Volterra series, and ridge regression. The model is known, hereafter as SDC2R2. In the developed downscaling model, the use of ridge regression, instead of multiple linear regression, is proposed to downscale daily rainfall with wide range (WR) predictors. The WR predictors were applied to sufficiently incorporate climate change signals. The developed model also captured the non-linear interactions of the climate variables by applying the transformation of Volterra series realization over WR predictors. This transformation was performed by applying principal components as orthogonal filters. Further, these principal components were clustered by using c-means clustering and non-linear transformations were applied on these membership functions, to improve the prediction ability of the model. The reanalysis of climate data from the National Centres for Environmental Prediction (NCEP) was used to develop the model and was validated by using the Global Climate Model (GCM) for four locations in the Manawatu River basin. The developed model was used to obtain future daily rainfall projections from three Representative Concentrative Pathways (RCP 2.6, RCP 4.5, and RCP 8.5) scenarios from the Canadian Earth System Model (CanESM2) GCM. The performance of the model was compared with a widely used statistical downscaling model (SDSM). It was observed that the model performed better than SDSM in downscaling rainfall on a daily basis. Every scenario indicated that there is a probability of obtaining high future rainfall frequency. The results of this study provide valuable information for decision-makers since climate change may potentially impact the Manawatu basin.

Assessing the impact of climate change on agricultural production in central Afghanistan

Afghanistan has faced extreme climatic crises such as drought, rising temperature, and scarce precipitation, and these crises will likely worsen in the future. Reduction in crop yield can affect food security in Afghanistan, where the majority of population and economy are completely dependent on agriculture. This study assessed the interaction between climate change and crop yield in Kabul of Afghanistan during the reference (1990–2020) and future (2025–2100) periods. Climate data (1990–2020) were collected from four meteorological stations and three local organizations, and wheat yield data (1990–2020) were acquired from the United States Agriculture Department. Data during the reference period (1990–2020) were used for the validation and calibration of the statistical downscaling models such as the Statistical Downscaling Model (SDSM) and Long Ashton Research Station Weather Generator (LARS-WG). Furthermore, the auto-regression model was used for trend analysis. The results showed that an increase in the average annual temperature of 2.15°C, 2.89°C, and 4.13°C will lead to a reduction in the wheat yield of 9.14%, 10.20%, and 12.00% under Representative Concentration Pathway (RCP)2.6, RCP4.5, and RCP8.5 during the future period (2025–2100), respectively. Moreover, an increase in the annual maximum temperature of 1.79°C, 2.48°C, and 3.74°C also causes a significant reduction in the wheat yield of 2.60%, 3.60%, and 10.50% under RCP2.6, RCP4.5, and RCP8.5, respectively. Furthermore, an increase in the annual minimum temperature of 2.98°C, 2.23°C, and 4.30°C can result in an increase in the wheat yield of 6.50%, 4.80%, and 9.30% under RCP2.6, RCP4.5, and RCP8.5, respectively. According to the SDSM, the decrease of the average monthly precipitation of 4.34%, 4.10%, and 5.13% results in a decrease in the wheat yield of 2.60%, 2.36%, and 3.18% under RCP2.6, RCP4.5, and RCP8.5, respectively. This study suggests that adaptation strategies can be applied to minimize the consequences of climate change on agricultural production.

Bias correction and ensemble techniques in statistical downscaling model for rainfall prediction using Tweedie-LASSO in West Java, Indonesia

Rainfall is a climate element with high variations in space and time scales, so it is not easy to predict. One way to predict rainfall is statistical downscaling (SD). SD can predict local rainfall based on Global Circulation Model (GCM) data. The Decadal Climate Prediction Project (DCPP), one of the GCMs, originates from adjacent grids and experiences multicollinearity problems. Rainfall as a response variable is Tweedie Compound Poisson Gamma (TCPG) distribution data because it has a discrete component (rainfall events) and a continuous component (rainfall intensity), so SD modelling will be carried out using Tweedie-LASSO. This research aims to compare the performance of bias correction and ensemble methods in SD in predicting rainfall in West Java, Indonesia. Bias correction uses Empirical Quantile Mapping (EQM) with CHIRPS data, and the ensemble method uses a stacking technique with Random Forest (Stacking-RF) due to the varied characteristics of DCPP model sources. Evaluation results using Root Mean Square Error Prediction (RMSEP) and correlation coefficient show that bias correction improves single-model performance but not ensemble models. Besides that, ensemble models outperform single models both before and after bias correction. The combination of bias correction and ensemble modelling can be recommended when conducting SD to enhance the prediction capability of rainfall at stations and other areas.

