Downscaling Model Research Articles

Climate Change Flood Risk Analysis: Application of Dynamical Downscaling and Hydrological Modeling

Floods are a recurring natural phenomenon during the rainy season in many Brazilian municipalities. Nevertheless, shifts in weather patterns have contributed to an increased incidence of these events in urban areas, where their impact can be amplified by the way the surrounding catchment is occupied. Hence, the present study sought to evaluate the susceptibility of the urban drainage infrastructure in João Monlevade, Brazil, to the effects of climate change by undertaking a comprehensive assessment of the Carneirinhos catchment, including its morphometric characteristics. For this purpose, we employed a hydrological model driven by regional rainfall projections from a high-resolution climate model (HadGEM2-ES downscaled to 5 km resolution) under the Representative Concentration Pathways (RCP8.5) scenario. Several combinations of rainfall occurrence were simulated, incorporating temporal aspects (different durations and return times), as well as spatial aspects (concentrated and distributed rainfall within the catchment). The results showed that the area of exposed soil in the Carneirinhos catchment experienced an increase of more than 140% from 2016 to 2019, favoring runoff surface and evaporation, which contributed to the increase in the number of flood events in the region. In addition, only 1 of the 56 heavy rainfall event simulations performed did not exceed the capacity of the macro drainage gallery.

Impact of Future Climate and Land Use Changes on Runoff in a Typical Karst Basin, Southwest China

Climate change and land use change are the two main factors affecting the regional water cycle and water resources management. However, runoff studies in the karst basin based on future scenario projections are still lacking. To fill this gap, this study proposes a framework consisting of a future land use simulation model (FLUS), an automated statistical downscaling model (ASD), a soil and water assessment tool (SWAT) and a multi-point calibration strategy. This frameword was used to investigate runoff changes under future climate and land use changes in karst watersheds. The Chengbi River basin, a typical karst region in southwest China, was selected as the study area. The ASD method was developed for climate change projections based on the CanESM5 climate model. Future land use scenarios were projected using the FLUS model and historical land use data. Finally, the SWAT model was calibrated using a multi-site calibration strategy and was used to predict future runoff from 2025–2100. The results show that: (1) the developed SWAT model obtained a Nash efficiency coefficient of 0.83, which can adequately capture the spatial heterogeneity characteristics of karst hydro-climate; (2) land use changes significantly in all three future scenarios, with the main phenomena being the interconversion of farmland and grassland in SSPs1-2.6, the interconversion of grassland, farmland and artificial surfaces in SSPs2-4.5 and the interconversion of woodland, grassland and artificial surfaces in SSPs5-8.5; (3) the average annual temperature will show an upward trend in the future, and the average annual precipitation will increase by 11.53–14.43% and (4) the future annual runoff will show a significant upward trend, with monthly runoff mainly concentrated in July–September. The variability and uncertainty of future runoff during the main-flood period may increase compared to the historical situation. The findings will benefit future water resources management and water security in the karst basin.

Spatio‐temporal downscaling emulator for regional climate models

AbstractRegional climate models (RCM) describe the mesoscale global atmospheric and oceanic dynamics and serve as dynamical downscaling models. In other words, RCMs use atmospheric and oceanic climate output from general circulation models (GCM) to develop a higher resolution climate output. They are computationally demanding and, depending on the application, require several orders of magnitude of compute time more than statistical climate downscaling. In this article, we describe how to use a spatio‐temporal statistical model with varying coefficients (VC), as a downscaling emulator for a RCM using VC. In order to estimate the proposed model, two options are compared: INLA, and varycoef. We set up a simulation to compare the performance of both methods for building a statistical downscaling emulator for RCM, and then show that the emulator works properly for NARCCAP data. The results show that the model is able to estimate non‐stationary marginal effects, which means that the downscaling output can vary over space. Furthermore, the model has flexibility to estimate the mean of any variable in space and time, and has good prediction results. INLA was the fastest method for all the cases, and the approximation with best accuracy to estimate the different parameters from the model and the posterior distribution of the response variable.

