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Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security, notably from climate change and, for that purpose, remote sensing is routinely used. However, identifying specific crop types, cropland, and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral signatures. This study applied a methodology to identify cropland and specific crop types, including tobacco, wheat, barley, and gram, as well as the following cropping patterns: wheat-tobacco, wheat-gram, wheat-barley, and wheat-maize, which are common in Gujranwala District, Pakistan, the study region. The methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning (ML) methods, namely a Decision Tree Classifier (DTC) and a Random Forest (RF) algorithm. The best time-periods for differentiating cropland from other land cover types were identified, and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML algorithms. The methodology was subsequently evaluated using Landsat images, crop statistical data for 2020 and 2021, and field data on cropping patterns. The results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images, together with ML techniques, for mapping not only the distribution of cropland, but also crop types and cropping patterns when validated at the county level. These results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan, adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries.

Changes in rainfall patterns, humidity, and temperature, as well as greater exposure to severe weather events, has led to the need for adapting cultural heritage to climate change. However, there is limited research accomplished to date on the process of adaptation of cultural heritage to climate change. This paper examines the perceptions of experts involved in the management and preservation of cultural heritage on adaptation to climate change risks. For this purpose, semi-structured interviews were conducted with experts from the UK, Italy, and Norway as well as a participatory workshop with stakeholders. The results indicate that the majority of interviewees believe that adaptation of cultural heritage to climate change is possible. Opportunities for, barriers to, and requirements for adapting cultural heritage to climate change, as perceived by the interviewees, provided a better understanding of what needs to be provided and prioritized for adaptation to take place and in its strategic planning. Knowledge of management methodologies incorporating climate change impacts by the interviewees together with best practice examples in adapting cultural heritage to climate change are also reported. Finally, the interviewees identified the determinant factors for the implementation of climate change adaptation. This paper highlights the need for more research on this topic and the identification and dissemination of practical solutions and tools for the incorporation of climate change adaptation in the preservation and management of cultural heritage.

Predicting rainfall is a challenging and critical task due to its significant impact on society. Timely and accurate predictions are essential for minimizing human and financial losses. The dependence of approximately 60% of agricultural land in India on monsoon rainfall implies the crucial nature of accurate rainfall prediction. Precise rainfall forecasts can facilitate early preparedness for disasters associated with heavy rains, enabling the public and government to take necessary precautions. In the North-Western Himalayas, where meteorological data are limited, the need for improved accuracy in traditional modeling methods for rainfall forecasting is pressing. To address this, our study proposes the application of advanced machine learning (ML) algorithms, including random forest (RF), support vector regression (SVR), artificial neural network (ANN), and k-nearest neighbour (KNN) along with various deep learning (DL) algorithms such as long short-term memory (LSTM), bi-directional LSTM, deep LSTM, gated recurrent unit (GRU), and simple recurrent neural network (RNN). These advanced techniques hold the potential to significantly improve the accuracy of rainfall prediction, offering hope for more reliable forecasts. Additionally, time series techniques, including autoregressive integrated moving average (ARIMA) and trigonometric, Box-Cox transform, arma errors, trend, and seasonal components (TBATS), are proposed for predicting rainfall across the altitudinal gradients of India’s North-Western Himalayas. This approach can potentially revolutionise how we approach rainfall forecasting, ushering in a new era of accuracy and reliability. The effectiveness and accuracy of the proposed algorithms were assessed using meteorological data obtained from six weather stations at different elevations spanning from 1980 to 2021. The results indicate that DL methods exhibit the highest accuracy in predicting rainfall, as measured by the root mean squared error (RMSE) and mean absolute error (MAE), followed by ML algorithms and time series techniques. Among the DL algorithms, the accuracy order was bi-directional LSTM, LSTM, RNN, deep LSTM, and GRU. For the ML algorithms, the accuracy order was ANN, KNN, SVR, and RF. These findings suggest that altitude significantly affects the accuracy of the models, highlighting the need for additional weather stations in this mountainous region to enhance the precision of rainfall prediction.

