Tag: Climate Change Downscaling Techniques

Decomposing impact of climate change and land surface alterations on catchment hydrology in Eastern Himalaya

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Abstract

Climate change is intensifying water challenges in the Eastern Himalayas. Bridging climate change and water demand is vital, especially in India’s ungauged Himalayan catchments. The proposed novel method aims to differentiate climate change and anthropogenic activity impacts on catchment hydrology. In this study, evaporative demand was assessed by coupling SWAT and Budyko framework at the micro-watershed scale for understanding climate and surface changes in local hydrology. The coupled model considers climate parameters through SWAT to decompose the impacts of climate change and anthropogenic activities on watershed hydrology. The Budyko framework depicted the relationship between precipitation, evapotranspiration (ET), and potential evapotranspiration (PET) resulting in partitioning evaporative demand into green and blue ET. Parameters like d-statistics, responsivity, and elasticity assessed the buffer capacity of catchments against climate change. The study unveiled catchment characteristic (ω) values within the range of 1.70–1.56 from 2005 to 2030, indicative of a discernible shift in hydrological patterns attributed to changes in land use land cover (LULC) and climate variables. The investigation underscored the significant impact of LULC changes, manifesting as a reduction in snow and glacier cover alongside an augmentation in blue ET. The variation in precipitation, LULC, PET, and temperature were identified as primary influencers on hydrological regimes, rendering watersheds increasingly susceptible to climatic variations. Six catchments are unable to cope with or buffer against climate change and have low coping capacity. The study emphasizes the exigency to water resource management in ecologically delicate Eastern Himalayan ecosystem.

Advances in remote sensing based soil moisture retrieval: applications, techniques, scales and challenges for combining machine learning and physical models

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Abstract

Soil Moisture (SM) monitoring is crucial for various applications in agriculture, hydrology, and climate science. Remote Sensing (RS) offers a powerful tool for large-scale SM retrieval. This paper explores the advancements in RS techniques for SM estimation. We discuss the applications of these techniques, along with the advantages and limitations of traditional physical models and data-driven Machine Learning (ML) based approaches. The paper emphasizes the potential of combining ML and physical models to leverage the strengths of both approaches. We explore the challenges associated with this integration and future research directions to improve the accuracy, scalability, and robustness of RS-based SM retrieval. Finally, the paper also discusses a few issues such as input data selection, data availability, ML complexity, the need for public datasets for benchmarking, and analysis.

Time-series analysis of remotely sensed biophysical parameters and their effects on land surface temperature (LST): a case study of Aligarh region, India

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Abstract

The temporal behaviors of land surface temperature (LST) coupled with its associated parameters play a crucial role in determining the microclimate at the city scale. The increasing pattern of LST and consequent changes in biophysical parameters (parameters specify the amalgamation of living system with their physical characteristics including vegetation, water, built-up, bareness and drought parameters) at monthly, seasonal and annual time spans and from regional to global scale need to be comprehensively evaluated. The present study deals with LST estimation along with other spectral indices including Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Built-up Index (NDBI), Normalized Difference Bareness Index (NDBaI), and Normalized Multi-band Drought Index (NMDI) using Landsat series datasets from 1991 to 2022 of Aligarh city, Uttar Pradesh, India. The spatial pattern of LST indicates that the areas having water bodies and dense vegetation are colder such as the Aligarh Muslim University Campus and the catchment of Ganga canal areas, whereas the areas of high urbanization and bare grounds reflect high LST trends. Study finds a positive correlation of LST with NDBI (R2-0.56), NDBaI (R2-0.22) and NDWI (R2-0.22), whereas a negative correlation with NDVI (R2-0.35), MNDWI (R2-0.36) and NMDI (R2-0.41). Land use land cover (LULC)-based change detection in land cover classes was found consistent with the obtained results for spectral indices and LST patterns in the study area. Finally, the cross-validation using Modern-Era Retrospective analysis for Research and Applications (MERRA) reanalysis-based products of earth skin temperature and rainfall showed a good fit between observed and reanalysis products.

Comparative assessment of univariate and multivariate imputation models for varying lengths of missing rainfall data in a humid tropical region: a case study of Kozhikode, Kerala, India