A spatially adaptive multi-resolution generative algorithm: Application to simulating flood wave propagation

We propose a statistical model suitable for large spatio-temporal data sets exhibiting complex patterns such as simulated by physics-based hydraulic models over high resolution (HR) 2D meshes. Although necessary for impact studies such as urban flood hazard assessment, their long computation times limit their applicability leading to the development of statistical models that may emulate them quickly. Our model draws from the strengths of multi-resolution analysis and relies on an extension of the lifting scheme, a flexible implementation of discrete wavelet transforms, for spatio-temporal data. The extended lifting scheme exploits the idea that dominant spatial features, that may be identified with clustering, remain present through time. An easily interpretable non-parametric representation can be derived from the lifting scheme by combining a smoothed version of the data (obtained by simple averaging) with details (given by local regression residuals). A generative algorithm is built by introducing the information provided by a low resolution model, whose computation times are orders of magnitude smaller, yielding a downscaling model. This downscaling model assumes that sufficiently representative HR spatial patterns can be inferred from the training set. Our model is applied to a 2D dam break experiment using a synthetic urban configuration and to a field-scale test case simulating the propagation of a dike break flood wave into a Sacramento urban area. A comparison, carried out with spatial interpolation schemes and with a variant of our model based on principal component analysis, shows that the spatio-temporal lifting scheme based model is better at reproducing extreme events.

A wind-driven snow redistribution module for Alpine3D v3.3.0: adaptations designed for downscaling ice sheet surface mass balance

Abstract. Ice sheet surface mass balance describes the net snow accumulation at the ice sheet surface. On the Antarctic ice sheet, winds redistribute snow, resulting in a surface mass balance that is variable in both space and time. Representing wind-driven snow redistribution processes in models is critical for local assessments of surface mass balance, repeat altimetry studies, and interpretation of ice core accumulation records. To this end, we have adapted Alpine3D, an existing distributed snow modeling framework, to downscale Antarctic surface mass balance to horizontal resolutions up to 1 km. In particular, we have introduced a new two-dimensional advection-based wind-driven snow redistribution module that is driven by an offline coupling between WindNinja, a wind downscaling model, and Alpine3D. We then show that large accumulation variability can be at least partially explained by terrain-induced wind speed variations which subsequently redistribute snow around rolling topography. By comparing Alpine3D to airborne-derived snow accumulation measurements within a testing domain over Pine Island Glacier in West Antarctica, we demonstrate that our Alpine3D downscaling approach improves surface mass balance estimates when compared to the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), a global atmospheric reanalysis which we use as atmospheric forcing. In particular, when compared to MERRA-2, Alpine3D reduces simulated surface mass balance root mean squared error by 23.4 mmw.e.yr-1 (13 %) and increases variance explained by 24 %. Despite these improvements, our results demonstrate that considerable uncertainty stems from the employed saltation model, confounding simulations of surface mass balance variability.

Downscaling of GPM satellite precipitation products based on machine learning method in complex terrain and limited observation area

Precipitation products with high spatial and temporal resolutions are crucial in hydrological monitoring, meteorological forecasting, and disaster warning. However, obtaining such products in regions with complex terrain and limited observation conditions is a challenging task. To address this issue, a statistical downscaling model for satellite precipitation products using an optimized Back Propagation (BP) neural network was developed. The model employed key environmental variables selected by the Mean Impact Value (MIV) index, including enhanced vegetation (EVI), land surface temperature (LST_D, LST_N), evapotranspiration (ET), digital elevation model (DEM), slope, and latitude and longitude (Lon, Lat). The downscaled precipitation products were corrected twice using a combination of the model error correction and observation data merging methods. Finally, the satellite precipitation products with a resolution of 0.1° (∼10 km) were downscaled to 1 km. A comprehensive evaluation of the results showed that: (1) the accuracy of the precipitation products obtained using this method was better than that of the original satellite precipitation products; (2) the method effectively supplemented precipitation data in areas with few ground stations, with better results in areas with a dense distribution of ground stations; (3) this method demonstrated the efficacy of satellite precipitation products for downscaling in areas with complex terrain.

