Long-term multi-source precipitation estimation with high resolution (RainGRS Clim)

This paper explores the possibility of using multi-source precipitation estimates for climatological applications. A data-processing algorithm (RainGRS Clim) has been developed to work on precipitation accumulations such as daily or monthly totals, which are significantly longer than operational accumulations (generally between 5 min and 1 h). The algorithm makes the most of additional opportunities, such as the possibility of complementing data with delayed data, access to high-quality data that are not operationally available, and the greater efficiency of the algorithms for data quality control and merging with longer accumulations. Verification of the developed algorithms was carried out using monthly accumulations through comparison with precipitation from manual rain gauges. As a result, monthly accumulations estimated by RainGRS Clim were found to be significantly more reliable than accumulations generated operationally. This improvement is particularly noticeable for the winter months, when precipitation estimation is much more difficult due to less reliable radar estimates.</p

Flood Warning and Monitoring System (FWMS) using GSM Technology

Floods are natural disasters that occur due to climate factors. The impact of floods on property and lives can be too high, resulting in the need to establish a monitoring system. FWMS is a personal flood monitoring system that has the ability to call and send warning via SMS. The warning was sent to not only system users, but also directly to the Fire and Rescue Station as floodwaters rose rapidly to dangerous levels. FWMS is developed using Arduino Uno microcontroller, ultrasonic sensor (HC-SR04) and GSM SIM900A module. Using FWMS, users can apply for flood status in their area in real time via SMS. In FWMS, there are three different levels of flood warning system. The first is the "normal" level, when the situation is normal. The second is the "warning" level, where an SMS will be sent to the users each time a flood is detected. Meanwhile, the third is known as the "danger" level, which will be sent when the depth of the flood that occurs is detected to be higher. For both levels of “warning†and “dangerâ€, a loud buzzer will be issued as a warning sound notification to users. Three types of tests were performed on the FWMS to measure its level of performance. These tests were functional tests, prototype system tests, and GSM network performance tests. They were done to ensure that FWMS can be used and function properly as required. The tests have produced positive results and it is potentially to be further enhanced

Quality control and bias adjustment of crowdsourced wind speed observations

Wind observations collected at citizen weather stations (CWSs) could be an invaluable resource in climate and meteorology studies, yet these observations are underutilised because scientists do not have confidence in their quality. These wind speed observations have systematic biases, likely caused by improper instrumentation and station sitings. Such systematic biases introduce spatial inconsistencies that prevent comparison of these stations spatially and limit the possible usage of the data. In this paper, we address these issues by improving and developing new methods for identifying suspect observations and adjusting systematic biases. Our complete quality control and bias adjustment procedure consists of four steps: (a) performing within-station quality control tests to check the plausible range and the temporal consistency of observations, (b) adjusting the systematic bias using empirical quantile mapping, (c) implementing between-station quality control to compare observations from neighbouring stations to identify spatially inconsistent observations, and (d) providing estimates of the true wind when CWSs falsely report zero wind speeds, as a complement to the bias adjustment. We apply these methods to CWSs from the Weather Observation Website (WOW) in the Netherlands, comparing the crowdsourced data with official data, and statistically assessing the improvements in data quality after each step. The results demonstrate that the crowdsourced wind speed data are more comparable with official data after quality control checks and bias adjustment steps. Our quality assessment methods therefore give confidence in CWSs, converting their observations into a usable data product and an invaluable resource for applications in need of additional wind observations

Learning from the past: Analysis of disaster management structures, policies and institutions in Pakistan