Rivers play an essential role to humans and ecosystems, but they also burst their banks during floods, often causing extensive damage to crop, property, and loss of lives. This paper characterizes the 2014 flood of the Indus River in Pakistan using the US Army Corps of Engineers Hydrologic Engineering Centre River Analysis System (HEC-RAS) model, integrated into a geographic information system (GIS) and satellite images from Landsat-8. The model is used to estimate the spatial extent of the flood and assess the damage that it caused by examining changes to the different land-use/land-cover (LULC) types of the river basin. Extreme flows for different return periods were estimated using a flood frequency analysis using a log-Pearson III distribution, which the Kolmogorov–Smirnov (KS) test identified as the best distribution to characterize the flow regime of the Indus River at Taunsa Barrage. The output of the flood frequency analysis was then incorporated into the HEC-RAS model to determine the spatial extent of the 2014 flood, with the accuracy of this modelling approach assessed using images from the Moderate Resolution Imaging Spectroradiometer (MODIS). The results show that a supervised classification of the Landsat images was able to identify the LULC types of the study region with a high degree of accuracy, and that the most affected LULC was crop/agricultural land, of which 50% was affected by the 2014 flood. Finally, the hydraulic simulation of extent of the 2014 flood was found to visually compare very well with the MODIS image, and the surface area of floods of different return periods was calculated. This paper provides further evidence of the benefit of using a hydrological model and satellite images for flood mapping and for flood damage assessment to inform the development of risk mitigation strategies.

Bias correction of global and regional climate models is essential for credible climate change projections. This study examines the bias of the models of the Coupled Model Inter-comparison Project Phase 5 (CMIP5) in their simulation of the spatial pattern of sea surface temperature (SSTs) in different phases of the El Niño Southern Oscillation (ENSO) and their teleconnections—highlighting the strengths and weaknesses of the models in different oceanic sectors. The comparison between the model outputs and the observations focused on the following three features: (i) the typical horseshoe pattern seen in the Pacific Ocean during ENSO events with anomalies in SSTs opposite to the warm/cool tongue, (ii) different signature in the tropical Pacific Ocean from that of the North and tropical Atlantic Ocean, and (iii) spurious signature in the southern hemisphere beyond 45° S. Using these three cases, it was found that the model simulations poorly matched the observations, indicating that more attention is needed on the tropical/extratropical teleconnections associated with ENSO. More importantly, the observed SST coupling between the tropical Pacific Ocean and the Atlantic Ocean is missing in almost all models, and differentiating the models between high/low top did not improve the results. It also found that SSTs in the tropical Pacific Ocean are relatively well simulated when compared with observation. This work has improved our understanding of the simulation of ENSO and its teleconnections in the CMIP5 models and has raised awareness of the bias existing in the models, which requires further attention by climate modellers.

The architectural style found in Wa and Tamale is renowned for its distinctive use of earthen construction, which features square buildings with flat roofs and circular compounds with conical thatch roofs. Recently, there has been a growing inclination towards the use of alternative construction techniques in which nontraditional materials such as cement, bitumen, and used car engine oil are utilised to render wall surfaces. These structures show how the materials and design in northern Ghana have substantially evolved. However, what forces drive the changes in cob construction in this region? Furthermore, how might these changes impact the preservation of cultural heritage in Ghana? To explore the factors that contribute to the departure from traditional earthen building methods that rely on local materials, this study employs a constructivist research approach. Participants in a survey that informed this study revealed that they struggled to access building materials to construct their houses. While most of the people who responded to the survey have resided in buildings constructed with a mixture of beini and dawadawa, they hesitate to use plant-based biostabilisers in new constructions. Factors that hinder the ongoing construction and preservation of earthen buildings include shifting cultural and social norms, environmental changes, difficulties accessing local building resources, flood risks, regular maintenance requirements, and societal influences. Thus, this study concludes that if communities are empowered to take ownership and recognise the value of their cultural heritage, they are likely to be increasingly aware and appreciative of their architectural heritage. Thus, their local heritage will be preserved.

Pakistan is amongst the most water-stressed countries in the world, with changes in the frequency of extreme events, notably droughts, under climate change expected to further increase water scarcity. This study examines the impacts of climate change and anthropogenic activities on the runoff of the Kunhar River Basin (KRB) in Pakistan. The Mann Kendall (MK) test detected statistically significant increasing trends in both precipitation and evapotranspiration during the period 1971–2010 over the basin, but with the lack of a statistically significant trend in runoff over the same time-period. Then, a change-point analysis identified changes in the temporal behavior of the annual runoff time series in 1996. Hence, the time series was divided into two time periods, i.e., prior to and after that change: 1971–1996 and 1997–2010, respectively. For the time-period prior to the change point, the analysis revealed a statistically significant increasing trend in precipitation, which is also reflected in the runoff time series, and a decreasing trend in evapotranspiration, albeit lacking statistical significance, was observed. After 1996, however, increasing trends in precipitation and runoff were detected, but the former lacked statistical significance, while no trend in evapotranspiration was noted. Through a hydrological modelling approach reconstructing the natural runoff of the KRB, a 16.1 m3/s (or 15.3%) reduction in the mean flow in the KRB was simulated for the period 1997–2010 in comparison to the period 1971–1996. The trend analyses and modeling study suggest the importance of anthropogenic activities on the variability of runoff over KRB since 1996. The changes in streamflow caused by irrigation, urbanization, and recreational activities, in addition to climate change, have influenced the regional water resources, and there is consequently an urgent need to adapt existing practices for the water requirements of the domestic, agricultural and energy sector to continue being met in the future.