Improve the downscaling accuracy of high-resolution precipitation field using classification mask

Due to the low spatial and temporal density of early meteorological observations in China, The China Meteorological Administration Land Data Assimilation System V3.0 (CLDAS3.0) could not obtain high-quality historical, high-resolution assimilated data. In particular, with high-resolution precipitation assimilation data, it is difficult to estimate the precipitation range accurately because of its substantial spatiotemporal heterogeneity. To solve this problem, our team proposes a two-stage downscaling framework for the precipitation field based on multi-task learning, CLDASSD-PRCP. It improves the accuracy of the downscaling task by predicting the precipitation classification mask first. We verify the algorithm using CLDAS3.0 and site observations as ground truth in Yunnan, China. The results show that CLDASSD-PRCP can accurately generate three types of precipitation masks and reduce the MAE of downscaling results by 0.32 mm/h after adding multi-classification masks (MutiClassesMask). In addition, the effects of light and extreme precipitation on precipitation spatial downscaling are also revealed. CLDASSD-PRCP is significant for establishing statistical downscaling models based on deep learning and provides a new way to deal with atmospheric elements with significant spatiotemporal heterogeneity.

STAR‐ESDM: A Generalizable Approach to Generating High‐Resolution Climate Projections Through Signal Decomposition

AbstractHigh‐resolution climate projections are critical to assessing climate risk and developing climate resilience strategies. However, they remain limited in quality, availability, and/or geographic coverage. The Seasonal Trends and Analysis of Residuals empirical statistical downscaling model (STAR‐ESDM) is a computationally‐efficient, flexible approach to generating such projections that can be applied globally using predictands and predictors sourced from weather stations, gridded data sets, satellites, reanalysis, and global or regional climate models. It uses signal processing combined with Fourier filtering and kernel density estimation techniques to decompose and smooth any quasi‐Gaussian time series, gridded or point‐based, into multi‐decadal long‐term means and/or trends; static and dynamic annual cycles; and probability distributions of daily variability. Long‐term predictor trends are bias‐corrected and predictor components used to map predictand components to future conditions. Components are then recombined for each station or grid cell to produce a continuous, high‐resolution bias‐corrected and downscaled time series at the spatial and temporal scale of the predictand time series. Comparing STAR‐ESDM output driven by coarse global climate model simulations with daily temperature and precipitation projections generated by a high‐resolution version of the same global model demonstrates it is capable of accurately reproducing projected changes for all but the most extreme temperature and precipitation values. For most continental areas, biases in 1‐in‐1000 hottest and coldest temperatures are <0.5°C and biases in the 1‐in‐1000 wet day precipitation amounts are <5 mm/day. As climate impacts intensify, STAR‐ESDM represents a significant advance in generating consistent high‐resolution projections to comprehensively assess climate risk and optimize resilience globally.

Climate change scenario analysis in Spree catchment, Germany using statistically downscaled ERA5-Land climate reanalysis data

This study focuses on the statistical downscaling of ERA5-Land reanalysis data using the Statistical DownScaling Model (SDSM) to generate climate change scenarios for the Spree catchment. Linear scaling was used to reduce the biases of the Global Climate Model for precipitation and temperature. The statistical analyses demonstrated that this method is a promising and straightforward way of correcting biases in climate data. SDSM was used to generate climate change scenarios, which considered three emission scenarios: RCP 2.6, RCP 4.5, and RCP 8.5. The results indicated that higher precipitation is expected under higher emission scenarios. Specifically, the summer and autumn seasons were projected to experience up to 50 mm more rainfall in the next 80 years, and the temperature was projected to increase by up to 1∘C by 2100. These projections of climate data for different scenarios are useful for assessing water management studies for agricultural and hydrologic applications considering changing climate conditions. This study highlights the importance of statistical downscaling and scenario generation in understanding the potential impacts of climate change on water resources. The results of this study can provide valuable insights into water resource management, especially on adapting to changing climate conditions.