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Abstract

Accurate measurement of meteorological parameters is crucial for weather forecasting and climate change research. However, missing observations in rainfall data can pose a challenge to these efforts. Traditional methods of imputation can lead to increased uncertainty in predictions. Additionally, varying lengths of missing data and nonlinearity in rainfall distribution make it difficult to rely on a single imputation method in all situations. To address this issue, our study compared univariate and multivariate imputation models for different lengths of missing daily rainfall observations in a humid tropical region. We used 33 years of weather data from Kozhikode, an urban city in Kerala region, and evaluated the selected models using accuracy measures such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Nash–Sutcliffe Efficiency (NSE) and Mean Absolute Relative Error (MARE). Among the considered univariate and multivariate imputation models, Kalman filter coupled time series models like Kalman–Arima ( \(\overline{\mathrm{RMSE} }\)  = 11.90, \(\overline{\mathrm{MAE} }\)  = 4.46) and Kalman Smoothing with structure time series ( \(\overline{\mathrm{RMSE } }\)  = 11.37, \(\overline{\mathrm{MAE} }\)  = 5.28) were found to be best for small (< 7 days) range imputation of rainfall data. Random Forest ( \(\overline{\mathrm{RMSE} }\)  = 16.57, \(\overline{\mathrm{MAE } }\)  = 8.0) and Kalman Smoothing with structure time series ( \(\overline{\mathrm{RMSE} }\)  = 16.84, \(\overline{\mathrm{MAE} }\)  = 8.09) performed well for medium range (8–15 days) of rainfall imputation. Random Forest technique was found to be suitable for large (≤ 30 days) ( \(\overline{\mathrm{RMSE} }\)  = 15.45, \(\overline{\mathrm{MAE } }\)  = 6.77), and very large (> 30 days) ( \(\overline{\mathrm{RMSE} }\)  = 12.91, \(\overline{\mathrm{MAE } }\)  = 3.42) missing length groups and Kalman–ARIMA performed best for mixed day series (RMSE = 9.7, MAE = 3.52). NSE and MARE values for different gap margins in rainfall data (≥ 1 mm) suggest that Kalman Smoothing (KS) connected models, as a representative univariate model, perform exceptionally well when dealing with a small number of missing observations. Notably, multivariate models like Principal Component Analysis (PCA) and Random Forest outperformed univariate models for medium to large gap margins. Considering these findings, utilizing multivariate techniques is recommended for imputing a large number of missing rainfall values and univariate models can be limited for small range of rainfall missing data imputation. The identified imputation models provide effective solutions for filling missing data of various lengths in all stations' datasets in humid tropical regions, thus enhancing rainfall-related analysis and enabling more accurate weather forecasts and climate change research.

Performance ranking of multiple CORDEX-SEA sensitivity experiments: towards an optimum choice of physical schemes for RegCM over Southeast Asia

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Abstract

This study conducted and evaluated 44 experiments using the non-hydrostatic version of the regional climate model RegCM4 (RegCM4-NH) and an additional three experiments with RegCM version 5 (RegCM5) over Southeast Asia for the period 2010–2015. The initiative was part of the coordinated regional climate downscaling experiment—Southeast Asia (CORDEX-SEA) project, in preparation for downscaling the latest coupled model intercomparison project Phase 6 (CMIP6) global climate models (GCMs). The RegCM4-NH experiments, forced by the ERA5 reanalysis, were configured using combinations of four cumulus, three planetary boundary layer (PBL), and three explicit moisture schemes. The spatiotemporal variability of simulated 2 m-temperature and rainfall for 2010–2015 was evaluated against observational datasets. The best experiments demonstrated reasonable reproduction of observed annual cycles and spatial distribution, while many exhibited unrealistic biases. A score ranking system was implemented to objectively compare the performance of experiments, enabling the identification of top-ranked experiments for Southeast Asia. The ensemble mean of the 44 RegCM4-NH experiments exhibited commendable performance, ranking 11th overall. Furthermore, the three additional RegCM5 experiments did not yield improved results compared to RegCM4-NH under the same physical configuration, suggesting that opting for RegCM4-NH would be a prudent choice for the CORDEX-SEA community in the forthcoming CMIP6 downscaling cycle for Southeast Asia.

Ecological sensitivity and its driving factors in the area along the Sichuan–Tibet Railway

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Understanding spatial and temporal characteristics and driving factors of ecological sensitivity are an essential prerequisite for effectively managing environmental changes and steering the rational use of land resources. This study employed the Analytic Hierarchy Process and Coefficient of Variation methods to calculate the weights of ten indicators from 2000 to 2018. Then, spatiotemporal change patterns of ecological sensitivity along the Sichuan–Tibet Railway were analyzed. At the same time, four individual parameters, including soil erosion, land use status, topographic factors, and climate conditions, were evaluated to create a multi-perspective understanding of the entire ecological sensitivity. The key factors affecting ecological sensitivity were explored through a geographic detector model. The results indicate that the ecological sensitivity along the Sichuan–Tibet Railway is predominantly high or moderate, with higher sensitivity observed in the western regions and lower sensitivity in the eastern regions. From 2000 to 2018, the ecological environment showed a trend of deterioration, and the spatial and temporal distribution patterns of the four parameters are closely related to the extensive ecological sensitivity. Based on the GeoDetector results, the spatial distribution of ecological sensitivity is mainly related to digital elevation model, precipitation, and air temperature. The interaction between different factors can enhance the effect on ecological sensitivity. The interaction between precipitation and Vegetation Coverage (FVC) has the largest effect.