The Ca Mau Peninsula (CMP) in Vietnam’s Lower Mekong Delta faces pressing challenges, including sea-level rise (SLR), land subsidence, flooding, and saltwater intrusion. Recent years have witnessed an earlier and more severe dry season, leading to heightened saltwater intrusion. As many CMP provinces rely on the Mekong River for their water supply, they are highly susceptible to prolonged drought and salinization. This study employs the MIKE 11 hydraulic model to project saltwater intrusion scenarios in the CMP up to 2050, based on Vietnam’s 2016 Ministry of Natural Resources and Environment (MONRE) SLR projections, considering water regulation from the Cai Lon-Cai Be sluice system. The modelled discharge, water level and salinity were calibrated and validated successfully based on di_erent statistical measures. The projections indicate that saltwater intrusion during the dry season could start 1 to 1.5 months earlier by 2050, with salinity levels exceeding 30 g/l in February. The findings underscore the importance of developing adaptation strategies to address the challenges of climate change and saltwater intrusion, notably in the region’s significant agricultural sector.

Purpose – The concept of vulnerability in climate change literature is underpinned by numerous theoretical contributions across different disciplines leading to disparate understandings of what climate change vulnerability entails, as well as different methodological frameworks for assessment. This multiplicity of contributions helped not only to frame and shape different understandings of vulnerability but also to define the conceptual and analytical elements considered as critical in any climate change vulnerability assessment. The purpose of this paper is to review the literature on climate change vulnerability and explore and synthesize those conceptual and analytical aspects considered fundamental in a vulnerability assessment in climate change.Design/methodology/approach – Drawing on existing literature on climate change vulnerability and vulnerability assessment frameworks, the paper provides a review of the conceptual elements regarded as critical in integrated assessments of climate change vulnerability to date.Findings – A review of the existing literature identified nine critical elements in vulnerability assessments: the coupled human‐environment system and place‐based analysis; key components of vulnerability; multiple perturbations; scales of analysis; causal structures of vulnerability; engaging stakeholders; differential vulnerability; historical and prospective analysis; and dealing with uncertainty. The paper concludes by highlighting some of the remaining challenges and limitations for the development of integrated vulnerability assessment in climate change research.Originality/value – The paper presents a synthesis that draws on existing literature on climate change vulnerability theory, as well as vulnerability assessment frameworks that attempt to apply those concepts in the assessment of climate change vulnerability.

The Grombalia Basin, located in Northern Tunisia, is facing significant challenges related to water scarcity. The cultivation of citrus fruits in this region, supported by the government, has become increasingly vulnerable to the impacts of climate change, including reduced rainfall and more frequent drought periods. The agricultural sector faces a crisis due not only to the lack of water resources but also to inadequate management (water losses in irrigation systems). This study aims to delineate the most suitable areas for implementing solar photovoltaic (PV) desalination farms utilizing abandoned brackish groundwater. A Fuzzy Analytical Hierarchy Process (FAHP), integrated with Geographic Information Systems (GIS), is employed as a Multi-Criteria Decision Analysis (MCDA) approach. This paper evaluates potential sites based on climatic, socioeconomic, and environmental factors. The FAHP framework determines criteria weights through pairwise comparisons, ensuring robust and systematic decision-making. The results indicate that the most suitable sites are located north of the Grombalia basin, which currently lacks access to external water resources for irrigation. The \"Dependence of Farmers on Water Resources (DFWR)\" is the most sensitive criterion, and the most suitable sites remain relatively the same despite variations in weighting. These findings will assist farmers in using solar energy to desalinate brackish groundwater, thus ensuring the sustainability of their crops and preserving their citrus heritage.