Patrick Laux

Climate trends of temperature, precipitation and river discharge in the Volta Basin of West Africa

Abstract The impact of climate change on precipitation and water availability is of major concern for policy makers in the Volta Basin of West Africa, whose economy mainly depends on rainfed agriculture and hydropower generation. It is therefore essential to know if, and to which extent climate trends in the Volta Basin exist that impact water availability. In this study, the present trends in precipitation, temperature, and river discharge for the Volta Basin were analysed. Linear trend and corresponding levels of significance were calculated for time series of annual and monthly maxima and corresponding means respectively. Trends of total annual precipitation and standard deviations for all considered variables were analysed. In addition, the stability of linear trends was considered via reverse arrangement test. Clear positive trends with high levels of significance were found for temperature time series. Precipitation time series showed both positive and negative trends, whereas most significant trends were negative. However, due to the small number of significant cases, only weak trends towards a decrease in precipitation can be concluded. Most of the significant trends of the standard deviation in precipitation were negative. Due to this observation a trend towards a decrease in the variability of precipitation is concluded. In case of discharge time series, a small amount of (predominantly positive) significant trends for the wet season was observed. The majority of the significant trends for the dry season were negative. For discharge no clear trend could be evaluated though, as the anthropogenic influences (e.g. building of dams, intensified irrigation) could not be quantified. Both, standard deviation of temperature and of river discharge show positive and negative significant trends. Thus one can not draw the conclusion of a change in temperature and river discharge variability. It is additionally shown that monthly precipitation trends can be weakly linked to climate indices. This was achieved by linear correlation analysis between monthly precipitation amounts and the climate indices NAO, SOI, TNA, TSA.

Detection of regional weekly weather cycles across Europe

Daily rainfall and temperature data of 158 weather stations in eight European countries and Iceland are investigated to set up a weekly cycle. The time series are divided into five time slices that are analyzed separately. As they depend strongly on the data availability, the significance of weekly cycles is generally higher for the past three time slices of 1931–1960, 1961–1990, and 1991–2005 compared to the two earlier analyzed time slices of 1871–1900 and 1901–1930. Precipitation does not follow any distinct significant weekly cycle. For temperature, however, significant weekly cycles exist in all analyzed countries. The weekly periodicities cannot be explained by random effects. A clear weekly signal is detected by means of a stationary block bootstrap approach. The cycles of temperature vary with the region and the time slice. However, they are found to be more stable for the last two time slices. For the dominant pattern of the weekly cycle in Germany, a coinciding significant weekly cycle of the large-scale circulation is detected for the time slice 1991–2005. In Germany, persistence can be observed for the weekday holding the minimum value of the temperature variables. The minimum is observed to occur on Saturday for the past two time slices. When judging from significant results exclusively, most other countries also show persistence for the past two time slices, except for the weekday with the maximum value of the temperature variables. This weekday either is Tuesday for Iceland and the UK or Wednesday for Sweden and Norway.

A Crop Model and Fuzzy Rule Based Approach for Optimizing Maize Planting Dates in Burkina Faso, West Africa

AbstractIn sub-Saharan Africa, with its high rainfall variability and limited irrigation options, the crop planting date is a crucial tactical decision for farmers and therefore a major concern in agricultural decision making. To support decision making in rainfed agriculture, a new approach has been developed to optimize crop planting date. The General Large-Area Model for Annual Crops (GLAM) has been used for the first time to simulate maize yields in West Africa. It is used in combination with fuzzy logic rules to give more flexibility in crop planting date computation when compared with binary logic methods. A genetic algorithm is applied to calibrate the crop model and to optimize the planting dates at the end. The process for optimizing planting dates results in an ensemble of optimized planting rules. This principle of ensemble members leads to a time window of optimized planting dates for a single year and thereby potentially increases the willingness of farmers to adopt this approach. The optimiz...