Mapping Blue Economy Potential Using Spatial Statistical Downscaling Model: Analysis of the Impact of Climate Change on Freshwater Fish Resources

Blue Economy is a sustainable economic concept that focuses on utilizing economic resources in marine, coastal and land ecosystems. Sustainable use of freshwater resources in the blue economy for inland waters supports economic growth with environmental balance. The Statistical Downscaling method is used to understand the impact of climate change on freshwater fish resources. To carry out mapping of the potential of the blue economy, it is carried out by statistical downscaling modeling with satellite variables with Integrated Nested Laplace Approximation parameter estimates. The response variable is satellite variables in the form of average rainfall. The modeling results show that Kalipuro District has the highest blue economy potential, while Kalibaru has the lowest. From the research results, that satellite data on average rainfall is a strong basis for printing statistical downscaling, increasing efficiency with open source digital data sources. Satellite data integration, maximizing analysis and comprehensive blue economy potential efficiency.

Spatiotemporal variation in rice water requirements and area in the cold rice cultivation region of China: Past and Future

Studying rice water requirements (ETc) and area is crucial for developing irrigation schedules and adjusting crop structures. The cold rice cultivation region is China’s main high-quality rice production base and has the lowest temperature, highest latitude, and greatest sensitivity to climate. However, little research has focused on its past rice distributions and ETc, and its future variations are not yet clear. Past ETc was calculated by the Penman–Monteith and crop-corrected coefficient method, and past rice distribution patterns were analyzed based on the annual paddy rice planting area datasets. Future reference crop evapotranspiration (ET0) was projected by the statistical downscaling model (SDSM) based on the CMIP6 ensemble of daily predictor variables. The food and agriculture projections to 2050 datasets were used to project future rice areas, and the patch-generating land use simulation (PLUS) model simulated rice distribution. Finally, we projected future rice water supply demand. The results showed that ETc showed a decreasing trend, and the total rice area and production increased by 4.52 and 5.49 times from 1990 to 2020. Both in the past and future, rice was densely distributed in and expanded into the Sanjiang and Songnen Plains, and the spatial characteristic of ETc was as follows: the west was larger than the east, the north was larger than the south, and the central was in between. In the four future periods, the total ETc demonstrated an upward trend, increasing by 7.44–21.04, 15.64–34.74, 24.74–45.84, and 16.14–41.14 mm under the SSP126, SSP245, SSP370, and SSP585 scenarios, respectively, compared to the historical period. And it is generally ranked as SSP370 > SSP245 > SSP126. Finally, we projected future rice water supply meet the demand caused by rice area expansion.

Projected Intensified Hydrological Processes in the Three‐River Headwater Region, Qinghai Tibetan Plateau