Generating high-resolution climate maps from sparse and irregular observations using a novel hybrid RBF network

ABSTRACT Sparse and irregular climate observations in many developing countries are not enough to satisfy the need of assessing climate change risks and planning suitable mitigation strategies. The wide-used statistical downscaling model (SDSM) software tools use multi-linear regression to extract linear relations between large-scale and local climate variables and then produce high-resolution climate maps from sparse climate observations. The latest machine learning techniques (e.g. SRCNN, SRGAN) can extract nonlinear links, but they are only suitable for downscaling low-resolution grid data and cannot utilize the link to other climate variables to improve the downscaling performance. In this study, we proposed a novel hybrid RBF (Radial Basis Function) network by embedding several RBF networks into new RBF networks. Our model can well incorporate climate and topographical variables with different resolutions and extract their nonlinear relations for spatial downscaling. To test the performance of our model, we generated high-resolution precipitation, air temperature and humidity maps from 34 meteorological stations in Bangladesh. In terms of three statistical indicators, the accuracy of high-resolution climate maps generated by our hybrid RBF network clearly outperformed those using a multi-linear regression (MLR), Kriging interpolation or a pure RBF network.

A Statistical Downscaling Model Based on Multiway Functional Principal Component Analysis for Southern Australia Winter Rainfall

Abstract We propose a statistical downscaling model based on multiway functional principal component analysis (FPCA) for rainfall prediction. The model mainly explains the relationship between the winter mean sea level pressure (MSLP) and rainfall in southern Australia from the perspective of functional data. In comparison with the traditional approach of feature extraction based on principal component analysis, the multiway FPCA needs fewer principal components not only to capture the most variance in MSLP but also to greatly avoid the loss of spatial information. A functional principal component (FPC) regression is further developed to simulate both current and future rainfall. The main results show that the first five leading FPCs are sufficient to capture the spatial characteristics of winter MSLP, achieving the purpose of efficient dimensionality reduction. Specifically, no more than three FPCs are required to develop the functional downscaling models for the winter rainfall over four studied regions. The functional downscaling model provides a good skill in terms of the correlation higher than 0.7 between the predictions and observations and the ratio of root-mean-square error to the climatology of winter rainfall below 20% over four regions. The developed downscaling models are further used to interpret the MSLP patterns from four CMIP5 climate models [ACCESS1.3, BCC_CSM1.1(m), CESM1(CAM5), and MPI-ESM-MR], which have been used to simulate both present-day and future climate. The resulting downscaled values based on ensemble MSLP provide 1) a closer representation of observed present-day rainfall than the raw climate model values and 2) alternative estimates of future changes in rainfall that arise from changes in MSLP.

EVALUATION OF SDSM MODELS FOR CLIMATE PREDICTIONS IN BANGLADESH

As one of the poorest countries in the world, agriculture is Bangladesh’s economic pillar, leading to Bangladesh’s economy becoming vulnerable to global warming. The generation of high-resolution climate predictions in Bangladesh can help to reduce the huge damage and losses inflicted by disasters linked to climate. The statistical downscaling model (SDSM) is the most widely used software on robust climate downscaling and prediction analysis. In this study, by using the SDSM model, we established the statistical relationship between observed climate data in Bangladesh and the large-scale low-resolution NCEP data and used three statistical indicators to evaluate the prediction performance of the SDSM software. Our results show that the SDSM software is more suitable for forecasting humidity/temperature in Bangladesh than rainfall.

