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In this study, the aridity index (AI) based on gridded climate data was validated for defining aridity and classifying aridity zones in Iraq through comparison with the results obtained by the station-based aridity index. Gauge-based gridded climate data taken from Climatic Research Unit Timeseries (CRU TS) were used to determine the annual value of four aridity indices (Lang, De Martonne, Ernic and UNEP AI) over the period 1998-2011. The results showed that the aridity distribution maps derived using grid-based aridity indices were reasonably close to those found using station-based ones. The four aridity indices properly identified similar aridity (dryness) classifications in both the station-based and grid-based aridity maps. The area percentage of each aridity class predicted by grid-based AIs was also compared with that obtained by the station-based AIs. The results showed that the variances between the area percentages predicted by grid-based AIs and those estimated using station-based AIs are fairly slight. The Lang AI exhibited the least variance (0.4%) while the De Martonne AI had the biggest variance (-4.8%). Despite these minor variances, it is however possible to conclude that the grid-based aridity index classified the aridity zones of Iraq as properly as the station-based aridity index did.

Satellite snow cover observations have been shown to enhance the calibration of conceptual hydrologic models. Recent advance in the mapping of snow cover fraction brings new satellite products and datasets. This study assesses the accuracy and potential of a newly developed snow cover fraction (SCF) product derived from Sentinel-3 observations. The product is developed using a physically based spectral unmixing approach that maps daily snow cover fractions at a 200 m spatial resolution over mountain regions. The main objective of this study is to evaluate the potential of the SCF for improving hydrological simulations at the regional scale. The specific aims are to compare the accuracy of snow cover mapping with daily snow depth observations at 631 climate stations and to assess and compare the runoff and snow model efficiencies obtained from multiple-objective calibration and calibration to runoff only. The analysis is performed using 188 lowland and alpine catchments in Austria. The results show that SCF agrees very well with snow depth observations at climate stations as documented by the median of overall accuracy, which exceeds 95%. The SCF helps to enhance runoff and snow simulations for 39% and 84% of the overall catchments in validation period, respectively. The use of SCF in model calibration improves the efficiency of runoff model, particularly in lowland catchments.

The objective of study of the impacts of climate change on mountain hydrology is to develop a methodology to assess the net impacts of climate change on the hydrological response in mountainous regions. This is done through a case study in the Peruvian Andes. There are few examples of predictions of the impact of climate change on resource availability and even fewer examples of the applications of such predictions to planning for sustainable economic development. This report presents a summary of the efforts of a Bank energy and climate change team to develop methodological tools for the assessment of climate impacts on surface hydrology in the Peruvian Andes. The importance of analyzing the potential climate impacts on hydrology in Peru arises in part from concerns about the retreat of tropical glaciers, the drying of unique Andean wetland ecosystems, as well as increased weather variability and weather extremes, all of which will affect water regulation. The study, together with a recently published report by the World Bank, Peru Overcoming the Barriers to Hydropower, is intended to inform plans for energy development in Peru and enable provides some insights into how hydrology may behave under future climate scenarios in Peru, the main purpose is to contribute to the methodological approaches to anticipate impacts from climate change in the Andes Region and other mountain ranges

The present study aims to evaluate meteorological data -with real time actualization- from the Climate Forecast System Reanalysis (CFRS) of the National Centers for Environmental Prediction (NCEP), comparing them with data from local stations in two mesoregions: Sertão de Pernambuco (SP) and Sertão do São Francisco (SFF), semi-arid region of Pernambuco, Brazil. Statistical performance indicators were used for the period since 1979 to 2014 and the variables: precipitation (P), average, minimum and maximum temperature (Tm, Tn, Tx respectively), relative humidity (HR), wind speed (Vv), solar radiation (RS) and potential evapotranspiration (ETo). Tn, Tm and Tx showed the best results for the determination coefficient (R2), Willmott concordance index (d), Nash-Sutcliffe efficiency index (NSE) and percentage bias (PBIAS). ETo, P and HR obtained acceptable values for R2, d and NSE. CFSR data shows good performance with d values between 0.63 and 0.94.