Improving the Short-Range Precipitation Forecast of Numerical Weather Prediction through a Deep Learning-Based Mask Approach

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Due to various technical issues, existing numerical weather prediction (NWP) models often perform poorly at forecasting rainfall in the first several hours. To correct the bias of an NWP model and improve the accuracy of short-range precipitation forecasting, we propose a deep learning-based approach called UNetMask, which combines NWP forecasts with the output of a convolutional neural network called UNet. The UNetMask involves training the UNet on historical data from the NWP model and gridded rainfall observations for 6-hour precipitation forecasting. The overlap of the UNet output and the NWP forecasts at the same rainfall threshold yields a mask. The UNetMask blends the UNet output and the NWP forecasts by taking the maximum between them and passing through the mask, which provides the corrected 6-hour rainfall forecasts. We evaluated UNetMask on a test set and in real-time verification. The results showed that UNetMask outperforms the NWP model in 6-hour precipitation prediction by reducing the FAR and improving CSI scores. Sensitivity tests also showed that different small rainfall thresholds applied to the UNet and the NWP model have different effects on UNetMask’s forecast performance. This study shows that UNetMask is a promising approach for improving rainfall forecasting of NWP models.

Projecting Spring Consecutive Rainfall Events in the Three Gorges Reservoir Based on Triple-Nested Dynamical Downscaling

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Abstract

Spring consecutive rainfall events (CREs) are key triggers of geological hazards in the Three Gorges Reservoir area (TGR), China. However, previous projections of CREs based on the direct outputs of global climate models (GCMs) are subject to considerable uncertainties, largely caused by their coarse resolution. This study applies a triple-nested WRF (Weather Research and Forecasting) model dynamical downscaling, driven by a GCM, MIROC6 (Model for Interdisciplinary Research on Climate, version 6), to improve the historical simulation and reduce the uncertainties in the future projection of CREs in the TGR. Results indicate that WRF has better performances in reproducing the observed rainfall in terms of the daily probability distribution, monthly evolution and duration of rainfall events, demonstrating the ability of WRF in simulating CREs. Thus, the triple-nested WRF is applied to project the future changes of CREs under the middle-of-the-road and fossil-fueled development scenarios. It is indicated that light and moderate rainfall and the duration of continuous rainfall spells will decrease in the TGR, leading to a decrease in the frequency of CREs. Meanwhile, the duration, rainfall amount, and intensity of CREs is projected to regional increase in the central-west TGR. These results are inconsistent with the raw projection of MIROC6. Observational diagnosis implies that CREs are mainly contributed by the vertical moisture advection. Such a synoptic contribution is captured well by WRF, which is not the case in MIROC6, indicating larger uncertainties in the CREs projected by MIROC6.

European hot and dry summers are projected to become more frequent and expand northwards

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Abstract

Heatwaves and dry spells are major climate hazards with far-reaching implications for health, economy, agriculture, and ecosystems. The frequency of compound hot and dry summers in Europe has risen in recent years. Here we present an examination of past extreme summers and compare them to future climate conditions. We use reanalysis data (2001–2022) and model data at three global warming levels: +1.2 °C, +2 °C, and +3 °C for nine selected sub-regions. Key findings indicate a significant increase in the frequency of most extreme past occurrences under 2 °C and 3 °C warming scenarios. For specific summers, the occurrence probability rises by up to 5–6 times from 2 °C to 3 °C. Moreover, our analysis reveals a notable northward shift in the climatology of hot and dry summers under 3 °C warming. The hot and dry climate observed in Eastern Europe under current conditions is anticipated to extend into substantial parts of the Baltic coast, Finland, and Scandinavia.

Monthly climate prediction using deep convolutional neural network and long short-term memory

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Abstract

Climate change affects plant growth, food production, ecosystems, sustainable socio-economic development, and human health. The different artificial intelligence models are proposed to simulate climate parameters of Jinan city in China, include artificial neural network (ANN), recurrent NN (RNN), long short-term memory neural network (LSTM), deep convolutional NN (CNN), and CNN-LSTM. These models are used to forecast six climatic factors on a monthly ahead. The climate data for 72 years (1 January 1951–31 December 2022) used in this study include monthly average atmospheric temperature, extreme minimum atmospheric temperature, extreme maximum atmospheric temperature, precipitation, average relative humidity, and sunlight hours. The time series of 12 month delayed data are used as input signals to the models. The efficiency of the proposed models are examined utilizing diverse evaluation criteria namely mean absolute error, root mean square error (RMSE), and correlation coefficient (R). The modeling result inherits that the proposed hybrid CNN-LSTM model achieves a greater accuracy than other compared models. The hybrid CNN-LSTM model significantly reduces the forecasting error compared to the models for the one month time step ahead. For instance, the RMSE values of the ANN, RNN, LSTM, CNN, and CNN-LSTM models for monthly average atmospheric temperature in the forecasting stage are 2.0669, 1.4416, 1.3482, 0.8015 and 0.6292 °C, respectively. The findings of climate simulations shows the potential of CNN-LSTM models to improve climate forecasting. Climate prediction will contribute to meteorological disaster prevention and reduction, as well as flood control and drought resistance.