Purpose: The paper provides a historical analysis of the disaster management structure, policies and institutions in Pakistan between 1947 and 2005, and highlights the contemporary challenges in view of the learning from the past. Design/methodology/approach: The paper uses a historic-integrative case study approach to disaster management and risk reduction policy, planning and practice. Qualitative data was collected through purposive sampling and a case study design was adopted. A broad range of actors was recruited as research participants. In total, 22 semi-structured in-depth interviews were conducted in relation to this study in six different districts of Pakistan to achieve insight into the role of different institutions and stakeholders. Findings: Overall, the post-colonial flood-centric policy framework and fragmented responsibilities of different disaster management institutions show the lack of an effective institutional structure for disaster management and mitigation in Pakistan, particularly at the local level. Until the event of the 2005 earthquake, policies heavily relied on attaining immediate and short-term goals of Response and Relief while ignoring the long-term objectives of strategic planning for Prevention and Preparedness as well as capacity building and empowerment of local institutions and communities. Practical implications: The analysis explains, in part, why disaster planning and management needs to be given due attention in the developing countries at different policy scales (from local to national) especially in the face of limited resources, and what measures should be taken to improve effectiveness at different phases of the disaster management cycle. Originality/value: The paper advances the importance of a historical case study approach to disaster management and mitigation. The empirical work provides original research evidence about the approaches to dealing with disasters in Pakistan and thus enriches existing knowledge of disaster management policy and planning about the country

Transforming Weather Index-Based Crop Insurance in India: Protecting Small Farmers from Distress. Status and a Way Forward. Research Report IDC-8

In India, agriculture contributes 14% of the GDP and employs 54% of the workforce (NCAER 2013). It accounts for 8.56% of the country’s exports. Despite agriculture’s steady decline in share in the GDP, it remains the largest economic sector and plays a significant role in the country’s overall socioeconomic development. However, agriculture is fundamentally a risky economic activity, particularly for small and marginal farm households because the climate risks, including aberrant rainfall, and natural calamities and input risks have a significant impact on yields. Low investment potential combined with poor coping ability render farming households vulnerable to debt and poverty traps in the face of adverse weather shocks. It is estimated that about 60% of the variation in yield can be attributed to various weather-related shocks. Since 70% of crop production in India is subject to the vagaries of the monsoon, crop insurance has been in existence through many public sector insurance companies for decades. Different agricultural insurance products have been tried out on a limited, ad-hoc and scattered manner..

A Monte Carlo simulation study of the factors influencing the performance of flood early warning systems

PhD ThesisIn recent decades, flood early warning systems (FEWSs) have been widely used as complementary non-structural mitigation measures in order to improve the population resilience to floods. FEWS research focusses mainly on flood forecasting techniques or social aspects of warning response, and end-to-end modelling frameworks that represent the entire FEWS forecast-decisionresponse/impact chain are rarely developed. A generic Monte Carlo simulation framework has been developed that represents an end-to-end FEWS in a versatile way, allowing factors influencing FEWS performance to be explored which cannot be analysed easily based on limited realworld data. The framework has been applied to a simulated generic fluvial case, where factors influencing FEWS performance in terms of reliability and economic effectiveness are explored. A new reliability performance measure based on inundation maps has been proposed. The framework has also been used to explore factors controlling the performance of a simulated FEWS representing an urban polder in Nanjing, China, with performance metrics based on waterlogging and pumping costs. For the generic fluvial case, the main results show that: i) the correlation between forecasts and observed values controls reliability; ii) probabilistic forecasts based on optimising a probabilistic threshold are robust to forecast biases in the mean and variance, iii) a FEWS based on uncertain forecasts is characterised by an optimal lead time that represents a balance between an adequate time to act in response and a reasonably good forecast; iv) the performance of the proactive action is the most important factor influencing the economic effectiveness of a FEWS. For the simulated flood-prone polder system case study, the results show that probabilistic forecasts of storm rainfall and runoff volume can considerably enhance the waterlogging and pumping metrics. The results of this research can be used to improve the performance of fluvial FEWSs, and to design FEWSs for polder systems.Ph.D. scholarship from the Secretary for Higher Education, Science, Technology and Innovation (SENESCYT) of the Government of Ecuador

Automatic quality control of telemetric rain gauge data for operational applications at IMGW-PIB

The automatic procedure of real-time quality control (QC) of telemetric rain gauge measurements (G) has been developed to produce quantitative precipitation estimates mainly for the needs of operational hydrology. The developed QC procedure consists of several tests: gross error detection, a range check, a spatial consistency check, a temporal consistency check, and a radar and satellite conformity check. The output of the procedure applied in real-time is quality index QI(G) that quantitatively characterised quality of each individual measurement. The QC procedure has been implemented into operational work at the Institute of Meteorology and Water Management since 2016. However, some elements of the procedure are still under development and can be improved based on the results and experience collected after about two years of real-time work on network of telemetric rain gauge