In this study, it is aimed to create a PLC controlled SRMS to be used in rural areas. Firstly a SRMS hardware was prepared consisting of power supply, PLC, analogue module and pyranometer units. Then, a SRMS software was written using CODESYS programming language to measure and record data and to control the hardware by PLC. SRMS software firstly collected the solar radiation in cumulatively by measuring every 30 minutes during the one-day period, and determined the daily total solar radiation. Then it calculated the daily average solar radiation by dividing the daily total solar radiation by the number of measurements. It recorded the daily total and average solar radiation amounts on the SD card. SRMS was tested in Kahramanmaraş Sütçü İmam University (KSU) during the July-November period of the 2019. The daily average solar radiation data recorded at KSU were compared with the data measured in the same period at the Eastern Mediterranean Transition Area Agricultural Research Institute (DAGTEM), located 10 km away. The daily average solar radiation data measured in KSU and DAGTEM varied between 3.63-33.48 MJ m-2 day-1 and 3.00-33.00 MJ m-2 day-1, respectively. Five-month averages of daily solar radiation data measured at both regions were determined 20.20 MJ m-2 day-1 and 19.64 MJ m-2 day-1, respectively. The difference between the mean of KSU and DAGTEM data groups was not found to be statistically significant (p> 0.05). This result revealed that the daily average solar radiation values measured in both regions can be used interchangeably. As an expression of the deviation between data groups measured in both regions, the MAPE and RMSE were determined as 14.57% and 2.68 MJ m-2 day-1. The compatibility level of the data groups was obtained as “good” (MAPE= 10-20%). It was concluded that SRMS could measure the daily average solar radiation with high accuracy and could be used in sensitive measurements. Bu çalışmada, kırsal alanlarda kullanılabilecek PLC kontrollü bir SRMS oluşturulması amaçlanmıştır. İlk olarak güç kaynağı, PLC, analog modül ve piranometre birimlerinden oluşan bir SRMS donanımı hazırlanmıştır. Daha sonra verileri ölçmek, kaydetmek ve donanımı PLC ile kontrol etmek amacıyla CODESYS programlama dili kullanılarak bir SRMS yazılımı yazılmıştır. SRMS yazılımı, ilk olarak bir günlük süre boyunca her 30 dakikada bir ölçüm yaparak solar radyasyonu kümülatif olarak toplamış ve günlük toplam solar radyasyonu belirlemiştir. Daha sonra günlük toplam solar radyasyonu ölçüm sayısına bölerek, günlük ortalama solar radyasyonu hesaplamıştır. Günlük toplam ve ortalama solar radyasyon miktarlarını SD karta kaydetmiştir. SRMS, Kahramanmaraş Sütçü İmam Üniversitesi’nde (KSÜ) 2019 yılı Temmuz-Kasım dönemi boyunca test edilmiştir. KSÜ’de kaydedilen günlük ortalama solar radyasyon verileri 10 km uzaklıkta bulunan Doğu Akdeniz Geçit Kuşağı Tarımsal Araştırma Enstitüsünde (DAGTEM) aynı dönemde ölçülen günlük veriler ile karşılaştırılmıştır. KSÜ ve DAGTEM’de ölçülen günlük ortalama solar radyasyon verileri sırasıyla 3.63-33.48 MJ m-2 gün-1 ve 3,00-33,00 MJ m-2 gün-1 arasında değişmiştir. Her iki bölgede ölçülen günlük ortalama solar radyasyon verilerinin beş aylık ortalamaları sırasıyla 20.20 MJ m-2 gün-1 ve 19.64 MJ m-2 gün-1 olarak belirlenmiştir. KSU ve DAGTEM veri gruplarının ortalamaları arasındaki fark istatistiksel olarak önemli bulunmamıştır (p> 0.05). Bu sonuç her iki bölgede ölçülen günlük ortalama solar radyasyon değerlerinin birbirlerinin yerine kullanılabileceğini ortaya koymuştur. Her iki bölgede ölçülen veri grupları arasındaki sapmanın bir ifadesi olarak MAPE ve RMSE sırasıyla %14.57 ve 2.68 MJ m-2 gün-1 olarak belirlenmiştir. Veri gruplarının uyumluluk düzeyi “iyi” olarak elde edilmiştir (MAPE=% 10-20). SRMS’nin günlük ortalama solar radyasyonu yüksek doğrulukla ölçebileceği ve hassas ölçümlerde kullanılabileceği sonucuna ulaşılmıştır.

Adapting to climate change is not a new phenomenon for the Arab world. For thousands of years, the people in Arab countries have coped with the challenges of climate variability by adapting their survival strategies to changes in rainfall and temperature. Their experience has contributed significantly to the global knowledge on climate change and adaptation. But over the next century global climatic variability is predicted to increase, and Arab countries may well experience unprecedented extremes in climate. Temperatures may reach new highs, and in most places there may be a risk of less rainfall. Under these circumstances, Arab countries and their citizens will once again need to draw on their long experience of adapting to the environment to address the new challenges posed by climate change. This report prepared through a consultative process with Government and other stakeholders in the Arab world assesses the potential effects of climate change on the Arab region and outlines possible approaches and measures to prepare for its consequences. It offers ideas and suggestions for Arab policy makers as to what mitigating actions may be needed in rural and urban settings to safeguard key areas such as health, water, agriculture, and tourism. The report also analyzes the differing impacts of climate change, with special attention paid to gender, as a means of tailoring strategies to address specific vulnerabilities. The socioeconomic impact of climate change will likely vary from country to country, reflecting a country's coping capacity and its level of development. Countries that are wealthier and more economically diverse are generally expected to be more resilient. The report suggests that countries and households will need to diversify their production and income generation, integrate adaptation into all policy making and activities, and ensure a sustained national commitment to address the social, economic, and environmental consequences of climate variability. With these coordinated efforts, the Arab world can, as it has for centuries, successfully adapt and adjust to the challenges of a changing climate.