Shrinking of the Arabian Sea oxygen minimum zone with climate change projected with a downscaled model

In Arabian Sea (AS), land-locked northern boundary and strong seasonal productivity lead to the formation of one of the most intense open ocean Oxygen Minimum Zones (OMZs). Presence of this perennial OMZ has significant consequences on adjacent coastal fisheries and ecosystem. Simulations from CMIP5 suggest significant weakening of both monsoonal winds and productivity under high emission scenario. But the fate of AS OMZ in this scenario - whether it will expand or shrink - still remains elusive, mainly due to poor representation of extent and strength of AS OMZ in CMIP5 present-day simulations. To address this, we analyze the distribution of O2 in AS from a subset of three contrasted CMIP5 simulations, and complemented with a set of regional downscaled model experiments which we forced at surface and open boundaries using information from those three CMIP5 models. We tested two regional downscaling approaches - with and without correction of CMIP5 biases with respect to observations. Using a set of sensitivity experiments, we disentangle the contributions of local (atmospheric) forcing vs. remote (at the lateral boundaries) forcing in driving the future projected O2 changes. While CMIP5 projects either shrinking or expansion of the AS OMZ depending on the model, our downscaling experiments consistently project a shrinking of AS OMZ. We show that projected O2 changes in OMZ layer are affected by both local and remote processes. In the southern AS, the main response to climate change is oxygenation that originates from the boundaries, and hence downscalled and CMIP5 model responses are similar. In contrast, in northern AS, downscaling yields a substantial reduction in O2 projection discrepancies because of a minimal influence of remote forcing there leading to a stronger sensitivity to improved local physics and improved model representation of present-day conditions. We find that when corrected for present-day biases, projected deoxygenation in the northern AS is shallower. Our findings indicate the importance of downscaling of global models in regions where local forcing is dominant, and the need for correcting global model biases with respect to observations to reduce uncertainties in future O2 projections.

Evolution of Drought Trends under Climate Change Scenarios in Karst Basin

Karst basins have a relatively low capacity for water retention, rendering them very vulnerable to drought hazards. However, karst geo-climatic features are highly spatially heterogeneous, making reliable drought assessment challenging. To account for geo-climatic heterogeneous features and to enhance the reliability of drought assessment, a framework methodology is proposed. Firstly, based on the history of climate (1963–2019) from the Global Climate Model (GCM) and station observations within the Chengbi River karst basin, a multi-station calibration-based automated statistical downscaling (ASD) model is developed, and the Kling–Gupta efficiency (KGE) and Nash–Sutcliffe efficiency (NSE) are selected as performance metrics. After that, future climate (2023–2100) under three GCM scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) are obtained by using the ASD model. Finally, the Standardized Precipitation Evapotranspiration Index (SPEI), calculated by future climate is applied to assess drought conditions. The results indicate that the multi-station calibration-based ASD model has good performance and thus can be used for climate data downscaling in karst areas. Precipitation mainly shows a significant upward trend under all scenarios with the maximum variation (128.22%), while the temperature shows a slow upward trend with the maximum variation (3.44%). The drought condition in the 2040s is still relatively severe. In the 2060s and 2080s, the basin is wetter compared with the historical period. The percentage of drought duration decreases in most areas from the 2040s to the 2080s, demonstrating that the future drought condition is alleviated. From the SSP1-2.6 scenario to the SSP5-8.5 scenario, the trend of drought may also increase.

Deep Learning for Downscaling Tropical Cyclone Rainfall to Hazard‐Relevant Spatial Scales

AbstractFlooding, driven in part by intense rainfall, is the leading cause of mortality and damages from the most intense tropical cyclones (TCs). With rainfall from TCs set to increase under anthropogenic climate change, it is critical to accurately estimate extreme rainfall to better support short‐term and long‐term resilience efforts. While high‐resolution climate models capture TC statistics better than low‐resolution models, they are computationally expensive. This leads to a trade‐off between capturing TC features accurately, and generating large enough simulation data sets to sufficiently sample high‐impact, low‐probability events. Downscaling can assist by predicting high‐resolution features from relatively cheap, low‐resolution models. Here, we develop and evaluate a set of three deep learning models for downscaling TC rainfall to hazard‐relevant spatial scales. We use rainfall from the Multi‐Source Weighted‐Ensemble Precipitation observational product at a coarsened resolution of ∼100 km, and apply our downscaling model to reproduce the original resolution of ∼10 km. We find that the Wasserstein Generative Adversarial Network is able to capture realistic spatial structures and power spectra and performs the best overall, with mean biases within 5% of observations. We also show that the model can perform well at extrapolating to the most extreme storms, which were not used in training.