Toward a seasonal precipitation prediction system for West Africa: Performance of CFSv2 and high-resolution dynamical downscaling

Seasonal precipitation forecasts are important sources of information for early drought and famine warnings in West Africa. This study presents an assessment of the monthly precipitation forecast of the Climate Forecast System version 2 (CFSv2) for three agroecological zones (Sudan-Sahel, Sudan, and Guinean zone) of the Volta Basin. The CFSv2 performance is evaluated for the Sahel drought 1983 and for all August months of the reforecast period (1982–2009) with lead times up to 8 months using a quantile-quantile transformation for bias correction. In addition, an operational experiment is performed for the rainy season 2013 to analyze the performance of a dynamical downscaling approach for this region. Twenty-two CFSv2 ensemble members initialized in February 2013 are transferred to a resolution of 10 km × 10 km using the Weather and Research Forecasting (WRF) model. Since the uncorrected CFSv2 precipitation forecasts are characterized by a high uncertainty (up to 175% of the observed variability), the quantile-quantile transformation can clearly reduce this overestimation with the potential to provide skillful and valuable early warnings of precipitation deficits and excess up to 6 months in ahead, particularly for the Sudan-Sahel zone. The operational experiment illustrates that CFSv2-WRF can reduce the CFSv2 uncertainty (up to 69%) for monthly precipitation and the onset of the rainy season but has still strong deficits regarding the northward progression of the rain belt. Further studies are necessary for a more robust assessment of the techniques applied in this study to confirm these promising outcomes.

Setting Up Regional Climate Simulations for Southeast Asia

Climate change and climate variability are main drivers for land–use, especially for regions dominated by agriculture. Within the framework of the project Land–Use and Climate Change Interactions in Central Vietnam (LUCCi) regional climate simulations are performed for Southeast Asia in order to estimate future agricultural productivity and to derive adaptive land–use strategies for the future. Focal research area is the Vu Gia-Thu Bon (VGTB) river basin of Central Vietnam. To achieve the goals of this project reliable high resolution climate information for the region is required. Therefore, the regional non-hydrostatic Weather Research and Forecasting (WRF) model is used to dynamically downscale large-scale coupled atmosphere–ocean general circulation model (AOGCM) information. WRF will be driven by the ECHAM5-GCM data and the business-as-usual scenario A1B for the period 1960–2050. The focus of this paper is on the setup of WRF for East Asia. Prior to running the long-term climate simulation in operational mode, experimental simulations using different physical parameterizations have been conducted and analyzed. Different datasets have been used to drive the WRF model and to validate the model results. For the evaluation of the parameterization combination special emphasis is given to the representation of the spatial patterns of rainfall and temperature. In total, around 1.7Mio CPUh are required to perform the climate simulations. The required computing resources have been approved from the Steinbuch Centre for Computing (KIT, SCC).

Seasonal Prediction of Surface Air Temperature across Vietnam Using the Regional Climate Model Version 4.2 (RegCM4.2)

To investigate the ability of dynamical seasonal climate predictions for Vietnam, the RegCM4.2 is employed to perform seasonal prediction of 2 m mean (T2m), maximum (Tx), and minimum (Tn) air temperature for the period from January 2012 to November 2013 by downscaling the NCEP Climate Forecast System (CFS) data. For model bias correction, the model and observed climatology is constructed using the CFS reanalysis and observed temperatures over Vietnam for the period 1980–2010, respectively. The RegCM4.2 forecast is run four times per month from the current month up to the next six months. A model ensemble prediction initialized from the current month is computed from the mean of the four runs within the month. The results showed that, without any bias correction (CTL), the RegCM4.2 forecast has very little or no skill in both tercile and value predictions. With bias correction (BAS), model predictions show improved skill. The experiment in which the results from the BAS experiment are further successively adjusted (SUC) with model bias at one-month lead time of the previous run showed further improvement compared to CTL and BAS. Skill scores of the tercile probability forecasts were found to exceed 0.3 for most of the target months.

High Resolution Climate Change Information for the Lower Mekong River Basin of Southeast Asia

