The monsoons in Pakistan have been exceptionally harsh in recent decades, resulting in extraordinary drought conditions and record flooding events. The changing frequency of extreme events is widely attributed to climate change. However, given this region's long history of floods and droughts, the role of natural climate variability cannot be rejected without a careful diagnosis. Here, we examine how oceanic and atmospheric variability has contributed to unusual precipitation distributions in West South Asia. Variations in sea surface temperatures in the tropical Pacific and northern Arabian Sea, and internal atmospheric variability related to the circumglobal teleconnection pattern and the subtropical westerly jet stream, explain more than 70% of monthly summer precipitation variability in the 21st century. Several of these forcings have co-occurred with record strength during episodes of extreme monsoons, which have exacerbated the overall effect. Climate change may have contributed to increased variability and the in-phase co-occurrences of the identified mechanisms, but further research is required to confirm any such connection.

An accurate assessment of hydrometeorological variables/ observations over an urban area is crucial to policy-makers and civic bodies to address an extensive range of water resources and environmental problems for informed decision-making related to the water distribution system and drainage networks. This necessitates the establishment of hydrometeorological monitoring networks that can efficiently obtain consistent and reliable information about the spatiotemporal variability of multiple hydrometeorological observations while being economically sustainable. However, the urban catchments especially in underdeveloped and developing countries are often subjected to spatial, environmental as well as monetary limitations which hinders the application of conventional approaches followed to set up the hydrometeorological networks. With this context, we propose a novel rationalization framework to record numerous hydrometeorological variables and acquire maximum information at an optimal cost. We have attempted to combine a multivariate statistical technique, Principal Component Analysis (PCA) with a multi-attribute decision-making method, Technique for Order of Preference by Similarity to Ideal Solutions (TOPSIS) to rank the significant hydrometeorological stations of an existing Automatic Weather Stations (AWS) network. It is observed that the set of rationalized AWS network obtained from this framework can capture the spatiotemporal information of the hydrometeorological variables considered in this study as efficiently as the entire AWS network. Additionally, the comparison of flood inundation and hazard maps derived from a 3-way coupled hydrodynamic flood modeling framework for the rationalized and original network also reflects its credibility to capture the flooding characteristics for the catchment. This proposed framework has been applied over Mumbai city, India, a major flood-prone area, and is characterized by high spatiotemporal variability of hydro-meteorological observations and space constraints due to dense population. This framework is generic and can be employed to reevaluate the prevailing hydro-meteorological networks in other catchments and help in the reduction of the maintenance cost while efficiently capturing the variability of observations.

Optimization in irrigation scheduling using weather forecast has been proven to achieve better productivity along with reduced irrigation water requirements. We developed a farm-scale hydrological model coupled with a chance-constraint optimization to take short to medium range weather forecast and prescribe the optimal irrigation amount determined by developing the conditional probability density functions of the rainfall and subsequently the soil moisture for the days in forecast range. The stress-avoidance was ensured by maintaining the probability of crops undergoing water stress is less than a prescribed threshold (reliability factor, α). The framework was implemented for irrigation decision simulation at extended range by downscaling the forecast with Nonhomogeneous Hidden Markov Model (NHMM) as an input and produce irrigation decision in extended range (15 to 30 days). The optimization framework ensured minimal water use without significant crop water stress. The method was tested at two site locations in Nashik district in the state of Maharashtra, both being involved in grape cultivation (referred herein as Site 1 and Site 2). In short-to-medium range weather scale, the model was implemented with varied α (0.5 to 0.95) and interval between two subsequent irrigation application (1, 3 and 7 days) and significant amount of water savings with respect to the farmer’s applied irrigation could be achieved. The simulation-optimization framework was only tested with α=0.95 and once in 7 days irrigation application for extended range, and yet no significant detrimental effect on yield was observed whereas in kharif season significant potential of water savings was observed both in Site 1 and 2. While the framework in short to medium range is useful for optimal real time irrigation decision making, in the extended range, it can be implemented in planning of irrigation for the upcoming month to avoid the inconvenience of instant arrangement of water, especially in case of drought-hit regions. Considering that irrigation accounts for over 80% of the total water use worldwide, the value of such an approach as a decision-support tool for irrigation optimization is self-evident.