Impacts of climate and land use/cover change on mini-hydropower generation in River Kyambura watershed in South Western part of Uganda

Abstract This study explored the combined impacts of climate and land-use change on mini-hydropower generation in the Kyambura watershed. The soil and water assessment tool (SWAT) was used as a hydrological model whereas the statistical downscaling model (SDSM) was used to downscale meteorological data for the Kyambura watershed for the year 2050. The results show that there will be an increase in urban land by 11.89%, barren land by 25.78%, water by 0.49% and a reduction in percentage area coverage of vegetation by 38.17% by the year 2050. A 10.6 and 17.7% increase is anticipated in average annual hydropower generated by the year 2050. There is, therefore, a need to develop governing policies to regulate management practices to preserve the integrity of the watersheds and ensure the reliability of power production.

Development of high-resolution gridded data for water availability identification through GRACE data downscaling: Development of machine learning models

Estimation of total water availability has paramount importance in planning sustainable development of a region, particularly in arid water-scarce areas. Coarse-resolution of existing total water availability or terrestrial water storage anomaly (TWSA) data is the major limitation of their applications in different sectors. An attempt has been made to downscale Gravity Recovery and Climate Experiment (GRACE) TWSA data to develop a high-resolution gridded data product of the total water availability of Iraq. European reanalysis (ERA5) precipitation, evapotranspiration, surface runoff, subsurface runoff and soil water contents data were used to downscale GRACE 1.0° spatial resolution monthly TWSA to 0.1° spatial resolution for the period 2002–2020. A machine learning (ML)-based recursive feature elimination algorithm was used to identify the optimum input combination according to the nonlinear relationship of ERA5 variables with GRACE water equivalence data. The selected subset of inputs was used to develop the downscaling models using three classical ML algorithms for the available GRACE measurement points over Iraq. The models were calibrated at 70% of GRACE grid point locations and validated in the rest of the points. Finally, the model was used to predict TWSA at each ERA5 grid point to generate Iraq's high-resolution water availability dataset. The results showed higher performance of random forest in downscaling TWSA compared to other algorithms. The model estimated the TWSA at validation points with Kling-Gupta Efficiency (KGE) in the range of 0.5–0.91 and Nash-Sutcliff Efficiency (NSE) between 0.54 and 0.88. The modelled high-resolution TWSA data shows higher availability of water resources in the north, particularly northeast of Iraq, and the least in the southeast. The technique developed in this study can be implemented in developing a high-resolution gridded water availability dataset from satellite GRACE data in the region where in-situ estimation is very limited.

Assessing Three Perfect Prognosis Methods for Statistical Downscaling of Climate Change Precipitation Scenarios

AbstractUnder the perfect prognosis approach, statistical downscaling methods learn the relationships between large‐scale variables from reanalysis and local observational records. These relationships are subsequently applied to downscale future global climate model (GCM) simulations in order to obtain projections for the local region and variables of interest. However, the capability of such methods to produce future climate change signals consistent with those from the GCM, often referred to as transferability, is an important issue that remains to be carefully analyzed. Using the EC‐Earth GCM and focusing on precipitation, we assess the transferability of generalized linear models, convolutional neural networks and a posteriori random forests (APRFs). We conclude that APRFs present the best overall performance for the historical period, and future local climate change signals consistent with those projected by EC‐Earth. Moreover, we show how a slight modification of APRFs can greatly improve the temporal consistency of the downscaled series.