Regional climate projections are derived for Southeast Asia with the goal to estimate future agricultural productivity and to derive adaptive land use strategies for the future. Therefore, the regional non-hydrostatic Weather Research and Forecasting (WRF) model is used to dynamically downscale large-scale coupled atmosphere–ocean general circulation model information. WRF is driven by ECHAM5 data for the control period 1960–2000 as well as for the period 2001–2050 using the two different SRES scenario A1B and B1. In addition to these long-term climate simulations, a 30-year WRF simulation using ERA40 reanalysis data (1971–2000) is performed to validate the performance of the WRF simulations for this region and to enable calibration of climate impact models. In total, around 1.7 Mio CPUh were used to finalize the climate simulations at the Steinbuch Centre for Computing (KIT, SCC). Trend analysis of the past reveals significant positive trends up to 0.04 K/year for the northern part of the Lower Mekong River Basin. Except for relatively small regions, precipitation shows positive trends with a magnitude of approximately + 10 mm/year. Temperature, in general, is expected to be increased in the future following both the A1B and the B1 scenario. The magnitude of increase, however, strongly depends on the scenario. For the period 2021–2050 the magnitude ranges between + 3. 5 K to + 1. 5 K on average for the A1B and the B1 scenario, respectively. For precipitation, however, the signal is not that clear. While for the B1 scenario an increase of precipitation is expected for almost the whole basin, both positive and negative signals are found for A1B.

Hydrological and Agricultural Impacts of Climate Change in the Vu Gia-Thu Bon River Basin in Central Vietnam

This paper summarizes some of the climate (change) impact modeling activities conducted in the Land use and Climate Change interactions in Central Vietnam (LUCCi) project. The study area is the Vu Gia-Thu Bon (VGTB) river basin in Central Vietnam, which is characterized by recurrent floods during the rainy season, but also water shortages during the dry season. The impact modeling activities, such as the validation of the models are hindered by the scarcity of hydrometeorological data and an unfavorable distribution of the observation network, i.e., station data is available only for the lowlands. In total, two different process-based and distributed hydrological models are applied in concert with climate change and land use projections. Based on that, the magnitudes and return periods of extreme flows are estimated. The modeling results suggest increases of extreme high flows due to climate change. A multi-objective agro-economical model was developed for a typical irrigation scheme in the region in order to optimize the area for cropping, irrigation-techniques and schedules. The model results suggest the irrigation technique Alternate Wetting and Drying, which has the potential to increase the benefits for the farmers and help to mitigate greenhouse gases at the same time. In addition, the regional-scale crop model GLAM is applied for groundnut under rainfed conditions, which is capable to identify regions suitable for cropping in the future. The paper further synthesizes recommendations for local stakeholders in Central Vietnam.

Impacts of Land-Use/Land-Cover Change and Climate Change on the Regional Climate in the Central Vietnam

Nowadays, it is widely accepted that both elevated greenhouse gas (GHG) concentration as well as land-use/land-cover change (LULCC) can influence the regional climate dynamics. It is a matter of fact that changes in the land use/land cover are often ignored in long-term regional climate projections . Even worse, often an outdated (RCM default) LU map is applied for modeling. In the framework of the LUCCi project, we applied the Weather Research and Forecasting Model WRF in combination with an updated LULC map to study (1) the impacts of an improved and updated Land-Use Land-Cover map on the regional climate in the VuGia-ThuBon basin in Central Vietnam ; (2) the impacts separately of both the changed LU map and climate change (CC) on the regional climate; and (3) the sensitivity of land-use conversions in WRF simulations . It is found that the impacts of the outdated LU map exceed those of climate change, at least for the period 2001–2030. In addition, the deforestation scenario does not provide statistically significant signals of the most crucial surface climate variables, whereas the urbanization scenario provides evidence for a temperature signal (temperature increase) over the converted area, but no clear signal for precipitation is found.

Future high and low flow estimations for Central Vietnam : a hydrometeorological modelling chain approach

ABSTRACT We apply a complex hydro-meteorological modelling chain for investigating the impact of climate change on future hydrological extremes in Central Vietnam, a region characterized by limited data availability. The modelling chain consists of six General Circulation Models (GCMs), six Regional Climate Models (RCMs), six bias correction (BC) approaches, the fully distributed Water Flow and Balance Simulation Model (WaSiM), and extreme values analysis. Bias corrected and raw climate data are used as input for WaSiM. To derive hydrological extremes, the generalized extreme value distribution is fitted to the annual maxima/minima discharge. We identify limitations according to the fitting procedure and the BC methods, and suggest the usage of the delta change approach for hydrological decision support. Tendencies towards increased high- and decreased low flows are concluded. Our study stresses the challenges in using current GCMs/RCMs in combination with state-of-the-art BC methods and extreme value statistics for local impact studies.