Agriculture is the mainstay of economy in Malawi - the warm heart of Africa. It employs 85 % of the labour force, and produces one third of the Gross Domestic Product (GDP) and 90 % of foreign exchange earnings. Maize farming covers over 92 % of Malawi’s agricultural land and contributes over 54 % of national caloric intake. With a subtropical climate and ~99 % rainfed agriculture, Malawi relies heavily on precipitation for its agricultural production. Given the significance of rainfed maize for the nation’s labour force and GDP, we have investigated climate change effects on this staple crop. We show that rainfed maize production in the Lilongwe District, the largest maize growing district in Malawi, may decrease up to 14 % by mid-century due to climate change, rising to as much as 33 % loss by the century’s end. These declines can substantially harm Malawi’s food production and socioeconomic status. Supplemental irrigation, crop diversification and natural conservation methods are promising adaptation strategies to improve Malawi’s food security and socioeconomic stability.

The coastal areas of Florida, United States, are exposed to increasing risk of flooding due to sea level rise as well as severe hurricanes. Florida regulations suggest constructing stormwater retention ponds as an option to retain excess runoff generated by the increased impervious area and to protect the environment by reducing pollutants from new developments. Groundwater level rise can significantly lower the soil storage capacity and infiltration at retention ponds, in turn, reducing the pond’s capacity to capture consecutive storms due to longer pond volume recovery time. Partial groundwater inundation can affect retention ponds’ ability to decrease peak flow rates and keep the post-development outflow lower than or equal to pre-development conditions. In this paper, the reliability and performance of a retention pond near Tampa Bay, Florida, was evaluated under sea level rise conditions. An integrated surface water and groundwater model was developed, and the groundwater table was projected for future conditions as a function of sea level rise. The results showed that sea level rise could increase the seasonal high water elevation of the retention pond up to 40 cm by mid-21st century. This increase lowered the reliability of the retention pond by about 45%. The pond failed to recover the designed treatment volume within required 72 h because of the high groundwater table, increasing the risk of pollutant discharge. Furthermore, the peak flow and volume of runoff significantly increased under sea level rise and associated groundwater table rise conditions. The study results suggest that it is imperative to consider future sea level rise conditions in stormwater design in low-lying coastal areas of Florida and around the world to prevent poor pond performance and increased risk of flooding in the future.

This paper applies HEC-HMS to the Karkheh River basin (KRB), Iran, and facilitates the calibration of a continuous hydrologic model (CHM) with soil moisture accounting (SMA) and snowmelt degree-day parameters. Manual calibration was performed to ensure the physical relevance of HEC-HMS parameter values. Because manual calibration entails changing each parameter value in a user-defined setting, it is often a time-consuming procedure complicated by multitude of interacting parameters. To address this setback, an event-based calibration technique (EBCT) was implemented in KRB and its interior sub-basins whereby the governing parameters of specific fall, spring, and winter events were initially estimated in a precalibration step and used as inputs to facilitate calibration of the CHM. Model performance analyzed based on goodness-of-fit criteria with respect to peak flows, low flows, and hydrograph shape reflects uncertainties associated with streamflow naturalization and use of average annual parameter values for the snowmelt component. Sensitivity analysis provided insights into the basin’s snowfall and melt characteristics, distinguishing antecedent temperature index (ATI) cold rate coefficient and baseflow recession coefficient as key parameters affecting hydrograph shape and magnitude of the peak flow, respectively. Results based on goodness of fit metrics suggest that event-based parameter estimation using seasonal characteristics improved the efficiency and accuracy of the continuous HEC-HMS model (CORRL and NSE 0.78–0.87 and 0.5–0.7, respectively) while facilitating application to a large, data-poor river basin with heterogeneous climatic conditions.

Measurement/estimation of snow water equivalent (SWE) is a difficult task in water resources studies of snowy regions. SWE point data is measured at snow courses that are normally operated with low density owing to high costs and great difficulty in reaching the stations in cold seasons. Moreover, snow is known to exhibit high spatial variability, which makes SWE studies based solely on sparse station data more uncertain. Ever‐increasing availability of satellite images is a promising tool to overcome some of the difficulties associated with analyzing spatial variability of snow. Although National Oceanic and Atmospheric Administration (NOAA) satellite images have low spatial resolution with approximately 1.1‐km pixel size, they are adequate for mapping snow cover at regional scales and enjoy a moderate length of record period. In this paper, rain and snow records of synoptic stations and the time series of NOAA‐based snow cover maps were used to map average SWE of a vast area in southwestern Iran. First, monthly and annual snow coefficient (SC) at synoptic stations were determined on the basis of analysis of hourly observation of type and amount of precipitation. Then, two new spatially distributed snow characteristics were introduced, namely, average frequency of snow observation (FSO) and monthly frequency of maximum snow observation (FMSO), on the basis of existing satellite snow observations. FSO and monthly FMSO maps were prepared by a geographic information system on the basis of snow map time series. Correlation of these two parameters with SC was studied and spatial distribution of SC was estimated on the basis of the best correlation. Moreover, the distribution of mean annual precipitation was derived by comparing a number of interpolation methods. SWE map was generated by multiplying SC and precipitation maps and its spatial variability in the region was analyzed. Copyright © 2007 Royal Meteorological Society

Karkheh River Basin (KRB) is the major water supply source for more than four million people in southwest Iran. Five riparian provinces of the KRB system have been pursuing aggressive development projects involving construction of multiple dams. These projects, together with increasing water withdrawals by Iran's neighboring country, Iraq, has resulted in the demise of the Hoor-Al-Azim marshland in the outlet of the basin. The frequent droughts in the region over the past decades have exacerbated the reliability of KRB water resources and the health of Hoor-Al-Azim marshland. This study evaluates the effects of climate change on KRB's ability to meet the increasing water demand in the region. Given the uncertainty in climate change projections, different climate change scenarios are selected through a clustering method, which tries to identify the scenarios with dissimilar characteristics. This helps evaluating the system under a range of climate change scenarios while minimizing redundancy in simulation model runs. Different systems performance indicators are used to examine the status of the KRB system under future climatic and water demand projections. Finally, different adaptation measures are identified and tested to minimize the undesirable effects of future changes on the system.