Prediction of climate change impacts on availability of surface water resources in the semi-arid Tugwi Mukosi catchment of Zimbabwe

Climate change impact drivers includes increased rainfall variability, increased extreme events, and changes in the mean behaviour of hydro-meteorological variables such as air temperature, rainfall, and runoff. As such, this study assessed the potential impacts of climate change on surface water resources in a semi-arid Tugwi Mukosi catchment of Zimbabwe. The Mann-Kendall trend analysis, climate change downscaling and streamflow modelling were used as the primary methodology. The analysis of historical hydro-climatic data trends were performed using the Mann-Kendall trend test and Sen's slope method. Equally, observed daily rainfall was extended using bias-corrected CHIRPS satellite rainfall. Thus, a total of 9 Global Climate Models including (CCCma-CanESM2, Miroc Miroc 5, MOHCHadGEM2, MPI-M-MPI-ESM-LR, and NCCNorESM1-M) on the Sveriges Meteorologiska och Hydrologiska Institut (SMHI) Rosby Centre Atmosphere model version 4 (RCA4) RCM under the Representative Concentrated Pathways 4.5 and 8.5 were extracted and analysed for the 2030s (2021–2050) and 2060s (2051–2080) period. The results show significant (p<0.05) decreasing rainfall trends at the analysed stations, as well as significant decrease in rainfall for the 2030s and the 2060s under GCMs CCCma-CanESM 2 model. The Hydrologic Engineering Centre-Hydrological modeling System (HECHMS) (1997–1999) was calibrated and validated for the period 1981–2016. Calibration results on gauged catchment runoff and peak flows are satisfactory (Ngezi observed peak discharge and simulated peak discharge at 42.4 m3/s and 34.8 m3/s respectively, NSE 62.8%, Upper Muzhwi 30 m3/s and 19.5 m3/s, NSE 53.55%, Musogwezi 18.7 m3/,s and 16.5 m3/s, NSE 58.4%). The GCM-CanESM RCP 4.5 model showed a decrease in the runoff predicted of above 17% in both the 2030s and a significant decrease in the 2060s of 28.41% and 5% respectively. The combination of climate downscaling and water resources modelling is paramount to the management of water resources in a semi-arid area. Hydrological modelling and climate downscaling techniques gives important insights for water resources planning and development to support the water sector adaptation to climate change. The methodology applies to other data-scarce and semi-arid areas whose climate change impacts require further investigation.

Mapping and predicting of water erosion using RUSLE in Mediterranean context: Case of El Kharroub watershed (Western Rif, Northern Morocco)

Worldwide, soil erosion is one of the hazards posing a serious threat to soil and water resources, especially in the Mediterranean context. In Morocco, this phenomenon is a major problem in the natural territory, especially in the Rif Mountains in the north of the country, where fragile rocks dominate on steep slopes. Thus, this work aims to assess the soil erosion in the El Kharroub River watershed over the baseline period 2000-2020 and two future periods 2021-2030 and 2031-2050, using the Revised Universal Soil Loss Equation (RUSLE). For the future climate periods (2021-2030 and 2031-2050), precipitations were produced using a classical statistical downscaling model (SDSM). Over the current period, the results showed that the annual rainfall erosivity and the vegetation cover decreased from 2002 to 2020 by 34.3% and 28.6% respectively. The annual soil loss maps showed a decline of about 54.8% during the baseline period. The changes in rainfall and vegetation cover are largely due to climate changes effect and the deforestation/reforestation that the region has experienced, which subsequently leads to changes in soil erosion due to the important function of these two factors. Furthermore, projected scenarios revealed that the average annual erosivity could decrease to 268.4 and 267.1 for 2031-2050 and 2031-2050 scenarios compared with the current period. As a result, the average annual soil losses could decrease by 21.3 and 21.8% for the projected scenarios.