AbstractThe Three‐River Headwater Region, also known as China's water tower, is highly sensitive to climate change and has experienced profound hydrological alterations in the last few decades. This study assessed the potential impacts of climate change on all the important hydrological components such as precipitation, evapotranspiration, streamflow, snow‐melt flow, and soil moisture (SM) content in the region. For this, climate data (i.e., temperature, precipitation, relative humidity, and windspeed) of three Global Climate Models (i.e., CanESM5, MPI‐ESM1.2‐HR, and NorESM2‐MM) was downscaled with the Statistical DownScaling Model (SDSM) and their ensemble was forced into a hydrological model to simulate the hydrological processes for 1981–2100. The screening process, which is central to all downscaling techniques, is very subjective in the SDSM. Therefore, we developed a quantitative screening approach by modifying the method applied by Mahmood and Babel (2013, https://doi.org/10.1007/s00704‐012‐0765‐0) for the selection of a set of logical predictors to cope with multi‐collinearity and their ranking. The analyses were performed for the near future period (NFP, 2021–2060) and far future period (FFP, 2061–2100) relative to the baseline period (BLP, 1981–2020). The results showed that the region will be hotter and wetter in the future, with intensive and frequent floods. For example, temperature, precipitation, evapotranspiration, and streamflow will increase by 1.0–1.5 (1–1.9)°C, 9–21 (15–27)%, 6–17 (9–29)%, and 9–46 (22–64)% in the NFP and by 2.0–2.8 (2.7–4.6)°C, 16–40 (43–87)%, 11–31 (24–73)%, and 20–95 (60–198)% in the FFP, respectively, under SSP2‐4.5 (SSP5‐8.5). Similar projections were explored for other hydrological components. Among all, surface flow showed an unprecedented increase (500%–1,000%) in the FFP. Peak flows will be much higher and will shift forward, and snowmelt will start earlier in the future. The results of the present study can be a good source for understanding the hydrological cycle and be used for the planning and management of water resources of the highly elevated and complex region of the Qinghai Tibetan Plateau.

Effects of climate change on streamflow in the Dez Basin of Iran using the IHACRES model based on the CMIP6 model

Abstract Understanding the changes in river flow is an important prerequisite for designing hydraulic structures as well as managing surface water resources in basins. By using the LARS-WG statistical downscaling model, the outputs of the general circulation model of the sixth report, including the ACCESS-ESM1 and BCC-CSM-MR models, under the SSP5.8.5 and SSP2.4.5 release scenarios. A more accurate spatial scale and daily precipitation and temperature time series were obtained for the studied area during the period of 2015–2043. Then the IHACRES rainfall-runoff model was calibrated in the study area. Based on the fit statistics in the calibration and validation stages, the overall performance of the developed model was evaluated as satisfactory. The calibrated hydrological model was driven by rainfall data and reduced air temperature to predict the effect of climate change on the output of the studied basin. The study showed that the studied basin has more rainfall (on average, 20.8% in the ACCESS-ESM1 model and 33.2% in the BCC-CSM2-MR model). The flow rate of the main river in the ACCESS-ESM1 model will decrease by 15% compared to the base period, and in the BCC-CSM2-MR model, it will increase by 16% compared to the base period.

Improving the statistical downscaling performance of climatic parameters with convolutional neural networks

Abstract This study examines two downscaling techniques, convolutional neural networks (CNNs) and feedforward neural networks for predicting precipitation and temperature, alongside statistical downscaling as a benchmark model. The daily climate predictors were extracted from the European Center for Medium-range Weather Forecast (ECMWF) ERA5 dataset spanning from 1979 to 2010 for Tabriz city, located in the northwest of Iran. The biases in precipitation data of ERA5 predictors were corrected through the empirical quantile mapping method. Also, two nonlinear predictor screening methods, random forest and mutual information, were employed, alongside linear correlation coefficient. While these methods facilitate identification of dominant regional climate change drivers, it is essential to consider their limitations, such as sensitivity to parameter settings, assumptions about data relationships, potential biases in handling redundancy and correlation, challenges in generalizability across datasets, and computational complexity. Evaluation results indicated that CNN, when applied without predictor screening, achieves coefficient of determination of 0.98 for temperature and 0.71 for precipitation. Ultimately, future projections were employed under two shared socioeconomic pathways (SSPs), SSP2-4.5 and SSP5-8.5, and concluded that the most increase in temperature by 2.9 °C and decrease in precipitation by 3.5 mm may occur under SSP5-8.5.