In the Jizera Mountains (Czech Republic) the density of climate station network was tested in relation to spatial data interpolation, and watershed management targets. Point weather data (precipitation, air temperature, humidity and wind velocity) were interpolated by the nearest neighbourhood (NN), inverse distance weighting (IDW), spline (SPL), hypsometric (HYP) and kriging (KRI) methods. The results were assessed by the root mean square error (RMSE). The interpolation effectiveness showed the following order: HYP, IDW, KRI, NN and SPL. The advantage of the hypsometric method was recognised, particularly, by providing reasonable outputs in marginal catchments of the region and outside of the main instrumented area. However, in case of a higher density of observation points (11 hectares per station), all interpolation methods manifested comparable and realistic outputs in the focused mountain watersheds.

Drought is one of the most critical environmental hazards for the viability and productive development of crops, especially in a climate change environment. To this end, drought assessment is a process of paramount importance to make vulnerable agricultural regions more resilient. The primary aim of this paper is an integrated drought assessment through time and space in one of the most susceptible (in terms of water availability limitations) and agriculturally productive regions in Greece and the Mediterranean, namely, the Thessaly region. Supplementary objectives consist of the determination of the two most extreme years in terms of drought and wetness, so that we may reveal any potential climatological cycles/patterns from 1981 to 2020. Additionally, the methodology includes the annual and seasonal analysis using one of the most widely used drought indices, namely, the Standardized Precipitation Index (SPI), so that consistent measurements are available across a large study area, avoiding the possible scarcity/deficiency of data coming from a sparse land weather network. The innovative element of this paper is the integrated spatiotemporal drought assessment in multiple time scales through the estimation of the SPI making use of remotely sensed data, such as CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data). The outcomes highlight that the study area faced two severe years of drought in 1988 and 1989, which led to moderate and extreme drought conditions, respectively. In contrast, extremely wet conditions were observed in 2002–2003, whereas 2009–2010 experienced moderately wet conditions. The central and western part of the region tends to suffer the most in terms of drought severity, especially at the most extreme years. The validity of the results has been confirmed by the adoption of R2 where the index is approaching 0.67 despite the large size of the pixels (5 × 5 km). In this context, the mapping of spatial and seasonal variability across the study area permits more targeted measures (e.g., precision farming) instead of horizontal policies.

The Climate Hazard Group InfraRed Precipitation with Stations (CHIRPS) dataset was examined for its variability and performance in explaining precipitation variations, forecasting, and drought monitoring in Southeast Asia (SEA) for the period of 1981–2020. By using time-series analysis, the Standardized Precipitation Index (SPI), and the Autoregressive Integrated Moving Average (ARIMA) model this study established a data-driven approach for estimating the future trends of precipitation. The ARIMA model is based on the Box Jenkins approach, which removes seasonality and keeps the data stationary while forecasting future patterns. Depending on the series, ARIMA model annual estimates can be read as a blend of recent observations and long-term historical trend. Methods for determining 95 percent confidence intervals for several SEA countries and simulating future annual and seasonal precipitation were developed. The results illustrates that Bangladesh and Sri Lanka were chosen as the countries with the greatest inaccuracies. On an annual basis, Afghanistan has the lowest Mean Absolute Error (MAE) values at 33.285 mm, while Pakistan has the highest at 35.149 mm. It was predicted that these two countries would receive more precipitation in the future as compared to previous years.

Although urban heat islands (UHIs) have been found in many cities throughout the world, work on smaller settlements is still limited, especially concerning variations connected to climate zones. Meteorological stations are often regarded as rural when located in a village or small town, and any temperature bias is assumed negligible. In this paper, we therefore present air temperature variations and their connection to land cover in 3 European villages, boreal Haparanda, temperate Geisenheim, and Mediterranean Cazorla, all of them hosting long temperature records that might be biased. The villages were equipped with temperature sensors, and the surrounding areas were digitized to compare UHI effects and to evaluate the contribution of land cover on local cooling and warming. This sensor network reveals significant village UHIs in all 3 climate zones, with seasonal maximum intensities decreasing from north (1.4°C) to south (0.9°C). During summer, urban warming is most emphasized in minimum temperatures in boreal Haparanda and temperate Geisenheim but weakest in Mediterranean Cazorla, presumably because of limited plant transpiration due to high insolation and drought stress. Urban warming is correlated with building density in all 3 settlements and shows little seasonal variation. Even though the mountain river passing Cazorla substantially cools ambient temperatures at distances <100 m, mitigation of warming through water bodies is limited in the Central and Northern European sites. Our results suggest to treat rural instrumental station data with care and to avoid using data recorded in villages as unbiased reference records to adjust measurements from larger cities.