Impacts of Climate Change on Extreme Climate Indices in Türkiye Driven by High-Resolution Downscaled CMIP6 Climate Models

In this study, the latest release of all available Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models with two future scenarios of Shared Socio-Economic Pathways, SSP2-4.5 and SSP5-8.5, over the period 2015–2100 are utilized in diagnosing climate extremes in Türkiye. Coarse-resolution climate models were downscaled to a 0.1° × 0.1° (~9 km) spatial resolution using the European Centre for Medium-Range Weather Forecasts Reanalysis 5-Land (ERA5-Land) dataset based on three types of quantile mapping: quantile mapping, detrended quantile mapping, and quantile delta mapping. The temporal variations of the 12 extreme precipitation indices (EPIs) and 12 extreme temperature indices (ETIs) from 2015 to 2100 consistently suggest drier conditions, in addition to more frequent and severe precipitation extremes and warming temperature extremes in Türkiye, under the two future scenarios. The SSP5-8.5 scenario indicates more severe water stress than the SSP2-4.5 scenario; the total precipitation decreases up to 20% for Aegean and Mediterranean regions of Türkiye. Precipitation extremes indicate a decrease in the frequency of heavy rains but an increase in very heavy rains and also an increasing amount of the total precipitation from very heavy rain days. Temperature extremes such as the coldest, warmest, and mean daily maximum temperature are expected to increase across all regions of Türkiye, indicating warming conditions by up to 7.5 °C by the end of the century. Additionally, the coldest daily maximums also exhibit higher variability to climate change in the subregions Aegean, Southeastern Anatolia, Marmara, and Mediterranean regions of Türkiye while the mean daily maximum temperature showed greater sensitivity in the Black Sea, Central Anatolia, and Eastern Anatolia regions.

GRACE Downscaler: A Framework to Develop and Evaluate Downscaling Models for GRACE

Monitoring and managing groundwater resources is critical for sustaining livelihoods and supporting various human activities, including irrigation and drinking water supply. The most common method of monitoring groundwater is well water level measurements. These records can be difficult to collect and maintain, especially in countries with limited infrastructure and resources. However, long-term data collection is required to characterize and evaluate trends. To address these challenges, we propose a framework that uses data from the Gravity Recovery and Climate Experiment (GRACE) mission and downscaling models to generate higher-resolution (1 km) groundwater predictions. The framework is designed to be flexible, allowing users to implement any machine learning model of interest. We selected four models: deep learning model, gradient tree boosting, multi-layer perceptron, and k-nearest neighbors regressor. To evaluate the effectiveness of the framework, we offer a case study of Sunflower County, Mississippi, using well data to validate the predictions. Overall, this paper provides a valuable contribution to the field of groundwater resource management by demonstrating a framework using remote sensing data and machine learning techniques to improve monitoring and management of this critical resource, especially to those who seek a faster way to begin to use these datasets and applications.

Rainfall-runoff modelling based on global climate model and tropical rainfall measuring mission (GCM -TRMM): A case study in Hulu Terengganu catchment, Malaysia

The hydropower Plant in Terengganu is one of the major hydroelectric dams currently operated in Malaysia. For better operating and scheduling, accurate modelling of natural inflow is vital for a hydroelectric dam. The rainfall-runoff model is among the most reliable models in predicting the inflow based on the rainfall events. Such a model's reliability depends entirely on the reliability and consistency of the rainfall events assessed. However, due to the hydropower plant's remote location, the cost associated with maintaining the installed rainfall stations became a burden. Therefore, the study aims to create a continuous set of rainfall data before, during, and after the construction of a hydropower plant and simulate a rainfall-runoff model for the area. It also examines the reliability of alternative methods by combining rainfall data from two sources: the general circulation model and tropical rainfall measuring mission. Rainfall data from ground stations and generated data using inverse distance weighted method will be compared. The statistical downscaling model will obtain regional rainfall from the general circulation model. The data will be divided into three stages to evaluate the accuracy of the models in capturing inflow changes. The results revealed that rainfall data from TRMM is more correlated to ground station data with R2 = 0.606, while SDSM data has R2 = 0.592. The proposed inflow model based on GCM-TRMM data showed higher precision compared to the model using ground station data. The proposed model consistently predicted inflow during three stages with R2 values ranging from 0.75 to 0.93.