Climate change projection using statistical downscaling model over southern coastal Iran

Iran is highly vulnerable to climate change, particularly evident in shifting precipitation and temperature patterns, especially in its southern coastal region. With these changing climate conditions, there is an urgent need for practical and adaptive management of water resources and energy supply to address the challenges posed by future climate change. Over the next two to three decades, the effects of climate change, such as precipitation and temperature, are expected to worsen, posing greater risks to water resources, agriculture, and infrastructure stability. Therefore, this study aims to evaluate the alterations in mean daily temperature (Tmean) and total daily rainfall (rrr24) utilizing climate change scenarios from both phases 5 and 6 of the Coupled Model Inter-comparison Project (CMIP5 and CMIP6, respectively) in the southern coastal regions of Iran (Hormozgan province), specifically north of the Strait of Hormuz. The predictions were generated using the Statistical Downscaling Model (SDSM) and National Centre for Environmental Prediction (NCEP) predictors, incorporating climate change scenarios from CMIP5 with Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5 and CMIP6 with Shared Socioeconomic Pathways (SSPs) 1, 2, and 5. The analysis was conducted for three distinct time periods: the early 21st century (2021–2045), middle 21st century (2046–2071), and late 21st century (2071–2095). The results indicated that the CMIP5 model outperformed the CMIP6 model in simulating and predicting Tmean and rrr24. In addition, a significant increase in Tmean was observed across all the scenarios and time periods, with the most pronounced trend occurring in the middle and late 21st century future periods. This increase was already evident during the base period of 2021–2045 across all scenarios. Moreover, the fluctuations in precipitation throughout the region and across all scenarios were significant in the three examined future periods. The results indicated that among CMIP5 scenarios, RCP8.5 had highest changes of Tmean (+1.22 °C) in Bandar Lengeh station in 2071–2095 period. The lowest change magnitude of Tmean among CMIP5 scenarios was found in RCP4.5 (−1.94 °C) in Ch station in 2046–2070 period. The results indicated that among CMIP5 scenarios, RCP8.5 had highest changes of rrr24 (+150.2 mm) in Chabahar station in 2071–2095 period. The lowest change magnitude of rrr24 among CMIP5 scenarios was found in RCP8.5 (−25.8 mm) in Bandar Abbas station in 2046–2070 period. In conclusion, the study reveals that the coastal area of Hormozgan province will experience rising temperatures and changing rainfall patterns in the future. These changes may lead to challenges such as increased water and energy consumption, heightened risks of droughts or floods, and potential damage to agriculture and infrastructure. These findings offer valuable insights for implementing local mitigation policies and strategies and adapting to emerging climate changes in Hormozgan's coastal areas. For example, utilizing water harvesting technologies, implementing watershed management practices, and adopting new irrigation systems can address challenges like water consumption, agricultural impacts, and infrastructure vulnerability. Future research should accurately assess the effect of these changes in precipitation and temperature on water resources, forest ecosystems, agriculture, and other infrastructures in the study area to implement effective management measures.

Prediction of groundwater level using GMDH artificial neural network based on climate change scenarios

One of the main challenges regarding the prediction of groundwater resource changes is the climate change phenomenon and its impacts on quantitative variations of such resources. Groundwater resources are treated as one of the main strategic resources of any region. Given the climate change phenomenon and its impacts on hydrological parameters, it is necessary to evaluate and predict future changes to achieve an appropriate plan to maintain and preserve water resources. In this regard, the present study is put forward by utilizing the Statistical Down-Scaling Model (SDSM) to forecast the main climate variables (i.e., temperature and precipitation) based on new Rcp scenarios for greenhouse gas emissions within a period from 2020 to 2060. The results obtained from the prediction of climate parameters indicate different values in each emission scenario, so the limit, minimum and maximum values occur in the Rcp8.5, Rcp2.6 and Rcp4.5 scenarios, respectively. Also, a model is developed by utilizing the GMDH artificial neural network technique. The developed model predicts the average groundwater level based on the climate variables in such a way that by implementing the climate parameters forecasted by the SDSM model, the groundwater level within a time period from 2020 to 2060 is predicted. The results obtained from the verification and validation of the model imply its proper performance and reasonable accuracy in predicating groundwater level based on the climate variables. The findings derived from the present paper indicate that compared to the years prior to the prediction period, the groundwater level of the Sahneh Plain has dramatically dropped so that based on the Rcp scenarios, the groundwater level values are in their lowest state within the period from 2046 to 2056. The findings of this paper can be used by managers and decision makers as a layout for evaluating climate change effects in the Sahneh Plain.

Assessing Future Hydrological Variability in a Semi-Arid Mediterranean Basin: Soil and Water Assessment Tool Model Projections under Shared Socioeconomic Pathways Climate Scenarios

Climate is one of the main drivers of hydrological processes, and climate change has caused worldwide effects such as water scarcity, frequent floods and intense droughts. The purpose of this study was to analyze the effects of climate change on the water balance components, high flow and low flow stream conditions in a semi-arid basin in Iran. For this reason, the climate outputs of the CanESM5 model under Shared Socioeconomic Pathways (SSP) scenarios SSP126, SSP245, and SSP585 were spatially downscaled by the Statistical Downscaling Model (SDSM). The hydrological process was simulated by the Soil and Water Assessment Tool (SWAT) model. Key findings include a 74% increase in evapotranspiration, a reduction by up to 9.6% in surface runoff, and variations in discharge by up to 53.6%. The temporal analysis of snow melting changes revealed an increase in the volume of snow melting during winter months and a reduction in the volume during spring. The projected climate change is expected to cause notable variations in high and low flow events, particularly under the SSP585 scenario, which anticipates significant peaks in flow rates. This comprehensive analysis underscores the pressing need for adaptive strategies in water resource management to mitigate the anticipated impacts of climate variability.

RAINFALL FORECASTING OF SALT PRODUCING AREAS IN PANGKEP REGENCY USING STATISTICAL DOWNSCALING MODEL WITH LINEARIZED RIDGE REGRESSION DUMMY

Pangkep Regency is one of the regions in South Sulawesi that is the center of national salt production. Salt production in the area is still dependent on sea water evaporation so that rainfall is one of the determining factors for the success of salt productivity. Statistical downscaling is an accurate method for rainfall forecasting by linking the local scale rainfall in Pangkep Regency (response variable) with the global scale of the global circulation model/GCM output (predictor variable). However, the GCM output rainfall has a large dimension, which is an 8×8 grid (64 predictor variables), causing multicollinearity. The linearized ridge regression (LRR) method is used to overcome this problem. This method combines the performance of generalized ridge regression and Liu-type methods to reduce multicollinearity. In addition, dummy variables based on the K-means clustering technique were added to the model to overcome heteroscedasticity. The purpose of this study is to obtain the results of rainfall forecasting in Pangkep Regency using the LRR method in the statistical downscaling model. The model generated from the LRR method with dummy variables is better at explaining the variability of rainfall in Pangkep Regency. The value is higher (72%) than without dummy variables (57%). The addition of dummy variables in the LLR model has better accuracy in forecasting rainfall. The actual rainfall correlation of Pangkep Regency with has the largest correlation (0.76) with the smallest mean absolute percentage error value (0.49). The results obtained are that the months of May - November tend to have relatively low rainfall, so that salt farmers can produce salt with good quantity and quality.

Statistical downscaling of high-resolution precipitation in India using convolutional long short-term memory networks

Abstract Statistical downscaling of the General Circulation Model (GCM) simulations are widely used for accessing climate changes in the future at different spatiotemporal scales. This study proposes a novel Statistical Downscaling (SD) model established on the Convolutional Long Short-Term Memory (ConvLSTM) Network. The methodology is applied to obtain future projection of rainfall at 0.25° spatial resolution over the Indian sub-continental region. The traditional multisite downscaling models typically perform downscaling on a single homogeneous rainfall zone, predicting rainfall at only one grid point in a single model run. The proposed model captures spatiotemporal dependencies in multisite local rainfall and predicts rainfall for the entire zone in a single model run. The study proposes a Shared ConvLSTM model providing a single end-to-end supervised model for predicting the future precipitation for entire India. The model captures the regional variability in rainfall better than a region-wise trained model. The projected future rainfall for different scenarios of climate change reveals an overall increase in the rainfall mean and spatially non-uniform changes in future rainfall extremes over India. The results highlight the importance of conducting in-depth hydrologic studies for different river basins of the country for future water availability assessment and making water resource policies.